Data-Driven Approach for Lithium-Ion Battery Remaining Useful Life Prediction: A Literature Review

• 영문명: Data-Driven Approach for Lithium-Ion Battery Remaining Useful Life Prediction: A Literature Review
• 발행기관: 한국스마트미디어학회
• 저자명: Luon Tran Van Choi Deokjai Lam Tran Ha
• 간행물 정보: 『스마트미디어저널』Vol11, No.11, 63~74쪽, 전체 12쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2022.12.31

영문 초록

Nowadays, lithium-ion battery has become more popular around the world. Knowing when batteries reach their end of life (EOL) is crucial. Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is needed for battery health management systems and to avoid unexpected accidents. It gives information about the battery status and when we should replace the battery. With the rapid growth of machine learning and deep learning, data-driven approaches are proposed to address this problem. Extracting aging information from battery charge/discharge records, including voltage, current, and temperature, can determine the battery state and predict battery RUL. In this work, we first outlined the charging and discharging processes of lithium-ion batteries. We then summarize the proposed techniques and achievements in all published data-driven RUL prediction studies. From that, we give a discussion about the accomplishments and remaining works with the corresponding challenges in order to provide a direction for further research in this area.

목차

Ⅰ. Introduction

Ⅱ. Battery Historical Data

Ⅲ. Data-Driven Approach for RUL Prediction: Research Summarization

Ⅳ. Discussion

Ⅴ. Conclusions

Acknowledgement

REFERENCES

키워드

Lithium-ion Battery Remaining Useful Life Data-Driven Approach Artificial Intelligence