A Robust Parameter Identification Approach with Anti-Outlier Characteristics for Lithium-Ion Batteries

Abstract

Accurate acquisition of model parameters of lithium-ion batteries (LIBs) is imperative for precious estimation state of charge. However, due to the interference of noise, the inevitable uncertainty may lead to outliers appearing in measurement data, which often leads to inaccurate parameter identification. To solve the problem, a robust Kalman filter (RKF) that uses the Mahalanobis Distance Criterion (MDC) is proposed for the parameter identification of LIBs. Firstly, the one-order equivalent circuit model of the LIBs is created. Subsequently, the gain of robust factor is implemented into the Kalman filter to decrease filter gain and swell the measurement noise covariance when facing the outlier measurements. Finally, the dynamic stress test condition of the electric vehicle is used to prove the effectiveness of the RKF-MDC approach. The simulation reveals that the RKF-MDC can reduce the influence of outlier values for the parameter identification of LIBs.