Intelligent Diagnosis of Abnormal Charging for Electric Bicycles Based on Improved Dynamic Time Warping

The widespread penetration of electric bicycles (E-bicycles) raises numerous charging safety concerns. However, online diagnosis of charging safety for E-bicycles remains challenging due to the limited data and involvement of multiple factors, such as battery, charger, charging mode, and user behavior. To overcome this difficulty and promote charging safety, this article proposes a nonintrusive charging safety intelligent diagnosis scheme on the inputted power grid side. First, more than 150 000 charging records are collected from the grid side, and various charging current patterns are formally identified according to the working principles of different batteries, charging modes, and user behaviors. Then, on the basis of longest similar substring (LSS), an improved dynamic time warping (DTW) model, referred to as LSS-DTW, is established to efficiently identify the charging current profile similarities and meanwhile restrict the overregularization of DTW. By this manner, the abnormal charging processes can be accurately identified. Experimental results reveal that the built LSS-DTW model can distinguish the unsafe charging processes online, and achieve the average identification precision, recall, and F1-score of 94%. Furthermore, the proposed algorithm can be extended to similar charging safety identifications in electric vehicles and other battery-powered systems and provides early warnings to avoid catastrophic consequences.

Automated summary

The widespread use of electric bicycles (E-bikes) has raised concerns about charging safety. However, diagnosing charging safety for E-bikes online is challenging due to limited data and multiple factors involved. This article proposes a nonintrusive intelligent diagnosis scheme on the inputted power grid side to overcome this challenge. By using an improved dynamic time warping model, the proposed scheme can accurately identify abnormal charging processes and achieve a high precision rate of 94%.