Editor’s Choice Articles

In order to simplify the stator winding structure of traditional variable reluctance (VR) resolvers and enhance their performance under high-speed operating conditions, this paper proposes a design for a variable reluctance self-coupling resolver based on ultrahigh-frequency (UHF) square wave excitation. The proposed solution optimizes the traditional winding structure by eliminating the separate excitation winding and integrating both excitation and detection functions into the two-phase sine and cosine windings. By optimizing the arrangement of the sine and cosine windings, a single-layer equal-turn winding design is successfully implemented, significantly simplifying the winding layout and reducing copper usage. In terms of excitation signal, this paper innovatively replaces the traditional sinusoidal excitation with UHF square wave excitation. Compared to sinusoidal excitation, square wave excitation not only generates higher electromotive force (EMF) peaks but also simplifies engineering implementation, reducing the complexity of system hardware. To validate the feasibility and advantages of the proposed structure, a complete experimental testing platform was built, and comparative experiments were conducted under various rotational speeds. The experimental results show that the proposed self-coupling resolver can achieve high-precision rotor position detection across the entire speed range, significantly improving the detection accuracy and dynamic response of traditional methods under high-speed conditions. Ultimately, the design demonstrates strong engineering application potential and provides a new solution for high-precision, high-dynamic response rotor position detection. Full article

This study presents an integrated simulation framework that combines agent-based transport modeling with probabilistic load-flow analysis to quantify power system loading of long-haul heavy-duty electrification. The approach is applied to a case study considering fully electrified road freight in the Skåne region in Sweden, using high-resolution transport demand data and the actual power grid model used by the grid owner in the study area. The synthetic freight population covers the full long-haul truck segment intersecting Skåne. Both public en-route fast charging and end-of-trip depot charging are considered. The analysis reveals two fundamentally different charging demand profiles: a heavily fluctuating profile for public en-route charging, accounting on average for 82% of the total daily charging energy, and a stable profile for end-of-trip depot charging, covering on average the remaining 18%. The latter is achieved through a Linear Programming (LP) optimization model that flattens the load by scheduling charging across depot stay windows. These profiles serve as inputs to a probabilistic load-flow simulation that computes loading distributions for substation transformers. The simulation results show that in 4 of the 43 primary substations studied, the maximum transformer loading exceeds 100% following the introduction of truck charging, with peak loading at the most affected substation rising from 99% to 159%. This stress is primarily caused by the public charging demand, which peaks from late morning to noon, aligning with the early stages of logistics operations. However, there is no clear correlation between the magnitude of the truck charging load and the impact on transformer loading, since this is also highly dependent on local grid conditions. These findings highlight the value of integrated transport-energy simulations for planning resilient infrastructure and guiding targeted grid reinforcements. Full article

This paper presents the results from in-lab chassis dynamometer testing of two battery electric vehicles of the same make and model: a 2022 model year vehicle with a heat pump and a 2020 model year vehicle with a resistive positive temperature coefficient (PTC)-type heater. The vehicles were tested over a series of standard drive cycles at −10 °C, −7 °C, 0 °C, and 25 °C to determine the impacts of the different heating systems on vehicle energy consumption and driving range in cold temperatures. The results indicate that in most (but not all) heating situations the heat pump heated its vehicle’s cabin more efficiently than the PTC heater did, especially at 0 °C. At the lowest temperature, −10 °C, the heat pump used more energy than the PTC heater on cold-start but was more efficient than the PTC heater once the cabin was warmed up. Over standard drive cycles and using SAE J1634 calculation methods to obtain a single range value for each cycle type, the improvement in the percentage of driving range retained by the heat pump-equipped vehicle over the PTC heater-equipped vehicle varied between 1% and 15% depending on ambient conditions and drive cycle, with the average advantage in percentage range retained being 7% over the UDDS cycle, 7% over the HWFET cycle, and 4% over the US06 cycle for all cold temperatures combined. Full article

Energy Management Strategies (EMSs) are crucial for enhancing fuel economy and reducing emissions in light commercial vehicles (LCVs). This paper presented three EMS approaches for LCVs with hybrid powertrains: Rule-Based Control (RBC) and two optimization-based strategies, the Equivalent Consumption Minimization Strategy (ECMS) and Model Predictive Control (MPC). To enhance robustness under varying operating conditions, optimization algorithms were designed and tuned using the WLTC City driving cycle, and adaptive components were included. For a fair assessment of overall efficiency, all strategies were compared under identical constraints on hydrogen and electrical energy consumption. The results showed that, under these constraints, MPC achieved the longest driving distance, highlighting its superior energy utilization capability. In a broader comparative analysis, both the ECMS and MPC outperformed the benchmark RBC, with MPC demonstrating the most consistent performance, enhanced stability, and strong adaptability in dynamic scenarios. The findings indicate that MPC offers notable advantages for LCV energy management, combining efficiency, robustness, and interpretability, positioning it as a promising candidate for practical implementation in future hybrid powertrain systems. Full article

This paper presents an exploratory cross-cultural analysis of autonomous vehicle expectations through a 57-question survey distributed in the United States (n = 50), Germany (n = 66), and Panama (n = 41). Five scales are presented and validated: Driving Behavior Aggressiveness (DBA), Self-Driving Car Aggressiveness (SDCA), Artificial Intelligence (AI) Trust (AIT), AI Driving Mechanics Trust (AIDMT), and Driver Safety Score (DSS). Each scale is validated via confirmatory factor analysis and multi-group measurement invariance testing. Results show that drivers prefer a self-driving car driving style more conservative than their own; however, participants who are more trustful of AI show DBA–SDCA equivalence, consistent with acceptance of a driving style comparable to their own. Significant cross-cultural differences emerge, with Panama diverging from the United States and Germany on DBA, SDCA, AIDMT, and DSS; these country effects largely persist after controlling for demographics. These findings suggest that self-driving car behaviors should be tailored to regional expectations and passenger trust profiles to improve adoption. Full article

