@inproceedings{712a56994d5d485ba1d595736428bdab,
title = "Big data driven Lithium-ion battery modeling method: A cyber-physical system approach",
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\%.",
keywords = "battery management, big data, cyber-physical system, deep learning, electric vehicle, lithiumion battery",
author = "Shuangqi Li and Jianwei Li and Hanxiao Wang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019 ; Conference date: 06-05-2019 Through 09-05-2019",
year = "2019",
month = may,
doi = "10.1109/ICPHYS.2019.8780152",
language = "English",
series = "Proceedings - 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "161--166",
booktitle = "Proceedings - 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019",
address = "United States",
}
