重庆理工大学学报2024，Vol.38Issue(11)：11-20,10.DOI:10.3969/j.issn.1674-8425(z).2024.06.002
基于等效电路模型的云端动力电池寿命估计
Battery life estimation of cloud-based based on equivalent circuit modeling
摘要
Abstract
The current power battery management system (BMS)has such problems as small storage and low arithmetic power,causing capacity errors with increasing cumulative State of Charge (SOC)errors of batteries.To realize the accurate estimation of power battery life,this paper proposes a power battery capacity estimation model based on Equivalent Circuit Model (ECM).The model is based on the relationship between Open Circuit Voltage (OCV)and SOC,linking the first-order RC model directly to capacity.The cloud data will be data augmented,the representative charging data in the data will be screened out,the capacity will be recognized by substituting it into the first-order RC model and outputting the simulated end voltage,and the results of parameter recognition will be evaluated by the RMSE between the simulated end voltage and the actual end voltage.Based on the Particle Swarm Optimization (PSO) algorithm,the minimum RMSE identification result for initial capacity estimation is optimized. Our identification results are then optimized by combining Polynomial Curve Fitting (PCF)controlled Kalman filter (KF).Our results show the inclusion of filtering effectively improves the stability of the estimation results.Finally cloud data from the power packs of five vehicles are employed to validate the methodology. The root-mean-square error (RMSE)for both is less than 3％ and the maximum absolute error less than 2Ah,demonstrating the method accurately estimates the capacity of the power batteries.关键词
动力电池/云数据/电池寿命估计/等效电路模型/参数辨识Key words
power battery/cloud data/battery life estimation/equivalent circuit model/parameter identification分类
信息技术与安全科学引用本文复制引用
陈金荣,孙跃东,邵裕新,王冠,陈星光,郑岳久..基于等效电路模型的云端动力电池寿命估计[J].重庆理工大学学报,2024,38(11):11-20,10.基金项目
国家自然科学基金面上项目(52277222) （52277222）
上海市自然科学基金项目(22ZR1444500) （22ZR1444500）