Model predictive current control (MPCC), owing to its straightforward design and convenient multi-objective optimization, has been widely adopted in applications demanding high dynamic performance. However, the conventional MPCC suffers from poor current steady-state performance and severe parameter dependence. To address these issues, this paper proposes a virtual-vector based model-free predictive current control (MFPCC) scheme for permanent magnet synchronous machine (PMSM) drives with adaptive control-gain. The proposed approach is developed based on the ultra-local model (ULM) concept to simplify the control structure and enhance robustness. The disturbance is observed by a linear extended state observer (LESO) and the effect of control-gain deviation on disturbance observation is analyzed. In addition, a control gain adaptive method is introduced to weaken the high-frequency components of the integrated disturbance, which can further improve the performance of observer. Furthermore, the virtual-vector control set is built where symmetrical vector sequences are included to reduce torque ripple. An improved optimization strategy is also developed that reduces computation and improves steady-state performance. Comprehensive experimental results confirm the effectiveness and superiority of the proposed method in terms of steady-state performance, robustness, and computational burden. Full article

Traffic sign recognition is critical for intelligent transportation systems and autonomous driving. Conventional convolutional neural networks (CNNs) typically utilize the ReLU activation function for its computational efficiency; however, alternative activation functions can improve computing effectiveness capacity in recognition tasks. In this study, we propose a CNNs model enhanced with the Gaussian Error Linear Unit (GELU) activation function. We evaluate its performance on benchmark datasets and compare it against both ReLU and Leaky ReLU baseline. Experimental results show that the proposed GELU-activated CNNs achieves a recognition accuracy of 99.75% and provides small but consistent improvements over ReLU and Leaky ReLU models, particularly under challenging conditions such as occlusion and low lighting. These findings highlight GELU’s potential to enhance the robustness and reliability of traffic sign recognition in Electric Vehicles for autonomous driving applications. Full article

Under the dual impact of environmental issues and the energy crisis, new energy vehicles (NEVs) have gradually become a phenomenal emerging industry in China, also essentially becoming a new engine to support the growth of China’s economy. While topics related to the NEV industry have gained widespread attention, there is a lack of studies specifically focusing on the characteristics of its industrial spatial distribution pattern. Based on the data related to NEV-listed companies located in the Yangtze River Delta (YRD) region in 2022, this study constructs the corresponding city network using the method of social network analysis (SNA) and interprets the structural features of this network. The results reveal the following: (1) The network exhibits three fundamental characteristics: low density, short path length, and multiple centers. (2) The NEV industry in the YRD has formed the agglomeration pattern of three major “clubs”, projected on the map in the shape of a “golden bow”. (3) Cities in the YRD show a “pyramid-type” collaboration in the NEV industry. (4) Collaboration between cities in the NEV industry can cross the limits of geographic proximity and even administrative boundaries. Full article

As the number of electric vehicles (EV) increases, the demand for recommending the best charging location when using a large-scale charge network to charge is also increasing. A successful recommendation will utilize the user’s preference and the operational constraints of the charging network to make sure that it also takes into account the real-time operational requirements of the network. Most current papers focus on optimizing individual algorithmic components in isolation; consequently, many of these papers neglect to provide a holistic view of an integrated system. In addition, there are many operational requirements that current research does not consider, such as cold-start personalization for new users and enforcing real-time operational constraints like station availability, power capacity, maintenance windows, etc. This paper describes a deployable multi-stage recommendation system that creates a candidate list based on location and ranks preferences based on user, station and context features. The recommendation system also adds a configurable rule-based re-ranking layer to ensure that both hard constraints (i.e., charger availability and power-cap limits) and soft objectives (i.e., load balancing and operator priority) are enforced. A method for enabling mixed use between stable Bayesian and adaptive Bayesian methods was developed to provide users starting with cold-start performance that do not have adequate histories. Evaluation of this method using 100k+ real charging sessions showed that the fraction of sessions where the ground-truth station appears in the top-two recommendations (Hit@2) for the recommendation system was 0.82, representing a 37% increase in performance compared to proximity-based recommendation methods. The online deployed recommendation system has a 99th-percentile serving latency (P99) of less than 200 ms. The findings of this paper provide a framework for the implementation of operationally-relevant user-centric recommendation systems for EV services at scale. Full article

This study provides a comprehensive assessment of the global implications arising from China’s dominant position in the electric vehicle (EV) transition. By 2030, under current policy trends, China is projected to account for approximately 57% of the global EV stock (238 million vehicles) and 53% of the worldwide EV-driven oil displacement (2.75 million barrels per day). Its demand for automotive batteries will reach 1516 GWh, representing 47% of the global total. Employing LMDI-I decomposition, we find that China’s outsized impact is driven not merely by the scale but by the higher vehicle utilization intensity (contributing 61% of its advantage) and policy support for efficient vehicle types like plug-in hybrids and two/three-wheelers (contributing 31%). The extreme geographic concentration creates a significant systemic risk; our Monte Carlo simulation indicates a 92% probability that a moderate supply shock in China would trigger a severe global battery shortage. Conversely, China stands to gain substantial economic benefits, estimated at USD 117 billion annually by 2030 (90% CI: 78–173 billion) from the avoided oil imports and potential carbon revenues. These findings highlight a central paradox of the energy transition: while China delivers immense climate and energy security benefits, its dominance introduces unprecedented supply chain vulnerabilities and a highly asymmetric distribution of economic gains, necessitating urgent policy responses for diversification and resilience. Full article

The deployment of artificial intelligence in safety-critical industrial systems is hindered by a core trust deficit, as models trained via empirical risk minimization often fail catastrophically in out-of-distribution (OOD) scenarios. We address this challenge by developing a physics-informed hybrid ensemble that achieves state-of-the-art accuracy and robustness for Permanent Magnet Synchronous Motor (PMSM) temperature forecasting. Our methodology first calibrates a Lumped-Parameter Thermal Network (LPTN) to serve as a physics engine for generating physically consistent data augmentations, which then pre-trains a Temporal Convolutional Network (TCN) encoder via self-supervision, with the final prediction assembled from the physics model’s baseline guess and a correction learned by an ensemble of gradient boosting models on a rich, multi-modal feature set. Evaluated against a suite of strong baselines, our hybrid ensemble achieves a state-of-the-art Root Mean Squared Error of 5.24 °C on a challenging OOD stress test composed of the most chaotic operational profiles. Most compellingly, our model’s performance improved by an unprecedented −10.68% under these extreme stress conditions where standard, purely data-driven models collapsed. This demonstrated robustness, combined with a statistically valid Coverage Under Shift (CUS) Gap of only 1.43%, provides a complete blueprint for building high-performance, trustworthy AI, enabling safer and more efficient control of critical cyber-physical systems and motivating future research into physics-guided pre-training for other industrial assets. Full article

