You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.

Search for Articles:

Title / Keyword

Author / Affiliation / Email

Journal

Article Type

Advanced Search

Section

Special Issue

Volume

Issue

Number

Page

Logical OperatorOperator

Search Text

Search Type

Optimizing Market Scenarios for Battery Electric Vehicles Through a Machine Learning-Based Manufacturer Agent
Unlocking the Value of Public EV Chargers: A Data-Driven Case Study from Gothenburg, Sweden
Factors of Electric Vehicle Adoption in Central Asia: A Multivariate Analysis of Consumer Purchase Intentions in Uzbekistan
Deep Koopman Observer for Lithium-Ion Battery Temperature Estimation

Journal Description

World Electric Vehicle Journal

World Electric Vehicle Journal (WEVJ) is the first international, peer-reviewed, open access journal that comprehensively covers all studies related to battery, hybrid, and fuel cell electric vehicles, published monthly online. It is the official journal of the World Electric Vehicle Association (WEVA) and its members, the E-Mobility Europe, Electric Drive Transportation Association (EDTA), and Electric Vehicle Association of Asia Pacific (EVAAP).

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, and other databases.
Journal Rank: JCR - Q2 (Engineering, Electrical and Electronic) / CiteScore - Q1 (Automotive Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.7 days after submission; acceptance to publication is undertaken in 3.7 days (median values for papers published in this journal in the first half of 2026).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 3.3 (2025); 5-Year Impact Factor: 3.3 (2025)

Imprint Information Journal Flyer Open Access ISSN: 2032-6653

Latest Articles

14 pages, 6118 KB

Open AccessArticle

Design and Performance Analysis of an Adaptive PID Controller for Brushless DC Motor Systems in Electric Vehicles

by Md Mahmud, S. M. Rakibul Islam and S. M. A. Motakabber

World Electr. Veh. J. 2026, 17(8), 422; https://doi.org/10.3390/wevj17080422 - 12 Aug 2026

Brushless DC (BLDC) motors are now the dominant propulsion choice for electric vehicles (EVs) because of their high torque density, efficiency and reliability, but their nonlinear dynamics, electronic commutation, and wide load and speed range make fixed-gain control difficult. A single set of [...] Read more.

Brushless DC (BLDC) motors are now the dominant propulsion choice for electric vehicles (EVs) because of their high torque density, efficiency and reliability, but their nonlinear dynamics, electronic commutation, and wide load and speed range make fixed-gain control difficult. A single set of proportional–integral–derivative (PID) gains tuned at one operating point degrades when inertia, back-EMF, or load torque change. This paper presents a hybrid adaptive PID speed controller for a BLDC EV drive that couples an online PID auto-tuner that re-estimates the gains from a frequency response estimate of the plant, with a fast fixed-structure PID that supplies the rapid corrective action that the auto-tuner cannot provide during its estimation interval. The novelty of this work is this explicit two-element decomposition operating on a cascaded speed/voltage loop driven by Hall sensor feedback, which removes the need for an exact analytical feedback model while retaining the transparency of classical PID. A full analytical model of the BLDC machine and the closed-loop transfer functions is derived and implemented in MATLAB/Simulink. Across step references of 1000–1800 rpm and load steps to 10 N·m, and against a conventional fixed-gain PID and a Flower Pollination Algorithm (FPA)-tuned PID, the proposed controller holds overshoot below 1% at low-to-mid speed and a consistently lower torque ripple, while a 12.4% transient undershoot at 1800 rpm under sudden load identifies the present operating limit and a direction for future work. Full article

(This article belongs to the Section Vehicle and Transportation Systems)

► Show Figures

Figure 1

15 pages, 843 KB

Open AccessArticle

Route-Specific Total Cost of Ownership for Electric Trucks: A Danish Distribution Case Study

by Lars Boserup Iversen and Christina Rehmeier

World Electr. Veh. J. 2026, 17(8), 421; https://doi.org/10.3390/wevj17080421 - 11 Aug 2026

The decarbonisation of heavy-duty road transport is central to European climate policy, yet the economic viability of battery-electric trucks under real-world route conditions remains uncertain. This study presents a route-specific total cost of ownership (TCO) analysis comparing an 18-tonne battery-electric truck with a [...] Read more.

The decarbonisation of heavy-duty road transport is central to European climate policy, yet the economic viability of battery-electric trucks under real-world route conditions remains uncertain. This study presents a route-specific total cost of ownership (TCO) analysis comparing an 18-tonne battery-electric truck with a comparable diesel truck across four Danish distribution routes. An eight-year net present value (NPV) model at 5% discount rate captures acquisition, energy, taxation, and maintenance costs. The analysis incorporates seasonal variation and distinguishes between depot charging (0.75 DKK/kWh) and public fast charging (3.00 DKK/kWh). The electric truck achieves NPV advantages of DKK 1.01–2.14 million across all routes, with break-even periods of 1.8 to 3.3 years. Charging strategy is the most influential economic factor. The findings demonstrate that fleet-averaged TCO estimates are insufficient and route-level analysis is essential for informed electrification decisions, though results are contingent on the specific policy and operational context of this single-operator case study. Full article

(This article belongs to the Collection Feature Papers in “Charging Infrastructure and Grid Integration” Section)

► Show Figures

Figure 1

29 pages, 16938 KB

Open AccessArticle

Online Parameter-Reconfigured Model Predictive Control for Integrated Trajectory Tracking of Distributed Four-Wheel Steering Vehicles

by Hao Zhang, Gang Li, Jingxue Zhang and Dong Zhang

World Electr. Veh. J. 2026, 17(8), 420; https://doi.org/10.3390/wevj17080420 - 10 Aug 2026

To overcome the limitations of conventional model predictive control (MPC) for trajectory tracking of distributed-drive four-wheel-steering (4WS) vehicles, particularly its fixed weighting matrices and prediction and control horizons, this study investigates the integrated trajectory tracking and stability control of an automated distributed-drive electric [...] Read more.

