World Electric Vehicle Journal

38 pages, 1913 KB

Open AccessArticle

Development of a Hybrid Particle Whale Optimization Algorithm for Electric Vehicle Battery Thermal Runaway Prediction

by Buasa Andy Mayingi, Bonginkosi A. Thango and Daniel Okojie

World Electr. Veh. J. 2026, 17(7), 354; https://doi.org/10.3390/wevj17070354 - 10 Jul 2026

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, [...] Read more.

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

(This article belongs to the Section Storage Systems)

29 pages, 10302 KB

Open AccessArticle

Load Profiles of Charging Stations for Long-Haul Electric Trucks

by Michele Garau, Ida Buttingsrud Stokke and Odd André Hjelkrem

World Electr. Veh. J. 2026, 17(7), 353; https://doi.org/10.3390/wevj17070353 - 9 Jul 2026

Abstract

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 [...] Read more.

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

(This article belongs to the Special Issue EVS38—International Electric Vehicle Symposium and Exhibition (Gothenburg, Sweden))

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18 pages, 2972 KB

Open AccessArticle

Coordinated Regulation Strategy for Electric Vehicles and Air-Conditioning Based on a Stackelberg–Evolutionary Game Framework

by Lu Xie, Jun Li, Feng Yang and Ye Li

World Electr. Veh. J. 2026, 17(7), 352; https://doi.org/10.3390/wevj17070352 - 8 Jul 2026

Abstract

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, [...] Read more.

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 57.9% while reducing users’ electricity costs by 41.2%, thereby validating its effectiveness. Full article

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

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16 pages, 1183 KB

Open AccessReview

Vehicle Grid Integration with Smart Meters Data in Europe: A Review on Current and Future Challenges to Enable Advanced Smart Charging Schemes and Flexibility Services

by Andrea Cazzaniga, Filippo Colzi, Michele Garau, Tesfaye Amare Zerihun, Josh Eichman, Antonio Pepiciello, Mattia Secchi, Mattia Marinelli, Aytug Yavuzer and Antonello Monti

World Electr. Veh. J. 2026, 17(7), 351; https://doi.org/10.3390/wevj17070351 - 8 Jul 2026

Abstract

Considering that smart meter roll-out has already been completed in several European countries for some years now, this review assesses the current state and future opportunities for the direct integration of commercial wallboxes and smart meters in Europe. Despite successful smart meter roll-outs, [...] Read more.

Considering that smart meter roll-out has already been completed in several European countries for some years now, this review assesses the current state and future opportunities for the direct integration of commercial wallboxes and smart meters in Europe. Despite successful smart meter roll-outs, direct integration remains challenging: while commercial wallboxes are sold on international markets and follow recognized standards, installed smart meters and related cloud platforms are mostly national or regional products, and grid operators have developed proprietary technologies to support their own Advanced Metering Infrastructures (AMI). Here, we first advocate the case for direct integration, noting that it is particularly well suited for local load management when EVs are the only flexible loads and for the provision of novel flexibility services based on real-time grid signals. We then review smart meters data exchange protocols and communication interfaces and identify the common issues hindering effective smart meters exploitation. We eventually propose a set of recommendations to tackle current smart metering infrastructures limitations and unlock their identified potential. Full article

(This article belongs to the Special Issue EVS38—International Electric Vehicle Symposium and Exhibition (Gothenburg, Sweden))

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31 pages, 809 KB

Open AccessArticle

How Should Chinese Administrative Agencies Protect Data Security in Autonomous Driving?

by Yajie Wang, Haojie Tang and Chunlin Li

World Electr. Veh. J. 2026, 17(7), 350; https://doi.org/10.3390/wevj17070350 - 6 Jul 2026

Abstract

The continuous collection of road information by autonomous vehicles (AVs) has intensified regulatory pressure on data security protection. At present, the Chinese government adopts a proactive stance on protecting AV data security. Nevertheless, relevant requirements are scattered across various regulatory regimes, including data [...] Read more.

