World Electr. Veh. J., Volume 17, Issue 7 (July 2026) – 57 articles

Driven by the dual-carbon target strategy, the new energy vehicle industry has achieved large-scale and rapid development. As the core protective component of power batteries, the surface quality of battery shells directly determines the operational safety and reliability of batteries. However, defects such as scratches, pits, and cracks easily occur on battery shells during forming processes, including stamping and deep drawing. Traditional manual detection suffers from bottlenecks, such as high labor intensity, low detection efficiency, and high false detection rates, making it difficult to adapt to the large-scale and high-cycle production requirements of modern industry. Firstly, this study systematically elaborates the material system, preparation process, and defect formation mechanism of battery shells, and clarifies the coupling mechanisms of material properties, process parameters, and equipment and environmental conditions for defect evolution. Subsequently, it compares and analyzes the principles, advantages and disadvantages, and applicable scenarios of traditional machine-vision- and deep-learning-based detection technologies, and focuses on analyzing the application performance and optimization paths of single-stage and two-stage object detection algorithms in shell defect recognition. Furthermore, it addresses the core challenges of deep-learning-based battery shell defect detection technologies in data, algorithm deployment, detection dimensions, and other aspects, and proposes targeted optimization strategies. Finally, the development directions, such as system integration and online learning, are forecasted. This study can provide theoretical support and technical references for the intelligent manufacturing of battery shell stamping and forming, as well as for surface defect detection. Full article

The rapid adoption of electric vehicles (EVs) has intensified the demand for advanced energy storage technologies capable of delivering high energy density, high power density, enhanced safety, and extended service life. Although lithium-ion batteries remain the dominant energy storage technology for EVs, their limited power capability, thermal degradation, and accelerated aging under high transient loads constrain vehicle performance. Battery–supercapacitor hybrid energy storage systems (HESSs) have emerged as a promising solution by combining the high energy density of batteries with the high-power density and rapid charge–discharge capability of supercapacitors. However, the increasing complexity of HESS architecture requires intelligent energy management strategies to optimize power allocation, battery protection, thermal regulation, and overall system efficiency. Existing review papers primarily address individual aspects of HESS architecture, battery management, or artificial intelligence (AI)-based control, leaving a lack of a unified review integrating these topics. This paper addresses this gap by reviewing 181 publications published between 2020 and 2026, covering HESS architectures, conventional and AI-driven energy management strategies, machine learning, deep learning, reinforcement learning, battery state estimation, diagnostics, prognostics, thermal management, and fault diagnosis. The reviewed studies are critically analyzed to assess the impact of AI on battery lifetime, regenerative braking, charging performance, thermal behavior, and energy efficiency. The review further identifies emerging research directions, including explainable AI, digital twins, federated learning, edge intelligence, vehicle-to-grid integration, and cybersecurity-aware energy management. The findings indicate that AI-based approaches generally demonstrate greater adaptability, predictive capability, and battery protection than conventional methods under dynamic operating conditions, although challenges related to computational complexity, real-time implementation, data availability, explainability, cybersecurity, and standardization remain significant barriers to large-scale deployment. Full article

Electric vehicles equipped with in-wheel motors provide packaging, controllability, and drivetrain-simplification advantages; however, the increase in wheel-side unsprung mass can intensify vibration transmission to the chassis, seat, and occupant. This paper presents a coordinated active seat and active suspension control strategy for an integrated 8-DOF quarter-car model that includes an in-wheel motor, an active seat suspension, and a 4-DOF seated driver body model. The proposed controller combines a Harmony Search (HS)-optimized proportional–integral–derivative (PID) feedback baseline with a repeatable-disturbance feedforward compensation term. The HS-PID loop provides baseline transient attenuation, while the feedforward term compensates the repeatable component of the bump-induced disturbance transmitted through the coupled seat–vehicle system. The controller is evaluated against passive suspension, active-seat-only control, active-vehicle-suspension-only control, and an HS-PID baseline under repeated bump/shock excitation. The results show that coordinated actuation reduces occupant displacement and acceleration responses relative to the benchmark cases. The discussion explains the active-seat-only peak-acceleration amplification, the different magnitudes of displacement and acceleration improvements, and the practical implications of suspension stroke and actuator-force limits. The reported conclusions are therefore confined to the repeated bump/shock condition considered in this numerical study; broader ride-comfort generalization requires standardized whole-body vibration metrics, random-road validation, speed variation, parametric uncertainty analysis, and drivetrain energy evaluation. Full article

This paper presents a comprehensive review of control techniques, simulation mechanisms, and validation methods applied to DC-DC converters, with a focus on high-step-up topologies used in renewable energy systems such as photovoltaic and wind power applications. Control strategies including classical PID, fuzzy logic, sliding mode, and model predictive control (MPC) are analyzed in terms of performance, robustness, and implementation complexity. Simulation platforms and hardware-in-the-loop (HIL) validation frameworks are also discussed as key enablers for rapid prototyping. The findings reveal a clear trend toward intelligent and hybrid control schemes that combine nonlinear techniques with artificial intelligence to address the inherent nonlinearities and parametric uncertainties of DC-DC converters. However, challenges remain in real-time implementation due to computational demands, which drives the need for future developments focused on the (i) integration of AI-based controllers with low-cost embedded platforms, (ii) standardization of HIL-based validation workflows, and (iii) optimization of converter topologies for specific applications such as electric vehicle charging and photovoltaic grid integration. Looking forward, the convergence of advanced control algorithms, real-time validation platforms, and application-specific converter design is expected to define the next generation of power electronics systems, enabling more efficient, reliable, and scalable renewable energy integration. Full article

