TY - GEN
T1 - Big data driven Lithium-ion battery modeling method
T2 - 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019
AU - Li, Shuangqi
AU - Li, Jianwei
AU - Wang, Hanxiao
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - Batteries are the bottleneck technology of electric vehicles (EV), which host complex and hardly observable internal chemical reactions. Therefore, a precise mathematical model is crucial for the battery management system (BMS) to ensure the secure and stable operation of the battery. Aiming at achieving a flexible, self-configuring, reliable BMS, this paper mainly focuses on the following research points: Firstly, a Cloud-based BMS (C-BMS) is established based on the Cyber-Physical system (CPS), and the conjunction working mode between the C-BMS and the BMS in vehicles (V-BMS) is also proposed. Then, we make the first attempt to apply the Deep Belief Network-Back Propagation (DBN-BP) algorithm to battery modeling issues. The idea is to fully excavate the hidden features in battery big data. Using the battery data extracted from electric buses, the effectiveness and accuracy of the model are validated. The error of the estimated battery terminal voltage is within 2.5%.
AB - Batteries are the bottleneck technology of electric vehicles (EV), which host complex and hardly observable internal chemical reactions. Therefore, a precise mathematical model is crucial for the battery management system (BMS) to ensure the secure and stable operation of the battery. Aiming at achieving a flexible, self-configuring, reliable BMS, this paper mainly focuses on the following research points: Firstly, a Cloud-based BMS (C-BMS) is established based on the Cyber-Physical system (CPS), and the conjunction working mode between the C-BMS and the BMS in vehicles (V-BMS) is also proposed. Then, we make the first attempt to apply the Deep Belief Network-Back Propagation (DBN-BP) algorithm to battery modeling issues. The idea is to fully excavate the hidden features in battery big data. Using the battery data extracted from electric buses, the effectiveness and accuracy of the model are validated. The error of the estimated battery terminal voltage is within 2.5%.
KW - battery management
KW - big data
KW - cyber-physical system
KW - deep learning
KW - electric vehicle
KW - lithiumion battery
UR - http://www.scopus.com/inward/record.url?scp=85070913235&partnerID=8YFLogxK
U2 - 10.1109/ICPHYS.2019.8780152
DO - 10.1109/ICPHYS.2019.8780152
M3 - Conference contribution
AN - SCOPUS:85070913235
T3 - Proceedings - 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019
SP - 161
EP - 166
BT - Proceedings - 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 May 2019 through 9 May 2019
ER -