This paper focuses on alternatives to the CAFC-NEV credits policy in the automotive industry of China. It considers a dual-channel supply chain consisting of a manufacturer and a retailer that can simultaneously produce and sell new energy vehicles (NEVs) and internal combustion engine vehicles (ICEVs). Differential game theory is employed to explore dynamic optimal decisions under CAFC-NEV credits and carbon credit policies. The results suggest that the strategies combining CAFC-NEV credits and carbon credit policies are equivalent to a single CAFC-NEV credits policy. Therefore, implementing the carbon credit policy on the basis of the CAFC-NEV credits policy does not affect the increase in NEV range. If the NEV credit score is below a certain threshold, the carbon credit policy will result in a higher range increase and brand goodwill of NEV. In the transition process of implementing the carbon credit policy based on CAFC-NEV credits and subsequently canceling the CAFC-NEV credit policy, the profits of supply chain members change slightly. The findings provide a theoretical basis for the timely exit of the CAFC-NEV credits policy. Full article

Under dual-carbon targets, electric vehicles (EVs) have become central to transport decarbonization, making EV sales a key indicator of market diffusion and policy effectiveness. Despite the growing body of research, studies on EV sales remain fragmented and lack systematic integration. This study provides a structured review of EV sales research published between 2016 and 2025. Based on searches in Scopus and Web of Science, 1518 records were identified, and 194 peer-reviewed journal articles were retained after a multi-stage screening process. Temporal analysis reveals a clear stage-based evolution of EV sales research, with limited publications prior to 2020 and a marked expansion after 2021. The literature is categorized into two main streams: (i) determinants of EV sales and (ii) forecasting approaches. For determinants, a macro–meso–micro analytical framework is developed to organize policy, market, and behavioral factors. For forecasting, quantitative analysis shows that econometric and statistical models remain dominant (54%), while machine learning (18%), behavior simulation (14%), hybrid models (8%), and deep learning (4%) are increasingly adopted. This indicates a gradual shift toward data-driven and model integration approaches. This review offers a structured synthesis of determinant mechanisms and forecasting paradigms, identifies methodological imbalances, and outlines future research directions toward improved multi-level integration and mechanism-based modeling of EV sales dynamics. Full article

The transition to electric mobility requires the coordinated evolution of vehicles, charging infrastructure, power systems, and intelligent digital platforms. This study examines the role of Industry 4.0 technologies in enabling large-scale electric vehicle (EV) adoption and effective EV grid integration and synthesizes the existing evidence into a coherent analytical framework to support planning and policy decision-making. A systematic review of 27 peer-reviewed studies published between 2018 and 2025 was conducted in accordance with PRISMA 2020 guidelines, capturing the acceleration of electromobility following the consolidation of Industry 4.0 technologies and the emergence of large-scale policy commitments worldwide. The analysis covers six technology families, including the Internet of Things, big data and analytics, artificial intelligence and machine learning, blockchain, digital twins, and extended reality, and examines their applications in smart charging, grid vehicle coordination, fleet optimization, and vehicle-to-grid services. The findings show that analytics and artificial intelligence consistently enhance operational reliability and efficiency, while digital twins are increasingly applied to infrastructure siting, grid impact assessment, and scenario analysis. Building on these results, the study proposes a three-layer analytical framework composed of physical, digital, and decision layers, together with a functional EV grid generation integration model that links technology readiness to system-level deployment. In addition, a transition timeline for the 2025–2040 period and a concise set of key performance indicators are introduced to support evaluation and comparison. Policy implications for Ecuador and Latin America emphasize interoperability, data governance, realistic cost assessment, and a phased approach to vehicle-to-grid deployment. Full article

As the oil price has skyrocketed, the attraction towards electric vehicles has gone up. This scenario has also increased the demand for charging infrastructure. This paper proposes a novel charging infrastructure for electric vehicles which is energized by a solar photovoltaic unit, integrated with a distribution static compensator. The output of the photovoltaic array is regulated by a DC–DC converter, which uses maximum power point tracking to support optimal solar energy conversion. The compensator is integrated into the grid through a zigzag-star transformer, which helps with neutral current compensation, promoting balanced and distortion-free operation. The control algorithm is designed to ensure superior power quality during grid synchronization and sustainable energy management. This novel architecture ensures bidirectional power flow, enabling the charge–discharge dynamics of the electric vehicles, which can be termed Grid-to-Vehicle and Vehicle-to-Grid modes. Better grid flexibility and resilience are ensured by this dynamic power exchange. The control strategy based on the Linear Kalman Filter provides reactive power balance and maintains steady voltage at the point of common coupling, and it ensures enhanced power quality during power flow, resulting in efficient and reliable grid operations. The effectiveness of the control algorithm is tested and validated under Grid-to-Vehicle, Vehicle-to-Grid, nonlinear, unbalanced, and isolated solar conditions. Analytical tuning of the gains in the controller, by using the conventional methods, is not efficient under dynamic conditions and nonlinear loads. An optimization technique is used to estimate the proportional–integral control gains, which avoids the difficulty of tuning the controllers. Simulation of the system is carried out using MATLAB 2022b/SIMULINK. Simulation results under diverse operating scenarios confirm the system’s capability to sustain superior power quality, maintain grid stability, and support a robust and reliable charging infrastructure. By enabling regulated bidirectional energy exchange and autonomous operation during grid disturbances, the charger operates as a resilient grid-support asset rather than as a passive electrical load. Full article

Sustainability and circular economy are becoming increasingly important for new products due to new EU legislation and serve as orientation for product development. This is particularly significant for the automotive industry, as “new” products such as electric vehicles present new challenges. This sustainability orientation influences fundamental requirements of a product, such as material, construction, joining techniques, design, production and R-strategies. Some of these requirements are determined particularly in the early phases of the innovation process. Decisions are made almost exclusively based on theoretical data, while the inclusion of physical prototypes in the evaluation and decision-making process often only takes place at later stages. In particular, early, disruptive, sustainability-oriented idea generation, which is not only based on a data-based evaluation but is also supported by physical realization in the early phase, contributes significantly to well-founded decision-making processes. An approach for idea generation on sustainable and circular products in the early phase is presented in this article, which was applied and evaluated by a sustainable and circular vehicle component (center console). Full article

