Etransportation最新文献

{"title":"Smart battery with intelligent management system: A lifespan perspective","authors":"Xuebing Han ,&nbsp;Yanan Wang ,&nbsp;Dongxu Guo ,&nbsp;Shuoyuan Mao ,&nbsp;Xiaodong Xu ,&nbsp;Depeng Wang ,&nbsp;Yukun Sun ,&nbsp;Yue Pan ,&nbsp;Yuebo Yuan ,&nbsp;Xuning Feng ,&nbsp;Yuejiu Zheng ,&nbsp;Languang Lu ,&nbsp;Jianqiu Li ,&nbsp;Minggao Ouyang","doi":"10.1016/j.etran.2026.100587","DOIUrl":"10.1016/j.etran.2026.100587","url":null,"abstract":"<div><div>With the global goal of decarbonization and net-zero emission, lithium-ion batteries (LIBs) with high density act as important energy supply in transportation and energy storage system. Considering LIB as an electrochemical element with chemical, electrical, thermal, and physical characteristics, the battery safety, dynamics, and degradation are main concerns of battery management system (BMS) during applications, and the next generation BMS needs to be comprehensive and intelligent enough to cover these concerns. Hence, we provide an overview of smart battery with intelligent management for the next generation BMS in this perspective. The proposed framework includes intelligent perception, intelligent monitoring, and intelligent management during battery lifetime, resulting in a multi-dimensional intelligence from material science to system science. Intelligent perception is smart battery with sensors and control strategies, intelligent monitoring is discussed with battery defect evolution and safety monitoring methods, and intelligent management is analyzed by algorithms iteration with AI and mechanism through battery lifetime. This work presents the framework covering the various stages of LIBs, that is, design, manufacturing, monitoring, control, protection, and recycling, to try to describe an integrated flowchart for battery intelligence. Opportunities and challenges are also presented in this perspective to show possible blueprint for the future development of smart battery with intelligent management.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"28 ","pages":"Article 100587"},"PeriodicalIF":17.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147849994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploiting physics-knowledge from unlabeled data to enhance battery lifetime prediction","authors":"Aihua Tang ,&nbsp;Yuehan Li ,&nbsp;Jinpeng Tian ,&nbsp;Quanqing Yu ,&nbsp;Ning Yu ,&nbsp;Yuchen Xu","doi":"10.1016/j.etran.2026.100560","DOIUrl":"10.1016/j.etran.2026.100560","url":null,"abstract":"<div><div>Accurately predicting battery lifetime is essential for ensuring the long-term operation of electrochemical energy storage systems. While machine learning has provided promising solutions, its performance degrades significantly in the absence of sufficient full-life degradation data on which it heavily depends. In this study, although direct acquisition of remaining useful life and cycles to knee-point labels from battery degradation data without reaching end-of-life is infeasible, valuable physics-related degradation knowledge can still be extracted from such incomplete data to enhance lifetime prediction. Accordingly, a physics-knowledge guided lifetime prediction method is proposed to utilize one-cycle constant-current curve to jointly predict remaining useful life and cycles to knee-point. More critically, this method can implicitly guide convolutional neural network training with incremental capacity knowledge obtained from incomplete-lifespan degradation data. This yields a pre-trained model that can be rapidly adapted using only a few remaining useful life and cycles to knee-point labels. The validity of the proposed method has been extensively validated on three full-lifespan degradation datasets comprising over 40,000 samples. The validation results show that by using only 10 % of the lifetime labels from the samples, the proposed method can achieve prediction with an error of less than 21 cycles on cells with the end-of-life distribution of 100–500 cycles, which reduces the error by more than 50 % compared with the traditional method. In conclusion, this study emphasizes the prospect of enhancing battery lifetime prediction through physics-knowledge in rare-label cases.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"28 ","pages":"Article 100560"},"PeriodicalIF":17.