To overcome the limitations of conventional model predictive control (MPC) for trajectory tracking of distributed-drive four-wheel-steering (4WS) vehicles, particularly its fixed weighting matrices and prediction and control horizons, this study investigates the integrated trajectory tracking and stability control of an automated distributed-drive electric vehicle equipped with four independently controlled in-wheel motors and a four-wheel-steering system. The main novelty of this study lies in the simultaneous online adaptation of the MPC weighting matrices and reconfiguration of the prediction and control horizons, together with the coordinated integration of four-wheel steering and direct yaw moment control (DYC) within a unified trajectory tracking framework. Unlike conventional adaptive MPC methods that primarily adjust weighting parameters, the proposed adaptive prediction and control horizon adjustment (APCHA) strategy jointly updates the prediction and control horizons according to the integrated tracking error, error variation rate, and control input variation rate. Meanwhile, a fuzzy adaptive weighting mechanism adjusts the MPC weighting matrices online. At the lower control layer, a torque allocation method considering both the tire load ratio and vertical tire loads is employed to realize the required direct yaw moment. Finally, CarSim–Simulink co-simulation is conducted to verify the effectiveness of the proposed control strategy. Simulation results demonstrate that, at a vehicle speed of 60 km/h and a road adhesion coefficient of

μ = 0.5

, the proposed Improved MPC-4WS controller reduces the maximum lateral tracking error by 34.9% compared with the conventional MPC-4WS controller, thereby demonstrating superior trajectory tracking performance. Furthermore, the ablation study verifies the effectiveness of the proposed hierarchical architecture by quantifying the contributions of the DYC module and the optimized torque allocation strategy. Full article

(This article belongs to the Section Automated and Connected Vehicles)

► Show Figures

Figure 1

24 pages, 10884 KB

Open AccessArticle

Research and Analysis on Stability Control of Four-Wheel-Independent-Drive Electric Vehicles Based on Phase Plane

by Xian Zheng and Tongqun Han

World Electr. Veh. J. 2026, 17(8), 419; https://doi.org/10.3390/wevj17080419 - 10 Aug 2026

To address the insufficient control accuracy of traditional vehicle stability control methods under nonlinear conditions, this paper proposes a combined stability control strategy for distributed-drive electric vehicles based on the phase plane method. A two-degree-of-freedom vehicle dynamics model incorporating the Magic Formula tire [...] Read more.

To address the insufficient control accuracy of traditional vehicle stability control methods under nonlinear conditions, this paper proposes a combined stability control strategy for distributed-drive electric vehicles based on the phase plane method. A two-degree-of-freedom vehicle dynamics model incorporating the Magic Formula tire model is established. The

β - \dot{β}

phase plane is selected, and a dynamic stability boundary function is constructed through saddle point analysis and road adhesion coefficient fitting. An instability index is defined to quantify the deviation from the stable state. Based on this index, a hierarchical control strategy is designed: within the stable region, model predictive control (MPC) is employed for yaw moment optimization via differential torque distribution among the four in-wheel motors; when the vehicle enters the unstable region, sliding mode control-based active rear-wheel steering (ARS) is activated. The strategy is validated through CarSim-Simulink co-simulation under step steering and slalom maneuvers. Results show that under the high-speed step steering condition, compared with the uncontrolled case, the combined control reduces the peak yaw rate by 5.3%, the overshoot from 32.86% to 27.38%, the settling time from 9.87 s to 8.02 s, and the oscillation amplitude by 36.5%; under the slalom condition, the yaw rate amplitude is reduced by 5.4%. The proposed strategy effectively improves vehicle handling stability. Full article

(This article belongs to the Section Vehicle Control and Management)

► Show Figures

Figure 1

27 pages, 3181 KB

Open AccessArticle

Can the Promotion of New Energy Vehicles Contribute to Economic Green Development? Evidence from Prefecture-Level Cities in China

by Lin Chen, Yingwen Chen, Yujiao He, Xiaoyi Wu and Hailin Yang

World Electr. Veh. J. 2026, 17(8), 418; https://doi.org/10.3390/wevj17080418 - 10 Aug 2026

Against the backdrop of intertwined economic advancement and ecological governance dilemmas confronting developing economies, this paper centers on the green growth objective embedded within China’s New Energy Vehicle Pilot (NEVP) Policy as its analytical focal point. Adopting the propensity score matching difference-in-differences (PSM-DID) [...] Read more.

Against the backdrop of intertwined economic advancement and ecological governance dilemmas confronting developing economies, this paper centers on the green growth objective embedded within China’s New Energy Vehicle Pilot (NEVP) Policy as its analytical focal point. Adopting the propensity score matching difference-in-differences (PSM-DID) framework, this study empirically evaluates the causal impacts of the NEVP policy on green economic development efficiency. The results indicate that the promotion of new energy vehicles yields a statistically significant improvement in green economic efficiency. Notably, the effect of the NEVP policy is more pronounced in cities with higher levels of economic development. Through mechanism analysis, we find that new energy vehicles play a crucial role in promoting green economic growth and sustainable development. Furthermore, this study also highlights the spatial effects of new energy vehicle promotion on green economic development. This research provides empirical evidence to guide the strategic promotion of new energy vehicles in developing regions to improve environmental quality and underscores the sustainable growth potential of aligning economic and environmental goals. Full article

(This article belongs to the Section Marketing, Promotion and Socio Economics)

► Show Figures

Figure 1

20 pages, 3989 KB

Open AccessArticle

Online Multi-Parameter Identification of PMSM Drives Using a Fuzzy PI-Tuned MRAS Observer

by Jishun Neng, Bo Huang, Shen Xu, Xiao Ju, Xu Wang and Jingbin Niu

World Electr. Veh. J. 2026, 17(8), 417; https://doi.org/10.3390/wevj17080417 - 9 Aug 2026

Permanent magnet synchronous motors (PMSMs) are widely used in AC drive systems, and their control performance depends strongly on accurate motor parameters. Conventional proportional-integral model reference adaptive system (PI-MRAS) observers use fixed adaptation gains, resulting in a trade-off between rapid convergence and low [...] Read more.

