On-Line Multi-Time Scale Adaptive Parameter Identification Based on Improved Lithium-Ion Batteries Hysteresis Characteristic-Electrical Equivalent Circuit Modeling

Abstract

Accurate identification of model parameters is a key aspect of lithium battery state estimation. To accurately identify battery model parameters, this paper establishes Hysteresis Characteristic-Electrical Equivalent Circuit (HC-EEC) modeling by analyzing the influence of the hysteresis effect on the battery State of Charge (SOC). For the high-precision identification of battery model parameters, an Online Multi-Time Scale Adaptive Parameter Identification Strategy (OM-TSAPIS) is proposed in this paper. According to the different dynamic response links in the HC-EEC model, the strategy performs parameter identification through different time scale links and uses the adaptive step size as the starting identification condition for the multi-time scale links, thereby improving the parameter identification accuracy of the HC-EEC model. The absolute average error of OM-TSAPIS was 0.0437 mV and 0.298 mV under the Urban Dynamometer Driving Schedule (UDDS) and Beijing Bus Dynamic Street Test (BBDST) conditions, respectively. Simulation results show that the identification accuracy of the proposed algorithm is high.