Junjie Tao
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
Shunli Wang
Wen Cao
Paul Takyi-Aninakwa
Dr Carlos Fernandez c.fernandez@rgu.ac.uk
Associate Professor
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 |
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Copyright Statement
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use [https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms], but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11581-024-05686-z.