Personalized Velocity and Energy Prediction for Electric Vehicles With Road Features in Consideration

Electric vehicles (EVs) seem to be an eminent alternative for ground transportation. Yet, human drivers may suffer from EV's range anxiety, which is engendered by EV's limited battery capacity and sparse charging stations, particularly in rural areas. A reliable prediction of EV battery energy consumption for the intended route before traveling can alleviate this feeling of unease. Fundamentally, an accurate speed trajectory forecast lays the foundation for a dependable energy consumption prediction. This article originates a data-driven paradigm to predict an EV's speed considering various road features and individuals' driving characteristics. Two modified transformer neural networks, which outperform the traditional recurrent neural networks (RNNs), are exploited to extract information from input features and predict vehicular acceleration or velocity profile. Additionally, a novel energy consumption model is suggested to pioneeringly take the tire slip ratio into account and utilize a neural-network-predicted powertrain efficiency map. By design, the proposed algorithm can offer EV speed and energy consumption estimations prior to the start of a trip. Experimental data collected in real-world EV test drives on four different routes are employed to validate the method. Compared to a baseline approach, the proposed scheme yields a superior accuracy on both velocity and energy consumption predictions for EVs.