Engineering

Abstract: The energy consumed by thermal management systems strongly affects the driving range of battery electric vehicles. This study develops an integrated control strategy that couples the Sparrow Search Algorithm (SSA) with Nonlinear Model Predictive Control (NMPC) to simultaneously reduce energy consumption and satisfy cabin comfort and battery safety requirements. A multi-loop coupled, heat pump based integrated thermal management model is constructed, including a compressor, heat exchangers, expansion valves, and an electro thermal battery sub model. Bench and vehicle level tests confirm that the model predicts refrigerant mass flow rate and heating capacity with mean relative errors of 4.76 % and 4.30 %, respectively. The SSA is used to tune the NMPC weighting parameters offline, minimizing the mean absolute errors of the cabin temperature, battery temperature, and total system energy consumption. The resulting SSA NMPC strategy is evaluated under NEDC and CLTC P driving cycles. Under the NEDC cycle, the strategy limits cabin temperature overshoot to 0.35°C and battery temperature fluctuation to 0.26°C, while achieving a 6.31 % energy saving under high speed cruising. The proposed framework focuses on cabin and battery thermal regulation and considers motor waste heat recovery. These results demonstrate that the SSA NMPC approach can improve thermal management performance under the investigated operating conditions.

Abstract: Aerodynamic drag is one of the two principal external sources of energy loss in on-road vehicles – the other being rolling resistance – and it critically affects the range of battery-electric and fuel cell-electric vehicles. To ensure accurate early-stage analysis such as vehicle range prediction and sizing of energy storage and powertrain components, it is essential to incorporate realistic representations of air resistance. Despite its importance, due to limited data availability air resistance is often simplified using zero crosswind and "nominal air conditions", which tend to underestimate the actual energy required to overcome aerodynamic drag. This approach also fails to capture the variability introduced by changing environmental conditions, leading to significant discrepancies in energy consumption and, consequently, vehicle range. As a result, evaluating system robustness and conducting meaningful trade-off analyses between different vehicles or vehicles configurations becomes challenging. This study demonstrates how publicly available meteorological data can be utilized to quantify long-term variations in aerodynamic drag. By analyzing multiple years of weather observations, we derive realistic distributions of aerodynamic energy losses – capturing not only mean values but also the full range of variability. These distributions enable probabilistic modeling of vehicle performance, thereby supporting robust system design and informed trade-off decisions across various levels of vehicle architecture. To demonstrate this, we compare two different tractor/semitrailer configurations.

Abstract: Lithium-ion batteries are prone to internal short circuits and subsequent thermal runaway under compression and impact loads during electric vehicle crashes, posing a critical safety challenge for the industry. However, existing studies lack systematic comparative analysis between quasi-static and dynamic loading conditions. In this study, 26 Ah ternary pouch lithium-ion batteries were used as research objects. A test platform for synchronous acquisition of mechanical load, electrical voltage and thermal temperature was established. Quasi-static compression and drop-weight impact tests were conducted to investigate the effects of indenter diameter, impact velocity and state of charge (SOC) on the multiphysics responses of batteries. The results show significant differences in failure modes between the two loading conditions: quasi-static loading causes progressive plastic deformation and stable short-circuit voltage decay, while dynamic loading induces brittle shear fracture and soft short-circuit voltage rebound. Under non-thermal runaway conditions, the temperature rise amplitude under dynamic impact is approximately 20% higher than that under quasi-static compression. High SOC alters the heat release pathway during thermal runaway, leading to deviations in surface temperature measurements. These findings provide critical experimental support for the crash safety design of power batteries and the formulation of thermal runaway prevention and control strategies.

