摘要
本文以容量和能量为电池健康表征参数进行电池健康状态(State of health,SOH)评估方法研究。首先分别采用两种方法进行健康状态估计:一种是直接输入原始电池容量、能量序列,利用灰色预测算法(Metabolic grey algorithm,MGA)对电池容量和能量进行预测;另一种是先输入原始模型参数,利用灰色预测算法对简化电化学-老化模型(Simplifiedelectrochemical model,SEM)参数进行预测,将预测后的参数值代入到模型当中,拟合电池端电压曲线,再通过积分法获取电池的容量和能量。针对两种健康表征参数衰退速度、估计精度等问题,提出基于数据-模型混合驱动的锂离子电池健康状态的综合评估方法,实现电池健康状态的准确估计。
In this work,a battery’s state of health(SOH)estimation method is developed with capacity and energy as characterization parameters.Two methods are used to estimate the SOH.First,the metabolic grey algorithm(MGA)is used to predict the battery capacity and energy by insetting the original battery capacity and energy sequence directly.Second,the original model parameters are imput,the parameters of simplified electrochemical model(SEM)are predicted by using grey prediction algorithm,the predicted parameter values are brought back to the model,the battery terminal voltage curve is fit,and the battery capacity and energy are obtained by integration method.Aiming at the decay rate and estimation accuracy of the two characterization parameters,and a comprehensive battery health state estimation method based on data-model hybrid drive is developed to realize the accurate prediction of battery SOH.
作者
方德宇
楚潇
刘涛
李俊夫
FANG Deyu;CHU Xiao;LIU Tao;LI Junfu(School of Automotive Engineering,Harbin Institute of Technology(Weihai),Weihai 264201)
出处
《电气工程学报》
CSCD
2022年第4期20-31,共12页
Journal of Electrical Engineering
基金
中国博士后科学基金面上资助项目(2021M690740)。
关键词
锂离子电池
健康状态
灰色预测算法
简化电化学-老化模型
Lithium-ion battery
state of health
metabolic grey prediction algorithm
simplified electrochemical-aging model
