Practical State of Health Estimation for LiFePO4 Batteries Based on Gaussian Mixture Regression and Incremental Capacity Analysis

Abstract

High accuracy, high adaptability and low complexity have always been the goals of on-board state of health (SoH) estimation in a well-designed battery management system for electric vehicles. In this article, we proposed a practical SoH estimation method for LiFePO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> batteries based on Gaussian mixture regression (GMR) and incremental capacity (IC) analysis. According to the close correlation between the SoH and the IC curve of LiFePO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> battery, the peak coordinates of the curve are extracted as capacity feature to train an aging model using GMR. The capacity degradation of batteries is well described by multiple Gaussian distributions, and the estimation accuracy of SoH is improved. Furthermore, the coordinates of three peaks in the curve are separately analyzed as capacity feature to enhance the adaptability of the aging model to different charging regions. Finally, the complexity of the proposed method is compared with that of other methods. The results show that taking the coordinates of any peak as the capacity feature, SoH can be accurately estimated based on GMR even for different tested batteries, and the mean absolute error and root mean square error are both less than 1%.