Etransportation最新文献

{"title":"Modeling study on fire propagation behavior and analysis of energy flow paths in double-layer LFP battery module","authors":"Yan Wang ,&nbsp;Yu Wang ,&nbsp;Chengshan Xu ,&nbsp;Feng Dai ,&nbsp;Xilong Zhang ,&nbsp;Hewu Wang ,&nbsp;Xuning Feng","doi":"10.1016/j.etran.2025.100508","DOIUrl":"10.1016/j.etran.2025.100508","url":null,"abstract":"<div><div>With the global transformation of energy structure and rapid development of energy storage technologies, battery energy storage stations (BESS) have been widely applied. However, vertically stacked lithium iron phosphate (LFP) battery modules in BESS are highly susceptible to thermal runaway (TR) jet combustion, leading to collective violent battery combustion under the influence of vertical flames. Extensive experimental investigations have been conducted on the failure process involving heat generation - jet combustion - (thermal runaway propagation)TRP in double - layer LFP battery modules. However, experimental methods struggle to clarify the influence mechanisms of vertical flames on battery TRP and energy flow pathways. This paper establishes a chain-type failure model for heat generation - jet combustion - TRP in double-layer battery modules based on non-premixed combustion theory. After calibrating critical parameters such as battery temperature and heat release rate (HRR), the model predicts battery TRP patterns and energy flow pathways in double-layer quadruple-battery modules under vertical flame influence. Results revealed that 71.2 % of flame-released heat radiates to the lateral surface of the upper battery module, while only 28.8 % radiates to the battery bottom. At 2133 s, the battery module has a group deflagration with a maximum HRR of 336.5 kW. The outermost upper-layer battery absorbs heat most rapidly, leading to earliest valve venting and TR. This creates an “inverse sequence” TRP pattern in the upper battery layer. The findings provide theoretical references for fire propagation protection and safety design in BESS.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100508"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465207","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":"Lightweight fault diagnosis for EV battery packs via SpikingFormer and frequency slice wavelet transform","authors":"Qian Huo ,&nbsp;Zhikai Ma ,&nbsp;Tao Zhang ,&nbsp;Zepeng Gao","doi":"10.1016/j.etran.2025.100503","DOIUrl":"10.1016/j.etran.2025.100503","url":null,"abstract":"<div><div>Accurate fault diagnosis of power batteries is crucial for ensuring the safe and reliable operation of electric vehicles (EVs). Existing fault diagnosis methods have increasingly adopted deep neural networks due to their powerful learning and feature extraction capabilities. However, two significant limitations remain. Firstly, these methods fail to exploit the time–frequency coupling characteristics from multiple battery operational signals, leading to suboptimal feature representation. Secondly, the employed deep network models, such as Transformers, often require substantial computational resources, making them unsuitable for real-time deployment. To address these challenges, this paper proposes a novel fault diagnosis framework that integrates frequency slice wavelet transform (FSWT) with a lightweight SpikingFormer architecture. FSWT is employed to decompose and analyze multiple raw battery signals, capturing comprehensive time–frequency domain features that enhance fault representation. SpikingFormer, inspired by spiking neural networks, serves as an efficient alternative to the Transformer model, reducing computational complexity through event-driven processing while maintaining its capability to capture long-term dependencies. The proposed method, validated using real-world EV battery datasets collected from 100 EVs over a period of 6 to 12 months, demonstrates superior performance compared to state-of-the-art (SOTA) techniques. Specifically, it achieves a 4%–6.8% increase in mean fault-diagnosis accuracy and reduces the time-to-fault error by 1.2–3.2 min. Moreover, its inference time accounts for only 2.8%–28.4% of that required by SOTA methods, while its energy consumption is limited to 13.3%–14.4% of their levels.