Prediction of Road-level Energy Consumption of Battery Electric Vehicles

Predicting the energy consumption of electric vehicles (EVs) plays an essential role in many EV planning and operation problems, such as route planning, eco-driving guidance, and charging facility design. This study analyzes the correlations of EV energy consumption with vehicle-related and environmental features from the one-year (Nov. 2017 to Nov. 2018) real-world trajectory data collected in Ann Arbor, Michigan, USA. The result suggests that the importance of these features is different for different road types. Since the road conditions and other external factors are transient and usually absent in the planning phase, we develop the problem for estimating the prediction intervals (PIs) for road-level EV energy consumption, which can consider the uncertainties. Several machine learning algorithms, such as Gradient Boosted Regression Tree (GBRT) and Extreme Gradient Boosting (XGBoost), are applied to estimate the PIs. The performances are evaluated by the percentage of observations that fall into PIs (coverage percentages) and the width of PIs (interval width). We find that GBRT provides coverage percentages over 90% and interval widths around 0.2 kWh/km for each level of roads under the confidence level of 85 %, which simultaneously satisfies a high prediction accuracy and a narrow prediction interval.

I. Introduction

Electric vehicles (EV s) are considered a promising alternative to gasoline vehicles to cut emissions in the transportation sector. The global EV sale has reached 6.7 million in 2021 (8.3% of all vehicle sales) [1], which is expected to reach 70% of global vehicle sales by 2030 [2]. Many countries like China, France, and Norway established strong policies to encourage EV adoption and increase the proportion of new EVs in the market sales [3]. Many states in the U.S. set aggressive targets and announced that they would stop the sales of gasoline vehicles by 2040 [4]. These raise the need for strategic planning for EV infrastructures and operations.