Permanent magnet synchronous motors (PMSMs) are widely used in AC drive systems, and their control performance depends strongly on accurate motor parameters. Conventional proportional-integral model reference adaptive system (PI-MRAS) observers use fixed adaptation gains, resulting in a trade-off between rapid convergence and low steady-state fluctuation. To address this limitation, this paper proposes a fuzzy proportional integral (Fuzzy-PI)-tuned MRAS observer for the simultaneous online identification of stator resistance (Rs) and stator inductance (Ls). The parameter-error dynamics are formulated from the PMSM model, and the adaptation laws are derived using Popov hyperstability theory. A fuzzy tuner uses the absolute identification error and its rate of change to schedule the proportional and integral gains online, thereby accelerating transient error convergence when the identification error is large and reducing estimation oscillations during steady-state operation. The method is evaluated through simulation and laboratory experiments involving rated operation, speed variation, parameter perturbation, and load disturbance. Under the investigated conditions, the identification errors of Rs and Ls are 3.8% and 0.18%, respectively. Compared with the conventional PI-MRAS, the reported Rs identification error decreases from 8.1% to 3.8% and the Ls identification error decreases from 0.91% to 0.18%. The results demonstrate an improved identification accuracy and disturbance recovery within the tested operating range. The implementation on an Infineon TC233 platform also demonstrates real-time feasibility, while broader validation under temperature variation, magnetic saturation, inverter nonlinearity, and measurement noise remains necessary. Full article

(This article belongs to the Section Vehicle Control and Management)

► Show Figures

Figure 1

31 pages, 10562 KB

Open AccessArticle

Evolution of Battery Parameters, State of Charge, and State of Health in Aging Lithium Batteries Using a PSO Algorithm at High Temperature

by Hamza Benhammou, Kamal Anoune and Abdelali Tajmouati

World Electr. Veh. J. 2026, 17(8), 416; https://doi.org/10.3390/wevj17080416 - 7 Aug 2026

To encourage a wild spread of EVs, accurate SoC and SoH estimation under thermally accelerated aging is critical for advanced BMS systems. This study presents a lifelong degradation analysis of LiBs cells over a 7700 cycle at 40 °C. An adaptive third-order ECM, [...] Read more.

To encourage a wild spread of EVs, accurate SoC and SoH estimation under thermally accelerated aging is critical for advanced BMS systems. This study presents a lifelong degradation analysis of LiBs cells over a 7700 cycle at 40 °C. An adaptive third-order ECM, coupled with a hybrid polynomial–logarithmic OCV formulation, is continuously identified via PSO. The experimental data reveals a distinct degradation profile where the cell crosses the 80% SoH EoL threshold at cycle 5500, steadily declining to a terminal state of 75.7% SoH. Continuous parameter tracking isolates key electrochemical transitions: an initial kinetic stabilization phase is followed by a synchronized thermodynamic OCV realignment near cycle 2000, consistent with increasing LLI. Mid-life aging features a pronounced increase in the time constants, while a late-life degradation is characterized by increasing transport limitations, reflected in the evolution of the slow diffusion-related model parameters, inducing parameter boundary clipping in the slow diffusion branch. Despite these physical non-linearities, the proposed framework maintains high global fidelity throughout the 7700-cycle lifespan, strictly bounding the SoC RMSE below 2.5%, keeping Voltage RMSE under 35 mV, and preserving an R² above 0.970. Comparative evaluations indicate that the proposed framework achieves a favorable balance between computational efficiency, tracking accuracy, and long-term diagnostic stability. Full article

(This article belongs to the Section Storage Systems)

► Show Figures

Graphical abstract

19 pages, 1871 KB

Open AccessArticle

Comparative Life Cycle Assessment of Battery Electric and Internal Combustion Engine Passenger Cars Under a Fossil-Dominated Electricity Grid: The Case of Saudi Arabia

by Ahmed S. Alghamdi

World Electr. Veh. J. 2026, 17(8), 415; https://doi.org/10.3390/wevj17080415 - 7 Aug 2026

This study quantifies whether vehicle electrification reduces greenhouse gas emissions on one of the world’s most fossil-intensive electricity grids. A transparent, ISO 14040/14044-conformant cradle-to-grave life cycle assessment compares a mid-size battery electric vehicle (BEV, 60 kWh) with a comparable gasoline car over 225,000 [...] Read more.

This study quantifies whether vehicle electrification reduces greenhouse gas emissions on one of the world’s most fossil-intensive electricity grids. A transparent, ISO 14040/14044-conformant cradle-to-grave life cycle assessment compares a mid-size battery electric vehicle (BEV, 60 kWh) with a comparable gasoline car over 225,000 km, using a fully source-traceable process-sum inventory and life cycle (well-to-wheel) emission factors for both energy carriers. On the 2024 Saudi grid (692 g CO₂e/kWh, 99.8% fossil) the BEV emits 37.8 t CO₂e (168 g CO₂e/km) against the gasoline car’s 50.6 t (225 g CO₂e/km)—a 25% reduction, with the BEV’s 1.9 times higher production emissions repaid at 76,000 km, approximately three years of typical Saudi driving. The advantage rises to 44% on the world-average grid, 53% under Saudi Arabia’s 50% renewable-electricity target for 2030, and 66–80% on the EU and French grids; grid parity would require 991 g CO₂e/kWh, above any national grid. The result is robust to hot climate energy consumption (+15%, advantage 25%), Gulf-sourced materials (break-even shortens to 68,000 km), battery capacity (40–80 kWh), and 10,000-run Monte Carlo uncertainty propagation (BEV superior in 99.6% of draws). Electrification is therefore a sound climate strategy even in fossil-grid economies, and its benefit roughly doubles with the announced power-sector transition. Full article

(This article belongs to the Section Energy Supply and Sustainability)

► Show Figures

Graphical abstract

13 pages, 425 KB

Open AccessArticle

Perceived Environmental Benefits and Electric Vehicle Intentions in Canada: Separate Analyses of Car Owners’ Purchase Likelihood and Non-Car Owners’ Stated Preference

by Naeleh Motamedi

World Electr. Veh. J. 2026, 17(8), 414; https://doi.org/10.3390/wevj17080414 - 7 Aug 2026

Believing that electric vehicles (EVs) benefit the environment may be associated with EV intentions, but current car owners and non-car owners answer different practical questions. This cross-sectional online survey of 328 adults residing in Canada therefore analyzes the groups separately. The focal item—“The [...] Read more.

