Crowd Sourced Energy Estimation in Connected Vehicles

Accurately forecasting the energy consumption profile of a vehicle is a key requirement of many growing research areas such as horizon based energy management and eco-routing. However, the energy consumption rate of a vehicle depends on many factors making it very difficult to estimate. Many of these factors such as traffic light timing, traffic congestion and weather, change from day to day and trip to trip. While real time traffic information and traffic light timing schedules can be used to help predict the effect of the first two factors, the impact of weather cannot be as easily predicted based on a weather report. Depending on the topology of the route including other vehicles on the road, the local wind speed relative to a vehicle can differ greatly from a predicted bulk wind speed. The effect of precipitation is also difficult to predict because it depends on the amount falling and the amount accumulated on the road. In this paper it is first shown that energy consumption prediction errors due to un-modeled effects, including most notably weather, exhibit a high amount of trip-to-trip variation and a smaller amount of variation within a trip. Next, it is demonstrated that moderate wind speeds have an observable effect on energy consumption and this effect varies based on the direction of travel and wind direction. This analysis also illustrates the challenges in predicting the effect of wind speed and precipitation on energy consumption based on a weather forecast. Finally, a case is made for future research involving the use of current and recent data from a large population of vehicles to provide a more accurate energy consumption profile by reducing the prediction errors due to un-modeled effects.