TY - GEN
T1 - Investigation of Deep Learning Based Techniques for Prognostic and Health Management of Lithium-Ion Battery
AU - Saleem, Umar
AU - Li, Weilin
AU - Liu, Weinjie
AU - Ahmad, Ibtihaj
AU - Aslam, Muhammad Mobeen
AU - Lateef, Hafiz Umair
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - deep-learning
KW - lithium-Ion
KW - prognostic
KW - remaining-useful-life
KW - state-of-charge
KW - state-of-health
UR - http://www.scopus.com/inward/record.url?scp=85168135489&partnerID=8YFLogxK
U2 - 10.1109/ECAI58194.2023.10194122
DO - 10.1109/ECAI58194.2023.10194122
M3 - 会议稿件
AN - SCOPUS:85168135489
T3 - 15th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2023 - Proceedings
BT - 15th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2023
Y2 - 29 June 2023 through 30 June 2023
ER -
