RouteE: A Vehicle Energy Consumption Prediction Engine
Abstract
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 conventionalmore »
- Publication Date:
- Research Org.:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
- OSTI Identifier:
- 1774940
- Report Number(s):
- NREL/JA-5400-78089
Journal ID: ISSN 0148-7191; MainId:31998;UUID:7290858a-2d0c-494d-b32d-951699ea41f8;MainAdminID:21215
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Society of Automotive Engineers Technical Paper Series
- Additional Journal Information:
- Journal Volume: 2; Journal Issue: 5; Journal ID: ISSN 0148-7191
- Publisher:
- SAE International
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 33 ADVANCED PROPULSION SYSTEMS; RouteE; green routing; eco-routing; energy estimation; trip; road link; FASTSim; TSDC; CAV; connected vehicles; automated vehicles; SMART Mobility
Citation Formats
Holden, Jacob, Reinicke, Nicholas, and Cappellucci, Jeffrey. RouteE: A Vehicle Energy Consumption Prediction Engine. United States: N. p., 2020.
Web. doi:10.4271/2020-01-0939.
Holden, Jacob, Reinicke, Nicholas, & Cappellucci, Jeffrey. RouteE: A Vehicle Energy Consumption Prediction Engine. United States. https://doi.org/10.4271/2020-01-0939
Holden, Jacob, Reinicke, Nicholas, and Cappellucci, Jeffrey. Tue .
"RouteE: A Vehicle Energy Consumption Prediction Engine". United States. https://doi.org/10.4271/2020-01-0939. https://www.osti.gov/servlets/purl/1774940.
@article{osti_1774940,
title = {RouteE: A Vehicle Energy Consumption Prediction Engine},
author = {Holden, Jacob and Reinicke, Nicholas and Cappellucci, Jeffrey},
abstractNote = {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.},
doi = {10.4271/2020-01-0939},
journal = {Society of Automotive Engineers Technical Paper Series},
number = 5,
volume = 2,
place = {United States},
year = {Tue Apr 14 00:00:00 EDT 2020},
month = {Tue Apr 14 00:00:00 EDT 2020}
}
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