This study presents an empirical fleet-level assessment of 110 autonomous-range trolleybuses using anonymized records collected over 12 months. The dataset comprises 40,150 vehicle-day operating records, 40,150 energy records, 3960 pack-month SOH records, and 584 maintenance, failure, and downtime events. Outcomes are reported in absolute units: RUB/km for LCC, kg CO₂-eq/km for ELC, events per 100,000 km, and downtime hours per 10,000 km. Autonomous operation accounted for 24.5% of mileage. Average net energy consumption was 1.520 kWh/km, whereas mode-distributed gross energy was 1.521 kWh/km in contact-supply mode and 1.752 kWh/km in autonomous mode. The daily-energy model achieved a full-sample fit of R² = 0.860 and MAPE = 8.119%. Validation of vehicle-grouped data using the generated dataset showed R² = 0.842 and MAPE = 8.74%. Mean SOH decreased from 89.98% to 85.94%, accompanied by higher internal resistance. In the central 6.5-year scenario, diagnostic-gated strategy B2 reduced estimated LCC from 29.52 to 26.16 RUB/km. The event-weighted control effect by RPN decreased from 125.4 to 80.4 (35.9%). Baseline ELC decreased only from 0.6646 to 0.6594 kg CO₂-eq/km because operational electricity dominated the total. The contribution is an observation-linked framework that integrates vehicle-day operation, pack-month diagnostics, and event-level maintenance data to compare cost, emissions, and risk under explicit battery-eligibility and service-coverage constraints. The novelty is therefore the empirical, observation-level coupling and joint calibration of existing energy, battery-condition, life-cycle, and reliability methods within one auditable fleet workflow, rather than the introduction of a new standalone degradation or reliability model. Full article

Autonomous vehicles (AVs) are moving from pilots to regular urban service, yet the speed of large-scale implementation remains uncertain. While prior research emphasizes technological feasibility and adoption, less attention has been paid to the socio-technical dynamics that constrain deployment. This study examines how unintended consequences shape the pace of AV implementation in cities. Drawing on a mixed-methods design combining a structured scoping review with 18 expert interviews, interrelated dynamics are identified across institutional, behavioral, economic-platform, spatial, and normative-societal domains. The findings indicate that implementation speed is not determined by technology alone but emerges from reinforcing feedback loops that generate systemic frictions, including governance lag, demand rebound, spatial bottlenecks, and legitimacy challenges. The study advances a systems-oriented framework that conceptualizes implementation speed as an emergent property of socio-technical dynamics, highlighting the importance of adaptive and anticipatory governance for sustainable urban mobility transitions. Full article

Vehicle Ad Hoc Networks (VANETs) face critical challenges in trust management, privacy preservation, and scalability, particularly with the integration of 5G networks in Intelligent Transportation Systems (ITS). Traditional centralized trust models present single points of failure and privacy concerns that compromise network security and user anonymity. This paper presents a novel decentralized trust model leveraging blockchain technology, Interplanetary File System (IPFS) integration, and post-quantum cryptographic algorithms to address these limitations. Our proposed TrustChain-VANET framework implements advanced privacy-preserving encryption techniques including threshold and homomorphic encryption, geographical sharding for scalability, and edge-assisted consensus mechanisms. Performance evaluation demonstrates significant improvements: 40% reduction in authentication latency (90–120 ms vs. 150–300 ms), 90% malicious node detection rate (+15% improvement), 300% increase in transaction throughput (2000–2150 TPS), and 100% scalability enhancement supporting up to 5000 nodes. The system integrates seamlessly with 5G network slicing (URLLC, eMBB, mMTC) while maintaining quantum resistance through CRYSTALS-Dilithium, KYBER, and FALCON algorithms. Real-world deployment considerations including OBU computational constraints, standardization gaps, and energy efficiency are comprehensively analyzed. Results indicate that the proposed decentralized approach provides robust security, enhanced privacy, and improved scalability for next-generation vehicular networks, making it suitable for large-scale ITS deployment. The main contribution of this work is the development of a unified TrustChain-VA 48NET framework. The proposed framework integrates blockchain-based trust management, IPFS-assisted storage, 5G network slicing, Mobile Edge Computing (MEC), geographical sharding, and post-quantum cryptographic mechanisms within a single architecture for next-generation VANET environments. While these technologies have been investigated separately in previous studies, this work presents a consolidated framework that analyzes their interoperability, identifies integration challenges, and evaluates their combined impact on trust management, scalability, privacy preservation, and deployment feasibility in Intelligent Transportation Systems. Full article