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100503"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362275","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":"Quantitative evaluation of venting-induced heat flux in semi-confined battery packs during lithium-ion battery thermal runaway","authors":"Rongqi Peng ,&nbsp;Ping Ping ,&nbsp;Depeng Kong ,&nbsp;Wei Gao ,&nbsp;Gongquan Wang ,&nbsp;Yihe Dong ,&nbsp;Juntao Huo ,&nbsp;Song Zhang ,&nbsp;Zehao Li","doi":"10.1016/j.etran.2025.100492","DOIUrl":"10.1016/j.etran.2025.100492","url":null,"abstract":"<div><div>The high-temperature multiphase flow vented by lithium-ion batteries (LIBs) during thermal runaway (TR) can significantly influence thermal runaway propagation (TRP) within confined battery packs. Quantitative analysis of the heating effect of unignited TR venting on neighboring cells is essential for understanding and predicting TRP behavior, particularly under semi-confined packaging conditions. In this study, we designed a modular experimental platform featuring an adjustable ceiling and peripheral baffles to emulate the semi-confined space of a battery pack. A distributed array of temperature-monitoring plates surrounding the triggered cell was used to record the transient heat flux induced by TR venting. Three critical parameters were systematically investigated in a stepwise spatial confinement framework: ceiling gap, trigger position, and state of charge (SOC). Reducing the ceiling gap from 100 mm to 15 mm markedly intensified the venting-induced heating: the maximum temperature rise of the plate adjacent to the trigger cell increased from approximately 44.1 °C–102.8 °C, while its thermal exposure integral (TEI) more than doubled. Center-triggered venting produced a more uniform but lower-intensity heat distribution over a wider area. In contrast, side-triggered venting-constrained by the enclosure-generated a localized high-heat region, where the maximum temperature rise and TEI on adjacent plates were approximately 20 % higher than in the center case, albeit over a smaller affected zone. Higher SOCs amplified the heating effect: at 100 % SOC, maximum temperature rise and TEI on adjacent plates were nearly double those observed at 50 % SOC. Based on these findings, an empirical heat-flux correlation incorporating multiphase venting effects was derived. While currently applicable to LFP cells under the tested conditions, this methodology can be extended to other battery configurations, supporting TRP modeling and informing future pack-level thermal protection strategies.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100492"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219597","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 multi-physics, fully liquid-cooled battery pack model development for winter-summer driving using a holistic reverse-engineering method","authors":"Ratnak Sok,&nbsp;Jin Kusaka","doi":"10.1016/j.etran.2025.100499","DOIUrl":"10.1016/j.etran.2025.100499","url":null,"abstract":"<div><div>Controlling battery temperature can reduce cell aging, internal resistance, and overheating, and improve pack performance. These require advanced battery thermal management systems (BTMS) for all-weather driving. A full liquid-cooled pack has hundreds to thousands of cells and coolant channels. Therefore, designing a full pack requires a thorough understanding of battery thermal, flow, and electrical responses under various driving and thermal conditions. This work presents a holistic reverse-engineering method to model and validate a production-based, liquid-cooled, 75-kWh lithium-ion battery pack, including its BTMS via multi-physics simulation. The model includes 4416 cells, 28 side-coolant lines, 784 coolant flow channels, and all plate bends. The channel geometries (width, height, bend angle, radius, and length) are optimized using a genetic algorithm. Firstly, a design-of-experiment is performed by changing the inlet coolant flow rate (<span><math><mrow><msub><mi>V</mi><mrow><mi>c</mi><mi>o</mi><mi>o</mi><mi>l</mi></mrow></msub></mrow></math></span> = 0–16 L/min) to measure steady-state and transient pressure drops. A sensitivity analysis of the channel geometries to the coolant flow characteristics is performed for the pack's flow model validation. A full battery-electric SUV equipped with the battery pack and dual e-motors was tested under a 60 km/h driving (winter test with ambient temperature <span><math><mrow><msub><mi>T</mi><mi>a</mi></msub></mrow></math></span> = −10 °C) and repeated WLTC and (FTP75+HWFET) cycles in summer (25–40 °C). The pack performances were recorded under battery heating (initial <span><math><mrow><msub><mi>T</mi><mrow><mi>a</mi><mo>,</mo><mi>i</mi></mrow></msub></mrow></math></span> &lt; initial <span><math><mrow><msub><mi>T</mi><mrow><mi>b</mi><mo>,</mo><mi>i</mi></mrow></msub></mrow></math></span>) and cooling (<span><math><mrow><msub><mi>T</mi><mrow><mi>a</mi><mo>,</mo><mi>i</mi></mrow></msub></mrow></math></span> &gt; <span><math><mrow><msub><mi>T</mi><mrow><mi>b</mi><mo>,</mo><mi>i</mi></mrow></msub></mrow></math></span>) modes. The battery model is based on the 2RC equivalent-circuit model, calibrated against an electrochemical NCA/Gr-SiO_x cell model to accelerate simulation time. Using the optimized flow and cell models, the model accurately (over 90 % accuracy) predicts the pack's responses (voltage, state of charge, flow, temperature) under steady-state and dynamic conditions. The detailed approach to building a comprehensive pack model can serve as a guideline for future BTMS development.