TY - JOUR
T1 - Overview of Machine Learning Methods for Lithium-Ion Battery Remaining Useful Lifetime Prediction
AU - Sui, Xin
AU - Jin, Siyu
AU - Huang, Xinrong
AU - Wang, Shunli
AU - Teodorescu, Remus
AU - Stroe, Daniel-Ioan
PY - 2021
Y1 - 2021
N2 - Lithium-ion batteries play an indispensable role, from portable electronic devices to electric vehicles and home storage systems. Even though they are characterized by superior performance than most other storage technologies, their lifetime is not unlimited and has to be predicted to ensure the economic viability of the battery application. Furthermore, to ensure the optimal battery system operation, the remaining useful lifetime (RUL) prediction has become an essential feature of modern battery management systems (BMSs). Thus, the prediction of RUL of Lithium-ion batteries has become a hot topic for both industry and academia. The purpose of this work is to review, classify, and compare different machine learning (ML)-based methods for the prediction of the RUL of Lithium-ion batteries. First, this article summarizes and classifies various Lithium-ion battery RUL estimation methods that have been proposed in recent years. Secondly, an innovative method was selected for evaluation and compared in terms of accuracy and complexity. DNN is more suitable for RUL prediction due to its strong independent learning ability and generalization ability. In addition, the challenges and prospects of BMS and RUL prediction research are also put forward. Finally, the development of various methods is summarized.
AB - Lithium-ion batteries play an indispensable role, from portable electronic devices to electric vehicles and home storage systems. Even though they are characterized by superior performance than most other storage technologies, their lifetime is not unlimited and has to be predicted to ensure the economic viability of the battery application. Furthermore, to ensure the optimal battery system operation, the remaining useful lifetime (RUL) prediction has become an essential feature of modern battery management systems (BMSs). Thus, the prediction of RUL of Lithium-ion batteries has become a hot topic for both industry and academia. The purpose of this work is to review, classify, and compare different machine learning (ML)-based methods for the prediction of the RUL of Lithium-ion batteries. First, this article summarizes and classifies various Lithium-ion battery RUL estimation methods that have been proposed in recent years. Secondly, an innovative method was selected for evaluation and compared in terms of accuracy and complexity. DNN is more suitable for RUL prediction due to its strong independent learning ability and generalization ability. In addition, the challenges and prospects of BMS and RUL prediction research are also put forward. Finally, the development of various methods is summarized.
KW - Lithium-ion batteries
KW - Battery Management System
KW - Remaining useful lifetime prediction
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85121216890&partnerID=8YFLogxK
U2 - 10.3390/electronics10243126
DO - 10.3390/electronics10243126
M3 - Journal article
SN - 2079-9292
VL - 10
JO - Electronics
JF - Electronics
IS - 24
M1 - 3126
ER -