World Electric Vehicle Journal

This article investigates the feasibility of hydrogen-based retrofitting solutions for light commercial vehicles operating in urban freight transport. The analysis is based on a mission-driven methodology applied to a representative urban case study in the city of Rome, using synthetic route profiles and vehicle specifications derived from manufacturer datasheets. Three representative urban delivery missions are defined, characterised by cumulative daily distances of approximately 190–200 km and associated energy requirements in the range of 54–57 kWh. These mission profiles are first used to assess a commercially representative battery electric vehicle configuration, for which the usable onboard battery energy is estimated at 41.6 kWh. The results show that, under the considered operating conditions, the battery electric configuration is not able to complete the planned routes without intermediate recharging. On this basis, a fuel cell hybrid electric vehicle retrofit configuration is evaluated, combining a 35 kWh battery, a 45 kW fuel cell system and 3.5 kg of onboard hydrogen storage at 350 bar. The resulting estimated driving range is approximately 293 km, which is sufficient to satisfy the defined mission requirements. This study is framed as a technical feasibility assessment and does not aim to provide optimisation or experimental validation. The proposed methodology can be applied to other urban contexts by adapting route characteristics and daily mileage requirements. Full article

Reliable prediction of energy consumption for electric motorcycles in sub-Saharan Africa requires models that reflect local riding conditions and measured component behaviour. This paper presents a validated, physics-based simulator for the Roam Air electric motorcycle that combines longitudinal dynamics with empirically derived motor and inverter efficiency maps obtained from dynamometer testing. The model ingests measured drive cycles and elevation-derived gradients to compute tractive effort and battery power flow and is validated against six real-world city and highway trips in Nairobi. The simulator reproduces temporal battery-power profiles with strong correlations between 0.87 and 0.91 and predicts energy per distance with small positive bias, achieving errors between 0.4% and 11.3%, where the measured energy consumption per distance ranges between 30.2 and 51.7 Wh/km. A sensitivity analysis quantifies the influence of key design parameters, and a scenario analysis assesses the impact of representative African driving conditions, including terrain, posture, payload, and surface type. The resulting framework is compact, transparent, and potentially adaptable to a wide range of electric two-wheelers, supporting design optimisation and electrification planning in the region. Full article

Norway reached a Battery Electric Vehicle market share of 96% in 2025. The fleet share reached 33%. Other countries are 5–10 years behind Norway. The extraordinary Norwegian development is the result of a 35-year-long complex process involving BEV testing from 1990 and Norwegian BEV industrialization from 1998, supported by a large package of incentives. The incentive package remained in place after the Norwegian actors went bankrupt in 2010 and the global OEMs took over the BEV supply. Norway has a had head start over other countries with high visibility, awareness, and a BEV fleet that accounted for 35% of all BEVs in Europe to build a market from. The incentives made the new OEM BEVs immediately competitive, contrasting with other countries’ insufficient incentives and slow development. A second market expansion followed from 2017 with access to lower-cost and long-range BEVs in more market segments. The EU’s new vehicle CO₂-regulation forced OEMs to sell BEVs on a large scale. BEV technology improved rapidly with longer range and faster charging at a reduced cost, making the incentive even more efficient. The model availability increased rapidly from 2020, while ICEV model availability declined rapidly from 2022, enabling Norway to reach the national target of only selling BEVs from 2025. Norway solved the demand-side challenges of BEV adoption through large market pull incentives. The early supply-side challenges were attempted to be solved with Norwegian BEV production targeting a small-city BEV niche. When that failed, a window of opportunity opened to solve the supply-side challenges with the availability of OEM BEVs. The market scope broadened to commuters and multi-vehicle households and eventually to all new vehicle buyers. By 2020, all demand-side and supply-side challenges were solved, and the transition was accelerated by societal processes. Full article

