电池2024，Vol.54Issue(6)：815-820,6.DOI:10.19535/j.1001-1579.2024.06.011
基于恒压充电数据的锂离子电池SOH估计
SOH estimation of Li-ion battery based on constant voltage charging data
摘要
Abstract
Accurate estimation of state of health(SOH)is crucial for the safe operation of the Li-ion battery.Based on the current data under the constant voltage(CV)charging scenario,the difference parameters of the current curves are extracted as health indicators(HI).In order to obtain the SOH estimation model,the gray wolf optimization(GWO)and the support vector regression(SVR)algorithms are combined to construct the mapping relation between HI and SOH.The validation based on two public battery test datasets demonstrates that under both the complete and the incomplete CV charging scenarios,the root-mean-square errors of the SOH estimation by the proposed method are overall less than 2%.The SOH estimation errors are compared with algorithms including GWO-SVR,SVR and Gaussian process regression.it indicates that the proposed method has better comprehensive performance.关键词
锂离子电池/健康状态(SOH)估计/恒压(CV)充电/灰狼优化(GWO)算法/支持向量机回归(SVR)Key words
Li-ion battery/state of health(SOH)estimation/constant voltage(CV)charge/grey wolf optimization(GWO)algorithm/support vector regression(SVR)分类
信息技术与安全科学引用本文复制引用
杨驹丰,李哲,王振,邬明宇,马迷娜,栗欢欢..基于恒压充电数据的锂离子电池SOH估计[J].电池,2024,54(6):815-820,6.基金项目
国家自然科学基金(52307244),国家资助博士后研究人员计划(GZC20231585),中国博士后科学基金第74批面上项目(2023M742255),江苏省自然科学基金(BK20210773),河北省自然科学基金(E2022210060),智能绿色车辆与交通全国重点实验室开放基金(KFY2408) （52307244）
