IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v300y2024ics0360544224013483.html
   My bibliography  Save this article

Remaining useful life prediction and state of health diagnosis of lithium-ion batteries with multiscale health features based on optimized CatBoost algorithm

Author

Listed:
  • Zhou, Yifei
  • Wang, Shunli
  • Xie, Yanxing
  • Zeng, Jiawei
  • Fernandez, Carlos

Abstract

Due to the large-scale application of electric vehicles, the remaining service life prediction and health status diagnosis of lithium-ion batteries as their power core is particularly important, and the essence of RUL prediction and SOH diagnosis is the prediction of remaining capacity. Through the aging experiment of cycle charging and discharging of lithium-ion batteries, the health features of experimental data are extracted for the prediction of remaining capacity. In this paper, a deep feature extraction method based on Bilinear CNN combined with CatBoost algorithm based on fractional order method optimization particle swarm optimization, and ant colony optimization algorithm is proposed for battery remaining capacity prediction. Seven groups of health features extracted from ten groups of battery data were used to input them into the optimized CatBoost algorithm for regression prediction. The results show that the proposed model achieves accurate SOH and RUL prediction, the three evaluation indicators MAE, RMSE, and MAPE of SOH are all within 1.7 % and the error rate of RUL is not higher than 1.5 %, and the test of multiple batteries also proves its strong robustness.

