Artificial deep neural network enables one-size-fits-all electric vehicle user behavior prediction framework

As greener mobility becomes the norm with the advent of electric vehicles (EVs), a natural question arises: how big of a change are we seeing in terms of the stochastic energy demands imposed by EVs? There have been considerable difficulties in analyzing the adoption of all types of EV infrastructure due to the lack of publicly available individual EV user data across various service providers, such as distribution network operators, EV aggregators, EV users, utilities, regulators, and academics. In this study, we introduce the JETPANN (Joint EV energy consumption and charging duration Training Prediction using Artificial Neural Networks), a novel jointly trainable artificial deep neural network framework for predicting stochastic EV user behavior by predicting their stay/charging duration and energy consumption simultaneously. We used a large-scale dataset of individual EV users’ charging transactions collected at a multi-site UCLA campus, the Los Angeles Department of Water and Power (LADWP), the Port of LA, the City of Santa Monica, and the City of Pasadena over a five-year period. This includes more than 50 EVSEs (Electric Vehicle Supply Equipment) and 216 EV charging points. Over 50,000 real-time transactions were recorded in our database with 341 distinct EV users. In this research, essential attributes of each charging session were identified, extracted from our database, and then fed into our JETPANN network. Using a jointly trained framework, our AI-enabled network predicts the stay/charging duration and the energy consumption of all the recorded EV users with an accuracy of above 99 percent. The proposed technique was tested and validated using all the collected historical charging data from individual EV users via realizing mean-absolute errors of training loss versus validation loss. Utilizing hyperparameter tuning and semi-grid search, the JETPANN with semi-optimized hyperparameters was achieved with the lowest mean-absolute errors of 0.927 and 0.068 in predicting stay/charging durations and energy consumptions, respectively, in a jointly trainable framework. This study demonstrates the potential of the JETPANN framework in the prediction of EV users’ behavior by using large-scale real-world data collected from a diverse pool of EV users, including faculty and students, occasional and frequent users, and early and late adopters.