Believing that electric vehicles (EVs) benefit the environment may be associated with EV intentions, but current car owners and non-car owners answer different practical questions. This cross-sectional online survey of 328 adults residing in Canada therefore analyzes the groups separately. The focal item—“The use of EVs will help protect the environment”—is treated as a perceived environmental benefit of EVs rather than as a validated general environmental-concern scale. For 226 car owners with complete focal variables, an ordered logistic model including personal environmental responsibility produced an odds ratio (OR) of 1.82 per one-category increase in perceived environmental benefit (95% confidence interval [CI] 1.51–2.19; p < 0.001). The association remained positive in the available demographic sensitivity model (OR 2.05, 95% CI 1.68–2.51). For 72 non-car owners, the parsimonious exploratory model produced an OR of 1.47 (95% CI 1.04–2.08; p = 0.029), but the estimate was attenuated after broader demographic adjustment (OR 1.39, 95% CI 0.96–2.02; p = 0.080). Cluster-robust stacked cumulative-logit diagnostics found no evidence against proportional odds in the primary models. Because owners reported five-category purchase likelihood for a plug-in electric vehicle and non-owners reported seven-category stated EV preference, no formal group comparison was conducted. The findings are associational, based on single items and a non-probability sample, and are consistent only with selected propositions of Value–Belief–Norm theory. Evidence is strongest for a positive owner–context association and suggestive, but less stable, for non-car owners. Full article

(This article belongs to the Section Marketing, Promotion and Socio Economics)

► Show Figures

Figure 1

21 pages, 2014 KB

Open AccessArticle

An Ordered Charging–Discharging Optimization Strategy for Electric Vehicles Considering Discharge Restraint and Carbon Emission Reduction

by Yan-Mei Tang, Jian-Feng Li, Yang Du, Kang Li, Tao-Yong Li, Qin Yan and Shuang Liang

World Electr. Veh. J. 2026, 17(8), 413; https://doi.org/10.3390/wevj17080413 - 6 Aug 2026

Uncoordinated charging and discharging of large-scale electric vehicles (EVs) exacerbates grid peak–valley fluctuations, while deep discharging accelerates battery degradation. To address these challenges, this study proposes a coordinated charging–discharging optimization strategy integrating dynamic discharge restraint and a three-dimensional weighted comprehensive objective covering electricity [...] Read more.

Uncoordinated charging and discharging of large-scale electric vehicles (EVs) exacerbates grid peak–valley fluctuations, while deep discharging accelerates battery degradation. To address these challenges, this study proposes a coordinated charging–discharging optimization strategy integrating dynamic discharge restraint and a three-dimensional weighted comprehensive objective covering electricity price signals, grid operational constraints and battery health state. First, an EV travel behavior model is established to characterize spatiotemporal availability. Subsequently, a coupled battery aging model is developed by combining a power-law-based cycle aging formulation with a square-root calendar aging model, based on which an adaptive linkage mechanism between the depth-of-discharge upper bound and a net-revenue threshold is introduced. Building on these components, this model is constructed to jointly optimize three sub-objectives: charging station revenue maximization, battery lifetime cost minimization, and load fluctuation suppression, thereby mitigating grid peak–valley differences while reducing battery degradation and discharge costs. Multi-scenario simulations demonstrate that the proposed strategy, by coupling discharge restraint with spatiotemporal dynamic pricing, enables precise peak shaving of discharge power. For a fleet of 50 EVs, the charging station revenue reaches 1073.7 CNY, the grid peak–valley difference is reduced by 9.8%, and the battery degradation cost decreases by 23.1% compared with conventional strategies, corresponding to a carbon emission reduction of 386.4 tCO₂. When scaled to 100 EVs, the revenue increases by 101.9%, while the peak–valley difference is further reduced by 0.6%, demonstrating the effectiveness of the proposed strategy in enhancing economic performance, extending battery lifetime, and supporting grid stability. Full article

(This article belongs to the Collection Feature Papers in “Charging Infrastructure and Grid Integration” Section)

► Show Figures

Graphical abstract

22 pages, 8222 KB

Open AccessArticle

State Estimation Method for Electric Vehicle Semi-Active Suspensions Considering Time-Varying Parameters and Non-Gaussian Noise

by Yunxing Liao, Zhaoxue Deng, Chong Peng, Xiaolin Wang, Hongwen Zhang and Shuangshuang Zhao

World Electr. Veh. J. 2026, 17(8), 412; https://doi.org/10.3390/wevj17080412 - 6 Aug 2026

An Adaptive-Parameter Maximum Correntropy Kalman Filter (APMCKF) algorithm is proposed to address state estimation degradation in semi-active suspensions caused by non-linear coupling between time-varying physical parameters and non-Gaussian noise. First, a time-varying dynamic model with non-linear damping is established via bench tests. A [...] Read more.

