| Description |
1 online resource (7 pages) : color illustrations, color maps. |
|
text txt rdacontent |
|
computer c rdamedia |
|
online resource cr rdacarrier |
| Series |
NREL/PR ; 5400-76921 |
|
NREL/PR ; 5400-76921.
|
| Note |
"June 16-18, 2020." |
| Funding |
National Renewable Energy Laboratory DE-AC36-08GO28308 |
| Note |
Description based on online resource; title from PDF title page (NREL, viewed on Oct. 21, 2021). |
| Summary |
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. |
| Subject |
SAE International (Society)
|
|
Energy conservation -- United States.
|
|
Energy consumption -- United States.
|
|
Économies d'énergie -- États-Unis.
|
|
Énergie -- Consommation -- États-Unis.
|
|
Energy conservation
|
|
Energy consumption
|
|
United States https://id.oclc.org/worldcat/entity/E39PBJtxgQXMWqmjMjjwXRHgrq
|
| Indexed Term |
eco-routing |
|
machine learning |
|
modeling |
|
routee |
|
transportation |
|
vehicles |
| Genre/Form |
Congress |
|
proceedings (reports)
|
|
Conference papers and proceedings
|
|
Conference papers and proceedings.
|
|
Actes de congrès.
|
| Added Author |
Reinicke, Nicholas, author.
|
|
Cappellucci, Jeff, author.
|
|
National Renewable Energy Laboratory (U.S.), issuing body.
|
| Note |
At head of title: WCX Digital Summit |
| Added Title |
Vehicle energy consumption prediction engine |
| Standard No. |
1765594 OSTI ID |
|
0000-0002-4384-1171 |
|
0000-0003-4183-6034 |
| Gpo Item No. |
0430-P-09 (online) |
| Sudoc No. |
E 9.22:NREL/PR-5400-76921 |
|
