Early in the middle of the 1800s. Up until about 1900, the automotive land speed record 1 was held by an electrical vehicle. Unfortunately, internal combustion engine vehicles did not do 2 well in the market due to their high cost, limited speed, and short range in the 20th century, which 3 resulted in a global fall in their use as private motor vehicles. Since there are no fuel emissions into 4 the environment, there are important uses for electrical vehicles, Electric cars, or EVs, have a closed 5 circuit, cheap operating costs, no maintenance requirements, and excellent performance. The goal 6 of this project is to assess the current state of electric vehicles and the issues surrounding the global 7 advancement of electric vehicle technology. Because of the changing climate.8

Electric mobility is one of the ways to contain greenhouse gas and local pollutants emissions in urban areas. Nevertheless, the massive introduction of battery-powered electric vehicles (EVs) brings some concerns related to their energy demand. Modelling vehicle usage and charging behavior is essential for charge demand forecasting and energy consumption estimation. Therefore, it is crucial to understand how the charging decisions of EV owners are influenced by different factors, ranging from the charging infrastructure characteristics to the users’ profiles. This review intends to investigate the approaches used to investigate on charging behavior and highlight trends and differences between the results, remarking on any gaps worthy of further investigation.

The transportation sector is seeing an increased adoption rate of electric vehicles (EV), which comprise battery electric vehicles (BEV), hybrid electric vehicles (HEV) and plug-in hybrid electric vehicles (PHEV). This mode of transportation is likely to displace automobiles powered by internal combustion engines (ICE) in the nearest future, as indicated by the current trend. Each of the key EC components is home to a variety of technologies that are either currently in use or will be in the near future. Electric vehicles have the potential to have a significant impact on a variety of fields, including the economy, the energy infrastructure, and the natural environment. As a result of the production of carbon dioxide gas from the combustion of fossil fuels, transportation is the sector that is responsible for the second-highest amount of greenhouse gas emissions. Electric vehicles, often known as EVs, are getting a lot of attention as a potentially game-changing solution to this issue. It is possible for electric vehicles to produce fewer emissions of carbon dioxide (CO2) than conventional automobiles due to the fact that an electric motor serves as the vehicle's propeller rather than an internal combustion engine. EVs have the potential to become zero-emission vehicles if they are paired with renewable energy sources. This document presents an overview of the numerous types of EV drive circuits, covering their design as well as the benefits and drawbacks associated with each type. The current state of battery technology, particularly that pertaining to the batteries utilized in electronic vehicles, is discussed in this paper. This article also discusses electric motor efficiency, power density, fault tolerance, dependability, cost, and the best electric motor for EVs. Then, a thorough study of future EV implementation's obstacles and prospects is done. Charging times and battery performance are examples of technological challenges, but government regulation of EVs continues to be a substantial non-technical hurdle.

Abstract: As the policies and regulations currently in place concentrate on environmental protection and greenhouse gas reduction, we are steadily witnessing a shift in the transportation industry towards electromobility. There are, though, several issues that need to be addressed to encourage the adoption of EVs at a larger scale. To this end, we propose a solution capable of addressing multiple EV charging scheduling issues, such as congestion management, scheduling a charging station in advance, and allowing EV drivers to plan optimized long trips using their EVs. The smart charging scheduling system we propose considers a variety of factors such as battery charge level, trip distance, nearby charging stations, other appointments, and average speed. Given the scarcity of data sets required to train the Reinforcement Learning algorithms, the novelty of the recommended solution lies in the scenario simulator, which generates the labelled datasets needed to train the algorithm. Based on the generated scenarios, we created and trained a neural network that uses a history of previous situations to identify the optimal charging station and time interval for recharging. The results are promising and for future work we are planning to train the DQN model using real-world data.

This article provides a comprehensive exploration of the electric vehicle (EV) business, investigating its current state, market dynamics, and pivotal role in sustainable transportation. It delves into factors driving EV adoption, challenges faced by the industry, and the landscape of key players. The discussion extends to investments, infrastructure development, consumer trends, and the technological innovations shaping the future. The article concludes with insights into the anticipated growth and transformative impact of EVs, emphasizing their significance in fostering a cleaner, more sustainable future for global transportation.

The predominance of traffic lights in urban settings often induces fluctuations in traffic patterns and energy utilization among vehicles. To counteract the adverse effects of traffic lights on the energy efficiency of electric vehicles (EVs), a Multi-Intersections-Based Eco-Approach and Departure strategy (M-EAD) is proposed. This strategy aims to enhance vehicle energy efficiency, traffic flow, and battery longevity, all while upholding satisfactory driving comfort. The M-EAD strategy unfolds in two distinct stages: the optimization of an eco-friendly green signal window and the refinement of speed trajectories. The initial stage tackles the optimization of traffic light green signal windows, underpinned by the minimization of travel delays via solving the shortest path problem. In the subsequent stage, a receding horizon framework takes center stage, leveraging an iterative dynamic programming algorithm to tackle the speed optimization challenge. The objective here is to curtail energy consumption and reduce battery wear by finding an optimal speed trajectory. Furthermore, the real-world efficacy of this approach is substantiated through on-road vehicle tests, attesting to its viability in actual road scenarios.

In this work, we develop a detailed analysis of the current panorama for electric vehicle charging technology, focusing on the various levels and types of charging protocols and used connectors. We propose a charging station for electric cars powered by solar photovoltaic energy, performing the analysis of the solar resource in the selected location, sizing the photovoltaic power plant to cover the demand completely, and exploring different configurations such as grid connection or physical and virtual electric energy storage. Despite the current development applying for specific working conditions, operating voltage, charging rate, power demand, etc., the proposed configuration is modular, adaptable, and resilient. The simulated system operates within the 360 V to 800 V range of continuous current for charging the electric vehicles, with a selectable power range between 20 and 180 kW. The basic layout includes four charging poles, each servicing all working voltages. An oversized PV plant powers the charging station at any time of the year, saving money compared to the alternative of the electric storage unit. In addition, we build simulation tools and algorithms that optimize the design of future projects, providing a solid basis for sustainable energy infrastructure planning and design.

In recent years, the introduction of electric vehicles as a means of transportation has been gradually growing in most developing and developed countries, especially in terms of energy efficiency, energy diversification, and environmental sustainability. While developed countries have been trying to keep up with electric vehicle usage and buying trends, most developing countries, especially African countries, are still riddled with severe power outages and electricity load shedding. The growing adoption of electric vehicles (EVs) has led to a corresponding increase in the availability of charging infrastructure, thereby exerting notable impacts on the power grids of most African countries. Recent evidence suggests that various charging strategies and grid integration approaches are being developed to mitigate the negative impacts of electric vehicle (EV) charging and enhance the advantages of integrating EVs with the grid. However, transportation researchers have struggled to develop applicable and sensible mitigation strategies to tackle this issue, which has seriously rendered the sustainability of electric vehicle usage in Africa in serious doubt. The primary aim of this research is to provide a conceptual theoretical framework based on previous and current literature reviews on electric vehicles to examine the challenges facing the adoption of electric vehicles in Africa. In addition, we explored the environmental and economic benefits of electric vehicles in Africa regarding urban sustainability and charging infrastructure on the existing transportation infrastructure in the African transportation network. This study contributes to the expanding body of literature on electric vehicles by emphasising the significance of incorporating sustainability principles in the adoption and usage of electric vehicles in Africa. It also examines the effects of electric vehicle usage on the existing transportation infrastructure.

This paper presents a thermal runaway prognosis scheme based on the big-data platform and entropy method for battery systems in electric vehicles. It can simultaneously realize the diagnosis and prognosis of thermal runaway caused by the temperature fault through monitoring battery temperature during vehicular operations. A vast quantity of real-time voltage monitoring data was collected in the National Service and Management Center for Electric Vehicles (NSMC-EV) in Beijing to verify the effectiveness of the presented method. The results show that the proposed method can accurately forecast both the time and location of the temperature fault within battery packs. Furthermore, a temperature security management strategy for thermal runaway is proposed on the basis of the Z-score approach and the abnormity coefficient is set to make real-time precaution of temperature abnormity.

The advancement of technology has led to a shift in the automotive industry from gasoline vehicles to electric vehicles. One of the key factors in modern vehicles is the communication between control devices and commands. This thesis presents a prototype of communication in the form of CAN BUS Protocol with CAN STANDARD SAE.J1939 for All-Terrain Vehicle (ATV) using MRS Developers Studio software to control the CAN Bus system. This thesis discusses the operation of communication from the assembly of the ATV, the packing of a 72 V 25Ah battery as a power source for the vehicle, the layout of the wiring system for receiving/sending communication signals for the 3 CAN Bus control boxes, and programming with MRS Developers Studio software used in communication. The communication of the vehicle in the ATV consists of receiving/sending signals of switches, headlights, left turn signal, right turn signal, brake light, emergency light, horn, forward-reverse gear, and accelerator. The results of the operation of the CAN Bus Protocol found that the CAN Bus control box has only 6 input and output pins, making the communication that has both receiving and sending signals insufficient. Therefore, starting from the CAN Bus1 control box acts as a receiver of signals from the forward-reverse gear switch sent to the output of the CAN Bus3 control box, the CAN Bus1 control box acts as a receiver of all light signals sent to the output of the CAN Bus2 control box. The signal transmission between devices will send signals through the CAN HUB line to ensure stability and minimal interference. In addition, the communication between devices from testing the operation of various switches on and off up to 1000 times found that this communication does not cause errors, making this communication reliable and stable.

Autonomous vehicles (AV)s will transform transport, but public opinion will play a key role in the decision how widely and quickly, they will be adopted. The purpose of the study presented here, was to investigate community’s views on the transition. As a method for primary data collection on public awareness, attitudes, and readiness to use autonomous cars, survey was conducted in Saudi Arabia. Following that, we have used statistical tools to analyse responses. Our findings indicate that the participants are mainly open to use new technology and had favourable attitude towards transition. Ordinal logistic regression model showed a wide variation in public opinion regarding the expected benefits that may accompany the transition, despite an average high score on this factor. Our findings reveal that awareness of AVs' benefits is more positively correlated to the age of participants. Perceived cost, on one side, and convenience, and safety, on the other side, have substantial impact on opinions. Investigation, presented here, shows how AVs are seen in Saudi Arabia. This can guide the development of AVs and deployment in that region.

The evolution of transportation has been inextricably tied to the progress of civilization. Through innovation, the automobile business has been working to improve safety, quality, and compliance with environmental regulations. Electric vehicles have made significant strides in this area, but optimizing their efficiency requires a special focus because they expend energy that can be recovered in a variety of ways. Energy harvesting, a cutting-edge technology that captures wasted energy from vehicles, has recently received a lot of attention as it constitutes a means to improve the efficiency of electric vehicles. Dissipated energy can be converted into electricity using regenerative energy recovery systems and put to various uses. This study tenders a thorough examination into energy recovery technologies which could be applied to the various types of energy dissipated in electric vehicles. Firstly, the paper investigates the possible sources of energy recoverable from an electric vehicle, as well as the various types of energy dissipated. Secondly, the article examines the energy recovery technologies most frequently used in vehicles, categorizing them according to the type of energy and application. Finally, it determines that with further research and development, energy harvesting holds considerable potential for improving the energy efficiency of electric vehicles. New and innovative methods for capturing and utilizing wasted energy in electric vehicles can be established. The potential benefits of applying energy recovery systems in electric vehicles is a vital issue for the automobile industry to focus on due to the potential benefits involved. The ongoing progress currently being made in this field is expected to play a significant role in shaping the future of transportation.

The motivations for the move to electrified vehicles are discussed with reference to their improved energy efficiency, their potential for lower CO2 emissions (if the electricity system is decarbonized), their lower (or zero) NOx/particulate matter (PM) tailpipe emissions, and the lower overall costs for owners. Some of the assumptions made in life-cycle CO2 emissions calculations are discussed and the effect of these assumptions on the CO2 benefits of electric vehicles are made clear. A number of new tribological challenges have emerged, particularly for hybrid vehicles that have both a conventional internal combustion engine and a battery, such as the need to protect against the much greater number of stop-starts that the engine will have during its lifetime. In addition, new lubricants are required for electric vehicle transmissions systems. Although full battery electric vehicles (BEVs) will not require engine oils (as there is no engine) they will require a system to cool the batteries – alternative cooling systems are discussed, and where these are fluid based, the specific fluid requirements are outlined.