The continuous collection of road information by autonomous vehicles (AVs) has intensified regulatory pressure on data security protection. At present, the Chinese government adopts a proactive stance on protecting AV data security. Nevertheless, relevant requirements are scattered across various regulatory regimes, including data security, cybersecurity, personal information protection and AV access regulation. It has given rise to ambiguous judgement criteria and inconsistent law enforcement practices among local authorities. As a leading developed economy worldwide, the European Union has continuously refined legislation on data security protection for AVs since 2018, establishing a globally sophisticated protective framework. Against this backdrop, this paper adopts comparative analysis and normative analysis to focus on examining the characteristics of the EU’s advanced rules. The research reveals that the EU has integrated risk prevention, monitoring and reporting mechanisms into a unified regulatory framework. It implements market access and safety assessment before operation, conducts ongoing safety management during operation, and launches data reporting and recall procedures after operation. After evaluating the applicability of the EU model in China, this paper suggests China adopt phased AV data security rules. Governance should focus on early detection of data breach risks before operation, real-time data monitoring during operation, and data reporting after operation. This proposal clarifies responsibilities among regulators, automakers and data service providers, improves the predictability and enforceability of relevant governance and facilitates safe, innovative and sound development of the autonomous driving industry. Full article

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

34 pages, 1842 KB

Open AccessReview

Vehicle-to-Grid Systems for Renewable Energy Integration: Scheduling, Economics, and User Engagement

by Peiying Zhang, Xiangguo Zheng, Yujie Yuan, Xi Chen and Chun Sing Lai

World Electr. Veh. J. 2026, 17(7), 349; https://doi.org/10.3390/wevj17070349 - 6 Jul 2026

Abstract

With the rapid growth of electric vehicles (EVs) and renewable energy generation, Vehicle-to-Grid (V2G) technology has emerged as a promising approach for transforming EVs from passive charging loads into flexible distributed energy storage resources. By enabling bidirectional power exchange between EV batteries and [...] Read more.

With the rapid growth of electric vehicles (EVs) and renewable energy generation, Vehicle-to-Grid (V2G) technology has emerged as a promising approach for transforming EVs from passive charging loads into flexible distributed energy storage resources. By enabling bidirectional power exchange between EV batteries and the power grid, V2G can support renewable energy accommodation, peak shaving, demand response, ancillary services, and local grid balancing. This review provides a systematic synthesis of recent advances in V2G systems for renewable energy integration, with particular emphasis on coordinated scheduling, economic mechanisms, battery degradation, and user engagement. First, the technical foundations of V2G are introduced, including Vehicle-to-Everything operating modes, bidirectional charging architecture, aggregation mechanisms, grid-support services, and renewable accommodation pathways. Second, major scheduling strategies are reviewed, including price-based, load-based, renewable-forecast-driven, centralized, distributed, and hybrid approaches. Third, the economic feasibility of V2G is examined from the perspectives of revenue streams, pricing mechanisms, business models, battery aging costs, and compensation schemes. In addition, user participation barriers, such as range anxiety, battery lifetime concerns, loss of control, uncertain financial returns, and data privacy, are discussed. Key challenges related to communication standards, interoperability, cybersecurity, market access, policy design, and pilot-scale validation are also summarized. Finally, future development directions are identified, including AI-based scheduling, aggregator platforms, fleet-scale V2G, degradation-aware optimization, carbon-aware electricity markets, and user-centered participation mechanisms. This review highlights that large-scale V2G deployment requires the integrated coordination of technical scheduling, economic incentives, battery health protection, and user acceptance in renewable-rich power systems. Full article

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

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38 pages, 6961 KB

Open AccessArticle

Reliability Analysis of a PCM–Liquid Hybrid Battery Thermal Management System for Electric Vehicles

by Shujaat Husain, Haroon Ashfaq, Mohammad Asjad, Pratibha Kumari and Rajeev Kumar

World Electr. Veh. J. 2026, 17(7), 348; https://doi.org/10.3390/wevj17070348 (registering DOI) - 6 Jul 2026

Abstract

Electric vehicles (EVs) utilize batteries that generate thermal energy during charging and discharging processes. Inadequate heat management can result in thermal runaway, which is marked by a rapid and uncontrolled increase in battery temperature and may lead to fire or explosion. Battery Thermal [...] Read more.

