SOH estimation of Li-ion battery based on constant voltage charging data
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.
Li-ion batterystate of health(SOH)estimationconstant voltage(CV)chargegrey wolf optimization(GWO)algorithmsupport vector regression(SVR)
国家科技期刊平台
NSTL
万方数据