An Adaptive-Parameter Maximum Correntropy Kalman Filter (APMCKF) algorithm is proposed to address state estimation degradation in semi-active suspensions caused by non-linear coupling between time-varying physical parameters and non-Gaussian noise. First, a time-varying dynamic model with non-linear damping is established via bench tests. A genetic algorithm (GA) globally optimizes key physical parameters to suppress model mismatch. Second, the APMCKF integrates an adaptive suspension parameter update mechanism. This closed-loop mechanism refreshes the system state matrix in real-time, effectively overcoming state-tracking lag. Concurrently, the maximum correntropy criterion (MCC) is embedded within the Sage–Husa recursive framework to dynamically reconstruct the observation noise covariance matrix, ensuring robust filtering under heavy-tailed noise. Simulations under ISO Class A–D random road profiles demonstrate that the APMCKF reduces the root-mean-square error (RMSE) by 62.33–81.24% compared to the adaptive Kalman filter (AKF). It also outperforms the adaptive-parameter Kalman filter (APKF), yielding a 27.49% accuracy improvement on Class D roads where non-Gaussian noise is most severe. Moreover, comparative evaluations against standard non-linear Bayesian filters demonstrate that the APMCKF successfully overcomes the truncation errors of the Extended Kalman Filter (EKF) and the tracking hysteresis of the Unscented Kalman Filter (UKF), reducing the average RMSE by up to 74.98% and 60.76%, respectively, under severe Class D non-Gaussian excitations. Furthermore, the algorithm exhibits excellent disturbance rejection under transient speed bump impacts and maintains stable error reduction across vehicle speeds of 10–25 m/s. Ultimately, the APMCKF delivers high-precision estimation and exceptional robust stability under variable speeds and non-Gaussian disturbances. Full article

(This article belongs to the Section Vehicle Control and Management)

► Show Figures

Figure 1

31 pages, 2565 KB

Open AccessArticle

An Interaction-Aware NI-EA Framework for EV Charging-Station Siting: Source-Conditioned Robust Candidate Sets and Bounded Spatial Evidence in Dubai

by Ghassan Malkawi, Azmi Alazzam, Ahmed Abdelaziz Elsayed, Asem Omari, Said Badreddine, Bakeel Hussein, Mohammed Alhagyan and Abdelrahman Altigani

World Electr. Veh. J. 2026, 17(8), 411; https://doi.org/10.3390/wevj17080411 - 6 Aug 2026

Public-data electric-vehicle charging-station siting needs a screening workflow that can use spatial proxies while keeping demand, grid-capacity, and implementation claims separate from the score. This study develops an interaction-aware Nonlinear Interaction–Einstein Aggregation (NI-EA) framework for Dubai and extends it with source-conditioned robust candidate-set [...] Read more.

Public-data electric-vehicle charging-station siting needs a screening workflow that can use spatial proxies while keeping demand, grid-capacity, and implementation claims separate from the score. This study develops an interaction-aware Nonlinear Interaction–Einstein Aggregation (NI-EA) framework for Dubai and extends it with source-conditioned robust candidate-set diagnostics. From 7410 admitted candidate/amenity records, 5097 inside-boundary candidates are scored using a candidate-derived activity-density proxy, a charger-coverage-gap proxy, and a grid-access proxy. The analysis compares NI-EA with WSM, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Einstein aggregation; reconstructs a 63-scenario interaction/curvature/blending rank matrix; evaluates weighting, road-network, and official-DEWA source sensitivities; and reports necessary and possible top-K candidate sets, family-balanced finite-scenario acceptability, rank-displacement summaries, and bounded spatial-evidence context from official community, transport, parking, DEWA, and OpenStreetMap-derived sources. The baseline leader is S1421/Boonmax, while official-DEWA coordinate-source reconciliation changes the leader to S3473. Across the reconstructed interaction, weighting, road-network, and official-DEWA scenario families, the top-15 necessary core contains 12 candidates, and the top-15 possible envelope contains 18 candidates. Activity-radius and charger-count coverage alternatives are reported separately as proxy-definition sensitivities. TOPSIS has 0/15 top-15 overlap with NI-EA because it favors a different profile with much higher coverage-gap scores but low activity density. The reported output is therefore a source-conditioned planning shortlist and robustness audit, not an observed-demand map, feeder-capacity validation, financial feasibility assessment, or construction recommendation. Full article

(This article belongs to the Section Charging Infrastructure and Grid Integration)

► Show Figures

Figure 1

15 pages, 23248 KB

Open AccessArticle

Effects of Simulated Battery-Relevant Contaminants on the Electrical Conductivity of Silicone Oil Under Controlled Conditions

by Ningning Wei and Lei Huo

World Electr. Veh. J. 2026, 17(8), 410; https://doi.org/10.3390/wevj17080410 - 6 Aug 2026

Silicone oil is a promising dielectric coolant for battery immersion cooling, yet the ability of commercially available conductivity sensors to detect battery-related contamination remains poorly quantified. In this study, simulated carbonaceous particles, electrolyte, and mixed solid–liquid contaminants were introduced into silicone oil under [...] Read more.

Silicone oil is a promising dielectric coolant for battery immersion cooling, yet the ability of commercially available conductivity sensors to detect battery-related contamination remains poorly quantified. In this study, simulated carbonaceous particles, electrolyte, and mixed solid–liquid contaminants were introduced into silicone oil under controlled conditions using a closed-loop circulation platform, and conductivity was monitored in real time. Pristine silicone oil exhibited a baseline conductivity near the instrumental detection limit (approximately 1.26 μS·cm⁻¹). No measurable conductivity increase was observed for particle concentrations up to 10 g·L⁻¹ or electrolyte additions up to 3.0 vol%. Only under an intentionally extreme condition involving 20 vol% electrolyte and vigorous mixing were transient conductivity spikes of 350–550 μS·cm⁻¹ detected. Thus, within the application-relevant concentration range examined, conductivity monitoring showed limited sensitivity to progressive contamination. These findings concern the response of a commercial low-field conductivity sensor and do not constitute a complete assessment of leakage current, dielectric strength, or full thermal-runaway conditions. Full article

(This article belongs to the Section Storage Systems)

► Show Figures

Figure 1

21 pages, 4086 KB

Open AccessArticle

Current-Division-Aware Modeling and Detuning Design of a Receiver-Side LCC-Type Three-Coil Wireless Power Transfer System for Fixed-Frequency CC/CV Charging

by Kai Yan, Ruirong Dang and Zhen Yang

World Electr. Veh. J. 2026, 17(8), 409; https://doi.org/10.3390/wevj17080409 - 5 Aug 2026

To satisfy the constant-current/constant-voltage (CC/CV) charging requirements of electric-vehicle batteries, this paper investigates the extension of an established fixed-frequency reconfigurable three-coil wireless power transfer system using a modified receiver-side LCC-type network. The receiver-side shunt capacitor introduces a current-dividing path, so the receiver-coil current [...] Read more.

