The multi-innovation extended Kalman filter algorithm for battery SOC estimation

Abstract

For a lithium battery, a second-order equivalent circuit model is adopted by studying the battery characteristic, and a state space equation with state of charge (SOC) being one state is constructed. To promote the SOC estimation precision of the extended Kalman filter (EKF) method for a lithium battery, this paper explores a multi-innovation extended Kalman filter (MI-EKF) algorithm to estimate the battery SOC by expanding a single innovation at current instant to multi-innovations containing information from current and previous instants. The aim is to increase the amount of information, and to get the more accurate estimated SOC. In addition, based on the battery difference equation, a stochastic gradient algorithm with a forgetting factor (FFSG) is used to identify the battery parameters. Finally, a lithium battery test bench is set up to sample charge-discharge data and to implement MATLAB simulation experiment; the experiment results confirm that the MI-EKF algorithm can accurately and effectively estimate the battery SOC.