IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v277y2023ics0360544223008034.html
   My bibliography  Save this article

Inconsistency modeling of lithium-ion battery pack based on variational auto-encoder considering multi-parameter correlation

Author

Listed:
  • He, Xitian
  • Sun, Bingxiang
  • Zhang, Weige
  • Su, Xiaojia
  • Ma, Shichang
  • Li, Hao
  • Ruan, Haijun

Abstract

—The accurate battery pack model is of great significance for the strategy development and functional verification of battery management system with the advantages of the high repeatability, fast state switching, and high safety. In this paper, a total of 49 dimensions of battery pack parameters are obtained through systematic experiments, including equivalent circuit model parameters and thermal characteristic parameters. Since the variational auto-encoder (VAE) can well preserve the correlation between parameters by training the neural network with small samples (95 samples), a novel battery pack inconsistency method is proposed based on VAE. Moreover, the parameter correlation and similarity are further considered based on the original loss function, which enables parameters generation for any amount of samples. The simulation results of the 95 and 500 generated samples illustrated that the proposed method can well maintain the similarity with the original parameters in both parameters distribution and parameters correlation, compared with the Copula-based and Metropolis-Hastings-based method. The average temperature error of the VAE-based method (0.02%) is much smaller than that of the Copula-based method (0.80%), and the temperature standard deviation error of the VAE-based method can be reduced to 4.53%, while the Copula-based method can reach 105.38%.

Suggested Citation

  • He, Xitian & Sun, Bingxiang & Zhang, Weige & Su, Xiaojia & Ma, Shichang & Li, Hao & Ruan, Haijun, 2023. "Inconsistency modeling of lithium-ion battery pack based on variational auto-encoder considering multi-parameter correlation," Energy, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:energy:v:277:y:2023:i:c:s0360544223008034
    DOI: 10.1016/j.energy.2023.127409
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223008034
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.127409?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Huang, Deyang & Chen, Ziqiang & Zhou, Shiyao, 2021. "Model prediction-based battery-powered heating method for series-connected lithium-ion battery pack working at extremely cold temperatures," Energy, Elsevier, vol. 216(C).
    2. Jiang, Jiuchun & Ruan, Haijun & Sun, Bingxiang & Zhang, Weige & Gao, Wenzhong & Wang, Le Yi & Zhang, Linjing, 2016. "A reduced low-temperature electro-thermal coupled model for lithium-ion batteries," Applied Energy, Elsevier, vol. 177(C), pages 804-816.
    3. Yao, Lei & Fang, Zhanpeng & Xiao, Yanqiu & Hou, Junjian & Fu, Zhijun, 2021. "An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine," Energy, Elsevier, vol. 214(C).
    4. Wu, Lifeng & Zhang, Yu, 2023. "Attention-based encoder-decoder networks for state of charge estimation of lithium-ion battery," Energy, Elsevier, vol. 268(C).
