Combining Multi-Indirect Features Extraction and Optimized Gaussian Process Regression Algorithm for Online State of Health Estimation of Lithium-Ion Batteries

Abstract

With the wide application of lithium batteries (LIBs) in electrified transportation and smart grids, especially in the pure electric vehicle industry, the accurate health maintenance monitoring of LIBs has emerged as critical to safe battery operation. Although many data-driven methods with state of health (SOH) estimation for LIBs have been proposed, the problems of industrial application and computational cost still need to be improved further. In contrast, this article carried out a low-complexity SOH estimation method for LIBs. Specifically, the seven health indicators are extracted firstly to characterize battery health status from voltage, current, temperature, and other data that can be obtained online. Then, the optimized Gaussian process regression (GPR) algorithm is proposed with proper computational cost. Ultimately, by combining a multi-indirect features extraction and optimized GPR algorithm, the online SOH estimation for LIBs was established and verified with NASA experiment data. The experimental results show that the maximum MAPE of SOH estimation from the proposed method is 1.4496 and the minimum MAPE only reaches 0.5635. More importantly, the optimized GPR for SOH estimation can achieve a maximum 65.37% improvement under multiple evaluation criteria compared to traditional GPR. The method proposed in this article is helpful for realizing online SOH estimation in battery management systems.