The large-scale integration of electric vehicles (EVs) introduces significant operational challenges for power systems, particularly when grid-favourable operating periods coincide with high marginal carbon emissions. This paper proposes a carbon-aware rolling-horizon energy management framework for EV fleets coordinated through virtual power plants (VPPs), explicitly addressing such carbon–grid conflict conditions. The proposed framework prioritises grid-friendly scheduling through power and ramp constraints while enforcing energy-service equivalence and a policy-level carbon budget consistent with carbon peak and carbon neutrality objectives. Carbon awareness is incorporated as a secondary steering term within the rolling-horizon optimisation, enabling temporal shifting of EV charging toward low-carbon periods without compromising grid stability. A Pareto-based trade-off analysis is conducted to characterise the relationship between grid stress mitigation and carbon reduction, and a knee point is identified to select a balanced operating regime. Simulation results using real EV charging demand combined with a conflict-driven carbon intensity signal demonstrate that grid-oriented scheduling alone can increase emissions under carbon–grid mismatch. In the evaluated conflict scenario, the proposed carbon-aware rolling-horizon strategy achieves a 17.35% reduction in total CO₂ emissions relative to RH-NoCarbon scheduling while maintaining peak–valley load variation below 11.03 kW compared with 43.65 kW under uncontrolled charging. These results confirm that explicit carbon-aware coordination can significantly mitigate emissions without compromising grid operational stability. All control strategies are evaluated in a simulation environment using real EV charging demand data as exogenous inputs, ensuring realistic demand representation while enabling controlled assessment of operational performance. These findings highlight the necessity of embedding carbon considerations directly into operational EV scheduling and establish VPP-based rolling-horizon coordination as a practical mechanism for low-carbon power system operation. Full article

Covalent organic frameworks (COFs) have emerged as promising candidates for solid-state electrolytes (SSEs) in lithium-ion batteries (LIBs) due to their tunable pore sizes, high surface areas, and exceptional thermal stability. However, the rational design of COF-based SSEs is hindered by the vast combinatorial chemical space, synthetic complexity, and the need for precise control over structure-property relationships. Machine learning (ML) has revolutionized the development of COF materials by enabling high-throughput screening, predictive modeling, and optimization of synthesis conditions. This review systematically explores the integration of ML in COF-based SSE development, focusing on structure prediction, synthesis-performance optimization, and the application of digital twin strategies. We highlight the role of ML in accelerating the discovery of high-performance COF-based solid-state electrolytes, optimizing ionic conductivity, and enhancing interfacial stability. By summarizing the synergistic pathways between computational simulations and experimental validation, this review offers strategic guidelines for overcoming traditional “trial-and-error” R&D bottlenecks, paving the way for the next generation of high-energy-density LIBs. Full article

Hydrogen fuel cell tractors (HFCTs) represent a critical frontier in the development of modern green agricultural equipment. Due to the heavy-duty and highly variable nature of tractor operations, current fuel cell-powered platforms face significant challenges, including insufficient energy sustainability and low-efficiency consumption. This study addresses the issues of sluggish dynamic response and durability degradation during complex plowing tasks through systematic power system modeling and energy management strategy (EMS) research. First, a control-oriented fuel cell model coupling mechanical inertia, manifold filling-and-emptying dynamics, and electrochemical reactions is established, which quantitatively reveals the physical boundaries of load-change ramp rates. On this basis, a multi-dimensional performance evaluation framework for HFCTs is constructed. This framework innovatively proposes fuel cell dynamic response indicators and a non-linear calculation model for continuous operational duration, achieving a non-linear mapping between onboard energy storage capacity and operating time for quantitative endurance assessment. Subsequently, guided by this evaluation system, a dynamic program considering the coordination of energy system durability and the energy consumption economy (DP-CoDE) is developed. By establishing an online update mechanism for power-change rates, synergistic optimization of system durability and economy is achieved based on the DP-CoDE strategy. Model-in-the-loop simulation results under plowing conditions demonstrate that, compared to the DP-CoDE strategy, the proposed strategy enhances response stability by 44.44% and reduces response tracking error by 41.17% at a marginal cost of only a 0.15% increase in total hydrogen consumption. These findings significantly improve the system’s tracking capability under transient complex loads and provide a robust theoretical foundation for the control system design of HFCTs. Full article

As the electrification of heavy-duty trucks accelerates, conventional charging methods face challenges, including long charging durations and reduced transportation efficiency. This paper compares and evaluates various charging methods for electric heavy-duty trucks (EHDTs), including slow charging, fast charging, battery swapping, and electric roads, from both technological and economic perspectives. A case study in a harbor setting further examines the cost and efficiency implications of a 22 kW slow charger, a 150 kW fast charger, and battery swapping (the swappable battery is charged with 150 kW). The analysis provides insights into selecting the most suitable charging solution by assessing annual charging costs, truck and infrastructure cost amortization, and downtime across different scenarios. The findings of this paper indicate that slow charging is cost-effective in low-demand operations but becomes impractical as operational demand increases, leading to excessive downtime exceeding 37,000 h annually in high-demand scenarios. Fast charging significantly reduces downtime but requires higher infrastructure investment and charging costs. Battery swapping minimizes downtime to less than 300 h annually in high-demand scenarios, and, despite a higher initial infrastructure cost, it emerges as the most cost-effective option over five years for medium- and high-utilization fleets, with a total cost of approximately €1.67 million in the studied harbor case. Thus, selecting a suitable charging solution depends on operational priorities, such as minimizing cost or maximizing fleet availability within a specific use-case context. Full article

Battery electric trucks (BETs) are a promising option to reduce emissions from heavy-duty vehicles. However, the transition to BETs will cause an additional demand for electricity. Future charging strategies will influence the future peak load as well as the operational and technical feasibility of BETs. We simulated 2410 representative single-day German truck driving profiles with three different charging strategies: (1) as slow as possible, (2) as fast as possible, and (3) slowly at depots and as fast as possible at public locations. Assuming a 33% electrification rate by 2030 and near-complete fleet conversion by 2045, we scaled our results to the German truck fleet. We found that charging as fast as possible leads to additional peak loads up to 6 GW in 2030 and 18 GW in 2045, while the other charging strategies reduce additional peak loads to 3 GW in 2030 and 8 GW in 2045. Therefore, implementing wise charging strategies will reduce future peak load. Full article

