Big data driven Lithium-ion battery modeling method: a Cyber-Physical System approach

Shuangqi Li, Jianwei Li, Hanxiao Wang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

1 Citation (Scopus)

Abstract

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%.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS)
PublisherIEEE
Pages161-166
Number of pages6
ISBN (Print)9781538685006
DOIs
Publication statusPublished - May 2019
Externally publishedYes
EventIEEE International Conference on Industrial Cyber Physical Systems - Taipei, Taiwan, Province of China
Duration: 6 May 20199 May 2019

Conference

ConferenceIEEE International Conference on Industrial Cyber Physical Systems
Abbreviated titleICPS 2019
CountryTaiwan, Province of China
CityTaipei
Period6/05/199/05/19

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  • Cite this

    Li, S., Li, J., & Wang, H. (2019). Big data driven Lithium-ion battery modeling method: a Cyber-Physical System approach. In 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS) (pp. 161-166). IEEE. https://doi.org/10.1109/icphys.2019.8780152