Electric-vehicle gearboxes operate at high rotational speeds and under low acoustic masking, making tonal excitation and unit-to-unit variability important design concerns. Contemporary loaded tooth contact, multibody, finite-element, and vibroacoustic models can represent the nominal excitation–transfer–response–radiation chain in considerable detail, but their inputs often remain disconnected from the manufactured and assembled gearbox. This review develops a structured framework for identifying which physical parameters, numerical representations, and validation evidence are required before a model can credibly represent a nominal design, a tolerance space, an as-built unit, or a production population. Parameters are classified jointly based on the physical origin and noise, vibration, and harshness (NVH) function and are mapped to contact, system-dynamic, structural, acoustic, and hybrid data-driven models. Four simulation-readiness levels are defined: nominal, tolerance-based, measurement-based, and variability-aware. Explicit transition gates, validation quantities, and permitted claims are assigned to each level. A stage-specific validation matrix distinguishes contact-level, interface-force, structural-response, and acoustic evidence. Literature-grounded quantitative examples demonstrate validated elastic multibody modeling and manufacturing-data-based gear-whine prediction while clarifying the limits of the available evidence. The framework provides a traceable basis for model planning, measurement selection, uncertainty analysis, and readiness-aware reporting of electric-vehicle gearbox NVH simulations. Full article

With the increasing complexity of electrical and electronic (E/E) components in modern powertrains, functional safety requires more systematic assessment methods. This paper presents a simulation-based approach for automated Hazard Analysis and Risk Assessment (HARA) of battery electric vehicles in accordance with ISO 26262. The method combines operational-situation parameters, including vehicle speed, road surface, vehicle gap, and road inclination, to define a structured set of hazardous events. Severity, Exposure, and Controllability are evaluated using rule-based criteria, including a dedicated Controllability rule set for longitudinal motion hazards. Quantitative erroneous acceleration and deceleration thresholds associated with different ASILs are derived using a bisection search algorithm, enabling quantifiable and testable safety goals. Comparison with manual HARA reveals systematic biases: low speed does not necessarily imply improved Controllability due to shorter vehicle gaps, while Severity may be underestimated at low speeds because of high instantaneous electric-machine torque. The maximum ASIL is often identified similarly by simulation and experts, whereas lower-ASIL hazardous events may lack consistency and coverage in manual HARA. As a practical application, the approach can be integrated into existing automotive safety workflows as a HARA support tool, improving the consistency of lower-ASIL events while allowing engineers to focus on maximum ASIL cases. Full article

Connected and Automated Electric Vehicles (CAEVs) are rapidly evolving into complex Cyber-Physical-Social Systems (CPSS), generating structural tensions between technological innovation and public safety. Current research in public governance exhibits significant fragmentation. Scholars frequently isolate data privacy compliance from algorithmic safety auditing, treating them as distinct silos. To bridge this gap, this study applies the PRISMA framework to systematically synthesize 135 core peer-reviewed articles, exposing the endogenous limitations of unidimensional regulatory paradigms. Our analysis yields three central insights. First, traditional “notice-and-consent” models fail under the ubiquitous data collection demands of modern V2X environments. Macro-level policies must translate into foundational Privacy-Enhancing Technologies (PETs) through “Law-as-Code” mechanisms. Second, the opacity of end-to-end algorithmic decision-making deconstructs traditional tort liability systems. This necessitates ex-ante quantitative auditing mechanisms—such as Explainable Artificial Intelligence (XAI) and enhanced Threat Analysis and Risk Assessment (TARA 2.0)—to mitigate adversarial attacks and physical-level safety hazards. Third, overcoming cross-national regulatory fragmentation requires constructing a “dual-track synergistic” governance architecture. This framework institutionalizes the coupling of data lifecycle quality workflows with the algorithmic Safety of the Intended Functionality (SOTIF). Ultimately, this review advocates for adaptive regulatory sandboxes and advances the harmonization and mutual recognition of global standards (e.g., ISO/SAE 21434, UN R155/156). Addressing current methodological and empirical data constraints, future academic inquiry must pivot. Researchers should target the value alignment challenges of Large Language Models (LLMs) in autonomous driving and implement multi-stakeholder participatory policy pilots designed to reconcile diverse social values. Full article

With the growing demand for electricity, the penetration of Renewable Distributed Generators (RDGs), alongside the transition from Internal Combustion Engine Vehicles (ICEVs) to Electric Vehicles (EVs), has become a pressing challenge for the stable and efficient operation of distribution networks. This research focuses on a critical task of determining the optimal integration of RDGs, including solar photovoltaic systems, wind turbines, biomass units, and EV charging stations, into an Unbalanced Radial Distribution System (URDS). This work proposes an optimization approach aiming to minimise the total costs (TCs), active power losses (APLs), voltage unbalance factor (VUF), and voltage deviation (VD) of the network under consideration simultaneously. The integration of RDGs is carried out using a metaheuristic technique, which accounts for the intermittent nature of renewable energy sources, the stochastic behaviour of EVs, and the variability of load demands over 24 h a day. Fuzzy decision-making is applied to select an optimal trade-off solution from the Pareto front. The effectiveness of the developed approach is assessed comprehensively on a Pakistani 60-bus URDS as a primary study, while the IEEE-123 bus system is employed as a validation case to demonstrate the applicability and scalability of the proposed methodology. Among the five analysed case studies, the simulation results indicate that coordinated integration of RDGs and EVCSs into the system yields significant benefits, including a decreased reliance on conventional centralised generation, with a reduction of 56.29% in costs, 46.61% in losses, 7.17% in voltage unbalance, and 27.13% in voltage deviation as compared to the base case. Full article

