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Machine Learning Approach to Predict the Second-Life Capacity of Discarded EV Batteries for Microgrid Applications

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Intelligent Computing and Optimization (ICO 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1324))

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Abstract

Batteries retired from electric vehicles (EV), known as second-life batteries, have enough potential in them to be used storage applications in other domains. By estimating the remaining capacity of these retired batteries, a reliable low-cost alternative can be provided for microgrid applications, and measures are taken to increase their remaining lifetime. To estimate the storage capacity available in these batteries, a predictive model that can provide the second-life capacity of the battery accurately is required. Since, second-life batteries are discarded from fresh (First life) batteries, whose state of health is in the range 80%–100%, a machine learning model is developed that can predict the operational characteristics of the second-life, utilizing that of its first life. This research proposes two different battery models, distinctive in terms of the input parameters, for the above purpose. Based on the obtained results, the prediction model using the charging voltage, charging current, battery capacity for every cycle alongside all three input parameters for 5 lag cycles, provided an accurate prediction of the second-life operation, in comparison to the other. Mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and R-squared was considered as performance indicating parameters.

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Authors and Affiliations

  1. Department of Energy, Environment and Climate Change, School of Environment Resources and Development, Asian Institute of Technology, Khlong Luang, 12120, Pathum Thani, Thailand

    Ankit Bhatt, Weerakorn Ongsakul & Nimal Madhu

Authors
  1. Ankit Bhatt
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  2. Weerakorn Ongsakul
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  3. Nimal Madhu
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Corresponding author

Correspondence to Weerakorn Ongsakul .

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Editors and Affiliations

  1. Department of Fundamental and Applied Sciences, Universiti Teknologi Petronas, Tronoh, Perak, Malaysia

    Pandian Vasant

  2. Faculty of Electrical Engineering and Computer Science, VŠB TU Ostrava, Ostrava, Czech Republic

    Ivan Zelinka

  3. Faculty of Engineering Management, Poznan University of Technology, Poznan, Poland

    Gerhard-Wilhelm Weber

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Bhatt, A., Ongsakul, W., Madhu, N. (2021). Machine Learning Approach to Predict the Second-Life Capacity of Discarded EV Batteries for Microgrid Applications. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_55

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