This paper presents an innovative approach to modified electrochemical impedance spectroscopy (EIS) for real-time health monitoring of galvanic cells, particularly batteries and fuel cells in high-dynamic applications such as vehicles. Traditional methodologies, including cell voltage monitoring, offer limited diagnostic value. In contrast, conventional EIS provides comprehensive system insights; however, its applicability is constrained by prolonged measurement durations, rendering it impractical for dynamic conditions. This article presents a method that iteratively selects specific frequency bands and key points, thereby substantially reducing measurement time without compromising critical system information. This approach was initially validated using battery systems, which exhibit well-regulated operational behavior, thus facilitating a rigorous evaluation of the concept. Experimental results demonstrated that the modified EIS method achieves performance comparable to conventional EIS but with measurement times reduced by up to 92%. This validation underscores its reliability and precision, thereby supporting proactive maintenance strategies and extending system longevity. The reduction in measurement time enables more precise analyses across diverse dynamic operational spectra. Consequently, this approach constitutes a robust solution for health monitoring of fuel cells and batteries in dynamic environments, capitalizing on the advantages of EIS while addressing its inherent limitations. Full article

The In-Wheel Motor represents a non-conventional propulsion architecture in which the electric motor is integrated into the wheel, offering advantages such as improved energy efficiency, individual torque control, and drivetrain simplification. In this study, two architectures, inboard and outboard, were developed using an original three-dimensional motor–brake–suspension–steering assembly model, in which disk brake position and In-Wheel Motor integration act as primary design drivers influencing vehicle dynamics. Both architectures were developed in CATIA V5 and exported to Altair Motion 2025 for multibody dynamics simulations. The study evaluates the impact of inboard versus outboard disk brake positioning on vehicle dynamics and provides a qualitative assessment of the associated architectures in terms of mechanical complexity, serviceability, sealing requirements, bearing load asymmetry, and packaging constraints. The results indicate that the inboard architecture exhibits more linear and stable kinematics and compliance (K&C) behavior compared to the outboard configuration, at the expense of increased mechanical complexity and reduced serviceability. By contrast, the outboard architecture preserves a simpler, more conventional MacPherson-like layout with a lower component count and improved service access but is dynamically outperformed under the imposed geometric constraints of the present study. Full article

Because of their great energy density and efficiency, lithium-ion batteries (LIBs) are essential to renewable energy systems and electric vehicles. Effective battery management requires precise estimation of the state of health (SoH) and state of charge (SoC). In order to overcome the difficulties caused by parameter fluctuations and real-world disturbances, this work presents a novel μ-analysis-based methodology designed to improve the resilience and accuracy of online SoC and SoH estimations in LIBs. In contrast to conventional techniques, the suggested strategy successfully manages both structured and unstructured uncertainties in battery systems by combining μ-analysis with model-based estimation. The framework creates an estimator that is resistant to parameter drift and outside perturbations by combining model-based estimation approaches with μ-analysis tools. Simulations using UDDS, US06, and HWFET driving cycles are used to verify its performance. When evaluating battery health and condition in dynamic and uncertain operating scenarios, the μ-analysis-based estimator demonstrates superior accuracy compared to conventional H∞-pole placement filter methods. The proposed approach enhances system robustness, achieving an 8 dB improvement in disturbance attenuation, as verified through MATLAB/Simulink. Stability analysis reveals the μ-analysis controller maintains robust performance up to ‖∆‖∞ = 3.5 at 10 Hz, compared to only ‖∆‖∞ = 1.5 for the H∞-pole placement controller—demonstrating significantly greater tolerance to parameter variations and unmodeled dynamics. These capabilities make the μ-analysis approach particularly suitable for electric vehicle applications requiring next-generation battery management systems. Full article

Indonesia’s long-term climate strategy targets net-zero emissions by 2060. In this context, this paper develops a simulation for the Java–Madura–Bali (Jamali) grid to quantify the joint impact of electric vehicle (EV) uptake and rooftop photovoltaic (PV) integration on system performance from 2025 to 2060. Historical statistics and national planning projections were used to calibrate annual capacity, peak load, and energy trajectories, which were downscaled to hourly resolutions. EV charging demand, generated using state-of-charge-dependent Alternating Current (AC) and Direct Current (DC) load profiles, and PV output were modeled across a 36-year span under a 5 × 5 policy matrix, producing a 900-scenario-year. These scenarios range from Business-as-usual (BAU) to aggressive interventions (including subsidies, regulation, and smart management). The scenarios were evaluated using a min–max composite index weighting supply–demand balance, production–consumption balance, and policy cost. Based on this simulation inputs, results indicate that the scenario combining regulated EV growth with BAU PV adoption achieves the highest average composite score. While charge-time management strategies provided the best adequacy, highly interventionist EV–PV packages were the most expensive without delivering proportional benefits. The study concludes that, with this current parameter input, moderate and regulation-driven strategies outperform aggressive interventions when adequacy, balance, and cost are jointly considered. Full article

