Progress in the prognosis of battery degradation and estimation of battery states

Lithium-ion batteries (LIBs) have gained immense popularity as a power source in various applications. Accurately predicting the health status of these batteries is crucial for optimizing their performance, minimizing operating expenses, and preventing failures. In this paper, we present a comprehensive review of the latest developments in predicting the state of charge (SOC), state of health (SOH), and remaining useful life (RUL) of LIBs, and particularly focus on machine learning techniques. This paper delves into the degradation mechanisms of LIBs and their underlying theories, providing an in-depth analysis of the strengths and limitations of various machine learning techniques used to predict SOC, SOH and RUL. Furthermore, this review sheds light on the challenges encountered in the practical application of electric vehicles, especially concerning battery degradation. It also offers valuable insights into the future research directions for LIBs. While machine learning methods hold great promise in enhancing the accuracy of predicting SOC, SOH, and RUL, there remain numerous technical and practical obstacles that must be overcome to make them more applicable in real-world scenarios.