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification and forecasting of reserve capacity from electric trains","authors":"Agnes Nakiganda ,&nbsp;Martin Lindahl ,&nbsp;Callum Henderson ,&nbsp;Agustí Egea-Àlvarez ,&nbsp;Lars Herre","doi":"10.1016/j.etran.2025.100524","DOIUrl":"10.1016/j.etran.2025.100524","url":null,"abstract":"<div><div>This paper explores the quantification and forecasting of reserve capacity from electric trains for participation in power system ancillary service markets. We first map train electricity consumption – traction and non-traction – to suitable reserve products, considering operational and regulatory constraints. Using historical data from the Danish railway operator DSB, we estimate the available flexibility for frequency containment reserves, focusing on controllable non-traction loads such as heating and air conditioning. To support market participation, we develop a low-resolution stochastic forecasting model based on conformal prediction, capable of estimating reserve availability for both day-ahead and hour-ahead horizons. Results show that a fleet of approximately 60 active trains can provide up to 10<!--> <!-->MW of downward regulation and 1.5<!--> <!-->MW of upward regulation from non-traction loads. Additionally, traction power from 25 trains can provide up to 5<!--> <!-->MW of upward reserve in certain time periods. The findings demonstrate a viable pathway for integrating electric trains into flexibility markets, offering new revenue opportunities for operators and enhancing grid stability.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"27 ","pages":"Article 100524"},"PeriodicalIF":17.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online energy management strategy for fuel cell hybrid powertrain based on multi-objective constraint rules embedded in soft actor-critic learning","authors":"Baobao Hu ,&nbsp;Zhiguo Qu ,&nbsp;Jianfei Zhang ,&nbsp;Pingwen Ming","doi":"10.1016/j.etran.2025.100532","DOIUrl":"10.1016/j.etran.2025.100532","url":null,"abstract":"<div><div>The fuel cell/battery hybrid powertrain offers a promising solution for fuel cell vehicles by integrating the high energy density of hydrogen fuel cells with the high-power density of batteries. However, real-time energy management of such a multi-source system faces challenges in simultaneously achieving economic efficiency, durability, and adaptability. To address this, this study proposes an online energy management strategy called MOCR-SAC. It incorporates multi-objective constraint rules (including hydrogen consumption, fuel cell degradation, battery degradation, fuel cell optimal efficiency deviation, and battery optimal state of charge deviation) within a Soft Actor-Critic reinforcement learning framework, enabling adaptive and intelligent power allocation. Evaluated on a 12-m fuel cell bus under standard Chinese driving cycles, MOCR-SAC reduces hydrogen consumption by at least 4.28 % and operating costs by 7.32 % compared to conventional SAC (without constraints or using single rules). It also outperforms other online reinforcement learning methods in component degradation, cost, battery <em>SOC</em> regulation, and hydrogen economy. Compared to the global optimum obtained by dynamic programming, its operating cost deviation remains within 4.50 %, while hydrogen consumption is 5.63 % lower. Under both deterministic and uncertain driving cycles, the total operating cost deviates by less than 10 %, demonstrating strong robustness and adaptability. The proposed strategy can be pre-trained offline and deployed online with minimal computational overhead, meeting the real-time requirements of vehicle energy management. In summary, MOCR-SAC significantly enhances the performance, efficiency, and durability of fuel cell hybrid powertrains, offering a practical and scalable solution for sustainable transportation.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"27 ","pages":"Article 100532"},"PeriodicalIF":17.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lab-to-field gap in battery aging studies: Mismatch of operating conditions between laboratory environments and real-world automotive applications","authors":"Markus Schreiber,&nbsp;Lukas Leonard Köning,&nbsp;Georg Balke,&nbsp;Kareem Abo Gamra,&nbsp;Jonas Kayl,&nbsp;Brian Dietermann,&nbsp;Raphael Urban,&nbsp;Cristina Grosu,&nbsp;Markus Lienkamp","doi":"10.1016/j.etran.2025.100518","DOIUrl":"10.1016/j.etran.2025.100518","url":null,"abstract":"<div><div>In response to the growing demand for electric vehicles, ensuring the longevity of traction batteries has become a central focus of scientific research. While most aging studies rely on accelerated aging testing with tightened stress factors, real-world battery operation reveals fundamentally different load profiles and aging conditions. To disclose the gap between the laboratory and the real-world application, we collected and assessed almost 2600 stress factor combinations from 201 different calendar and cycle aging studies. Moreover, we gathered and analyzed vehicle data from over 72<!