Conventional unscented Kalman filtering faces two main problems in state of charge(SOC)estimation:firstly,the low accuracy of SOC estimation due to the fixed parameters of the battery model;secondly,the failure of SOC estimation due to the occurrence of non-positive timing in the covariance matrix.A forgetting factor recursive least square(FFRLS)algorithm combined with square root unscented Kalman filter(SRUKF)is proposed for SOC estimation algorithm.Firstly,a second-order resistance capacitance(RC)equivalent circuit model is established.Secondly,the FFRLS algorithm is used to identify the circuit model parameters online and correct the battery equivalent circuit model in real time,based on which the SRUKF algorithm is used to estimate the SOC.Finally,the proposed algorithm is validated by intermittent constant-current pulse discharging and dynamic stress test conditions.The text results prove that the average absolute error of the algorithm is lower than 0.011 5,and the root-mean-square error is lower than 0.012.Compared with the SRUKF algorithm,FFRLS-SRUKF algorithm has a better SOC estimation performance,which provides a reliable basis for the battery management system to solve the inconsistency of the lithium batteries.
维普
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