Electric vehicles (EVs) utilize batteries that generate thermal energy during charging and discharging processes. Inadequate heat management can result in thermal runaway, which is marked by a rapid and uncontrolled increase in battery temperature and may lead to fire or explosion. Battery Thermal Management Systems (BTMSs) are implemented to reduce the risk of thermal runaway by maintaining battery temperature within a defined safe operating range. Hybrid BTMS configurations integrate multiple cooling methods to enhance operational effectiveness and reliability, surpassing the performance of conventional liquid coolant systems. This work investigates the enhanced efficiency of an integrated phase change material (PCM)–liquid hybrid approach evaluated under severe 3C discharge conditions over a 10,000 h operational reliability window. Our study carefully investigates the system-level reliability using a Functional Fault Tree Analysis (FTA) framework to classify top, intermediate, and basic events. Multi-physics and probabilistic evaluation results demonstrate that the hybrid system achieves a 5 °C reduction in peak cell temperature and a 3 °C improvement in spatial temperature uniformity compared to standard liquid cooling. Reliability assessment establishes a system top-event failure probability of 6% over the 10,000 h window, identifying the coolant pump as the primary failure bottleneck with an individual contribution of 27%. These quantitative insights advance our understanding of hybrid safety architectures, providing essential baseline metrics for future electric vehicle thermal management development. Full article

(This article belongs to the Section Storage Systems)

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31 pages, 2330 KB

Open AccessArticle

Future Projections of Lifecycle Cost and Greenhouse Gas Emissions of Light-Duty Vehicles

by Karim Hamza, Kenneth Laberteaux, Kang-Ching Chu and Peter Benoliel

World Electr. Veh. J. 2026, 17(7), 347; https://doi.org/10.3390/wevj17070347 - 3 Jul 2026

Abstract

Vehicles with electrified powertrains carry the promise of significant reductions in greenhouse gas (GHG) emissions from a lifecycle analysis (LCA) standpoint compared to conventional internal combustion engine (CICE) vehicles. However, trade-offs exist between different types of electrified powertrains in terms of cost, consumer [...] Read more.

Vehicles with electrified powertrains carry the promise of significant reductions in greenhouse gas (GHG) emissions from a lifecycle analysis (LCA) standpoint compared to conventional internal combustion engine (CICE) vehicles. However, trade-offs exist between different types of electrified powertrains in terms of cost, consumer acceptance, and GHG reduction efficacy for different operating conditions. The open-source tool CarGHG was developed with an aim to enable the exploration of a plethora of parametric study scenarios, including the cost of electrification technologies, different driving patterns and charging habits, and the cost and carbon intensity of electricity and fuel blends. This paper introduces the framework of CarGHG, then showcases total cost of ownership (TCO) and LCA GHG results for select models of light-duty vehicles. Another capability of CarGHG, which is the ability to estimate the performance of “virtual” vehicle models (perceived vehicle design specifications not yet on the market), is utilized to explore future scenarios of electrification and low-carbon fuel blends for Small Sports Utility Vehicles (SUVs), a popular light-duty vehicle segment in North America. With opportunities, but also uncertainties, in future scenarios, it is likely wise to continue pursuing multiple ways towards the reduction of LCA GHG. Full article

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

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19 pages, 2146 KB

Open AccessArticle

Configuration Analysis and Path Optimization of Digital Economic Empowerment for the New Energy Vehicle Industry Chain Security

by Chagen Luo, Deyang Kong and Jinsuo Zhou

World Electr. Veh. J. 2026, 17(7), 346; https://doi.org/10.3390/wevj17070346 - 3 Jul 2026

Abstract

The security of new energy vehicle (NEV) industry chains has become a strategic issue for industrial competitiveness, the energy transition, and economic security. This study examines how digital economy capabilities jointly support NEV industry chain security across 30 provincial-level administrative regions in China. [...] Read more.

The security of new energy vehicle (NEV) industry chains has become a strategic issue for industrial competitiveness, the energy transition, and economic security. This study examines how digital economy capabilities jointly support NEV industry chain security across 30 provincial-level administrative regions in China. Drawing on Organizational Information Processing Theory and Dynamic Capability Theory, we conceptualize artificial intelligence capability (AIC), big data analytics capability (BDA), cloud computing infrastructure (CCI), and blockchain application level (BCL) as complementary information-processing and reconfiguration capabilities. We combine Necessary Condition Analysis (NCA), fuzzy-set Qualitative Comparative Analysis (fsQCA), and Random Forest/SHAP analysis. The revised results show that AIC is a practically necessary condition for supply chain resilience, BDA is a necessary condition for achieving a high cybersecurity level, and BCL is a dimension-specific necessary condition for data security. Four sufficient configurational paths—technology-driven, data-driven, infrastructure-driven, and security-synergistic—lead to high comprehensive NEV industry chain security. Robustness checks using alternative calibration anchors and consistency thresholds show that the core configurations are stable. A revised machine learning specification using only digital economy predictors confirms the high relative importance of AIC. It also shows that the marginal contribution of AIC tends to flatten beyond the upper-middle range. The findings provide a configurational and regionally differentiated perspective on digital economy empowerment while avoiding overgeneralization beyond the Chinese provincial context. Full article