Abstract: The transition to a hydrogen-based energy economy emphasizes the potential of hydrogen as a fuel for plug-in hybrid electric vehicles (PHEVs). The performance of a hydrogen engine within a PHEV depends on the choice of its operating modes, which influence both efficiency and emissions. This study proposes a method for developing engine operating lines (EOLs) on engine maps based on minimizing nitrogen oxide (NOx) emissions while considering constraints on maximum engine power. A total of 15 EOLs are proposed for configurations with both constant and variable maximum engine power. Using mathematical modeling of PHEV operation under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC), the impact of EOL selection on engine characteristics, as well as on battery and generator parameters, is analyzed. For a comprehensive evaluation of EOL effectiveness, five criteria are introduced, considering fuel energy consumption, NOx emissions, wear, mechanical fatigue, and noise, vibration, and harshness (NVH). The Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are applied to determine the weighting factors of the criteria and to rank the proposed EOLs, thereby identifying the most efficient configurations. The results show that, for the base hydrogen engine configuration, selecting appropriate operating modes alone enables NOx emissions to be reduced significantly below Euro 6 limits, without any hardware modifications or exhaust aftertreatment.

Abstract: Automated valet parking requires a difficult balance between reliable long-range empty spot seeking within structured parking lots and precise, low-speed maneuvers into tight terminal poses. Traditional controllers often struggle to bridge these two distinct operational domains. This paper proposes a hierarchical cruise-to-park framework that leverages the strengths of classical and learning-based control by using a Nonlinear Model Predictive Controller (NMPC) for predefined-route cruising and a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent for final parking. The system is implemented in a high-fidelity Simulink environment with Unreal Engine-based sensors. During the cruising phase, a camera-based module detects available slots to trigger a seamless transition to the parking phase. The NMPC utilizes a custom cost function to minimize error on curved approaches, while the TD3 policy is trained with reward shaping and an explicit time penalty to promote efficient and stable low-speed docking using LiDAR feedback. Simulation results demonstrate smooth phase transition, accurate cruising, and effective terminal parking in the training slot. The validation results on six previously unseen target slots within the same structured parking-lot environment show encouraging intra-lot transferability without retraining, supporting the proposed modular architecture as a practical baseline for integrated cruise-to-park automated valet parking studies.

Abstract: Light electric vehicles within the L category are expected to play a significant role in promoting sustainable urban transport, advantageous for both society and the environment. The batteries in these vehicles are well-suited for swapping, necessitating appropriate standards. This paper outlines the standardization framework relevant to this application, as studied by the ongoing European Stan4SWAP project.

Abstract: Considerable progress has been made in predicting nominal NVH behavior in electric drivetrains, but the acoustic scatter observed across manufactured units remains insufficiently understood. In practice, nominally identical drive units may still exhibit noticeably different tonal behavior because small deviations in gears, shafts, bearings, fits, centering features, or assembly phase modify the excitation, transfer, and radiation mechanisms of the system. This review examines how manufacturing and assembly variability influences NVH performance in electric drive units and e-axles, with particular focus on the rotor–shaft–gear–bearing–housing system. Unlike broader EV NVH reviews, the present work focuses specifically on variability-induced acoustic scatter and its propagation along the drivetrain NVH generation and transmission path. To support transparency and consistency, the literature search and selection process followed a structured, PRISMA-inspired approach to ensure transparency and consistency across Scopus, Web of Science, Google Scholar, and SAE Mobilus for the 2015–2026 period. From 387 identified records, 50 studies were retained after duplicate removal, screening, and full-text assessment. The selected literature was synthesized into eight thematic categories: imbalance; run-out and eccentricity; bearing clearance and preload; spline and pilot centering; thermal effects; phase indexing; transmission error and sidebands; and end-of-line NVH diagnostics. The reviewed literature shows that manufacturing- and assembly-induced deviations can significantly alter transmission error, sideband structure, shaft-order content, and final tonal response, even when individual components remain within nominal tolerance limits. Beyond synthesizing the evidence base, the review proposes a general simulation methodology for variability-aware NVH prediction based on explicit deviation parameterization, hierarchical model fidelity, intermediate excitation metrics, thermal-state awareness, and closer integration with production and measurement data. Overall, the findings support a shift from nominal NVH assessment toward robustness-oriented, production-representative prediction of acoustic scatter, and establish a structured methodology for variability-aware NVH engineering in electric drivetrains.