With firm commitment to energy labelling, it is not a question of if but only of when the European Union will introduce an energy label for electric cars. To support such policy efforts, we conducted a scoping analysis of energy consumption and efficiency trade-offs for 342 electric car models sold in Europe. The results suggest that certified and real-world energy consumption average 19 ± 4 kWh/100 km and 21 ± 4 kWh/100 km, translating into drive ranges of 440 ± 120 km and 380 ± 110 km, respectively. Energy consumption is correlated with vehicle mass, frontal area, and battery capacity, but less so with rated power and vehicle price. Each 100 kg of vehicle mass and 0.1 m2 of frontal area tend to increase energy consumption by 0.20 ± 0.06 kWh/100 km and 0.72 ± 0.05 kWh/100 km. Raising battery capacity by 10 kWh increases vehicle mass by 145 kg, energy consumption by 0.6 kWh/100 km, and drive range by 45 km. Efficient vehicles are available at any price but drive range has a cost. Our findings point to considerable efficiency trade-offs that could be tapped by a dedicated energy label. We propose several options for categorizing vehicles on an efficiency scale from A to G, with and without additional utility parameters. Our analysis provides rationale for energy labelling of electric cars in Europe and could inspire similar analyses for other vehicle categories such as e-scooters, lightweight three- and four-wheelers, e-busses, e-trucks, and electric non-road machinery.

The work aims to improve the battery performance in hybrid electric vehicles (HEVs) by reducing the vehicle speed. We develop a specific protocol for managing battery use and optimizing the energy consumption rate to achieve this goal. The protocol automatically controls the driving operation, avoiding incompatible driving patterns with energy-saving mode and performance improvement. The protocol is submitted to a simulation process to predict the energy rate lowering and battery performance enhancement. The proposed protocol applies to any hybrid electric vehicle type and route conditions since it uses vehicle mass, drag and rolling coefficients, and road slope as variable parameters to determine the minimum energy consumption rate. We run experimental tests to validate the simulation data and the proposed protocol. Furthermore, the protocol applies to variable starting vehicle speeds, from 10 to 50 km/h, corresponding to the current driving patterns, sport, normal, and eco, set up by car manufacturers. A reduction of 10% in vehicle speed in urban and peripheral routes achieves a minimum energy rate, enhancing battery management. Current vehicle speed shows a deviation from optimum management of 18% while applying the vehicle speed reduction limits the deviation to 0.2%. Experimental results show a good agreement with simulation data with 94% accuracy. We tested the protocol for urban and peripheral routes with maximum vehicle speed limits of 60 and 90 km/h.

This paper provides a comprehensive overview of recent advancements in autonomous electric vehicle (AEV) within the specified region. It elaborates on the progress and comparative analysis of diverse subsystems, including energy storage, cell balancing for battery systems, vehicle charger layouts, electric vehicle motor mechanisms, and braking systems. Furthermore, this paper showcases several prototype autonomous electric vehicles as conclusive study findings.

This study conducts a comprehensive review of the integration of Hybrid Electric Turbochargers in mass-produced road vehicles. The primary objective is to inform the readers about the potential advantages and innovative applications of HETs. Employing a SWOT analysis, the research compares the performance of HETs on thermal engines versus traditional turbochargers and HETs on thermal engines versus HETs on hybrid engines based on a synthesis of multiple research findings. The study elucidates the fundamental mechanics of turbochargers, emphasizing their inherent benefits. Additionally, it introduces a novel approach with the integration of the hy-brid-electric turbocharger, demonstrating its capacity, supported by practical examples. The in-corporation of turbochargers with energy recovery mechanisms, including HETs, is shown to significantly contribute to emissions reduction. Moreover, the electric drive facilitates the utili-zation of hybrid turbines, particularly on low-displacement engines, resulting in enhanced effi-ciency, torque, and liter power. In conclusion, the research underscores the potential of HETs to substantially mitigate the environmental impact of the transportation sector by reducing emis-sions and conserving energy. Their ability to optimize the fuel efficiency and power output of internal combustion engines, coupled with the feasibility of downsized engines, positions HETs as an attractive option for both manufacturers and consumers. However, it is acknowledged that the implementation of HETs is in its early stages, necessitating further research to comprehensively understand their environmental impact. Despite this, the promising positive impact on the en-vironment designates HETs as a technology deserving close attention in the forthcoming years.

A number of decision support tools facilitating the use of Electric Vehicles (EVs) have been recently developed. Due to the EVs’ limited autonomy, routing and path planning are the main challenges treated in such tools. Specifically, determining at which Charging Stations (CSs) to stop, and how much the EV should charge at them is complex. This complexity is further compounded by the fact that charging times depend on the CS technology, the EV characteristics, and follow a nonlinear function. Considering these factors, we propose a path planning methodology for EVs with user preferences, where charging is performed at public CSs. To achieve this, we introduce the Electric Vehicle Shortest Path Problem with time windows and user preferences (EVSPPWP) and propose an efficient solution algorithm for it. Given an origin and a destination, the algorithm prioritizes CSs close to Points of Interest (POIs) that match user inputted preferences, and user-defined time windows are considered for activities such as lunch and spending the night at hotels. The algorithm produces flexible solutions by considering clusters of charging points (CPs) as separate CSs. Furthermore, the algorithm yields resilient paths by ensuring that recommended paths have a minimum number of CSs in their vicinity. The main contributions of our methodology are: modeling user-defined time windows, including user-defined weights for different POI categories, creating CSs based on clusters of CPs with sufficient proximity, using resilient paths, and proposing an efficient algorithm for solving the EVSPPWP. To facilitate the use of our methodology, the algorithm was integrated into a web interface. We illustrate the use of the web interface, giving usage examples and comparing different settings.

Electric Vehicles (EVs) are the need of the hour due to growing climate change problems linked with the transportation sector. Battery Thermal Management System (BTMS), which is accountable for certifying safety and performance of lithium-ion batteries (LiB), is the most vital part of an EV. LiB has auspicious gravimetric energy density but the heat generation due to chemical reactions inside a LiB during charging and discharging causes temperature rise which has a direct effect on LiB performance and safety. This study specifically focuses on aircooled BTMS, defines different types of air-cooled BTMS (active and Passive), discusses limitations associated with air-cooled BTMS, and investigates different optimization techniques and parameters to improve performance of air-cooled BTMS. Maintaining temperature within optimum range and uniform temperature distribution between cells of a battery pack are the major design parameters for improving the performance and efficiency of air-cooled BTMS. Various optimization techniques including cell arrangement with a battery pack, air-flow channel optimization, and air inlet/outlet position variations are discussed and each technique is thoroughly reviewed. Finally, it’s noted that passive air-cooled BTMS is not that effective for long-distance vehicles so most researchers shifted their focus toward active air-cooled BTMS. Active air-cooled BTMS requires a lot of power for effective performance. Lastly, the most recent field of air-cooled BTMS technology which is Air-Hybrid BTMS is discussed and declared a very promising solution for overcoming major limitations associated with air-cooled BTMS.

An indisputable fact about our planet is that its atmospheric temperature has risen dramatically during the past century. Combustion of fossil fuels and their subsequent greenhouse gas emissions are thought to be the main contributors to recent changes within the Earth’s ecosystem. The transportation sector and electricity generating power plants are each responsible for approximately one-third of these emissions. Shifting towards a cleaner and renewable resources to generate electricity is believed to omit a big portion of polluting substances. Improvements in vehicles’ fuel efficiency and the introduction of alternative fuels besides strategic plans to control travel demand are among the most promising approaches to alleviate emissions from the transportation sector. Recent technology advancements, however, drew much attention to the production and manufacturing of alternative fuel vehicles, electric vehicles in particular. Since these vehicles use electricity as part of or all their powertrain, assessing the amount of emissions they produce is closely tied to the cleanliness of the electricity source. In order for a valid comparison to be made between internal combustion and electric vehicles, hence, a life cycle assessment procedure needs to be followed from production stages to terminal life of vehicles. Involvement of numerous affecting factors during the lifetime of a vehicle on one hand, and the ambiguity in the exact source of electricity used to charge electric vehicles on the other hand bring about more complexities. The latter case is more commonly known as the marginal grid problem, which deals with how a combination of sources used to generate electricity can influence the life cycle emissions. There are also other concerns regarding the growth in fuel-efficient and electric vehicles. Transportation planners argue that new developments in the vehicle industry may attract more people to owning and driving cars. This phenomenon which is better known as a rebound effect not only will result in increased traffic congestion, but it can also outpace the environmental benefits from utilizing electric vehicles. Moreover, since fuel taxes comprise the majority of Highway Trust Funds, alternative ways to compensate for state and federal revenues should be devised. This paper is an attempt to review the existing literature to better elaborate on the role of the transportation sector in controlling climate change threats. More specifically, issues around the use of electric vehicles and how they can contribute to more environmentally friendly communities are discussed.

Due to nonlinear components in the charging piles of electric vehicles, harmonics and non-stationary signals in the electric vehicle charging load brings voltage and current distortion, seriously affecting the accuracy of the power-related calculation in non-sinusoidal environments. This paper proposed a new approach to calculate the active power and root mean square values from decomposed components using the adaptive chirp mode decomposition (ACMD) method on voltage and current. The advantage of the ACMD-based method is that it correctly provides the power-related quantities of harmonics or non-stationary components for the electric vehicle charging load. The performance of the proposed method was verified using synthetic signals and simulation tests. The experimental results presented better estimations for each quantity defined in IEEE Standard 1459-2010, compared with the discrete wavelet transform approach.

Smart cities and smart technologies have been incorporated into several axes to increase the comfort of life. The connected building's concept was introduced for this reason. However, it was utilized in power management for better organizing, greater buildings management, and monetary savings. Cars technologies and the number of vehicles are also involved; Nowadays, each house has at least one car. Technological evolution helped to make those cars intelligent and connected. In the latest versions, the majority of those cars were equipped with several sensors, several communication protocols and a principal electrical control unit (ECU), especially for the electric vehicle model. This type of architecture was an essential element in a smart city, thus, it helps to manage power and decide when a vehicle needs to be charged. Based on the smart city concept and using possible network communication between buildings and vehicles, EVs can share their own information related to the powerful experience on a specific path. This information can be gathered in a gigantic database and used for managing the power inside these vehicles. In this field, we propose in this paper a new approach for power management inside an electric vehicle based on bi-communication between vehicles and buildings. The proposed approach is founded on two essential parts; the first is related to vehicles’ classification and buildings’ recommendation according to different car positions. Two algorithms, related to the SVC and neural network was employed in this work for implementing the final process. Different possibilities and situations were discussed for this approach. The proposed method was tested and validated using Simulink/Matlab application. The state of charge of the used battery was compared at the end of this work, for two specified cases, for showing the contribution of this approach.

Transportation electrification has been identified as a crucial shift to be made for achieving energy security and emission reduction targets. Electric Vehicles (EVs) play a significant role in achieving these targets; therefore, there is a growing trend to develop new EV technologies and associated infrastructure. Along with these developments, EVs' Quality of Service (QoS) aspects should be enhanced to increase the number of EVs on the road. Various QoS factors are defined to assess the QoS of EVs, and one of the two aims of this paper is to provide a comprehensive review of these factors. The other aim is to review recent developments of associated communication technologies which are essential for exchanging information between EVs and charging stations. This paper serves as a comprehensive review and useful reference for researchers working on QoS aspects of EVs and associated communication systems aiming to enhance the QoS.

The thermal degradation of brakes has a significant impact on the stability of pure electric commercial vehicles during long downhill braking. Based on road slope and using fuzzy control method, design strategies for motor regenerative braking force and friction braking force distribution, with the aim of reducing friction braking force, improving braking stability, and recovering braking energy. By establishing driving conditions on roads with different slopes and using numerical analysis methods to verify the proposed control strategy. the results show that under the 6% constant slope driving condition, the vehicle maintains a constant speed of 30km/h downhill driving, and the braking energy recovery rate reaches 50.93%; Under the condition of 3% -6% gradient driving, the vehicle speed fluctuates slightly, but it can still be maintained at a constant speed of 30km/h for downhill driving through the adjustment of the motor's reverse drag characteristic, with a braking energy recovery rate of 49.97%; During the process of driving downhill at a constant speed, the frictional braking force does not participate in braking; The braking energy recovery rate is determined by the slope and braking deceleration.

The results shown in this paper extend our research group's previous work, which presents the theoretically achievable hydrogen engine-out NOx (H2-NOx) Pareto front of a hydrogen hybrid electric vehicle (H2-HEV). While the Pareto front is calculated offline, which requires significant computing power and time, this work presents an online-capable algorithm to tackle the energy management of a H2-HEV with explicit consideration of the H2-NOx trade-off. Through the inclusion of realistic predictive data on the upcoming driving mission, a model predictive control algorithm (MPC) is utilized to effectively tackle the conflicting goal of achieving low hydrogen consumption while simultaneously minimizing the NOx. In a case study it is shown that the MPC is able to satisfy user-defined NOx limits over the course of various driving missions. Moreover, a comparison to the optimal Pareto front highlights the MPC's ability to achieve close-to-optimal fuel-performance for any desired cumulated NOx target on four realistic routes for passenger cars.

