A Dynamic Warning Method for Electric Vehicle Charging Safety Based on CNN-BiGRU Hybrid Model

다국어 초록 (Multilingual Abstract)

Electric vehicles (EVs) are prone to spontaneous combustion during charging, which can lead to safetyaccidents. Therefore, it is critical to accurately obtain the charging crises of EVs for timely fault identification andearly warning. This paper proposes a hybrid convolutional neural networks (CNN) and bi-directional gated recurrentunit (BiGRU) dynamic early warning method for EV charging safety. The method combines CNN and BiGRUfeatures to rapidly extract deep characteristics of EV charging data, establish charging safety prediction models,and train it with historical normal charging data. After training, real-time EV charging data is input for predictionto identify whether EV charging processes are irregular. Sliding windows are used with the residual analysis of thehistorical data forecast outcomes to generate the safety dynamic warning threshoThe energy rules. The experimentalresults demonstrated that the CNN-BiGRU model has a superior prediction effect and accuracy. With eRMSE andeMAPE as the evaluation criteria, the charging current is 0.2393 A and 0.1888%, the charging voltage is 0.3859 Vand 0.084%, and the temperature is 0.0543◦C and 0.1658%; The charging current, voltage and temperature data canbe used for early fault warning, which can be advance by 20.7 s, 20.2 s and 17.7 5 s, respectively.