Etransportation最新文献

{"title":"A thermo-mechanical-chemical composite barrier for suppressing thermal runaway propagation in NCM811 battery module","authors":"Shaw Kang WONG,&nbsp;Yan Hong,&nbsp;Chengshan Xu,&nbsp;Yong Peng,&nbsp;Siqi Zheng,&nbsp;Xuning Feng","doi":"10.1016/j.etran.2025.100451","DOIUrl":"10.1016/j.etran.2025.100451","url":null,"abstract":"<div><div>High-nickel cathode lithium-ion batteries have gained widespread use in electric vehicles. However, the thermal safety risks associated with battery failure remain a significant challenge. Conventional thermal insulation materials have proven suboptimal in preventing thermal runaway propagation among high-specific-energy battery module. Thermal runaway of these cells can result in temperatures exceeding 1000 °C, leading to combustion when the fire triangle conditions are met. This makes it difficult to guarantee system-wide thermal safety through insulation alone. This paper introduces a composite material primarily composed of porous fiber and high enthalpy phase-change materials, specifically designed as a protective barrier positioned between adjacent cells, functioning as a passive safety measure. This composite material exhibits a tri-stage temperature-responsive behavior characterized by thermal control, dissipation, and insulation, thereby achieving effective thermal regulation. In addition, it demonstrates thermo-mechanical-chemical responsiveness, making it particularly well-suited for application in high-energy-density battery modules. With a compact thickness of only 2.5 mm, the material effectively prevents thermal runaway propagation and combustion in NCM811 battery modules, while also providing structural reinforcement, thermal mitigation, and flame suppression. Compared to conventional insulation materials, this innovative barrier delivers significantly enhanced performance in both safety and multifunctionality.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100451"},"PeriodicalIF":17.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757868","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":"Challenges and numerical solutions for multi-domain and multi-physics coupling in heterogeneous lithium-ion battery model simulation","authors":"Qiyu Chen ,&nbsp;Lance Zhao ,&nbsp;Xinhong (Susan) Chen ,&nbsp;Zhe Li","doi":"10.1016/j.etran.2025.100452","DOIUrl":"10.1016/j.etran.2025.100452","url":null,"abstract":"<div><div>In electrochemistry, the heterogeneous model effectively characterizes the microstructural features of porous electrodes by distinctly resolving both solid and liquid phases with respective spatial distributions and interfacial interfaces. The model incorporates essential characteristics including particle size distributions and non-uniform porosity, enabling spatiotemporal representation of coupled physicochemical processes. However, modeling and numerically solving the heterogeneous model presents significant challenges. This study introduces computational solutions to critical challenges in heterogeneous lithium-ion battery simulation. (1) Distinct material phases occupy spatially resolved domains, with various phenomena occurring either bulk phases or interfaces. We develop domain decomposition/combination strategy with morphology-specific approaches. (2) Regions with similar compositions may exhibit significant variations in physical properties. Our novel transfer coefficient matrix method enables global solutions for concentration equations across interfaces with varying porosity. (3) Batteries represent inherently mass-charge coupled systems, where lithium-ion transport is driven by both electric potential and concentration gradients. The composite potential field method rigorously ensures flux continuity while resolving coupled transport mechanisms. We implement above methods to our self-developed simulation framework, rigorously validating accuracy against experimental measurements and COMSOL benchmarks. This work provides a fundamental theoretical foundation for both the development of next-generation ultra-high-performance batteries and the technological upgrade of industrial battery simulation software.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100452"},"PeriodicalIF":17.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757613","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":"Cloud-based SOC optimization for predictive energy management and zero emission zone compliance in PHEVs","authors":"Paul Muthyala ,&nbsp;Florian Wessel ,&nbsp;Joschka Schaub ,&nbsp;Stefan Pischinger","doi":"10.1016/j.etran.2025.100443","DOIUrl":"10.1016/j.etran.2025.100443","url":null,"abstract":"<div><div>With deteriorating air quality in many cities worldwide failing to meet World Health Organization (WHO) standards, effective countermeasures are urgently needed. In response, cities are implementing zero-emission zones, restricting entry to only zero-emission vehicles like Battery Electric Vehicles and Fuel Cell Electric Vehicles. These measures aim to reduce urban air pollution and improve public health significantly. Despite their ability to operate in pure electric mode under city driving conditions, Plug-in Hybrid Electric Vehicles (PHEVs) are typically prohibited from zero-emission zones due to the potential use of their Internal Combustion Engines, which could compromise air quality improvement efforts. However, advancements in digital maps and Vehicle-to-Everything (V2X) technology present a viable solution to this challenge. Geofencing technology can now be employed to carefully plan and prepare PHEVs’ battery State of Charge (SOC), ensuring that SOC usage is strictly restricted within zero-emission zones.