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

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18 pages, 5809 KB

Open AccessArticle

Flow and Atomization Characteristics of Biodiesel in Equilateral Triangular Nozzles with Different Side Lengths Under Ultra-High Pressure

by Bokai Su, Sunyang Zhang and Zhihua Li

World Electr. Veh. J. 2026, 17(7), 345; https://doi.org/10.3390/wevj17070345 - 3 Jul 2026

Abstract

Facing the stringent demands of ultra-high pressure fuel injection systems on atomization quality and mixing efficiency, non-circular nozzle geometries have shown significant potential. Biodiesel, as a renewable alternative fuel, suffers from poor atomization due to its high viscosity, low volatility, and large surface [...] Read more.

Facing the stringent demands of ultra-high pressure fuel injection systems on atomization quality and mixing efficiency, non-circular nozzle geometries have shown significant potential. Biodiesel, as a renewable alternative fuel, suffers from poor atomization due to its high viscosity, low volatility, and large surface tension, posing greater challenges for injector design. Among non-circular designs, the equilateral triangular orifice offers distinct advantages in promoting atomization of high-viscosity fuels and inducing jet axis-switching. This study demonstrates that such triangular nozzles under ultra-high pressure conditions exhibit intense turbulent vorticity at the outlet and distinctive cavitation development, which significantly affect the primary breakup of biodiesel. During spray development, a pronounced axis-switching behavior is observed, characterized by alternating spray cone angles between the major and minor axes. This phenomenon intensifies with higher injection pressure but is mitigated by increased ambient backpressure. The comparative analysis quantitatively establishes these macro–micro coupling characteristics over ultra-high injection pressures of 160–200 MPa, using fixed orifice lengths of 1.5 mm across exit cross-sectional areas ranging from 24,942 to 29,272 μm². The axis-switching process is accompanied by vigorous air entrainment, which significantly enlarges the spray projected area, accelerates liquid breakup, and shortens penetration distance, collectively enhancing the mixing rate and uniformity of biodiesel with air. This work systematically investigates the atomization characteristics and axis-switching behavior of equilateral triangular orifices with varying side lengths when injecting biodiesel under ultra-high pressure conditions, providing an effective technical pathway for the active control of spray morphology and atomization enhancement of biodiesel. Full article

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

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25 pages, 585 KB

Open AccessArticle

Electric Vehicle Infrastructure Deployment in the Mid-Atlantic Region: Comparative Evolution of NEVI Implementation from 2022 to 2026

by Saddam Alkhamaiesh

World Electr. Veh. J. 2026, 17(7), 344; https://doi.org/10.3390/wevj17070344 - 2 Jul 2026

Abstract

The National Electric Vehicle Infrastructure (NEVI) Program is a major federal initiative to expand electric vehicle (EV) charging infrastructure and support transportation electrification in the United States. This study examines the evolution of NEVI implementation across New York, New Jersey, and Pennsylvania between [...] Read more.

The National Electric Vehicle Infrastructure (NEVI) Program is a major federal initiative to expand electric vehicle (EV) charging infrastructure and support transportation electrification in the United States. This study examines the evolution of NEVI implementation across New York, New Jersey, and Pennsylvania between 2022 and 2026. A qualitative comparative longitudinal approach was used to analyze 23 official documents, including NEVI deployment plans, annual implementation updates, Federal Highway Administration guidance, and Joint Office of Energy and Transportation resources. The findings show that implementation evolved beyond compliance with the Alternative Fuel Corridor toward broader transportation electrification, characterized by adaptive governance, infrastructure scalability, and operational resilience. New York demonstrated the most advanced implementation through extensive interagency coordination, infrastructure integration, and long-term planning. New Jersey emphasized metropolitan charging accessibility, adaptive planning, and alignment with statewide zero-emission vehicle objectives. Pennsylvania followed a more gradual implementation trajectory shaped by phased deployment, regional accessibility priorities, and procurement-related challenges. The study demonstrates that implementation trajectories differed despite a common federal framework and contributes to the literature by providing a comparative longitudinal perspective on how governance and institutional adaptation influence large-scale EV infrastructure deployment. Full article

