Earticle

초록

영어

This study proposes a deep learning-based LSTM model to predict the state of charge (SOC) of lithium-ion batteries. The model was trained using data collected under various temperature and load conditions, including measurement data from the CS2 lithium-ion battery provided by the University of Maryland College of Engineering. The LSTM model effectively models temporal patterns in the data by learning long-term dependencies. Performance evaluation by epoch showed that the predicted SOC improved from 14.8400 at epoch 10 to 12.4968 at epoch 60, approaching the actual SOC value of 13.5441. The mean absolute error (MAE) and root mean squared error (RMSE) also decreased from 0.9185 and 1.3009 at epoch 10 to 0.2333 and 0.5682 at epoch 60, respectively, indicating continuous improvement in predictive performance. This study demonstrates the validity of the LSTM model for predicting the SOC of lithium-ion batteries and its potential to enhance battery management systems.

목차

Abstract
1. INTRODUCTION
2. RESEARCH MODEL
3. STRUCTURAL DESIGN OF LSTM MODEL
4. IMPLEMENTATION AND RESULTS
5. CONCLUSION
REFERENCES

키워드

저자

• Sang-Hyun Lee [ Associate Prof., Dept. of Computer Engineering, Honam University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Culture Technology(IJACT)

• 간기

  계간

• pISSN

  2288-7202

• eISSN

  2288-7318

• 수록기간

  2013~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 600 DDC 700