</div><div>This study proposes a predictive control strategy for PHEVs, utilizing route information from digital map providers to enable electric driving within zero-emission zones. To achieve this, a supervisory control with Dynamic Programming (DP) is developed in the upper layer to calculate an optimal SOC trajectory considering the zero-emission zone and guide the rule-based controller in the lower level. The high computational effort of DP is addressed by running it in the cloud. In addition, the optimization can be repeated multiple times during driving. The proposed methodology is tested and validated on a demonstrator vehicle in a real-world drive cycle.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100443"},"PeriodicalIF":17.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721335","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":"Challenges and perspectives towards multi-physics modeling for porous electrode of ultrahigh performance durable polymer electrolyte membrane fuel cells","authors":"Ning Wang ,&nbsp;Tao Lai ,&nbsp;Wenkai Wang ,&nbsp;Zhiguo Qu ,&nbsp;Xuhui Wen ,&nbsp;Guangyou Xie ,&nbsp;Wenquan Tao","doi":"10.1016/j.etran.2025.100449","DOIUrl":"10.1016/j.etran.2025.100449","url":null,"abstract":"<div><div>The development of ultrahigh-performance, durable polymer electrolyte membrane fuel cells (PEMFCs) is crucial for achieving large-scale commercialization. A comprehensive insight into multi-physics phenomena within advanced porous electrode designs provide motivation for the ambitious targets. Modeling is an indispensable tool in multi-physics transfer understanding and offers a promising pathway for electrode structural designs and material architecture selections. Despite the progress, the modeling community continues to face significant challenges, including oversimplification, difficulties in coupling complex features, unclear physical knowledge, and unavoidable discrepancies. This perspective highlights the current status of porous electrode modeling, identifies ongoing challenges, and explores future directions for key technologies and potential countermeasures. Specifically, the characteristics and limitations of macro-scale, meso-scale, and micro-scale models regarding intricate porous electrode microstructures are compared, including ordered structure, mesoporous carbon support, various catalyst architectures, etc. Potential solutions to these challenges are proposed for the next generation of porous electrode designs. Furthermore, three alternatives to advancing cross-scale, full-morphology, and full-coupling modeling are developed and discussed, including layer-by-layer physical property transfer, interfacial data transfer and direct numerical simulation, and data-driven assisted cross-scale modeling, which are expected to be evaluated and validated in the foreseeable future.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100449"},"PeriodicalIF":15.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633204","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":"Mechanism of battery expansion failure due to excess solid electrolyte interphase growth in lithium-ion batteries","authors":"Dongdong Qiao ,&nbsp;Xuezhe Wei ,&nbsp;Jiangong Zhu ,&nbsp;Guangxu Zhang ,&nbsp;Shuai Yang ,&nbsp;Xueyuan Wang ,&nbsp;Bo Jiang ,&nbsp;Xin Lai ,&nbsp;Yuejiu Zheng ,&nbsp;Haifeng Dai","doi":"10.1016/j.etran.2025.100450","DOIUrl":"10.1016/j.etran.2025.100450","url":null,"abstract":"<div><div>Revealing the aging and failure mechanisms of lithium-ion batteries is crucial for extending battery life and improving battery safety. This paper presents a mechanism of solid electrolyte interphase (SEI) film overgrowth and battery failure caused by deep aging of cylindrical batteries. Firstly, multiple 18650-type cylindrical battery accelerated aging experiments were designed. Differential voltage analysis (dV/dQ) and electrochemical impedance spectroscopy (EIS) are used to investigate battery degradation mechanisms non-destructively. Secondly, batteries under different degradation degrees were disassembled, and the scanning electron microscope (SEM), liquid nitrogen cooled argon-ion cross-sectional polishing, and X-ray photoelectron spectroscopy (XPS) technology were used to investigate the surface and cross-sectional SEI evolution of electrodes. Obvious increases in SEI thickness and resistance occur when the battery capacity fade is less than 30 %. Finally, the mechanism of excessive growth of SEI on the graphite negative electrode surface of the cylindrical battery, leading to the expansion and rupture failure of the metal shell, was revealed. This work provides crucial insights for the safe service, management, and residual value assessment of lithium-ion batteries throughout their entire lifecycle.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100450"},"PeriodicalIF":15.