MagnetoRheological Brakes (MRBs) can provide wear-free, electrically controllable braking torque, but repeated high-load braking can cause rapid heat accumulation in the narrow rotor–stator gap and degrade MRF performance. This study evaluates rotor-only, stator-only and combined rotor–stator liquid-cooling configurations using transient 3-D conjugate heat-transfer CFD in ANSYS Fluent 2024 R1 for a UN Regulation No. 13-H-based 10-cycle duty profile (8.5 s acceleration, 20 s constant speed and 2.5 s braking per cycle). The activated MRF is modeled as an incompressible laminar Herschel–Bulkley fluid during braking, while the field-OFF phases use a Newtonian viscosity of 0.114 Pa·s; viscous dissipation and coil volumetric heating are included as internal heat sources. Cooling simulations apply water with a 130 kPa (absolute) inlet pressure and a conservative +20% heat-load margin with adiabatic external boundaries. Baseline uncooled dynamometer data (no integrated cooling) verify the thermal implementation, with a 7.06% underprediction of the measured temperature rise. In the uncooled case, the MRF reaches a temperature of 501 K after ten cycles; rotor-only and stator-only cooling reduce temperatures but do not fully suppress cumulative heating, whereas the combined configuration maintains the MRF below 400 K after ten cycles. These results indicate that cooling both dominant heat paths is required for stable MRB thermal operation under severe repeated braking. Full article

This paper presents the design, digital implementation, and experimental validation of a new 50 W scaled prototype of a quadratic buck converter (48 V to 5 V, 10 A) controlled by a finite control set model predictive control (FCS-MPC) strategy. The converter utilizes its quadratic step-down topology to achieve high voltage conversion gain without extreme duty cycles, making it suitable for low-power applications requiring precise voltage regulation. The proposed methodology encompasses the theoretical design of the power stage, the development of the experimental prototype based on a C2000 Digital Signal Processor DSP, and a comparative performance assessment between the proposed FCS-MPC and a conventionally tuned PD controller. An iterative tuning and real-time validation process is employed to optimize both the converter parameters and the control law, ensuring closed-loop stability and enhanced dynamic response under line and load disturbances. The experimental results demonstrate that the FCS-MPC strategy significantly outperforms the PD controller in terms of output voltage regulation, settling time (4.2 s vs. 5 ms), and disturbance rejection (<2 ms recovery). The main contribution of this work is the construction of a new scaled prototype and the experimental validation of a predictive control strategy for a high-gain DC–DC converter, positioning the FCS-MPC-controlled quadratic buck converter as a viable solution for modern applications demanding high energy efficiency and robustness. Full article

Accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) is critical to ensuring the safety and reliability of battery management system (BMS). To achieve precise estimation, this study proposes a hybrid framework that integrates variational mode decomposition (VMD), kernel principal component analysis (KPCA), polar lights optimizer (PLO), and physics-informed neural network (PINN) for SOH estimation. First, multidimensional health features are extracted and decomposed by VMD into intrinsic mode functions (IMFs), which are then compressed into a one-dimensional principal component via KPCA, retaining over 95% of the original information. Subsequently, the PLO algorithm is used to adaptively optimize three key hyperparameters of the PINN-based model: the learning rate, the number of collocation points, and the regularization loss weight. Finally, the optimized PINN is deployed to predict the SOH of the Center for Advanced Life Cycle Engineering (CALCE) battery dataset. Experimental results demonstrate that the proposed VMD-KPCA-PLO-PINN exhibits high prediction accuracy under both 7:3 and 5:5 training-to-testing data partitions. For example, under the 5:5 partition, the proposed model achieves an average $R^2$ of 0.983 and an average RMSE of 0.0085 on the tested CALCE cells. Full article

Electric vehicle (EV) charging has a large, spatially clustered, schedulable load whose vehicle-to-grid flexibility can be sold back to the power system. That flexibility has grid value only when the committed quantity can be reliably delivered under uncertainty. Open forecasting benchmarks operators rely on report-only point predictions. The dispatch models that turn forecasts into firm commitments assume a constant round-trip efficiency, so the committed flexibility is systematically over-scheduled. This study contributes two complementary modules, validated separately on public data. The first is a calibrated probabilistic charging forecaster that provides, to our knowledge, the first prediction intervals with reported empirical coverage on the UrbanEV benchmark. It is a gradient-boosted quantile-regression model that combines each zone’s own-history lags with adjacency-weighted neighbor-mean features and exogenous price and calendar inputs. It is calibrated by conformalized quantile regression and scored over thirty zones across a 120-day hourly window. The second is a deliverable-flexibility envelope whose returnable-energy bounds are set by measured, state-of-charge- and rate-dependent vehicle-to-grid (V2G) discharge efficiency rather than a constant round-trip number. These bounds are fit to the measured discharge traces of three V2G-capable vehicles in the Esser bidirectional-charging dataset. Chosen as a lightweight, reproducible baseline, the forecaster keeps its prediction intervals within a five-percentage-point coverage tolerance at both the 80% and 90% nominal levels. Measured coverage is 0.823 and 0.911. It also improves on the continuous ranked probability score of its conformalized-point counterpart at matched point accuracy. This calibration holds across the hyperparameter neighborhood and under data deficiency. On the delivery side, a leave-one-vehicle oracle shows the efficiency-aware envelope short-delivers less than the constant-average-efficiency aggregator on held-out vehicles. Its residual shortfall is 1.21% against the aggregator’s 2.03% at the conservative operating point. The margin widens as commitments grow more aggressive and discharges reach the lowest states of charge. Each of these two measured properties, calibrated demand-side uncertainty and state-dependent discharge physics, imposes a material, separately validated constraint on how much contracted EV flexibility can be delivered, a constraint the point-forecasting frontier leaves unaddressed. Full article

