Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects

Lithium-ion battery aging mechanism analysis and health prognostics are of great significance for a smart battery management system to ensure safe and optimal use of the battery system. This paper provides a comprehensive review of aging mechanisms and the state-of-the-art health prognostic methods and summarizes the main challenges and research prospects for battery health prognostics. First, the complex relationships among aging mechanisms, aging modes, influencing factors, and aging types are reviewed and summarized. Then, the battery health prognostic methods are divided according to different time scales and objectives, which include the short-term state of health estimation, long-term end-of-life prediction, and degradation trajectory prediction, followed by a detailed review of each prognostic task and method. For consistency, we first provide a clear and concise description of each method, showing the similarities and peculiarities of these methods, and then review several representative works. After that, comparative evaluations are conducted. The main advantages and disadvantages of each prognostic task and prognostic method are analyzed in detail. Next, key challenges are presented by considering the specific characteristics of each prognostic task. Moreover, for each challenge, potential solutions are presented and discussed. These proposed potential solutions to the main challenges are beneficial and can be considered by researchers in their further studies. Finally, the future trends of battery health prognostics are discussed, and several new ideas for battery health prognostics are proposed.

Automated summary

This paper provides a comprehensive review of aging mechanisms and health prognostic methods for lithium-ion batteries, and discusses the main challenges and research prospects. The complex relationships between aging mechanisms, modes, factors, and types are summarized. Prognostic methods are categorized based on time scales and objectives, followed by detailed reviews and comparative evaluations. Key challenges are presented and potential solutions are discussed. Future trends and new ideas for battery health prognostics are proposed.