Electric Vehicle Velocity and Energy Consumption Predictions Using Transformer and Markov-Chain Monte Carlo

Abstract

Although electric vehicles (EVs) are becoming a promising alternative for ground transportation, the issue of limited battery energy hinders its market penetration. Accurate prediction of electrical energy consumption along a given route would significantly relieve drivers’ anxieties and build their confidence in EVs. A high-fidelity energy estimation relies on an accurate velocity forecast. To this end, this article proposes a novel hybrid deterministic-stochastic methodology that utilizes inputs encompassing the route information, the driver’s characteristics, and the traffic flow’s uncertainties to predict the EV’s future velocity profile and energy consumption. The method comprises two components: a deterministic machine-learning-based transformer network and a stochastic Markov-chain Monte Carlo (MCMC) algorithm. Real-world EV data collected in four routes of different lengths and features are used to evaluate the method. The results demonstrate the enhanced performance of the approach in both velocity prediction and energy consumption estimation, compared to two popular baseline algorithms.