The transportation sector accounts for 25% of CO₂ global emissions. Europe aims for carbon neutrality by 2050 through new light-duty vehicle technologies and stricter regulations, though these efforts may be insufficient. This work aims to assess a small neighborhood by analyzing over 19,500 routes to calculate an indicator that identifies streets with the highest impacts to evaluate the individual impacts of various light-duty vehicle technologies and examines how different combinations of technologies, based on traffic distribution, influence overall energy and emissions outcomes. The results highlight how uphill steep roads increase energy use, while downhill sections allow for energy recovery. A Street VSP Impact Factor (SVIF) was developed to identify streets with high energy use and emissions, offering insights into targeted urban planning strategies. The findings suggest that promoting BEV adoption and optimizing street infrastructure are key to reducing energy consumption and emissions in cities. Full article

Battery management systems are essential in electric vehicles and renewable energy applications, especially in terms of ensuring optimal battery health and performance and regarding the state of charge (SOC) in batteries consisting of many cells. The lifetime and efficiency of the battery depend on the accuracy of the SOC parameter estimation. Moreover, systems that apply active balancing technology are able to move cells with high SOC data to cells with low SOC. Many methods have been developed, but their long execution time makes them less optimal when applied. High-speed SOC estimation is required in active balancing technology, in addition to high accuracy. Therefore, this study proposes the estimation of SOC parameters using a statistical and metaheuristic approach from voltage and current input data in each battery cell. The experimental results showed that the metaheuristic-based method (ANFIS) had better RSME and R² values compared with the polynomial and linear regression or even the machine learning-based method (recurrent neural network) for training data. Full article

The efficiency of power transfer is a critical issue for wireless charging applications in electric vehicles. The misalignment between the transmitter coil and the receiver coil in wireless charging leads to a significant reduction in efficiency. This article investigates improving coil misalignment performance in wireless power transfer for electric vehicles using magnetic flux density analysis. The objective is to study the effect of the automatic alignment transmitter system’s movement on error distance. The automatic alignment transmitter system was integrated with a wireless power transfer system to realign the transmitter coil whenever lateral misalignment occurred between the transmitter and receiver coils. The experiment was performed with a horizontal misalignment of 0.35 m and was repeated three times. The gap between the coils was held constant at 0.15 m. The wireless charging system was designed according to the Society of Automotive Engineers (SAE) standard. The experimental results demonstrated that the movement error distance was 0.001 m, with an average error of 0.33%. These findings indicate that the automatic alignment transmitter system achieved an operational effectiveness of 99.67%. The maximum wireless charging efficiencies of 75.78% and 75.59% were recorded for the X-axis and Y-axis adjustments, respectively. Full article

Urban bus operations under signalized traffic conditions are characterized by frequent stop-and-start behaviors which significantly degrade fuel economy, especially for fuel cell buses (FCB). In this paper, a collaborative optimization method is proposed that combines speed planning and energy management for FCB in this situation. The method calculates the target speed of FCB using traffic light phase information and the remaining signal time. With an intelligent driving model, the vehicle can adjust its speed in advance when approaching intersections so it can pass through intersections without stopping. At the same time, a learning-based energy management strategy is used to reasonably share power between the fuel cell and the battery. The results indicate that the method proposed in this paper reduces hydrogen consumption by approximately 11.3% compared to the standard method. Full article