    5. Fan, Zhaohui & Fu, Yijie & Liang, Hong & Gao, Renjing & Liu, Shutian, 2023. "A module-level charging optimization method of lithium-ion battery considering temperature gradient effect of liquid cooling and charging time," Energy, Elsevier, vol. 265(C).
    6. Guo, Shanshan & Ma, Liang, 2023. "A comparative study of different deep learning algorithms for lithium-ion batteries on state-of-charge estimation," Energy, Elsevier, vol. 263(PC).
    7. Liu, Xinhua & Ai, Weilong & Naylor Marlow, Max & Patel, Yatish & Wu, Billy, 2019. "The effect of cell-to-cell variations and thermal gradients on the performance and degradation of lithium-ion battery packs," Applied Energy, Elsevier, vol. 248(C), pages 489-499.
    8. Chen, Junxiong & Feng, Xiong & Jiang, Lin & Zhu, Qiao, 2021. "State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network," Energy, Elsevier, vol. 227(C).
    9. Bingxiang Sun & Xitian He & Weige Zhang & Yangxi Li & Minming Gong & Yang Yang & Xiaojia Su & Zhenlin Zhu & Wenzhong Gao, 2020. "Control Strategies and Economic Analysis of an LTO Battery Energy Storage System for AGC Ancillary Service," Energies, MDPI, vol. 13(2), pages 1-16, January.
    10. Zhang, Caiping & Jiang, Yan & Jiang, Jiuchun & Cheng, Gong & Diao, Weiping & Zhang, Weige, 2017. "Study on battery pack consistency evolutions and equilibrium diagnosis for serial- connected lithium-ion batteries," Applied Energy, Elsevier, vol. 207(C), pages 510-519.
    11. He, Xitian & Sun, Bingxiang & Zhang, Weige & Fan, Xinyuan & Su, Xiaojia & Ruan, Haijun, 2022. "Multi-time scale variable-order equivalent circuit model for virtual battery considering initial polarization condition of lithium-ion battery," Energy, Elsevier, vol. 244(PB).
    12. Yang, Ruixin & Xiong, Rui & Ma, Suxiao & Lin, Xinfan, 2020. "Characterization of external short circuit faults in electric vehicle Li-ion battery packs and prediction using artificial neural networks," Applied Energy, Elsevier, vol. 260(C).
    13. Li, Xiaoyu & Xu, Jianhua & Hong, Jianxun & Tian, Jindong & Tian, Yong, 2021. "State of energy estimation for a series-connected lithium-ion battery pack based on an adaptive weighted strategy," Energy, Elsevier, vol. 214(C).
    14. Jiang, Yan & Jiang, Jiuchun & Zhang, Caiping & Zhang, Weige & Gao, Yang & Mi, Chris, 2019. "A Copula-based battery pack consistency modeling method and its application on the energy utilization efficiency estimation," Energy, Elsevier, vol. 189(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Kuijie & Chen, Long & Gao, Xinlei & Lu, Yao & Wang, Depeng & Zhang, Weixin & Wu, Weixiong & Han, Xuebing & Cao, Yuan-cheng & Wen, Jinyu & Cheng, Shijie & Ouyang, Minggao, 2024. "Implementing expansion force-based early warning in LiFePO4 batteries with various states of charge under thermal abuse scenarios," Applied Energy, Elsevier, vol. 362(C).
    2. Wang, Xiaowei & Kang, Qiankun & Gao, Jie & Zhang, Fan & Wang, Xue & Qu, Xinyu & Guo, Liang, 2024. "Distribution network restoration supply method considers 5G base station energy storage participation," Energy, Elsevier, vol. 289(C).

