IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i22p5808-d1525582.html
   My bibliography  Save this article

Development of a Battery Diagnostic Method Based on CAN Data: Examining the Accuracy of Data Received via a Communication Network

Author

Listed:
  • Balázs Baráth

    (Zalaegerszeg Innovation Park, Széchenyi István University, H-8900 Zalaegerszeg, Hungary)

  • Gergő Sütheö

    (Zalaegerszeg Innovation Park, Széchenyi István University, H-8900 Zalaegerszeg, Hungary)

  • Letícia Pekk

    (Zalaegerszeg Innovation Park, Széchenyi István University, H-8900 Zalaegerszeg, Hungary)

Abstract

In order to reduce the emissions caused by internal combustion engine vehicles, the industry is introducing more and more electric or hybrid vehicles to the market nowadays. The battery cells and modules of these vehicles require a lot of care, as improper or improperly maintained battery units can cause serious problems inside vehicles and can be extremely dangerous. The safest solution is to keep this unit of a vehicle under constant supervision so that it can be repaired immediately in case of an issue. Since all necessary data can be extracted from a vehicle’s communication network(s) through standard communication protocols, it is advisable to use them for continuous monitoring and diagnostics of units, while also considering cost-effectiveness and simplicity. The data received from here can also be used for measurement of electric powertrains and other parameters. However, since these data go through many conversions and computers (ECUs) before reaching us, their accuracy is questionable. In this study, we present our own custom battery diagnostic tool based on data extracted from a communication network. With the help of commercially available diagnostic tools, we also compare several measurements of the extent of the error limits of the data arriving at the communication network, how far they differ from the real values, and with the help of these, we analyze the accuracy of the device we have made. We present the commonly used Controller Area Network (CAN) communication protocol for passenger vehicles and briefly describe the construction of the high-voltage battery unit of the test vehicle.

Suggested Citation

  • Balázs Baráth & Gergő Sütheö & Letícia Pekk, 2024. "Development of a Battery Diagnostic Method Based on CAN Data: Examining the Accuracy of Data Received via a Communication Network," Energies, MDPI, vol. 17(22), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5808-:d:1525582
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/22/5808/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/22/5808/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jaguemont, J. & Boulon, L. & Dubé, Y., 2016. "A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures," Applied Energy, Elsevier, vol. 164(C), pages 99-114.
    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. Das, Himadry Shekhar & Tan, Chee Wei & Yatim, A.H.M., 2017. "Fuel cell hybrid electric vehicles: A review on power conditioning units and topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 268-291.
    2. 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.
    3. Ragab El-Sehiemy & Mohamed A. Hamida & Ehab Elattar & Abdullah Shaheen & Ahmed Ginidi, 2022. "Nonlinear Dynamic Model for Parameter Estimation of Li-Ion Batteries Using Supply–Demand Algorithm," Energies, MDPI, vol. 15(13), pages 1-20, June.
    4. Xuliang Tang & Heng Wan & Weiwen Wang & Mengxu Gu & Linfeng Wang & Linfeng Gan, 2023. "Lithium-Ion Battery Remaining Useful Life Prediction Based on Hybrid Model," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
    5. Kriegler, Johannes & Hille, Lucas & Stock, Sandro & Kraft, Ludwig & Hagemeister, Jan & Habedank, Jan Bernd & Jossen, Andreas & Zaeh, Michael F., 2021. "Enhanced performance and lifetime of lithium-ion batteries by laser structuring of graphite anodes," Applied Energy, Elsevier, vol. 303(C).
    6. Hong Shi & Mengmeng Cheng & Yi Feng & Chenghui Qiu & Caiyue Song & Nenglin Yuan & Chuanzhi Kang & Kaijie Yang & Jie Yuan & Yonghao Li, 2023. "Thermal Management Techniques for Lithium-Ion Batteries Based on Phase Change Materials: A Systematic Review and Prospective Recommendations," Energies, MDPI, vol. 16(2), pages 1-23, January.
    7. Wang, Chun & Yang, Ruixin & Yu, Quanqing, 2019. "Wavelet transform based energy management strategies for plug-in hybrid electric vehicles considering temperature uncertainty," Applied Energy, Elsevier, vol. 256(C).
    8. Jiangbo Wang & Kai Liu & Toshiyuki Yamamoto, 2017. "Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations," Energies, MDPI, vol. 10(1), pages 1-12, January.
    9. Santhakumari, Manju & Sagar, Netramani, 2019. "A review of the environmental factors degrading the performance of silicon wafer-based photovoltaic modules: Failure detection methods and essential mitigation techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 83-100.
    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. Huang, Yi-Huan & Cheng, Yi-Xin & Zhao, Rui & Cheng, Wen-Long, 2020. "A high heat storage capacity form-stable composite phase change material with enhanced flame retardancy," Applied Energy, Elsevier, vol. 262(C).
    12. Yigeng Huangfu & Jiani Xu & Dongdong Zhao & Yuntian Liu & Fei Gao, 2018. "A Novel Battery State of Charge Estimation Method Based on a Super-Twisting Sliding Mode Observer," Energies, MDPI, vol. 11(5), pages 1-21, May.
    13. Zhang, Jiangyun & Shao, Dan & Jiang, Liqin & Zhang, Guoqing & Wu, Hongwei & Day, Rodney & Jiang, Wenzhao, 2022. "Advanced thermal management system driven by phase change materials for power lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    14. Xian Zhao & Siqi Wang & Xiaoyue Wang, 2018. "Characteristics and Trends of Research on New Energy Vehicle Reliability Based on the Web of Science," Sustainability, MDPI, vol. 10(10), pages 1-25, October.
    15. Liang, Lin & Zhao, Yaohua & Diao, Yanhua & Ren, Ruyang & Zhu, Tingting & Li, Yan, 2023. "Experimental investigation of preheating performance of lithium-ion battery modules in electric vehicles enhanced by bending flat micro heat pipe array," Applied Energy, Elsevier, vol. 337(C).
    16. Gu, Huaying & Liu, Zhixue & Qing, Qiankai, 2017. "Optimal electric vehicle production strategy under subsidy and battery recycling," Energy Policy, Elsevier, vol. 109(C), pages 579-589.
    17. Yang, Jufeng & Xia, Bing & Huang, Wenxin & Fu, Yuhong & Mi, Chris, 2018. "Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis," Applied Energy, Elsevier, vol. 212(C), pages 1589-1600.
    18. Wen, Jianping & Zhao, Dan & Zhang, Chuanwei, 2020. "An overview of electricity powered vehicles: Lithium-ion battery energy storage density and energy conversion efficiency," Renewable Energy, Elsevier, vol. 162(C), pages 1629-1648.
    19. Shang, Wenxu & Yu, Wentao & Xiao, Xu & Ma, Yanyi & Chen, Ziqi & Ni, Meng & Tan, Peng, 2022. "Optimizing the charging protocol to address the self-discharge issues in rechargeable alkaline Zn-Co batteries," Applied Energy, Elsevier, vol. 308(C).
    20. 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).

    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:17:y:2024:i:22:p:5808-:d:1525582. 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.