Generative pre-training via neural rendering has become a cornerstone for scaling 3D perception in autonomous driving. However, prevalent approaches relying on implicit Neural Radiance Fields (NeRFs) face two fundamental limitations: the shape-radiance ambiguity inherent in vision-centric optimization and the prohibitive computational overhead of volumetric ray marching. To address these challenges, we propose AES-Gaussian, a novel multi-modal pre-training framework grounded in the efficient 3D Gaussian Splatting (3DGS) representation. Diverging from symmetric fusion paradigms, our core innovation is an Asymmetric Encoder architecture that couples a deep semantic vision backbone with a lightweight, physics-aware LiDAR branch. In this framework, LiDAR data serve not merely for semantic extraction, but as sparse physical anchors. By employing a novel Explicit Feature Synergy mechanism, we directly inject raw LiDAR intensity and depth priors into the Gaussian decoding process, thereby rigidly constraining scene geometry in open-world environments. Extensive empirical validation on the nuScenes dataset demonstrates the superiority of our approach. AES-Gaussian achieves state-of-the-art transfer performance, yielding a substantial 7.0% improvement in NDS for 3D Object Detection and a 4.8% mIoU gain in 3D semantic occupancy prediction compared to baselines. Notably, our method reduces geometric reconstruction error by over 50% while significantly improving training and inference efficiency, attributed to the streamlined asymmetric design and rapid Gaussian rasterization. Ultimately, by enhancing both perception accuracy and system efficiency, this work contributes to the development of safer and more reliable autonomous driving systems. Full article

Dimensioning the energy storage systems for a heavy-duty fuel cell hybrid electric vehicle is not straightforward. This study proposes a methodology to address this challenge, aiming to maximize efficiency while mitigating the aging effects on the energy storage systems. Various configurations of storage system ratios have been analyzed using the concept of hybridization percentage, which represents the ratio between the supercapacitor weight and the total weight of the energy storage elements. Simulations were conducted using models developed in AVL Cruise MTM. A case study is included to test the methodology, incorporating commercial components, a standard driving cycle, and a rule-based energy management strategy. The conclusions of this application example illustrate the types of results that can be obtained by using this hybrid energy storage system sizing methodology. Findings for this case study suggest that for cycles lacking extreme power peaks, non-hybridized configurations can be the optimal solution, as the battery size reduction outweighs the benefits of hybridization in terms of efficiency, achieving 76.08% without supercapacitors compared to 65.7% with a high hybridization grade of 32.4%, and overall cost. However, sensitivity analysis reveals that if the optimization weights are adjusted to prioritize aging over efficiency, the optimal configuration shifts to a 6.48% hybridization grade at a 0.3C threshold. Full article

This study investigates the prospects for implementing hydrogen mobility in Bulgaria within the broader context of transport decarbonization. Using a three-dimensional framework—policy, technology, and geography—it combines analysis of European and national strategic documents, technological feasibility assessment, and a pilot case study in the city of Ruse. The pilot scenario includes a regional hydrogen ecosystem with a photovoltaic-powered electrolyzer, two refueling stations, deployment of 20 hydrogen buses, and retrofitting of a river vessel with fuel cell propulsion. Results indicate that hydrogen technologies can significantly reduce transport-related emissions, particularly where battery-electric solutions face operational constraints. Total Cost of Ownership (TCO) analysis shows that hydrogen buses remain more expensive than diesel or battery-electric alternatives under current conditions, even with locally produced green hydrogen. Sensitivity analysis demonstrates that cost competitiveness may be achieved after 2030 with large-scale investments, policy support, and reduced hydrogen prices. The study highlights the importance of coherent national strategies, public–private partnerships, and targeted financial instruments to enable sustainable integration of hydrogen in urban and river transport systems. Full article

This paper investigates the coordination between logistics and policy decisions for electric vehicle (EV) exports under the Belt and Road Initiative. Focusing on the two modes—maritime shipping and the China Railway Express (CR Express)—along with government production subsidies, import tariffs, and service premium, a Stackelberg game model for a cross-border supply chain comprising a domestic manufacturer and an overseas retailer is constructed. The equilibrium outcomes under four scenarios formed by combining subsidy policies and transportation modes (Models NM, NR, GM and GR) are compared theoretically and numerically, with further evaluation of capacity constraints and power structures, as well as the robustness verification of the core findings. Results show that the CR Express mode exhibits a service-driven nonlinear cost pattern, where its service premium amplifies positive market responses. Its appeal to the manufacturer, however, is tightly constrained by fixed cost. Furthermore, government subsidies can overcome this barrier by synergizing with the service premium, turning the CR Express into a relatively advantageous strategy. Moreover, subsidy efficacy is conditional, depending heavily on the service premium level and logistics cost coefficient, leading to a proposed differentiated subsidy framework. This study offers a theoretical basis for corporate logistics strategy and targeted policy design. Full article

To address the waste of mechanical energy from suspension vibrations during vehicle operation, this study proposes a vehicle suspension vibration energy harvester based on the piezoelectric effect and nonlinear magnetic coupling. It aims to recover the mechanical energy generated by suspension vibrations in the course of vehicle operation. The device adopts a multi-cantilever beam array structure. Permanent magnets are symmetrically arranged on the free ends of cantilevers and suspension springs, which enables non-contact excitation and system frequency regulation. It converts mechanical energy into electrical energy by virtue of the direct piezoelectric effect. A finite element simulation model was developed in the study. A dedicated vibration test platform was also constructed. Experimental results show the following performance: Under the operating conditions of 16.75 Hz excitation frequency and 10 kΩ load resistance, a single cantilever beam can generate a peak voltage of 9.59 V. Its maximum output power reaches 7.67 mW. Under simulated Class D road conditions and at a vehicle speed of 90 km/h, the array made up of eight cantilever beams delivers a total output power of 414.37 mW. This study provides a viable technical solution for vehicle suspension vibration energy recovery. It promotes the full utilization of wasted energy, and it is of great significance for advancing sustainable development in the transportation sector. Full article