The rapid development of autonomous vehicle technologies represents a major transformation in contemporary transportation systems; however, their successful integration depends not only on technological maturity but also on societal acceptance. This study investigates public attitudes toward autonomous vehicles, with particular emphasis on generational and gender-based differences, aiming to identify key factors influencing acceptance, usage intention, and purchase-related decision-making. A quantitative, cross-sectional research design was applied using an online questionnaire survey conducted between January and September 2025. The final sample consisted of 655 respondents, with a balanced gender distribution and representation across multiple generational cohorts. Statistical analyses included one-way and two-way analysis of variance (ANOVA), complemented by non-parametric tests when distributional assumptions were not fully met. The results indicate significant generational differences across all examined dimensions. Younger generations, particularly Generations Y and Z, exhibit significantly higher willingness to try autonomous vehicles, greater openness to new technologies, and stronger consideration of autonomous functions in vehicle purchasing decisions. Gender-based differences were also identified, with men generally demonstrating higher technological openness than women. Moreover, a significant interaction effect between generation and gender was found, suggesting that gender differences vary across generational groups and are less pronounced among younger cohorts. Despite these contributions, the study has several limitations. Its cross-sectional design captures attitudes at a single point in time and does not allow causal inference or longitudinal analysis of attitude change. The use of self-reported, hypothetical measures may not fully reflect actual behaviour in real-world adoption scenarios. Additionally, online data collection may introduce self-selection bias, favouring respondents with higher digital literacy and technological interest. Overall, the findings highlight the importance of considering demographic heterogeneity when developing, communicating, and regulating autonomous vehicle technologies, while also underscoring the need for longitudinal and behaviour-based research in future studies. Full article

Modulated twelve-voltage-vector model predictive current control (MPCC), which applies two or three voltage vectors per control period, exhibits superior steady-state performance compared to modulated six-active-voltage-vector MPCC and conventional MPCC. However, implementing modulated twelve-voltage-vector MPCC requires accurate knowledge of the permanent magnet synchronous motor drive’s inductance and permanent magnet (PM) flux linkage parameters for selecting suboptimal and optimal voltage vectors, as well as computing the duty cycles of optimal vectors. Consequently, its control performance is more sensitive to model parameter inaccuracies. To mitigate parameter sensitivity, a robust modulated twelve-voltage-vector MPCC algorithm based on a model reference adaptive system (MRAS) is proposed. The MRAS-based observer estimates the inductance and PM flux linkage parameters in real time, enhancing model accuracy. The observer is designed with a stability analysis framework, where the proportional and integral gains of the MRAS are theoretically derived to ensure precise parameter estimation. The effectiveness of the proposed algorithm is validated through simulation results, demonstrating satisfactory control performance even under parameter mismatches. Specifically, the torque ripple is reduced from 1.1 A to 0.6 A, corresponding to a reduction of 45.5%. Similarly, the stator flux ripple decreases from 1.75 A to 1 A (42.9% reduction), while the total harmonic distortion (THD) is reduced from 8.39% to 5.48%, representing a 34.7% improvement. Full article

Aiming at the challenge of simultaneously optimizing ride comfort and wheel grounding performance for mining dump trucks under severe road conditions, this paper proposes a hydro-pneumatic suspension parameter design method based on an improved multi-objective particle swarm optimization (IMOPSO) algorithm. First, a dynamic model of the hydro-pneumatic suspension is established, incorporating the coupled nonlinear characteristics of the valve system and the gas chamber. The accuracy of the model is verified through bench tests. Subsequently, the influence of key parameters, including the damping orifice diameter, check valve seat hole diameter, and initial gas charging height, on the vertical dynamic performance of the vehicle, is systematically analyzed. On this basis, a multi-objective optimization model is constructed with the objective of minimizing the root mean square (RMS) values of both the sprung mass acceleration and the dynamic tire load. To enhance the global search capability and convergence performance of the MOPSO algorithm, adaptive inertia weighting, dynamic flight parameter update, and an enhanced mutation strategy are introduced. Simulation results demonstrate that the optimized suspension achieves significant improvements under various road conditions. On class-C roads, the RMS values of the sprung mass acceleration (SMA) and the dynamic tire load (DTL) are reduced by 37.6% and 15.8%, respectively, while the suspension rattle space (SRS) decreases by 10.2%. Under transient bump roads, the peak-to-peak (Pk-Pk) values of the same two indicators drop by 38.9% and 44.9%, respectively. Furthermore, compared to the NSGA-II algorithm, the proposed method demonstrates superior performance in terms of convergence stability and overall performance balance. These results indicate that the proposed design effectively balances ride comfort, wheel grounding performance, and driving safety. This study provides a theoretical foundation and an engineering-feasible method for the performance balancing and parameter co-design of suspension systems in heavy-duty engineering vehicles. Full article

