Review of SOH Prediction Methods for Lithium-Ion Batteries

Lithium-ion batteries are widely utilized due to their outstanding performance in the energy storage sector, spanning various applications such as smartphones, automobiles, and laptops. As a crucial functionality within Battery Management Systems (BMS), monitoring the State of Health (SOH) of lithium-ion batteries not only enables the BMS to make timely adjustments to extend battery life but also ensures the safe utilization of batteries, preventing potential accidents. Given its significance, numerous researchers conduct extensive studies on predicting battery SOH and propose various methods. This paper collects various methods for predicting the State of Health (SOH) categorizes current SOH prediction methods into three main types: model-based methods, data-based methods, and hybrid methods. Each method is elaborated upon in detail, and the performance differences among them are analyzed. Finally, the paper summarizes the challenges facing current SOH predictions and explores potential research directions for the future.