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

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

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

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

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

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

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

Large-scale collection and annotation of sensitive facial data in real-world traffic scenarios face significant hurdles regarding privacy protection, temporal consistency, and high costs. To address these issues, this work proposes an integrated method specifically designed for sensitive information anonymization and semi-automatic image annotation (AIA). Specifically, the Nullface anonymization model is applied to remove identity information from facial data while preserving non-identity attributes including pose, expression, and background that are relevant to downstream vision tasks. Secondly, the Qwen3-VL multimodal foundation model is combined with the Grounding DINO detection model to build an end-to-end annotation platform using the Dify workflow, covering data cleaning and automated labeling. A traffic-sensitive information dataset with diverse and complex backgrounds is then constructed. Subsequently, the systematic experiments on the WIDER FACE subset show that Nullface significantly outperforms baseline methods including FAMS and Ciagan in head pose preservation and image quality. Finally, evaluation on object detection further confirms the effectiveness of the proposed approach. The accuracy achieved by the proposed method reaches 91.05%, outperforming AWS, and is almost identical to the accuracy of manual annotation. This demonstrates that the anonymization process maintains critical semantic details required for effective object detection. Full article

Although the combination of solar power and electric vehicles is widely considered beneficial, practical applications reveal substantial variance. To determine the proportion of solar energy used for charging and to identify the main drivers of a high solar share, a dataset containing measured 5 min energy time series of 725 households with PV and EVs was analyzed. In the existing literature, this represents a novelty, as most studies in this field are simulation-based, rely on synthetic profiles, use lower time resolutions, or are based on questionnaires. The share of solar energy used for EV charging is highly dispersed and varies by about ±40% around a median of 60%. The analysis shows that clustering by preferred charging times has strong explanatory potential: at the median, EVs charged predominantly during the daytime achieve a solar share that is more than 40% higher than those charged in the evening. In the latter case, home battery storage increases the solar share by an average of 20 percentage points. A similar magnitude of a 25-percentage-point increase could be reached with solar surplus charging compared to uncontrolled charging. On average, households with PV, battery, and EVs cover more than 56% of their total demand with self-generated solar energy; with solar-adapted charging, median values exceed 77%. If a heat pump is used on site, the self-sufficiency decreases but can still reach median values above 45% and up to 61% for optimized households. Full article

This perspective provides a multidisciplinary assessment of the use of lithium-ion batteries from electric vehicles (EVs) for second-life applications, motivated by the need to improve resource efficiency, reduce environmental impacts, and support a circular battery economy. Second-life deployment requires the integrated consideration of technical performance, legal compliance, and economic viability. The analysis combines a technical evaluation of battery aging mechanisms, operational load effects, and qualification strategies with a legal assessment of the EU Batteries Regulation (EU) 2023/1542 and an economic analysis of market potential and business models (BM). From a technical perspective, the limitations of State of Health (SOH) as a standalone indicator are demonstrated, highlighting the need for multiple health indicators and degradation-aware qualification. A scalable two-step qualification approach, combining qualitative inspection with a standardized quantitative measurement protocol, is discussed. From a legal perspective, regulatory requirements and barriers related to repurposing, waste classification, and conformity assessment are analyzed. From an economic perspective, business model patterns and market dynamics are evaluated, identifying Automated Guided Vehicles (AGVs) and industrial Energy Storage Systems (ESSs) for renewable firming as particularly promising applications. The paper concludes with recommendations for action and key research needs to enable safe, economically viable, and legally compliant second-life deployment. Full article

Lithium-ion batteries are widely used in portable devices and electronic vehicles (EVs) due to their excellent performance. Because of their internal chemistry, these batteries have non-linear characteristics, their parameters being dependent on temperature and varying over time due to aging. Since electric vehicles are marketed in different regions of the globe with different climates, this has led to increased attention to the problem of the reduced performance of EVs in colder environments. The purpose of this research is to study the effects of preconditioning on Li-ion cells and determine the need for preconditioning in EVs that operate under low-temperature conditions. Additionally, based on the results, alternative coping strategies are also suggested which can be used instead of preconditioning when this is not a viable option. Given this, the 18650 Li-ion cells studied were divided into two categories, cells to be charged/discharged permanently at low temperatures and cells that were to be exposed to the same low temperatures but then preconditioned to ambient temperature before the charge/discharge cycle for a total of 100 performed cycles. It was observed that low temperatures have a direct negative impact on the usable capacity of the cells, accounting for a drop of 8% of the initial value. These effects can be completely negated by preconditioning the cells prior to charging/discharging. After that, the effects of medium-term storage on the capacity of the batteries were investigated to study the possible recovery in the capacity of the cells. Finally, the need for preconditioning the cells is analyzed and alternative methods to mitigate the issues are suggested for equipment where preconditioning is not possible. Full article

Autonomous vehicles, often described as “computers on wheels,” must collect extensive data, including personal information, and employ data analysis to enhance their self-learning capabilities. In this process, users’ personal information is particularly vulnerable to excessive collection, leakage, and misuse. Accordingly, establishing a robust legal framework for the protection of personal information in the context of autonomous driving is of critical importance. China has not yet implemented an Autonomous Driving Law, and the related legal provisions on protecting of personal information in the field of autonomous vehicles still unclear. We conducted a comparative analysis of the policies and legislation on automated driving and personal information protection in various countries and regions. The results indicate that China could benefit from the EU’s approach to expanding protection. Considering the current state of China’s legal system and legislative trends, it is more suitable to guide the legal application of personal information protection for automated driving through legal interpretation, alongside the existing laws on personal information protection. Full article

This paper reviews recent developments in the design optimization of electrical drive systems for electric vehicles (EVs) and proposes a pathway to develop next-generation AI design platforms that integrate system-level optimization methods and digital twins. First, a comprehensive review is presented to five design optimization models for EV motors, including multiphysics, multiobjective, multimode, robust, and topology optimization, as well as six efficient optimization strategies, such as multilevel optimization and AI-based approaches. Several recommendations on the practical application of these optimization strategies are also presented. Second, representative optimization methods for power converters and control systems of EV drives are summarized. Third, application-oriented and robust system-level design optimization strategies for EV drive systems are discussed. Finally, two proposals are presented and discussed for the design of next-generation EV drive systems and their integration with battery management systems. They are AI-powered automatic design optimization platforms that integrate large language models and a digital-twin-assisted system-level optimization framework. Two case studies on in-wheel motors and drive systems are also included to demonstrate the performance and effectiveness of various optimization methods. Full article