--> <!-->000 km of everyday usage of seven vehicles in public road traffic in Germany and extracted the related battery-specific load spectra. The stress factor combinations chosen in the literature show a trend towards high temperatures and state of charges (SOCs) during storage in calendar aging studies. In contrast, cycle aging tests are predominantly performed at full depth of discharge (DOD) or elevated average SOC levels, with current rates of primarily <span><math><mrow><mo>±</mo><mn>1</mn><mspace></mspace><mtext>C</mtext></mrow></math></span> at 25<!--> <!-->°C or slightly elevated temperatures. Contrary to this, the field data analysis reveals the following main findings: Driving events rarely exceed 30<!--> <!-->km in distance or 40<!--> <!-->min in duration, with an average driving speed of 61.1 km h<span><math><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></math></span>. This leads to average current rates of <span><math><mo>−</mo></math></span>0.2 C in discharging and 0.1 C in charging direction and average cycle depths of less than 30%, while the average battery pack temperature ranges around 17<!--> <!-->°C. Comparing laboratory test conditions with stress conditions in field applications reveals three major discrepancies: First, the stress levels applied are substantially higher than the stresses acting in real-world operation. Second, the dynamic load characteristic of real-world vehicle operation is rarely reflected; most studies work with synthetic constant current load cycles. Third, intermediate rest periods, which are predominant in real-world use, are omitted in most studies. This raises concerns about the transferability and applicability of findings from accelerated aging tests to automotive real-world applications.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"27 ","pages":"Article 100518"},"PeriodicalIF":17.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time AI-enabled digital twin for battery health estimation and fast charging using partial-discharge data","authors":"Mohammad Qasem,&nbsp;Jeff Stubblefield,&nbsp;Moath Qandil,&nbsp;Yazan Yassin,&nbsp;Mariana Haddadin,&nbsp;Mahesh Krishnamurthy","doi":"10.1016/j.etran.2025.100528","DOIUrl":"10.1016/j.etran.2025.100528","url":null,"abstract":"<div><div>Digital twin technology has emerged as a promising approach for integrating multi-physics models in real-time to optimize the operation of electric vehicles (EVs) and electric vertical take-off and landing (eVTOLs), particularly in terms of battery performance. However, the mitigation of dynamic lithium plating and solid electrolyte interphase (SEI) growth during fast charging remains unaddressed in current studies. This paper proposes an AI-enabled digital twin that uses partial-discharge data, data from incomplete discharge cycles, for real-time battery-health estimation and couples this insight with an age-aware fast-charging controller that adaptively controls the charging current to mitigate lithium plating and SEI growth. The experimental results demonstrated the framework’s robustness across varying ambient temperatures and initial state of charge (SoC) conditions. A novel real-time estimation model within the framework achieved a root mean square error (RMSE) of less than 0.5% and 0.4% for both battery capacity and internal resistance. Additionally, the proposed framework preserved battery capacity of 87.6% at 25 °<span><math><mi>C</mi></math></span> compared to 81.4% and 64.3% for MCC-CV and CC-CV, respectively, representing relative improvements of +7.6% and +36.2% over MCC-CV and CC-CV, respectively. This approach helped mitigate battery side reactions during fast charging, while it reduced the time required to reach 80% SoC to less than 25 min, which was 28.6% faster than MCC-CV (35 min) and 35.9% faster than CC-CV (39 min) after 200 cycles. These results support practical deployment in embedded BMS and EV/eVTOL charging to enhance safety, reduce plating risk, and extend service life.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"27 ","pages":"Article 100528"},"PeriodicalIF":17.