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

被引:4
|
作者
Liu, Hongling [1 ]
Bie, Chuanyu [1 ]
Luo, Fan [1 ]
Kang, Jianqiang [2 ,3 ]
Zhang, Yuping [4 ]
机构
[1] Wuhan Power Battery Recycling Technol Co Ltd, Wuhan 431400, Peoples R China
[2] Wuhan Univ Technol, Hubei Key Lab Adv Technol Automot Components, Wuhan 430070, Peoples R China
[3] Wuhan Univ Technol, Hubei Collaborat Innovat Ctr Automot Components Te, Wuhan 430070, Peoples R China
[4] GEM Co Ltd, Shenzhen 518101, Peoples R China
基金
中国国家自然科学基金;
关键词
Ni-MH batteries; multi-parameter; Pearson correlation coefficient; KS-test; SVR; STATE; MODEL;
D O I
10.3390/en15239156
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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 Delta d, AC internal resistance R, terminal voltage U of the battery, charge and discharge polarization internal resistance R-f1 and R-f2 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.
引用
收藏
页数:11
相关论文
共 11 条
  • [1] Online Parameter Estimation of the Ni-MH Batteries Based on Statistical Methods
    Piao, Chang-hao
    Fu, Wen-li
    Lei, Gai-hui
    Cho, Chong-du
    ENERGIES, 2010, 3 (02) : 206 - 215
  • [2] Ni-MH batteries state-of-charge prediction based on immune evolutionary network
    Cheng Bo
    Zhou Yanlu
    Zhang Jiexin
    Wang Junping
    Cao Binggang
    ENERGY CONVERSION AND MANAGEMENT, 2009, 50 (12) : 3078 - 3086
  • [3] Prediction of residual capacity of MH/Ni batteries based on neural network
    Deng, Chao
    Shi, Peng-Fei
    Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology, 2003, 35 (11): : 1405 - 1408
  • [4] Areal-time seismic intensity prediction model based on multi-parameter driven machine learning
    Ding YiTian
    Hu JinJun
    Zhang Hui
    Jin ChaoYue
    Hu Lei
    Wang ZhongWei
    Tang Chao
    CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2023, 66 (07): : 2920 - 2932
  • [5] Upcycling of nickel oxide from spent Ni-MH batteries as ultra-high capacity and stable Li-based energy storage devices
    Pham, Hong Duc
    Krishnan, Syam G.
    Wang, Tony
    Fernando, Joseph F. S.
    Padwal, Chinmayee
    Golberg, Dmitri V.
    Dubal, Deepak P.
    SUSTAINABLE MATERIALS AND TECHNOLOGIES, 2023, 36
  • [6] Remaining discharge energy estimation of lithium-ion batteries based on average working condition prediction and multi-parameter updating
    Lai, Xin
    Weng, Jiahui
    Yang, Yipeng
    Qiu, Changqing
    Huang, Yunfeng
    Yuan, Ming
    Yao, Yi
    Zheng, Yuejiu
    JOURNAL OF SOLID STATE ELECTROCHEMISTRY, 2024, 28 (01) : 229 - 242
  • [7] Remaining discharge energy estimation of lithium-ion batteries based on average working condition prediction and multi-parameter updating
    Xin Lai
    Jiahui Weng
    Yipeng Yang
    Changqing Qiu
    Yunfeng Huang
    Ming Yuan
    Yi Yao
    Yuejiu Zheng
    Journal of Solid State Electrochemistry, 2024, 28 : 229 - 242
  • [8] Multi-parameter correlation analysis and multi-step prediction of seawater quality based on graph spatio-temporal analysis network
    Zhu, Qiguang
    Shen, Zhen
    Qiao, Wenjing
    Wu, Zhen
    Chen, Ying
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2025, 36 (01)
  • [9] Parametric study and response surface analysis of hatch sealing structure based on multi-parameter leakage rate prediction model
    Huang, Xiaoming
    Zhong, Xiaochen
    Li, Ming
    Yu, Xinli
    Liu, Yu
    Xu, Guoliang
    NUCLEAR ENGINEERING AND DESIGN, 2024, 424
  • [10] Establishment of a novel system for the preoperative prediction of adherent perinephric fat (APF) occurrence based on a multi-mode and multi-parameter analysis of dual-energy CT
    Li, Guan
    Dong, Jie
    Huang, Wei
    Zhang, Zhengyu
    Wang, Di
    Zou, Mingyu
    Xu, Qinmei
    Lu, Guangming
    Cao, Zhiqiang
    TRANSLATIONAL ANDROLOGY AND UROLOGY, 2019, 8 (05) : 421 - 431