IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v131y2020ics1364032120303087.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032120303087
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2020.110017?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

    for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    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. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    2. 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.
    3. 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).
    4. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    5. Fei, Zicheng & Yang, Fangfang & Tsui, Kwok-Leung & Li, Lishuai & Zhang, Zijun, 2021. "Early prediction of battery lifetime via a machine learning based framework," Energy, Elsevier, vol. 225(C).
    6. Khan, Ayesha & Naqvi, Ijaz Haider & Bhargava, Cherry & Lin, Chun-Pang & Boles, Steven Tyler & Kong, Lingxi & Pecht, Michael, 2025. "Safety and reliability analysis of lithium-ion batteries with real-time health monitoring," Renewable and Sustainable Energy Reviews, Elsevier, vol. 212(C).
    7. Tang, Xiaopeng & Liu, Kailong & Lu, Jingyi & Liu, Boyang & Wang, Xin & Gao, Furong, 2020. "Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter," Applied Energy, Elsevier, vol. 280(C).
    8. Shaheer Ansari & Afida Ayob & Molla Shahadat Hossain Lipu & Aini Hussain & Mohamad Hanif Md Saad, 2021. "Data-Driven Remaining Useful Life Prediction for Lithium-Ion Batteries Using Multi-Charging Profile Framework: A Recurrent Neural Network Approach," Sustainability, MDPI, vol. 13(23), pages 1-25, December.
    9. Yang, Yixin, 2021. "A machine-learning prediction method of lithium-ion battery life based on charge process for different applications," Applied Energy, Elsevier, vol. 292(C).
    10. S. Tamilselvi & S. Gunasundari & N. Karuppiah & Abdul Razak RK & S. Madhusudan & Vikas Madhav Nagarajan & T. Sathish & Mohammed Zubair M. Shamim & C. Ahamed Saleel & Asif Afzal, 2021. "A Review on Battery Modelling Techniques," Sustainability, MDPI, vol. 13(18), pages 1-26, September.
    11. Mathieu, Romain & Baghdadi, Issam & Briat, Olivier & Gyan, Philippe & Vinassa, Jean-Michel, 2017. "D-optimal design of experiments applied to lithium battery for ageing model calibration," Energy, Elsevier, vol. 141(C), pages 2108-2119.
    12. Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    13. Kaizhi Liang & Zhaosheng Zhang & Peng Liu & Zhenpo Wang & Shangfeng Jiang, 2019. "Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles," Energies, MDPI, vol. 12(24), pages 1-17, December.
    14. 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).
    15. Chen, Long & Ding, Shicong & Wang, Li & Zhu, Feng & Zhu, Xiayu & Zhang, Songtong & Dai, Haifeng & He, Xiangming & Cao, Gaoping & Qiu, Jinyi & Zhang, Hao, 2024. "Electrochemical model boosting accurate prediction of calendar life for commercial LiFePO4|graphite cells by combining solid electrolyte interface side reactions," Applied Energy, Elsevier, vol. 376(PA).
    16. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    17. Ni, Yulong & Xu, Jianing & Zhu, Chunbo & Pei, Lei, 2022. "Accurate residual capacity estimation of retired LiFePO4 batteries based on mechanism and data-driven model," Applied Energy, Elsevier, vol. 305(C).
    18. Lehtola, Timo, 2025. "Vehicle-to-grid applications and battery cycle aging: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 208(C).
    19. Xue, Qiao & Li, Junqiu & Xu, Peipei, 2022. "Machine learning based swift online capacity prediction of lithium-ion battery through whole cycle life," Energy, Elsevier, vol. 261(PA).
    20. Yan, Dongxiang & Lu, Languang & Li, Zhe & Feng, Xuning & Ouyang, Minggao & Jiang, Fachao, 2016. "Durability comparison of four different types of high-power batteries in HEV and their degradation mechanism analysis," Applied Energy, Elsevier, vol. 179(C), pages 1123-1130.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:rensus:v:131:y:2020:i:c:s1364032120303087. 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.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.