Hybrid electric vehicles (HEVs) are perceived as a first step towards the future of sustainable transport. Of course, battery electric vehicles (EVs) are currently ideal for what is wanted to stand for transport in the future. The lack of infrastructure for these vehicles makes many potential users choose hybrid vehicles in these conditions. This paper analyzes the influence of the engine and electric motor of hybrid vehicles on their performances. Three engines with slightly different power and close characteristics were considered for configured models. Also, two electric motors with very different power but having a very close maximum continuous torque have been used in models. One is an induction motor, and the other is a permanent magnet synchronous motor. ADVISOR software has been used for vehicles configuration and simulation. Series and parallel hybrid vehicles have been considered. The main dynamic performances and the fuel consumption in two driving cycles were compared for the configured models. Three conventional models with the same engines used in HEVs have also been simulated for reference. The results highlight that in the case of HEVs, the choice of combination engine/electric motor is crucial in obtaining the best report between dynamic performances and low fuel consumption, and implicitly a low negative impact on the environment.

This work simulates the performance of lithium batteries for electric vehicles under different charge and discharge rates. The simulation is based on similarity factors for power, voltage and current, reproducing the real operation conditions of an electric vehicle at the model scale. Most current driving modes have been analysed corresponding to discharge rates from 0.1C to 0.37C. The simulation has also been applied to determine charging time using charge power in real conditions, from 6.1 kW to 18.3 kW (0.1 C to 0.3C). Driving conditions are obtained using equations for vehicle motion including all forces. Tests have been run under two configurations, continuous and alternate current circuits, to reproduce the two types of engines that electric vehicles use. The simulation shows very good agreement in charge and discharge processes, average deviation of 3% related to real conditions, and 1.6% between them, which proves the validity of the simulation process.

The recent advancements in the field of communication have led data sharing to become an integral part of today's smart cities with the evolution of concepts such as the internet of vehicles (IoV) paradigm. As a part of IoV, Electric Vehicles (EVs) have recently gained momentum as authorities have started expanding their Low Emission Zones (LEZ) in an effort to build green cities with low carbon footprints. Energy is one of the key requirements of EVs not only to support the smooth and sustainable operation of EV itself but to also ensure connectivity between the vehicles and infrastructure with controlling devices like sensors and actuators installed within an EV. In this context, renewable energy sources (such as wind energy) dramatically play their parts in the automobile sector towards designing the energy harvesting electric vehicles (EH-EV) to pare the energy reliance on the national grid. In this article, a novel approach is presented to achieve electric generation due to vehicle mobility to support the communication primitives in electric vehicles which enables plenty of IoV use cases in the presence of surplus energy at hand. A small-scale wind turbine is designed to harness wind power for converting it into mechanical power. This power is then fed to the onboard DC generator to produce electrical energy. Furthermore, the acquired power is processed through a regulation circuitry to consequently achieve the desired power supply for the end load, i.e. the batteries installed. The suitable orientation for efficient power generation is proposed on ANSYS-based aerodynamics analysis. The voltages induced by DC generator at No-Load condition are 35V while at Full-Load 25V are generated at rated current of 6.9A, along with the generation of power at around 100W (at constant voltage) at the rated speed of 90 mph for nominal battery charging. Moreover, the acquired data can be monitored via an android application interface by using a Bluetooth module.

Electric vehicles (EVs) are being endorsed as the uppermost successor to fuel-powered cars, with timetables for banning the sale of petrol-fueled vehicles announced in many countries. However, the range and charging times of EVs are still considerable concerns. Fast charging could be a solution to consumers' range anxiety and the acceptance of EVs. Nevertheless, it is a complicated and systematized challenge to realize the fast charging of EVs because it includes the coordinated development of battery cells, including electrode materials, EV battery power systems, charging piles, electric grids, etc. This paper aims to serve as an analysis for the development of fast-charging technology, with a discussion of the current situation, constraints and development direction of EV fast-charging technologies from the macroscale and microscale perspectives of fast-charging challenges. It is emphasized that to essentially solve the problem of fast charging, the development of new battery materials, especially anode materials with improved lithium ion diffusion coefficients, is the key. It is highlighted that red phosphorus is the most promising anode that can simultaneously satisfy the double standards of high-energy density and fast-charging performance to a maximum degree.

Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles has been discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we have compared the key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For the energy storage systems employing ultra capacitors, we have presented the characteristics such as cell voltage, cycle life, power density and energy density. Furthermore, we have discussed and evaluated the interconnection topologies for the existing energy storage systems. We have also discussed the hybrid battery-flywheel energy storage system as well as the mathematical modeling of the battery-ultracapacitor energy storage system. Toward the end, we have discussed energy efficient powertrain for hybrid electric vehicles.

Facing the charging difficulties of free-floating shared electric vehicle and the high cost of mobile charging for single demand, this article proposes a collaborative charging planning method based on the complementary advantages of fixed charging stations and mobile charging vehicles, which can charge shared electric vehicles more efficiently and reduce the charging cost at the same time. A bi-level programming model for fixed and mobile cooperative charging is constructed. The upper level of the model is the minimization model of system charging total cost, seeking the optimal charging scheme and the number of mobile charging vehicles. The lower model is a fixed and mobile cooperative charging path planning model, which calculates the optimal routes for mobile charging vehicles and the shared electric vehicles that need to be transferred to the fixed charging station. The example results show that the cost of the proposed fixed-mobile cooperative charging scheme is reduced by 12.6% compared with the pure fixed charging scheme and 14.9% compared with the pure mobile charging scheme

The utilization of fuel cells (FC) in automotive technology has experienced significant growth in recent years. Fuel cell hybrid electric vehicles (FCHEVs) are powered by a combination of fuel cells, batteries, or/and ultracapacitors (UCs). By integrating power converters with these power sources, the FCHEV system can overcome the limitations of using them separately. The performance of an FCHEV is influenced by the efficiency of the power electronics converter controller, as well as the technical efficiency of the power sources. FCHEVs need intricate energy management systems (EMSs) to function effectively. Poor EMS can lead to low efficiency and accelerated fuel cell and battery degradation. The literature discussed various types of EMSs, such as equivalent consumption minimization strategy, classical PI controller, fuzzy logic controller, and Mutative Fuzzy Logic Controller (MFLC). However, comparing different EMSs can be challenging due to the varying vehicle and system parameters, which might lead to false claims being made regarding system performance. This research aims to categorize and discuss the various topologies of FCHEVs, highlighting their pros and cons, and comparing several EMSs based on performance metrics such as State of Charge (SOC) and FC deterioration.

This paper presents a new methodology to evaluate the performance of an electric vehicle hybrid power system consisting of a fuel cell and a supercapacitor. The study compares the results with those obtained for a battery-electric vehicle. The methodology extends to three driving modes, ECO, NORMAL, and SPORT, corresponding to conservative, moderate, and aggressive acceleration, and three driving conditions, low, medium, and high energy demand. We develop a simulation process to evaluate the energy consumption and the energy rate of a specific electric vehicle used as a prototype for the study. The methodology applies to a driving route that includes acceleration, deceleration, braking, and constant speed segments reproducing standard driving conditions in urban journeys. The proposed method considers combined driving modes, ECO, NORMAL, and SPORT, in each acceleration process, with variable fractions, 0% to 100%, for each mode. This methodology optimizes the simulation results as it fits the current driving way in urban environments. The simulation results show an average reduction in energy consumption of 37% and 27.1% in vehicle weight, contributing to lower energy use. The study concludes that using a hybrid power system, fuel cell/supercapacitor, instead of a battery in electric vehicles is beneficial, especially in journeys with frequent acceleration processes.

In recent years, integration of electric vehicles (EVs) has increased dramatically due to their lower carbon emissions and reduced fossil fuel dependency. However, charging EVs could have significant impacts on the electrical grid. One promising method for mitigating these impacts is the use of renewable energy systems. Renewable energy systems can also be useful for charging EVs where there is no local grid. This paper proposes a new strategy for designing a renewable energy charging station consisting of wind turbines, a photovoltaic system, and an energy storage system to avoid the use of diesel generators in remote communities. The objective function is considered to be the minimization of the total net present cost, including energy production, components setup, and financial viability. The proposed approach, using stochastic modeling, can also guarantee profitable operation of EVs and reasonable effects on renewable energy sizing, narrowing the gap between real-life daily operation patterns and the design stage. The proposed strategy should enhance the efficiency of conventional EV charging stations. The key point of this study is the efficient use of excess electricity. The infrastructure of the charging station is optimized and modeled.

Microgrid is one of the important ways to accommodate distributed PV generation. However, the optimal planning scheme of microgrids is closely related to solar resources, load characteristics, and the key components of microgrid systems. This paper uses HOMER software to conduct collaborative planning for grid-connected PV-storage microgrids with electric vehicles in multiple scenarios. Specifically, we construct a multi-scenario capacity optimization model for PV generation, energy storage, and converters, considering both the cleanliness and economic performance of microgrids. The performance is quantified using indices such as net present cost, levelized cost of electricity, and carbon dioxide emission under multiple scenarios. Finally, we conduct extensive case studies on a business park in Wuhan, China, to compare and discuss the planning performance under multiple scenarios, as well as sensitivity analysis with specific cases.

Aiming at the current optimization problem of electric vehicle charging path planning, a charging path optimization strategy for electric vehicles under the "Traffic-Price-Distribution" mode is proposed. This strategy builds an electric vehicle charging and navigation system based on road traffic network model, real-time electricity price model and distribution network model. Based on Dijkstra shortest path algorithm and Monte Carlo time-space prediction method, the goal is to minimize the charging cost of electric vehicles. Optimal charging path. The simulation results of MATLAB and MATPOWER show that the electric vehicle charging path optimization strategy can better solve the local traffic congestion problem and improve the safety and stability of the distribution network on the basic of fully considering the convenience of electric vehicle charging.

As the emission regulations get more and more stringent in the different fields of energy and environmental systems, the electric and fuel cell vehicles (FCV) have attracted growing attention by automakers, governments, and customers. Research and development efforts have been focused on devising novel concepts, low-cost systems, and reliable electric/fuel cell powertrain. In fact, electric and fuel cell vehicles coupled with low-carbon electricity sources offer the potential for reducing greenhouse gas emissions and exposure to tailpipe emissions from personal transportation. In particular, Pedal Assisted Bicycles (PAB) popularity is rising in urban areas due to their low energy consumption and environmental impact. In fact, when electrically moved, they are zero emission vehicles with very low noise emissions, as well. These positive characteristics could be even improved by coupling a PAB with a fuel cell based power generation system, thus increasing the vehicle autonomy without influencing their emissions and consumption performances. In this paper, four types of vehicles are compared from an environmental and accessibility point of view: conventional car, bus, electric PAB and hydrogen fuel cell PAB; for such vehicles, the respective utilization stages are accounted for, i.e. without considering the manufacturing process. The analysis has been carried out comparing different vehicles performance along different routes of an Italian middle-size city, Viterbo, which represents a very good pilot case as its Municipality is adopting many solutions suggested by European Union (EU) through the planning tool called Sustainable Energy Action Plan (SEAP). The comparison is based on an ad-hoc developed mathematical procedure, which includes environmental (greenhouse gas and air pollution emissions), health (pollutants toxicity levels) and accessibility time (waiting times) indicators. According to this analysis, electric and fuel cell PAB exhibit interesting advantages over the other vehicles. However, the global economic efficiency of electric or fuel cell apparatus depends substantially on the exploited source of electrical energy.

- Power grids of the future will likely incorporate more renewable energy distributed generation (REDG), also known as alternative energy systems. REDG units are increasingly being used in electrical transmission networks because to the overall positive effects they have on power networks. REDG systems are the backbone of smart electric networks and are essential to the operation of the smart grid. These REDG systems can additionally improve system reliability by providing some customers with a backup generator in the event of power interruptions. The report offers a thorough evaluation of the existing body of information on the topic of electric vehicles' (EVs') future interactions with the smart grid. The combination of the EVs' potential deployment and the smart grid's conceptual goal presents challenges in electric grid infra-structure, communication, and control. The proposal for connecting EVs to the grid is based on research into cutting-edge smart metering and communication systems. In the context of the vehicle-to-grid (V2G) phenomenon, the possibilities, benefits, and limitations of various EV smart charging systems are also fully examined. There is a fast growing percentage of distributed energy that is wind and solar photovoltaic. Their variable power output, however, introduces fresh challenges for those responsible for organizing, operating, and controlling the power grid. While fluctuations in the electric grid are problematic, they may be mitigated by the entry of EVs into the energy market. As such, we do a comprehensive literature search to learn more about the exciting research gap that needs to be filled and the most recent possible answer that involves EVs. Additionally, we take a close look at the practicality of the sophisticated V2G technology. The smart grid is a developing concept for the world's energy infrastructure, and this study analyzes in depth how EVs interact with it.

Electric vehicles, being able to reduce pollutant and greenhouse gas emissions and shift the economy away from oil products, can play a major role in the transition towards low-carbon energy systems. However, the related increase in electricity demand inevitably affects the strategic planning of the overall energy system as well as the definition of the optimal power generation mix. With this respect, the impact of electric vehicles may vary significantly depending on the composition of both total primary energy supply and electricity generation. In this study, Italy and Germany are compared to highlight how a similarity in their renewable shares not necessarily leads to a CO₂ emissions reduction. Different energy scenarios are simulated with the help of EnergyPLAN software assuming a progressive increase in renewable energy sources capacity and electric vehicles penetration. Results show that, for the German case, the additional electricity required leads to a reduction in CO₂ emissions only if renewable capacity increases significantly, whereas the Italian energy system benefits from transport electrification even at low renewable capacity. Smart charging strategies are also found to foster renewable integration; however, power curtailments are still significant at high renewable capacity in the absence of large-scale energy storage systems.

