International Journal of Internet, Broadcasting and Communication

  • 제16권3호
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  • Pages.236-241
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  • 2024
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  • 2288-4920(pISSN)
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  • 2288-4939(eISSN)
한국인터넷방송통신학회 (The Institute of Internet, Broadcasting and Communication)
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A Study on the Lifetime Prediction of Lithium-Ion Batteries Based on the Long Short-Term Memory Model of Recurrent Neural Networks

  • Sang-Bum Kim (Department of Robotdrone Engineering, Honam University)
  • 투고 : 2024.06.19
  • 심사 : 2024.06.30
  • 발행 : 2024.08.31
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초록

Due to the recent emphasis on carbon neutrality and environmental regulations, the global electric vehicle (EV) market is experiencing rapid growth. This surge has raised concerns about the recycling and disposal methods for EV batteries. Unlike traditional internal combustion engine vehicles, EVs require unique and safe methods for the recovery and disposal of their batteries. In this process, predicting the lifespan of the battery is essential. Impedance and State of Charge (SOC) analysis are commonly used methods for this purpose. However, predicting the lifespan of batteries with complex chemical characteristics through electrical measurements presents significant challenges. To enhance the accuracy and precision of existing measurement methods, this paper proposes using a Long Short-Term Memory (LSTM) model, a type of deep learning-based recurrent neural network, to diagnose battery performance. The goal is to achieve safe classification through this model. The designed structure was evaluated, yielding results with a Mean Absolute Error (MAE) of 0.8451, a Root Mean Square Error (RMSE) of 1.3448, and an accuracy of 0.984, demonstrating excellent performance.

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과제정보

This result was supported by "Regional Innovation Strategy (RIS)" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021RIS-002)

참고문헌

  1. T.H. Chen, T.Y. Hsieh, N.C. Yang, J. S. Yang and C. J. Liao, "Evaluation of advantages of an energy storage system using recycled EV batteries," International Journal of an energy storage systems, Vol. 45, Issue. 1, pp. 264-270, February 2013. DOI: https://doi.org/10.1016/j.ijepes.2012.08.037
  2. S.B. Kim and S.H. Lee, "Design and Development of a Public Waste Battery Diagnostic Device," The International Journal of Advanced Culture Technology(IJACT), Vol. 10, No. 3, pp. 281-286, July 2022. DOI:https://www.earticle.net/Article/A417941
  3. S.B. Kim and S.H. Lee, "Development of Aging Diagnosis Device Through Real-time Battery Internal Resistance Measurement," The International Journal of Internet, Broadcasting and Communication(IJIBC), Vol. 14, No. 2, pp. 129-135, February 2022. DOI: https://doi.org/10.7236/IJIBC.2022.14.2.129
  4. M.J. Choi and S.B. Kim, "A Study on Impedance Change Trend and Battery Life Analysis through Battery Performance Deterioration Factors," The International Journal of Internet, Broadcasting and Communication(IJIBC), Vol. 15, No. 3, pp. 141-146, August 2023. DOI: https://doi.org/10.7236/IJIBC.2023.15.3.141
  5. Y. U. Mun and S. B. Kim,"A study on the prediction of SOH estimation of waste lithium-ion batteries based on SVM model,"IJCC
  6. S. B. Kim, and S. H. Lee,"Research on Hyper-parameter Optimization Method based on Genetic Algorithm of Deep Neural Network,"O2O IICCC 2020.
  7. M.J. Choi and S.B. Kim, "A Study on The Life Prediction of Lithium Ion Batteries Based on a Convolutional Neural Network Model," The International Journal of Internet, Broadcasting and Communication(IJIBC), Vol. 15, No. 3, pp. 130-133, August 2023. DOI: https://doi.org/10.7236/IJIBC.2023.15.3.130