State-of-Charge Observer Design for Batteries With Online Model Parameter Identification: A Robust Approach

Abstract

The state-of-charge (SOC) indicates a lithium-ion battery's remaining capacity, and an accurate SOC estimation plays a crucial role in the battery's operation optimization and lifetime extension. This article studies a robust model-based SOC estimation strategy for batteries. Based on a battery equivalent circuit model, a robust recursive-least-squares algorithm is utilized for the model parameters online extraction, which avoids unnecessary experiments prior to SOC estimation for parameter identification. Compared with the conventional recursive least squares, it can effectively guarantee the parameter identification performance in spite of outliers in battery measurement signals. Then, a robust observer with the estimated model parameters is designed for the battery's SOC estimation, which can suppress the disturbance caused by unknown model errors. Theoretical analysis and extensive experimental results demonstrate the effectiveness of the designed SOC observer combined with robust recursive least-squares-based model identification.