IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v260y2022ics0360544222020175.html
   My bibliography  Save this article

Battery capacity trajectory prediction by capturing the correlation between different vehicles

Author

Listed:
  • Li, Jinwen
  • Deng, Zhongwei
  • Liu, Hongao
  • Xie, Yi
  • Liu, Chuan
  • Lu, Chen

Abstract

Advances in battery technology and dwindling oil resources have greatly boosted the popularity of electric vehicles (EVs). Accurate prediction of battery capacity trajectory is critical to ensure safety and timely maintenance of EVs. However, present studies only based on laboratory data. To bridge this gap, this paper proposes a data-driven capacity prediction framework using the vehicle field data, which can automatically match the aging pattern (AP) of the vehicle and fully utilizes the correlation between vehicles and does not need to extract features. The real capacity of battery pack in vehicle is calculated by ampere-hour integration method combined with open circuit voltage correction. To assess the overall effectiveness of this diagnostic methodology, the target vehicle's accessible data is divided into three stages: early, middle, and late. It is demonstrated that after automatic matching of AP, the average mean absolute percentage error (MAPE) of capacity prediction based on the multioutput spectral mixture Gaussian process (MOSMGP) are reduced by 9.82%, 21.25%, and 26.92% respectively in three different aging stages. Compared with the other six machine learning methods, the MOSMGP has the highest accuracy in early prediction. Its average MAPE and average root mean squared error (RMSE) are only 1.39% and 1.92Ah, respectively.

Suggested Citation

  • Li, Jinwen & Deng, Zhongwei & Liu, Hongao & Xie, Yi & Liu, Chuan & Lu, Chen, 2022. "Battery capacity trajectory prediction by capturing the correlation between different vehicles," Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:energy:v:260:y:2022:i:c:s0360544222020175
    DOI: 10.1016/j.energy.2022.125123
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222020175
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125123?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shehzar Shahzad Sheikh & Mahnoor Anjum & Muhammad Abdullah Khan & Syed Ali Hassan & Hassan Abdullah Khalid & Adel Gastli & Lazhar Ben-Brahim, 2020. "A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach," Energies, MDPI, vol. 13(14), pages 1-16, July.
    2. Zheng, Yuejiu & Cui, Yifan & Han, Xuebing & Ouyang, Minggao, 2021. "A capacity prediction framework for lithium-ion batteries using fusion prediction of empirical model and data-driven method," Energy, Elsevier, vol. 237(C).
    3. Deng, Zhongwei & Yang, Lin & Cai, Yishan & Deng, Hao & Sun, Liu, 2016. "Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery," Energy, Elsevier, vol. 112(C), pages 469-480.
    4. Cai, Yishan & Yang, Lin & Deng, Zhongwei & Zhao, Xiaowei & Deng, Hao, 2018. "Online identification of lithium-ion battery state-of-health based on fast wavelet transform and cross D-Markov machine," Energy, Elsevier, vol. 147(C), pages 621-635.
    5. Shen, Sheng & Sadoughi, Mohammadkazem & Li, Meng & Wang, Zhengdao & Hu, Chao, 2020. "Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 260(C).
    6. Chang, Yang & Fang, Huajing & Zhang, Yong, 2017. "A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery," Applied Energy, Elsevier, vol. 206(C), pages 1564-1578.
    7. Che, Yunhong & Deng, Zhongwei & Li, Penghua & Tang, Xiaolin & Khosravinia, Kavian & Lin, Xianke & Hu, Xiaosong, 2022. "State of health prognostics for series battery packs: A universal deep learning method," Energy, Elsevier, vol. 238(PB).
    8. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
    9. Deng, Zhongwei & Deng, Hao & Yang, Lin & Cai, Yishan & Zhao, Xiaowei, 2017. "Implementation of reduced-order physics-based model and multi-parameters identification strategy for lithium-ion battery," Energy, Elsevier, vol. 138(C), pages 509-519.
    10. Huang, Huanyang & Meng, Jinhao & Wang, Yuhong & Feng, Fei & Cai, Lei & Peng, Jichang & Liu, Tianqi, 2022. "A comprehensively optimized lithium-ion battery state-of-health estimator based on Local Coulomb Counting Curve," Applied Energy, Elsevier, vol. 322(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Meng, Jinhao & You, Yuqiang & Lin, Mingqiang & Wu, Ji & Song, Zhengxiang, 2024. "Multi-scenarios transferable learning framework with few-shot for early lithium-ion battery lifespan trajectory prediction," Energy, Elsevier, vol. 286(C).
    2. Zhao, Guangcai & Kang, Yongzhe & Huang, Peng & Duan, Bin & Zhang, Chenghui, 2023. "Battery health prognostic using efficient and robust aging trajectory matching with ensemble deep transfer learning," Energy, Elsevier, vol. 282(C).
    3. Lin, Mingqiang & Wu, Denggao & Meng, Jinhao & Wang, Wei & Wu, Ji, 2023. "Health prognosis for lithium-ion battery with multi-feature optimization," Energy, Elsevier, vol. 264(C).

