A comprehensive review of multiple physical and data-driven model fusion methods for accurate lithium-ion battery inner state factor estimation.

Tao, Junjie; Wang, Shunli; Cao, Wen; Fernandez, Carlos; Blaabjerg, Frede

Abstract

With the rapid global growth in demand for renewable energy, the traditional energy structure is accelerating its transition to low-carbon, clean energy. Lithium-ion batteries, due to their high energy density, long cycle life, and high efficiency, have become a core technology driving this transformation. In lithium-ion battery energy storage systems, precise state estimation, such as state of charge, state of health, and state of power, is crucial for ensuring system safety, extending battery lifespan, and improving energy efficiency. Although physics-based state estimation techniques have matured, challenges remain regarding accuracy and robustness in complex environments. With the advancement of hardware computational capabilities, data-driven algorithms are increasingly applied in battery management, and multi-model fusion approaches have emerged as a research hotspot. This paper reviews the fusion application between physics-based and data-driven models in lithium-ion battery management, critically analyzes the advantages, limitations, and applicability of fusion models, and evaluates their effectiveness in improving state estimation accuracy and robustness. Furthermore, the paper discusses future directions for improvement in computational efficiency, model adaptability, and performance under complex operating conditions, aiming to provide theoretical support and practical guidance for developing lithium-ion battery management technologies.

Citation

TAO, J., WANG, S., CAO, W., FERNANDEZ, C. and BLAABJERG, F. 2024. A comprehensive review of multiple physical and data-driven model fusion methods for accurate lithium-ion battery inner state factor estimation. Batteries [online], 10(12), article 442. Available from: https://doi.org/10.3390/batteries10120442

• Journal Article Type: Article
• Acceptance Date: Dec 12, 2024
• Online Publication Date: Dec 13, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Jan 9, 2025
• Publicly Available Date: Jan 9, 2025
• Journal: Batteries
• Electronic ISSN: 2313-0105
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 12
• Article Number: 442
• DOI: https://doi.org/10.3390/batteries10120442
• Keywords: Lithium-ion battery; State of charge estimation; Physical modeling approach; Data-driven approach; Multi-model fusion approach
• Public URL: https://rgu-repository.worktribe.com/output/2626135