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Enhancing Predictive Battery Maintenance Through the Use of Explainable Boosting Machine

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Novel & Intelligent Digital Systems: Proceedings of the 3rd International Conference (NiDS 2023) (NiDS 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 784))

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Abstract

Battery Remaining Useful Life (RUL) prediction is crucial for the predictive maintenance of lithium-ion batteries. This paper presents a study on applying an Explainable Boosting Machine (EBM) for RUL prediction of lithium-ion batteries. EBM is a machine learning technique that combines the benefits of gradient boosting and rule-based systems, making it highly interpretable and suitable for applications in safety-critical domains. We evaluated the performance of EBM compared to other machine learning techniques and demonstrated its superiority in accuracy, interpretability, and robustness. The results show that EBM can accurately predict the RUL of lithium-ion batteries; the interpretability of EBM provides insights into the factors that affect battery RUL and enables a better understanding of battery degradation mechanisms. The proposed method highlights the potential of EBM for battery RUL prediction and guarantees the secure and reliable functioning of batteries.

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Acknowledgements

This research was financially supported by the Ministry of Small and Medium-sized Enterprises (SMEs) and Startups (MSS), Korea, under the “Regional Specialized Industry Development Plus Program (R &D, S3246057)” supervised by the Korea Technology and Information Promotion Agency for SMEs (TIPA). This work was also financially supported by the Ministry Of Trade, Industry & ENERGY (MOTIE) through the fostering project of The Establishment Project of Industry-University Fusion District.

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Authors and Affiliations

  1. Department of Electronic Engineering, Institute of Information Science and Technology, Jeju National University, Jeju, 63243, South Korea

    Sadiqa Jafari

  2. Department of Computer Engineering, Major of Electronic Engineering, Jeju National University, Institute of Information Science & Technology, Jeju, 63243, South Korea

    Yung-Cheol Byun

Authors
  1. Sadiqa Jafari
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  2. Yung-Cheol Byun
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Correspondence to Yung-Cheol Byun .

Editor information

Editors and Affiliations

  1. Department of Environment, Ionian University, Zakynthos, Greece

    Katerina Kabassi

  2. Department of Informatics and Computer Engineering, University of West Attica, Egaleo, Greece

    Phivos Mylonas

  3. College of Engineering, University of the Philippines, Diliman, Philippines

    Jaime Caro

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Jafari, S., Byun, YC. (2023). Enhancing Predictive Battery Maintenance Through the Use of Explainable Boosting Machine. In: Kabassi, K., Mylonas, P., Caro, J. (eds) Novel & Intelligent Digital Systems: Proceedings of the 3rd International Conference (NiDS 2023). NiDS 2023. Lecture Notes in Networks and Systems, vol 784. Springer, Cham. https://doi.org/10.1007/978-3-031-44146-2_6

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