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623667","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":"Enhancing safety of electric aircraft Batteries: Degradation and thermal runaway behavior at extreme altitudes","authors":"Wenjie Jiang ,&nbsp;Canbing Li ,&nbsp;Xinxi Li ,&nbsp;Yuhang Wu ,&nbsp;Yunjun Luo ,&nbsp;Dequan Zhou ,&nbsp;Zhaowei Lin ,&nbsp;Kang Xiong ,&nbsp;Jianzhe Liu","doi":"10.1016/j.etran.2025.100448","DOIUrl":"10.1016/j.etran.2025.100448","url":null,"abstract":"<div><div>The operating performance and thermal safety of lithium-ion batteries (LIBs) in high-altitude scenarios are prime concerns for their reliable applications in various fields. High-altitude environments, characterized by low ambient pressure and temperature, can accelerate LIB degradation and increase the risk of thermal runaway (TR). Unlike previous studies focusing solely on ambient pressure or ambient temperature, this work quantifies their high-altitude coupled effects on the battery performance as well as the TR characteristics. Herein, experiments and simulations are combined to analyze the hybrid pulse charge/discharge behavior, direct current internal resistance (DCIR), over-discharge and recharge/re-discharge performance, and TR characteristics of 26650 NiCoMn LIBs under various ambient pressure and temperature conditions. The results show that low ambient pressure at 20 kPa increases the DCIR of LIB by 7.16 mΩ, raises battery temperature by 4.3 °C, lowers energy efficiency to 92.2 %, and advances TR occurrence with mass loss increasing to 7.5 g. Low ambient temperature at 50 °C causes abrupt changes in battery voltage (up to 6.8009 V during pulse charge and down to 2.0641 V during pulse discharge) and increases the DCIR to 284.8 mΩ. When low ambient pressure and low ambient temperature are combined, energy efficiency decreases to 93.5 % and the peak TR temperature of LIB reduces to 214.2 °C at 20 kPa &amp; −50 °C. The research elucidates the relationship between performance/TR behaviors of LIB and individual/coupled environmental factors, shedding new insights into the operation and safety of LIB in the aviation sector. This facilitates to establishing tailored LIB designs and adaptive thermal management strategies to mitigate failure risks in high-altitude applications.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100448"},"PeriodicalIF":15.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604499","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":"Mechanical information enhanced battery state-of-health estimation","authors":"Xubo Gu ,&nbsp;Xinyuan Wang ,&nbsp;Yao Ren ,&nbsp;Wenqing Zhou ,&nbsp;Xun Huan ,&nbsp;Jason Siegel ,&nbsp;Weiran Jiang ,&nbsp;Ziyou Song","doi":"10.1016/j.etran.2025.100440","DOIUrl":"10.1016/j.etran.2025.100440","url":null,"abstract":"<div><div>Accurate estimation of the state of health (SOH) is crucial for the safe operation of batteries. Mechanical features, in particular, offer significant potential for improving SOH estimation by directly reflecting key internal processes within batteries. However, research on the contribution of mechanical features to SOH estimation remains limited. This study demonstrates the effectiveness of mechanical features for SOH estimation in pouch cells under various operating conditions and scenarios. The results show that mechanical features provide reliable SOH estimates across different temperatures, C-rates, and charging profiles, and they are especially robust under real-world driving conditions. The mechanical features typically achieve at least a 28.26% reduction in prediction error. Notably, in the driving scenario, the mean absolute percentage error reaches an impressive low of 0.65%. Furthermore, this work introduces an evaluation framework to systematically benchmark features derived from electrical, thermal, and mechanical signals based on their overall predictive capabilities. Finally, detailed physical interpretations are provided to explain the effectiveness of mechanical features.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100440"},"PeriodicalIF":15.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572421","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":"Battery temperature anomaly early warning for electric vehicles under real driving conditions using a temporal convolutional network","authors":"Shaopeng Li ,&nbsp;Hui Zhang ,&nbsp;Daniela Anna Misul ,&nbsp;Federico Miretti ,&nbsp;Matteo Acquarone ,&nbsp;Naikan Ding ,&nbsp;Dingan Ni ,&nbsp;Ninghao Hou ,&nbsp;Yanjie He ,&nbsp;Yijun Zhang ,&nbsp;Yifan Sun","doi":"10.1016/j.etran.2025.100445","DOIUrl":"10.1016/j.etran.2025.100445","url":null,"abstract":"<div><div>For preventing thermal runaway accidents in electric vehicles (EVs), it is crucial to conduct early warning for temperature anomaly in battery pack. Based on data collected by a naturalistic driving experiment with 20 EVs, this study proposes a temporal convolutional network (TCN) algorithm for battery temperature anomaly prediction. Firstly, 40 features encompassing battery signals, thermal management state, ambient temperature, and driving condition are extracted from micro-segments. Then, the most effective input features are selected between the 40 features through maximum information coefficient (MIC) correlation analysis, and the principal component analysis (PCA). After obtaining the optimal hyperparameters, the TCN model is trained using the data from four EVs. The