A Variable-length scale Parameter Dependent State of Charge Estimation of Lithium Ion Batteries by Kalman Filters

This paper proposes a new state of charge ( SOC ) estimation algorithm based on Kalman filters (KF). In the first stage, the equivalent circuit model’s parameters are estimated by a least square estimation window-wise, assuming a linear SOC and open-circuit voltage ( OCV ) relation. The algorithm accurately estimates the parameters and observes the changes that depend on SOC . Moreover, based on the estimated parameters, the OCV values are identified. In the next stage, window-wise Kalman filter(ES-KF) without hysteresis voltage and extended Kalman filter (ES-EKF) and sigma-point Kalman filter (ES-SPKF) algorithm with hysteresis voltage are executed to estimate SOC . Having fewer state equations and hysteresis parameters tuned up, the ES-EKF and ES-SPKF perform accurately and improve the results of previous algorithms. The proposed methods are validated by experiments with three different datasets obtained from lab tests. We also show SOC-OCV curve can be obtained in a simple way that replaces the time-consuming C/30 tests.