The increase in the emission of greenhouse gases (GHG) is one of the most important world problems. Decreasing of GHG emission is a big challenge in the future. Transportation sector uses a significant part of petroleum production in the world and it leads to an increase in the emission of GHG. The result of this issue is that population of the world befoul environment of transportation system automatically. Electric Vehicles (EV) have a potentiality to solve a big part of the GHG emission and energy efficiency issues such as stability and reality of energy. The energy actors and their research team determine some targets for 2050; hence, they hope to decrease the world temperature to 6 °C in the best and 2 °C in the normal condition. Fulfillment of these scenarios needs suitable grid infrastructure but in most of the countries, the grid does not have a suitable background to apply those scenarios. In this paper, some problems about energy scenarios, energy storage systems, grid infrastructure and communication systems in supply and demand side of the grid and its solutions have been investigated.

Electric vehicles (EVs) will have a greater need for the amount of electricity needed to charge them as their popularity grows. It is anticipated that in order to accomplish this objective, it will be essential to implement a variety of solutions for grid transportation that are designed to complement one another and to make significant changes to the transmission infrastructure. It is possible to reduce the amount of energy that is lost on the power network through strategic planning and control, which may include economic models and methods to engage and reward users. This would eliminate the need for grid upgrades. Charging electric vehicles can also assist alleviate problems with transmission systems that are caused by the allocation of electric vehicles (EVs) using bidirectional charging method. The most significant problems that can occur with a transmission network are power loss and unstable voltage. Adding EV units to the transmission network is typically an effective method for resolving these challenges. As a result, EVs need to have the appropriate arrangement and dimensions. This research establishes where and how many electric vehicles (EVs) should be in a radial transmission network both before and after the adjustment is made. An artificially intelligent (AI) approach, known as a hybrid genetic algorithm particle swarm optimization (HGAIPSO), is used both before and after the radial network modification to find the optimal EV location and size. When electric vehicles are coordinated in an active transmission network, power losses are decreased, voltage profiles are raised, and system stability is increased. These benefits can be attributed to the greater use of electric vehicles. The simulation found that incorporating EVs into the testing system resulted in a considerable decrease in the quantity of power that was wasted. The minimal bus voltage of the system also undergoes similar kinds of enhancements. According to the findings of the comparative study, the proposed method mitigates both the voltage fluctuations and the power losses that occur in the transmission system. For type 1, type 2, and type 3 EV allocations, the IEEE-30 bus test system reduced real power loss by 40.70%, 36.24%, and 42.94%, respectively. IEEE-30 bus voltage reaches 1.01 pu.

There is currently an exponential growth in the electric vehicle market, which will require an increase in the electrical grid capacity to meet the demand of charging. If, on the one hand, the introduction of energy generation from renewable sources can be used to meet that requirement, the intermittent nature of some of these sources poses challenges to the required real time equilibrium between generation and consumption. The impossibility of controlling generation from some of these sources leads to the attempt of controlling and manage the consumption of electricity, according with the levels of generation. In this context, the emergence of smart grids has introduced mechanisms that guarantee a balance between consumption and generation of electricity. One of these mechanisms is the smart charging of electric vehicles. Effective smart charging relies on communication between the supply equipment and the electric vehicle, en-abling the adjustment of the energy transfer according with the generation levels. Thus, there is a necessity for a standardised system that guarantees this communication. In this context, the ISO 15118 standard, allows high level communication mechanisms, much beyond the basic control solutions offered by the IEC 61851-1 specification. In this context, this paper presents the development of a charge emulation system, based on the ISO 15118 communication protocol and discusses its application for demand response purposes.

Coaxial rotors can be found in multirotor vehicles for the added thrust compared to independent rotors while keeping similar area footprints but, performance losses should be considered. This experimental study analyzes the effects of varying motor throttle and propeller pitch values in motor-propeller systems with two to four coaxial rotors. The results show that in a two-rotor coaxial system, to lessen the adverse effects of a front rotor’s backwash and to operate at the maximum performance, only the back motor should be operated initially up to 75% duty cycle before using the front motor up to its 75% duty cycle. Additional thrust requirements should be generated from the back rotor and then from the front rotor up to their maximum duty cycles. In two, three, and four-rotor coaxial setups, total thrust output generated is 1.6, 2.1, and 2.5 times the thrust output at system thrust performance of 86%, 76%, and 66%, respectively of that of an isolated rotor. In a four-rotor coaxial setup, maximum system performance is achieved when the propeller pitch values are gradually increased from the first to the last rotor. The gradual increments in propeller pitch values also result in more uniform thrust sharing among rotors.

Nowadays, there are several electric vehicle (EV) on the market, due to the innovation of technology that promotes the new components such as battery, transmission, electric motors. This paper proposes a design approach for the configuration synthesis and simulation of the in-wheel-hub motor transmissions with the six-link mechanisms. The synthesis process shows 6 mechanisms with six members and eight joints, 15 new clutchless motor transmissions and 16 new clutched motor transmissions. A novel motor transmission in the feasibility of the synthesized configurations is selected as an example to analyze the working principle with operation modes and power flow paths. And, this design is modeled for the simulation process that generates the results of operation mode transition and energy regulation.

In this study, a novel two-layer control scheme aimed at enhancing the dynamic performance of a nonlinear vehicle through the utilization of an optimal control approach grounded in predictive control principles is presented. The first layer of our control system focuses on the computation of an optimized rotational torque that ensures lateral dynamic stability. This torque is subsequently translated into differential forces acting on the wheels, employing the inverse tire model to derive the desired longitudinal slips. These slip values are then transmitted to the second layer of our control system. In the second layer, the electric motors associated with the vehicle's wheels dynamically adjust the input torque to accurately track the desired slip, thereby ensuring the overall stability of the electric vehicle. In view of the paramount importance of energy consumption in electric vehicles, we adopt optimal control strategies to substantially minimize battery utilization. To this end, careful selection of appropriate weighting coefficients in the control laws enables us to maintain the electric motors within their permissible operational range while simultaneously minimizing battery energy consumption for desired slip tracking. Extensive simulation results validate the effectiveness of our proposed control system in proficiently managing nonlinear effects and safeguarding the vehicle's stability.

Concern about the planet's future has motivated governments around the world to adopt policies that promote more responsible energy consumption. Reducing emissions of the principal cause of climate change man-made greenhouse gases requires greater system efficiency and the utilization of renewable energy sources. The generation of electricity is the single largest source of hazardous pollutants, followed by the transportation sector. This is true due to the extreme reliance on petroleum and its derivatives that these industries have. Electric mobility is receiving more attention from businesses, governments, and academic institutions as a means to reduce the negative impacts of current energy consumption. Without measures to address common technical issues, electric vehicles would strain electrical infrastructure. In order to determine the best strategy for switching operations and feeder reconfiguration, this research presents a helpful hybrid genetic algorithm particle swarm optimization (HGAPSO) method. Meet transmission limits while decreasing real power losses and enhancing the system's bus voltage profile. The effects of HGAPSO on power losses and voltage distributions in the network are analyzed. Then, a report is produced contrasting GA and PSO. This cross-breeding of GA and PSO led to HGAPSO. This important problem can be solved more effectively with the use of new or improved algorithms. To find the optimal reconfiguration and properly rearrange transmission network interconnection, it employs a plethora of heuristic optimization algorithms. The objective is to limit bus voltage variations while maintaining the system's radial topology and minimizing power usage. The reliability and performance of the procedure were evaluated using MATLAB's IEEE 33-bus communication network. The outcomes demonstrate the effectiveness of the proposed technique, which cuts down on power waste during standalone runs and speeds up the processing time.

This paper defines the two main causes of global warming in the UK and seeks to cut climate change using green hydrogen made from renewable electricity sources. Combustion vehicles powered by fossil gasoline and diesel emit a third of UK climate-warming CO2 while buildings heated by natural gas provide a quarter. First, current UK grid problems are defined: Electricity grid, gas grid and petroleum grid. Then experiments on the private energy community of Keele University allow electrolysis demonstrations producing green hydrogen for buildings and vehicle refueling. Next, the model supply chain is planned and tested. Finally, experiments and calculations are outlined, analyzing the optimum system design criteria proposed. We conclude that economic green hydrogen can displace diesel and gasoline in vehicle transport, while also displacing natural gas for combined heat and power in buildings. In addition, the prospect in 2023 is that profits can now be made all along the green hydrogen supply chain, such that new businesses involved in clean communities can beat the National Grid monopoly and other large dominant fossil grid companies.

The adoption of a fully battery-based Electric Vehicles (EVs) in Kuwait apparently seems to be less than one percent and hence Kuwait has the lowest indexed-rank among countries around the globe. The absence of fast-charging stations for fully-battery-based EVs and landlords' reluctance to install home fast-charging plugs are the primary reasons for extremely low adoption rates in this country. Because the government of Kuwait is reluctant to give permission for expatriates to own property in Kuwait, fully battery-based EV ownership is restricted to Kuwaiti nationals who reside in their private properties. To accomplish the present objectives, a quantitative de-scriptive method (closed-ended questions) was used to collect data from a sample of 227 Kuwaiti nationals who owned gasoline cars. The finding of the present study indicates that more than half of the participants stated that they preferred to purchase an EVs within the next three years if certain criteria were met, inclusive of government-controlled pricing policies and charging stations availability, fast-driving lanes, and free of charge parking spaces. Furthermore, more than 40 percent of respondents stated that they would contemplate purchasing an electric vehicle if the price of gasoline or diesel increased by 19 to 50 percent. The finding also indicates that more than forty percent of respondents said that believed that EVs are fire- and crash-safe and roughly half of the participants would pay between 6 and 20% more for an EVs because they be-lieve that EVs are environmentally friendly and significantly faster than gasoline vehicles. In addition, participants rewarded EVs with an excellent mark for their environmental, economic, and technological attributes and benefits.

With the continuous improvement and observable benefits of electric vehicles (EVs), major logistic companies are introducing more EVs into their conventional fleets. This gives rise to a new type of vehicle routing problem with mixed vehicles, where heterogeneous internal combustion vehicles (ICVs) and electric vehicles are considered in route planning. In addition, certain deliveries that are not efficient on any type of vehicles, are outsourced to third-party common carriers. In this paper, we define this problem as a mixed vehicle routing problem with common carriers (MVRPC). The objective of such problems is to minimize the transportation costs by considering routes with ICVs and EVs, the possibility of visiting recharging stations, outsourcing options, and drivers’ layover regulations. This variant of the vehicle routing problems has many practical applications, particularly in the design of long-haul transportation and last-mile delivery services. Effective MVRPC solutions play a key role in promoting the going Green image and optimally allocating resources. The problem has received limited attention in the literature likely because addressing all the needed aspects is especially challenging. To solve the large-scale problem, we develop a branch-and-cut pricing framework that relies on strong cuts and customized labeling algorithms. Numerical experiments highlight the effectiveness of our algorithm. This success can be attributed to tailored critical resources, dynamically bounded bidirectional labeling procedures, strong dominance criteria, and implementation strategies.

The article studies and analyzes a hybrid power source system, lithium battery, and fuel cell assisted by flexible photovoltaic panels for hybrid electric vehicles (HEVs). The new configuration operates based on optimizing the performance of the dual power source to maximize the available energy and operating time. The operational mode simulates specific driving conditions in urban, peripheral, and intercity routes under variable driving patterns and traffic conditions. The simulation results show that the new configuration increases the hybrid electric vehicle performance, providing higher reliability despite the more complex design. The proposed system reduces the external power supply dependence and avoids unexpected sudden stops due to energy exhaustion. The new configuration also extends the vehicle driving range depending on driving mode and route conditions. The vehicle driving range extension may reach up to 276.3 km. The hybridization of fuel cells with lithium battery and PV panel assistance enhances battery management, reducing battery servicing and increasing lifespan. The proposed topology is cheaper than a single fuel cell power system by 17.2%, reducing the total cost related to the single battery power system if we consider the battery recharge for its entire lifespan. The cost saving is 2.6% for the American market and 14.8% for the European one.

