Understanding How Wind Affects EV's Energy Consumption and Optimal EV Routing

What is it about?

Electric vehicles (EVs) are becoming more popular, raising the demand for accurate range estimation. Nevertheless, the literature addressing the wind in energy consumption estimation of EVs is relatively scarce. In this paper, we found that while the wind can notably impact the vehicle’s energy, the effects of small variations in the wind’s condition on energy consumption could be masked by the effects of vehicle acceleration and instantaneous velocity. We proposed a fuzzy-set approach to incorporate wind into energy prediction, which shows a potential for improvement (3.62%) over a baseline model that does not include wind. The improvement can be even more pronounced (~7%) for trips with more substantial headwind or tailwind. Recognizing the interplay between range prediction and route selection, we described an approach to optimal route planning that considers battery charge and travel time, accounts for the impact of the wind, and includes planning stops at the charging stations.

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Photo by Denys Nevozhai on Unsplash

Photo by Denys Nevozhai on Unsplash

Why is it important?

The unique aspect of our work is its focus on the wind, a factor that has not been studied as extensively in the literature on EV range prediction. By incorporating the wind into a prediction model, we show a potential for an improvement in the accuracy of energy consumption estimates. Improving range prediction can reduce customers’ "range anxiety” - the fear of running out of battery before reaching a destination or charging station. We have also shown that the wind information can be integrated into the optimal route selection along with the planning of stops at charging stations.

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