To satisfy the constant-current/constant-voltage (CC/CV) charging requirements of electric-vehicle batteries, this paper investigates the extension of an established fixed-frequency reconfigurable three-coil wireless power transfer system using a modified receiver-side LCC-type network. The receiver-side shunt capacitor introduces a current-dividing path, so the receiver-coil current differs from the equivalent rectifier-load current and the strict self-resonant value of the L₂—C₂ branch no longer provides the intended CC regulation. A current-division-aware fundamental-harmonic model is established, and a closed-form correction gives C₂ = 21.950 nF instead of the strict resonant value of 22.825 nF at 85 kHz. Calculated results show that the maximum CC target error over RL = 15–35 Ω is reduced from 8.948% to 0.031%, while the calculated CC/CV transition resistance is restored from 39.25 Ω to 34.97 Ω. Comparative analytical results are provided for the series-compensated baseline, the strictly resonant LCC-type configuration, and the corrected LCC-type configuration, together with receiver-current and coupling-sensitivity assessments. At RL = 35 Ω, the retained prototype measurements give an output voltage of 109.14 V, an output current of 3.121 A, an output power of 0.341 kW, and a DC-DC efficiency of 93.120%. The prototype measurements confirm the nominal operating target of the corrected design, while the analytical and numerical results clarify the modeling, correction, and current-stress tradeoffs introduced by the modified receiver network. Full article

(This article belongs to the Section Storage Systems)

► Show Figures

Figure 1

27 pages, 3798 KB

Open AccessArticle

Optimum Copula-Based Stochastic Planning of Electric Vehicle Fast-Charging Stations in Coupled Electric-Transport Networks

by Payam Farhadi, Seyed-Masoud Moghaddas-Tafreshi and Amir Shahirinia

World Electr. Veh. J. 2026, 17(8), 408; https://doi.org/10.3390/wevj17080408 - 4 Aug 2026

The increasing penetration of electric vehicles (EVs) introduces significant uncertainties into fast-charging station (FCS) planning due to the stochastic nature of EV charging behavior. Accurately representing these uncertainties is essential for making reliable planning decisions in coupled transportation–power networks. This paper proposes a [...] Read more.

The increasing penetration of electric vehicles (EVs) introduces significant uncertainties into fast-charging station (FCS) planning due to the stochastic nature of EV charging behavior. Accurately representing these uncertainties is essential for making reliable planning decisions in coupled transportation–power networks. This paper proposes a copula-based stochastic planning framework for the optimal allocation of FCSs while accounting for the correlated uncertainties associated with EV charging behavior. A multivariate copula model is employed to capture the dependency structure among key charging variables and generate realistic stochastic charging scenarios, which are subsequently incorporated into the EV charging load forecasting process over the planning horizon. Based on the resulting stochastic charging demand, a multi-objective optimization model is developed to simultaneously minimize investment costs and EV users’ travel distances, improve distribution network performance, and maximize environmental benefits through decarbonization. In addition, distributed generation (DG) units are optimally integrated to improve voltage profiles and reduce power losses. The proposed framework is implemented using MATLAB R2013a and R.4.0.2 and evaluated using both the IEEE 33-bus test system and a realistic 37-bus coupled transportation–power network in Meshgin-Shahr, Iran. The results demonstrate the effectiveness of the proposed stochastic planning framework in addressing uncertainties in EV charging behavior and identifying robust FCS deployment strategies. Full article

(This article belongs to the Collection Feature Papers in “Charging Infrastructure and Grid Integration” Section)

► Show Figures

Figure 1

20 pages, 2832 KB

Open AccessSystematic Review

Consumer Purchase Intention for Sustainable Passenger Vehicles: A Bibliometric and PRISMA-Guided Systematic Review of a Decade of Research (2015–2026)

by Radhhika Katyal, Shilpi Khandelwal and Namita Rajput

World Electr. Veh. J. 2026, 17(8), 407; https://doi.org/10.3390/wevj17080407 - 4 Aug 2026

The gap between consumers’ intent to buy electric cars and their actual purchasing behaviour of petrol cars has motivated researchers to conduct numerous investigations over the last decade. Nevertheless, with the explosive development of the field, bibliographic mapping remains a difficult task. This [...] Read more.

