Etransportation最新文献

{"title":"Impact of shutdown operations on the recovery of reversible degradation in proton exchange membrane fuel cells","authors":"Ruitao Li ,&nbsp;Julong Zhou ,&nbsp;Naiyuan Yao ,&nbsp;Yuhan Zhou ,&nbsp;Weikang Lin ,&nbsp;Zishun Xu ,&nbsp;Jianbin Su ,&nbsp;Lei Shi ,&nbsp;Tiancai Ma","doi":"10.1016/j.etran.2025.100468","DOIUrl":"10.1016/j.etran.2025.100468","url":null,"abstract":"<div><div>In proton exchange membrane fuel cells, partial performance loss accumulated during long-term operation can be recovered through optimized operation or shutdown, a process known as reversible degradation. However, most existing studies focus on small-scale single cells and laboratory-scale test conditions, without adequately considering the constraints inherent to practical system-level operation. Therefore, investigating the influence of practically adjustable shutdown parameters on recovery effectiveness is crucial. Moreover, large-scale stacks exhibit spatial heterogeneity in degradation and recovery, both across cell positions and within individual cells. This heterogeneity plays a key role in identifying reversible degradation and formulating recovery strategies. In this study, accelerated stress tests were conducted on a full-size short stack under New European Driving Cycle conditions. Experimental variables included shutdown temperature, operating temperature, and purging methods, to evaluate their effects on recovery. Changes in stack consistency and electrochemically active surface area before and after recovery were analyzed. Results indicate that moderate retention of condensed water promotes ionomer rehydration and performance recovery, while uneven water distribution leads to spatial differences in recovery. Inter-cell and in-plane inconsistencies increase with current density, with voltage deviations exceeding 40 mV between cells and 20 mV within cells at 594 A. The outlet region exhibited weaker recovery consistency and greater sensitivity to load fluctuations, with response amplification reaching approximately 300 %. Cooling measures during recovery improved both steady-state performance and dynamic response. This work provides important insights into the optimization of shutdown parameters and spatial performance variation in large PEMFC stacks, supporting the development of improved operational strategies to enhance durability and efficiency in practical applications.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100468"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019421","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 state-of-the-art review on eVTOL thermal management: system architectures, key components and emerging technologies","authors":"Zhe Li ,&nbsp;Peng Xie ,&nbsp;Cheng Lin ,&nbsp;Guoyu Liu","doi":"10.1016/j.etran.2025.100480","DOIUrl":"10.1016/j.etran.2025.100480","url":null,"abstract":"<div><div>Electric vertical takeoff and landing aircraft (eVTOL) represent a transformative solution for modern transportation, offering high speed, low noise, and operational flexibility. However, the performance of their powertrains is highly temperature-sensitive, and their operating scenarios and mission profiles differ significantly from those of ground-based electric vehicles (EVs). In addition, cabin thermal regulation significantly affects energy consumption, thereby influencing the flight range. Consequently, an efficient thermal management system (TMS) is essential for eVTOL applications. This paper first reviews eVTOL powertrain architectures, followed by a systematic examination of the corresponding TMS architectures, including their operating principles, characteristics, and limitations. The thermal management requirements of key powertrain components are then analyzed, along with the review of relevant thermal management technologies. Moreover, emerging technologies applicable to eVTOLs are discussed, with an emphasis on their potential to enhance system performance. Finally, current research gaps are identified, and directions for future investigation are proposed. To the best of our knowledge, this is the first dedicated review of thermal management technologies for eVTOLs, aiming to clarify the state of the art, identify existing challenges, and provide valuable insights for researchers and industry practitioners.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100480"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096103","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":"State-of-charge estimation over full battery lifespan under diverse fast-charging protocols: A lightweight base-error joint modeling framework","authors":"Ganglin Cao ,&nbsp;Shouxuan Chen ,&nbsp;Yuanfei Geng ,&nbsp;Shuzhi Zhang ,&nbsp;Yao Jia ,&nbsp;Rong Feng ,&nbsp;Yongjun Liu","doi":"10.1016/j.etran.2025.100474","DOIUrl":"10.1016/j.etran.2025.100474","url":null,"abstract":"<div><div>Accurate state-of-charge (SOC) online estimation during various multi-stage constant current (MCC) fast-charging protocols over battery entire lifespan holds significant importance. In this work, we develop a lightweight-training oriented data-driven base-error joint modeling framework to fill this