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

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 LiFePO₄ 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 LiFePO₄ 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%.

I. Introduction

The global average temperature is gradually rising due to greenhouse gas emissions worldwide. The development of electric vehicles (EVs) and renewable energy power generation is trying to improve this dilemma [1]. Lithium-ion batteries have become their most popular energy storage carrier owing to the high energy density, low self-discharge rate and long service life [2]. However, the capacity of lithium-ion battery gradually decreases, and the internal resistance gradually rises with use and aging [3]. It is generally believed that when the capacity of battery is less than 80% of the initial value, it cannot continue to serve EVs and needs to be retired to avoid safety problems [4]. In addition, other battery states are closely related to its aging from the perspective of battery life cycle [5]. For example, state of charge (SoC) is defined as the ratio of the remaining electric quantity to the maximum available capacity, and the capacity should be adjusted with battery aging to ensure estimation accuracy [6]. Battery power estimation takes SoC as input, and is greatly affected by internal resistance [7]. The peak power gradually decreases as the battery ages. In short, accurate estimation of state of health (SoH) is an extremely important function of battery management system (BMS) in EVs.