A comprehensive review of state-of-charge and state-of-health estimation for lithium-ion battery energy storage systems.

Tao, Junjie; Wang, Shunli; Cao, Wen; Takyi-Aninakwa, Paul; Fernandez, Carlos; Guerrero, Josep M.

Abstract

With the gradual transformation of energy industries around the world, the trend of industrial reform led by clean energy has become increasingly apparent. As a critical link in the new energy industry chain, lithium-ion (Li-ion) battery energy storage system plays an irreplaceable role. Accurate estimation of Li-ion battery states, especially state of charge (SOC) and state of health (SOH), is the core to realize the safe and efficient utilization of energy storage systems. This paper presents a systematic and comprehensive evaluation and summary of the most advanced Li-ion battery state estimation methods proposed in the past 3 years, focusing on analyzing data-driven state estimation algorithms. At the same time, the latest Li-ion battery data sets and data selection methods are analyzed, and future research trends and possible challenges are proposed. This review will provide a valuable reference for future academic research in Li-ion battery state estimation.

Citation

TAO, J., WANG, S., CAO, W., TAKYI-ANINAKWA, P., FERNANDEZ, C. and GUERRERO, J.M. 2024. A comprehensive review of state-of-charge and state-of-health estimation for lithium-ion battery energy storage systems. Ionics [online], 30(10), pages 5903-5927. Available from: https://doi.org/10.1007/s11581-024-05686-z

• Journal Article Type: Review
• Acceptance Date: Jun 30, 2024
• Online Publication Date: Jul 12, 2024
• Publication Date: Oct 31, 2024
• Deposit Date: Jul 26, 2024
• Publicly Available Date: Jul 13, 2025
• Journal: Ionics
• Print ISSN: 0947-7047
• Electronic ISSN: 1862-0760
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 30
• Issue: 10
• Pages: 5903–5927
• DOI: https://doi.org/10.1007/s11581-024-05686-z
• Keywords: Li-ion battery state estimation; Equivalent circuit model; Parameter identification; Kalman filtering; Deep learning; Based on data-driven algorithms
• Public URL: https://rgu-repository.worktribe.com/output/2418912