research gap. Through deep learning-based initial-cycle data training and lightweight machine learning-based typical-cycle data training, we only extract approximately 1 % of whole battery data for data-driven base-error joint modeling. With consideration of SOC time-dependency, short-term Ampere-hour is further combined via a simple filter structure to guarantee final SOC estimation accuracy. The validation, derived from a public battery degradation dataset comprising 8 different MCC fast-charging protocols from 46 cells, demonstrates that our framework allows rapid data-driven base-error joint modeling with training time only about l min, where both average mean absolute error and average root mean square error of SOC estimation during various MCC fast-charging protocols over battery entire lifespan are roughly below 0.3 %. Our work, for the first time, reveals the possibility of joint data-driven model trained via extremely few data on accurate SOC online estimation with consideration of various MCC fast-charging protocols and battery degradation status, and also offers a pretty concise but efficient solution for multi-scenario battery aging diagnosis and voltage dynamics forecast. The code accompanying this work is available at <span><span>https://github.com/szzhang96/A-light-weighted-training-oriented-data-driven-base-error-joint-modeling-method-for-SOC-estimation</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100474"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010679","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 flight challenges in PEMFC-powered UAVs: Towards intelligent management and sustainable propulsion","authors":"Xikai Tu ,&nbsp;Jin Wu ,&nbsp;Zhiming Tao ,&nbsp;Chang Wen ,&nbsp;Zhengkai Tu","doi":"10.1016/j.etran.2025.100506","DOIUrl":"10.1016/j.etran.2025.100506","url":null,"abstract":"<div><div>Proton exchange membrane fuel cell (PEMFC)-powered unmanned aerial vehicles (UAVs) exhibit nonlinear and coupled behavior under dynamic flight conditions. To enable intelligent management, this study proposes an adaptive air stoichiometric ratio (ASR) strategy that dynamically responds to real-time variations in payload and acceleration. Although ASR optimization has been studied under steady-state conditions, its regulation under realistic UAV dynamics remains underexplored and experimentally unverified. We develop a coupled framework integrating UAV flight dynamics, an air-cooled PEMFC model, and mass–heat transfer multiphysics, and validate it through dynamic flight tests. Results show that optimal ASR varies significantly with operating conditions: with a 40 kg payload, ASR increases from 52 to 81 as acceleration changes from −0.6 to 0.6 m/s²; at 0.6 m/s², raising the payload from 25 to 40 kg increases ASR from 24 to 81. Optimal ASR regulation improves stack voltage by 0.170 V, reduces hydrogen consumption by 2.05 mg per 100 m of flight, lowers temperature by 18.32 %, and enhances efficiency, voltage uniformity, and temperature uniformity. Notably, ASR exhibits a nonlinear influence on performance: it improves markedly from 25 to 28 kg, remains stable between 28 and 31 kg, and rises again at 40 kg. Experimental validation (error &lt;1.1 %) confirms model accuracy and demonstrates the effectiveness of ASR optimization in PEMFC-powered UAVs. Beyond UAV applications, the proposed adaptive ASR strategy offers a pathway toward intelligent air management in fuel-cell propulsion systems, with direct relevance to emerging electric transportation modes such as urban air mobility vehicles, cargo drones, and hybrid-electric aircraft.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100506"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416082","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":"Early warning of lithium-ion battery thermal runaway based on gas sensors","authors":"Yuhang Song ,&nbsp;Xin Jiang ,&nbsp;Nawei Lyu ,&nbsp;Hongfei Lu ,&nbsp;Di Zhang ,&nbsp;Hong Li ,&nbsp;Yang jin","doi":"10.1016/j.etran.2025.100502","DOIUrl":"10.1016/j.etran.2025.100502","url":null,"abstract":"<div><div>Lithium-ion batteries (LIBs) are widely used in electric vehicles and battery energy storage systems, but the risks of thermal runaway (TR) pose significant safety challenges. Gas sensing technology offers a promising solution for early TR warning by detecting hazardous gases. This paper provides a comprehensive review of gas generation mechanisms across the lifecycle and systematically analyzes gas generation characteristics under varying conditions (material chemistries, state of charge (SOC), abuse conditions, state of health, environment, battery package and capacity). Based on 247 reported TR cases, principal component analysis with SOC-balanced resampling was applied to quantitatively assess the effects of material chemistries, SOC, and abuse conditions on gas composition. These insights enable more targeted selection of warning gases. Mainstream gas sensors, including metal oxide semiconductor, electrochemical and optical types, are evaluated for their suitability in LIBs safety applications. Furthermore, optimal gas sensor selection strategies are proposed for batteries with different material chemistries, enhancing the precision of early warning systems. Finally, the challenges of gas sensing technologies in TR early warning are analyzed, and an outlook on future development directions is provided, paving the way for more reliable and effective safety strategies.