Suggested Citation

  • Zhou, Yifei & Wang, Shunli & Xie, Yanxing & Zeng, Jiawei & Fernandez, Carlos, 2024. "Remaining useful life prediction and state of health diagnosis of lithium-ion batteries with multiscale health features based on optimized CatBoost algorithm," Energy, Elsevier, vol. 300(C).
    • Handle: RePEc:eee:energy:v:300:y:2024:i:c:s0360544224013483
    DOI: 10.1016/j.energy.2024.131575
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224013483
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.131575?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. Chen, Dan & Meng, Jinhao & Huang, Huanyang & Wu, Ji & Liu, Ping & Lu, Jiwu & Liu, Tianqi, 2022. "An Empirical-Data Hybrid Driven Approach for Remaining Useful Life prediction of lithium-ion batteries considering capacity diving," Energy, Elsevier, vol. 245(C).
    2. 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).
    3. He, Xinbo & Wang, Yong & Zhang, Yuyang & Ma, Xin & Wu, Wenqing & Zhang, Lei, 2022. "A novel structure adaptive new information priority discrete grey prediction model and its application in renewable energy generation forecasting," Applied Energy, Elsevier, vol. 325(C).
    4. Yang, Bo & Qian, Yucun & Li, Qiang & Chen, Qian & Wu, Jiyang & Luo, Enbo & Xie, Rui & Zheng, Ruyi & Yan, Yunfeng & Su, Shi & Wang, Jingbo, 2024. "Critical summary and perspectives on state-of-health of lithium-ion battery," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    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, Alan G. & West, Alan C. & Preindl, Matthias, 2022. "Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review," Applied Energy, Elsevier, vol. 316(C).
    7. Ding, Pan & Liu, Xiaojuan & Li, Huiqin & Huang, Zequan & Zhang, Ke & Shao, Long & Abedinia, Oveis, 2021. "Useful life prediction based on wavelet packet decomposition and two-dimensional convolutional neural network for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    8. Ma, Jian & Shang, Pengchao & Zou, Xinyu & Ma, Ning & Ding, Yu & Sun, Jinwen & Cheng, Yujie & Tao, Laifa & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou, 2021. "A hybrid transfer learning scheme for remaining useful life prediction and cycle life test optimization of different formulation Li-ion power batteries," Applied Energy, Elsevier, vol. 282(PA).
    9. Gallo, Marco & Costabile, Carmine & Sorrentino, Marco & Polverino, Pierpaolo & Pianese, Cesare, 2020. "Development and application of a comprehensive model-based methodology for fault mitigation of fuel cell powered systems," Applied Energy, Elsevier, vol. 279(C).
    10. Vichard, L. & Ravey, A. & Venet, P. & Harel, F. & Pelissier, S. & Hissel, D., 2021. "A method to estimate battery SOH indicators based on vehicle operating data only," Energy, Elsevier, vol. 225(C).
    11. Kong, Jin-zhen & Yang, Fangfang & Zhang, Xi & Pan, Ershun & Peng, Zhike & Wang, Dong, 2021. "Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries," Energy, Elsevier, vol. 223(C).
    12. Güven, Aykut Fatih & Yörükeren, Nuran & Samy, Mohamed Mahmoud, 2022. "Design optimization of a stand-alone green energy system of university campus based on Jaya-Harmony Search and Ant Colony Optimization algorithms approaches," Energy, Elsevier, vol. 253(C).
    13. Khosravi, Nima & Dowlatabadi, Masrour & Abdelghany, Muhammad Bakr & Tostado-Véliz, Marcos & Jurado, Francisco, 2024. "Enhancing battery management for HEVs and EVs: A hybrid approach for parameter identification and voltage estimation in lithium-ion battery models," Applied Energy, Elsevier, vol. 356(C).
    14. Chen, Lin & Ding, Yunhui & Liu, Bohao & Wu, Shuxiao & Wang, Yaodong & Pan, Haihong, 2022. "Remaining useful life prediction of lithium-ion battery using a novel particle filter framework with grey neural network," Energy, Elsevier, vol. 244(PA).
    15. Kim, Sung Wook & Oh, Ki-Yong & Lee, Seungchul, 2022. "Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries," Applied Energy, Elsevier, vol. 315(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. 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).
    2. 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).
    3. Zhao, Bo & Zhang, Weige & Zhang, Yanru & Zhang, Caiping & Zhang, Chi & Zhang, Junwei, 2024. "Research on the remaining useful life prediction method for lithium-ion batteries by fusion of feature engineering and deep learning," Applied Energy, Elsevier, vol. 358(C).
    4. Zhang, Ying & Li, Yan-Fu, 2022. "Prognostics and health management of Lithium-ion battery using deep learning methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    5. Wu, Ji & Fang, Leichao & Dong, Guangzhong & Lin, Mingqiang, 2023. "State of health estimation of lithium-ion battery with improved radial basis function neural network," Energy, Elsevier, vol. 262(PB).
    6. Li, Chuan & Zhang, Huahua & Ding, Ping & Yang, Shuai & Bai, Yun, 2023. "Deep feature extraction in lifetime prognostics of lithium-ion batteries: Advances, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    7. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Shang, Zuogang & Yan, Ruqiang & Chen, Xuefeng, 2023. "Explainability-driven model improvement for SOH estimation of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    8. Du, Jingcai & Zhang, Caiping & Li, Shuowei & Zhang, Linjing & Zhang, Weige, 2024. "Two-stage prediction method for capacity aging trajectories of lithium-ion batteries based on Siamese-convolutional neural network," Energy, Elsevier, vol. 295(C).
    9. Wang, Shunli & Fan, Yongcun & Jin, Siyu & Takyi-Aninakwa, Paul & Fernandez, Carlos, 2023. "Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    10. Kurucan, Mehmet & Özbaltan, Mete & Yetgin, Zeki & Alkaya, Alkan, 2024. "Applications of artificial neural network based battery management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    11. Chunxiang Zhu & Zhiwei He & Zhengyi Bao & Changcheng Sun & Mingyu Gao, 2023. "Prognosis of Lithium-Ion Batteries’ Remaining Useful Life Based on a Sequence-to-Sequence Model with Variational Mode Decomposition," Energies, MDPI, vol. 16(2), pages 1-16, January.
    12. Guo, Junyu & Wan, Jia-Lun & Yang, Yan & Dai, Le & Tang, Aimin & Huang, Bangkui & Zhang, Fangfang & Li, He, 2023. "A deep feature learning method for remaining useful life prediction of drilling pumps," Energy, Elsevier, vol. 282(C).
    13. Zhou, Yifei & Wang, Shunli & Xie, Yanxing & Shen, Xianfeng & Fernandez, Carlos, 2023. "Remaining useful life prediction and state of health diagnosis for lithium-ion batteries based on improved grey wolf optimization algorithm-deep extreme learning machine algorithm," Energy, Elsevier, vol. 285(C).
    14. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
    15. Wang, Huan & Li, Yan-Fu & Zhang, Ying, 2023. "Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    16. Zhou, Jianguo & Xu, Zhongtian, 2023. "Optimal sizing design and integrated cost-benefit assessment of stand-alone microgrid system with different energy storage employing chameleon swarm algorithm: A rural case in Northeast China," Renewable Energy, Elsevier, vol. 202(C), pages 1110-1137.
    17. Xiangang Cao & Pengfei Li & Song Ming, 2021. "Remaining Useful Life Prediction-Based Maintenance Decision Model for Stochastic Deterioration Equipment under Data-Driven," Sustainability, MDPI, vol. 13(15), pages 1-19, July.
    18. Xu, Yuan-wu & Wu, Xiao-long & Zhong, Xiao-bo & Zhao, Dong-qi & Sorrentino, Marco & Jiang, Jianhua & Jiang, Chang & Fu, Xiaowei & Li, Xi, 2021. "Mechanism model-based and data-driven approach for the diagnosis of solid oxide fuel cell stack leakage," Applied Energy, Elsevier, vol. 286(C).
    19. 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).
    20. Fu, Shiyi & Tao, Shengyu & Fan, Hongtao & He, Kun & Liu, Xutao & Tao, Yulin & Zuo, Junxiong & Zhang, Xuan & Wang, Yu & Sun, Yaojie, 2024. "Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method," Applied Energy, Elsevier, vol. 353(PA).

    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:300:y:2024:i:c:s0360544224013483. 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.