This study employed an integrated experimental–computational methodology to investigate the critical role of the layer-stacking sequence in the acoustic performance of multi-layer porous materials for vehicle NVH applications. The acoustic properties of four distinct single-layer materials were first characterized via impedance tube measurements. A finite element simulation model based on the Johnson–Champoux–Allard (JCA) theory was subsequently developed in COMSOL Multiphysics 6.2 and rigorously validated. Leveraging this validated model, a systematic analysis was conducted on six different layer sequences under a fixed total thickness of 30 mm. The simulation results showed excellent agreement with experimental data, with a root-mean-square error (RMSE) below 5%. It was demonstrated that the stacking sequence significantly governed the mid-to-high frequency sound absorption behavior, which was strongly correlated with the modulation of the real and imaginary parts of the normalized surface acoustic impedance. This study thus demonstrated that the layer sequence—a previously underexplored design factor—critically determines the absorption performance of multi-layer materials at a fixed total thickness. A full design-space analysis revealed that performance shifts are governed by changes in interfacial acoustic impedance. This physics-driven insight provides a practical framework for tailoring absorbers to specific frequency bands, offering a viable path toward lightweight acoustic solutions for electric vehicle applications. Full article

Heavy-duty vehicles dominate global freight movement and primarily rely on fossil-derived diesel fuel. However, fluctuations in crude oil prices and evolving emissions regulations have prompted interest in alternative powertrains to enhance fleet energy resiliency. This study paired real-world operational data from a large commercial fleet with high-fidelity vehicle models to evaluate the potential for replacing diesel internal combustion engine (ICE) trucks with alternative powertrain architectures. The baseline vehicle for this analysis is a diesel-powered ICE truck. Alternatives include ICE trucks fueled by bio- and renewable diesel, compressed natural gas (CNG) or hydrogen (H₂), as well as plug-in hybrid (PHEV), fuel cell electric (FCEV), and battery electric vehicles (BEV). While most alternative powertrains resulted in some payload capacity loss, the overall fleetwide impact was negligible due to underutilized payload capacity for the specific fleet considered in this study. For sleeper cab trucks, CNG-powered trucks achieved the highest replacement potential, covering 85% of the fleet. In contrast, H₂ and BEV architectures could replace fewer than 10% and 1% of trucks, respectively. Day cab trucks, with shorter daily routes, showed higher replacement potential: 98% for CNG, 78% for H₂, and 34% for BEVs. However, achieving full fleet replacement would still require significant operational changes such as route reassignment and enroute refueling, along with considerable improvements to onboard energy storage capacity. Additionally, the higher total cost of ownership (TCO) for alternative powertrains remains a key challenge. This study also evaluated lifecycle impacts across various fuel sources, both fossil and bio-derived. Bio-derived synthetic diesel fuels emerged as a practical option for diesel displacement without disrupting operations. Conversely, H₂ and electrified powertrains provide limited lifecycle impacts under the current energy scenario. This analysis highlights the complexity of replacing diesel ICE trucks with admissible alternatives while balancing fleet resiliency, operational demands, and emissions goals. These results reflect a US-based fleet’s duty cycles, payloads, GVWR allowances, and an assumption of depot-only refueling/recharging. Applicability to other fleets and regions may differ based on differing routing practices or technical features such as battery swapping. Full article

For hybrid energy storage systems requiring efficient energy management to achieve optimal power allocation between the power battery and supercapacitor, this study proposes an optimal energy management method integrating whole-process particle swarm optimization with fuzzy logic control, which simultaneously considers braking safety and energy efficiency optimization. First, a zonal braking force distribution strategy based on the I-curve, ECE regulations curve, and front wheel lockup curve is designed to maximize energy recovery while ensuring braking safety. On this basis, a whole-process “driving–braking” fuzzy logic control strategy for power distribution is constructed, aiming at maximizing braking energy recovery efficiency and minimizing energy consumption per 100 km. The parameters of the membership functions in the fuzzy controller are optimized using the particle swarm optimization algorithm to achieve global optimization of the control process. Finally, simulation validation of the optimization results demonstrates that, compared with traditional logic threshold control under NEDC conditions, the proposed strategy improves braking energy recovery efficiency by 10.32%, reduces energy consumption per 100 km by 0.96 kWh, and decreases the peak current of the power battery by 6.4%, thereby effectively enhancing vehicle economy and extending battery lifespan. Full article