RouteE Powertrain

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RouteE Powertrain#

RouteE-Powertrain is a Python package that allows users to work with a set of pre-trained mesoscopic vehicle energy prediction models for a variety 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.

Quickstart#

RouteE Powertrain is available on PyPI and can be installed with pip:

pip install routee.powertrain

Then, you can import the package and use a pre-trained model from the RouteE model catalog:

import pandas as pd
import routee.powertrain as pt

# List the available pre-trained models (returns a list of ModelId objects)
print(pt.list_available_models())

# [
#   toyota/camry_ice/2016/rf_base_fe510e40/v1,
#   chevrolet/bolt_bev/2017/rf_base_fe510e40/v1,
#   ...
# ]

# You can also query available models with filters for more detail
results = pt.query_available_models(make="toyota", model="camry")

# Load a pre-trained model using its registry path
model = pt.load_model("toyota/camry_ice/2016/rf_base_fe510e40/v1")

# Inspect the model to see what it expects for input
print(model)

# ========================================
# Model Summary
# --------------------
# Vehicle description: 2016_Toyota_Camry_4cyl_2WD | fastsim 3.1.0 | routee-powertrain 2.0.1
# Powertrain type: ICE
# ========================================
# Estimator Summary
# --------------------
# Feature: speed_mph (mph)
# Feature: grade_pct (percent)
# Feature: distance_mi (miles)
# Distance: distance_mi (miles)
# Target: fuel_gge (gallons gasoline)
# Raw Predicted Consumption: 31.012 (miles/gallons gasoline)
# Real World Predicted Consumption: 26.597 (miles/gallons gasoline)
# Predict Method: RATE
# ========================================

# Predict energy consumption for a set of road links
links_df = pd.DataFrame(
    {
        "distance_mi": [0.1, 0.2, 0.3], # miles
        "speed_mph": [30, 40, 50], # mph
        "grade_pct": [-5.0, 0, 5.0], # percent
    }
)

energy_result = model.predict(links_df)
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