Battery State of Health Estimate Strategies: From Data Analysis to End-Cloud Collaborative Framework
Source abstract
Abstract
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).
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Research context
OpenAlex topics: Advanced Battery Technologies Research · Advancements in Battery Materials · Advanced Battery Materials and Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Research Institute of Aero-Engine, Beihang University, Beijing 100191, China
School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
Department of Earth Science and Engineering, Imperial College London, London SW7 2AZ, UK
Institute for Clean Growth and Future Mobility, Coventry University, Coventry CV1 5FB, UK
China First Automobile Group Corporation, Changchun 130013, China
Dyson School of Design Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
Funding
National Natural Science Foundation of China
References (100+)
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