Rapid Prediction of Retired Ni-MH Batteries Capacity Based on Reliable Multi-Parameter Driven Analysis

Hongling Liu, Chuanyu Bie (), Fan Luo, Jianqiang Kang and Yuping Zhang ()
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Hongling Liu: Wuhan Power Battery Recycling Technology Co., Ltd., Wuhan 431400, China
Chuanyu Bie: Wuhan Power Battery Recycling Technology Co., Ltd., Wuhan 431400, China
Fan Luo: Wuhan Power Battery Recycling Technology Co., Ltd., Wuhan 431400, China
Jianqiang Kang: Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
Yuping Zhang: GEM Co., Ltd., Shenzhen 518101, China

Energies, 2022, vol. 15, issue 23, 1-11

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 Δ d , AC internal resistance R , terminal voltage U of the battery, charge and discharge polarization internal resistance R f1 and R f 2 and pulse charging power P 2 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: Ni-MH batteries; multi-parameter; Pearson correlation coefficient; KS-test; SVR (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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