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Performance Comparison Analysis of Artificial Intelligence Models for Estimating Remaining Capacity of Lithium-Ion Batteries

첫 페이지 보기
  • 발행기관
    국제문화기술진흥원 바로가기
  • 간행물
    International Journal of Advanced Culture Technology(IJACT) KCI 등재 바로가기
  • 통권
    Volume 11 Number 3 (2023.09)바로가기
  • 페이지
    pp.310-314
  • 저자
    Kyu-Ha Kim, Byeong-Soo Jung, Sang-Hyun Lee
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A436834

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원문정보

초록

영어
The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.

목차

Abstract
1. INTRODUCTION
2. RESEARCH OF METHOD
3. IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE MODEL EVALUATION
4. ARTIFICIAL INTELLIGENCE MODEL PERFORMANCE EVALUATION RESULT
5. CONCLUSION
ACKNOWLEDGEMENT
REFERENCES

키워드

Lithium-ion Battery Remaining Capacity Linear Regression Model Decision Tree Model Random Forest Model Neural Network Model Ensemble Model

저자

  • Kyu-Ha Kim [ Associate Prof., Dept. Computer Engineering, Honam University, Korea ]
  • Byeong-Soo Jung [ Prof., Dept. IT&Management, Nambu University, Korea ]
  • Sang-Hyun Lee [ Associate Prof., Dept. Computer Engineering, Honam University, Korea ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    국제문화기술진흥원 [The International Promotion Agency of Culture Technology]
  • 설립연도
    2009
  • 분야
    공학>공학일반
  • 소개
    본 진흥원은 문화기술(Culture Technology) 관련 산·학·연·관으로 구성된 비영리 단체이다. 문화기술(CT)은 정보통신기술(ICT), 문화적 사고 기반의 예술, 인문학, 디자인, 사회과학기술이 접목된 신융합기술(New Convergence Technology, NCT)로 정의한다. 인간의 삶의 질을 향상시키고, 진보된 방향으로 변화시키고, 문화기술 관련 분야의 학술 및 기술의 발전과 진흥에 공헌하기 위하여, 제3조의 필요한 사업을 행함을 그 목적으로 한다.

간행물

  • 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

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