Earticle

초록

영어

Recently, green energy support policies have been announced around the world in accordance with environmental regulations, and as the market grows rapidly, demand for batteries is also increasing. Therefore, various methodologies for battery diagnosis and recycling methods are being discussed, but current accurate life prediction of batteries has limitations due to the nonlinear form according to the internal structure or chemical change of the battery. In this paper, CS2 lithium-ion battery measurement data measured at the A. James Clark School of Engineering, University of Marylan was used to predict battery performance with high accuracy using a convolutional neural network (CNN) model among deep learning-based models. As a result, the battery performance was predicted with high accuracy. A data structure with a matrix of total data 3,931 ☓ 19 was designed as test data for the CS2 battery and checking the result values, the MAE was 0.8451, the RMSE was 1.3448, and the accuracy was 0.984, confirming excellent performance.

목차

Abstract
1. Introduction
2. Design of CNN-based model for predicting battery life
3. MAE and RMSE analysis of the data applied to CNN model
4. Conclusion
Acknowledgement
References

키워드

저자

• Mi-Jin Choi [ MJ-korea Co., Gwangju, Korea ]
• Sang-Bum Kim [ Professor, Department of Electronic Engineering, Honam University, Gwangju, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380