Engineering

Abstract: Reduced air density at altitude challenges spark-ignition combustion in light-duty vehicles, particularly in Andean countries where fleets operate between sea level and over 4000~m~a.s.l. We quantified the effect of altitude on gasoline combustion efficiency and pollutant formation using 94,263 second-by-second records (2021–2025) from ten Euro~2–5 vehicles travelling Andean and coastal corridors in Ecuador (0--4000~m~a.s.l.). Emissions were measured with a Brain Bee AGS-688 analyser with pressure compensation; fuel consumption via OBD-II; operational demand via smoothed Vehicle Specific Power ($\mathrm{PSV_{m10}}$) in six K-Means clusters. We propose $R_{\mathrm{CO}}=[\mathrm{CO}]/[\mathrm{CO_2}]$ and $R_{\mathrm{HC}}=[\mathrm{HC}]/[\mathrm{CO_2}]$ as standardised, displacement-independent combustion quality indicators. $R_{\mathrm{CO}}$ at 3500--4000~m exceeded the 500--1000~m value by 7.5$\times$, and $R_{\mathrm{HC}}$ by 18$\times$. Nitric oxide showed a non-linear ``N''-pattern, peaking locally at 2000–2500~m (212~ppm) and absolutely at 3500–4000~m (613~ppm), linked to EGR suppression and rising chamber temperature. CO emission factors reached 6.42~g/km at 2000--2500~m versus 1.32~g/km at the coast (4.9$\times$). These results provide the first naturalistic evidence base for calibrating high-altitude emission inventories in Andean corridors.

Abstract: The paper deals with a horizontal seat suspension system in which an electric motor is used as the force actuator. Its active system can operate in a motoring mode, but is also able to act as a generator in regenerative braking mode when the kinetic vibration energy is partially converted into electrical energy. Such an innovative approach to seat vibration control allows to improve the vibro-isolation properties of a suspension system together with reducing the energy consumption of a motor. Within the scope of this paper the effectiveness of an energy regeneration process under random vibration of various intensity is investigated experimentally. The presented results concern both the reduction of vibrations affecting the human body in sitting position, as well as the amount of energy recovered from the seat suspension system during its oscillatory motion.

Abstract: The rapid development of electric vehicles has increased the need for a better understanding of the relationships between vehicle dynamics, energy consumption and control strategies. This study presents an experimental investigation of the acceleration and coasting characteristics of a 2024 Kia Niro EV. Road tests were combined with laboratory measurements of the vehicle mass properties, including the centre of gravity. Vehicle motion parameters were recorded using a GNSS/INS measurement system, while electric powertrain data were acquired from the vehicle CAN bus using proprietary software developed by the authors. The influence of driving mode, accelerator pedal position and regenerative braking intensity was analysed. The results showed that the selected driving mode significantly affects the acceleration characteristics only at intermediate accelerator pedal positions, whereas identical maximum performance is obtained with the accelerator pedal fully depressed. The energy required to accelerate the vehicle to 90 km/h remained nearly constant under most operating conditions, indicating high electric powertrain efficiency. During coasting, regenerative braking recovered up to 50% of the energy previously required for acceleration. The obtained results provide valuable experimental data for the validation of vehicle dynamics and energy consumption models and support the development of more efficient electric vehicle control strategies.

