Citation

Shuai C, Yang F, Wang W, Shan J, Chen Z, Ouyang X. iScience 2023; 26(1): e105786.

Copyright

(Copyright © 2023, Cell Press)

DOI

10.1016/j.isci.2022.105786

PMID

36594019

PMCID

PMC9804133

Abstract

The worldwide penetration of electric bicycles has caused numerous charging accidents; however, online diagnosing charging faults remains challenging because of non-standard chargers, non-uniform communication manners and inaccessible battery inner status. The development of Internet of Things enables to acquire the input current information of chargers in the cloud platform, thereby supplying an alternative perspective to excavate underlying charge abnormalities. Through analyzing 181,282 charge records collected from the power-grid side, we establish an update-to-date deep neural network algorithm, which can automatically capture these charge feature variables, determine their dependencies and identify abnormal charge behaviors. Based on the only input current sequences, the algorithm can effectively diagnose the charging fault with the average accuracy of 85%, efficiently ensuring the charging safety of more than 20 million E-bicycles after substantial validations. Besides, this diagnosis framework can be extended to the real-time charge safety detection of electric vehicles and other similar energy storage systems.

Language: en

Keywords

Machine learning; Energy systems