Abstract: Pedestrian safety at urban intersections requires risk-aware mechanisms that extend beyond binary collision detection toward comparative prioritization among multiple agents. This study introduces the Intelligent Pedestrian Model (IPM). This reference-normalized scalar framework represents pedestrian risk as a function of trajectory, contextual, infra-structural, and behavioral factors, decomposed into Exposure and Severity components. Building on IPM, the Safety-Prioritized Trajectory Model (SPTM) operationalizes the Ex-posure component using an observation-only, leakage-free kinematic proxy embedded into a cost-aware negative log-likelihood objective. Evaluation on the ETH/UCY benchmark under a strictly inductive protocol shows that moderate prioritization (β ≈ 1.0) improves best-of-K multimodal performance (ALL FDE@K: 0.979 → 0.970 m) while maintaining mean displacement accuracy within seed-level variability. The results indicate that Expo-sure-based weighting does not act as a global accuracy enhancer but redistributes predictive capacity toward safety-relevant motion regimes. Validation is limited to a single benchmark fold; cross-fold generalization and full IPM instantiation remain future work.

Abstract: This study investigates the geometric parameters of commercially available or recently published models of catalyst substrates for passenger vehicles and provides a numerical evaluation of their influence on thermal behavior for use in urban areas. To ensure the plausibility of the results, a range of scenarios have been meticulously designed to replicate real operational conditions. To ensure a reliable basis for comparison, all geometries were standardized to the same gas-solid exchange surface by adapt the total monolith length. The simulation experiments conducted revealed that the primary role in question is played by the mass of the monolith and its internal surface area, while the heat transfer coefficient plays a secondary role. The necessity of adapting the geometry to the deployment scenario was demonstrated by comparing the performance of different scenarios. The employment of contemporary technologies, such as start-stop systems and automatic catalytic converter preconditioning, has also been factored. The study's findings suggest that geometries characterized by high cell density and low wall thickness, in conjunction with modern materials and intelligent engine control, are ready to become the norm for future ICE applications. This claim is particularly salient in the context of the requirements of mobile exhaust gas purification systems.

Abstract: Currently, motorsport is one of the most important directions of development in the automotive industry. Vehicle manufacturers (VMs) at such competitions conduct tests of new technical solutions and technologies. To meet increasingly stringent environmental emission requirements, automakers are intensively transitioning to electrified vehicles, which include both pure electric vehicles and vehicles with combined power plants or hybrid vehicles. Hybrid vehicles, in addition to being more environmentally friendly compared to traditional internal combustion engine vehicles, allow increasing the power of the vehicle's power plant through the use of an electric motor, and also have lower fuel consumption due to the use of regenerative braking. This article investigates a hybrid racing car. Its design includes a traction battery (TB), which under severe operating conditions has increased requirements for the cooling system. The cooling system is one of the most important systems of a hybrid vehicle. The purpose of this work is the development and investigation of an immersion cooling system for the traction battery of a hybrid racing car. To achieve this goal, a methodology for mathematical modeling of the traction battery cooling system was developed, the thermal power of the TB was determined, and an effective coolant for the TB cooling system was selected, the TB design was optimized, experimental studies of the TB separately and as part of a hybrid racing car were conducted. The results of experimental studies of the hybrid racing car showed that the temperature of the battery cells of the developed TB with an immersion cooling system does not exceed the maximum permissible values.

