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A novel battery pack inconsistency model and influence degree analysis of inconsistency on output energy

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  • An, Fulai
  • Zhang, Weige
  • Sun, Bingxiang
  • Jiang, Jiuchun
  • Fan, Xinyuan

Abstract

The battery pack inconsistency directly affects output energy, which is an important factor reflecting the driving range of electric vehicles. Therefore, this manuscript focuses on influence degree analysis of inconsistency on output energy. Firstly, a novel battery pack inconsistency model, consisting of Gaussian mixture model (GMM) which well describes the marginal distribution characteristics of single parameter and three-dimensional mixture Copula model (MCM) which well describes the correlation between parameters, is proposed. Secondly, the battery packs with different inconsistency characteristics are designed based on the built inconsistency quantification experimental platform, and the virtual ones with the same inconsistency are generated by the inconsistency parameters generation method. The output energy estimation errors between simulation and experiment are within ±1%. Thirdly, multiple linear regression analysis is used to study the influence degree of GMM and MCM model parameters on output energy respectively. The analysis results show that in GMM, the variance of SOC and the mean of capacity and SOC have greater impacts than the mean of resistance and the variance of capacity and resistance, and in MCM, the parameters of Gumbel and Frank have greater influence than that of Gaussian and Clayton. The results could provide support for battery pack performance evaluation.

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  • 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).
    • Handle: RePEc:eee:energy:v:271:y:2023:i:c:s0360544223004267
    DOI: 10.1016/j.energy.2023.127032
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    1. Feng, Fei & Hu, Xiaosong & Hu, Lin & Hu, Fengling & Li, Yang & Zhang, Lei, 2019. "Propagation mechanisms and diagnosis of parameter inconsistency within Li-Ion battery packs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 102-113.
    2. 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.
    3. Xue Li & Jiuchun Jiang & Caiping Zhang & Le Yi Wang & Linfeng Zheng, 2015. "Robustness of SOC Estimation Algorithms for EV Lithium-Ion Batteries against Modeling Errors and Measurement Noise," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-14, October.
    4. Liu, Guangming & Ouyang, Minggao & Lu, Languang & Li, Jianqiu & Hua, Jianfeng, 2015. "A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications," Applied Energy, Elsevier, vol. 149(C), pages 297-314.
    5. Yun Bao & Wenbin Dong & Dian Wang, 2018. "Online Internal Resistance Measurement Application in Lithium Ion Battery Capacity and State of Charge Estimation," Energies, MDPI, vol. 11(5), pages 1-11, April.
    6. Weiping Diao & Jiuchun Jiang & Hui Liang & Caiping Zhang & Yan Jiang & Leyi Wang & Biqiang Mu, 2016. "Flexible Grouping for Enhanced Energy Utilization Efficiency in Battery Energy Storage Systems," Energies, MDPI, vol. 9(7), pages 1-15, June.
    7. 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).
    8. Lai, Xin & Huang, Yunfeng & Gu, Huanghui & Han, Xuebing & Feng, Xuning & Dai, Haifeng & Zheng, Yuejiu & Ouyang, Minggao, 2022. "Remaining discharge energy estimation for lithium-ion batteries based on future load prediction considering temperature and ageing effects," Energy, Elsevier, vol. 238(PA).
    9. Yun Zhang & Yunlong Shang & Naxin Cui & Chenghui Zhang, 2017. "Parameters Identification and Sensitive Characteristics Analysis for Lithium-Ion Batteries of Electric Vehicles," Energies, MDPI, vol. 11(1), pages 1-15, December.
    10. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    11. 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).
    12. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    13. Diao, Weiping & Xue, Nan & Bhattacharjee, Vikram & Jiang, Jiuchun & Karabasoglu, Orkun & Pecht, Michael, 2018. "Active battery cell equalization based on residual available energy maximization," Applied Energy, Elsevier, vol. 210(C), pages 690-698.
    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).
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