RouteE: Route Energy Prediction Model

RouteE logo

Developed by NREL, the Route Energy Prediction Model (RouteE) predicts the energy consumption of a given vehicle over a proposed route.

The data-informed model accounts for driving conditions such as anticipated traffic congestion, traffic speed, road type (including number of lanes), road grade, and turns.


RouteE enables users to obtain energy estimates for any vehicle type—including existing and futuristic vehicles—for trips or routes where detailed drive cycle data may be unavailable.

Built as a modular Python package, RouteE features a library of "pretrained" models informed by roughly a million miles of real-world drive cycle data from NREL’s Transportation Secure Data Center and hundreds of Future Automotive Systems Technology Simulator powertrain models. Together, these pretrained models provide energy consumption behavior estimates over a representative sample of driving conditions in the United States.

Users can also employ the versatile tool to generate their own trained, validated models based on custom driving and energy consumption data.


Designed for use with a full range of vehicle types and sizes, RouteE applications include:

  • Vehicle route optimization and selection
  • Vehicle range estimation
  • Calculating the energy and time savings potential of specific green-routing options
  • Large-scale green-routing opportunity analysis
  • Energy accounting and optimization in transportation simulations
  • Trip planning energy estimates
  • Fuel economy impact analysis
  • Corridor energy analysis
  • Vehicle replacement analysis.
Image of map showing three different routes with the same starting and ending points. Accompanying table shows the distance (in miles), the energy savings percentage, and the time required for each route. Route 1 data: 7.1 miles, 6% energy savings, and 14 minutes; Route 2 data: 8.5 miles, 12% energy savings, and 15 minutes; and Route data 3: 9.4 miles, 0% energy savings, and 17 minutes.


RouteE: A Vehicle Energy Consumption Prediction Engine, SAE International Journal of Advances and Current Practices in Mobility (2020)

Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green-Routing Applications, Transportation Research Record: Journal of the Transportation Research Board (2018)

Development of a Trip Energy Estimation Model using Real-World Global Positioning System Driving Data, ITS World Congress (2017)


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