Promoting charging safety of electric bicycles via machine learning.

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 ca

SUBMITTER: Shuai C 

PROVIDER: S-EPMC9804133 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature