An Information Analysis Based Online Parameter Identification Method for Lithium-ion Batteries in Electric Vehicles
Ruohan Guo, Weixiang Shen
Abstract
This article proposes an information analysis-based multiple adaptive forgetting factors (FFs) recursive least squares (IA-MAFF-RLS) method to identify model parameters of lithium-ion batteries in electric vehicles. First, a Cramer–Rao lower bound (CRLB) based information analysis is implemented for each individual model parameter, and the constant information theory is introduced to update the CRLB for the associated estimation error covariance. Second, a switching-based adaptive strategy is proposed with two rules to fine-tune multiple FFs online by making a trade-off between the information richness in a memory window and the traceability to current-voltage profiles. Third, the IA-MAFF-RLS method is established to separately identify different model parameters with associated FFs. Unlike conventional methods, the proposed element-wise forgetting strategy directly focuses on battery model parameters rather than regression model coefficients, thereby avoiding an accuracy loss in the transformation between two models. According to the simulation and experimental results, the proposed method reduces the mean square deviation to −20.17 dB under noise interferences and benefits the generic extended Kalman filter in online SOC estimation with the estimation error less than 1% at different battery aging levels and temperatures.