State-of-charge estimation for the lithium-ion battery based on adaptive extended Kalman filter using improved parameter identification

The state of charge(SOC) of lithium-ion battery is an essential parameter of battery management system. Accurate estimation of SOC is conducive to give full play to the capacity and performance of the battery. For the problems of selection of forgetting factor and poor robustness and susceptibility to the noise of extended Kalman filtering algorithm, this paper proposes a SOC estimation method for the lithium-ion battery based on adaptive extended Kalman filter using improved parameter identification. Firstly, the Thevenin equivalent circuit model is established and the recursive least squares with forgetting factor(FFRLS) method is used to achieve the parameter identification. Secondly, an evaluation factor is defined, and fuzzy control is used to realize the mapping between the evaluation factor and the correction value of forgetting factor, so as to realize the adaptive adjustment of forgetting factor. Finally, the noise adaptive algorithm is introduced into the extended Kalman filtering algorithm(AEKF) to estimate the SOC based on the identification results, which is applied to the parameter identification at the next time and executed circularly, so as to realize the accurate estimation of SOC. The experimental results show that the proposed method has good robustness and estimation accuracy compared with other filtering algorithms under different working conditions, state of health(SOH) and temperature.