Investigation of Deep Learning Based Techniques for Prognostic and Health Management of Lithium-Ion Battery

• Published in: 2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) pp. 01 - 06
• Main Authors: Saleem, Umar, Li, Weilin, Liu, Weinjie, Ahmad, Ibtihaj, Aslam, Muhammad Mobeen, Lateef, Hafiz Umair
• Format: Conference Proceeding
• Language: English
• Published: IEEE 29-06-2023
• Subjects: Aerospace electronics; Deep learning; Feature extraction; Lead acid batteries; lithium-Ion; Lithium-ion batteries; prognostic; remaining-useful-life; Safety; state-of-charge; state-of-health; Training
• DOI: 10.1109/ECAI58194.2023.10194122

Lithium-ion batteries (LB) have become increasingly popular for use in electric vehicles, aircraft, and portable electronic devices due to their high-energy storage capacity and extended lifespan. As a result, the demand for Li-ion batteries has risen significantly compared to other rechargeable batteries. During normal working conditions, any fault in the battery may lead to severe damage to equipment or human. As a preventative measure, developing a Prognostic and Health Management (PHM) system that can detect faults early on is essential. PHM systems can provide early warning of faults and improve reliability and safety. A PHM system for batteries comprises three components: determining the State of Charge (SOC), the State of Health (SOH), and the Remaining Useful Life (RUL). This paper will explore deep learning (DL) techniques to predict the SOC, SOH, and RUL of batteries. Generally, DL based method for PHM has four main stages, data collection, extraction of features, training, and testing. DL-based techniques for PHM of LB will be discussed in detail and also make comparisons to understand the effectiveness. The investigation results can be used in future to improve the accuracy of PHM for LB.