A Robust Online Parameter Identification Method for Lithium-Ion Battery Model Under Asynchronous Sampling and Noise Interference

Abstract

Online identification of a battery model can capture the parameter variations in real time, thus, providing an accurate model under various conditions, such as a wide temperature range, different aging degree in electric vehicle application. However, the measurement noise and sampling asynchronization of voltage and current are usually inevitable in the battery management system (BMS), which can lead to a large identification error. In this article, the mechanism of sampling asynchronization and measurement noise is analyzed first, and then the identification sensitivity analyses on noise and sampling asynchronization are carried out. Simulation results indicate that even a small fluctuation as small as 5 mV or the sampling latency between voltage and current within 10 mS can cause relatively large identification errors, especially for polarization parameters. In order to guarantee the online identification accuracy and robustness in the BMS application, an improved robust recursive least-squares (RLS) algorithm with adaptive outlier boundary is proposed to eliminate the input outlier caused by sampling latency and suppress the effect of measurement noise utilizing the bias compensation method. The experimental results demonstrate that the proposed method can provide sufficient accuracy and robustness under noise interference and sampling latency in BMS application.