IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i24p9656-d1008476.html
   My bibliography  Save this article

Agnostic Battery Management System Capacity Estimation for Electric Vehicles

Author

Listed:
  • Lisa Calearo

    (Department of Wind and Energy Systems, Technical University of Denmark (DTU), Risø Campus, 2800 Roskilde, Denmark
    Ramboll Danmark A/S, 2300 Copenhagen, Denmark)

  • Charalampos Ziras

    (Department of Wind and Energy Systems, Technical University of Denmark (DTU), Risø Campus, 2800 Roskilde, Denmark)

  • Andreas Thingvad

    (Hybrid Greentech ApS, 4000 Roskilde, Denmark)

  • Mattia Marinelli

    (Department of Wind and Energy Systems, Technical University of Denmark (DTU), Risø Campus, 2800 Roskilde, Denmark)

Abstract

Battery degradation is a main concern for electric vehicle (EV) users, and a reliable capacity estimation is of major importance. Every EV battery management system (BMS) provides a variety of information, including measured current and voltage, and estimated capacity of the battery. However, these estimations are not transparent and are manufacturer-specific, although measurement accuracy is unknown. This article uses extensive measurements from six diverse EVs to compare and assess capacity estimation with three different methods: (1) reading capacity estimation from the BMS through the central area network (CAN)-bus, (2) using an empirical capacity estimation (ECE) method with external current measurements, and (3) using the same method with measurements coming from the BMS. We show that the use of BMS current measurements provides consistent capacity estimation (a difference of approximately 1%) and can circumvent the need for costly experimental equipment and DC chargers. This data can simplify the ECE method only by using an on-board diagnostics port (OBDII) reader and an AC charger, as the car measures the current directly at the battery terminals.

Suggested Citation

  • Lisa Calearo & Charalampos Ziras & Andreas Thingvad & Mattia Marinelli, 2022. "Agnostic Battery Management System Capacity Estimation for Electric Vehicles," Energies, MDPI, vol. 15(24), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9656-:d:1008476
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/24/9656/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/24/9656/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Luo, Guangzhao & Sui, Xin & Teodorescu, Remus, 2019. "Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles," Energy, Elsevier, vol. 185(C), pages 1054-1062.
    2. Berecibar, M. & Gandiaga, I. & Villarreal, I. & Omar, N. & Van Mierlo, J. & Van den Bossche, P., 2016. "Critical review of state of health estimation methods of Li-ion batteries for real applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 572-587.
    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. Xu, Zhicheng & Wang, Jun & Lund, Peter D. & Zhang, Yaoming, 2021. "Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data," Energy, Elsevier, vol. 225(C).
    2. Lai, Xin & Yi, Wei & Cui, Yifan & Qin, Chao & Han, Xuebing & Sun, Tao & Zhou, Long & Zheng, Yuejiu, 2021. "Capacity estimation of lithium-ion cells by combining model-based and data-driven methods based on a sequential extended Kalman filter," Energy, Elsevier, vol. 216(C).
    3. Sun, Li & Sun, Wen & You, Fengqi, 2020. "Core temperature modelling and monitoring of lithium-ion battery in the presence of sensor bias," Applied Energy, Elsevier, vol. 271(C).
    4. Wenxian Duan & Chuanxue Song & Silun Peng & Feng Xiao & Yulong Shao & Shixin Song, 2020. "An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery," Energies, MDPI, vol. 13(23), pages 1-19, December.
    5. Das, Kaushik & Kumar, Roushan & Krishna, Anurup, 2024. "Analyzing electric vehicle battery health performance using supervised machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    6. 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.
    7. Zhang, Yajun & Liu, Yajie & Wang, Jia & Zhang, Tao, 2022. "State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression," Energy, Elsevier, vol. 239(PB).
    8. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    9. 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).
    10. Zhao, Jingyuan & Wang, Zhenghong & Wu, Yuyan & Burke, Andrew F., 2025. "Predictive pretrained transformer (PPT) for real-time battery health diagnostics," Applied Energy, Elsevier, vol. 377(PD).
    11. Liu, Sijia & Winter, Michaela & Lewerenz, Meinert & Becker, Jan & Sauer, Dirk Uwe & Ma, Zeyu & Jiang, Jiuchun, 2019. "Analysis of cyclic aging performance of commercial Li4Ti5O12-based batteries at room temperature," Energy, Elsevier, vol. 173(C), pages 1041-1053.
    12. Jin, Haiyan & Ru, Rui & Cai, Lei & Meng, Jinhao & Wang, Bin & Peng, Jichang & Yang, Shengxiang, 2025. "A synthetic data generation method and evolutionary transformer model for degradation trajectory prediction in lithium-ion batteries," Applied Energy, Elsevier, vol. 377(PD).
    13. Penelope K. Jones & Ulrich Stimming & Alpha A. Lee, 2022. "Impedance-based forecasting of lithium-ion battery performance amid uneven usage," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    14. Zheng Chen & Xiaoyu Li & Jiangwei Shen & Wensheng Yan & Renxin Xiao, 2016. "A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles," Energies, MDPI, vol. 9(9), pages 1-15, September.
    15. Tian, Yong & Dong, Qianyuan & Tian, Jindong & Li, Xiaoyu & Li, Guang & Mehran, Kamyar, 2023. "Capacity estimation of lithium-ion batteries based on optimized charging voltage section and virtual sample generation," Applied Energy, Elsevier, vol. 332(C).
    16. Ummartyotin, S. & Bunnak, N. & Manuspiya, H., 2016. "A comprehensive review on modified clay based composite for energy based materials," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 466-472.
    17. Theodoros Kalogiannis & Md Sazzad Hosen & Mohsen Akbarzadeh Sokkeh & Shovon Goutam & Joris Jaguemont & Lu Jin & Geng Qiao & Maitane Berecibar & Joeri Van Mierlo, 2019. "Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model," Energies, MDPI, vol. 12(21), pages 1-35, October.
    18. Hector Beltran & Pablo Ayuso & Emilio Pérez, 2020. "Lifetime Expectancy of Li-Ion Batteries used for Residential Solar Storage," Energies, MDPI, vol. 13(3), pages 1-18, January.
    19. Yunlong Han & Conghui Li & Linfeng Zheng & Gang Lei & Li Li, 2023. "Remaining Useful Life Prediction of Lithium-Ion Batteries by Using a Denoising Transformer-Based Neural Network," Energies, MDPI, vol. 16(17), pages 1-16, August.
    20. Bian, Chong & He, Huoliang & Yang, Shunkun, 2020. "Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 191(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:15:y:2022:i:24:p:9656-:d:1008476. 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.