Abstract: Fallen pedestrians—those lying prone, supine, or crouching on roadways—represent a critical and largely unaddressed vulnerability in contemporary Advanced Driver Assistance Systems (ADAS). Standard pedestrian detection systems achieve only 21.4% true positive rate (TPR) at night for non-upright subjects, compared with 98.2% for upright pedestrians, creating a 76.8 percentage-point detection gap with direct fatality consequences. This article synthesises three complementary peer-reviewed contributions into a unified closed-loop safety architecture: (1) real-time multi-modal detection via the Advanced Fallen Object Detection System (AFODS); (2) physics-grounded post-collision kinematic reconstruction; and (3) injury-risk quantification translating detection latency into Head Injury Criterion (HIC) and AIS-grade fatality probability. The integrated framework, which forms the technical basis of Japanese Patent Application No. 2025-167440 (PCT deadline: October 3, 2026), demonstrates that fatal head injury probability is reducible from 66.2% (no detection baseline at 50 km/h) to 0.7% under worst-case AFODS detection. A five-stage empirical validation roadmap is presented, culminating in regulatory conformance assessment to ISO 26262, ISO 21448 (SOTIF), and Euro NCAP 2026 Post-Crash Safety protocols. The article identifies critical open challenges and defines the trajectory toward prototype deployment, real-world forensic validation, and commercialisation.

Abstract: The hydroforming performance of trailing arms is governed by the coupled effects of feed parameters, pressure schedules and frictional characteristics. Improper parameter matching readily induces typical forming defects such as wrinkling, cracking and uneven wall thickness. To address this issue, a multi-objective optimization method for hydroforming is proposed in this study. Taking the maximum wall thickness, minimum wall thickness and die-to-workpiece gap of the tubular blank as optimization objectives, and the internal pressure and right-side axial feed velocity as design variables, an integrated numerical simulation framework combining the Archive-based Micro Genetic Algorithm (AMGA) and LS-DYNA is established to analyze the hydroforming process. By adaptively adjusting the key control points of internal pressure and axial feed loading curves, the developed method expands the solution space and realizes the automatic optimization of loading paths. The results reveal that the maximum wall thickness reduction rate of the tubular component is reduced from 20.4% to 14.8%. Meanwhile, the wall thickness uniformity is improved and forming defects are effectively suppressed while the thickening rate remains stable. Moreover, multiple sets of Pareto optimal solutions can be obtained in a single calculation. This work provides new insight into the process optimization for forming similar structural components.

Abstract: Active suspension can deliver better ride comfort and stability than a passive layout system, but only when the controller is properly tuned. This work proposes a simple tuning method: retaining Proportional-Integral-Derivative (PID) structure and employing two off-the-shelf optimizers (Optuna and Particle Swarm) to select the gains. The full-vehicle 7-Degrees of freedom (DOF) benchmark of Kumar et al. was used as a virtual test bench to compare four controllers: passive, Kumar et al. (2020) PID. A manually tuned baseline, and the two optimizer-tuned versions. The cost function combines RMS body acceleration, pitch and roll angular rates, peak actuator force and jerk across three simulation scenarios (bump, speed-breaker, cornering), with soft penalties for actuator saturation, suspension travel, and tire lift-off. The Optuna gains cut the global cost by 19% relative to the manual baseline and by 8.5% relative the Kumar et al. (2020) PID, primarily by reducing the peak actuator force from 2.7 kN to 1.78 kN. A 100-vehicle Monte-Carlo study (±20% on sprung mass, ±15% on stiffness and damping) confirms that the performance advantage is robust to variations in the nominal parameters.

Abstract: The transition from internal combustion engine vehicles toward hybrid electric vehicles and battery electric vehicles has transformed not only automotive engineering but also the sensory and emotional experience of driving. While previous studies have examined environmental and performance differences between propulsion systems, limited researches investigated their direct impact on human neurophysiology and emotional perception during real-world driving. This study investigates the neurophysiological and autonomic responses of drivers exposed to three vehicle categories: electric vehicles, internal combustion engine vehicles, and hybrid electric vehicles. A standardized driving sessions were performed in urban driving environment, while electroencephalography, heart rate, heart rate variability, galvanic skin response and peripheral blood oxygen saturation were continuously recorded. Measurements were collected during three phases: a five-minute pre-driving baseline, twenty minutes of active driving, and a five-minute post-driving recovery period. Electroencephalography power in the theta (4–8 Hz), alpha (8–12 Hz), low beta (12–20 Hz), and high beta (20–30 Hz) frequency bands were analyzed as indicators of cognitive workload, cortical relaxation, and attentional engagement. Cardiovascular and electrodermal signals were interpreted as markers of autonomic arousal, sympathetic activation, and stress regulation. Peripheral oxygen saturation was included as complementary index of cardiorespiratory and metabolic demand across vehicle conditions.

