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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

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  • Li, Yi
  • Liu, Kailong
  • Foley, Aoife M.
  • Zülke, Alana
  • Berecibar, Maitane
  • Nanini-Maury, Elise
  • Van Mierlo, Joeri
  • Hoster, Harry E.

Abstract

Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.

Suggested Citation

  • Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    • Handle: RePEc:eee:rensus:v:113:y:2019:i:c:19
    DOI: 10.1016/j.rser.2019.109254
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    as
    1. You, Gae-won & Park, Sangdo & Oh, Dukjin, 2016. "Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach," Applied Energy, Elsevier, vol. 176(C), pages 92-103.
    2. Lingling Li & Pengchong Wang & Kuei-Hsiang Chao & Yatong Zhou & Yang Xie, 2016. "Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Gaussian Processes Mixture," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-13, September.
    3. Pan, Haihong & Lü, Zhiqiang & Wang, Huimin & Wei, Haiyan & Chen, Lin, 2018. "Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine," Energy, Elsevier, vol. 160(C), pages 466-477.
    4. 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.
    5. Donal P. Finegan & Mario Scheel & James B. Robinson & Bernhard Tjaden & Ian Hunt & Thomas J. Mason & Jason Millichamp & Marco Di Michiel & Gregory J. Offer & Gareth Hinds & Dan J.L. Brett & Paul R. Sh, 2015. "In-operando high-speed tomography of lithium-ion batteries during thermal runaway," Nature Communications, Nature, vol. 6(1), pages 1-10, November.
    6. 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.
    7. Hu, Chao & Jain, Gaurav & Zhang, Puqiang & Schmidt, Craig & Gomadam, Parthasarathy & Gorka, Tom, 2014. "Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery," Applied Energy, Elsevier, vol. 129(C), pages 49-55.
    8. de Hoog, Joris & Timmermans, Jean-Marc & Ioan-Stroe, Daniel & Swierczynski, Maciej & Jaguemont, Joris & Goutam, Shovon & Omar, Noshin & Van Mierlo, Joeri & Van Den Bossche, Peter, 2017. "Combined cycling and calendar capacity fade modeling of a Nickel-Manganese-Cobalt Oxide Cell with real-life profile validation," Applied Energy, Elsevier, vol. 200(C), pages 47-61.
    9. Berecibar, M. & Gandiaga, I. & Villarreal, I. & Omar, N. & Van Mierlo, J. & Van den Bossche, P., 2016. "Critical review of state of health estimation methods of Li-ion batteries for real applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 572-587.
    10. Kailong Liu & Kang Li & Qiao Peng & Yuanjun Guo & Li Zhang, 2018. "Data-Driven Hybrid Internal Temperature Estimation Approach for Battery Thermal Management," Complexity, Hindawi, vol. 2018, pages 1-15, July.
    11. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
    12. Hu, Xiaosong & Li, Shengbo Eben & Jia, Zhenzhong & Egardt, Bo, 2014. "Enhanced sample entropy-based health management of Li-ion battery for electrified vehicles," Energy, Elsevier, vol. 64(C), pages 953-960.
    13. An, Dawn & Choi, Joo-Ho & Kim, Nam Ho, 2013. "Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 161-169.
    14. Sarasketa-Zabala, E. & Martinez-Laserna, E. & Berecibar, M. & Gandiaga, I. & Rodriguez-Martinez, L.M. & Villarreal, I., 2016. "Realistic lifetime prediction approach for Li-ion batteries," Applied Energy, Elsevier, vol. 162(C), pages 839-852.
    15. Yang, Fangfang & Wang, Dong & Zhao, Yang & Tsui, Kwok-Leung & Bae, Suk Joo, 2018. "A study of the relationship between coulombic efficiency and capacity degradation of commercial lithium-ion batteries," Energy, Elsevier, vol. 145(C), pages 486-495.
    16. Wang, Limei & Pan, Chaofeng & Liu, Liang & Cheng, Yong & Zhao, Xiuliang, 2016. "On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis," Applied Energy, Elsevier, vol. 168(C), pages 465-472.
    17. Wu, Ji & Zhang, Chenbin & Chen, Zonghai, 2016. "An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks," Applied Energy, Elsevier, vol. 173(C), pages 134-140.
    18. 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.
    19. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
    20. Berecibar, Maitane & Garmendia, Maitane & Gandiaga, Iñigo & Crego, Jon & Villarreal, Igor, 2016. "State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application," Energy, Elsevier, vol. 103(C), pages 784-796.
    21. Li, Yi & Zou, Changfu & Berecibar, Maitane & Nanini-Maury, Elise & Chan, Jonathan C.-W. & van den Bossche, Peter & Van Mierlo, Joeri & Omar, Noshin, 2018. "Random forest regression for online capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 232(C), pages 197-210.
    22. Hu, Chao & Jain, Gaurav & Tamirisa, Prabhakar & Gorka, Tom, 2014. "Method for estimating capacity and predicting remaining useful life of lithium-ion battery," Applied Energy, Elsevier, vol. 126(C), pages 182-189.
    23. Jaguemont, J. & Boulon, L. & Dubé, Y., 2016. "A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures," Applied Energy, Elsevier, vol. 164(C), pages 99-114.
    24. Keith T. Butler & Daniel W. Davies & Hugh Cartwright & Olexandr Isayev & Aron Walsh, 2018. "Machine learning for molecular and materials science," Nature, Nature, vol. 559(7715), pages 547-555, July.
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