    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. Bingxiang Sun & Xinze Zhao & Xitian He & Haijun Ruan & Zhenlin Zhu & Xingzhen Zhou, 2023. "Virtual Battery Pack-Based Battery Management System Testing Framework," Energies, MDPI, vol. 16(2), pages 1-21, January.
    2. Chang, Chun & Wu, Yutong & Jiang, Jiuchun & Jiang, Yan & Tian, Aina & Li, Taiyu & Gao, Yang, 2022. "Prognostics of the state of health for lithium-ion battery packs in energy storage applications," Energy, Elsevier, vol. 239(PB).
    3. Liu, Yongjie & Huang, Zhiwu & Wu, Yue & Yan, Lisen & Jiang, Fu & Peng, Jun, 2022. "An online hybrid estimation method for core temperature of Lithium-ion battery with model noise compensation," Applied Energy, Elsevier, vol. 327(C).
    4. Qin, Yudi & Du, Jiuyu & Lu, Languang & Gao, Ming & Haase, Frank & Li, Jianqiu & Ouyang, Minggao, 2020. "A rapid lithium-ion battery heating method based on bidirectional pulsed current: Heating effect and impact on battery life," Applied Energy, Elsevier, vol. 280(C).
    5. Ma, Chen & Chang, Long & Cui, Naxin & Duan, Bin & Zhang, Yulong & Yu, Zhihao, 2022. "Statistical relationships between numerous retired lithium-ion cells and packs with random sampling for echelon utilization," Energy, Elsevier, vol. 257(C).
    6. An, Fulai & Zhang, Weige & Sun, Bingxiang & Jiang, Jiuchun & Fan, Xinyuan, 2023. "A novel battery pack inconsistency model and influence degree analysis of inconsistency on output energy," Energy, Elsevier, vol. 271(C).
    7. Li, Changlong & Cui, Naxin & Chang, Long & Cui, Zhongrui & Yuan, Haitao & Zhang, Chenghui, 2022. "Effect of parallel connection topology on air-cooled lithium-ion battery module: Inconsistency analysis and comprehensive evaluation," Applied Energy, Elsevier, vol. 313(C).
    8. Liang, Jialin & Gan, Yunhua & Tan, Meixian & Li, Yong, 2020. "Multilayer electrochemical-thermal coupled modeling of unbalanced discharging in a serially connected lithium-ion battery module," Energy, Elsevier, vol. 209(C).
    9. Fan, Xinyuan & Zhang, Weige & Sun, Bingxiang & Zhang, Junwei & He, Xitian, 2022. "Battery pack consistency modeling based on generative adversarial networks," Energy, Elsevier, vol. 239(PE).
    10. Yu, Hanqing & Zhang, Lisheng & Wang, Wentao & Li, Shen & Chen, Siyan & Yang, Shichun & Li, Junfu & Liu, Xinhua, 2023. "State of charge estimation method by using a simplified electrochemical model in deep learning framework for lithium-ion batteries," Energy, Elsevier, vol. 278(C).
    11. Li, Feng & Zuo, Wei & Zhou, Kun & Li, Qingqing & Huang, Yuhan & Zhang, Guangde, 2024. "State-of-charge estimation of lithium-ion battery based on second order resistor-capacitance circuit-PSO-TCN model," Energy, Elsevier, vol. 289(C).
    12. Wang, Shumao & Bao, Wenkang & Sun, Yuedong & Li, Xiangjun & Dai, Feng & Hua, Jianfeng & Zheng, Yuejiu, 2024. "Current sensorless diagnosis of the cell internal resistance consistency in a parallel module using relaxation voltage," Energy, Elsevier, vol. 301(C).
    13. Chen, Haosen & Fan, Jinbao & Zhang, Mingliang & Feng, Xiaolong & Zhong, Ximing & He, Jianchao & Ai, Shigang, 2023. "Mechanism of inhomogeneous deformation and equal-stiffness design of large-format prismatic lithium-ion batteries," Applied Energy, Elsevier, vol. 332(C).
    14. Li, Da & Zhang, Lei & Zhang, Zhaosheng & Liu, Peng & Deng, Junjun & Wang, Qiushi & Wang, Zhenpo, 2023. "Battery safety issue detection in real-world electric vehicles by integrated modeling and voltage abnormality," Energy, Elsevier, vol. 284(C).
    15. Jiong Yang & Fanyong Cheng & Maxwell Duodu & Miao Li & Chao Han, 2022. "High-Precision Fault Detection for Electric Vehicle Battery System Based on Bayesian Optimization SVDD," Energies, MDPI, vol. 15(22), pages 1-20, November.
    16. Cheng, Gong & Wang, Xinzhi & He, Yurong, 2021. "Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network," Energy, Elsevier, vol. 232(C).
    17. Bian, Chong & Duan, Zhiyu & Hao, Yaqian & Yang, Shunkun & Feng, Junlan, 2024. "Exploring large language model for generic and robust state-of-charge estimation of Li-ion batteries: A mixed prompt learning method," Energy, Elsevier, vol. 302(C).
    18. Jinrui Nan & Bo Deng & Wanke Cao & Jianjun Hu & Yuhua Chang & Yili Cai & Zhiwei Zhong, 2022. "Big Data-Based Early Fault Warning of Batteries Combining Short-Text Mining and Grey Correlation," Energies, MDPI, vol. 15(15), pages 1-19, July.
    19. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
    20. Zhang, Junwei & Zhang, Weige & Sun, Bingxiang & Zhang, Yanru & Fan, Xinyuan & Zhao, Bo, 2024. "A novel method of battery pack energy health estimation based on visual feature learning," Energy, Elsevier, vol. 293(C).

    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:eee:energy:v:277:y:2023:i:c:s0360544223008034. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.