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Prediction of Battery Remaining Useful Life Using Machine Learning Algorithms

Author

Listed:
  • J. N. Chandra Sekhar

    (Department of Electrical and Electronics Engineering, Sri Venkateswara University, Tirupati 517502, India)

  • Bullarao Domathoti

    (Department of Computer Science and Engineering, Shree Institute of Technological Education, Tirupati 517501, India)

  • Ernesto D. R. Santibanez Gonzalez

    (Department of Industrial Engineering and CES 4.0, Faculty of Engineering, University of Talca, Curico 3340000, Chile)

Abstract

Electrified transportation systems are emerging quickly worldwide, helping to diminish carbon gas emissions and paving the way for the reduction of global warming possessions. Battery remaining useful life (RUL) prediction is gaining attention in real world applications to tone down maintenance expenses and improve system reliability and efficiency. RUL forms the prominent component of fault analysis forecast and health management when the equipment operation life cycle is considered. The uprightness of RUL prediction is vital in providing the effectiveness of electric batteries and reducing the chance of battery illness. In assessing battery performance, the existing prediction approaches are unsatisfactory even though the battery operational parameters are well tabulated. In addition, battery management has an important contribution to several sustainable development goals, such as Clean and Affordable Energy (SDG 7), and Climate Action (SDG 13). The current work attempts to increase the prediction accuracy and robustness with selected machine learning algorithms. A Real battery life cycle data set from the Hawaii National Energy Institute (HNEI) is used to evaluate accuracy estimation using selected machine learning algorithms and is validated in Google Co-laboratory using Python. Evaluated error metrics such as Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-Squared, and execution time are computed for different L methods and relevant inferences are presented which highlight the potential of battery RUL prediction close to the most accurate values.

Suggested Citation

  • J. N. Chandra Sekhar & Bullarao Domathoti & Ernesto D. R. Santibanez Gonzalez, 2023. "Prediction of Battery Remaining Useful Life Using Machine Learning Algorithms," Sustainability, MDPI, vol. 15(21), pages 1-28, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15283-:d:1267238
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    References listed on IDEAS

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    Cited by:

    1. Despotovic, Miroslav & Glatschke, Matthias, 2024. "Challenges and Opportunities of Artificial Intelligence and Machine Learning in Circular Economy," SocArXiv 6qmhf, Center for Open Science.

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