Lithium-ion batteries have become the primary electrical energy storage device in commercial and industrial applications due to their high energy/power density, high reliability, and long service life. It is essential to estimate the state of health (SOH) of batteries to ensure safety, optimize better energy efficiency and enhance the battery life-cycle management. This paper presents a comprehensive review of SOH estimation methods, including experimental approaches, model-based methods, and machine learning algorithms. A critical and in-depth analysis of the advantages and limitations of each method is presented. The various techniques are systematically classified and compared for the purpose of facilitating understanding and further research. Furthermore, the paper emphasizes the prospect of using a knowledge graph-based framework for battery data management, multi-model fusion, and cooperative edge-cloud platform for intelligent battery management systems (BMS).
Bibliographical noteThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
FunderThis research was funded by the National Natural Science Foundation of China (No. 52102470).
- artificial intelligence
- multi-model fusion
- end-cloud collaboration