Accurate state of charge (SoC) estimation requires mathematical models that consider individual user usage patterns to ensure optimal performance of lithium-ion battery (LiB) systems. Accurate SoC estimation improves battery life, driving comfort and avoids failure. In this paper, Pi and T equivalent circuit models were developed and validated with the 1RC and 2RC Thevenin models. Simulations were carried out to test their behavior under different rates of charging and discharging (1C, 2C, and 3C). The proposed models were validated by using real data from battery tests. At high C-rates, the 1RC and 2RC models show large variations, but the Pi model provided the best agreement with the experimental data for all rates, and the T model provided the second-best fit. In this paper, the impact of battery aging and degradation on the accuracy of the Extended Kalman Filter (EKF) and Coulomb Counting (CC) methods for estimating the SoC was studied, emphasizing the need to include them in the models. The results emphasized the importance of advanced modeling approaches for efficient battery management and point to directions for future research. Full article

Under the “dual carbon” goals (carbon peak and carbon neutrality), the new energy vehicle (NEV) industry has become a strategic focus of great-power competition, and the resilience of its international supply chain is critical to industrial security and development initiatives. As a traditional automobile manufacturing hub in China, Hubei Province faces increasingly prominent global risks in its supply chain during the transition to NEVs; scientifically evaluating and enhancing its international supply chain resilience is therefore of great practical significance. Drawing on supply chain resilience theory, this paper constructs an evaluation index system comprising 18 specific indicators across four dimensions: robustness, redundancy, agility, and innovativeness. To overcome the limitations of a single weighting method, a combined subjective and objective weighting approach that integrates the Analytic Hierarchy Process (AHP) and the entropy weight method was employed to determine indicator weights. Subsequently, the TOPSIS model was applied to measure the supply chain resilience level of Hubei Province from 2018 to 2025, with horizontal comparisons conducted against Shanghai and Guangdong. Finally, an obstacle degree model was introduced to quantitatively diagnose the key factors constraining resilience improvement. The results indicate that the international supply chain resilience of Hubei’s NEV industry has shown a continuous upward trend. By 2025, it ranks in the first tier alongside Guangdong (with closeness coefficients of 0.8180 and 0.8181, respectively), approaching the level of Shanghai. Weaknesses are concentrated primarily in the agility dimension, while upstream resource dependence remains a salient issue within the robustness dimension. “External dependence on key raw materials,” “average recovery time from logistics disruptions,” and “level of supply chain information sharing” are still the top three obstacle factors. Fuzzy-set qualitative comparative analysis (fsQCA) further reveals that low resource autonomy and slow logistics recovery are core conditions leading to low resilience, and the coupling of multiple obstacle factors amplifies the risk transmission effect. Based on this, this study proposes optimization recommendations focusing on foundation strengthening and chain consolidation, digital chain connectivity, and innovation–chain integration, in order to enhance the resilience of the international supply chain for new energy vehicles in Hubei. This research provides a methodological reference for evaluating the supply chain resilience of regionally distinctive industries and offers a quantitative basis for Hubei Province and related enterprises to formulate targeted improvement strategies. Full article

The research utilizes Ansys Maxwell 2024 R2, a 3D Finite Element Analysis (FEA) software, to compare the electromagnetic coil parameters, losses, and efficiency of planar spiral circular, square, and round-cornered square (RCS) coils for wireless power transfer (WPT) systems in electric vehicle (EV) applications. This study focuses on key coil parameters such as self-inductance, mutual inductance, and the coupling coefficient, which are crucial for determining power transfer and system efficiency. While analytical calculations for these parameters are straightforward for air-core transformers, they become complex and inaccurate when ferrite cores are incorporated to improve efficiency. Ansys Maxwell overcomes this challenge by employing a numerical method. Simulation results indicate that RCS coils offer uniform magnetic field distribution and reduced losses, similar to circular coils. They also exhibit better coupling and good misalignment tolerance, akin to square coils. These characteristics suggest that RCS coils are a superior choice for WPT applications. Electro-thermal management (ETM) co-simulation of the RCS coil is performed and analyzed using Ansys Icepak 2024 R2. Furthermore, an Ansys Twin Builder 2024 R2 co-simulation of a double-sided LCL-compensated WPT system, incorporating the reduced-order model of the RCS coil, is performed. Under standardized EV conditions, say 85 kHz, 35 mm, and 50 Ω load, the RCS coil achieves an efficiency of 94.81%. The research also includes loss analysis and misalignment tolerance studies, confirming the superiority of RCS coils. Full article

High electricity prices remain a substantial barrier to electric vehicle (EV) diffusion. To address this challenge, we propose a bundled sales model that integrates EVs with distributed, operationally off-grid photovoltaic (PV) systems for self-consumption. Using a sequential game-theoretic framework and scenario analysis calibrated to U.S. and German data, we show that, within the calibrated scenarios and declared system boundaries, bundling accelerates EV adoption and reduces modeled oil dependency, measured as the physical volume of fossil fuel displaced by the bundled fleet. In Germany, bundling increases oil-dependency reduction by 7.8 percentage points, to 34.5%, relative to the traditional unbundled model. The bundled model also delivers stronger decarbonization, yielding incremental lifecycle emission reductions of 11% in the U.S. and 29% in Germany under the declared system boundary. Three insights follow. First, bundling is especially advantageous in markets with high grid tariffs, strong solar irradiance, or falling PV costs. Second, decoupling EV charging from carbon-intensive grids promotes household energy self-sufficiency and helps households become more resilient energy prosumers. Third, the threshold analysis indicates that the model is already viable in high-tariff markets such as Germany, while declining battery costs are likely to trigger a tipping point in lower-tariff markets such as the U.S., supporting a gradual diffusion pattern from suburbs to cities. These findings identify a viable pathway for low-carbon transport transitions through synergistic EV–solar integration. Full article