Abstract: This paper examines the state of academic literature on the development of after-sales and maintenance services for electric vehicles (EVs), highlighting their critical yet underexplored role in the transition to electrified mobility. Against the backdrop of rising EV sales, the study investigates how service ecosystems influence long-term adoption. A systematic review was conducted to identify recurring themes, barriers, and proposed solutions related to EV maintenance and after-sales systems. The findings indicate that, despite lower mechanical complexity compared to internal combustion vehicles, EVs generate new service demands due to their reliance on electronics, software, and high-voltage systems. Key barriers to EV adoption include high purchase costs, limited charging infrastructure, and shortages of skilled technicians, which collectively affect consumer confidence beyond the point of acquisition. The analysis shows that after-sales services constitute both a technical and economic bottleneck in large-scale EV diffusion. Existing literature predominantly emphasizes theoretical solutions, such as digitalized maintenance and data-driven business models, with limited focus on practical implementation strategies. The paper concludes that sustainable EV adoption depends not only on technological and infrastructural progress but also on workforce adaptation, proposing a transitional management framework to support independent workshops in shifting toward fully electric service operations.

Abstract: The rapid growth of electromobility is increasing pressure on the adequacy of charging infrastructure deployed along major transport corridors. This study presents a simulation-based framework for assessing the operational performance of electric vehicle charging infrastructure along the S19 Rzeszów–Barwinek section, a 90 km corridor forming part of the TEN-T and Via Carpathia networks. The methodology combines microscopic traffic simulation in PTV Vissim with probabilistic charging-demand modeling for passenger cars and heavy-duty vehicles, enabling the analysis of infrastructure utilization, queue formation, and unmet charging demand under realistic corridor conditions. Three electric vehicle penetration scenarios were examined: 10%, 25%, and 45% of the traffic stream. The results show that the charging system remains stable under the 10% scenario, begins to experience local overload and recurring congestion at 25%, and reaches structural insufficiency at 45%, where utilization exceeds 100% and unmet demand rises markedly. A key finding is that heavy-duty electric vehicles constitute the dominant operational bottleneck due to longer charging times, higher energy requirements, and the limited number of dedicated charging points. An additional expansion variant indicates that increasing the number of heavy-duty charging points can substantially improve system performance and restore a safer utilization range. The study demonstrates that minimum regulatory compliance should be treated as a baseline rather than a sufficient planning target and that dynamic, scenario-based simulation offers an effective decision-support tool for the adaptive development of corridor charging infrastructure.

Abstract: The proliferation of unmanned aerial vehicles (UAVs) in civil, commercial, and defence domains has exposed a critical architectural gap: existing platforms optimise either communication or perception independently, leaving safety coverage incomplete under simultaneous stress in Beyond Visual Line of Sight (BVLOS) operations. This paper proposes the Risk-Aware UAV Safety Architecture (RASA), a three-layer conceptual framework integrating multi-modal sensor fusion, satellite communication (SATCOM), and AI-driven risk modelling aligned with functional safety principles such as ISO 26262. The RASA framework quantifies operational risk as R(t) = α·U_sensor(t) + β·L_c_norm(t) + γ·U_sensor(t)·L_c_norm(t) — a function of normalised sensor uncertainty and normalised communication latency, with an interaction term capturing compound degradation effects — enabling onboard risk estimation without ground-in-the-loop dependency. Building on prior validated work in multi-modal sensor fusion for safety-critical human detection [10] and SATCOM communication architectures for UAV connectivity [15], this paper extends those contributions to the BVLOS domain. Monte Carlo simulations across three representative operational scenarios validate the risk model’s behaviour and demonstrate that the interaction term produces steeper risk escalation under compound failure conditions compared to the linear baseline. This paper addresses the critical gap in BVLOS UAV safety architectures by integrating perception and communication reliability within a single, auditable, risk-aware framework.

Abstract: Pedestrians who are already on the road surface — collapsed through medical emergency, intoxication, or displacement by a prior collision — represent one of the most lethal yet least-addressed categories in road traffic safety. Peer-reviewed forensic database studies from Japan report a fatality rate of 33.0% for collisions involving prostrate pedestrians, more than double the rate for standing victims [1,2]. Simulation-based evaluation of a novel multi-modal detection system — the Advanced Falling Object Detection System (AFODS) — has demonstrated a True Positive Rate of 98.2% for fallen pedestrian detection under night conditions, against a baseline of 21.4% for standard ADAS [3]. These results are promising. But a simulation benchmark is not a deployed safety system. This opinion paper argues that three key steps must now be taken: a physical prototype of AFODS must be built and validated under real-world conditions; its detection latency advantage must be translated into forensic injury outcome estimates using established biomechanical criteria; and regulatory bodies must extend pedestrian AEB test standards to encompass the non-upright pedestrian scenario. The evidence for the problem is conclusive. The technical pathway to the solution is published. The work that remains is a matter of will, not capability.

