汽车工程学报2024，Vol.14Issue(6)：1048-1060,13.DOI:10.3969/j.issn.2095‒1469.2024.06.12
面向多步快速充电场景的锂离子电池健康状况估计
Health State Estimation of Lithium-Ion Batteries for Multi-Step Fast Charging Scenarios
摘要
Abstract
In response to the challenges posed by the widespread adoption of fast charging in lithium-ion battery health assessment,this study develops a state-of-health estimation model for dynamic fast-charging scenarios.Twelve direct features are extracted from the partial voltage curve during the fast charging process,followed by a comprehensive analysis of degradation mechanisms strongly correlated with these features.Subsequently,feature selection is conducted based on degradation mechanisms and correlation analysis,and the radial basis function neural network(RBFNN)is deployed to establish the estimation model.The validation results indicate that the constructed data features exhibit excellent generalization across various battery degradation paths,improving accuracy by over 17% compared to traditional feature selection methods.Satisfactory estimation results are obtained even under different fast charging protocols and with a smaller training dataset.关键词
锂离子电池/健康状态/多步快充/特征筛选Key words
lithium-ion battery/state of health/multi-step fast charging/feature selection分类
信息技术与安全科学引用本文复制引用
宋伟萍,刘丹,李耀华,冯乾隆..面向多步快速充电场景的锂离子电池健康状况估计[J].汽车工程学报,2024,14(6):1048-1060,13.基金项目
2022年陕西国防工业职业技术学院校本科研课题(Gfy22-54):基于西安市工况的混合动力汽车能量管理策略研究 （Gfy22-54）