The gap between consumers’ intent to buy electric cars and their actual purchasing behaviour of petrol cars has motivated researchers to conduct numerous investigations over the last decade. Nevertheless, with the explosive development of the field, bibliographic mapping remains a difficult task. This study combines bibliometric analysis and a PRISMA-oriented systematic review of consumer purchase intentions towards sustainable vehicles. A systematic literature search in Scopus retrieved 1447 records between 2015 and June 2026, of which 706 empirical studies met the inclusion criteria for qualitative synthesis. Based on performance analysis and science mapping using VOS viewer software, one can conclude that the topic area develops at around 23%, gathers 43,758 citations, and shifts geographically over time. China contributes 418 papers, and India goes from publishing one piece in 2015 to occupying the third position overall. Keywords co-occurrence identifies five research clusters, whereas co-citations show that there are three intellectual bases for the field: behavioural theory, choice modelling, and methodological approaches based on the PLS-SEM framework. The results also emphasize some recurring gaps, namely the lack of a moderating effect of demographic characteristics, personality traits, intention-to-behaviour relationship, and longitudinal surveys. This study proposes an eight-point research agenda driven by keyword analysis. Full article

► Show Figures

Graphical abstract

19 pages, 1037 KB

Open AccessArticle

Understanding the Acceptance of Vehicle-to-Grid (V2G) Services: Evidence from Chongqing, China

by Qi Chen, Wenli Fan, Jian Chen and Yin Pan

World Electr. Veh. J. 2026, 17(8), 406; https://doi.org/10.3390/wevj17080406 - 4 Aug 2026

Amid global energy demand escalation, renewable energy intermittency, and electric vehicle (EV) charging demand concentration exacerbating power grid supply–demand contradictions, Vehicle-to-Grid (V2G) emerges as a solution, yet EV users’ V2G acceptance and participation willingness lack in-depth exploration. This study aims to fill this [...] Read more.

Amid global energy demand escalation, renewable energy intermittency, and electric vehicle (EV) charging demand concentration exacerbating power grid supply–demand contradictions, Vehicle-to-Grid (V2G) emerges as a solution, yet EV users’ V2G acceptance and participation willingness lack in-depth exploration. This study aims to fill this research gap by investigating Chongqing EV users’ V2G acceptance, behavioral intention, and influencing mechanisms to provide support for V2G promotion. It targets EV owners in Chongqing’s downtown areas, collecting 295 valid questionnaires, covering users’ demographics, travel-charging habits, and subjective attitudes. Based on technology acceptance and usage theories, it constructs a structural equation model (SEM) with perceived usefulness, ease of use, economic viability, and technological risk as latent variables to analyze their impacts on behavioral intention. Results show that perceived usefulness, perceived ease of use, and economic benefits positively affect behavioral intention, while technology risk perception exerts a negative effect; users with fixed commutes, low-range anxiety, and home charging piles are more receptive, and 70% support V2G but worry about battery wear and plug-in duration. Its innovation lies in integrating EV charging–discharging and travel patterns into the analysis, and its findings enrich new energy technology acceptance theory and provide a theoretical basis for transportation-energy system coordinated planning and V2G development. Full article

(This article belongs to the Section Marketing, Promotion and Socio Economics)

► Show Figures

Figure 1

20 pages, 1138 KB

Open AccessArticle

The Impact of Government Subsidies on R&D Investment of New Energy Vehicle Enterprises

by Jun Liu

World Electr. Veh. J. 2026, 17(8), 405; https://doi.org/10.3390/wevj17080405 - 3 Aug 2026

New energy vehicles constitute a crucial component of low-carbon economic systems and green development initiatives. Supported by government subsidy policies, the new energy vehicle industry has achieved remarkable development in recent years. This study conducts an empirical analysis based on panel data of [...] Read more.

New energy vehicles constitute a crucial component of low-carbon economic systems and green development initiatives. Supported by government subsidy policies, the new energy vehicle industry has achieved remarkable development in recent years. This study conducts an empirical analysis based on panel data of 93 listed new energy vehicle enterprises from 2012 to 2022 to explore the impacts of government subsidies on corporate R&D investment. Using Stata 17.0, we use return on assets, debt-to-asset ratio, enterprise size and operating efficiency as control variables. A two-way fixed-effect model is selected via the Hausman test, followed by linear regression analysis. Furthermore, a dynamic panel vector autoregression (PVAR) model is employed to examine the dynamic interaction between government subsidies and corporate R&D investment. This research perspective overcomes the limitations of traditional static innovation policy research, effectively supplements the empirical evidence on long-term policy incentive effects in the new energy vehicle industry, and enriches the theoretical and empirical literature on the intrinsic dynamic correlation between government subsidies and corporate innovation investment. The empirical results show that government subsidies exert a significantly positive effect on firms’ R&D investment, and that there exists a stable long-term two-way positive interaction and dynamic equilibrium between the two. However, such mutual promotion effects are economically weak in magnitude, and the long-term evolutionary trends of both variables are predominantly dominated by their respective internal self-reinforcing inertia. In view of the limited incentive contributions of existing subsidy policies, the results of this study suggest the need to optimize the precision and targeting of government subsidy mechanisms to amplify policy incentive efficiency, while enterprises should fully leverage their endogenous R&D inertia to strengthen their independent innovation capabilities. The presented findings provide empirical evidence and policy guidance for the promotion of stable R&D innovation and high-quality development of the new energy vehicle industry. Full article

(This article belongs to the Section Marketing, Promotion and Socio Economics)

► Show Figures

Figure 1

27 pages, 4952 KB

Open AccessArticle

Research on a Boomerang Aerodynamic Ellipse Optimization Algorithm–Informer–Autoregressive Integrated Moving Average-Based Forecasting Model for New Energy Vehicle Sales in China

by Shiming Lin, Wenhao Liu, Zhiyi Pang and Yi Li

World Electr. Veh. J. 2026, 17(8), 404; https://doi.org/10.3390/wevj17080404 - 3 Aug 2026

To improve the accuracy and stability of new energy vehicle (NEV) sales forecasting in China, this study develops a hybrid forecasting framework integrating the Informer model, autoregressive integrated moving average (ARIMA), and the Boomerang Aerodynamic Ellipse Optimization (BAEO) algorithm. Monthly data from January [...] Read more.