Abstract: This study presents an experimental safety assessment of degraded lithium-ion battery modules from a 2016 Nissan Leaf 30 kWh pack (106,394 km) exhibiting P33E6 faults, "turtle mode" activation, and sudden range drops. On-vehicle diagnostics using LeafSpy revealed severe cell voltage imbalance, with a 2323 mV spread across 96 cells under high-current load (165–170 A), individual cells dropping to 1.041 V. Laboratory capacity testing of modules 73–88 (16S2P configuration) measured 49.8 Ah (4.1–3.1 V) versus nominal 84 Ah, confirming SOH 59% (vs. BMS-reported 57%). High-current discharge (600 W → 40 A) of the weakest segment (cells 81–84) demonstrated critical safety limits: cell 82 voltage collapsed from 4.04 V to 1.2 V within 56 minutes, cell voltage delta escalated from 20 mV to 810 mV after 38 minutes, culminating in open-circuit failure. Infrared thermography recorded localized surface heating to 43 °C (ΔT = 24 K above 19 °C ambient) with irreversible cell swelling, indicating early thermal runaway precursors. The findings validate LeafSpy OBD diagnostics for identifying at-risk modules and underscore the necessity of high-power bench testing with thermal monitoring for aging EV packs. Fleet operators should prioritize module replacement for cells showing >200 mV load delta to prevent sudden power loss and thermal events in 30 kWh Nissan Leaf vehicles.

Abstract: Aquaculture production systems rely on the reliable operation of mechanical and electromechanical equipment to maintain stable environmental conditions. In shrimp farming, failures in critical assets may directly affect dissolved oxygen availability and compromise production stability. Despite the operational importance of these systems, structured methodologies for asset criticality assessment and maintenance prioritization in aquaculture remain limited. This study proposes a multi-criteria decision-making framework based on the Analytic Hierarchy Process (AHP) to evaluate and prioritize critical assets in shrimp aquaculture production systems. The model integrates nine technical and operational criteria related to reliability, maintainability, operational exposure, and production impact. The proposed methodology was applied to three key assets in the Primary Production stage: mechanical aerators, turbines, and stationary engines. The results indicate that mechanical aerators exhibit the highest criticality score (0.350), followed by stationary engines (0.328) and turbines (0.322). These findings highlight the dominant operational role of aeration systems in maintaining dissolved oxygen levels and ensuring production stability in shrimp farming systems. The proposed framework demonstrates that multi-criteria decision models can effectively support maintenance prioritization by transforming expert knowledge and operational information into a structured and consistent evaluation process. The methodology provides a replicable decision-support tool that can assist managers and maintenance planners in improving asset management and resource allocation in aquaculture production systems.

Abstract: Despite many efforts to curtail drunk driving, alcohol-related traffic fatalities and injuries continue to be a major public health problem in the U.S. and most of the world. Technologies exist that prevent an automobile from starting if the driver’s breath alcohol exceeds 20 mg/dL, but these devices are only fitted to vehicles of individuals who have been convicted of Driving Under the Influence (DUI). A new approach must be taken to reduce the incidence of drunk driving by integrating an alcohol sensor system in vehicles as part of the delivered hardware. The system must be fast, accurate, and contactless--meaning that a forced exhalation is not required to measure the concentration of alcohol on the breath. We report on a novel device, the Driver Alcohol Detection System for Safety (DADSS) Breath Alcohol Sensor System, which uses the mid-infrared region of the electromagnetic spectrum, is designed to concurrently monitor alcohol and expired carbon dioxide (CO2) to accurately quantify the breath alcohol concentration in samples that have been diluted in the atmosphere before being measured. The system was validated in a research laboratory with 70 male and female volunteers in 187 individual study days. Participants were given various doses of alcohol to consume and then breath and blood samples were collected simultaneously. Pearson correlation coefficients between the DADSS Breath Alcohol Sensor system and blood samples indicate a strong correlation between the measures, with an overall Pearson correlation of 0.8875 over an alcohol concentration range of 0 - 220 mg/dL. These results indicate that Incorporating the DADSS system into motor vehicles has the potential to reduce the incidence of drunk driving.

