IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i3p812-d1572318.html
   My bibliography  Save this article

Jointed SOH Estimation of Electric Bus Batteries Based on Operating Conditions and Multiple Indicators

Author

Listed:
  • Xiaohui Xu

    (Key Laboratory of Automobile Measurement and Control & Safety, School of Automobile & Transportation, Xihua University, Chengdu 610039, China
    Yibin Institute in Xihua University, Yibin 644000, China)

  • Ke Deng

    (Key Laboratory of Automobile Measurement and Control & Safety, School of Automobile & Transportation, Xihua University, Chengdu 610039, China
    Yibin Institute in Xihua University, Yibin 644000, China)

  • Jibin Yang

    (Key Laboratory of Automobile Measurement and Control & Safety, School of Automobile & Transportation, Xihua University, Chengdu 610039, China
    Yibin Institute in Xihua University, Yibin 644000, China)

  • Pengyi Deng

    (Key Laboratory of Automobile Measurement and Control & Safety, School of Automobile & Transportation, Xihua University, Chengdu 610039, China
    Yibin Institute in Xihua University, Yibin 644000, China)

  • Xiaohua Wu

    (Key Laboratory of Automobile Measurement and Control & Safety, School of Automobile & Transportation, Xihua University, Chengdu 610039, China
    Yibin Institute in Xihua University, Yibin 644000, China)

  • Linsui Cheng

    (Key Laboratory of Automobile Measurement and Control & Safety, School of Automobile & Transportation, Xihua University, Chengdu 610039, China
    Yibin Institute in Xihua University, Yibin 644000, China)

  • Haolan Zhou

    (Key Laboratory of Automobile Measurement and Control & Safety, School of Automobile & Transportation, Xihua University, Chengdu 610039, China
    Yibin Institute in Xihua University, Yibin 644000, China)

Abstract

Accurately estimating the battery State of Health (SOH) is crucial for the safe and reliable operation of electric vehicles. Based on the actual operating data of electric buses, this article proposes a battery SOH estimation method that can be applied to multiple operating conditions and indicators. Specifically, the complex operating conditions are simplified into charging and driving conditions through data preprocessing. Under charging conditions, combined with Coulomb counting and incremental capacity analysis methods, a battery SOH estimation model of capacity indicators based on the Bayesian optimization bidirectional gated recursive unit model (BO-BiGRU) is established. Under driving conditions, the adaptive forgetting factor recursive least squares method considering the influence of current is used to identify the battery internal resistance feature. In addition, two separate battery SOH estimation models are established: one for internal resistance indicators based on BO-BiGRU and another for power indicators derived from the actual operational data feature. Finally, a joint battery SOH estimation method considering temperature and different operating conditions is proposed based on the SOH estimation results of the three battery indicators. The verification results show that the average error of the battery SOH estimation method proposed in this article is less than 2%, which has better accuracy for actual vehicles.