In Electrified Vehicles, the cost, efficiency, and durability of electrified vehicles are dependent on the energy storage system (ESS) components, configuration and its performance. This paper, pursuing a minimal size tactic, describes a methodology for quantitatively and qualitatively investigating the impacts of a full bandwidth load on the ESS in the HEV. However, the methodology can be extended to other electrified vehicles. The full bandwidth load, up to the operating frequency of the electric motor drive (20 kHz), is empirically measured which includes a frequency range beyond the usually covered frequency range by published standard drive cycles (up to 0.5 Hz). The higher frequency band is shown to be more efficiently covered by a Hybrid Energy Storage System (HESS) which in this paper is defined as combination of a high energy density battery, an Ultra-Capacitor (UC), an electrolytic capacitor, and a film capacitor. In this paper, the harmonic and dc currents and voltages are measured through two precision methods and then the results are used to discuss about overall HEV efficiency and durability. More importantly, the impact of the addition of high-band energy storage devices in reduction of power loss during transient events is disclosed through precision measurement based methodology.

This investigation is part of a topical situation where wireless equipment is gradually being used for energy transfer, particularly for autonomous systems and the use of decarbonized en-ergies. A characteristic example of decarbonized autonomous use is linked to the substitution of thermal engine vehicles by electric vehicles (EVs) equipped with energy storage batteries. This response was considered in an ecological context of reducing air pollution and defending plane-tary biodiversity, which are currently vital. These EVs ultimately operate thanks to the wireless charging of their batteries when stationary or running. By changing long-established means of transport that have become a threat to biodiversity, it is necessary to ensure that innovative re-placement solutions protect this biodiversity. In addition, the construction of wireless power transfer (WPT) battery chargers for these EVs must offer an optimal ecology of clean energy saving. In such a context, the two concepts of One Health (OH) and Responsible Attitude (RA) will find their place in the design and control of WPT tools in EVs. This contribution aims to il-lustrate and analyze the roles of the green and non-wasteful OH and RA approaches in the de-sign and control of WPT embedded in EVs for the smart city (SC) environment. In the paper, WPT tools are first introduced. The design and control of EV battery charging tools are then examined. The biological effects on living tissues due to the electromagnetic field (EMF) radia-tion of WPT are analyzed. The phenomena and equations governing the design of WPT and the effects of EMF radiation are then exposed. The OH and RA approaches in the SC context are af-terward analyzed. The protection against the unsafe effects of WPT tools in the SC environment is consequently explored. The analyses followed in the paper are supported by examples of lit-erature.

Introduction: Transportation logistics play a pivotal role in facilitating both individual mobility and supply chain operations on a global scale. However, conventional transportation systems have contributed significantly to urban congestion and environmental degradation. In response to these challenges, there is a growing momentum towards the adoption of intelligent transportation systems (ITS) and sustainable transportation solutions. This research aims to explore the potential of renewable energy sources to power electric vehicles (EVs) and flying automobiles, thereby accelerating the transition towards a low-carbon future. Objectives: The primary objectives of this research are as follows: - Investigate the role of renewable fuels in powering both EVs and flying cars to reduce reliance on fossil fuels and mitigate carbon emissions. - Analyze the dynamic growth and sustainability implications of advancements in battery technology and alternative renewable energy sources, such as Hydrogen Fuel Cells. - Evaluate the challenges and opportunities associated with the integration of flying vehicles into existing transportation infrastructure, focusing on safety, airspace control, and infrastructure development. - Review insights and guidance to stakeholders on promoting sustainable transportation solutions for EVs and flying cars through the utilization of renewable energy sources and the reduction of carbon emissions from the literature. Methodology: This research employs a mixed-method approach, combining literature review, and causal loop diagram analysis. The literature review provides a comprehensive understanding of current trends, challenges, and opportunities in sustainable transportation and renewable energy technologies. A causal loop diagram is utilized to visualize the dynamic interactions between various factors influencing the adoption of EVs and flying cars, including technological advancements, policy interventions, and market dynamics. Results and Discussion: The findings of this research highlight the significant potential of renewable energy sources, such as solar, wind, and hydrogen, in powering both EVs and flying cars. Advances in battery technology and hydrogen fuel cells are identified as key drivers in accelerating the transition towards sustainable transportation systems. However, challenges remain in terms of infrastructure development, safety regulations, and airspace management for flying vehicles. Nevertheless, there is a noticeable advancement in reducing carbon emissions and reliance on fossil fuels with the widespread adoption of EVs. By harnessing renewable energy and implementing effective policy measures, stakeholders might able to promote the widespread adoption of sustainable transportation solutions, thereby contributing to environmental protection and societal well-being. Conclusion: Literally, this research underscores the importance of sustainable transportation solutions in addressing urban congestion, environmental pollution, and energy security concerns. By leveraging renewable energy sources and embracing technological advancements, such as EVs and flying cars, stakeholders pave the way for a more sustainable and resilient transportation system. However, concerted efforts are needed to overcome existing challenges and ensure the successful integration of these technologies into existing infrastructure. Through collaborative partnerships and innovative initiatives, a low-carbon future for transportation logistics can be achieved, benefiting both the environment and society as a whole.

This work aims to study and analyze a hybrid power system for electric vehicles consisting of a dual low and high-rate lithium battery block and a fuel cell. In this configuration, the high-rate lithium battery powers the electric vehicle in high power demand processes like acceleration mode or on an uphill road; the low-rate battery operates at a low output power range, servicing the auxiliary systems and low power loads and the fuel cell supplies energy at intermediate power demand conditions, normal driving mode, constant velocity, or flat and downhill terrain. The dual power system improves global efficiency since every power unit operates optimally depending on driving conditions. Power sharing optimizes the lithium battery performance and fuel cell capacity, minimizing the size and weight of each energy system and enlarging the driving range. A comparative study between different lithium battery configurations and fuel cell shows an efficiency improvement of 31.4% for the hybrid dual battery block and fuel cell operating in low, high, and intermediate output power ranges, respectively. The study bases on a simulation process recreating current driving conditions for electric cars in urban, peripheral, and intercity routes. An alternative solution consisting of a hybrid system, fuel cell and a high rate lithium battery produces a 29 % power gain.

Electric vehicles (EVs) are pivotal in addressing the escalating environmental crisis. While EV drivetrains excel compared to those of vehicles with internal combustion engines (ICE), their energy storage systems are hampered by limited range, lifespan, and lengthy charging times. Hybrid energy storage systems (HESS) present a viable current solution to these issues. This review thoroughly explores state-of-the-art in the emerging field of multisource EVs that utilize HESS, incorporating any combination of batteries (BTs), supercapacitors (SCs), flywheels (FWs), fuel cells (FCs), and/or transmotors. In addition, the paper systematically categorizes and evaluates different hybrid configurations, detailing potential topologies and their respective advantages and limitations. Moreover, the paper examines diverse control algorithms used to manage these complex systems, focusing on their effectiveness and operational efficiency. By identifying current research gaps and technological challenges, this study aims to delineate future research directions that could enhance the deployment and optimization of multisource EVs, thereby addressing critical challenges such as energy density, system reliability, and cost-effectiveness.

This article explores the crucial task of estimating State of Charge (SOC) in Hybrid Electric Vehicles (HEVs) and examines the applicability of various regression models for this purpose. We delve into the strengths and limitations of Linear Regression, Support Vector Regression (SVR), and Neural Network Regression (NNR) in the context of SOC estimation. Linear regression provides a simple and interpretable baseline, SVR extends this to nonlinear relationships, while NNR emerges as a powerful tool with adaptive capabilities. The choice of model depends on factors such as data characteristics, interpretability, and computational resources. As the field evolves, the article advocates for a nuanced approach, possibly incorporating hybrid models, to achieve robust and accurate SOC estimation, contributing to the ongoing enhancement of HEV efficiency and sustainability.

This paper proposes the impact of plug-in electric vehicles integrated into power distribution system based on voltage dependent control. The plug-in electric vehicles was modeled as the static load model in power distribution systems under balanced load condition. The power flow analysis is determined by using the basic parameters of the electrical network. The main point of this study are compare with voltage magnitude profiles, load voltage deviation, and total power losses of the electrical power system. There are investigating the affected from constant power load, constant current load, constant impedance load and plug-in electric vehicles load, respectively. The IEEE 33 bus test system is used to test the proposed method by assigning each load type to a balanced load in steady state and applied the solving methodology based on the bus injection to branch injection matric, branch current to bus voltage matrix, and current injection matrix to solve the power flow problem. The simulation results showed that the plug-in electric vehicles load had the lowest impact compared to other loads. The lowest plug-in values for the electric vehicle loads were 0.062, 119.67 kW and 79.31 kVar for the load voltage deviation, total active power loss and total reactive power loss, respectively. Therefore, this study can be verified that the plug-in electric vehicles load were affected to the lowest of the electrical power system in condition to same sizing and position. So that, in condition to the plug-in electric vehicles load added into the electrical power system with the conventional load type or complex load type could be considered that the affected from the plug-in electric vehicles load in next study.

Existence of hybrid electric vehicles nowadays is one of the emerging technologies worldwide. Different countries are using this technology on their transportation, and nowadays, a lot of other countries are gradually adapting on using this kind of innovation in their transport system. Philippines is among these countries which aims to have a sustainable mobility by embracing the use of hybrid - electric vehicles but still lots of consumers are lacking awareness on this technology. Lack of information on the social, environmental, and economic potential benefits are among the main issues of Philippines consumers that really affect its uptake. The goal of this study was to evaluate the consumer’s perception and purchase intention on the transition of hybrid - electric vehicles for sustainable mobility and transportation here in the Philippines. It aimed to assess consumer awareness with hybrid - electric vehicles and its potential benefits to human beings in terms of social, environmental, and economic aspects. The study also gauged the impact of this perception on consumer’s purchase intention on hybrid - electric vehicles. These objectives were attained using the Descriptive Statistics and Correlation Analysis. Using these methods, the results revealed that majority of the consumers believed on the positive benefits of transitioning to hybrid - electric vehicles in terms of social, environmental, and economic aspects. This study also showed that these benefits have high impact on the consumer’s intention of buying or having a hybrid – electric vehicle. Electric vehicle is one of the most effective measure of decarbonizing the transport system. Furthermore, government and automotive businesses must strengthen their drive in spreading awareness of hybrid – electric vehicle’s benefits to consumers to further support country’s effort for development.

This study establishes life cycle assessment model to quantitively evaluate and predict material resource consumption, fossil energy consumption and environmental emissions of plug-in hybrid electric vehicles (PHEVs) by employing the GaBi software. the envi-ronmental impact of different vehicle working conditions, power battery degradation scenarios and mileage scenarios on the operation and use stage of PHEV, BEV and HEV is distinguished. The findings indicate that under urban, highway, and aggressive driving conditions, PHEVs' life cycle material resource and fossil fuel consumption exceed that of BEVs but are less than HEVs. Battery degradation leads to increased material resource consumption, energy use, and environmental emissions for both PHEVs and BEVs, en-vironmental emission of BEVs are more sensitive than those of PHEVs to the impact of power battery degradation. Among different-mileage scenarios, PHEVs demonstrate the least sensitivity to increased mileage regarding life cycle material resource consumption, with the smallest increase. Future projections for 2025 and 2035 suggest life cycle GWP of HEV, PHEV and BEV in 2035 is 1.21×104, 1.12×104 and 1.01×104 kg CO2-eq respectively, which is decreased by 48.7%, 30.9% and 36.1% compared with those in 2025. The out-comes of this study are intended to bolster data support for the manufacturing and de-velopment of PHEV, BEV and HEV under different scenarios and offer insights into the growth and technological progression of the automotive sector.

Demand response flexible loads can provide fast regulation and ancillary services as reserve capacity in power systems. This paper proposes a joint optimization dispatch control strategy for source-load system with stochastic renewable power injection and flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs). Specifically, the optimization model is characterized by a chance constraint look-ahead programming to maximal the social welfare of both units and load agents. By solving the chance constraint optimization with sample average approximation (SAA) method, the optimal power scheduling for units and TCL/PEV agents can be obtained. Secondly, two demand response control algorithms for TCLs and PEVs are proposed respectively based on the aggregate control models of the load agents. The TCLs are controlled by its temperature setpoints and PEVs are controlled by its charging power such that the DR control objective can be fulfilled. The effectiveness of the proposed dispatch and control algorithm has been demonstrated by the simulation studies on a modified IEEE 39 bus system with a wind farm, a photovoltaic power station, two TCL agents and two PEV agents.

Electric vehicles are in focus and growing, with new adoptions every day. Its wide use represents a new scenario and challenge for the electrical power system due to the high charge storage requirements. Predicting these loads using artificial neural networks proves to be a very efficient tool for solving time series. This work uses a multilayer perceptron network through supervised backpropagation training with Bayesian regularization to improve generalization, reducing overfit- ting errors. In this research, the aggregation of the actual consumption of 200 homes and 348 plug-in electric vehicles is used in the training and forecasting process, and to validate the method developed, MAPE was used. Short-term forecasts were made considering the year’s four seasons, predicting the next 24 hours of total aggregate demand from homes and vehicles. The methodology presented significant and relevant results using hybrid training for the problem, with the potential to be applied in the real world.

