A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms

As an important energy storage device, lithium-ion batteries (LIBs) have been widely used in various fields due to their remarkable advantages. The high level of precision in estimating the battery’s state of health greatly enhances the safety and dependability of the application process. In contras...

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Veröffentlicht in:Energies (Basel) Jg. 16; H. 7; S. 3167
Hauptverfasser: Zhang, Ming, Yang, Dongfang, Du, Jiaxuan, Sun, Hanlei, Li, Liwei, Wang, Licheng, Wang, Kai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.04.2023
Schlagworte:
Accuracy
Aging
Algorithms
Analysis
Batteries
Data processing
data-driven algorithms
Datasets
Decomposition
Electrodes
Electrolytes
Energy industry
Graphite
Information management
LIB
Lithium
Machine learning
Methods
Neural networks
Power
SOH
Peru
ISSN:1996-1073, 1996-1073
Online-Zugang:Volltext
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Zusammenfassung:As an important energy storage device, lithium-ion batteries (LIBs) have been widely used in various fields due to their remarkable advantages. The high level of precision in estimating the battery’s state of health greatly enhances the safety and dependability of the application process. In contrast to traditional model-based prediction methods that are complex and have limited accuracy, data-driven prediction methods, which are considered mainstream, rely on direct data analysis and offer higher accuracy. Therefore, this paper reviews how to use the latest data-driven algorithms to predict the SOH of LIBs, and proposes a general prediction process, including the acquisition of datasets for the charging and discharging process of LIBs, the processing of data and features, and the selection of algorithms. The advantages and limitations of various processing methods and cutting-edge data-driven algorithms are summarized and compared, and methods with potential applications are proposed. Effort was also made to point out their application methods and application scenarios, providing guidance for researchers in this area.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en16073167