To improve the accuracy and stability of new energy vehicle (NEV) sales forecasting in China, this study develops a hybrid forecasting framework integrating the Informer model, autoregressive integrated moving average (ARIMA), and the Boomerang Aerodynamic Ellipse Optimization (BAEO) algorithm. Monthly data from January 2016 to December 2023 covering 31 provincial-level administrative regions in China (excluding Hong Kong, Macao, and Taiwan) were collected from authoritative statistical sources. A multidimensional feature system was established by incorporating factors related to charging infrastructure, transportation demand, market development, and environmental conditions. Data preprocessing techniques, including Min–Max normalization, lagged variables, rolling statistical features, and seasonal sine–cosine encoding, were applied to capture temporal dependencies and periodic patterns. The BAEO algorithm was employed to optimize the key hyperparameters of the Informer model, while the ARIMA model was introduced to correct linear patterns in forecasting residuals. The proposed BAEO–Informer–ARIMA framework was evaluated against seasonal autoregressive integrated moving average (SARIMA), Prophet, extreme gradient boosting (XGBoost), long short-term memory (LSTM), gated recurrent unit (GRU), Transformer, and Informer models under the same chronological evaluation strategy. Results show that the proposed framework achieved superior forecasting performance, with a coefficient of determination (R²) of 0.9544, mean absolute error (MAE) of 24,068, root mean square error (RMSE) of 26,822, and mean absolute percentage error (MAPE) of 3.39%. Furthermore, uncertainty analysis based on rolling-validation forecast errors was conducted to establish a 90% confidence interval for future projections. Forecast results for 2024–2030 reveal sustained NEV sales growth with gradually decreasing growth rates and persistent seasonal variations. This study provides quantitative insights for NEV market planning, charging infrastructure deployment, and low-carbon policy formulation. Full article

(This article belongs to the Section Marketing, Promotion and Socio Economics)

► Show Figures

Figure 1

28 pages, 4076 KB

Open AccessReview

New Energy Vehicles and Charging and Battery-Swapping Infrastructure: Development Patterns, Policy Drivers, and the Evolution of Vehicle–Grid Interaction

by Bo Zhao, Zhihang Ren, Zhibin Liu, Peng Yang, Zhiheng Liu, Changpeng Hu, Nahan Hao, Xiaoyin Ding and Lei Li

World Electr. Veh. J. 2026, 17(8), 403; https://doi.org/10.3390/wevj17080403 - 3 Aug 2026

The rapid expansion of electric mobility is reshaping both transport infrastructure and power-system operation. This narrative and critical review examines the connected evolution of new energy vehicle (NEV) markets, charging and battery-swapping infrastructure, policy mechanisms, and vehicle-to-grid (V2G) systems. In this paper, NEV [...] Read more.

The rapid expansion of electric mobility is reshaping both transport infrastructure and power-system operation. This narrative and critical review examines the connected evolution of new energy vehicle (NEV) markets, charging and battery-swapping infrastructure, policy mechanisms, and vehicle-to-grid (V2G) systems. In this paper, NEV includes battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), and fuel-cell electric vehicles (FCEVs); conventional non-plug-in hybrid electric vehicles are discussed only where regional statistics require clarification. Peer-reviewed studies, official statistics, policy documents, market reports, and technical standards available through June 2026 are synthesized thematically and compared across China, Europe, the United States, and selected emerging markets. The review distinguishes verified 2025 observations from scenario-based projections, evaluates policy instruments by their outcomes and limitations, and extends the V2G discussion to bidirectional charger requirements, interoperability, aggregation, DSO-TSO coordination, battery degradation, cybersecurity, and economic viability. Unlike reviews centered on a single technology or region, the proposed market–infrastructure–policy–V2G framework explains how market structure, infrastructure governance, standards, and electricity-market design jointly shape commercialization pathways. The synthesis indicates that infrastructure scale alone is insufficient: utilization, grid hosting capacity, interoperable communication, credible revenue stacking, and equitable access determine whether charging, battery swapping, and V2G can deliver system-level value. Full article

(This article belongs to the Section Charging Infrastructure and Grid Integration)

► Show Figures

Figure 1

More Articles...

Submit to WEVJ Review for WEVJ

Journal Menu

Journal Browser

► Journal Browser

Highly Accessed Articles

View More...

Latest Books

More Books and Reprints...

E-Mail Alert

News

5 August 2026
MDPI INSIGHTS: The CEO’s Letter #37 – Canada Summit, Sciforum Relaunch, 30 Years of Impactful Research & ISPRS 2026

31 July 2026
World Electric Vehicle Journal | Notable Papers in the Field of Microgrid Integration

31 July 2026
World Electric Vehicle Journal | Notable Papers in the Field of Second-Life Batteries

More News & Announcements...

Topics

Propose a Topic

Conferences

Propose a Conference Collaboration

21–24 August 2026 Meet Us at the 12th World Sustainability Forum, 21–24 August 2026, Hong Kong, China

12–14 April 2027 International Conference on Intelligent Electronics and Micro/Nanosystems (Intelectro)

21–23 August 2026 2026 6th Power System and Green Energy Conference (PSGEC 2026)

More Conferences...

Special Issues

Propose a Special Issue

Special Issue in WEVJ

Motion Planning and Control of Autonomous Vehicles: 2nd Edition Guest Editor: Guoxing Bai
Deadline: 15 January 2027

Special Issue in WEVJ

EVS39—International Electric Vehicle Symposium and Exhibition Guest Editor: Peter Van den Bossche
Deadline: 31 January 2027

Special Issue in WEVJ

SMART 2026: Electric Machines Drives Applied in Transportation Electrification Guest Editors: Zi-Qiang Zhu, Ahmed Masmoudi, Ayman EL-Refaie
Deadline: 10 February 2027

Special Issue in WEVJ

Electric and Hydrogen Mobility: Charging, Energy Storage, and Sustainable Transportation Guest Editor: Aritra Ghosh
Deadline: 20 February 2027

More Special Issues

Topical Collections

Topical Collection in WEVJ

Feature Papers in Propulsion Systems and Components in Electric Vehicle Collection Editors: Martin Doppelbauer, Paulo J. G. Pereirinha, Zhongze Wu

Topical Collection in WEVJ

Feature Papers in “Charging Infrastructure and Grid Integration” Section Collection Editor: Peter Van den Bossche

Back to TopTop