Comparing the Effectiveness of Deep Learning Approaches for Charging Time Prediction in Electric Vehicles: Kocaeli Example

The aim of this study is to compare the performance of various deep learning models in predicting the arrival and charging time of an electric vehicle at a charging station. The objective is to identify the most precise model capable of predicting the time from the driver’s location to the arrival at the charging station. Initially, an effort was made to ascertain which model type offers superior prediction accuracy, characterized by low error rates and high success scores. To this end, the study examined the prediction capabilities of the LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), Prophet, and ARIMA (Autoregressive Integrated Moving Average) models, which were trained with historical data using various metrics. These metrics included the R 2 (R-squared)success metric, MAE (Mean Absolute Error) and MSE (Mean Squared Error) error metrics, DTW (Dynamic Time Warping) metric scores, and model selection. In MAE metric scores, GRU has an error rate of 0.38, LSTM 0.50, Prophet 0.61, and ARIMA 2.31. For MSE metric scores, GRU has an error rate of 2.92, LSTM 3.05, Prophet 4.21, and ARIMA 65.54. In R 2 metric scores, GRU has a success rate of 0.99, LSTM 0.99, Prophet 0.13, and ARIMA −2.19. In DTW metric scores, GRU has a distance ratio of 125.5, LSTM 126.9, Prophet 185.9, and ARIMA 454.9. Based on these score values, it was decided that the GRU model made the most accurate time estimation. After observing the superior performance of the GRU model, the time prediction capability of this model is demonstrated through an interface program for charging stations in the province of Kocaeli, which serves as the model’s real-world application area. The main contribution of this article is that LSTM, GRU, Prophet, and ARIMA deep learning approaches, which are preferred in many studies, are used for the first time in the process of estimating electric vehicle charging time.