Suggested Citation

  • Xiaohui Xu & Ke Deng & Jibin Yang & Pengyi Deng & Xiaohua Wu & Linsui Cheng & Haolan Zhou, 2025. "Jointed SOH Estimation of Electric Bus Batteries Based on Operating Conditions and Multiple Indicators," Sustainability, MDPI, vol. 17(3), pages 1-24, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:812-:d:1572318
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/3/812/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/3/812/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bi, Jun & Zhang, Ting & Yu, Haiyang & Kang, Yanqiong, 2016. "State-of-health estimation of lithium-ion battery packs in electric vehicles based on genetic resampling particle filter," Applied Energy, Elsevier, vol. 182(C), pages 558-568.
    2. 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).
    3. Chang, Yang & Fang, Huajing, 2019. "A hybrid prognostic method for system degradation based on particle filter and relevance vector machine," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 51-63.
    4. Hong, Jichao & Zhang, Tiezhu & Zhang, Zhen & Zhang, Hongxin, 2023. "Investigation of energy management strategy for a novel electric-hydraulic hybrid vehicle: Self-adaptive electric-hydraulic ratio," Energy, Elsevier, vol. 278(C).
    5. Alexander Ly & Maarten Marsman & Eric†Jan Wagenmakers, 2018. "Analytic posteriors for Pearson's correlation coefficient," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(1), pages 4-13, February.
    6. Ma, Ruiyang & Lin, Boqiang, 2023. "Digitalization and energy-saving and emission reduction in Chinese cities: Synergy between industrialization and digitalization," Applied Energy, Elsevier, vol. 345(C).
    7. Guo, Fei & Wu, Xiongwei & Liu, Lili & Ye, Jilei & Wang, Tao & Fu, Lijun & Wu, Yuping, 2023. "Prediction of remaining useful life and state of health of lithium batteries based on time series feature and Savitzky-Golay filter combined with gated recurrent unit neural network," Energy, Elsevier, vol. 270(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. 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).
    2. Zhao, Hongqian & Chen, Zheng & Shu, Xing & Shen, Jiangwei & Lei, Zhenzhen & Zhang, Yuanjian, 2023. "State of health estimation for lithium-ion batteries based on hybrid attention and deep learning," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    3. Shao-Xun Liu & Ya-Fu Zhou & Yan-Liang Liu & Jing Lian & Li-Jian Huang, 2021. "A Method for Battery Health Estimation Based on Charging Time Segment," Energies, MDPI, vol. 14(9), pages 1-15, May.
    4. Yang, Duo & Wang, Yujie & Pan, Rui & Chen, Ruiyang & Chen, Zonghai, 2018. "State-of-health estimation for the lithium-ion battery based on support vector regression," Applied Energy, Elsevier, vol. 227(C), pages 273-283.
    5. Huang, Chenchen & Lin, Boqiang, 2024. "Digital economy solutions towards carbon neutrality: The critical role of energy efficiency and energy structure transformation," Energy, Elsevier, vol. 306(C).
    6. Chen, Xiaowu & Liu, Zhen, 2022. "A long short-term memory neural network based Wiener process model for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    7. Csordás, Adrián & Füzesi, István, 2023. "An Empirical Evaluation of Information Sharing's Impact on Profitability; Evidence from the Solar Sector," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 15(3), September.
    8. Shuo Sun & Junzhong Sun & Zongliang Wang & Zhiyong Zhou & Wei Cai, 2022. "Prediction of Battery SOH by CNN-BiLSTM Network Fused with Attention Mechanism," Energies, MDPI, vol. 15(12), pages 1-17, June.
    9. 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.
    10. Du, Jingcai & Zhang, Caiping & Li, Shuowei & Zhang, Linjing & Zhang, Weige, 2024. "Aging abnormality detection of lithium-ion batteries combining feature engineering and deep learning," Energy, Elsevier, vol. 297(C).
    11. Pingkuo Liu & Jiahao Wu, 2023. "Game Analysis on Energy Enterprises’ Digital Transformation—Strategic Simulation for Guiding Role, Leading Role and Following Role," Sustainability, MDPI, vol. 15(13), pages 1-33, June.
    12. Kalina Grzesiuk & Dorota Jegorow & Monika Wawer & Anna Głowacz, 2023. "Energy-Efficient City Transportation Solutions in the Context of Energy-Conserving and Mobility Behaviours of Generation Z," Energies, MDPI, vol. 16(15), pages 1-28, August.
    13. Kamrul Islam Shahin & Christophe Simon & Philippe Weber, 2024. "RUL management by production reference loopback," Journal of Risk and Reliability, , vol. 238(4), pages 873-888, August.
    14. Chen, Kui & Badji, Abderrezak & Laghrouche, Salah & Djerdir, Abdesslem, 2022. "Polymer electrolyte membrane fuel cells degradation prediction using multi-kernel relevance vector regression and whale optimization algorithm," Applied Energy, Elsevier, vol. 318(C).
    15. Duan, Wenjie & Yu, Xinhang & Wang, Zichuan & Chai, Xue, 2024. "Financial resilience-focused program: A single group pretest–posttest intervention to change financial risk perception among teenagers in an ethnic minority poverty-stricken area in China," Children and Youth Services Review, Elsevier, vol. 164(C).
    16. Tang, Ting & Yuan, Huimei, 2022. "A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    17. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    18. Fengdan Liu & Jiangyi Chen & Dongchen Qin & Tingting Wang, 2023. "Research on Appearance Detection, Sorting, and Regrouping Technology of Retired Batteries for Electric Vehicles," Sustainability, MDPI, vol. 15(21), pages 1-18, November.
    19. Cheng Jin & Zhifeng Jia & Ge Li & Lingke Zhao & Yuze Ren, 2024. "Effect of Soil Moisture Content on Condensation Water in Typical Loess and Sandy Soil," Land, MDPI, vol. 13(7), pages 1-16, June.
    20. Prakash, Om & Samantaray, Arun Kumar, 2021. "Prognosis of Dynamical System Components with Varying Degradation Patterns using model–data–fusion," Reliability Engineering and System Safety, Elsevier, vol. 213(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:jsusta:v:17:y:2025:i:3:p:812-:d:1572318. 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.