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
陈金荣 1孙跃东 1邵裕新 1王冠 1陈星光 1郑岳久1
作者信息
- 1. 上海理工大学 机械工程学院,上海 200093
- 折叠
摘要
由于目前动力电池管理系统(battery management system,BMS)存在存储小、算力低等问题,仅依靠BMS估计出的容量误差会随电池荷电状态(state of charge,SOC)累计误差增大而逐渐增大.为实现动力电池寿命准确估计,提出了基于等效电路模型(equivalent circuit model,ECM)的动力电池容量估计方法.模型基于开路电压(open circuit voltage,OCV)和SOC的关系,直接建立1阶RC模型和容量联系;通过粒子群优化算法(particle swarm optimization,PSO)寻优最小仿真端电压与实际端电压的均方根误差(root-mean-square error,RMSE),此时辨识结果为初步估计容量,结合多项式回归(polynomial curve fitting,PCF)控制卡尔曼滤波(kalman filter,KF)对辨识结果进行了优化.最后对云端实车与传统方法测得的容量进行对比验证,二者的RMSE小于3％且最大绝对误差小于2 Ah.与现有方法相比,该方法能够不单依赖BMS数据准确估计容量.同时,对于实车等复杂场景的应用做出了优化,可以实现实车场景下的容量精确估计.
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引用本文复制引用
基金项目
国家自然科学基金面上项目(52277222)
上海市自然科学基金项目(22ZR1444500)
出版年
2024
国家科技期刊平台
NSTL
万方数据