Predicting real life electric vehicle fast charging session duration using neural networks

Abstract

Predicting the time needed to charge an electric vehicle from X% to Y% is a difficult task due to the nonlinearity of the charging process and other external factors such as temperature and battery degradation. Using 28,000 real-life level 3 fast charging sessions from 15 different types of electric vehicles, we train models for this task. We compare learning models such as random forest, linear and seconddegree regressions, support vector regressions, and neural networks. The models take into consideration the external temperature, battery capacity, nominal capacity of the electric vehicle, number of charges made during the same day, maximum charging time allowed by the electric vehicle, target voltage, maximum voltage and maximum current asked by the electric vehicle. The models also take into consideration the vehicle type and the charging station type. We use a data augmentation technique (SMOTE) and hyperparameters optimization to enhance our model performances. The structure of the neural networks is optimized using Bayesian optimization. All models are trained and statistically compared in order to find the overall best model for all vehicle types. The overall best model is a neural network with a sub neural network pre-trained to predict the electric vehicle type.