Abstract
RouteE-Powertrain is a tool for predicting energy usage over a set of road links.
RouteE-Powertrain is a Python package that allows users to work with a set of pre-trained mesoscopic vehicle energy prediction models for a varity of vehicle types. Additionally, users can train their own models if "ground truth" energy consumption and driving data are available. RouteE-Powertrain models predict vehicle energy consumption over links in a road network, so the features considered for prediction often include traffic speeds, road grade, turns, etc.
The typical user will utilize RouteE's catalog of pre-trained models. Currently, the catalog consists of light-duty vehicle models, including conventional gasoline, diesel, hybrid electric (HEV), and battery electric (BEV). These models can be applied to link-level driving data (in the form of pandas dataframes) to output energy consumption predictions.
Users that wish to train new RouteE models can do so. The model training function of RouteE enables users to use their own drive-cycle data, powertrain modeling system, and road network data to train custom models.
https://pypi.org/project/nrel.routee.powertrain/
pip install nrel.routee.powertrain
- Developers:
-
Holden, Jacob [1] ; Cappellucci, Jeffrey [1] ; Wood, Eric [1] ; Gonder, Jeffrey [1]
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Release Date:
- 2023-09-27
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
Shell
Rust
Python
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO)Primary Award/Contract Number:AC36-08GO28308
- Code ID:
- 114447
- Site Accession Number:
- NREL SWR-19-19
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Country of Origin:
- United States
Citation Formats
Holden, Jacob, Cappellucci, Jeffrey, Wood, Eric, and Gonder, Jeffrey.
RouteE-Powertrain [SWR-19-19].
Computer Software.
https://github.com/NREL/routee-powertrain.
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO).
27 Sep. 2023.
Web.
doi:10.11578/dc.20231030.2.
Holden, Jacob, Cappellucci, Jeffrey, Wood, Eric, & Gonder, Jeffrey.
(2023, September 27).
RouteE-Powertrain [SWR-19-19].
[Computer software].
https://github.com/NREL/routee-powertrain.
https://doi.org/10.11578/dc.20231030.2.
Holden, Jacob, Cappellucci, Jeffrey, Wood, Eric, and Gonder, Jeffrey.
"RouteE-Powertrain [SWR-19-19]." Computer software.
September 27, 2023.
https://github.com/NREL/routee-powertrain.
https://doi.org/10.11578/dc.20231030.2.
@misc{
doecode_114447,
title = {RouteE-Powertrain [SWR-19-19]},
author = {Holden, Jacob and Cappellucci, Jeffrey and Wood, Eric and Gonder, Jeffrey},
abstractNote = {RouteE-Powertrain is a tool for predicting energy usage over a set of road links.
RouteE-Powertrain is a Python package that allows users to work with a set of pre-trained mesoscopic vehicle energy prediction models for a varity of vehicle types. Additionally, users can train their own models if "ground truth" energy consumption and driving data are available. RouteE-Powertrain models predict vehicle energy consumption over links in a road network, so the features considered for prediction often include traffic speeds, road grade, turns, etc.
The typical user will utilize RouteE's catalog of pre-trained models. Currently, the catalog consists of light-duty vehicle models, including conventional gasoline, diesel, hybrid electric (HEV), and battery electric (BEV). These models can be applied to link-level driving data (in the form of pandas dataframes) to output energy consumption predictions.
Users that wish to train new RouteE models can do so. The model training function of RouteE enables users to use their own drive-cycle data, powertrain modeling system, and road network data to train custom models.
https://pypi.org/project/nrel.routee.powertrain/
pip install nrel.routee.powertrain},
doi = {10.11578/dc.20231030.2},
url = {https://doi.org/10.11578/dc.20231030.2},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20231030.2}},
year = {2023},
month = {sep}
}