World Electric Vehicle Journal

To address the poor adaptability to diverse driving cycles and the imbalance between optimization performance and computational efficiency in existing energy management strategies (EMSs) for plug-in hybrid electric vehicles (PHEVs), this paper proposes a lightweight intelligent EMS (IEMS) with adaptive rule-parameter updating. The key contributions lie in constructing an optimized rule library using parameter optimization, and developing an online adaptive updating mechanism for rule parameters combined with driving cycle prediction, realizing dynamic self-adjustment of energy management rules. The results show that compared with the rule-based EMS (RBEMS), the strategy reduces energy consumption by 9.09%, 10.85% and 9.25% under NEDC, WLTC and real-world test cycles, respectively, with drastically lower computation times than dynamic programming (DP). The proposed IEMS can effectively balance fuel economy, driving cycle adaptability and computational efficiency. Full article

The global transition to electric mobility is crucial for reducing transportation-related emissions, although there is a scarcity of empirical research on customer adoption psychology in transition economies in Central Asia. This study investigates the economic and structural drivers of electric vehicle purchase intention in the Republic of Uzbekistan. Data collected from prospective customers across large city hubs were analyzed using a dual hierarchical multiple linear regression model, supported by an empirical bootstrapping procedure with 2000 resamples, based on the rational choice theory and bounded rationality. The structural model shows that baseline socio-demographics explain insignificant initial variance ($R^2$ = 0.105); however, the integration of primary theoretical constructs yields a significant incremental variance change ($\Delta R^2$ = 0.096), explaining 20.1% of the total variance. Inferential tracking confirms that government incentives are the only statistically significant driver of the purchase intention (p = 0.009). Conversely, purchase cost (p = 0.251) and charging infrastructure (p = 0.475) lack direct significance. However, partial collinearity and infrastructure expectation effects systematically change these localized contact points. The study concludes that consumer intent in this emerging marketplace is primarily anchored to macro-level institutional policy signaling rather than immediate vehicle-specific characteristics or current physical network constraints. Full article

Against the backdrop of the global aviation industry’s commitment to achieving the “Net Zero Carbon Emissions by 2050” goal, the issue of superimposed peak loads on distribution networks—arising from the large-scale transition from fossil-fueled to electric Ground Service Equipment (GSE) at civil airports—has become increasingly prominent, emerging as a critical constraint on green airport development. Focusing on the high-value airside area, this paper presents the first systematic review of how Vehicle-to-Grid (V2G) technology can transform electric Ground Service Equipment (e-GSE) from mere “charging loads” into “dispatchable energy storage resources.” The study proposes that, through bidirectional DC charging/discharging and intelligent aggregation technologies, e-GSE fleets operating on predictable schedules can be integrated as flexible regulation units within airport microgrids. To realize this pathway, the study comprehensively examines the core technological framework, encompassing wide-power-range bidirectional charging infrastructure, grid-forming power conversion topologies, standardized communication and grid interconnection interfaces, flight-schedule-based potential assessment and dispatch algorithms, and photovoltaic storage–charging hybrid system integration schemes. The review demonstrates that this technology can not only enhance grid resilience and promote renewable energy accommodation through peak shaving, valley filling, and ancillary services but also yields significant economic benefits. Finally, the study identifies the technical, standardization, and business model barriers hindering large-scale deployment, thereby providing a theoretical reference and a technology roadmap for the energy system planning and construction of future “zero-carbon smart airports”. Full article

This paper presents the modelling and dynamic analysis of a Virtual Synchronous Generator (VSG) operating under three representative load models: constant impedance (Z), constant power load (CPL), and composite ZIP (constant impedance, constant current, and constant power) loads. The VSG control strategy enables voltage-source converters to emulate the inertial behavior of synchronous machines. However, load characteristics strongly affect the stability of such systems, and CPLs can be particularly destabilizing because of their negative incremental impedance. This study provides a theoretical and simulation-based analysis of VSG performance under Z-, CPL, and ZIP load conditions. A swing-equation-based control model is linearized to obtain a reduced-order small-signal stability model. The incremental impedance properties of the load types are evaluated analytically, showing that CPL behavior reduces effective damping and can destabilize the system. The resulting analytical stability condition provides a practical basis for selecting virtual inertia and damping parameters. Practical DC-side energy storage and current-limiting constraints associated with inertia emulation are also discussed. The analysis is supported by simulation studies that quantify the influence of load dynamics on frequency stability and transient response. In contrast to current research, this paper offers a single comparative framework in which all load types are analyzed under the same operating conditions and derives analytical stability conditions that inform the selection of virtual inertia and damping parameters. Full article

