Velocity and Energy Consumption Prediction of Medium-Duty Electric Trucks Considering Road Features and Traffic Conditions

Electric vehicles (EVs) have emerged as a promising solution to address environmental concerns, especially benefiting urban delivery and last-mile fleets due to their unique operational characteristics. Despite the potential advantages, the integration of electric trucks (eTrucks) into delivery fleets has been slow, mainly due to the challenge posed by limited driving range. Consequently, a reliable method for predicting energy consumption in fleet route planning is essential, with the accuracy of the velocity trajectory forecast forming the fundamental basis. This paper introduces a data-driven paradigm to predict the velocity and energy consumption of medium-duty eTrucks, considering various road features, payload, and traffic conditions. A Gated Recurrent Unit (GRU) is trained using traffic-labeled characteristic features specific to each road segment within a delivery route. For every predefined route, the GRU generates the velocity profile by analyzing a sequence of traffic states predicted from the Maximum Entropy Markov Model (MEMM). Corresponding energy consumption is estimated using an Autonomie truck model. Real-world EV data collected are used to evaluate the proposed method, and the results demonstrate that the model effectively utilizes all information from the features, achieving high accuracy in predicting both velocity and energy consumption.