IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v282y2023ics0360544223021333.html
   My bibliography  Save this article

State of health estimation of lithium-ion batteries based on fine-tuning or rebuilding transfer learning strategies combined with new features mining

Author

Listed:
  • Huang, Kai
  • Yao, Kaixin
  • Guo, Yongfang
  • Lv, Ziteng

Abstract

Accurate state of health (SOH) estimation of lithium-ion batteries is essential to ensure the reliability of power equipment. However, the degradation trajectory of different cells and different types of batteries is not repeatable. At present, there is no unified model or method to effectively predict SOH for all batteries. Therefore, a new SOH estimation method is proposed in the paper. Firstly, two types of new features are proposed in this paper. One is the voltage features extracted from the constant-current charging stage, and the other is the capacity recovery feature. They are used to reflect the nonlinear degradation process of the battery. Secondly, the relationship between features and SOH is established by using the LSTM model, which can prevent the problem of gradient vanishing and gradient explosion during model learning. Finally, for the inconsistencies between the same type or different types of batteries, two different transfer learning strategies (fine-tuning and rebuilding) are proposed in this paper, and the effectiveness of the proposed features and transfer learning strategies is verified on three open-source battery data sets (NASA, Oxford, and CALCE). Experimental results show that the SOH estimation method proposed in the paper has good universality, robustness, and accuracy.