Interior permanent magnet synchronous motor (IPMSM) has advantages with high power density, wide speed range, small size, and high efficiency, and is widely used in the drive system of electric vehicles. Compared to other types of motors, permanent magnet synchronous motors (PMSMs) have some irreplaceable advantages, but there are also some disadvantages. As a type of PMSM, IPMSMs have problems with large fluctuations in permanent magnet (PM) magnetic field and demagnetization. At present, irreversible demagnetization of PMs is the most serious problem faced by IPMSMs. Once irreversible demagnetization of PMs occurs, it can cause a decrease in the performance of IPMSMs and can even damage the entire drive system. This paper takes an IPMSM with 48 slots, 8 poles, and 66 kW as the research object. Based on the reasons for PM demagnetization, a PM demagnetization model is established to obtain the demagnetization law of PMs. Firstly, the magnetic properties of PM materials were described based on their characteristic curves. The demagnetization mechanism of PMs was analyzed, and the demagnetization process of PMs was studied in combination with the reasons for demagnetization. Secondly, the basic parameters and torque performance of IPMSMs were calculated and analyzed. We analyzed the demagnetization curves of PM materials at different temperatures, calculated the operating points of PMs under various working conditions, and analyzed whether PMs undergo irreversible demagnetization based on the relationship between the operating points of PMs and the knee points of demagnetization curves. A high-fidelity electromagnetic–thermal coupling simulation model has been established, combined with the characteristics of electric vehicle driving conditions, to accurately characterize the temperature rise distribution and electromagnetic parameter changes of IPMSMs under different operating conditions and achieve multi-physics field collaborative analysis. Finally, a finite element model is adopted to simulate uniform and local demagnetization of PMs, and the changing characteristics of motor performance parameters under demagnetization are summarized. Different magnitudes of d-axis reverse current are applied as demagnetization excitation to analyze PM behaviors under various demagnetization degrees. The variations in magnetic flux density, output torque, and no-load back electromotive force (EMF) before and after demagnetization are simulated and analyzed. For the investigated motor and specific magnet grade, this work summarizes the irreversible demagnetization characteristics and corresponding practical judgment references. Full article

This study examines Connecticut’s strategic approach to electric vehicle (EV) workforce development within the framework of the Bipartisan Infrastructure Law (BIL) and its National Electric Vehicle Infrastructure (NEVI) program. Amid the U.S. goal to transition to a zero-emission vehicle fleet by 2050, this research investigates whether Connecticut’s current policies sufficiently address the need to reskill automotive mechanics into qualified EV technicians. Using a qualitative case study methodology, semi-structured interviews were conducted with state workforce representatives and analyzed through inductive coding within Kotter’s 8-Step Change Model. Findings reveal that while Connecticut aligns with federal NEVI goals for infrastructure, it lacks a dedicated budget and clearly defined pathways for technician training. Stakeholder collaboration remains fragmented, and efforts to empower workforce transformation are in the early stages. The study concludes that Connecticut risks falling behind unless it integrates a robust workforce development strategy that includes cross-sector partnerships, pilot training programs, and transparent certification pathways. These findings highlight the importance of aligning state-level EV infrastructure planning with human capital development and offer actionable insights for other states navigating similar transitions. Full article

The growing adoption of electric vehicles (EVs) and the rapid expansion of public charging infrastructure pose new challenges and opportunities for energy systems, particularly in urban settings. This study presents an optimization-based evaluation of different EV charging strategies including direct charging, average-based methods, smart charging, and vehicle-to-grid (V2G) at public parking lots using real-world charging session data. This data-driven model is set to optimize the public EV charging of vehicles in Gothenburg, without sacrificing on the energy requirement while minimizing charging costs for the operators. Results indicate that direct charging scenarios lead to significantly higher peak loads (up to 1286 kW) and costs (around 370 k€), highlighting their inefficiency under unmanaged operation. In contrast, smart charging reduces peak loads by approximately 47% and overall costs by around 74%, showcasing its potential for cost-effective grid-friendly operation. Two different V2G scenarios were tested based on the impact of discharged power accounted for in peak costs, though it enables energy discharge back to the grid, the benefits remain modest under current assumptions due to tight operational constraints and limited incentives. The study emphasizes the value of smart optimization and appropriate market design in enhancing the flexibility and cost efficiency of public EV charging systems. Full article

Electric vehicles (EVs) relying solely on lithium-ion (Li-ion) batteries face limitations related to range, mass, charging time, and battery downsizing. This study develops a dynamic system-level modeling framework for integrating an aluminum–air (Al–air) battery with a Li-ion traction battery within a MATLAB/Simulink electric vehicle platform. Two integration strategies were evaluated: (i) Al–air operation as a range extender activated through SOC-based control logic, and (ii) Al–air operation as an auxiliary power unit supplying non-traction loads. The Al–air subsystem was implemented using an experimentally informed polarization-based model coupled with aluminum consumption tracking and DC–DC converter integration. Vehicle performance was evaluated under UDDS, HWFET, WLTP, and FTP-75 drive cycles. Results show that coupling a 24.6 kWh Al–air pack with a downsized 20.3 kWh Li-ion pack enabled driving ranges of 379 km (UDDS), 523 km (HWFET), and 450 km (WLTP), exceeding the baseline full-capacity Li-ion configuration while reducing total battery-system mass by more than 50%. When operated as an auxiliary power unit under a constant 3 kW auxiliary load, the Al–air system increased the vehicle range by 44–96 km depending on the drive cycle. The results demonstrate the feasibility of Al–air-assisted dual-energy-storage architectures for extending the EV range while reducing dependence on large Li-ion battery packs. Full article

To meet climate goals, the automotive industry is transitioning to electromobility, reshaping vehicle model variants, market composition and therefore influencing purchasing decisions. To cover the full range of possible vehicle models for the German passenger vehicle market, a machine learning-based manufacturer agent was developed, incorporating a comprehensive technology database and historical vehicle data. Over 3000 new BEV models were generated and evaluated for possible year of market entry. Relevant models were integrated into the VECTOR21 vehicle technology scenario model to assess their market potential against competing drivetrains. The scenario results for Germany show that LFP vehicles can capture more than 18% overall market share in 2030, while Ni-rich cells remain competitive in long-range variants with up to 53% market potential by 2035. On the other hand, BEVs powered by sodium-ion batteries could reach up to 9% market potential by 2030, potentially exceeding 17% if cell prices fall below 50 EUR/kWh. However, sensitivity analysis reveals So-Ion market potential is highly sensitive to model availability, dropping to 6% or 2% in constrained scenarios, primarily replaced by LFP variants. These findings suggest that alongside cost reductions, sufficient model availability can also play a significant role in realizing the market potential of next-generation battery technologies. Full article

Accurate range prediction is crucial for reducing range anxiety and optimizing the energy management of electric two-wheelers (E2Ws). In this study, we propose a stacked ensemble model to predict the remaining driving range (RDR) of E2Ws with enhanced accuracy. Grid Search Cross-Validation (Grid Search CV) was employed for model selection and hyperparameter tuning to ensure the most effective regression models were chosen as base learners. The final ensemble consists of AdaBoost, CatBoost, Gradient Boosting, and Lasso Regression as base learners, while Ridge Regression serves as the meta-learner to refine predictions. To develop and validate the model, real-world driving data was collected from three different E2W models. The dataset underwent preprocessing and was evaluated using 10-fold cross-validation to ensure robustness. Experimental results demonstrate that the proposed stacked model achieves a Mean Absolute Error (MAE) of 0.13, corresponding to an average prediction error of 130 m per trip. Full article

Mobile emergency generators and mobile energy storage clusters are core flexible resources for the rapid recovery of critical loads in post-disaster distribution networks and the enhancement of resilience in isolated microgrids. However, due to the strong random changes in end-load loads, heterogeneous units are prone to problems such as large transient inrush currents, unstable phase-locked loops (PLLs), and reliance on manual synchronization adjustments when connected under load. To address these issues, this paper proposes a multi-timescale smooth grid-connected control architecture that combines data-driven feedforward and physical feedback. The architecture extracts spatiotemporal load features based on a CNN-BiLSTM-Attention model to achieve capacity optimization and baseline allocation. Predicted load voltage drop is converted into feedforward compensation to construct a virtual internal potential for coarse pre-grid connection adjustment. This is then combined with a closed-loop PLL to achieve fine-tuning of phase angle and voltage errors and autonomous decoupling of transient power after grid connection. Simulation and experimental results show that the proposed method suppresses voltage overshoot from 0.3 p.u. to within 0.03 p.u., increases the minimum frequency to above 49.8 Hz, reduces inrush current, and shortens synchronization time by 15.4%, significantly improving the system’s rapid connection and recovery capabilities. Full article

Despite the global movement toward sustainable mobility, the adoption of electric and autonomous vehicles (EVs/AVs) in Gulf Cooperation Council (GCC) countries is shaped by unique socio-cultural and structural contingencies. This study provides a significant theoretical contribution by exploring market entry strategies through a multidimensional value framework that captures symbolic and contextual dimensions overlooked by traditional models such as TAM and UTAUT. Drawing on in-depth interviews, focus groups, and participant observations, the research utilizes Kuwait as a case study to delineate the multidimensional construct of perceived value through Osterwalder’s Value Proposition Canvas (VPC). The findings reveal that consumer adoption is influenced not only by utility and efficiency but also by social, emotional, epistemic, conditional, and cost values. Dealers, in turn, demonstrate how these values guide entry strategies for non-conventional vehicles by aligning product offerings with specific “Pain relievers”, “Gain creators”, and “Jobs to be done” (JTBD). The study identifies distinct encroachment pathways: high-end entry for battery electric vehicles (BEVs) and low-end entry for hybrid electric vehicles (HEVs). Notably, a dual-encroachment strategy is identified for high-tech Chinese brands, which are aggressively disrupting emerging markets by leveraging manufacturing efficiencies to dominate the mid-market while simultaneously deploying premium models to challenge luxury incumbents. Finally, despite the structural constraints on public AV deployment, the research highlights vital applications for autonomous systems within “industrial sandboxes” such as aviation, seaports, military, and oil sectors. While centered on Kuwait, the findings offer potentially transferable strategic insights for the broader GCC region. Full article

Electrification of road transport is widely promoted as a pathway to reduce greenhouse gas (GHG) emissions; however, its effectiveness depends critically on electricity carbon intensity, renewable energy share, charging behavior, and grid capacity constraints. This study develops a multi-objective analytical and optimization framework to evaluate cost and carbon-optimal electric vehicles electrification by jointly minimizing system cost and carbon emissions under coupled transport–energy system conditions. A closed form cut-off condition is derived to determine the minimum renewable electricity share required for electric vehicles to achieve lower emissions than internal combustion engine vehicles, and the formulation is extended to mixed fleets including battery electric and plug-in hybrid electric vehicles. The framework integrates fleet-level emissions, electricity demand, renewable capacity limits, charging losses, carbon taxation, and peak charging constraints to define a feasible electrification region. Feasibility mapping, Monte Carlo exploration, and evolutionary multi-objective optimization are employed to characterize trade-offs between CO₂ emission and total system cost, and to identify Pareto-optimal and knee point solutions. The results show that electrification without sufficient renewable support or coordinated charging can increase emissions and violate grid limits, whereas integrated planning enables significant emission reduction within economically viable regions. These findings provide a quantitative and decision-oriented basis for cut-off-informed and grid-aware electrification planning in carbon-constrained power systems. Full article

