# <img src="docs/images/routeelogo.png" alt="Routee Powertrain" width="100"/>
<div align="left">
<img src="https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11-blue"/>
<a href="https://pypi.org/project/nrel.routee.powertrain/">
<img src="https://img.shields.io/pypi/v/nrel.routee.powertrain" alt="PyPi Latest Release"/>
</a>
</div>
## Overview
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), plugin hybrid electric (PHEV) and battery electric (BEV). These models can be applied to link-level driving data (in the form
of [pandas](https://pandas.pydata.org/) 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.
## Quickstart
RouteE Powertrain is available on PyPI and can be installed with `pip`:
```bash
pip install pip --upgrade
pip install nrel.routee.powertrain
```
If `pip` is unavailable, use `pip3`:
```bash
pip3 install pip --upgrade
pip3 install nrel.routee.powertrain
```
(For more detailed instructions, see [here](https://nrel.github.io/routee-powertrain/installation.html))
Then, you can import the package and use a pre-trained model from the RouteE model catalog:
```python
import pandas as pd
import nrel.routee.powertrain as pt
# Print the available pre-trained models
print(pt.list_available_models(local=True, external=True))
# [
# '2016_TOYOTA_Camry_4cyl_2WD',
# '2017_CHEVROLET_Bolt',
# '2012_Ford_Focus',
# ...
# ]
# Load a pre-trained model
model = pt.load_model("2016_TOYOTA_Camry_4cyl_2WD")
# Inspect the model to see what it expects for input
print(model)
# ========================================
# Model Summary
# --------------------
# Vehicle description: 2016_TOYOTA_Camry_4cyl_2WD
# Powertrain type: ICE
# Number of estimators: 2
# ========================================
# Estimator Summary
# --------------------
# Feature: speed_mph (mph)
# Distance: miles (miles)
# Target: gge (gallons_gasoline)
# Raw Predicted Consumption: 29.856 (miles/gallons_gasoline)
# Real World Predicted Consumption: 25.606 (miles/gallons_gasoline)
# ========================================
# Estimator Summary
# --------------------
# Feature: speed_mph (mph)
# Feature: grade_dec (decimal)
# Distance: miles (miles)
# Target: gge (gallons_gasoline)
# Raw Predicted Consumption: 29.845 (miles/gallons_gasoline)
# Real World Predicted Consumption: 25.596 (miles/gallons_gasoline)
# ========================================
# Predict energy consumption for a set of road links
links_df = pd.DataFrame(
{
"distance": [0.1, 0.2, 0.3], # miles
"speed_mph": [30, 40, 50], # mph
"grade_percent": [-0.5, 0, 0.5], # percent
}
)
energy_result = model.predict(links_df)
```
Raw data
{
"_id": null,
"home_page": null,
"name": "nrel.routee.powertrain",
"maintainer": null,
"docs_url": null,
"requires_python": "<3.13,>=3.9",
"maintainer_email": null,
"keywords": null,
"author": "National Renewable Energy Laboratory",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/bf/32/ef73e35b922304404731e71e54aef99dc42898067eb2f914eea04683e954/nrel_routee_powertrain-1.3.1.tar.gz",
"platform": null,
"description": "# <img src=\"docs/images/routeelogo.png\" alt=\"Routee Powertrain\" width=\"100\"/>\n\n<div align=\"left\">\n <img src=\"https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11-blue\"/>\n <a href=\"https://pypi.org/project/nrel.routee.powertrain/\">\n <img src=\"https://img.shields.io/pypi/v/nrel.routee.powertrain\" alt=\"PyPi Latest Release\"/>\n </a>\n</div>\n\n## Overview\n\nRouteE-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.\n\nThe typical user will utilize RouteE's catalog of pre-trained models. Currently, the\ncatalog consists of light-duty vehicle models, including conventional gasoline, diesel,\nhybrid electric (HEV), plugin hybrid electric (PHEV) and battery electric (BEV). These models can be applied to link-level driving data (in the form\nof [pandas](https://pandas.pydata.org/) dataframes) to output energy consumption predictions.\n\nUsers that wish to train new RouteE models can do so. The model training function of RouteE enables users to use their\nown drive-cycle data, powertrain modeling system, and road network data to train custom models.