    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. Hu, Chunsheng & Ma, Liang & Guo, Shanshan & Guo, Gangsheng & Han, Zhiqiang, 2022. "Deep learning enabled state-of-charge estimation of LiFePO4 batteries: A systematic validation on state-of-the-art charging protocols," Energy, Elsevier, vol. 246(C).
    2. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
    3. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    4. Ghorbanzadeh, Milad & Astaneh, Majid & Golzar, Farzin, 2019. "Long-term degradation based analysis for lithium-ion batteries in off-grid wind-battery renewable energy systems," Energy, Elsevier, vol. 166(C), pages 1194-1206.
    5. 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.
    6. Zhao, Xiaowei & Cai, Yishan & Yang, Lin & Deng, Zhongwei & Qiang, Jiaxi, 2017. "State of charge estimation based on a new dual-polarization-resistance model for electric vehicles," Energy, Elsevier, vol. 135(C), pages 40-52.
    7. Li, Qingbo & Lu, Taolin & Lai, Chunyan & Li, Jiwei & Pan, Long & Ma, Changjun & Zhu, Yunpeng & Xie, Jingying, 2024. "Lithium-ion battery capacity estimation based on fragment charging data using deep residual shrinkage networks and uncertainty evaluation," Energy, Elsevier, vol. 290(C).
    8. Haitao Min & Yukun Yan & Weiyi Sun & Yuanbin Yu & Rui Jiang & Fanyu Meng, 2023. "Construction and Estimation of Battery State of Health Using a De-LSTM Model Based on Real Driving Data," Energies, MDPI, vol. 16(24), pages 1-23, December.
    9. Mehta, Rohit & Gupta, Amit, 2024. "Mathematical modelling of electrochemical, thermal and degradation processes in lithium-ion cells—A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    10. Yang, Lin & Cai, Yishan & Yang, Yixin & Deng, Zhongwei, 2020. "Supervisory long-term prediction of state of available power for lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 257(C).
    11. Liyuan Shao & Yong Zhang & Xiujuan Zheng & Xin He & Yufeng Zheng & Zhiwei Liu, 2023. "A Review of Remaining Useful Life Prediction for Energy Storage Components Based on Stochastic Filtering Methods," Energies, MDPI, vol. 16(3), pages 1-22, February.
    12. Wu, Chunling & Hu, Wenbo & Meng, Jinhao & Xu, Xianfeng & Huang, Xinrong & Cai, Lei, 2023. "State-of-charge estimation of lithium-ion batteries based on MCC-AEKF in non-Gaussian noise environment," Energy, Elsevier, vol. 274(C).
    13. Deng, Zhongwei & Yang, Lin & Deng, Hao & Cai, Yishan & Li, Dongdong, 2018. "Polynomial approximation pseudo-two-dimensional battery model for online application in embedded battery management system," Energy, Elsevier, vol. 142(C), pages 838-850.
    14. Che, Yunhong & Zheng, Yusheng & Wu, Yue & Sui, Xin & Bharadwaj, Pallavi & Stroe, Daniel-Ioan & Yang, Yalian & Hu, Xiaosong & Teodorescu, Remus, 2022. "Data efficient health prognostic for batteries based on sequential information-driven probabilistic neural network," Applied Energy, Elsevier, vol. 323(C).
    15. Lin, Chun-Pang & Cabrera, Javier & Yang, Fangfang & Ling, Man-Ho & Tsui, Kwok-Leung & Bae, Suk-Joo, 2020. "Battery state of health modeling and remaining useful life prediction through time series model," Applied Energy, Elsevier, vol. 275(C).
    16. Ma’d El-Dalahmeh & Maher Al-Greer & Mo’ath El-Dalahmeh & Michael Short, 2020. "Time-Frequency Image Analysis and Transfer Learning for Capacity Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 13(20), pages 1-19, October.
    17. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    18. Deng, Zhongwei & Hu, Xiaosong & Lin, Xianke & Che, Yunhong & Xu, Le & Guo, Wenchao, 2020. "Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression," Energy, Elsevier, vol. 205(C).
    19. Cheng, Gong & Wang, Xinzhi & He, Yurong, 2021. "Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network," Energy, Elsevier, vol. 232(C).
    20. Cheng, Ming & Zhang, Xuan & Ran, Aihua & Wei, Guodan & Sun, Hongbin, 2023. "Optimal dispatch approach for second-life batteries considering degradation with online SoH estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(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:eee:energy:v:260:y:2022:i:c:s0360544222020175. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.