Electric vehicles (EVs) are widely promoted as a key strategy for reducing carbon dioxide (CO₂) emissions and advancing sustainable development. However, the real-world benefits of EV adoption may vary across countries with different income levels and energy systems. This study investigates the relationship between EV adoption and CO₂ emissions per capita, as well as overall sustainable development performance (SDG Index), across 50 countries from 2010 to 2023. Using panel quantile regression, we find that EV adoption is significantly associated with reduced CO₂ emissions particularly in the high-emitting countries in high-income countries (interaction coefficient at the 90th quantile = −0.24, p < 0.05) but positively associated with emissions in lower- and middle-income countries at lower quantiles of the emissions distribution. Similarly, while EV adoption correlates positively with the SDG Index in high-income countries, it shows negative effects at the median and several quantiles. These findings challenge the “zero-emission” assumption and demonstrate that the climate and development benefits of EV diffusion are context-dependent and unevenly distributed, highlighting the need for policies that link electrification to renewable energy deployment, infrastructure development, and equitable access. Full article

Worldwide the adoption of electric vehicles (EVs) is recognized as a key strategy for reducing transport-related greenhouse gas (GHG) emissions, a major contributor to global warming and climate change. The objective of this pilot study is to examine the key variables that might have influenced electric vehicle (EV) purchase decisions among current EV owners and how they are aligned or different for the prospective EV owners in North Carolina (NC). By adopting a web-based survey for data collection, the study specifically aims to identify economic, demographic, environmental, and commuting behaviors, along with existing government policies and incentives that might motivate consumer choices regarding EV adoption. Most existing EV owners who participated in the survey identified themselves as college-educated White men with USD 100 K or higher income, have more than two cars, commute more than 30 min, and live in single-family homes with EV charging. In contrast, among non-EV owners who plan to adopt an EV within the next three years, a significant proportion are non-White, women, and earn USD 50,000 or less annually. While home charging is important to both current EV owners and non-EV owners, EV incentive policies and proximity to public changing stations are found to be more important to non-EV owners’ decision to adopt EVs. A reasonable conclusion from this research is that expanding EV-friendly policies, incentives, and infrastructure will encourage first-time EV ownership in NC while providing deeper insights into the dynamics of sociodemographic among both EV owners and non-EV owners. Full article

With the rapid development of artificial-intelligence technologies in the field of automated driving, many jurisdictions have successively adopted legislation and policy guidance to regulate the safe use of such technologies and to promote their orderly development. This article takes as its objects of study a set of jurisdictions that are particularly representative in terms of legislation and practice across different legal systems. The study finds that liability regimes for traffic accidents involving automated driving fall mainly into four types: the driver liability regime, the system liability regime, the manufacturer or operator liability regime, and the composite liability regime. In application, each of these regimes reveals different types of institutional dilemmas, including blurred boundaries of liability, underdeveloped mechanisms for evidence production and fact-finding, imbalanced allocation of liability, and fragmentation of the rules governing liability determination. In response to these dilemmas, this article proposes corresponding optimisation pathways, including clarifying the boundaries of driver liability and improving supplementary liability mechanisms; specifying in greater detail the obligations of system providers and strengthening data-related fact-finding rules; developing a reasonable allocation of liability between manufacturers and operators together with supporting insurance arrangements; and enhancing institutional coordination under the composite liability regime. These optimisation pathways not only provide institutional reference for jurisdictions seeking to maintain risk controllability while fostering innovation amid rapid technological evolution, but also lay the groundwork for the systematic improvement of future governance of automated driving. Full article

The use of light electric vehicles (LEVs), such as electric bikes and electric scooters, is being increasingly adopted as a sustainable transportation solution in urban areas. This is driven by the need for cleaner, faster, and space-efficient mobility solutions in urban areas. Although research on LEVs has grown over time, it remains fragmented across disciplines, creating a need for an integrated study on how LEVs contribute to sustainable transport in urban areas. This study conducted a bibliometric review to identify key themes in LEVs and sustainable transport in urban areas, and proposed future research agendas based on conceptual patterns and research gaps. The Scopus database was utilised, with a focus on 552 publications covering the period from 2000 to 2025, retrieved on 30 September 2025. The Biblioshiny application (version 5.0) was used to perform bibliometric performance analysis and science mapping techniques. Results revealed that the publication trend steadily rose from 2015, with a significant upsurge after 2020, with an annual growth rate of 18.69%. Three dominant themes were identified, namely sustainability, integration with public transport, and technological innovations, alongside underexplored areas such as shared electric micromobility, freight delivery, and policy and governance. Research gaps remain in lifecycle impacts, social equity, and governance frameworks, highlighting the need for inclusive and sustainable LEV adoption. Future research should capture full lifecycle impacts, expand access to LEVs beyond current user groups, and align rapid technological advances with inclusive governance frameworks. Full article

In terms of their high efficiency and low environmental impact, proton exchange membrane fuel cells (PEMFC) are becoming increasingly essential in the development of hydrogen electric vehicles. Despite these advantages, optimizing hydrogen consumption remains difficult because of the highly nonlinear behavior of PEMFC systems and their sensitivity to variations in operating conditions. This article outlines an intelligent control approach based on extremum seeking control (ESC), based on an artificial neural network (ANN) model, to improve hydrogen utilization in hydrogen electric vehicles. Experimental data on current, voltage, and temperature are collected, preprocessed, and used to train the ANN model of the PEMFC. The ESC algorithm uses this predictive ANN model to adjust the fuel cell current in real time, ensuring voltage stability while reducing hydrogen consumption. The simulation results demonstrate that the ANN-based ESC system provides voltage stability under dynamic load variations and achieves approximately 2.7% hydrogen savings without affecting the experimental current profile, validating the efficacy of the suggested strategy for effective hydrogen management in fuel cell electric vehicles. Full article