\n\n## Quickstart\n\nRouteE Powertrain is available on PyPI and can be installed with `pip`:\n\n```bash\npip install pip --upgrade\npip install nrel.routee.powertrain\n```\n\nIf `pip` is unavailable, use `pip3`:\n```bash\npip3 install pip --upgrade\npip3 install nrel.routee.powertrain\n```\n\n(For more detailed instructions, see [here](https://nrel.github.io/routee-powertrain/installation.html))\n\nThen, you can import the package and use a pre-trained model from the RouteE model catalog:\n\n```python\nimport pandas as pd\nimport nrel.routee.powertrain as pt\n\n# Print the available pre-trained models\nprint(pt.list_available_models(local=True, external=True))\n\n# [\n# '2016_TOYOTA_Camry_4cyl_2WD',\n# '2017_CHEVROLET_Bolt',\n# '2012_Ford_Focus',\n# ...\n# ]\n\n# Load a pre-trained model\nmodel = pt.load_model(\"2016_TOYOTA_Camry_4cyl_2WD\")\n\n# Inspect the model to see what it expects for input\nprint(model)\n\n# ========================================\n# Model Summary\n# --------------------\n# Vehicle description: 2016_TOYOTA_Camry_4cyl_2WD\n# Powertrain type: ICE\n# Number of estimators: 2\n# ========================================\n# Estimator Summary\n# --------------------\n# Feature: speed_mph (mph)\n# Distance: miles (miles)\n# Target: gge (gallons_gasoline)\n# Raw Predicted Consumption: 29.856 (miles/gallons_gasoline)\n# Real World Predicted Consumption: 25.606 (miles/gallons_gasoline)\n# ========================================\n# Estimator Summary\n# --------------------\n# Feature: speed_mph (mph)\n# Feature: grade_dec (decimal)\n# Distance: miles (miles)\n# Target: gge (gallons_gasoline)\n# Raw Predicted Consumption: 29.845 (miles/gallons_gasoline)\n# Real World Predicted Consumption: 25.596 (miles/gallons_gasoline)\n# ========================================\n\n# Predict energy consumption for a set of road links\nlinks_df = pd.DataFrame(\n {\n \"distance\": [0.1, 0.2, 0.3], # miles\n \"speed_mph\": [30, 40, 50], # mph\n \"grade_percent\": [-0.5, 0, 0.5], # percent\n }\n)\n\nenergy_result = model.predict(links_df)\n```\n",
"bugtrack_url": null,
"license": "BSD 3-Clause License Copyright (c) 2023, Alliance for Sustainable Energy, LLC",
"summary": "RouteE-Powertrain is a tool for predicting energy usage over a set of road links.",
"version": "1.3.1",
"project_urls": {
"Homepage": "https://github.com/NREL/routee-powertrain"
},
"split_keywords": [],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "af185edf56e96442a1fac38cfb2a9f6c260faf4b1fe6ed595e63e79be73149db",
"md5": "c8ea25071a94ff70dda3ec60a4d72c01",
"sha256": "6583bd7a63473e5cedf98b9c9ae9618adf1f2148855586a070359050b309ccdc"
},
"downloads": -1,
"filename": "nrel_routee_powertrain-1.3.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "c8ea25071a94ff70dda3ec60a4d72c01",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<3.13,>=3.9",
"size": 1964410,
"upload_time": "2025-07-24T21:35:10",
"upload_time_iso_8601": "2025-07-24T21:35:10.336441Z",
"url": "https://files.pythonhosted.org/packages/af/18/5edf56e96442a1fac38cfb2a9f6c260faf4b1fe6ed595e63e79be73149db/nrel_routee_powertrain-1.3.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "bf32ef73e35b922304404731e71e54aef99dc42898067eb2f914eea04683e954",
"md5": "470788a9dc9d1d59525c913e2083f123",
"sha256": "8b68ba9d192bb2b56199e73e9cd89f3f80ee417ec7ce62a16cb51fc2b8cb58ae"
},
"downloads": -1,
"filename": "nrel_routee_powertrain-1.3.1.tar.gz",
"has_sig": false,
"md5_digest": "470788a9dc9d1d59525c913e2083f123",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<3.13,>=3.9",
"size": 1930602,
"upload_time": "2025-07-24T21:35:12",
"upload_time_iso_8601": "2025-07-24T21:35:12.092574Z",
"url": "https://files.pythonhosted.org/packages/bf/32/ef73e35b922304404731e71e54aef99dc42898067eb2f914eea04683e954/nrel_routee_powertrain-1.3.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-07-24 21:35:12",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "NREL",
"github_project": "routee-powertrain",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "nrel.routee.powertrain"
}
JSON