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100499"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320596","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":"Machine learning-assisted optimization of NbTa alloy coating thickness via DC magnetron sputtering for SS316L bipolar plates in PEMFCs","authors":"Yasin Mehdizadeh Chellehbari ,&nbsp;Pramoth Varsan Madhavan ,&nbsp;Mohammadhossein Johar ,&nbsp;Leila Moradizadeh ,&nbsp;Abhay Gupta ,&nbsp;Xianguo Li ,&nbsp;Samaneh Shahgaldi","doi":"10.1016/j.etran.2025.100500","DOIUrl":"10.1016/j.etran.2025.100500","url":null,"abstract":"<div><div>Corrosion and high interfacial contact resistance (ICR) in metallic bipolar plates (BPPs) remain critical challenges limiting the durability of proton exchange membrane fuel cells (PEMFCs). This study employs a dual experimental-machine learning (ML) approach to optimize NbTa alloy coatings deposited on SS316L BPPs via DC-balanced magnetron sputtering. Electrochemical testing and surface characterization were conducted under simulated and accelerated PEMFC conditions, while an artificial neural network (ANN) model was developed to predict performance trends across coating thicknesses. A 2.5 μm coating exhibited the best overall performance, reducing corrosion current density to below 0.2 μA cm^-2 and ICR to 0.9 mΩ cm². Notably, the 1.7 μm coating also met U.S. DOE targets, representing a practical balance between cost and durability. The ANN model achieved high predictive accuracy (R² = 0.992), validating its use in guiding experimental optimization. A preliminary techno-economic assessment indicated that NbTa alloy coatings could achieve favorable payback periods of only a few years under plausible manufacturing scenarios, reinforcing their potential for large-scale PEMFC deployment. This integrated experimental-ML framework offers a powerful strategy for accelerating the development of corrosion-resistant, conductive coatings tailored for advanced PEMFC applications.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100500"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320594","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 spatiotemporal clustering method for mobile energy storage routing and vehicle-to-grid","authors":"Xinjiang Chen ,&nbsp;Jiayang Yao ,&nbsp;Guannan He","doi":"10.1016/j.etran.2025.100478","DOIUrl":"10.1016/j.etran.2025.100478","url":null,"abstract":"<div><div>Mobile Energy Storage (MES) has proven effective in integrating renewable energy and alleviating grid congestion due to its flexible deployment. However, in MES routing and Vehicle-to-Grid applications (such as energy arbitrage), the large-scale routing problem involving multiple vehicles and nodes encompasses high-dimensional spatiotemporal decision variables, making it challenging for general commercial solvers to solve efficiently. To address this challenge, we develop an improved time–space network-based model that uses feasible spatiotemporal arcs to represent the routing schemes for MES throughout the entire scheduling period. Furthermore, we propose an adaptive spatiotemporal clustering algorithm based on time–space network aggregation-split to solve the model quickly. In the aggregation phase, given the lower bound of cluster quantities, nodes with closely related spatiotemporal distances are clustered into one representative node. During the split phase, we design a spatiotemporal envelope method to identify nodes with potential arbitrage opportunities in each cluster and classify them into a separate cluster. We apply the proposed model and algorithm to the energy arbitrage of MES within the California power grid. The results reveal that, compared to the commercial solver, the proposed algorithm significantly reduces the average time overhead by 92.7%, while only sacrificing 0.9% in optimality in more than 300 daily scheduling cases.