Parameter identification of Li-ion battery model by MAFFRLS algorithm
The modeling method and the method of model parameter identification will affect the accurate estimation of the Li-ion battery state,especially under dynamic conditions.Therefore,the method of online identification of battery model parameters is very important.A modified adaptive forgetting factor recursive least squares(MAFFRLS)method is proposed,its superiority is that the optimal value of the forgetting factor can be adaptively updated within different error ranges.A second-order RC equivalent circuit model is chosen to validate the algorithm under dynamic operating conditions.The proposed algorithm is compared with the recursive least squares(RLS)method and the forgetting factor recursive least squares(FFRLS)method.Under the dynamic stress test(DST)condition,the voltage is estimated using the RLS,FFRLS and MAFFRLS algorithms with the average absolute error of 0.010 2 V,0.009 9 V and 0.004 6 V,respectively.The root mean square error is 0.015 5 V,0.015 0 V and 0.006 8 V.The MAFFRLS algorithm has a smaller mean absolute error and root mean square error,the accuracy is higher.
battery modelequivalent circuit modeladaptiveforgetting factor recursive least squares(FFRLS)method
万方数据
维普