Abstract: This work presents a comparative handling and stability analysis between conventional pneumatic tires and self-supporting run-flat tires (SSRFT) subject to severe inflation pressure loss. A comprehensive seven-degree-of-freedom (7-DOF) full-scale vehicle dynamics plant model was developed to evaluate vehicle performance across four distinct operational scenarios under the standardized closed-loop tracking constraints of the ISO 3888-2 double lane change maneuver. Dynamic vehicle behavior was quantified using a broad suite of handling metrics, including: individual tire lateral forces, transient lateral acceleration, yaw rate, body roll angle, exit speed, body sideslip angle, and Kamm friction circle envelopes. The simulation results demonstrate a severe degradation in trajectory tracking for deflated conventional configurations yielding critical understeer saturation. Conversely, the SSRFT configurations preserve sufficient cornering stiffness to remain within stable path boundaries. These findings provide high-value empirical insights essential for optimizing future active chassis management architectures and electronic stability control systems logic.

Abstract: The increasing complexity of automotive software driven by ADAS and autonomous driving has intensified the need for time-deterministic network validation beyond CAN/LIN, while HIL integration remains constrained by limited ECU prototypes and labor-intensive manual configuration. This paper presents a network-oriented virtual verification environment that couples Renode-based virtual ECUs (vECUs) with FMU-based Interaction Layer (FIL) Nodes automatically generated from DBC specifications. The vECUs provide instruction-accurate execution of unmodified target binaries without physical hardware, while the generated FIL Nodes encapsulate communication behavior as model-based FMUs to maintain continuity from MIL to SIL without manual signal mapping. The proposed framework constructs virtual CAN networks from communication-definition files, reproduces periodic and event-triggered traffic patterns, and supports synchronized multi-ECU co-simulation under master-controlled time stepping. Experimental results show that the vHIL environment reproduces physical ECU timing behavior with a maximum relative error of 0.086%–0.171% across all evaluated step sizes (10 μs to 1000 μs), confirming binary-level timing fidelity. Replacing vECUs with FIL Nodes for network communication processing significantly reduces wall-clock execution time in multi-node configurations, demonstrating improved scalability without sacrificing determinism. These results demonstrate that the proposed methodology effectively reduces early-stage integration bottlenecks while preserving timing fidelity for automotive networked ECU validation.

Abstract: Stability of vehicles is one of the most important factors of safety. When assessing the conditions for the stability of the vehicle, on-road and off road vehicles differ in many perspectives, and because this, specific methods are needed to describe the stability conditions for terrain vehicles. In this paper, the aspects of off-road vehicle stability are assessed, terrain vehicle specific stability modeling methods are shown. After that, the LiDAR based terrain mapping technology and the measurement and remote sensing possibilities for the variables affecting the stability, both from the side of the vehicle and terrain, are introduced. At the end, field measurements conducted with a small scale remote controlled vehicle are presented.