Photovoltaic (PV) systems, which are renewable energy sources, are increasingly being utilized in distributed generation. To maintain a stable energy balance, energy storage systems such as batteries (BAT) play a vital role. Electric vehicles (EVs) offer a promising solution with rechargeable battery systems for operation. The BATs must maintain their state of charge within design boundaries, despite intermittent PV and load power fluctuations, and advanced power control and management techniques are essential for their effective operation. The article evaluates an intelligent controller that uses a Sliding Mode Control adaptive deep learning algorithm Convolutional Neural Networks (SMC-CNN) and a unique Strategy for managing supervisory power. (SPMS) for photovoltaic systems with battery energy storage for better regulation of DC bus voltage. SMC-CNN offers exceptional resilience and seamless functionality, minimizing fluctuations in DC bus voltage in the control strategy design requirements. The primary goals are maintaining a consistent power supply and ensuring continuous service by preventing system components from exceeding capacity. This study aims to enhance the control of DC bus voltage in the PV and battery system, with the primary significance focusing on the following aspects. The advanced SPMS has been created, incorporating control system constraints to enhance SOC balancing speed and reduce fluctuations in DC bus voltage. Power flow management involves optimizing energy flow between the PV system, battery system, and load while minimizing battery capacity requirements. The proposed SMC-CNN and SPMS are demonstrated through real-time simulation using Matlab/Simulink, as demonstrated in comprehensive case studies.

Following electrification of automotive transport, studies on the penetration of Electric Vehicles (EVs) are widespread, especially in defined contexts, e.g. cities. As major transport hubs, airports fall within contexts worth of interest. In this work, a forecast of the demand for electric mobility in an Italian international airport (Rome, Fiumicino) is presented. First, a wide review of proposed sce-narios on the penetration of EVs at international and national level and available data on local automotive transport are presented, as preliminary study for the definition of reference scenarios for the local context. Then the methodology proposed is presented and applied to the specific case study. Finally, a preliminary sizing of the required charging infrastructure is reported. The proposed approach can be considered as reference for similar studies on electrical mobility in other airport areas around the world.

Electric vehicles (EV) are getting more commonplace in the transportation sector in recent times. As the present trend suggests, this mode of transport is likely to replace the internal combustion engine (ICE) vehicles in near future. Each of the main EV components has a number of technologies that are currently in use or can become prominent in the future. EVs can cause significant impacts on the environment, power system, and other related sectors. The present power system can face huge instabilities with enough EV penetration; but with proper management and coordination, EVs can be turned into a major contributor to the successful implementation of smart grid. There are possibilities of immense environmental benefits as well, as the EVs can extensively reduce the greenhouse gas emission from the transportation sector. However, there are some major obstacles for EVs to overcome before replacing the ICE vehicles totally. This paper is focused on reviewing all the useful data available on EV configurations, energy sources, motors, charging techniques, optimization techniques, impacts, trends, and possible directions of future developments. Its objective is to provide an overall picture of the current EV technology and ways of future development to assist in future researches in this sector.

To regulate the growth of battery-based electric vehicles (BEVs) in Indonesia and mitigate their impact on the power grid's supply-demand balance, the government can adjust BEV regulations and offer subsidies. The Jawa-Madura-Bali (Jamali) electrical system, being the largest in Indonesia [1], faces the challenge of accommodating the increasing number of vehicles [2]. Subsequent to analyzing Jamali's electricity supply using data from the National Electricity Company (RUPTL) [3], simulations are constructed to model the grid's demand side. Input variables such as Jamali's population, internal combustion engine (ICE) and electric vehicle populations, initial charging time (ICT), slow- and fast-charging ratios, and BEV charge load curves are simulated. Scenario variables like supply capacity growth speed, vehicle population growth speed, subsidy impact on EV attractiveness, ICT, and fast-charging ratio are subsequently simulated for 2024-2029. Four key simulation outcomes are identified: the best-case scenario (Scenario 1776), achieves the highest EV growth with minimal grid disruption, resulting in a 45.38% EV percentage in 2029 and requiring an annual allocation of 492 billion rupiah to match supply with demand. The worst outcome scenario leads to a 23.12% EV percentage, necessitating 47.566 billion rupiah for EV subsidies in 2029. Additionally, the most and least probable scenarios based on literature research are evaluated. This simulation and its results provide insights into EV growth's impact on the grid's balance in one presidential term from 2024 to 2029 [4], aiding the government in planning regulations and subsidies effectively. Improving the simulation's accuracy depends on refining input variables over time.

Abstract: This study investigates the effect of Electric Vehicle Charging Stations (EVCS) on light-duty Electric Vehicle sales and the Total California rebates. Electric Vehicle Charging Stations (EVCS) are infrastructure facilities that allow for the charging of electric vehicles (EVs). I applied the before and after model with California state county using its community-level attributes and used the difference-in-difference design to identify a strategy for estimating the causal effects of these attributes. These attributes include EVCS installations by time, EV sales by time, rebate applications by time, the number of multi-unit housing units, and the median income levels. The empirical ev-idence of this study shows the estimated relationship between public EV charging installation and EV sales overall by community income level, housing density, and other relevant factors, the result shows that EVCSs is highly correlated with light duty Electric Vehicle adoption. For the investors, policymakers, and other stakeholders, this study provides evidence of a threshold of EVCS on Light Duty Electric Vehicle adoption and rebate in California.

Electric field assisted combustion is an important means to improve fuel combustion efficiency. This paper conducts extensive research on flame characteristics under different forms and different application methods of electric fields, emission of soot particles and simulation status. Different flame parameter measurement methods will lead to different degrees of error, and perfect numerical simulation can make simple predictions on experimental data. Most of the current numerical simulations are in two dimensions, and it is necessary to develop a complete and accurate three-dimensional model to simulate and predict the characteristics of the flame under an electric field. The emission of soot particles is also affected by the electric field, and reasonable electric field parameters can greatly reduce the emission of soot particles. It is recommended to conduct centralized measurement of different fuels under the electric field under high pressure and temperature conditions, so as to be able to develop a wider and more accurate flame dynamics and chemical model under the electric field.

The goal of the study presented in this article is to investigate interconnection issues of the Intelligent Electrical Networks (IENs) with the existing ones, as well as the deployment of the appropriate load forecasting demand techniques. The concept of IENs has recently emerged as a potential solution to the global energy problem, due to the more flexible dispersed production units that are involved. In the same context, additional issues, such as the reduction of production-transfer-distribution costs as well as power losses are analyzed as well, via the penetration of Renewable Energy Sources (RESs). To this end, it is necessary to use appropriate advanced infrastructure, where in combination with modern telecommunication networks will make possible the full exploitation of IENs. Moreover, to make the electricity system more efficient, to avoid voltage and frequency imbalance issues and to implement optimal production and consumption planning, the Electric Load Forecast (ELF) is a vital process and plays a key role in the planning as well as in the management of electricity systems. Therefore, recent state of the art approaches in ELF methods are discussed as well.

The paper proposes a methodological way to compensate the imbalance between energy generation and consumption using the battery block of electric vehicles as energy reservoir through the well-known Vehicle to Grid system (V2G). The method is based on a simulation process developed by the authors that take into consideration the daily fluctuations of energy consumption as well as the power level generated by an energy source, either conventional, renewable or hybrid. The study shows that for very large electric vehicle fleet the system renders non-viable, since the remaining energy in the battery block to let the electric vehicle usable for running during daytime avoids to compensating for the energy grid imbalance, only allowing to cover a percentage of the energy imbalance, which the proposed methodology may optimize. The analysis of the proposed methodology also shows the viability of the system when being applied to a small fleet of electric vehicles, not only compensating for the energy imbalance but also preserving the required energy in the battery of the electric vehicle to make it run. The method allows predicting the optimum size of electric vehicle battery, which depends on the energy generation level, coverage factor of the energy imbalance and size of the electric vehicle fleet.

This article presents a novel approach to understanding the influence of electric Energy on space-time curvature. EFE actual calculations refer solely to gravitational charges. In this article, I expand them to include the electric charge with calculations carried specifically for electric charges, with exciting results. The article leverages the universal consistency of physical laws, applying the known effects of gravitational charges on space-time curvature to electric charges. The research focuses on calculating the specific impact of electric charges on space-time curvature, rooted in the principle that space-time curvature is omnipresent. The findings reveal that space-time curvature's alteration due to an electric charge is influenced by various factors, including the charge's location, magnitude, mass, density, associated electric field, and its motion through space-time. Notably, the conversion factor for electric charges differs from the conversion factor for gravitational charges. These insights significantly enhance our understanding of electromagnetism and general relativity, paving the way for a more profound comprehension of electric charges.

This paper provides a proof of concept for a linear electric generator that can be used to harvest energy from various sources of linear motion, such as vibrations, free-piston engines and wave energy. The generator can be used to power small electronic devices, such as sensors, or charge household batteries. Literature was reviewed to develop an understanding about the applications, control methods, excitation methods and mechanics of rotating and linear electric machines. A bidirectional, two-sided linear machine was designed with two stator cores and a single mover core. The stator windings and mover winding can be independently excited, allowing for three modes: no mover excitation, DC excited mover, and AC excited mover. Simulations results showed that the magnetic flux generated by DC excited stator cores were concentrated in the centre of the mover core. The use of two stator cores eliminates lateral flux in the mover core when it is not excited, minimising attraction and repulsion forces. Parametric analysis showed that flux cutting occurred in all operation modes, verifying that the generator will produce power when operating. Hardware tests produced an output current when the machine is electrically and mechanically excited, verifying the proposed design.

The state-of-the-art smart city has been calling for an economic but efficient energy management over large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze and control electric loads of all users in system. In this paper, we employ the popular computer vision techniques of AI to design a non-intrusive load monitoring method for smart electric energy management. First of all, we utilize both signal transforms (including wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods to map one-dimensional current signals onto two-dimensional color feature images. Second, we propose to recognize all electric loads from color feature images using a deep neural network with multi-scale feature extraction and attention mechanism. Third, we design our method as a cloud-based, non-intrusive monitoring of all users, thereby saving energy cost during electric power system control. Experimental results on both public and our private datasets have demonstrated our method achieves superior performances than its peers, and thus supports efficient energy management over large-scale Internet of Things.

This study conducted experiments to optimize the electro-dewatering of sludge using a dynamic model calculation method and the Wild Horse optimizer. Specifically, we explored two factors that might influence dewatering: the composition of the electrode materials and the strength of the electrical field. Compared to other electrodes commonly used in electro-dewatering—such as RuO2/Ir2O3-Ti, graphite, Ir2O3/Ta2O5-Ti, and Ti—the Electro-kinetic Geosynthetics (EKG) electrode exhibited superior performance. This was determined by considering both energy consumption and model establishment. Our findings also identified electric-field strength as a crucial factor determining the final moisture content. As the electrical intensity increased, the dewatering effect improved, aligning with our model calculations. However, we observed a steep increase in energy consumption per kilogram of filtrate when the electric-field strength exceeded a certain threshold. To optimize the energy consumption of the dehydration process, we constructed a multi-factor model. The simulation results from this model confirmed that electric-field intensity is the primary factor affecting dehydration. It was established that 10 V/cm is a critical threshold for the process.

The olfactory system is capable of distinguishing individual odorants from among a virtually unlimited number. Fish, for example, detect changes in the electric field environment induced by prey and other sources. Floral electric fields exhibit variations in pattern and structure, which can be discriminated by bumblebees. We have constructed an electric field sensor, which, in the course of focussing on achieving maximum sensitivity and consistency, ultimately resembles features of the insect sensorium. A “fingerprint” 3D plot ( time, frequency range, voltage amplitude), representing the emitted electric field profile, is presented for each of a variety of odorants and other chemicals. The substance-specific electric-field emission and identification is not impeded by containers or barriers or distance.

To enable the deployment of battery-electric vehicles (BEV) as passenger cars in the private transport sector, a suitable charging infrastructure is crucial. In this paper a methodology for efficient spatial distribution of charging infrastructure is evaluated by investigating a scenario with a market penetration of BEVs of 100 percent (around 1.3 million vehicles). It aims towards the development of various charging infrastructure scenarios - including public and private charging - which are suitable to cover the charging demand. Therefore, these scenarios are investigated in detail with focus on number of public charging points, their spatial distribution, the available charging power and the necessary capital costs. For the creation of those charging infrastructure scenarios a placement model is developed. It uses the data of a MATSim (Multi-Agent Transport Simulation) traffic simulation of the metropolitan area of Berlin to evaluate and optimize different distributions of charging infrastructure. The model uses a genetic algorithm and the principle of multi-objective optimization. The capital cost of the charging points and the mean detour car drivers must cover additionally are used as optimization criteria. Using these criteria should lead to cost efficient infrastructure solutions which provide high usability at the same time. The main advantage of the method selected is that multiple optimal solutions with different characteristics can be found and suitable solutions can be selected by using other criteria subsequently. The optimized charging infrastructure solutions show capital costs between 624 and 2950 million euro. Users must cover an additionally mean detour of 254m to 590m per charging process to reach an available charging point. According to the results a suitable ratio between charging points and vehicles is between 11:1 and 5:1. A share of fast charging infrastructure (>50kW) of less than ten percent seems to be sufficient, if it is situated at main traffic routes and highly frequented places.