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100478"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158652","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":"Strain-rate-dependent failure behavior of lithium-ion batteries: Role of liquid electrolyte in impact safety","authors":"Mingzhe Zhou ,&nbsp;Jinyu Yan ,&nbsp;Qingfei Ren ,&nbsp;Yongrou Zhang ,&nbsp;Lingling Hu","doi":"10.1016/j.etran.2025.100490","DOIUrl":"10.1016/j.etran.2025.100490","url":null,"abstract":"<div><div>The structural integrity of lithium-ion batteries (LIBs) under dynamic loading is critical to their safe deployment in electric transportation systems. While dry-state testing of battery components is common, the influence of liquid electrolyte on battery failure under dynamic loading remains largely unexplored. This study investigates the out-of-plane compressive behavior of lithium iron phosphate (LFP) pouch cells in both dry and electrolyte-saturated states across a wide range of strain rates (0.005/s to 2000/s), using quasi-static and Split Hopkinson Pressure Bar (SHPB) tests. High-speed imaging and transparent cell designs enabled real-time visualization of electrolyte migration and structural deformation. The results show that, although electrolyte presence has little effect under quasi-static loading, it significantly lowers peak stress, strain, and stiffness at elevated strain rates. Microscopy reveals that confined electrolyte flow induces internal pore pressure, accelerates microcrack initiation in separators and electrode coatings. A mechanistic framework is proposed to explain how fluid–solid interactions degrade structural integrity at high rates. The findings demonstrate that dry-state testing overestimates battery resilience under impact and highlight the need to account for electrolyte effects in crash safety assessments. This work provides new insights into battery failure mechanisms relevant to electric mobility and supports the development of impact-tolerant energy storage systems and more comprehensive testing protocols for crashworthiness analysis.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100490"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219601","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":"Component-level analysis for developing an energy consumption model for battery electric vehicles (BEVs) in operation","authors":"Dongmin Kim,&nbsp;Kitae Jang","doi":"10.1016/j.etran.2025.100472","DOIUrl":"10.1016/j.etran.2025.100472","url":null,"abstract":"<div><div>In battery electric vehicles (BEV), energy originates in the battery and is transmitted to the wheels through a series of energy conversion processes involving the inverter and motor. Therefore, understanding the energy conversion mechanisms in both the inverter and motor is essential for accurately modeling energy consumption. However, in previous studies, real-world driving data are often limited, making it challenging to fully analyze the complex and nonlinear relationships within each conversion component. In this study, we collected input–output data from the inverters and motors of fifty-four BEVs, measured repeatedly over time. The data revealed a piecewise nonlinear relationship between input and output, prompting us to partition the models by different phases: propulsion, regeneration, and battery status. For each phase, we applied linear mixed-effects models to account for the hierarchical structure of the data, estimating coefficients separately for the inverter and motor using a randomly selected 75% of the dataset. Through this component-level modeling approach, the models not only capture component-level random-effect parameters but also effectively model the nonlinear energy conversion characteristics at the component level. The two models were then integrated to estimate the total driving energy consumption of the BEVs, and the results were validated against actual observations using the total driving energy from the remaining 25% of the dataset. Model performance was evaluated using the Total Consumption Estimation Rate (TCER) and Mean Absolute Percentage Error (MAPE). The proposed model achieved at least 95.27% in TCER and 86.34% in MAPE, outperforming existing approaches with a 20% higher TCER and an MAPE approximately ten times lower on average. The comparison demonstrated that our model accurately estimates driving energy consumption, as it effectively captured the heterogeneous and nonlinear relationships between input and output energy for each component.