The rapid proliferation of Electric Vehicles (EVs) offers a promising pathway toward reducing greenhouse gas emissions and fostering a sustainable environment. However, the large-scale integration of EVs presents significant challenges to distribution networks, potentially increasing stress on grid infrastructure. To address these challenges, this study proposes the integration of a Virtual Power Plant (VPP) framework within EV charging stations as a novel approach to facilitate dynamic power management. The proposed framework integrates electric vehicle (EV) scheduling, battery energy storage (BES) charging, and vehicle-to-grid (V2G) support, while dynamically monitoring energy generation and consumption. This approach aims to enhance voltage regulation and minimize both EV charging durations and waiting periods. A modified IEEE 13-bus test network, equipped with six strategically placed EV charging stations, has been employed to evaluate the performance of the proposed model. Simulation results indicate that the proposed VPP-based method enables dynamic power coordination through EV scheduling, significantly improving the voltage stability margin of the distribution system and efficiently reduces charging times for EV users. Full article

Two-speed transmissions can improve battery electric vehicle (BEV) drivetrain efficiency. However, the additional losses caused by shifting actuators offset these efficiency gains. Particularly hydraulic actuated wet-running multi-plate clutches, which enable powershifts, typically require rotary feedthroughs. Commonly used rectangular sealing rings (RSR) demand continuous hydraulic power due to leakage and cause friction torque. This leads to high RSR temperatures, especially at high angular velocities of electric machines. This article introduces a two-speed BEV transmission concept using wet-running multi-plate clutches actuated via a rotating 5/3-way valve that can shut off, i.e., lock up the actuating pressure directly in the rotating system. Consequently, the rotary feedthrough is depressurized and contactless gap seals are usable. This reduces supply pressure requirements and minimizes hydraulic and friction losses while retaining powershift capability. Component-level tests evaluate leakage, pressure shut off, actuator dynamics and power consumption. Results show that actuating pressure in a shut-off clutch is maintained for longer than 60 min and electrical actuator power consumption is less than 20 W. During overlapping gearshifts, gap seal leakage is less than 1 L/min at 10 bar and sufficient pressure dynamics are achieved. These findings confirm the feasibility of the proposed actuator for multi-plate clutches in two-speed BEV transmissions. Full article

The integration of electric buses into urban transportation networks is a priority for policymakers aiming to promote sustainable public mobility. Among available technologies, electric Bus Rapid Transit (eBRT) systems offer an environmentally friendly and operationally effective alternative to conventional modes. This study introduces a Machine Learning Decision Support Framework designed to assess the feasibility of deploying eBRT systems in urban environments. Using a dataset of 28 routes in the Athens Metropolitan Area, the framework integrates diverse variables such as land use, population coverage, proximity to public transport, points of interest, road characteristics, and safety indicators. The XGBoost model demonstrated strong predictive performance, outperforming traditional approaches and highlighting the significance of points of interest, land use diversity, green spaces, and roadway infrastructure in forecasting travel times. Overall, the proposed framework provides urban planners and policymakers with a robust, data-driven tool for evaluating the practical and environmental viability of eBRT systems. Full article

Creating trust in society for new technologies, such as a new types of powertrains, and making them marketable requires transparent, neutral, and independent technical verification. This is crucial for the acceptance and success of electrified vehicles in the used car markets. A key component of electric vehicles is the traction battery, whose current and future condition, particularly regarding aging, determines its residual value and safe operation. This review aims to identify and evaluate methods for predicting the lifetime of onboard traction batteries, focusing on their applicability in technical inspections. A systematic literature and patent review was conducted using targeted keywords, yielding 22 patents and 633 publications. From these, 150 distinct lifetime prediction methods were extracted and categorized into a four-level mind map. These methods are summarized, cited, and structured in detailed tables. The relationships between approaches are explained to clarify the current research landscape. Long Short-Term Memory, Convolutional Neural Networks, and Particle Filters were identified as the most frequently used techniques. However, no methods were found suitable for predicting the lifetime of traction batteries during technical vehicle inspections, which operate under short test durations, limited data access, and diverse real-world operating conditions. Most studies focused on cell-level testing and did not address complete battery systems in operational vehicles. This gap highlights the need for applied research and the development of practical methods to support battery assessment in real-world conditions. Advancing this field is essential to foster confidence in battery systems and enable a sustainable transition to electromobility. Full article

Electrifying port horizontal transportation is constrained by downtime and deadheading from fixed charging/swapping systems, large battery sizes, and the lack of integrated decision tools for life-cycle emissions. This study develops a carbon-efficiency-centered bi-objective optimization framework benchmarking Mobile Charging Stations (MCSs) against Fixed Charging Stations (FCSs) and Battery Swapping Stations (BSWSs). The framework integrates operational parameters such as charging power, range, dispatch, and non-operational mileage, along with grid carbon intensity, battery embodied emissions, and carbon-market factors. It generates Pareto fronts using the NSGA-II algorithm with real port data. Port horizontal transportation refers to the movement of goods within the port area, typically involving the use of specialized vehicles to transport containers short distances across the terminal. Results show that MCSs can reuse idle windows to reduce deadheading and infrastructure demand, yielding significant economic improvements. The trade-off between emissions and profitability is context-dependent: at low-to-moderate reuse levels, low-carbon and profitable solutions coexist; beyond a threshold of approximately 0.5–0.75, the Pareto fronts shift to high emissions and high profits, highlighting the context-specific advantages of MCSs for port-infrastructure planning. MCSs thus provide context-dependent advantages over FCSs and BSWSs, offering practical guidance for port infrastructure planning and carbon-informed policy design. Full article

The global shift toward sustainable transport electric vehicles (EVs) is at the core of decarbonization efforts. While advanced economies have achieved their rapid adoption through strong policies and incentives, emerging markets face structural and behavioral barriers. This study investigates the paradox in Morocco, whereby a significant automotive capacity contrasts with a minimal domestic BEV market share of 0.6%, despite 143% growth from a small base, using a four-dimensional framework encompassing financial, infrastructural and energy, policy and institutional, and behavioral–social factors. The research integrates a literature review, a survey (n = 522), and secondary data on charging infrastructure and EV sales. Findings reveal a strong value–action gap: 69% of respondents acknowledged EVs’ environmental benefits yet only 1.1% owned one and 42% had considered buying. The high upfront costs of EVs influenced over 70% of participants, and a significant association was confirmed between charging availability and purchase intent (χ² = 34.80, p < 0.05). Urban-centric charging, fragmented governance, and skepticism persist as barriers. The study concludes that industrial strength alone cannot ensure adoption without targeted incentives, equitable infrastructure, and cultural shifts in ownership perception, offering key insights for policymakers in emerging economies pursuing sustainable mobility. Full article