Vehicle damage detection is an important task in intelligent transportation systems and insurance assessment, yet it remains challenging due to the subtle appearance, irregular shapes, and spatial dispersion of damage regions in complex environments. We propose a specialized structural synergy that organically integrates a GSConv-based Slim-Neck, a dynamic Bi-Level Routing Attention mechanism, and an orientation-aware SIoU loss. Rather than a superficial architectural combination, this cooperative design introduces a novel methodological framework engineered specifically to resolve the fundamental conflict between edge-deployment efficiency and fine-grained feature preservation in vehicle inspection. The method is evaluated on the publicly available Car Damage Detection dataset and compared with representative two-stage and one-stage detectors, including DETR, Faster R-CNN, YOLOv5n, YOLOv8n, and YOLO11n. Experimental results show that the proposed approach achieves a mAP50 of 67.9% and mAP50–95 of 53.8%, outperforming the baseline YOLO11n and other lightweight YOLO variants with only a moderate increase in computational cost. These results indicate that the proposed framework offers a favorable trade-off between detection accuracy and efficiency, showing potential for vehicle damage inspection under resource-constrained conditions. Full article

The integration of electric vehicles with grid vehicles promotes the creation of multi-energy microgrid models. One of the aims of these models is to decrease electricity usage through Vehicle-to-Grid planning. Effective management of microgrids necessitates sophisticated automation and control systems, which can prove challenging to establish and sustain. To tackle these challenges, the author introduces a hybrid model that merges a Linear Regression model and a Feedforward Neural Network, created using Matlab software. This combined algorithm adjusts the quantity of hidden neurons to enhance performance, guided by the evaluation criteria of Mean Squared Error, Root Mean Squared Error, and Mean Absolute Percentage Error based on batteries B0005, B0006, and B0007 from the NASA PCoE Research Center Dataset. The author forecasts the lifespan of the battery that most accurately reflects its degradation, revealing important implications for the future advancement of systems that employ Linear Regression and Feedforward Neural Networks for integrating electric vehicles into Vehicle-to-Grid systems. The comparison among the training, testing, and validation stages of the methodology serves to thoroughly demonstrate its effectiveness. Furthermore, the author indicates that the LR-FFN algorithm provides predictive tools relevant for the management of V2G-compatible EV systems and performs superiorly compared to other methods noted in the existing literature. Additionally, the author aimed to specifically identify the attributes of the LR-FNN model for prospective usages, emphasizing its efficacy in developing effective microgrid management, promoting energy efficiency, and ensuring that microgrids remain secure and resilient against failures or threats. Full article

Against the backdrop of global energy transition and the continuous growth in electric vehicle (EV) market penetration, accurate forecasting of EV charging load is critically important for guaranteeing the safe and stable operation of power grids. Most existing forecasting approaches rely on artificial intelligence (AI) models trained with large-scale and continuous historical data, which imposes stringent requirements on the collection of EV charging load data. To address this issue, this paper proposes a novel method for EV charging load forecasting under small sample and discontinuous data conditions. Firstly, the differences between the daily load curves of EV charging are characterized by local dynamic time warping (LDTW) distance. And a spectral clustering algorithm based on LDTW distance (SCLD) is proposed to realize the classification of daily EV charging load patterns. Secondly, feature correlation weights (FCWs) derived from eXtreme gradient boosting (XGBoost) with one-hot encoding of input features are introduced to quantify the influences of features such as district-level attributes and weather conditions on daily EV charging load. Then, a method for determining the category of daily EV charging load based on FCWs and Hamming distance is put forward. On this basis, a daily EV charging load forecasting framework is established via weighted fitting of similar intra-class samples based on category judgment. Finally, to validate the effectiveness of the proposed method, a case study is carried out using EV charging load data and corresponding feature data of 62 typical days across 16 administrative districts in Shanghai from 2023 to 2025. The results demonstrate that the proposed method effectively addresses the challenging problem of EV charging load forecasting under small sample and discontinuous data conditions. Full article

