Electric Bikes Charging Anomaly Detection from Alternating Current Side based on Big Data

Abstract

With the widespread use of electric bikes (E-bikes), charging safety incidents occur frequently, even causing serious hazards. However, detecting and warning of unsafe charging from the E-bikes side is a challenge due to the lack of full-featured battery management systems and communication means vehicles and chargers in majority E-bikes. Aim for this, a diagnosis scheme is proposed to detect E-bikes’ abnormal charging from the alternating current (AC) side of the charging pile. Initially, 91,282 charging records are collected from charging piles to analyze the correlations between the current features and the battery working principle, charging mode, and user behavior in depth. Then, ten current features and six feature sequences are formulated, and two algorithms based on the first-order difference and pattern-matching are proposed to recognize and extracted these features and feature sequences. A feature-based random forest model is presented to identify the abnormal charging. Empirical studies show that the anomaly recognition performance of the proposed framework exceeds that of the baselines, achieving a recognition precision of 0.89 and an F1-score of 0.86. The application of this scheme can provide early warning for unsafe charging from the charging pile side without modification of the existing E-bikes, and can be extended to diagnose the charging safety of other battery-powered system, such as electric vehicles. Meanwhile, the analysis of internal and external factors that lead to abnormal charging is beneficial for charging operation companies and government to develop charging security specifications and regulate charging behaviors.