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
Senior Lecturer
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 |
Files
This file is under embargo until Jul 13, 2025 due to copyright reasons.
Contact publications@rgu.ac.uk to request a copy for personal use.
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