Decoding battery aging in fast-charging electric vehicles: An advanced SOH estimation framework using real-world field data

作者：Caiping Zhang, Jinyu Wang, Linjing Zhang, Weige Zhang, Tao Zhu, Xiaoguang Yang, Andrew Cruden · 发表于：Energy storage materials · 年份：2025 · DOI：10.1016/j.ensm.2025.104236 · 被引用次数：22 · 研究领域：Advanced Battery Technologies Research、Advancements in Battery Materials、Electric Vehicles and Infrastructure

Accurately estimating the state of health (SOH) of in-vehicle batteries is critical for advancing electric vehicle (EV) technology. However, higher charging rates and more complex driving conditions have posed major challenges, with significant variations from vehicle-to-vehicle and cycle-to-cycle. In this study, we developed a SOH estimation framework to monitor battery capacity degradation, in EVs with multi-step constant-current fast charging and voltage balancing technology. The framework employs a customized data window approach, informed by a thorough analysis of EV charging behavior, and extracts hierarchical features from vehicle-, pack- and cell-levels for tracking battery aging. We collected real-world charging data from 300 pure EVs over 1.5 years, resulting in 193,180 samples for validation. The best-performing machine learning models achieved an absolute error of less than 2 % for 93.7 % of samples, a root mean square error (RMSE) of 1.05 %, and a maximum error of only 3.73 % whilst using only 30 % data for training. Our analysis indicates that the proposed model can be effectively developed without the need to pre-select vehicles based on specific driving habits or operating conditions. Notably, reliable and accurate estimations were produced using data from just one vehicle, achieving an RMSE of 1.82 %. Our results highlight the potential of user behavior-assisted feature engineering to decode battery pack aging under dynamically changing vehicle profiles. This...