In this research, the concept of the instability of tension in Microgrid still becomes the focus of study and purpose. Scrutinously, First-Swing Instability occurs in the system in noted changes with properties of making the system unstable or restore to a state of stability. The latter calls itself as a phenomenon of re-synchronization. Allied to aspects of system instability, it is evident that by connecting fast electric chargers for battery-electric vehicles to the distribution system from microgrids, they increase the necessary basic electrical charges and reduce the stability of the electrical energy system. Transient electrical currents have the characteristic of severely impacting microgrids not prepared in connected states or island of the main system. The occurrences of the electromagnetic incompatibility of the inrush current, due to their unpredictability and intermittent characteristic, have complex identification studies. With the elevation of electricity demand by battery-electric vehicle chargers, concerns about the effects of the integration of the electricity grid increase proportionally. The research described in this article emphasizes the influence of fast chargers’ initialization on the power variation related to the re-synchronization phenomenon and its mitigation of the stability of electricity microgrid. An optimization algorithm based on particle swarm optimization (PSO) implements the development of virtual impedances. The PSO optimization algorithm analyzes all possible operating points and aims to maximize the stability index of microgrid, maintaining reactive energy incompatibilities at the minimum level supported. The fractional objective function facilitates the service to these goals simultaneously. The optimization algorithm is implemented in a case study and the corresponding virtual impedance is incorporated into the microgrid. Voltage levels are verified as a condition in the optimization process, comparing system effectiveness.

Solar powering the increasing fleet of electrical vehicles (EV) demands more surface area than may be available for photovoltaic (PV) powered buildings. Parking lot solar canopies can provide the needed area to charge EVs, but are substantially costlier than roof- or ground-mounted PV systems. To provide a lower-cost PV parking lot canopy to supply EV charging beneath them, this study provides a full mechanical and economic analysis on three novel PV canopy systems: (1) exclusively wood, single parking spot spanning system, (2) wood and aluminum double parking spot spanning system, and (3) wood and aluminum cantilevered system for curbside parking. All systems can be scalable to any amount of EV parking spots. The complete designs and bill of materials (BOM) of the canopies are provided along with basic instructions and are released with an open source license that will enable anyone to fabricate them. The results found single-span systems have cost savings of 82%-85%, double-span systems save 43%-50%, and cantilevered systems save 31%-40%. In the first operation year, the PV canopies can provide 157% of energy needed to charge the least efficient EV currently on the market if it is driven the average driving distance in London ON, Canada.

The development of electric aircrafts is becoming an important technology for achieving the goals set by the European Commission for the reduction of gases emissions by 2050 in the aeronautical transportation system. However, there is a gap between the values of specific power in commercial electric machines and the ones required for aeronautical applications. Therefore, the search for alternative materials and non-conventional designs is mandatory. One emergent solution is using superconducting machines and systems to overcome the current limits of conventional electrical machines. This work reviews the new hybrid and all-electric aircraft tendencies, complementing it with recent research on the design and development of high specific power superconducting machines. This includes the main topologies for hybrid and all-electric aircraft, with an overview of the ongoing worldwide projects of these types of aircrafts, systematizing the main characteristics of their propulsion systems. It also includes the research on superconducting machines for the purpose of high specific power, considering the impact on the redesign of aircraft systems in the electrical, cooling, and fuel source sense.

In this paper, we examined the temperature variations of key constituents during the operational cycle of a custom-built electric vehicle. The vehicle was made employing a motor of 10kW, a KLS7275D controller, and a lithium-ion battery. Temperature sensors for monitoring was affixed to the motor, controller, and battery, facilitating a comprehensive analysis of thermal behaviors. A controlled 20 minute driving tests encompassing both high and low speeds were executed to scrutinize temperature variations. Our findings reveal that the controller exhibited the most substantial temperature fluctuation compared to the battery and motor. Results shows the importance of thermal management strategies for components to ensure optimal driving performance and component reliability in electric vehicles.

A self-propelled electric tea seed-planter is designed to address the lack of tea seed seeding equipment, low seeding efficiency, and poor seeding uniformity. Based on the physical characteristics of the tea seeds and the agronomic requirements of tea planting, the key structure of the planter is designed, and the structural parameters are determined. The motor and battery configurations are determined by calculating the power consumption of the entire machine. In a field experiment, the driving gear (A), ditching depth (B), and planting gear position (C) are used as test factors, and the acceptance index (AI), replay index (RI) and leakage index (LI) are used as test indexes. The test results showed that the driving speed and planting gear position significantly affect the acceptance index, replay, and leakage index, whereas the ditching depth has little effect on the performance indexes. When the driving speed was medium, ditching depth was 30 mm, and planting gear position was in the third gear, the corresponding acceptance index was 87.22%, the replay index was 8.89%, and the leakage index was 3.89%. The test prototype has a good working quality and can provide theoretical guidance for production practices.

Zero-emission trucks for regional and long haul missions are an option for fossil-free freight. The viability of such powertrains and system solutions was studied conceptually in project ESCALATE for trucks with GVW of 40 tonnes and beyond through various prime mover combinations. The study covers battery and fuel cell power sources with different degrees of battery electric as well as H2 and fuel cell operation. As a design basis, two different missions with a single-charge/H2 refill were analysed. The first mission was the VECTO long haul profile repeated up to 750 km, whereas the second was a real 520 km on-road mission in Finland. Based on the simulated energy consumption on the driving cycle, on-board energy demand was estimated, and the initial single-charge operational scenarios were analysed with five different power source topologies. The traction motors of the tractor were dimensioned so that a secondary mission of GVW up to 76 tons on a shorter route can be operated. Based on the powertrain and vehicle model, various infrastructure options for charging and H2 refuelling strategy as well as various operative scenarios with indicative total cost of ownership (TCO) were analysed.

The present study reports a critical review of NIO's business model, considering the evolving landscape of the electric vehicle market and servicing. The objective of this study is to develop a comprehensive framework that facilitates the identification of key elements characterizing a company's business model and highlights ongoing transformations, crucial for adaptation and survival in a rapidly changing environmental context. Focusing on the case study of NIO, a relatively young Chinese original equipment manufacturer (OEM) specializing in high-tech electric cars, the research delves into the challenging scenario of the Chinese electric vehicle market, which has recently faced a bubble in 2023. The market proliferation, supply chain disruptions, and price wars triggered by Tesla have resulted in a survival struggle for numerous automotive startups, leaving larger companies with increasing market shares. Despite facing adversities, NIO managed to secure a promising segment, catering to premium-range battery electric vehicles (BEVs), establishing a competitive advantage through differentiation. By pursuing ambitious investments, the company aims to create economies of scope and achieve cost leadership, venturing into new market sectors and vertically integrating the production chain. Given NIO's agility in adapting to market conditions, aggressive entry into new segments, and a strategic vision for the future, it serves as an excellent candidate for testing and validating the proposed framework. The research sheds light on NIO's trajectory and offers insights into its potential for sustained growth in the dynamic electric vehicle market.

As we all know, contemporary electromagnetic theory is based on the macroscopic electromagnetic theory established by the 19th century. Because of the lack of microscopic cognition in that era, various electromagnetic phenomena could only be explained and described at the macro level, and their origins could not be explained at the micro level. And because of the microscopic understanding of atomic physics in the 20th century, new progress has been made. Considering that it is now the 21st century, this paper attempts to establish a new theory of microscopic interpretation of classical electromagnetic theory based on the new cognition of atomic physics in the 20th century. Starting from the general assumption, the current will be interpreted as the momentum flow produced by the directed collision between electrons; the charge will be interpreted as a form of expression of electron motion; the voltage is interpreted as the potential difference (energy level difference) of the electron orbit. In the end, this paper successfully developed a new microscopic electromagnetic theory by introducing microscopic atomic physics and rigid body mechanics models to Maxwell's macroscopic electromagnetic theory.

Nanocrystalline La_1-xNd_xFeO₃ powders with different concentrations of Nd³⁺ have been synthesized by modified Pechini method. Their structure was studied by X-ray powder diffraction (XRD). Further, La_1-xNd_xFeO₃ nanoceramics were prepared by high pressure sintering technique. The luminescence spectra of the powders were investigated as a function of concentration of active dopant to check the possible energy transfers observed due to Nd³⁺ concentration changes. The electrical and magnetic properties of the powders and ceramics were investigated to determine the effect of Nd³⁺ doping on the dielectric permittivity and magnetization in the wide frequency range.

We report on ground-based measurements of the atmospheric electric field (Ez= -Potential Gradient (PG)) and current density (Jz) that were conducted at two locations in Israel. One is the Emilio Segre cosmic ray station located on Mt. Hermon (34.45 N, 2020 m AMSL) located in northern Israel near the Syrian-Lebanon border and at the other at the Wise astronomical observatory in the Negev desert highland plateau of southern Israel (31.18 N, 870 m AMSL). We searched for possible effects of strong, short-term solar events on the potential gradient and the vertical current density, as disruption to the Global Electric Circuit are often observed following strong solar events. The first case study (St. Patrick Day, 17 March 2015) was classified as the strongest event of 2015. The second case study (8 Sep 2017) was categorized as the strongest event of 2017 and one of the twenty strongest events on record to date. The results show that the electrical parameters measured at ground level at both stations were not affected during the two massive proton events and the ensuing geomagnetic storms. The magnetospheric shielding in lower latitudes is strong enough to shield against flux of energetic particles from solar events, obscuring any impact that may be noticeable above the local daily variations induced by local meteorological conditions (aerosol concentrations, clouds, high humidity, and wind speed) which were investigated as well.

By 2020, over 100 countries expanded electric and plug-in hybrid electric vehicle (EV/PHEV) technologies, with global sales surpassing 7 million units. Governments are adopting cleaner vehicle technologies due to proven environmental and health implications of internal combustion engine vehicles (ICEVs), evidenced by the recent COP26 meeting. This article proposes an agent-based model of vehicle activity as a tool for quantifying energy consumption by simulating a fleet of EV/PHEVs within an urban street network at various spatio-temporal resolutions. Driver behaviour plays a significant role in fuel consumption, thus, simulating various levels of individual behaviour enhancing heterogeneity should provide more accurate results of potential energy demand in cities. The study found that 1) energy consumption is lowest when speed limit adherence increases (low variance in behaviour) and is highest when acceleration/deceleration patterns vary (high variance in behaviour) and 2) on average, for tested vehicles, EV/PHEVs were £116.33 cheaper to run than ICEVs across all experiment conditions. The difference in the average fuel costs (electricity and petrol) shrinks at the vehicle level as driver behaviour is less varied (more homogeneous). This research should allow policymakers to quantify the demand for energy and subsequent fuel costs in cities.

This work targets the development of a mobility control system for high-speed series hybrid electric tracked vehicles, which operate with independent traction motors for each track. The scope of the research encompasses the modeling of the series hybrid powertrain specific to military tracked vehicles, as well as an in-depth analysis of their dynamic behavior. Subsequently, the study conducts a critical review of the mobility control approaches sourced from literature, identifying key techniques relevant to high-inertia vehicular applications. Building on the foundational models, the study puts forward a robust closed-loop mobility control system aimed at ensuring precise and stable off-road vehicle operations. The system's resilience and adaptability to a variety of driving conditions are emphasized, with a particular focus on handling maneuvers such as steering and pivoting, which are challenging operations for tracked vehicle agility. The performance of the proposed mobility control system is tested through a series of simulations, covering a spectrum of operational scenarios. These tests are carried out in both offline simulation settings, which permit meticulous fine-tuning of system parameters, and real-time environments that replicate actual field conditions. The simulation results demonstrate the system's capacity to improve vehicular response and highlight its potential impact on the future designs of mobility control systems for the heavy-duty vehicle sector, particularly in defense applications.

In some clinical applications pulsating currents are delivered into a body region for therapeutic purposes. In this paper we analyze the generated thermal field with the aim of determining the amplitude, period and duration of those stimuli, guaranteeing that the temperature of the interested tissue stays below the necrosis threshold.

ABSTRACT The work is focused on determining the energy transfer from EV’s to grid for different operating conditions. The study has been simulated on a scaled model and later validated through experimental tests on batteries of 20 kWh, 40 kWh, 60 kWh and 80 kWh for EV’s energy rates from 130 Wh/km to 180 Wh/km. Tests have been developed to evaluate the energy transfer from driving distance between 0 and 50 km. Results have proved there is a linear dependence between energy transfer and energy rate. Experimental and simulated values have been correlated showing a 99.6% accuracy. The ratio of energy transfer to battery energy capacity depends not only on this latter parameter, but also on the energy rate of the electric vehicle. Experimental tests have shown a variation from a minimum transfer ratio of 54.1% for the highest electric vehicle energy rate and lowest battery energy capacity and power transfer to grid rate to a maximum of 88.6% for the highest power transfer to grid rate and battery energy capacity and lowest electric vehicle energy rate.