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100502"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416084","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":"Breaking the voltage plateau barrier: Slope-adaptive state-of-charge estimation for LFP batteries with temperature-aware hysteresis modeling","authors":"Lisen Yan ,&nbsp;Jun Peng ,&nbsp;Heng Li ,&nbsp;Zhiwu Huang ,&nbsp;Dirk Uwe Sauer ,&nbsp;Weihan Li","doi":"10.1016/j.etran.2025.100473","DOIUrl":"10.1016/j.etran.2025.100473","url":null,"abstract":"<div><div>The open-circuit voltage (OCV) hysteresis effect significantly complicates state-of-charge (SOC) estimation of <span><math><msub><mrow><mtext>LiFePO</mtext></mrow><mrow><mtext>4</mtext></mrow></msub></math></span> batteries. While prior research has focused on major-loop hysteresis between full charge and discharge, accurately modeling minor-loop hysteresis during partial charge/discharge remains a persistent challenge. This paper proposes a data-driven hysteresis model that incorporates historical SOC and temperature data, with which an adaptive SOC estimator is designed to accommodate slope variations in minor-loop hysteresis. The proposed model accurately captures complex voltage hysteresis across different charge/discharge paths and temperature conditions using deep long short-term memory neural networks trained on hysteresis test data. This OCV component is integrated into a second-order equivalent circuit model, achieving both high-precision battery modeling and computational efficiency. The model parameters are optimized effectively using a multistep parameter identification method enhanced by a meta-heuristic algorithm. The proposed SOC estimator dynamically adjusts its covariance matrices in response to voltage slope variations during the plateau, improving Kalman gain matching to eliminate cumulative errors and enhance accuracy. Extensive experimental results show that over 95% of samples achieve a mean absolute error of less than 0.56% across various usage scenarios. The proposed method outperforms two state-of-the-art methods by 46.2% and 45.7% in root mean square error, demonstrating fast convergence and robust estimation even within the voltage plateau.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100473"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219600","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":"Multi-level passive-active thermal control for battery thermal runaway prevention and suppression in electric vehicles","authors":"Jiekai Xie ,&nbsp;Junlin Li ,&nbsp;Canbing Li ,&nbsp;Xinyan Huang ,&nbsp;Guoqing Zhang ,&nbsp;Xiaoqing Yang","doi":"10.1016/j.etran.2025.100467","DOIUrl":"10.1016/j.etran.2025.100467","url":null,"abstract":"<div><div>Resolving the contradiction between heat-dissipation during normal operation and thermal-insulation after thermal runaway (TR) is highly desirable for battery thermal safety system but remains challenges. Herein, a multi-leveled thermal control strategy, <em>i.e.</em>, passive cooling - active cooling - passive suppression - active suppression, has been proposed for TR prevention-suppression of the battery packs. The system is primarily designed by modular composite phase change material (CPCM), liquid cooling (LC) plates and aerogel plates (APs). Firstly, the passive cooling CPCM coordinated with active LC enables a suitable working temperature, low temperature gradient and low energy consumption of the battery pack under variable environments. Secondly, the modular design of the battery pack couples with the passive thermal-insulation effect of APs, successfully preventing TR from propagating to other modules. Thirdly, APs work synergistically with dynamic LC, greatly enhancing the directional heat-dissipation, and consequently, the TR propagation can be suppressed to the lowest level. By the flexible dynamic flow rate adjustment, the TR of large-scaled battery packs with different configurations of 4S12P, 6S8P, 8S6P and 12S4P can be successfully suppressed in the initially-triggered cell.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100467"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019420","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":"Bridging battery degradation and safety: Challenges and opportunities","authors":"Yiwen Zhao ,&nbsp;Zhenpo Wang ,&nbsp;Lisen Yan ,&nbsp;Zhenyu Sun ,&nbsp;Peng Liu ,&nbsp;Lei Zhang ,&nbsp;Weihan Li","doi":"10.1016/j.etran.2025.100497","DOIUrl":"10.1016/j.etran.2025.100497","url":null,"abstract":"<div><div>As lithium-ion batteries continue to empower the global shift toward transportation electrification and renewable energy integration, ensuring their reliable, long-term and safe operation has emerged as a topmost challenge. Despite extensive research on both degradation mechanisms and catastrophic failures such as thermal runaway, these phenomena are often investigated in