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

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16 pages, 15499 KB

Open AccessArticle

Study on Torque Production, Eddy Current Loss, and Demagnetization in Spoke-Type FI-IPM Motor Adopting Segmented Permanent Magnet Configurations

by Viet-Vu Do, Duc-Kien Ngo, Minh-Hoc Le Duong, Min-Fu Hsieh, Ho Quang Viet, Hong Viet Phuong Nguyen and Nguyen Gia Minh Thao

World Electr. Veh. J. 2026, 17(7), 343; https://doi.org/10.3390/wevj17070343 - 2 Jul 2026

Abstract

This paper investigates the impact of segmented permanent magnet (PM) configurations on torque production, eddy current loss, and demagnetization in spoke-type flux-intensifying interior permanent magnet (FI-IPM) motors. While PM segmentation has been explored in conventional interior permanent magnet synchronous motors (IPMSMs) for reducing [...] Read more.

This paper investigates the impact of segmented permanent magnet (PM) configurations on torque production, eddy current loss, and demagnetization in spoke-type flux-intensifying interior permanent magnet (FI-IPM) motors. While PM segmentation has been explored in conventional interior permanent magnet synchronous motors (IPMSMs) for reducing losses, its effect in flux-intensifying (FI) motors, characterized by reverse saliency, remains underexplored. To address this, five rotor designs with segmented PMs are analyzed against a baseline model using finite element analysis, maintaining identical stator and PM volume. Results show that segmentation increases reluctance torque, compensating for reduced PM torque, while simultaneously lowering eddy current loss and enhancing demagnetization resistance. These improvements validate segmented PMs as a viable strategy to enhance the durability and efficiency of FI-IPM motors for electric vehicle applications. Full article

(This article belongs to the Collection Feature Papers in Propulsion Systems and Components in Electric Vehicle)

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19 pages, 1985 KB

Open AccessArticle

Reproducible State-of-Charge and Range Evaluation of a 350 W Electric Scooter Under an Urban NEDC Driving Cycle

by Juan C. Castro-Galeano, Edgar E. Tibaduiza-Rincon and Freddy F. Valderrama

World Electr. Veh. J. 2026, 17(7), 342; https://doi.org/10.3390/wevj17070342 - 30 Jun 2026

Abstract

This article presents an experimental–computational methodology for evaluating the state of charge (SoC), energy consumption, terminal-voltage behavior, and driving range of a 350 W electric scooter powered by a 36 V, 7.8 Ah lithium-ion battery. The test was carried out using a 117 [...] Read more.

This article presents an experimental–computational methodology for evaluating the state of charge (SoC), energy consumption, terminal-voltage behavior, and driving range of a 350 W electric scooter powered by a 36 V, 7.8 Ah lithium-ion battery. The test was carried out using a 117 s elementary urban driving cycle derived from the low-speed section of the New European Driving Cycle (NEDC) and limited to the 32 km/h operating speed of the scooter. Laboratory measurements were performed on rollers under controlled conditions. Battery current and terminal voltage were recorded during the discharge test. The experimental SoC was reconstructed from the measured current by trapezoidal Coulomb counting. The voltage-derived SoC values included in the original laboratory file were kept only for traceability, since they did not correspond to current integration. A MATLAB/Simulink model was developed to reproduce the driving cycle, longitudinal vehicle dynamics, DC motor demand, battery current, and SoC evolution. The valid experimental endpoint occurred at 5233 s, when the terminal voltage reached 31.50 V. At this point, the tested distance was 16.49 km, the discharged capacity was 5.817 Ah, and the final experimental SoC was 25.42%. The simulation produced a discharged capacity of 5.147 Ah and a final SoC of 34.01%, with a charge deviation of 11.51%. Energy consumption was also evaluated from the measured and simulated electrical power. The experimentally integrated discharged energy was 208.10 Wh, equivalent to 12.62 Wh/km. The simulated electrical demand was 184.41 Wh, equivalent to 11.18 Wh/km. A semiempirical terminal-voltage reconstruction, based on the simulated SoC, current demand, an open-circuit-voltage curve, and a fixed internal resistance, reproduced the global voltage-decay trend observed in the experiment. The simplified model captured the general discharge behavior, although it underestimated the measured charge and energy demand. The proposed workflow provides a reproducible basis for comparing manufacturer-declared range, laboratory measurements, current-based SoC reconstruction, energy consumption, and simplified simulation results in light electric vehicles. Full article

