Artificial Intelligence-based Battery State-of-Health (SoH) Prediction through battery data characteristics analysis

• Published in: 2022 IEEE/IAS 58th Industrial and Commercial Power Systems Technical Conference (I&CPS) pp. 1 - 6
• Main Authors: Choi, Sungsan, Jang, Hyeonwoo, Han, Hohyeon, Park, Sangmin, Choi, Myeong-In, Park, Sehyun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 02.05.2022
• Subjects: Artificial Intelligence; Battery; Deep learning; Energy Data; Lithium-ion batteries; Machine learning; Machine learning algorithms; NASA; Prediction algorithms; SoH; Stability analysis; State-of-Health; Support vector machines
• ISSN: 2158-4907
• DOI: 10.1109/ICPS54075.2022.9773913

Batteries are used in various places, including portable devices and energy storage devices. However, due to aging batteries, it is broken or in severe cases, an explosion accident is occurring. Therefore, research on the stability and life of batteries continues. However, prediction of battery SoH is difficult due to various variables. Data-based artificial intelligence prediction can be made to solve this problem. This paper analyzed the battery data set provided by NASA to predict the remaining life of a lithium-ion battery, extracted the life characteristics, and predicted the SoH through artificial intelligence technology. Support Vector Machine (SVM) and Long Short-Terms Memory (LSTM) were used as artificial intelligence algorithms. As a result, for NASA battery data with temporal mechanism, 3 characteristics were extracted for each data set, and the RMSE of SVM showed lower results than LSTM, showing relatively high accuracy.