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An evaluation study of different modelling techniques for calendar ageing prediction of lithium-ion batteries

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  • Liu, Kailong
  • Ashwin, T.R.
  • Hu, Xiaosong
  • Lucu, Mattin
  • Widanage, W. Dhammika

Abstract

Prediction of battery calendar ageing is a key but challenging issue in the development of durable electric vehicles. This paper simultaneously evaluates three mainstream types of modelling techniques for calendar ageing prediction of Lithium-ion (Li-ion) batteries. They are the pseudo two dimensional (P2D)-based electrochemical model, Arrhenius law-based semi-empirical model, and Gaussian process regression (GPR)-based data-driven model. Specifically, both the electrochemical and semi-empirical models are consciously developed or selected from the state-of-the-art modelling literature. For the data-driven model, due to the limited research in the existing publications, a machine learning-enabled GPR model is derived and applied for calendar ageing prediction. An experimental setup is developed to load the commercial Panasonic NCR18650BD batteries and to collect the experimental calendar ageing data under different storage temperature and SOC levels over 435 days. Based upon this well-rounded database, each model is well trained through using its corresponding training solution. Then the prediction performances of these models are studied and evaluated in terms of the model accuracy, generalization ability and uncertainty management. Both the challenges and future prospects of each model type are highlighted to assist the industrial and academic research communities, thus boosting the progress of designing advanced modelling techniques in battery calendar ageing prediction domain.

Suggested Citation

  • Liu, Kailong & Ashwin, T.R. & Hu, Xiaosong & Lucu, Mattin & Widanage, W. Dhammika, 2020. "An evaluation study of different modelling techniques for calendar ageing prediction of lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
  • Handle: RePEc:eee:rensus:v:131:y:2020:i:c:s1364032120303087
    DOI: 10.1016/j.rser.2020.110017
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    1. 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.
    2. Zheng, Xiujuan & Fang, Huajing, 2015. "An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 74-82.
    3. Eddahech, Akram & Briat, Olivier & Vinassa, Jean-Michel, 2015. "Performance comparison of four lithium–ion battery technologies under calendar aging," Energy, Elsevier, vol. 84(C), pages 542-550.
    4. Patil, Meru A. & Tagade, Piyush & Hariharan, Krishnan S. & Kolake, Subramanya M. & Song, Taewon & Yeo, Taejung & Doo, Seokgwang, 2015. "A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation," Applied Energy, Elsevier, vol. 159(C), pages 285-297.
    5. Petit, Martin & Prada, Eric & Sauvant-Moynot, Valérie, 2016. "Development of an empirical aging model for Li-ion batteries and application to assess the impact of Vehicle-to-Grid strategies on battery lifetime," Applied Energy, Elsevier, vol. 172(C), pages 398-407.
    6. Yu Peng & Yandong Hou & Yuchen Song & Jingyue Pang & Datong Liu, 2018. "Lithium-Ion Battery Prognostics with Hybrid Gaussian Process Function Regression," Energies, MDPI, vol. 11(6), pages 1-20, June.
    7. 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.
    8. Ahmadian, Ali & Sedghi, Mahdi & Elkamel, Ali & Fowler, Michael & Aliakbar Golkar, Masoud, 2018. "Plug-in electric vehicle batteries degradation modeling for smart grid studies: Review, assessment and conceptual framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2609-2624.
    9. 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.
    10. 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.
    11. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    12. 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.
    13. Ouyang, Minggao & Feng, Xuning & Han, Xuebing & Lu, Languang & Li, Zhe & He, Xiangming, 2016. "A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery," Applied Energy, Elsevier, vol. 165(C), pages 48-59.
    14. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
    Full references (including those not matched with items on IDEAS)

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    17. Peng, Qiao & Li, Wei & Fowler, Michael & Chen, Tao & Jiang, Wei & Liu, Kailong, 2024. "Battery calendar degradation trajectory prediction: Data-driven implementation and knowledge inspiration," Energy, Elsevier, vol. 294(C).
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    19. Jia Guo & Yaqi Li & Kjeld Pedersen & Daniel-Ioan Stroe, 2021. "Lithium-Ion Battery Operation, Degradation, and Aging Mechanism in Electric Vehicles: An Overview," Energies, MDPI, vol. 14(17), pages 1-22, August.
    20. Neha Bhushan & Saad Mekhilef & Kok Soon Tey & Mohamed Shaaban & Mehdi Seyedmahmoudian & Alex Stojcevski, 2022. "Overview of Model- and Non-Model-Based Online Battery Management Systems for Electric Vehicle Applications: A Comprehensive Review of Experimental and Simulation Studies," Sustainability, MDPI, vol. 14(23), pages 1-31, November.

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