Abstract: The widespread adoption of hydrogen fuel cell electric vehicles (FCEVs) is currently hindered by the significant cost and lack of geometric flexibility of conventional Type IV pressure vessels made from carbon fiber reinforced plastic (CFRP). These tanks are difficult to integrate into future vehicle platforms optimized for modular batteries. This study, therefore, presents a novel compressed hydrogen storage system (CHSS) based on a modular assembly of seamless steel cylinders. The objective of this approach is to create a design-flexible and cost-effective alternative that adapts to the limited installation space of modern electric vehicle architectures while offering a sustainability advantage through the high recyclability of steel. The system was specifically designed to meet the stringent requirements of the UNECE R134 regulation and subsequently subjected to rigorous experimental validation. The evaluation included all four test sequences required for component certification: Baseline Tests, Performance Durability Test, On-Road Performance Test and Fire Test. The successful validation demonstrates that the developed modular steel-based CHSS meets all relevant safety and performance requirements. It, therefore, represents a technically and economically promising technology that can make a decisive contribution to accelerating hydrogen mobility through its superior design flexibility and sustainability.

Abstract: Electric vehicles (EVs) have fundamentally changed the noise, vibration, and harshness (NVH) landscape of automotive powertrains. In the absence of masking inter-nal-combustion-engine noise, harmonic components such as gear whine, electric-motor orders, and inverter-related tones become more perceptible and more critical to vehicle re-finement. This review synthesizes the current state of the art in harmonic NVH engineer-ing for electric drivetrains, focusing on the interactions between gear geometry, manufac-turing variability, electromechanical coupling, structural transfer, and human sound per-ception. Classical mechanisms of gear-mesh excitation are revisited together with emerg-ing EV-specific challenges, including long-wavelength flank deviations, ghost orders, lightweight housing dynamics, and psychoacoustic sound-quality requirements. The re-view further examines recent progress in predictive and data-driven approaches, includ-ing machine-learning-based gear-noise modeling, digital-twin concepts, and virtual NVH assessment workflows. Overall, the literature shows that harmonic NVH engineering in EVs is evolving from a conventional gear-noise problem into a multidisciplinary sys-tem-level task integrating gear dynamics, manufacturing science, structural acoustics, electric-drive control, psychoacoustics, and data-driven optimization. This review pro-vides a structured synthesis of these developments and identifies key research gaps and future directions for the next generation of refined electric drivetrains.

Abstract: In recent years we have seen Large Language Models (LLMs) demonstrating robust reasoning capabilities comparable to human performance. This makes them increasingly appealing for driver assistance, where adaptation to dynamic human context is essential. Yet, research in this area remains fragmented, often focusing on isolated applications, lacking utilization of LLM's full potential to deliver integrated, context-specific support and action. This survey synthesizes recent advancements in LLM-driven occupant monitoring systems, focusing on their capabilities for interpreting driver states and acting appropriately, enabling a new generation of intelligent driver assistance. We critically examine pioneering frameworks, benchmarks, and foundational datasets that employ techniques like reasoning chains, multimodality, and human-in-the-loop feedback to create personalized and safe driving experiences. We lay out the current trends, limitations, emerging patterns, in addition to a novel human-centered evaluation of the field, providing researchers with a roadmap towards transparent and trustworthy in-cabin systems, that bridge safety with driver experience.