IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i2p680-d1027350.html
   My bibliography  Save this article

Virtual Battery Pack-Based Battery Management System Testing Framework

Author

Listed:
  • Bingxiang Sun

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China)

  • Xinze Zhao

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China)

  • Xitian He

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China)

  • Haijun Ruan

    (Dyson School of Design Engineering, Imperial College London, London SW7 2AZ, UK)

  • Zhenlin Zhu

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China)

  • Xingzhen Zhou

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China)

Abstract

The battery management system (BMS) is a core component to ensure the efficient and safe operation of electric vehicles, and the practical evaluation of key BMS functions is thus of great importance. However, the testing of a BMS with actual battery packs suffers from a poor testing repeatability and a long status transition time due to the uncontrollable degradation of battery systems and testing environment variations. In this paper, to overcome this challenge, we propose an efficient BMS testing framework that uses virtual battery packs rather than actual ones, thus enabling a rapid and accurate evaluation of a BMSs key functions. A series-connected virtual battery pack model through leveraging Copula’s method is formulated to capture the dynamics and inconsistency of individual batteries in the pack. The developed lithium iron phosphate model features low computational efforts and is experimentally validated with different dynamical profiles, implying a high-precision virtual battery pack that is capable of reproducing the actual one. Furthermore, this framework includes a closed-loop testing platform, which can provide the state-of-charge/state-of-power references and thus automatically test and evaluate the states of the battery packs estimated from the BMS. Particularly, we consider the initial polarization that often exists in the batteries during the operation to accurately calibrate the available state-of-power benchmark of battery packs in the real world. The performed BMS testing results using the proposed framework illustrate that the tested BMS cannot adapt to the varied operation conditions, thus leading to high state estimation errors, which may result in the over-charge/discharge or over-temperature of the batteries. Therefore, this work highlights the value of effective BMS testing, providing the promising potential to achieve reliability and durability for battery systems.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:680-:d:1027350
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/2/680/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/2/680/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Ding, Xiaofeng & Zhang, Donghuai & Cheng, Jiawei & Wang, Binbin & Luk, Patrick Chi Kwong, 2019. "An improved Thevenin model of lithium-ion battery with high accuracy for electric vehicles," Applied Energy, Elsevier, vol. 254(C).
    3. Ruan, Haijun & Jiang, Jiuchun & Sun, Bingxiang & Su, Xiaojia & He, Xitian & Zhao, Kejie, 2019. "An optimal internal-heating strategy for lithium-ion batteries at low temperature considering both heating time and lifetime reduction," Applied Energy, Elsevier, vol. 256(C).
    4. 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.
    5. 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)

    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. 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).
    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. 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).
    4. 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).
    5. 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).
    6. 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).
    7. 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).
    8. 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).
    9. Dawei Song & Shiqian Wang & Li Di & Weijian Zhang & Qian Wang & Jing V. Wang, 2023. "Lithium-Ion Battery Life Prediction Method under Thermal Gradient Conditions," Energies, MDPI, vol. 16(2), pages 1-13, January.
    10. Hu, Lin & Hu, Xiaosong & Che, Yunhong & Feng, Fei & Lin, Xianke & Zhang, Zhiyong, 2020. "Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering," Applied Energy, Elsevier, vol. 262(C).
    11. 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).
    12. Shi, Xingyi & Li, Guangzhe & Zhang, Ruihan & Esan, Oladapo Christopher & Huo, Xiaoyu & Wu, Qixing & An, Liang, 2024. "Operation of rechargeable metal-ion batteries in low-temperature environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    13. 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).
    14. Xinwei Cong & Caiping Zhang & Jiuchun Jiang & Weige Zhang & Yan Jiang & Linjing Zhang, 2021. "A Comprehensive Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 14(5), pages 1-21, February.
    15. Yao Ahoutou & Adrian Ilinca & Mohamad Issa, 2022. "Electrochemical Cells and Storage Technologies to Increase Renewable Energy Share in Cold Climate Conditions—A Critical Assessment," Energies, MDPI, vol. 15(4), pages 1-30, February.
    16. Sun, Xilei & Fu, Jianqin, 2024. "Many-objective optimization of BEV design parameters based on gradient boosting decision tree models and the NSGA-III algorithm considering the ambient temperature," Energy, Elsevier, vol. 288(C).
    17. Xingxing Wang & Peilin Ye & Shengren Liu & Yu Zhu & Yelin Deng & Yinnan Yuan & Hongjun Ni, 2023. "Research Progress of Battery Life Prediction Methods Based on Physical Model," Energies, MDPI, vol. 16(9), pages 1-20, April.
    18. Bingxiang Sun & Xianjie Qi & Donglin Song & Haijun Ruan, 2023. "Review of Low-Temperature Performance, Modeling and Heating for Lithium-Ion Batteries," Energies, MDPI, vol. 16(20), pages 1-37, October.
    19. Hong, Jichao & Li, Kerui & Liang, Fengwei & Yang, Haixu & Zhang, Chi & Yang, Qianqian & Wang, Jiegang, 2024. "A novel state of health prediction method for battery system in real-world vehicles based on gated recurrent unit neural networks," Energy, Elsevier, vol. 289(C).
    20. Sun, Xilei & Zhou, Feng & Fu, Jianqin & Liu, Jingping, 2024. "Experiment and simulation study on energy flow characteristics of a battery electric vehicle throughout the entire driving range in low-temperature conditions," Energy, Elsevier, vol. 292(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:gam:jeners:v:16:y:2023:i:2:p:680-:d:1027350. 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.