(This article belongs to the Section Storage Systems)

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29 pages, 2468 KB

Open AccessArticle

Public Perceptions of Electric Vehicle Adoption in Kuwait: The Role of Low Electricity Tariffs, Charging Constraints, and Fire-Safety Concerns

by Saad Almutairi, Mubarak Alrumaidhi and Hamad Matar

World Electr. Veh. J. 2026, 17(7), 341; https://doi.org/10.3390/wevj17070341 - 30 Jun 2026

Abstract

This study examines public perceptions of electric vehicle (EV) adoption in Kuwait, a high-income petroleum-dependent country characterized by highly subsidized electricity, low fuel prices, limited charging infrastructure, and extreme climatic conditions. Using a structured survey of 1753 licensed drivers, the study evaluates how [...] Read more.

This study examines public perceptions of electric vehicle (EV) adoption in Kuwait, a high-income petroleum-dependent country characterized by highly subsidized electricity, low fuel prices, limited charging infrastructure, and extreme climatic conditions. Using a structured survey of 1753 licensed drivers, the study evaluates how economic incentives, practical constraints, environmental perceptions, technological confidence, and safety concerns shape expectations of future EV diffusion. Descriptive statistics, principal component analysis, and ordinal logistic regression were used to examine the factors associated with respondents’ expectation of widespread EV adoption in Kuwait over the next ten years. The regression results show that low-tariff/delayed-bill perception was the strongest positive predictor of expected EV adoption, indicating that Kuwait’s low-cost electricity environment may strengthen expectations of EV diffusion. However, the findings also demonstrate that electricity tariffs alone do not explain public expectations. EV performance perception, environmental benefit perception, workplace charging, battery warranty, prior passenger experience in an EV, and higher weekly fuel expenditure were also positively associated with stronger expectations of EV adoption. In contrast, perceived complexity was negatively associated with expected adoption, highlighting the importance of consumer familiarity and ease of use. Safety-related perceptions, particularly concerns regarding EV fire-extinguishing difficulty and lower perceived safety compared with conventional vehicles, were also significant, suggesting that fire safety remains a salient issue in the Kuwaiti context. The findings contribute to the literature on sustainable transportation adoption in petroleum-based economies and extreme climates by showing that EV diffusion depends on a combination of economic, infrastructural, technological, environmental, and safety-related factors. Policy efforts in Kuwait should therefore combine charging-infrastructure development, workplace charging expansion, consumer education, battery-warranty assurance, and EV-specific safety and emergency-response measures. Full article

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

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25 pages, 594 KB

Open AccessArticle

Driving the Mass Market: How Infrastructure Readiness and User Experience Shape Consumer Valuation of Electric Vehicles in Thailand

by Adisak Suvittawat and Nutchanon Suvittawat

World Electr. Veh. J. 2026, 17(7), 340; https://doi.org/10.3390/wevj17070340 - 29 Jun 2026

Abstract

Electric vehicles (EVs) are increasingly recognized as a sustainable transportation solution; however, mass-market adoption in Thailand remains limited due to infrastructure constraints, technological complexity, and evolving consumer perceptions. This study examines the effects of charging infrastructure accessibility, perceived ease of use, and psychological [...] Read more.

Electric vehicles (EVs) are increasingly recognized as a sustainable transportation solution; however, mass-market adoption in Thailand remains limited due to infrastructure constraints, technological complexity, and evolving consumer perceptions. This study examines the effects of charging infrastructure accessibility, perceived ease of use, and psychological driving experience on consumers’ willingness to pay (WTP) for EVs. A quantitative approach was employed using survey data collected from 400 EV users and analyzed through Structural Equation Modeling (SEM). Grounded in the Technology Acceptance Model (TAM) and the Theory of Consumption Values (TCV), the study reveals that charging infrastructure accessibility significantly enhances perceived ease of use, driving experience, and WTP. In addition, perceived ease of use and driving experience positively influence consumers’ financial commitment toward EV adoption and partially mediate the relationship between infrastructure accessibility and WTP. The findings indicate that EV consumer valuation is shaped by both functional infrastructure readiness and psychological user experience. The study contributes to EV consumer behavior literature by integrating cognitive and experiential perspectives and provides practical implications for policymakers and industry stakeholders seeking to accelerate EV adoption in Thailand. Full article