The mass and position of tractor components were unknown at the beginning of the design, making it impossible to accurately obtain parameters. To solve the problem, a mass and position co-optimization method was proposed. Firstly, a counterweight design method was used to ensure the tractor’s traction performance and operational stability. Secondly, under the premise of meeting power requirements, a power component design method was applied to match the fuel cell and motor. Then, the energy storage component design method was employed to calculate the required hydrogen tank and battery. Finally, a genetic algorithm was used to integrate the counterweight design method, power component design method, and energy storage component design method to form the mass and position co-optimization method. To verify the rationality of this method, the method was carried out under simulation of the plowing conditions. The results show that, compared with the rule design method, the fuel cell tractors using mass and equivalent hydrogen consumption were reduced by 4.37% and 3.59%, respectively, and the centroid shifted backward by 0.1 m. This method avoids unreasonable parameter design caused by unknown mass and position. Full article

Interior permanent magnet synchronous motors have high torque density and a high salient pole effect, combine low-speed high torque with constant-power wide speed regulation, and are increasingly favored by more and more car companies and widely used in electric vehicles. With the development of interior permanent magnet synchronous motors for electric vehicle towards high speed and large capacity, the eddy current loss generated inside the permanent magnet increases rapidly when the magnetic field alternates. Simulation results show that the excessive eddy current loss can raise the permanent magnet temperature of the I2V-type rotor up to 112 °C under rated operating conditions. Such a high temperature far exceeds the stable working temperature range of conventional NdFeB materials and greatly increases the risk of irreversible demagnetization. NdFeB permanent magnet materials have high electrical conductivity but weak heat-resistant capacity, so the temperature rise of permanent magnet is more serious, and even irreversible demagnetization occurs, which is fatal for the safe operation of motors. Therefore, it is necessary to analyze and study the eddy current loss of permanent magnets, explore methods to reduce magnet loss, and design reasonable and efficient cooling systems. Firstly, this paper selects three different rotor topologies as research objects, establishes two-dimensional parameterized finite element analysis models, and analyzes and compares magnet loss and the hysteresis loss, eddy loss, and copper loss of the stator. Secondly, to solve the problem that the I2V-type rotor generates higher magnet loss than the other two structures under all working conditions, magnetic isolation holes are arranged on each rotor pole to optimize the internal magnetic circuit. Simulation analysis results show that this method can effectively reduce magnet loss and stator hysteresis losses. Finally, the temperature of the shaft, magnet and stator winding are studied; aiming at characteristics of high torque density with small size, large torque, and high magnet temperature, a cooling method combining housing cooling and shaft cooling is proposed. Simulation results indicate that the new cooling method can greatly suppress the magnet temperature rise, which reduces the maximum permanent magnet temperature from 112 °C to 80 °C under rated operating conditions and can further improve the torque density and operating reliability of interior permanent magnet synchronous motors. This provides a feasible design reference for high-reliability vehicle interior permanent magnet synchronous motors. Full article

The commercialization of electric and hybrid vehicles in Colombia is a strategic component of the national energy-transition agenda and of the country’s climate-governance commitments. However, despite relevant regulatory progress, market deployment remains territorially uneven and institutionally fragmented. The article describes how regulation affects the market for electric and hybrid cars, considering regulatory consistency, inter-institutional cooperation, market obstacles, and the rollout of the energy transition. Based on doctrinal legal analysis, comparative public-policy evaluation, and contextual examination of official vehicle-registration information, the study employs a qualitative and comparative research design. The analysis reviews Colombian legal instruments, policy strategies, and governance arrangements related to electric mobility and compares them with selected experiences in Chile and Mexico. The findings show Colombia has developed tariff reductions, tax benefits, circulation privileges, charging-infrastructure obligations, interoperability rules, and strategic planning instruments. Commercialization faces structural barriers: fragmented regulation, uneven territorial implementation, insufficient charging infrastructure, weak public–private coordination, limited consumer awareness, and a lack of long-term governance for battery replacement and industrial adaptation. The results also show that hybrid electric vehicles dominate national registrations, while battery electric and plug-in hybrid vehicles remain comparatively limited. This distinction shows that Colombia’s current transition is still more strongly associated with hybridization than with full electrification. The article concludes that Colombia requires a more coherent governance architecture capable of integrating regulatory stability, territorial coordination, infrastructure deployment, market facilitation, and long-term energy-transition planning. Full article

