LiFePO<sub>4</sub> Battery SOC Estimation Under OCV–SOC Curve Error Based on Adaptive Multimodel Kalman Filter
Daniyaer Paizulamu, Lin Cheng, Helin Xu, Yingrui Zhuang, Ning Qi, Song Ci
Abstract
LiFePO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> batteries are widely used in electric vehicles and energy storage systems due to long cycle life and high safety performance. However, the open circuit voltage-state of charge curve (OSC) of these batteries features a long plateau region, making state of charge (SOC) estimation highly sensitive to OSC error, which arises due to aging and temperature. To address this, we propose an SOC estimation method that accounts for error in OSC. First, we establish battery equivalent circuit model and introduce a parameters identification algorithm based on adaptive recursive least squares. Next, we derive the relationship between the innovation’s cross-correlation matrix/auto-correlation matrix of the Kalman filter and the OSC error. We then develop an adaptive multi-model Kalman filter, which dynamically adjusts the measurement model parameters of each filter based on the sign of the OSC error. By assigning a probability to each filter according to its predicted voltage distribution function, the optimal filter is selected. The proposed method is tested under various OSC error types and operating conditions. Results demonstrate that the proposed method achieves high accuracy and robustness, with root mean square error of less than 0.03, also offers significant advantages in computational efficiency, interpretability, and generalizability.