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

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36 pages, 4513 KB

Open AccessArticle

An Intelligent Profiling and Classification Method for Load Adjustment Potential of Multi-Type Demand-Side Resources Considering Adjustment Willingness

by Can Wang, Xuesong Shao, Shihai Yang, Huiling Su and Yingwen Zhu

World Electr. Veh. J. 2026, 17(7), 339; https://doi.org/10.3390/wevj17070339 - 29 Jun 2026

Abstract

The rapid development of new energy has caused a sharp increase in the stochasticity on the source side of the new power system (NPS), and extreme weather along with climate variability have also led to increased stochasticity in power demand on the load [...] Read more.

The rapid development of new energy has caused a sharp increase in the stochasticity on the source side of the new power system (NPS), and extreme weather along with climate variability have also led to increased stochasticity in power demand on the load side; thus, how to achieve source-load matching and enable the load to track the source under the new situation is the key to the efficient operation of the power system. Aiming at the problem that existing load regulation potential evaluation mainly focuses on physical capacity, making it difficult to reflect users’ subjective willingness to participate as well as the dynamic changes in regulation capability under different operating scenarios, this paper proposes a two-stage dynamic profiling classification method for multi-type power user loads considering regulation willingness. First, an evaluation index system is constructed from three dimensions, physical reliability, execution reliability, and behavioral willingness, to achieve the unified characterization of the regulation capabilities of heterogeneous resources such as industrial loads and electric vehicle (EV) aggregators. Second, the DBSCAN algorithm is adopted to identify typical annual operating scenarios. Finally, the Dynamic Time Warping (DTW) distance is introduced to improve the K-Means++ algorithm, achieving the profiling classification of user regulation potential. This paper takes a certain NPS demonstration park as an example for verification, and the results show that the annual operating scenarios can be divided into 4 types of typical days; the proposed DTW-K-Means++ method has better classification performance compared with traditional Euclidean distance clustering, can effectively identify the differences and dynamic migration characteristics of user regulation potential under different operating scenarios, and stably classifies users into three types of profiles: deep regulation type, agile response type, and rigid constraint type. The research results aim to provide reliable data support for the refined dispatch of the power grid by effectively quantifying the dynamic migration patterns of heterogeneous resources under variable scenarios. Full article

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

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18 pages, 818 KB

Open AccessArticle

Comparing Exporting Competitiveness in the EV ERA: Determinants of RCA and REC Among Major Car Exporters

by Wanvilai Chulaphan, Jau-Rong Chen, Rujinan Koonwandee and Jorge Fidel Barahona

World Electr. Veh. J. 2026, 17(7), 338; https://doi.org/10.3390/wevj17070338 - 29 Jun 2026

Abstract

The global shift toward electric vehicles (EVs) may alter the competitiveness of automobile-exporting countries. This study measures revealed comparative advantage (RCA) and relative export competitiveness (REC) and examines their determinants for 10 major automobile-exporting countries from 2001 to 2022. The results show that [...] Read more.

The global shift toward electric vehicles (EVs) may alter the competitiveness of automobile-exporting countries. This study measures revealed comparative advantage (RCA) and relative export competitiveness (REC) and examines their determinants for 10 major automobile-exporting countries from 2001 to 2022. The results show that Mexico, Germany, Japan, and Republic of Korea have strong automobile export competitiveness, Thailand and the United States show moderate competitiveness, while China, Vietnam, Indonesia, and Malaysia record lower RCA and REC values. The regression results indicate that reported EV production status is positively associated with both RCA and REC. Physical capital per worker and labor productivity are also positively associated with competitiveness, while automobile production growth is negatively associated with both indicators. These findings suggest that automobile export competitiveness during the EV transition may depend not only on participation in EV production but also on capital intensity, labor productivity, production efficiency, product quality, and integration into export markets. Policy efforts should therefore support EV-related capabilities while also improving productivity, quality upgrading, and global value-chain integration. Full article

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

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22 pages, 5231 KB

Open AccessArticle

Cost-Aware Topology and Gun-to-Module Ratio Design for Modular Multi-Gun DC Fast Chargers

by Min Huang and Haoyu Wang

World Electr. Veh. J. 2026, 17(7), 337; https://doi.org/10.3390/wevj17070337 - 29 Jun 2026

Abstract

Modular multi-gun DC fast chargers can improve converter-capacity utilization by allowing charging guns to share power modules, but additional internal reachability also increases switching devices, layout complexity, reconfiguration exposure, and fault-related burden. This paper investigates topology and gun-to-module-ratio co-design for modular DC fast [...] Read more.

