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

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 es...

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Vydáno v:Ionics Ročník 30; číslo 10; s. 5903 - 5927
Hlavní autoři: Tao, Junjie, Wang, Shunli, Cao, Wen, Takyi-Aninakwa, Paul, Fernandez, Carlos, Guerrero, Josep M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2024
Springer Nature B.V
Témata:
Accuracy
Algorithms
Batteries
Chemistry
Chemistry and Materials Science
Clean energy
Clean technology
Condensed Matter Physics
Deep learning
Electric vehicles
Electrochemistry
Energy Storage
Energy utilization
Lithium-ion batteries
Machine learning
Methods
Neural networks
Optical and Electronic Materials
Renewable and Green Energy
Review
State estimation
State of charge
Trends
Deep learning
Kalman filtering
Li-ion battery state estimation
Equivalent circuit model
Parameter identification
Based on data-driven algorithms
ISSN:0947-7047, 1862-0760
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Shrnutí: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.
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ISSN:0947-7047
1862-0760
DOI:10.1007/s11581-024-05686-z