Abstract: Globally, it is estimated that the number of light duty vehicles will increase, with the potential to reach 2 billion by 2040, and that petrol, including biofuel additives such as ethanol, will continue to be used in substantial quantities for road transport applications until at least 2050. The main focus of vehicle manufacturers today is to ensure their future product ranges are compatible with forthcoming legislation through the development of zero tailpipe emission electric vehicles. One unforeseen consequence of this may be a reduction of research and development funding for internal combustion technology and a consequent slow-down in the recent progress in the reduction of internal combustion engine vehicle tailpipe emissions. The research presented in this paper provides a conceptual framework to systematize the interrelationships between specific tailpipe emissions reduction strategies and their effects on tailpipe greenhouse gas emissions. The application of a systems thinking approach seeks to leverage the benefits of previous investments and knowledge in the included reduction strategies through the identification of system weaknesses and intervention points, thus exploiting relationships in place of developing alternative technological solutions. This integrated approach is important as it has the potential to result in accelerated environmental gains, through the greater reduction of harmful gaseous tailpipe emissions, than would be possible from each strategy in isolation. A detailed causal diagram was constructed following the identification of the main emission reduction strategies, providing an ordered visual representation of the system and exposing the causality chains and relationships in the context of the greenhouse gas emissions reference mode. The analysis of this causal diagram revealed that each of the four main sub-model variables have several critical relationships: turbocharging and engine downsizing (7 apiece); bioethanol (6) and lightweighting (5). Further, that these can be exploited through a combination of changes in technology, behaviour, regulation and resource. The adoption of systems thinking methodology in this context is considered to be unique.

Abstract: The rapid increase in electric vehicle (EV) usage requires scalable and flexible charging solutions that minimize user effort and infrastructure constraints. One such solution is robot-based EV charging, which automates the charging process to increase convenience and ease of use. This work investigates the feasibility of deploying mobile charging robots in selected parking scenarios, with a focus on user acceptance and the economic viability from the perspective of infrastructure operators. The research is guided by three objectives: identifying user requirements; modelling realistic charging behaviour; and evaluating operational strategies for mobile charging fleets, including fleet sizing, impact of nearby charging infrastructure, and pricing approaches. A dedicated user survey and a predictive behavioural model informed the simulation studies used to assess optimal deployment strategies. The results suggest that, while mobile charging robots can enhance the user experience and increase infrastructure flexibility, their effectiveness depends on user behaviour, temporal demand patterns, and well-designed interaction and pricing mechanisms. These findings provide a basis for evaluating the integration of mobile charging robots as a complement to stationary charging infrastructure.

Abstract: This two-year study proposes the application of industrial computed tomography (CT) as a complementary technique to conventional capacity and internal resistance measurements for evaluating not only the state of health (SOH) of different lithium-ion battery types used in electric vehicles, but also to predict its past. While commonly used assessment methods primarily focus on electrical properties of batteries, industrial CT allows non-destructive, three-dimensional visualization and systematic evaluation of internal structural changes within individual battery cells and allows to compare different lithium battery type internal structure changes. The study investigates two lithium-ion battery chemistries: lithium iron phosphate (LFP) and nickel manganese cobalt oxide (NMC). The effects of different discharge rates (1C, 2C, and 3C) on battery degradation were analyzed by comparing CT scan data obtained for the cells in their initial (new) condition and after reaching 60% SOH following cycling-induced aging. The findings provide improved understanding of the physical processes associated with battery aging under varying discharge conditions, enabling a more complete evaluation of battery health.

Abstract: A compact rectangular microstrip patch antenna with enhanced impedance bandwidth is proposed for vehicular V2X applications operating at the 5.9 GHz ITS band. The design employs a single edge-notch slot to improve impedance matching and broaden the operational bandwidth. Full-wave electromagnetic simulations are performed using MATLAB Antenna Toolbox, enabling analysis of main antenna parameters including surface current distributions. Simulation results demonstrate that the slot excites an additional resonance that merges with the fundamental mode, increasing bandwidth from 240 MHz to approximately 500 MHz for VSWR < 2, fully covering the 5.85–5.925 GHz V2X band. Experimental measurements of VSWR show a bandwidth of approximately 490 MHz around the 5.9 GHz center frequency. Calculated peak gain (~8.3 dBi) and front-to-back ratio (~18.5 dB) are preserved. A parametric study of ground plane dimensions reveals the evolution of back-radiation patterns due to edge diffraction.

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 European Stan4SWAP project, which analyzed the standardization and regulation framework relevant to this application, and drafted the standardization roadmap which highlights development needs on short, medium and long term. This paper was originally presented on the EVS38 in Gothenburg [1] and has been expanded to include the outcomes of the project.

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.