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal economic integrated thermal management of battery and cabin for connected electric vehicles considering battery degradation","authors":"Qian Ma ,&nbsp;Yan Ma ,&nbsp;Jinwu Gao ,&nbsp;Hong Chen","doi":"10.1016/j.etran.2025.100540","DOIUrl":"10.1016/j.etran.2025.100540","url":null,"abstract":"<div><div>The integrated thermal management system (ITMS) for the battery and cabin is essential to improve thermal safety, energy efficiency, battery lifespan, and passenger comfort in connected electric vehicle (CEV). The ITMS consumes considerable energy to maintain battery and cabin temperatures in the optimal range, which severely reduces the CEV’s driving range. To solve the ITMS optimization problem for CEV and achieve eco-cooling, this article proposes a two-stage optimization strategy for ITMS based on multi-horizon economic nonlinear model predictive control (TS-MH-ENMPC), which considers the total economic cost of cooling system energy consumption and battery degradation. Firstly, a control-oriented nonlinear ITMS model is developed to predict the battery and cabin temperature changes. Then, a two-stage cooling optimization strategy based on economic nonlinear model predictive control (MPC) is proposed to achieve optimal driving economy, which divides the ITMS into fast cooling stage and temperature maintenance stage with different cooling objectives. Finally, to address the multi-timescale problem of slow dynamic response in thermal system and fast response in power transfer, a multi-prediction horizon MPC framework is introduced to fully utilize the intelligent transportation system (ITS) information to achieve optimal economic performance over long prediction horizon, which solves the optimization problem of the integrated system with dynamic responses at different time scales and reduces the computational burden. The simulation results under various conditions show that the proposed method reduces the total economic cost of energy consumption and battery degradation. And a sensitivity analysis is conducted on ambient temperatures, battery prices, and electricity prices. Compared to the traditional MPC, rule-based, the total economic cost of the TS-MH-ENMPC is reduced by 5.24% and 7.09%, and the driving distance is increased by 3.03% and 6.65%. The co-simulation results on real-world traffic data show that the proposed method improves driving economy and thermal performance under preview information uncertainty and model mismatch.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"27 ","pages":"Article 100540"},"PeriodicalIF":17.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of pulsating spray cooling for enhanced air-cooled radiator performance in fuel cell vehicles: An experimental and RSM study","authors":"Rajendran Prabakaran,&nbsp;M Mohamed Souby,&nbsp;Sung Chul Kim","doi":"10.1016/j.etran.2025.100517","DOIUrl":"10.1016/j.etran.2025.100517","url":null,"abstract":"<div><div>This study proposes a pulsating spray cooling (PSC) to enhance the performance of an air-cooled radiator (ACR) in a fuel cell vehicle (FCV). It explores the influence of duty cycle (DC) on heat dissipation and spray performance using both experimental methods and response surface methodology (RSM). Results revealed that employing PSC with a lower DC (&lt;40 %) caused greater fluctuations in both heat dissipation and coolant outlet temperature, indicating it is unsuitable for ACR. Conversely, non-optimized PSC with an 80 % DC demonstrated performance comparable to continuous spray cooling, achieving up to 75.5 % enhancement in heat dissipation compared to air cooling. Furthermore, spray efficiency increased from 8.4 % to 53.5 % as the DC decreased from 100 % to 20 %. In addition, spray pump power and water consumption were significantly reduced by up to 80 %. Importantly, the threshold limit of spray flow rate was experimentally determined to be 0.60 L/min. RSM optimization was then conducted to identify the optimal PSC conditions that balance thermal and spray performance. Spray flow rate, interval, and pulse duration were selected for optimization due to their key influence on heat dissipation, water use, and pump power in PSC system. The optimal conditions obtained were a spray flow rate of 0.522 L/min, a spray interval of 56.72 s, and a continuous spray duration of 10 s. Under these optimized conditions, the PSC-coupled ACR achieved a heat dissipation rate of 5.47 kW, a spray efficiency of 46.89 %, spray pump power of 2.62 W, and water consumption of 5.25 L/h. Moreover, the optimized water consumption was within the theoretical water production capacity (up to 10.6 L/h) of a real PEM-FC vehicle (up to 295 kW). Thus, the proposed PSC approach offers a promising solution for enhancing stack cooling performance using available water resources from the fuel cell itself, making it a viable option for future FCVs.