BACKGROUND: Functional constipation can lead to painful defecations, fecal incontinence, and abdominal pain, significantly affecting a child's quality of life. Treatment options include non-pharmacological and pharmacological approaches, but some cases are intractable and require alternative interventions like neuromodulation. A subtype of neuromodulation, called Transcutaneous Posterior Tibial Nerve Stimulation (TPTNS), comprises electrical stimulation at the ankle level, by means of electrodes fixed to the skin. TPTNS is a minimally invasive, easy-to-apply technique that can potentially improve constipation symptoms in the pediatric population by stimulating the sacral nerves. AIM: To evaluate the clinical results and applicability of TPTNS as an adjuvant treatment for children and adolescents with functional constipation. METHODS: Between April 2019 and October 2021, 38 patients diagnosed with functional constipation according to the Rome IV Criteria were invited to participate in the study. The study followed a single-center, uncontrolled, prospective cohort design. Patients received TPTNS for 4 or 8 weeks, with assessments conducted immediately after the periods of TPTNS and 4 weeks after the end of the intervention period. The data normality distribution was determined by the Shapiro-Wilk test. Wilcoxon test and Student's t-test for paired samples were used to compare quantitative variables, and the McNemar test was used to compare categorical variables. RESULTS: Of the 38 enrolled patients, 28 children and adolescents with intractable function constipation completed the study, receiving TPTNS for 4 weeks. Sixteen patients (57.1%) extended the intervention period for 4 extra weeks, receiving 8 weeks of intervention. TPTNS led to significant improvements in stool consistency, frequency of defecation, and bowel function scores, with a reduction in abdominal pain. Quality of life across physical and psychosocial domains showed substantial enhancements. The quality of life-related to bowel habits also improved significantly, particularly in lifestyle, behavior, and embarrassment domains. The positive effects of this intervention are seen relatively early, detected after 4 weeks of intervention, and even 4 weeks after the end of the intervention. TPTNS was well-tolerated, with an adherence rate of approximately 78%, and no adverse effects were reported. CONCLUSION: TPTNS is an adjuvant treatment for intractable functional constipation, improving bowel function and quality of life. The effects of TPTNS were observed relatively early and sustained even after treatment cessation.

Hybrid electric vehicles are certainly one of the key solutions for improving fuel efficiency and reducing emissions, especially in special vehicle application and with the use of CO2 neutral fuels. Determining the energy management strategy and finding the optimal solution with regard to the aforementioned goals re-mains the one of the main challenges in the design of HEV. This paper presents a new vehicle modeling method, with an emphasis on HEVs, which is based on the frequency analysis of emissions and consumption according to the current specific traction power of the vehicle. The evaluation of the newly introduced model in the RDE, NEDC and WLTP cycle was performed and the results were compared with the standard verified vehicle model that was created in AVL's CruiseM software package. Positive traction energies have positive deviations of between 0.35% and 2.85%. The largest deviation in CO2 emissions was recorded for the HEV model in the RDE cycle and in the non-hybrid model in the WLTP cycle of 3.79% and 4.4% respectively, all other combinations of cycle and vehicles had deviations of up to about 1%. As expected, the largest relative deviations were recorded for NOx emissions from 0.13% to 9.62% for HEV in the WLTP cycle.

Conversion of electromagnetic energy into magnetohydrodynamic energy occurs when the electric conductivity changes from negligible to finite values. This process is relevant during the epoch of reheating of the early Universe at the end of inflation and before the emergence of the radiation-dominated era. We find that conversion into kinetic and thermal energies is primarily the result of electric energy dissipation and that the magnetic energy plays only a secondary role in this process. This means that, since electric energy dominates over magnetic energy during inflation and reheating, significant amounts of electric energy can be converted into magnetohydrodynamic energy when conductivity emerges early enough, before the relevant length scales become stable.

In current electric vehicles, the traction battery is intended to store energy. When designing this battery different parameters must be taken into account in order to arrange the battery/module/cells in the mechanically and thermally safest configuration. Moreover, the battery layout must have a correct dynamic behavior in possible collisions. In the present study, different battery configurations plus added energy absorbers are analyzed. In order to do that, an internal combustion vehicle modeled with finite elements is applied as the reference model. The structural behavior of the different battery configurations in the event of a side collision is examined. Firstly, the safest arrangement is stablished, both with respect to cabin intrusion and thermal runaway propagation. Secondly, the safest arrangement that guarantees the safety of the occupants in the event of a side collision is analyzed but using the MADYMO. This software includes experimentally validated dummies which allow have an insight in the stresses experienced by occupants. On the one hand, the analysis states that the battery pack inclusion in the vehicle increases the stiffness of the car floor, resulting in fewer intrusions in the passenger compartment. Therefore, a greater safety for the occupants is reached. On the other hand, none of the configurations analyzed has shown enough safety against the phenomenon of Thermal Runaway.

The rhythmical nature of the cardiovascular system constantly generates dynamic mechanical forces. At the center of this system is the heart which must detect these changes and adjust its performance accordingly. Mechano-electric feedback provides a rapid mechanism for detecting even subtle changes in the mechanical environment and transducing these signals into electrical responses which can adjust a variety of cardiac parameters such as heart rate and contractility. However, pathological conditions can disrupt this intricate mechanosensory system and manifest as potentially life-threatening cardiac arrhythmias. Mechanosensitive ion channels are thought to be the main proponents of mechano-electric feedback as they provide a rapid response to mechanical stimulation and can directly affect cardiac electrical activity. Here we demonstrate that the mechanosensitive ion channel Piezo1 is expressed in zebrafish cardiomyocytes. Furthermore, chemically prolonging Piezo1 activation in zebrafish results in cardiac arrhythmias indicating that this ion channel plays an important role in mechano-electric feedback. This also raises the possibility that Piezo1 gain of function mutations could be linked to heritable cardiac arrhythmias such as atrial fibrillation in humans.

In this theoretical study we report on molecular electrostatic potential (MEP) of titled molecules confined by repulsive potentials of cylindrical symmetry mimicking a topology. Our calculations show that the spatial restriction significantly changes the picture of MEP of molecules in quantitative and qualitative sense. In particular, the drastic changes of MEP as a function of the strength of spatial confinement are observed for the BrCN molecule. This preliminary study is the first step in the investigations of the behavior of MEP of molecular systems under the orbital compression.

In view of the problem of charging and hydrogen filling facilities construction in the transition from fuel vehicles to electric vehicles and hydrogen fuel cell vehicles, in order to meet the electric energy demand of electric vehicles and hydrogen energy demand of hydrogen fuel cell vehicles at the same time, this paper puts forward a method of siting and capacity determination of electric-hydrogen refueling integrated station (EHRIS) based on Voronoi diagram and particle swarm algorithm based on calculating the demand of charging and filling of hydrogen in vehicles. Firstly, OD pair(Origin-Destination) is used to represent the starting point and end point of the car to portray the travel demand of the car, and on the basis of the traffic network model, the shortest driving path of the new energy car is determined by Dijkstra algorithm, and Monte Carlo simulation is used to get the electric-hydrogen energy demand of the car; secondly, the Voronoi diagram is introduced to classify the service scope of the electric-hydrogen charging station to determine the equipment capacity of the electric-hydrogen charging station, while taking into account the electric-hydrogen charging and refueling capacity. Secondly, a Voronoi diagram is introduced to divide the service scope of the EHRIS, determine the capacity of the equipment in the EHRIS, and consider the distance constraints between the sites of EHRIS, so as to make the division of the site and service scope more reasonable. Finally, a dynamic optimal current model framework for distribution networks based on second-order cone relaxation is established, and each element of the active distribution network is planned, so that the distribution network can operate safely and stably after being connected to the EHRIS. With the objective of minimizing the total social cost of EHRIS and considering the constraints of the charging equipment and hydrogen production and injection equipment of the EHRIS, a siting and capacity model to meet the electric-hydrogen energy demand of electric and hydrogen fuel cell vehicles is developed, and solved by a particle swarm algorithm. Finally, the simulation planning is carried out with Sioux Falls city and IEEE33 network, which ensures the stable operation of the power grid while meeting the energy demand of automobiles, and the results verify the effectiveness and feasibility of the proposed method.

Electrifying aircraft, a crucial advancement in the aviation industry, aims to cut pollutive emissions and boost energy efficiency. Traditional aircraft depend on fossil fuels, which contribute significantly to greenhouse gas emissions and environmental pollution. Despite progress in electric propulsion and energy storage technologies, challenges such as low energy density and integration issues persist. This paper provides a comprehensive thematic and bibliometric analysis to map the research landscape in aircraft electrification, identifying key research themes, influential contributors, and emerging trends. The study applies natural language processing to unstructured bibliographic data and cross-sectional statistical methods to analyze publications, citations, and keyword distributions across various categories related to aircraft electrification. The findings reveal significant growth in research output, particularly in energy management and multidisciplinary design analysis. Collaborative networks highlight key international partnerships, with the United States and China being key research hubs, while citation metrics highlight the impact of leading researchers and institutions in these countries. This study provides valuable insights for researchers, policymakers, and industry stakeholders, guiding future research directions and collaborations.

This study investigates the impact of geomagnetic storms that occurred on June 8, 22-23, and 25, 2015, on the ionosphere in the low-latitude and equatorial regions of Brazil. By examining various data sources, such as solar wind parameters from the ACE satellite, GPS vertical total electron content (VTEC), magnetometer data, and the SAMI2 model, we aimed to simulate the effects of storms on the ionosphere in these regions. Two methods were employed to separate DP2 and Disturbance dynamo (Ddyn) from the ionospheric disturbance current (Diono). Our analysis revealed a positive (negative) ionospheric storm in the VTEC during the main phase (recovery) of the June 22-23 and 25 storms. This observation can be attributed to the combined impact of the eastward prompt penetration of the magnetospheric convection electric field (westward disturbance dynamo electric field) and changes in the storm-time thermospheric [O]/[N_2] ratio based on the GUVI satellite imagery. Notably, the westward disturbance dynamo exhibited a significant amplitude on June 23 in Belem. The amplitude of the D_dyn at Belem (dip lat: - 0.47˚) was greater than that at Alta Floresta (dip lat: - 3.75˚) due to intensified cowling conductivity in Belem. Furthermore, we found that the SAMI2 model provided more accurate results when we replaced the default ExB drift with the vertical drift calculated from the ground-based magnetometer, enabling us to simulate the effect of the westward DDEF on VTEC during daytime.

For many years, energy monitoring at the most disaggregate level has been mainly sought through the idea of Non-Intrusive Load Monitoring (NILM). Nevertheless, a practical application of this concept in the residential sector should address the underlying concerns raised by the technical specifications of case studies. From one side, such an operation must handle common matters related to the essence of any NILM system. Although this aspect has been thoroughly investigated by basic research, it is limited to the properties of public datasets. On the other side, it must deal with specific concerns corresponding to uncommon situations. These circumstances impose further restrictions on existent NILM schemes, however, they have been overlooked due to the lack of pertinent databases to scrutinize. Accordingly, this paper presents applied research on a potential solution to NILM for Quebec residences. It carries out a relevant investigation into the multi-faceted nature of this problem in order to reveal barriers to feasible implementations in the context of Quebec. This work commences with a concise discussion about the NILM idea to highlight its essential requirements for a fruitful practice. Afterward, it provides a comparative statistical analysis to represent the specificity and potential challenges of the case study in accordance with NILM necessities. For this purpose, the examination exploits data from real-world measurement systems in the same and European regions. Subsequently, this study focuses on a load identification exercise by proposing a combinatory approach that utilizes the promise of sub-meter smart technologies to integrate the intrusive aspect of load monitoring with the non-intrusive one. The former is aimed at extracting overall heating demand from the aggregate one by a supervised procedure based on Deep Learning (DL) models while the latter is designed for disaggregating the residual load through an unsupervised process on the basis of clustering techniques. The results demonstrate that geographically-dependent cases create electricity consumption scenarios under which existing NILM methods can be questioned. From a realistic standpoint, this research elaborates on critical remarks to realize viable NILM systems, particularly, in Quebec houses.

Recently many researches of EOS (Electric Arc Furnace Oxidizing Slag) on application to construction industry have been carried out with increasing its production and limited reclamation site. EOS can be used as a fine aggregate for construction material, however, its engineering properties vary with the manufacturing process and producing district, causing a quality differences in material performance. In the work, EOS is obtained from steel manufacturing plants in South Korea and the engineering properties are evaluated for EOS and the cement mortar with EOS, respectively. From the tests, EOS is mainly made up of CaO, SiO₂, and FeO with 18.2% of larnite which has a crystal structure of β-C₂S with similar cement mineral. EOS mortar shows an increasing compressive strength with more EOS content, which is affected by a considerable amount of larnite (β-C2S) in EOS. The EOS based mortar with ERS (Electric Arc Furnace Reduction Slag) shows unsatisfactory results over the criteria for rate of change, which implies that more consideration must be taken for the usage of the mixed ERS and EOS for cement mortar due to swelling effect of ERS on dimensional stability.