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State-of-Health Prediction for Li-ion Batteries for Efficient Battery Management System Using Hybrid Machine Learning Model

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Abstract

Since Lithium-ion (Li-ion) batteries are frequently used for real-time applications, evaluating their State of Health (SoH) is crucial to guarantee their effectiveness and safety. Model-based methods with SoH prediction are helpful. However, the issues with battery modelling have led to a greater dependence on machine learning (ML). As a significant step in assessing the effectiveness of ML techniques, data preprocessing has also drawn much attention. In this work, a new preprocessing method using relative State of Charge (SoC) is proposed; further, this paper describes a hybrid learning model (HLM) that combines auto-regressive integrated moving average (ARIMA), gated recurrent unit (GRU) and convolutional neural network (CNN). Data: proposed HLM uses time-series and SoC domain data; the ARIMA + GRU algorithm trains the time-series data, while CNN trains the SoC domain data. Both outputs are mean averaged to get the final output prediction. The proposed HLM is evaluated for root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) using the National Aeronautics and Space Administration (NASA’s) randomized battery usage data set (RBUDS). The results indicate that the recommended HLM is more accurate and has a smaller error margin than existing ML models.

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Funding

The researchers would like to acknowledge the Deanship of Scientific Research at Taif University for funding this work.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, V.S.B College of Engineering Technical Campus, Coimbatore, Tamil Nadu, 642109, India

    Varatharaj Myilsamy

  2. Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, 627152, India

    Sudhakar Sengan

  3. Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia

    Roobaea Alroobaea & Majed Alsafyani

Authors
  1. Varatharaj Myilsamy
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  2. Sudhakar Sengan
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  3. Roobaea Alroobaea
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  4. Majed Alsafyani
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Contributions

VM: Writing-Review & Editing; SS: Investigation, Writing-Original Draft, Conceptualization, Methodology, Formal analysis; RA: Validation, Software, Data Curation; MA: Supervision, Funding.

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Correspondence to Sudhakar Sengan.

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Myilsamy, V., Sengan, S., Alroobaea, R. et al. State-of-Health Prediction for Li-ion Batteries for Efficient Battery Management System Using Hybrid Machine Learning Model. J. Electr. Eng. Technol. 19, 585–600 (2024). https://doi.org/10.1007/s42835-023-01564-2

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