TY - GEN
T1 - Machine learning algorithm based battery modeling and management method
T2 - 3rd Conference on Vehicle Control and Intelligence, CVCI 2019
AU - Li, Shuangqi
AU - He, Hongwen
AU - Li, Jianwei
AU - Yin, Peng
AU - Wang, Hanxiao
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - In recent years, in order to realize the accurate state monitoring and management of battery, the development of a flexible, self-reconfigurable and reliable model has become one of the most crucial technologies for electric vehicles. This paper mainly focuses on the battery management issues in new energy vehicles, in which the concept of artificial intelligence and grid-connected vehicle is introduced. Firstly, the concept of Cyber-Physical system (CPS) is applied in battery management issues in our work for a better use of battery data. To establish a precise battery model in cloud, the Support vector regression (SVR) algorithm, a classical artificial intelligence algorithm, is used in our work to model the battery. Finally, a rain-flow cycle counting algorithm-based battery degradation quantification method is proposed to deal with the influence of battery aging phenomenon during modeling the battery.
AB - In recent years, in order to realize the accurate state monitoring and management of battery, the development of a flexible, self-reconfigurable and reliable model has become one of the most crucial technologies for electric vehicles. This paper mainly focuses on the battery management issues in new energy vehicles, in which the concept of artificial intelligence and grid-connected vehicle is introduced. Firstly, the concept of Cyber-Physical system (CPS) is applied in battery management issues in our work for a better use of battery data. To establish a precise battery model in cloud, the Support vector regression (SVR) algorithm, a classical artificial intelligence algorithm, is used in our work to model the battery. Finally, a rain-flow cycle counting algorithm-based battery degradation quantification method is proposed to deal with the influence of battery aging phenomenon during modeling the battery.
KW - big data
KW - cyber-physical system
KW - electric vehicle
KW - grid-connected vehicle
KW - lithium-ion battery
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85078756186&partnerID=8YFLogxK
U2 - 10.1109/CVCI47823.2019.8951635
DO - 10.1109/CVCI47823.2019.8951635
M3 - Conference contribution
AN - SCOPUS:85078756186
T3 - 3rd Conference on Vehicle Control and Intelligence, CVCI 2019
BT - 3rd Conference on Vehicle Control and Intelligence, CVCI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 21 September 2019 through 22 September 2019
ER -
