LAPSE:2023.35796
Published Article
LAPSE:2023.35796
Research Progress of Battery Life Prediction Methods Based on Physical Model
Xingxing Wang, Peilin Ye, Shengren Liu, Yu Zhu, Yelin Deng, Yinnan Yuan, Hongjun Ni
May 24, 2023
Remaining useful life prediction is of great significance for battery safety and maintenance. The remaining useful life prediction method, based on a physical model, has wide applicability and high prediction accuracy, which is the research hotspot of the next generation battery life prediction method. In this study, the prediction methods of battery life were compared and analyzed, and the prediction methods based on the physical model were summarized. The prediction methods were classified according to their different characteristics including the electrochemical model, equivalent circuit model, and empirical model. By analyzing the emphasis of electrochemical process simplification, different electrochemical models were classified including the P2D model, SP model, and electrochemical fusion model. The equivalent circuit model was divided into the Rint model, Thevenin model, PNGV model, and RC model for the change of electronic components in the model. According to the different mathematical expressions of constructing the empirical model, it can be divided into the exponential model, polynomial model, exponential and polynomial mixed model, and capacity degradation model. Through the collocation of different filtering methods, the different efficiency of the models is described in detail. The research progress of various prediction methods as well as the changes and characteristics of traditional models were compared and analyzed, and the future development of battery life prediction methods was prospected.
Keywords
lithium-ion battery, physical model, prediction method, residual life
Suggested Citation
Wang X, Ye P, Liu S, Zhu Y, Deng Y, Yuan Y, Ni H. Research Progress of Battery Life Prediction Methods Based on Physical Model. (2023). LAPSE:2023.35796
Author Affiliations
Wang X: School of Mechanical Engineering, Nantong University, Nantong 226019, China; School of Rail Transportation, Soochow University, Suzhou 215131, China [ORCID]
Ye P: School of Mechanical Engineering, Nantong University, Nantong 226019, China
Liu S: School of Mechanical Engineering, Nantong University, Nantong 226019, China
Zhu Y: School of Mechanical Engineering, Nantong University, Nantong 226019, China
Deng Y: School of Rail Transportation, Soochow University, Suzhou 215131, China
Yuan Y: School of Rail Transportation, Soochow University, Suzhou 215131, China
Ni H: School of Zhang Jian, Nantong University, Nantong 226019, China
Journal Name
Energies
Volume
16
Issue
9
First Page
3858
Year
2023
Publication Date
2023-04-30
ISSN
1996-1073
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Original Submission
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PII: en16093858, Publication Type: Review
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LAPSE:2023.35796
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https://doi.org/10.3390/en16093858
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May 24, 2023
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May 24, 2023
 
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Calvin Tsay
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