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Transferability of a Battery Cell End-of-Life Prediction Model Using Survival Analysis

Author

Listed:
  • Maya Santhira Sekeran

    (Digitalization, AVL Software and Functions GmbH, 93059 Regensburg, Germany)

  • Milan Živadinović

    (PTE/DAB Big Data Intelligence, AVL List GmbH, 8020 Graz, Austria)

  • Myra Spiliopoulou

    (Knowledge Management and Discovery Lab, Otto-von-Guericke University, 39106 Magdeburg, Germany)

Abstract

Electric vehicles are increasingly becoming the vehicle of choice in today’s environmentally conscious society, and the heart of an electric vehicle is its battery. Today, lithium-ion batteries are mainly used to power electric vehicles for its increased energy storage density and longevity. However, in order to estimate battery life, long and costly battery testing is required. Therefore, there is a need to investigate efficient ways that could reduce the amount of testing required by reusing existing knowledge of aging patterns from different kinds of battery chemistry. This work aims to answer two research questions. The first addresses the challenge of battery cell testing data that contain battery cells that do not reach the End-of-Life (EOL) threshold by the time the testing has been completed. For this challenge, we propose to implement survival analysis that is able to handle incomplete data or what is referred to as censored data. The second addresses how to reuse a model trained on one type of battery cell chemistry to predict the EOL of another battery cell chemistry by implementing transfer learning. We develop a workflow to implement a prediction model for one type of battery cell chemistry and to reuse this pre-trained model to predict the EOL for another type of battery cell chemistry.

Suggested Citation

  • Maya Santhira Sekeran & Milan Živadinović & Myra Spiliopoulou, 2022. "Transferability of a Battery Cell End-of-Life Prediction Model Using Survival Analysis," Energies, MDPI, vol. 15(8), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2930-:d:795215
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    References listed on IDEAS

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    1. Shuai Wang & Lingling Zhao & Xiaohong Su & Peijun Ma, 2014. "Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression," Energies, MDPI, vol. 7(10), pages 1-17, October.
    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. Ng, Selina S.Y. & Xing, Yinjiao & Tsui, Kwok L., 2014. "A naive Bayes model for robust remaining useful life prediction of lithium-ion battery," Applied Energy, Elsevier, vol. 118(C), pages 114-123.
    4. 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.
    5. Yi Chen & Qiang Miao & Bin Zheng & Shaomin Wu & Michael Pecht, 2013. "Quantitative Analysis of Lithium-Ion Battery Capacity Prediction via Adaptive Bathtub-Shaped Function," Energies, MDPI, vol. 6(6), pages 1-15, June.
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    Cited by:

    1. Dimitris Papathanasiou & Konstantinos Demertzis & Nikos Tziritas, 2023. "Machine Failure Prediction Using Survival Analysis," Future Internet, MDPI, vol. 15(5), pages 1-26, April.

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