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"27 ","pages":"Article 100517"},"PeriodicalIF":17.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145616221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards intelligent online diagnosis and degradation prognostics of lithium-ion batteries: A mechanism–data fusion approach","authors":"Shilong Guo ,&nbsp;Yaxuan Wang ,&nbsp;Lei Zhao ,&nbsp;Junfu Li ,&nbsp;Zhenbo Wang","doi":"10.1016/j.etran.2025.100513","DOIUrl":"10.1016/j.etran.2025.100513","url":null,"abstract":"<div><div>Lithium-ion batteries experience complex degradation governed by multiple interacting mechanisms, posing challenges for real-time aging-mode identification. To overcome this issue, we propose a mechanism–data fusion framework that couples an extended single-particle model (SPM) with a multi-task learning (MTL) architecture. The electrochemical model explicitly incorporates solid–electrolyte interphase (SEI) growth and lithium plating side reactions, and employs a multi-swarm cooperative adaptive particle swarm optimization (MSCPSO) algorithm to achieve accurate parameter identification across different temperatures and C-rates. A three-branch MTL framework is then constructed to jointly predict key degradation indicators—including the loss of lithium inventory (LLI), loss of active material (LAM), SEI and plating layer thicknesses, and plating-induced capacity loss—while also classifying the occurrence of lithium plating. Experimental validation demonstrates strong physical consistency and robustness of the proposed framework under various operating conditions. Among the tested architectures, the MT-LSTM model exhibits the best overall performance, achieving a lithium-plating detection accuracy of 99.63 % and an R² exceeding 0.97 for multi-target regression tasks. This unified and scalable framework enables quantitative identification of multiple degradation mechanisms directly from charge–discharge data, offering a practical, real-time, and physically interpretable tool for next-generation battery health management systems.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"27 ","pages":"Article 100513"},"PeriodicalIF":17.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145616283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A non-intrusive integration of wireless chargers into electric vehicles: 95.60 % dc-dc efficiency at 0.51 LD-to-CL ratio with on-vehicle demonstration","authors":"Songyan Niu ,&nbsp;Wei Liu ,&nbsp;Chang Liu ,&nbsp;Chunchun Jia ,&nbsp;Marco Liserre ,&nbsp;Kwok Tong Chau","doi":"10.1016/j.etran.2026.100547","DOIUrl":"10.1016/j.etran.2026.100547","url":null,"abstract":"<div><div>Wireless electric vehicle charging (WEVC) is recognized as a promising technology to enhance user convenience and support autonomous mobility. Yet, the development of WEVC systems may face compatibility issues, potential intrusion into EV electronics, and privacy concerns when directly interfacing with the batteries. Moreover, under the large power transmission distance imposed by high-clearance EVs, the systems may fail to interoperate, especially when misalignments between transmitter and receiver are considered and the allowed installation space in EVs is limited. To address these challenges, this work proposes a non-intrusive framework of systems that target the onboard charger, enabling their seamless integration into EVs with a coupler that remains efficient under the severe coupling constraints. Sensitivity analysis and multi-objective optimization are first applied to identify dominant geometric parameters and balance efficiency, misalignment tolerance, and material usage. On this basis, a parasitic-aware circuit model is established to capture interlayer capacitance effects inherent to dual-layer coils. Guided by this model, a winding method is proposed to reduce voltage differences between layers, thereby minimizing dielectric losses, while an additional spacer design further reduces losses by suppressing parasitic capacitance at its source. A 3.17 kW WEVC prototype is built and installed on a commercial autonomous electric shuttle, demonstrating 95.60 % dc–dc efficiency under aligned operation and 92.64 % efficiency under 112.5 mm lateral misalignment. These outcomes confirm the practicality of the framework and system design, providing a scalable pathway for safe and compatible deployment of WEVC in real EV fleets.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"27 ","pages":"Article 100547"},"PeriodicalIF":17.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}