# <img src="docs/images/routeelogo.png" alt="Routee Powertrain" width="100"/>
<div align="left">
<img src="https://img.shields.io/badge/python-3.8%20%7C%203.9%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
```
(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(
{
"miles": [0.1, 0.2, 0.3], # miles
"speed_mph": [30, 40, 50], # mph
"grade_dec": [-0.05, 0, 0.05], # decimal
}
)
energy_result = model.predict(links_df)
```
Raw data
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"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-3.8%20%7C%203.9%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\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 \"miles\": [0.1, 0.2, 0.3], # miles\n \"speed_mph\": [30, 40, 50], # mph\n \"grade_dec\": [-0.05, 0, 0.05], # decimal\n }\n)\n\nenergy_result = model.predict(links_df)\n```\n",
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