In response to the frequent safety incidents associated with the core electrical systems (i.e., traction battery, charging system, and drive motor) of new energy vehicles (NEVs) and the lack of forward-looking quantitative risk assessment methods in existing detection and diagnostic technologies, this study introduces the Layer of Protection Analysis (LOPA) methodology into the field of NEV safety. Unlike qualitative methods (e.g., FMEA, FTA) or purely data-driven diagnosis, this work establishes a tailored semi-quantitative LOPA framework that defines scenario-specific independent protection layer (IPL) identification criteria and probability of failure on demand (PFD) assignment rules for NEV applications. Typical risk scenarios, including battery thermal runaway, electrical faults in charging systems, overheating of drive motors, and battery internal short circuits caused by mechanical abuse, are systematically analyzed in terms of their failure mechanisms and evolution processes. A tailored quantitative risk assessment framework is established and applied to conduct full-process risk evaluations for the four scenarios. The results indicate that, under the synergistic effect of multiple protection layers—including inherently safe design, basic process control systems, safety instrumented systems, and physical protection measures—the accident consequence frequencies of all scenarios are significantly lower than the tolerable risk thresholds. This verifies the applicability and effectiveness of the LOPA method in NEV safety analysis. The proposed quantitative framework provides a scientific basis for safety design optimization, identification of critical protective elements, and operation and maintenance strategy formulation throughout the lifecycle of NEVs. Furthermore, the limitations of data portability from process industries are discussed, and sensitivity analyses are conducted to confirm the robustness of the conclusions. Full article

Precision battery-health monitoring and rapid degradation detection are essential for improving the security, durability, and efficacy of electric vehicles (EVs). By incorporating short-term State-of-Health (SoH) forecasting, mid-term deterioration alarms, and degradation acceleration risk modeling into a temporally consistent machine learning architecture, this research suggests a hierarchical predictive-maintenance framework. The rolling-origin cross-validation approach is implemented to maintain the chronological order of the data and prevent any potential information leaks. The predictive core employs an ensemble learning approach that integrates Random Forest, Extremely Randomized Trees, and Histogram-Based Gradient Boosting. Validation-driven model blending and training only feature selection are implemented to improve generalizability. The one-hour SoH forecasting model for short-horizon monitoring exhibits exceptional accuracy in an assessment of health prediction, with an R² of 0.9254, an RMSE of 0.0033, and a MAPE of 0.32%. Early detection of anomalies and the provision of a seven-day degradation warning may be achieved by a proactive maintenance scheduling model with an area under the curve (AUC) of 0.7838 and a recall of 0.8205. In addition, the degradation acceleration risk module could identify rapid health decline with a robustness of 0.8796 and a precision–recall AUC of 0.7101 when operating under significant stress. Reliability in critical domains is demonstrated through validation using scenarios that simulate severe temperature and stress conditions. Achieving intelligent predictive maintenance of electric vehicle battery packs is now feasible due to the proposed multi-layer ensemble structure. Full article

Accurate bearing fault diagnosis in electric drive systems is crucial for ensuring the safety and reliability of new energy vehicles. Aiming at the problem of inaccurate bearing fault diagnosis caused by the failure to fully utilize the time-frequency feature information of bearing faults and the lack of an adaptive selection mechanism for features, an intelligent bearing fault diagnosis method based on wavelet packet decomposition (WPD) time-frequency feature enhancement is proposed in this paper. First, the collected vibration signals are enhanced using WPD to obtain the full-frequency-band time-frequency information, which provides input for the bearing fault diagnosis model. Second, a hybrid neural network CNN-BiLSTM-AM for bearing fault diagnosis is constructed. On the basis of using the convolutional neural network (CNN) improved with cross-convolutional layers to extract multiscale spatial features of the input data and the bidirectional long short-term memory network (BiLSTM) to capture the bidirectional temporal dependence between features, the attention mechanism (AM) is introduced to adaptively weight and enhance key global features. Finally, a fully connected layer is employed to achieve intelligent classification of bearing fault states. Validation on a laboratory test dataset shows that the proposed method achieves an average diagnostic accuracy of 98.67%, outperforming existing benchmark models and exhibiting strong generalization ability. This study provides an effective and practical intelligent fault diagnosis scheme for bearings in electric drive systems. Full article