model's performance in predicting temperature is assessed over the data of the remaining 16 vehicles. The results demonstrate that the model achieves accurate prediction with the maximum and minimum mean relative error (MRE) of 0.0132 and 0.0072 across the 16 test vehicles. Moreover, the model proves to be robust against different testing seasons, SOCs, and traffic conditions. Compared to convolutional neural network (CNN), long short-term memory network (LSTM), and CNN-LSTM models with same hyperparameters, the developed TCN model consistently obtains the lowest MRE on both training and testing. For two kinds of scenarios where the probe temperature changes slowly and rapidly, the TCN model can predict an impending temperature anomaly up to 40 min in advance, and forecast the temperature anomaly within the future 8 min, respectively. Among the 16 vehicles, 81.25 % demonstrate a high prognosis accuracy, with an average F1 score of 0.951 across 10 of the vehicles. Thus, the proposed method can provide accurate battery temperature anomaly early warning for EVs under actual driving conditions.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100445"},"PeriodicalIF":15.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623777","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":"Dynamic bus charge scheduling by model predictive control to maximize local PV surplus power utilization","authors":"Fumiaki Osaki ,&nbsp;Yu Fujimoto ,&nbsp;Yutaka Iino ,&nbsp;Yuto Ihara ,&nbsp;Masataka Mitsuoka ,&nbsp;Yasuhiro Hayashi","doi":"10.1016/j.etran.2025.100441","DOIUrl":"10.1016/j.etran.2025.100441","url":null,"abstract":"<div><div>As the electrification of public transport and the adoption of variable renewable energy accelerate the transition to carbon neutrality, integrating local photovoltaic (PV) surplus power into electric bus charging operations becomes increasingly critical. However, uncertainties in PV generation and traffic delays often reduce the effective utilization of PV surplus due to missed charging opportunities. To address these challenges, this study proposes a dynamic charging scheduling method based on model predictive control (MPC), which adaptively updates the schedule using quasi-real-time, district-scale information. The framework integrates real-time traffic delays in the General Transit Feed Specification format (GTFS Realtime), smart meter measurements, and meteorological satellite observations—data sources currently available in real cities. At each update step, the system forecasts PV surplus power using a machine learning model that captures temporal weather conditions and localized PV surplus trends around charging stations, while detecting bus delays at each station. Based on this information, the optimal charging schedule is updated every 30 min to adaptively maximize PV surplus utilization. Numerical experiments simulating an entire year demonstrate the effectiveness of the proposed method. Compared to a fixed day-ahead schedule and a rule-based charging method, it improves the annual average PV surplus utilization rate by up to 11.9% and reduces annual average grid power purchases by up to 15.6%. These results highlight the potential of combining MPC with quasi-real-time, district-scale data to proactively and robustly integrate renewable energy into public electric bus operations under uncertainty.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100441"},"PeriodicalIF":15.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588837","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":"Hybrid fusion for battery degradation diagnostics using minimal real-world data: Bridging laboratory and practical applications","authors":"Yisheng Liu ,&nbsp;Boru Zhou ,&nbsp;Tengwei Pang ,&nbsp;Guodong Fan ,&nbsp;Xi Zhang","doi":"10.1016/j.etran.2025.100446","DOIUrl":"10.1016/j.etran.2025.100446","url":null,"abstract":"<div><div>Unpredictability of battery lifetime has been a key stumbling block to technology advancement of safety-critical systems such as electric vehicles and stationary energy storage systems. In this work, we present a novel hybrid fusion strategy that combines physics-based and data-driven approaches to accurately predict battery capacity. This strategy, implemented via a convolutional neural network, achieves an average estimation error of only 0.63 % over the entire battery lifespan, utilizing merely 45 real-world data segments along with over 1.7 million simulated data segments derived from random partial charging cycles. By leveraging a thoroughly validated reduced-order electrochemical model, we extract typical aging patterns from laboratory aging data and extend them into a more comprehensive parameter space, encompassing diverse battery aging states in potential real-world applications while accounting for practical cell-to-cell variations. By bridging the gap between controlled laboratory experiments and real-world usage scenarios, this method highlights the significant potential of transferring underlying knowledge from high-fidelity physics-based models to data-driven models for predicting the behavior of complex dynamical systems.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100446"},"PeriodicalIF":15.0,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549436","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}