Battery management systems (BMSs) in electric vehicles (EVs) are instrumented with an increasing number of heterogeneous sensors, many of which contribute redundant or noisy measurements that increase computational cost without improving diagnostic accuracy. This paper proposes a Binary Hybrid Particle Whale Optimization Algorithm (BHPWOA) for multi-objective feature selection targeting three-class BMS fault diagnosis: OK, Warning, and Critical. The method is evaluated using an 18-feature EV charging dataset with $n=500$ samples. BHPWOA encodes candidate feature subsets as binary masks in a continuous $\left[\mathrm{0,1}\right]$ position space. It executes a Binary Particle Swarm Optimization (BPSO) phase during the first 50 iterations to rapidly identify a promising subset region, then transfers the global-best mask as the Whale Optimization Algorithm (WOA) leader for the remaining 50 iterations of bubble-net exploitation. A multi-objective fitness function simultaneously penalises classifier error and subset size, directly optimising the accuracy–cost trade-off. BHPWOA selects four features out of 18, corresponding to a $77.8\mathrm{\%}$ reduction, and achieves accuracy $=0.710$ and macro-F1 $=0.4455$ on the held-out test set. It outperforms all-feature KNN $\left(F_1=0.2997\right)$, standalone BPSO with six selected features $\left(F_1=0.4603\right)$, BWOA with two selected features $\left(F_1=0.4026\right)$, and BSFSA with five selected features $\left(F_1=0.4216\right)$ on the Pareto-dominant combined fitness objective. The selected subset $\left\{{\mathrm{C}\mathrm{e}\mathrm{l}\mathrm{l}\mathrm{V}\mathrm{o}\mathrm{l}\mathrm{t}\mathrm{a}\mathrm{g}\mathrm{e}}_V,{\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{C}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t}}_A,{\mathrm{S}\mathrm{O}\mathrm{C}}_{\mathrm{\%}},{\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{P}\mathrm{o}\mathrm{w}\mathrm{e}\mathrm{r}}_{kW}\right\}$ achieves the best fitness score of $-0.5555$, enabling a $77.8\mathrm{\%}$ sensor-cost reduction while improving fault detection. Stability analysis across five independent random seeds confirms a mean feature count of $4.0\pm 0.7$ and a mean macro-F1 of $0.441\pm 0.021$, demonstrating algorithmic robustness. Full article

The rapid surge of Electric Two-Wheelers (E2Ws) in high-density urban villages imposes severe strain on low-voltage residential distribution networks. Unlike formal Electric Vehicles, E2W charging is decentralized and highly constrained by short pedestrian walking thresholds, frequently forcing users to adopt non-compliant “fly-wire charging” when public facilities are scarce. Traditional top-down load models fail to capture these localized, micro-behavioral single-phase grid impacts. To address this deficit, this study proposes a GIS-integrated “Digital Sandbox” simulation framework that projects individual behavioral mutations directly onto feeder networks via a hyper-granular “particle tracking” mechanism, treating each E2W as an autonomous agent. As a case study focused on a representative urban-village area, we validate the model using field data from the site. A 30-day simulation reveals that unmanaged fly-wire charging generates a peak load of 17.64 kW (nearly double the public station peak) and accounts for 38.9% of aggregate energy consumption—concentrated within the top 10 buildings and coinciding with evening peaks, inducing severe phase imbalance. While the numerical results are case-specific, the framework itself is transferable to other service areas through re-calibration against local data. This foundational digital twin blueprint shifts E2W planning from guesswork to particle-level risk prediction. Full article

Power-switch open-circuit (OC) faults distort electric-vehicle (EV) inverter phase currents and require fast on-board diagnosis for fault-tolerant control. Trajectory-image methods encode the $\alpha$–$\beta$ current-vector trajectory as a binary image and classify it with a convolutional neural network (CNN); however, the baseline uses $6.46\times {10}^5$ parameters and $3.31\times {10}^7$ multiply–accumulate (MAC) operations per inference, which is costly for motor-control microcontrollers (MCUs). Here, each one-cycle trajectory is represented by a 36-dimensional normalized angular sector-energy vector and classified by a compact two-stage multilayer perceptron. Sector accumulation averages zero-mean measurement noise in the representation, without relying on noise-augmented training. The locating stage uses $1.58\times {10}^4$ parameters and $1.56\times {10}^4$ MACs per inference, $97.55\%$ and $99.95\%$ fewer than the baseline CNN; the complete pipeline runs on a TI F28379D in $0.52$ ms. On measured resistive-load currents, both methods reach $100\%$ accuracy from 40 to 20 dB, whereas the proposed method remains more accurate at 15 and 10 dB, including under $88\%$ phase-current unbalance. A supplementary balanced $RL$-load experiment preserves $100\%$ clean accuracy, confirming MCU-executable diagnosis under a lagging power-factor load for embedded EV inverter protection. Full article

The study provides an institutional strategic assessment of the development of electric mobility in the Republic of Kazakhstan, taking into account international trends and national characteristics. Market diagnostics are based on statistics, regulatory and legal analysis, surveys of electric vehicle owners, and interviews with stakeholders. This study represents one of the first integrated institutional and strategic assessments of the development of electric mobility in Kazakhstan, combining regulatory analysis, consumer survey data, stakeholder interviews and semi-quantitative structures of PESTEL and SWOT in the context of the emerging electric vehicle market in Central Asia. The market’s high sensitivity to institutional policy is demonstrated: with state support, the strategic position becomes stable (SWOT index +1.65), and without it, critically vulnerable (−4.05). The conclusions of the study form a scientifically sound basis for the development of a comprehensive state program for the development of electric mobility, focused on regulatory unification, infrastructure modernization, and institutional coordination. Full article

