Effect of Wind on Electric Vehicle Energy Consumption: Sensitivity Analyses and Implications for Range Estimation and Optimal Routing

Abstract

The energy consumption of electric vehicles (EVs) depends on multiple factors. As it affects vehicle range, energy consumption must be accurately predicted. After a summary of the relevant literature, this paper focuses on two sensitivity studies: one on the impact of wind on energy consumption, and the other on the identifiability of wind in the absence of vehicles’ speed and acceleration profiles. The studies show that wind has a significant impact on the energy consumption for a trip, and without high-resolution knowledge of the acceleration and instantaneous velocity, minor variations in the wind condition do not drastically alter the energy consumption distribution. After that, data sources for the information on the wind velocity and direction are discussed. A data-driven approach based on fuzzy set theory is proposed to incorporate wind into the energy prediction; the best model from this approach shows a notable improvement (3.62%) over the currently implemented production-level predictive model for energy consumption on a data set of 35,139 real-world trips; the improvement is even more pronounced (∼ 7%) for trips with more substantial headwind or tailwind level. Recognizing the interplay between range prediction and route selection, we consider a Markov Decision Process (MDP) framework for battery-charge- and travel-time-aware optimal route planning that accounts for the impact of the wind and includes stops at the charging stations. Finally, we propose a framework that includes wind in the operation of EVs, which consists of learning the impact of wind, incorporating wind forecasting into range and energy prediction, and using that prediction to perform optimal routing.