RouteE: A Vehicle Energy Consumption Prediction Engine

Abstract

<div class="section abstract"><div class="htmlview paragraph">The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL’s Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.</div></div>

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

“…This method can be embedded in routing algorithms and used as one component in the optimization of the route algorithm's generalized cost function. Recently, Holden et al developed the Route Energy prediction Model (RouteE) for the accurate estimation of energy consumption for a variety of vehicle types (i.e., makes and models) over trips or sub-trips where detailed drive cycle data are unavailable (32). The RouteE models were established using the Future Automotive Systems Technology Simulator (FASTSim) paired with approximately 1 million miles of drive cycle data across the United States from the Transportation Secure Data Center (33).…”

“…Provided real-time speeds, volumes, grade, and link length, we have sufficient information to calculate nearreal-time energy consumed on each segment of road using the National Renewable Energy Laboratory's RouteE software (32). RouteE considers link featuressuch as average traffic speed, road grade, and link length-to accurately estimate energy consumption for particular vehicle types.…”

“…RouteE offers different estimator types-including explicit bin (lookup tables), linear regressors, and random forest regressors-that are best suited to different applications. In this pipeline, we used the random forest regressors because this was the most robust model for interpolating among discrete conditions (feature sets) captured in the original training data as documented in RouteE literature (32). The model features used in each estimator type are the average speed over a link in miles per hour (mph), the average grade across a link as a percentage, and the length of the link in miles.…”