Skip to main content

Research Repository

Advanced Search

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.

Authors

Junjie Tao

Shunli Wang

Wen Cao

Paul Takyi-Aninakwa

Josep M. Guerrero



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