IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i23p9156-d991800.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China)

  • Yuping Zhang

    (GEM Co., Ltd., Shenzhen 518101, China)

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.

Suggested Citation

  • Hongling Liu & Chuanyu Bie & Fan Luo & Jianqiang Kang & Yuping Zhang, 2022. "Rapid Prediction of Retired Ni-MH Batteries Capacity Based on Reliable Multi-Parameter Driven Analysis," Energies, MDPI, vol. 15(23), pages 1-11, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9156-:d:991800
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/23/9156/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/23/9156/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kang, Jianqiang & Yan, Fuwu & Zhang, Pei & Du, Changqing, 2014. "Comparison of comprehensive properties of Ni-MH (nickel-metal hydride) and Li-ion (lithium-ion) batteries in terms of energy efficiency," Energy, Elsevier, vol. 70(C), pages 618-625.
    2. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
    3. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    4. Fei, Zicheng & Yang, Fangfang & Tsui, Kwok-Leung & Li, Lishuai & Zhang, Zijun, 2021. "Early prediction of battery lifetime via a machine learning based framework," Energy, Elsevier, vol. 225(C).
    5. Zhu, Wenhua H. & Zhu, Ying & Davis, Zenda & Tatarchuk, Bruce J., 2013. "Energy efficiency and capacity retention of Ni–MH batteries for storage applications," Applied Energy, Elsevier, vol. 106(C), pages 307-313.
    6. Yu Miao & Patrick Hynan & Annette von Jouanne & Alexandre Yokochi, 2019. "Current Li-Ion Battery Technologies in Electric Vehicles and Opportunities for Advancements," Energies, MDPI, vol. 12(6), pages 1-20, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Jufeng & Huang, Wenxin & Xia, Bing & Mi, Chris, 2019. "The improved open-circuit voltage characterization test using active polarization voltage reduction method," Applied Energy, Elsevier, vol. 237(C), pages 682-694.
    2. Chen, Yongbao & Chen, Zhe & Xu, Peng & Li, Weilin & Sha, Huajing & Yang, Zhiwei & Li, Guowen & Hu, Chonghe, 2019. "Quantification of electricity flexibility in demand response: Office building case study," Energy, Elsevier, vol. 188(C).
    3. Desreveaux, A. & Bouscayrol, A. & Trigui, R. & Hittinger, E. & Castex, E. & Sirbu, G.M., 2023. "Accurate energy consumption for comparison of climate change impact of thermal and electric vehicles," Energy, Elsevier, vol. 268(C).
    4. Sandra Castano-Solis & Daniel Serrano-Jimenez & Lucia Gauchia & Javier Sanz, 2017. "The Influence of BMSs on the Characterization and Modeling of Series and Parallel Li-Ion Packs," Energies, MDPI, vol. 10(3), pages 1-13, February.
    5. Song, Jingeun & Cha, Junepyo & Choi, Mingi, 2024. "A study on 5-cycle fuel economy prediction model of electric vehicles using numerical simulation," Energy, Elsevier, vol. 309(C).
    6. Bingjie Jin & Guihua Zeng & Zhilin Lu & Hongqiao Peng & Shuxin Luo & Xinhe Yang & Haojun Zhu & Mingbo Liu, 2022. "Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load," Energies, MDPI, vol. 15(20), pages 1-20, October.
    7. Ming Cai & Weijie Chen & Xiaojun Tan, 2017. "Battery State-Of-Charge Estimation Based on a Dual Unscented Kalman Filter and Fractional Variable-Order Model," Energies, MDPI, vol. 10(10), pages 1-16, October.
    8. Anisa Surya Wijareni & Hendri Widiyandari & Agus Purwanto & Aditya Farhan Arif & Mohammad Zaki Mubarok, 2022. "Morphology and Particle Size of a Synthesized NMC 811 Cathode Precursor with Mixed Hydroxide Precipitate and Nickel Sulfate as Nickel Sources and Comparison of Their Electrochemical Performances in an," Energies, MDPI, vol. 15(16), pages 1-15, August.
    9. Alexandru Ciocan & Cosmin Ungureanu & Alin Chitu & Elena Carcadea & George Darie, 2020. "Electrical Longboard for Everyday Urban Commuting," Sustainability, MDPI, vol. 12(19), pages 1-14, September.
    10. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    11. Ozkurt, Celil & Camci, Fatih & Atamuradov, Vepa & Odorry, Christopher, 2016. "Integration of sampling based battery state of health estimation method in electric vehicles," Applied Energy, Elsevier, vol. 175(C), pages 356-367.
    12. Piotr Krawczyk & Anna Śliwińska, 2020. "Eco-Efficiency Assessment of the Application of Large-Scale Rechargeable Batteries in a Coal-Fired Power Plant," Energies, MDPI, vol. 13(6), pages 1-16, March.
    13. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    14. Thanh-Tung Nguyen & Abdul Basit Khan & Younghwi Ko & Woojin Choi, 2020. "An Accurate State of Charge Estimation Method for Lithium Iron Phosphate Battery Using a Combination of an Unscented Kalman Filter and a Particle Filter," Energies, MDPI, vol. 13(17), pages 1-15, September.
    15. Noshin Omar & Peter Van den Bossche & Thierry Coosemans & Joeri Van Mierlo, 2013. "Peukert Revisited—Critical Appraisal and Need for Modification for Lithium-Ion Batteries," Energies, MDPI, vol. 6(11), pages 1-17, October.
    16. Jonathan Berrisch & Micha{l} Narajewski & Florian Ziel, 2022. "High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks," Papers 2203.03342, arXiv.org, revised Nov 2022.
    17. Alhamwi, Alaa & Medjroubi, Wided & Vogt, Thomas & Agert, Carsten, 2018. "Modelling urban energy requirements using open source data and models," Applied Energy, Elsevier, vol. 231(C), pages 1100-1108.
    18. Wang, Yujie & Sun, Zhendong & Chen, Zonghai, 2019. "Energy management strategy for battery/supercapacitor/fuel cell hybrid source vehicles based on finite state machine," Applied Energy, Elsevier, vol. 254(C).
    19. Xiaojin Xie & Kangyang Luo & Zhixiang Yin & Guoqiang Wang, 2021. "Nonlinear Combinational Dynamic Transmission Rate Model and Its Application in Global COVID-19 Epidemic Prediction and Analysis," Mathematics, MDPI, vol. 9(18), pages 1-17, September.
    20. Jiang, Yunfeng & Xia, Bing & Zhao, Xin & Nguyen, Truong & Mi, Chris & de Callafon, Raymond A., 2017. "Data-based fractional differential models for non-linear dynamic modeling of a lithium-ion battery," Energy, Elsevier, vol. 135(C), pages 171-181.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9156-:d:991800. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.