LAPSE:2023.7596
Published Article
LAPSE:2023.7596
Rapid Prediction of Retired Ni-MH Batteries Capacity Based on Reliable Multi-Parameter Driven Analysis
Hongling Liu, Chuanyu Bie, Fan Luo, Jianqiang Kang, Yuping Zhang
February 24, 2023
Abstract
In order to solve the problems of long-time consumption and high energy consumption in existing capacity detection methods of retired Ni-MH batteries, a fast and reliable capacity prediction method for retired Ni-MH batteries by multi-parameter driven analysis was proposed in this paper. This method mainly obtains several parameters through short-time measurement and pulse rapid nondestructive testing. Then, Pearson correlation coefficient and KS-test were used to analyze the correlation between the two parameters and verify the same distribution. Finally, SVR was used to predict the battery discharge capacity. The results show that the volume expansion thickness difference Δd, AC internal resistance R, terminal voltage U of the battery, charge and discharge polarization internal resistance Rf1 and R and pulse charging power P2 of the battery are strongly negatively correlated with the discharge capacity, and these characteristic parameters can effectively and reliably reflect the internal structural characteristics of the battery. Additionally, the mean relative error of the established capacity model is 5.87%, and the lowest error is 1.32%. The prediction effect is good, which provides a certain reference value for the subsequent consistent sorting method.
Keywords
KS-test, multi-parameter, Ni-MH batteries, Pearson correlation coefficient, SVR
Suggested Citation
Liu H, Bie C, Luo F, Kang J, Zhang Y. Rapid Prediction of Retired Ni-MH Batteries Capacity Based on Reliable Multi-Parameter Driven Analysis. (2023). LAPSE:2023.7596
Author Affiliations
Liu H: Wuhan Power Battery Recycling Technology Co., Ltd., Wuhan 431400, China
Bie C: Wuhan Power Battery Recycling Technology Co., Ltd., Wuhan 431400, China
Luo F: Wuhan Power Battery Recycling Technology Co., Ltd., Wuhan 431400, China
Kang J: Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China; Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China
Zhang Y: GEM Co., Ltd., Shenzhen 518101, China
Journal Name
Energies
Volume
15
Issue
23
First Page
9156
Year
2022
Publication Date
2022-12-02
ISSN
1996-1073
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Original Submission
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PII: en15239156, Publication Type: Journal Article
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LAPSE:2023.7596
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https://doi.org/10.3390/en15239156
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