Hybrid energy storage systems combining LiFePO₄ batteries and supercapacitors can reduce high-rate battery loading in battery electric mining dump trucks operating under intensive regenerative braking conditions. This study proposes a constrained multi-objective sizing methodology for a semi-active battery–supercapacitor hybrid energy storage system applied to a 65 t payload-class mining electric dump truck. The model combines segment-level mining duty cycles, longitudinal vehicle dynamics, a first-order Thevenin battery representation, a usable supercapacitor energy window, bidirectional DC/DC converter limits, and constrained supervisory power splitting. Three mining duty cycles are considered: production haulage, reclamation/backfill operation, and mixed operation. The final sizing result is reported using a dimensionless usable energy buffer ratio rather than a direct comparison between supercapacitor capacitance and battery energy capacity. The results show that the required supercapacitor buffer is strongly duty cycle-dependent. For the regenerative-dominant backfill cycle, the hybrid configuration reduced peak battery charging current from approximately −950 A to −180 … −280 A and reduced battery root mean square (RMS) current by 52–64% relative to the pure battery configuration. The constrained stored fraction of regenerative energy also increased when the supercapacitor branch was included, while non-accepted braking power was assigned to the residual braking channel. The proposed approach provides a physically consistent basis for preliminary hybrid energy storage system (HESS) sizing and clarifies that battery current reduction should be interpreted as a degradation-relevant stress indicator rather than as a direct quantified lifetime prediction. Full article

Accurate prediction of battery thermal runaway (TR) is a critical requirement for electric vehicle (EV) battery management systems (BMSs), as TR remains one of the most severe failure modes in lithium-ion batteries. Conventional neural network training methods may suffer from local optimum entrapment, slow convergence, and unstable performance when applied to nonlinear battery safety data. To address these limitations, this paper proposes a Hybrid Particle Whale Optimization Algorithm-optimized feedforward neural network (HPWOA-FNN) for continuous TR probability prediction and binary high-risk event classification using multivariate EV charging sensor data. The proposed HPWOA combines the rapid convergence capability of Particle Swarm Optimization (PSO) during the initial exploration phase with the exploitation and refinement capability of the Whale Optimization Algorithm (WOA) during the second phase. A global-best transfer mechanism is introduced at the PSO-WOA phase boundary to preserve the best solution identified during exploration and initialize the WOA leader, thereby improving convergence continuity and reducing premature stagnation. The model is evaluated using a 500-sample EV battery-charging dataset containing 12 electrothermal, electrical, mechanical, and environmental features. The proposed HPWOA-FNN outperforms standalone PSO-, WOA-, and Stochastic Fractal Search Algorithm (SFSA)-optimized FNN models across all regression metrics, achieving MSE = 0.000989, RMSE = 0.031442, MAE = 0.027250, R² = 0.9702, and MAPE = 3.8075%. For binary high-risk event detection, HPWOA-FNN achieves the highest AUC of 0.9817 and the lowest false-negative count, reducing missed high-risk events to 7 compared with 9 for PSO, 12 for WOA, and 17 for SFSA. Feature-importance analysis identifies maximum temperature and internal resistance as the dominant predictors, consistent with established thermal runaway mechanisms. The results demonstrate that HPWOA-FNN provides an accurate, interpretable, and computationally practical framework for EV battery thermal runaway prediction and BMS decision support. Full article

Electric trucks play a crucial role in achieving a zero-emission future. As battery electric technology advances, electric trucks are expected to become a cost-effective and sustainable alternative to diesel trucks. Long-haul trucks have unique driving patterns that affect their charging needs, and investigating the expected load profiles is fundamental to conducting a proper assessment of the impact of truck fleet electrification on the charging infrastructure. This article presents an agent-based modeling approach to estimate high-power charging station load profiles, leveraging open data and driver decision-making patterns. The methodology is implemented in a software tool, ABChargingSim, which includes heterogeneous charging logic (distinguishing between urgent mid-shift and long-dwell off-shift charging, as well as different driver triggers to initiate charging) alongside a vehicle’s SOC-dependent power tapering charging patterns. A case study along a Norwegian highway demonstrates the framework’s applicability for evaluating grid impacts under various heavy-duty transport electrification scenarios. The findings illustrate how driver behavior and heavy-duty vehicle charging processes shape expected load profiles, emphasizing the value of such simulation frameworks as essential decision-support tools for the strategic planning and operation of future high-power charging networks. Full article

Load aggregators play a pivotal role in demand-side regulation by coordinating flexible resources between electricity retailers and end users. However, existing studies have rarely considered their dual-role attribute, namely acting as followers of electricity retailers while serving as leaders of end users. Moreover, most studies assume fully rational user behavior, which may not accurately reflect practical decision-making processes under heterogeneous preferences. To address these gaps, this paper proposes a coordination strategy for EV and air-conditioning loads based on a Stackelberg–evolutionary game framework. A three-layer Stackelberg–evolutionary game model is first constructed, with the electricity retailer serving as the leader and the load aggregator acting both as a follower and a leader, thereby revealing the interest interactions among multiple stakeholders. Subsequently, an evolutionary game based on the Logit protocol is introduced to establish a dynamic evolution equation for users’ collective strategy choices, which captures users’ heterogeneous trade-offs between electricity costs and thermal comfort, as well as their strategic interactions. Next, a genetic algorithm was used to solve the problem. Finally, case study results demonstrate that, compared with the pure Stackelberg game, the proposed strategy increases the aggregator’s profit by 56.7% while reducing users’ electricity costs by 41.2%, thereby validating its effectiveness. Full article