Modular multi-gun DC fast chargers can improve converter-capacity utilization by allowing charging guns to share power modules, but additional internal reachability also increases switching devices, layout complexity, reconfiguration exposure, and fault-related burden. This paper investigates topology and gun-to-module-ratio co-design for modular DC fast chargers from a device-level architecture perspective. A unified screening framework is developed to compare fixed, ring, partitioned, and semi-flexible layouts under common demand patterns, coefficient settings, and probabilistic module outages. A normalized cost-aware planning score evaluates delivered charging service against architecture burden, while exact small-scale benchmarks, repeated-seed sweeps, hotspot cases, robustness analysis, and continuous-operation references are combined to separate robust conclusions from conditional ones. The results show that fixed topology is the most conservative and robust option under balanced demand and high switching burden, whereas partitioned topology gives the most statistically regular behavior across broad sweeps. Semi-flexible layouts are not globally superior; their advantage appears mainly under persistent hotspot demand and moderate switching burden. These findings position bounded module sharing as a conditional charger-design regime for hotspot-prone applications. The results apply under homogeneous-module, graph-level, structured-demand, and proxy-cost assumptions and provide planning-stage architecture-screening guidance instead of hardware-calibrated cost predictions. Full article

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

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27 pages, 1984 KB

Open AccessArticle

Forecast-Time-Safe Load Forecasting for Connected and Automated EV Charging Operation: Periodicity-Aware Residual Correction on a Processed Distribution Load Proxy with Public EV Charging Validation

by Yaqi Liang

World Electr. Veh. J. 2026, 17(7), 336; https://doi.org/10.3390/wevj17070336 - 29 Jun 2026

Abstract

To address the challenge that connected and automated electric vehicle (EV) charging operation requires short-term load forecasts that preserve the current operating level while accurately capturing local ramps and peaks under strict forecast time information constraints, this paper proposes a forecast-time-safe periodicity-aware residual [...] Read more.

To address the challenge that connected and automated electric vehicle (EV) charging operation requires short-term load forecasts that preserve the current operating level while accurately capturing local ramps and peaks under strict forecast time information constraints, this paper proposes a forecast-time-safe periodicity-aware residual correction (PARC) framework. The primary experiment is a controlled benchmark on a 60-day processed distribution load proxy series, while a charging load series reconstructed from public Boulder, Colorado, EV charging transactions is used as a secondary traceable validation case. Rather than directly predicting the next load value, PARC uses the persistence forecast as the local operating state anchor and learns only the residual correction from admissible lag, rolling statistical, ramp, daily/weekly memory, and cyclic time features. This design enables a controlled comparison between direct load prediction and residual correction under the same feature boundary. In the primary proxy-series setting, PARC-HistGBR achieves a test mean absolute percentage error (MAPE) of 1.527% and a root mean square error (RMSE) of 37.051 kW, outperforming persistence, a validation-selected seasonal blend, same-feature direct tree learners, long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM). Additional XGBoost, LightGBM, and CatBoost residual variants, together with Seasonal-ETS and SARIMA-daily statistical baselines, support the interpretation that the residual target formulation, rather than one specific learner, accounts for the main gain. Rolling-origin checks, day-block bootstrap intervals, Diebold–Mariano tests, and Wilcoxon signed-rank tests provide supporting evidence within the short-data setting. In the Boulder EV validation case, the model ranking is metric-dependent, with simple persistence remaining strong for percentage metrics and residual/tree models improving selected absolute error metrics. The results indicate that PARC is useful as an auditable forecast-time-safe residual benchmarking framework for connected and automated EV charging operation; they should not be interpreted as evidence of universal superiority on fully traceable EV-rich feeders. Full article

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

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World Electric Vehicle Journal

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