Suggested Citation

  • Huang, Kai & Yao, Kaixin & Guo, Yongfang & Lv, Ziteng, 2023. "State of health estimation of lithium-ion batteries based on fine-tuning or rebuilding transfer learning strategies combined with new features mining," Energy, Elsevier, vol. 282(C).
    • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s0360544223021333
    DOI: 10.1016/j.energy.2023.128739
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223021333
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.128739?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. 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).
    2. 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).
    3. Wen, Jianping & Chen, Xing & Li, Xianghe & Li, Yikun, 2022. "SOH prediction of lithium battery based on IC curve feature and BP neural network," Energy, Elsevier, vol. 261(PA).
    4. Deng, Yuanwang & Ying, Hejie & E, Jiaqiang & Zhu, Hao & Wei, Kexiang & Chen, Jingwei & Zhang, Feng & Liao, Gaoliang, 2019. "Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries," Energy, Elsevier, vol. 176(C), pages 91-102.
    5. 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.
    6. Zuo, Wei & Zhang, Yuntian & E, Jiaqiang & Li, Jing & Li, Qingqing & Zhang, Guangde, 2022. "Performance comparison between single S-channel and double S-channel cold plate for thermal management of a prismatic LiFePO4 battery," Renewable Energy, Elsevier, vol. 192(C), pages 46-57.
    7. Wilberforce, Tabbi & Rezk, Hegazy & Olabi, A.G. & Epelle, Emmanuel I. & Abdelkareem, Mohammad Ali, 2023. "Comparative analysis on parametric estimation of a PEM fuel cell using metaheuristics algorithms," Energy, Elsevier, vol. 262(PB).
    8. Zuo, Wei & Chen, Zhijie & E, Jiaqiang & Li, Qingqing & Zhang, Guangde & Huang, Yuhan, 2023. "Effects of structure parameters of tube outlet on the performance of a hydrogen-fueled micro planar combustor for thermophotovoltaic applications," Energy, Elsevier, vol. 266(C).
    9. Lin, Chuanping & Xu, Jun & Shi, Mingjie & Mei, Xuesong, 2022. "Constant current charging time based fast state-of-health estimation for lithium-ion batteries," Energy, Elsevier, vol. 247(C).
    10. Zuo, Wei & Wang, Zijie & E, Jiaqiang & Li, Qingqing & Cheng, Qianju & Wu, Yinkun & Zhou, Kun, 2023. "Numerical investigations on the performance of a hydrogen-fueled micro planar combustor with tube outlet for thermophotovoltaic applications," Energy, Elsevier, vol. 263(PC).
    11. Waag, Wladislaw & Käbitz, Stefan & Sauer, Dirk Uwe, 2013. "Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application," Applied Energy, Elsevier, vol. 102(C), pages 885-897.
    12. Hashemi, Seyed Reza & Mahajan, Ajay Mohan & Farhad, Siamak, 2021. "Online estimation of battery model parameters and state of health in electric and hybrid aircraft application," Energy, Elsevier, vol. 229(C).
    13. Lin, Mingqiang & Yan, Chenhao & Meng, Jinhao & Wang, Wei & Wu, Ji, 2022. "Lithium-ion batteries health prognosis via differential thermal capacity with simulated annealing and support vector regression," Energy, Elsevier, vol. 250(C).
    14. Shi, Mingjie & Xu, Jun & Lin, Chuanping & Mei, Xuesong, 2022. "A fast state-of-health estimation method using single linear feature for lithium-ion batteries," Energy, Elsevier, vol. 256(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. Li, Dexin & Zuo, Wei & Li, Qingqing & Zhang, Guangde & Zhou, Kun & E, Jiaqiang, 2023. "Effects of pulsating flow on the performance of multi-channel cold plate for thermal management of lithium-ion battery pack," Energy, Elsevier, vol. 273(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. Chen, Zhijie & Zuo, Wei & Zhou, Kun & Li, Qingqing & Huang, Yuhan & E, Jiaqiang, 2023. "Multi-factor impact mechanism on the performance of high temperature proton exchange membrane fuel cell," Energy, Elsevier, vol. 278(PB).
    4. Shi, Mingjie & Xu, Jun & Lin, Chuanping & Mei, Xuesong, 2022. "A fast state-of-health estimation method using single linear feature for lithium-ion batteries," Energy, Elsevier, vol. 256(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. Zhang, Shuxin & Liu, Zhitao & Su, Hongye, 2023. "State of health estimation for lithium-ion batteries on few-shot learning," Energy, Elsevier, vol. 268(C).
    7. Ceglia, Francesca & Marrasso, Elisa & Roselli, Carlo & Sasso, Maurizio, 2023. "Energy and environmental assessment of a biomass-based renewable energy community including photovoltaic and hydroelectric systems," Energy, Elsevier, vol. 282(C).
    8. Shu, Xing & Li, Guang & Shen, Jiangwei & Lei, Zhenzhen & Chen, Zheng & Liu, Yonggang, 2020. "A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization," Energy, Elsevier, vol. 204(C).
    9. Gu, Xinyu & See, K.W. & Li, Penghua & Shan, Kangheng & Wang, Yunpeng & Zhao, Liang & Lim, Kai Chin & Zhang, Neng, 2023. "A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model," Energy, Elsevier, vol. 262(PB).
    10. 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).
    11. Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    12. Yan, Lisen & Peng, Jun & Gao, Dianzhu & Wu, Yue & Liu, Yongjie & Li, Heng & Liu, Weirong & Huang, Zhiwu, 2022. "A hybrid method with cascaded structure for early-stage remaining useful life prediction of lithium-ion battery," Energy, Elsevier, vol. 243(C).
    13. Bao, Zhengyi & Nie, Jiahao & Lin, Huipin & Jiang, Jiahao & He, Zhiwei & Gao, Mingyu, 2023. "A global–local context embedding learning based sequence-free framework for state of health estimation of lithium-ion battery," Energy, Elsevier, vol. 282(C).
    14. Goh, Taedong & Park, Minjun & Seo, Minhwan & Kim, Jun Gu & Kim, Sang Woo, 2018. "Successive-approximation algorithm for estimating capacity of Li-ion batteries," Energy, Elsevier, vol. 159(C), pages 61-73.
    15. Yang, Jufeng & Li, Xin & Sun, Xiaodong & Cai, Yingfeng & Mi, Chris, 2023. "An efficient and robust method for lithium-ion battery capacity estimation using constant-voltage charging time," Energy, Elsevier, vol. 263(PB).
    16. Miaomiao Zeng & Peng Zhang & Yang Yang & Changjun Xie & Ying Shi, 2019. "SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm," Energies, MDPI, vol. 12(16), pages 1-15, August.
    17. Zhang, Jiarui & Wang, Chao & Li, Jinzhong & Xie, Yuguang & Mao, Lei & Hu, Zhiyong, 2023. "A Bayesian method for capacity degradation prediction of lithium-ion battery considering both within and cross group heterogeneity," Applied Energy, Elsevier, vol. 351(C).
    18. Liu, Gengfeng & Zhang, Xiangwen & Liu, Zhiming, 2022. "State of health estimation of power batteries based on multi-feature fusion models using stacking algorithm," Energy, Elsevier, vol. 259(C).
    19. Yao, Jiachi & Han, Te, 2023. "Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data," Energy, Elsevier, vol. 271(C).
    20. Farmann, Alexander & Sauer, Dirk Uwe, 2018. "Comparative study of reduced order equivalent circuit models for on-board state-of-available-power prediction of lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 225(C), pages 1102-1122.

    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:282:y:2023:i:c:s0360544223021333. 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.