isolation, hindering the development of comprehensive safety strategies. This review bridges the critical gap between battery degradation and safety by establishing a unified framework that connects gradual degradation processes, fault evolution and extreme risks. We systematically examine how electrochemical degradation influences the emergence of safety-critical events and emphasize the importance of diagnostic strategies capable of identifying performance degradation, detecting early-stage faults and predicting impending thermal hazards. Such insights not only enhance safety risk awareness but also enable proactive interventions across the battery lifecycle. Looking ahead, we provide guidance on key pathways toward lifecycle-aware battery management system development and scalable methods for large-scale deployment.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100497"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362274","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 mechanical reliability and safety performance of a battery pack system for electric vehicles: A review","authors":"Ibna Kawsar,&nbsp;Honggang Li,&nbsp;Binghe Liu,&nbsp;Yongzhi Zhang,&nbsp;Yongjun Pan","doi":"10.1016/j.etran.2025.100469","DOIUrl":"10.1016/j.etran.2025.100469","url":null,"abstract":"<div><div>In electric vehicles (EVs), battery packs (BPs) are susceptible to mechanical and functional failures, where various environmental factors are influenced. Although standard testing procedures contribute to improved safety and overall performance, current research primarily examines individual factors, neglecting a comprehensive assessment of battery pack (BP) design solutions. This review comprehensively analyzes safety standards, empirical research, and advances in patent design to provide a broad perspective on the safety of battery pack systems (BPS). Specifically, it examines the responses of BPs to severe environmental conditions, including vibrations, mechanical shock, and collisions. The paper presents comprehensive design solutions, providing valuable knowledge on reducing the likelihood of failure and addressing safety concerns. The review emphasizes the importance of a complete optimization strategy for BPS, explicitly focusing on analyzing mechanical reactions, particularly concerning the reliability and efficacy of safety alerts. The conclusion highlights the imperative to meet operational requirements and safety standards in the design of BP, emphasizing the importance of adopting a robust structural design approach. The study suggested adopting harmonized standards for testing in realistic scenarios. Furthermore, this study makes an innovative contribution by exploring advanced technologies, such as FEA-DNN, reinforcement learning, and various intelligent optimization algorithms, to mitigate mechanical stresses, vibrations, shock impacts, and collision-induced damage in different work environments, providing engineering guidance to enhance the safety performance of BPS.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100469"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060521","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":"Digital twins for battery health prognosis: A comprehensive review of recent advances and challenges","authors":"Yujie Wang,&nbsp;Jiayin Xiao,&nbsp;Yin-Yi Soo,&nbsp;Yifan Chen,&nbsp;Zonghai Chen","doi":"10.1016/j.etran.2025.100489","DOIUrl":"10.1016/j.etran.2025.100489","url":null,"abstract":"<div><div>This review systematically examines the integration of Digital Twin (DT) technology with lithium-ion battery health prognosis systems. As electrification accelerates across multiple domains, accurate prediction of battery health indicators – including State of Charge (SOC), State of Health (SOH), Remaining Useful Life (RUL), and fault conditions – becomes increasingly critical for ensuring safety, reliability, and optimal performance. The core contribution of this review lies in proposing a novel four-layer conceptual framework, comprising the Physical, Data &amp; Communication, Virtual Model, and Twin Service layers, as an analytical tool for structuring the field. After establishing the theoretical foundations of DTs and battery aging, we leverage this framework to systematically survey recent advancements in data augmentation, online state estimation, and fault diagnosis. Through this structured analysis, we then identify critical implementation challenges, including performance in extreme degradation phases, battery pack inconsistencies, and operation under complex conditions. We conclude by proposing future research directions focused on enhancing model generalization and creating standardized architectures through the integration of cloud computing and IoT technologies, and applying federated learning to solve potential privacy and security problems. This review serves as a critical reference by providing a structured, application-centric understanding of DTs in battery health management.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100489"},"PeriodicalIF":17.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219594","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}