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100472"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158651","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":"Predicting battery degradation for electric vertical take-off and landing (eVTOL) aircraft: A comprehensive review of methods, challenges, and future trends","authors":"Baoji Wang ,&nbsp;Teng Xu ,&nbsp;Bailin Zheng ,&nbsp;Yue Kai ,&nbsp;Kai Zhang","doi":"10.1016/j.etran.2025.100477","DOIUrl":"10.1016/j.etran.2025.100477","url":null,"abstract":"<div><div>With the rapid development of intelligent technologies in aviation, electric vertical take-off and landing (eVTOL) aircraft have emerged as key players in the low-altitude economy, their battery performance directly impacts safety and cost, making accurate prediction essential. This paper presents a comprehensive review of the literature on battery degradation prediction methods for eVTOL aircraft, providing a brief overview on early modeling approaches and placing primary emphasis on recent advances in their applicability and limitations under unique operational scenarios of eVTOL, such as frequent takeoffs and landings, high power loads, and complex environmental conditions. Current prediction efforts primarily target key indicators including battery lifespan, health status, and capacity retention, employing a range of technical approaches such as electrochemical modeling, equivalent circuit modeling, data-driven algorithms like machine learning and deep learning, and hybrid physics-informed models that integrate domain knowledge with data analysis. The review systematically summarizes the main prediction methods and their evolution in different phases of the development of eVTOL technology. On this basis, we highlight existing technical bottlenecks and unresolved challenges, including the high demand for data and computational resources limiting real-time performance, poor accuracy of traditional models under high discharge rates and extreme conditions, challenges in accurately modeling complex multi-physics interactions and achieving a stable balance among prediction accuracy, interpretability, and real-time computational efficiency, as well as the scarcity of historical flight data affecting model reliability and generalization. This review also proposes future research directions to enhance the reliability and accuracy of battery degradation forecasting for eVTOL applications.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100477"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158654","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":"Insights into the failure mechanisms of leaky lithium-ion batteries for electric vehicles by a systematic multiscale analytical framework","authors":"Jing Hu ,&nbsp;Caiping Zhang ,&nbsp;Haoteng Guo ,&nbsp;Jing Xu ,&nbsp;Linjing Zhang ,&nbsp;Tao Zhu ,&nbsp;Yanru Zhang","doi":"10.1016/j.etran.2025.100504","DOIUrl":"10.1016/j.etran.2025.100504","url":null,"abstract":"<div><div>Electrolyte leakage poses serious safety risks by shortening service life and elevating the risk of thermal runaway. A comprehensive understanding of the failure mechanisms is essential for effective safety management. However, such studies are hindered by the lack of reliable fault imitation methods, poor reproducibility of experimental data, and the complexity of side reactions. To address these challenges, this paper proposes a systematic, analytical framework that integrates reproducible fault imitation, cell regeneration, and systematic in-situ and ex-situ analyses to investigate external behaviors and reveal the failure mechanisms. Failure scenarios are imitated by drilling holes into the annular indentation of the aluminum safety valve. In-situ analyses reveal nonlinear degradation behavior and severe kinetic deterioration, primarily attributed to the degradation of the solid electrolyte interphase (SEI). Ex-situ techniques, including cell regeneration and comprehensive material characterization, are employed to distinguish between the impacts of electrolyte depletion and electrode damage. Electrolyte depletion is identified as the primary failure mechanism, which drives severe kinetic degradation and ultimately causing battery performance deterioration or even failure. In contrast, the electrode structure remains largely intact. Moreover, regeneration experiments have confirmed that partial performance recovery can be achieved through electrolyte replenishment. These methods and findings are expected to offer valuable insights for battery fault detection and recycling strategies.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100504"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362273","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}