Accurate State of Charge Estimation With Model Mismatch for Li-Ion Batteries: A Joint Moving Horizon Estimation Approach

Abstract

The accurate state of charge (SOC) estimation plays a significant role in charge/discharge control, balance control, and safe management of lithium-ion batteries (LIBs). However, due to the model mismatch issues, either from battery inconsistency or battery dynamic characteristics difference, the accuracy of the model-based SOC estimation method is usually unsatisfactory. To solve this problem, a joint moving horizon estimation (joint-MHE) approach that can simultaneously estimate the model parameter and state is proposed here. In this paper, the circuit-equivalent battery model is first constructed by parameterizing the circuit parameters as polynomial function of SOC. Then, by the sensitivity analysis, the update parameters are selected and added to the state-space model as additional states. Finally, the joint-MHE strategy is conducted for the simultaneous parameter and SOC estimation. To investigate the performance of the proposed method thoroughly, three model mismatch conditions are considered, including battery inconsistency, battery dynamic characteristics difference, and the combination of both. The results demonstrate that the joint-MHE approach is an effective way to solve the model mismatch problem. Moreover, compared to joint extended Kalman filtering, the proposed approach can offer a more reliable, robust, and accurate SOC estimation of LIBs under various model mismatch conditions.