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Transferable data-driven capacity estimation for lithium-ion batteries with deep learning: A case study from laboratory to field applications

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  • Wang, Qiao
  • Ye, Min
  • Cai, Xue
  • Sauer, Dirk Uwe
  • Li, Weihan

Abstract

Capacity estimation plays a vital role in ensuring the health and safety management of lithium-ion battery-based electric-drive systems. This research focuses on developing a transferable data-driven framework for accurately estimating the capacity of lithium-ion batteries with the same chemistry but different capacities in field applications. The proposed approach leverages universal information from a laboratory dataset and utilizes a pre-trained network designed for small-capacity batteries with constant-current discharging profiles. By applying this framework, capacity estimation for large-capacity batteries under drive cycles can be efficiently achieved with improved performance. In addition, the incremental capacity analysis is employed on two datasets, selecting a robust voltage interval for health indicator extraction with physical interpretations and uncertainty awareness of different fast charging protocols. The feature extraction and dimension increase processes are automated, utilizing the last short charging sequences in wide voltage intervals while considering the uncertainty related to various user charging habits. Results demonstrate that the proposed strategy significantly enhances both robustness and accuracy. When compared to conventional methods, the proposed method exhibits an average root mean square error improvement of 68.40% and 65.89% in the best and worst cases, respectively. The robustness of the proposed strategy is further verified through 30 randomized health indicator verifications. This research showcases the potential of transferable deep learning in improving capacity estimation by leveraging universal information for field applications. The findings emphasize the importance of sharing knowledge across different capacities of lithium-ion batteries, enabling more effective and accurate capacity estimation techniques.

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  • Wang, Qiao & Ye, Min & Cai, Xue & Sauer, Dirk Uwe & Li, Weihan, 2023. "Transferable data-driven capacity estimation for lithium-ion batteries with deep learning: A case study from laboratory to field applications," Applied Energy, Elsevier, vol. 350(C).
    • Handle: RePEc:eee:appene:v:350:y:2023:i:c:s030626192301111x
    DOI: 10.1016/j.apenergy.2023.121747
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    as
    1. Wang, Qiao & Ye, Min & Wei, Meng & Lian, Gaoqi & Li, Yan, 2023. "Deep convolutional neural network based closed-loop SOC estimation for lithium-ion batteries in hierarchical scenarios," Energy, Elsevier, vol. 263(PB).
    2. Li, Yuanyuan & Sheng, Hanmin & Cheng, Yuhua & Stroe, Daniel-Ioan & Teodorescu, Remus, 2020. "State-of-health estimation of lithium-ion batteries based on semi-supervised transfer component analysis," Applied Energy, Elsevier, vol. 277(C).
    3. 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).
    4. Reniers, Jorn M. & Howey, David A., 2023. "Digital twin of a MWh-scale grid battery system for efficiency and degradation analysis," Applied Energy, Elsevier, vol. 336(C).
    5. Björn Nykvist & Måns Nilsson, 2015. "Rapidly falling costs of battery packs for electric vehicles," Nature Climate Change, Nature, vol. 5(4), pages 329-332, April.
    6. Xiankai Li & Mingjie Li & Jie Xu & Jun You & Zhiqin Yang & Chaoxu Li, 2019. "Evaporation-induced sintering of liquid metal droplets with biological nanofibrils for flexible conductivity and responsive actuation," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    7. D. M. Davies & M. G. Verde & O. Mnyshenko & Y. R. Chen & R. Rajeev & Y. S. Meng & G. Elliott, 2019. "Combined economic and technological evaluation of battery energy storage for grid applications," Nature Energy, Nature, vol. 4(1), pages 42-50, January.
    8. Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
    9. 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).
    10. Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo & Xie, Jiale, 2022. "A data-fusion framework for lithium battery health condition Estimation Based on differential thermal voltammetry," Energy, Elsevier, vol. 239(PC).
    11. Zhao, Yang & Wang, Zhenpo & Shen, Zuo-Jun Max & Zhang, Lei & Dorrell, David G. & Sun, Fengchun, 2022. "Big data-driven decoupling framework enabling quantitative assessments of electric vehicle performance degradation," Applied Energy, Elsevier, vol. 327(C).
    12. Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
    13. Khaleghi, Sahar & Karimi, Danial & Beheshti, S. Hamidreza & Hosen, Md. Sazzad & Behi, Hamidreza & Berecibar, Maitane & Van Mierlo, Joeri, 2021. "Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network," Applied Energy, Elsevier, vol. 282(PA).
    14. Yang, Jufeng & Cai, Yingfeng & Mi, Chris, 2022. "Lithium-ion battery capacity estimation based on battery surface temperature change under constant-current charge scenario," Energy, Elsevier, vol. 241(C).
    15. 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).
    16. 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).
    17. Wang, Qiao & Ye, Min & Wei, Meng & Lian, Gaoqi & Li, Yan, 2023. "Random health indicator and shallow neural network based robust capacity estimation for lithium-ion batteries with different fast charging protocols," Energy, Elsevier, vol. 271(C).
    18. Xiong, Rui & Pan, Yue & Shen, Weixiang & Li, Hailong & Sun, Fengchun, 2020. "Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    19. 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).
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    Cited by:

    1. Zhongxian Sun & Weilin He & Junlei Wang & Xin He, 2024. "State of Health Estimation for Lithium-Ion Batteries with Deep Learning Approach and Direct Current Internal Resistance," Energies, MDPI, vol. 17(11), pages 1-14, May.
    2. Fahmy, Hend M. & Alqahtani, Ayedh H. & Hasanien, Hany M., 2024. "Precise modeling of lithium-ion battery in industrial applications using Walrus optimization algorithm," Energy, Elsevier, vol. 294(C).

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