Model Prediction

RouteE models can be loaded from a large library of pre-trained models. Conventional gasoline (CV), hybrid electric (HEV), plug-in hybrid electric (PHEV), and battery electric (BEV) powertrain types are all available.

A note on PHEVs: Plug-in hybrids have two general operating modes 1) "Charge Depleting" or "EV" mode, where the vehicle relies only on energy from the battery to power the motor and 2) "Charge Sustaining" or "Hybrid" mode, where the vehicle operates like a typical parallel hybrid, using a combination of the combustion energy and electric motor for tractive effort and regenerative braking. Since the operating mode depends on battery state-of-charge and driver decisions, pre-trained RouteE-Powertrain models for both operating modes are provided for all PHEVs and it is up to the user to decide which is most appropriate for a particular application.

import nrel.routee.powertrain as pt

pt.list_available_models()

['2016_TOYOTA_Camry_4cyl_2WD',
 '2017_CHEVROLET_Bolt',
 '2010_Mazda_3_i-Stop',
 '2012_Ford_Focus',
 '2012_Ford_Fusion',
 '2016_AUDI_A3_4cyl_2WD',
 '2016_BMW_328d_4cyl_2WD',
 '2016_BMW_i3_REx_PHEV_Charge_Depleting',
 '2016_BMW_i3_REx_PHEV_Charge_Sustaining',
 '2016_CHEVROLET_Malibu_4cyl_2WD',
 '2016_CHEVROLET_Spark_EV',
 '2016_CHEVROLET_Volt_Charge_Depleting',
 '2016_CHEVROLET_Volt_Charge_Sustaining',
 '2016_FORD_C-MAX_(PHEV)_Charge_Depleting',
 '2016_FORD_C-MAX_(PHEV)_Charge_Sustaining',
 '2016_FORD_C-MAX_HEV',
 '2016_FORD_Escape_4cyl_2WD',
 '2016_FORD_Explorer_4cyl_2WD',
 '2016_HYUNDAI_Elantra_4cyl_2WD',
 '2016_HYUNDAI_Sonata_PHEV_Charge_Depleting',
 '2016_HYUNDAI_Sonata_PHEV_Charge_Sustaining',
 '2016_Hyundai_Tucson_Fuel_Cell',
 '2016_KIA_Optima_Hybrid',
 '2016_Leaf_24_kWh',
 '2016_MITSUBISHI_i-MiEV',
 '2016_Nissan_Leaf_30_kWh',
 '2016_TESLA_Model_S60_2WD',
 '2016_TOYOTA_Camry_4cyl_2WD',
 '2016_TOYOTA_Corolla_4cyl_2WD',
 '2016_TOYOTA_Corolla_4cyl_2WD_Stochastic',
 '2016_TOYOTA_Highlander_Hybrid',
 '2016_Toyota_Prius_Two_FWD',
 '2017_CHEVROLET_Bolt',
 '2017_Maruti_Dzire_VDI',
 '2017_Prius_Prime_Charge_Depleting',
 '2017_Prius_Prime_Charge_Sustaining',
 '2017_Toyota_Highlander_3',
 '2020_Chevrolet_Colorado_2WD_Diesel',
 '2020_Chevrolet_Colorado_2WD_Diesel_Stochastic',
 '2020_VW_Golf_1',
 '2020_VW_Golf_2',
 '2021_BMW_iX_xDrive40',
 '2021_Cupra_Born',
 '2021_Fiat_Panda_Mild_Hybrid',
 '2021_Honda_N-Box_G',
 '2021_Peugot_3008',
 '2022_Ford_F-150_Lightning_4WD',
 '2022_MINI_Cooper_SE_Hardtop_2_door',
 '2022_Renault_Megane_E-Tech',
 '2022_Renault_Zoe_ZE50_R135',
 '2022_Tesla_Model_3_RWD',
 '2022_Tesla_Model_Y_RWD',
 '2022_Tesla_Model_Y_RWD_Stochastic',
 '2022_Toyota_Avanza_E_J_MT',
 '2022_Toyota_RAV4_Hybrid_LE',
 '2022_Toyota_RAV4_Hybrid_LE_Stochastic',
 '2022_Toyota_Yaris_Hybrid_Mid',
 '2022_Volvo_XC40_Recharge_twin',
 '2023_Mitsubishi_Pajero_Sport',
 '2023_Polestar_2_Long_range_Dual_motor',
 '2023_Volvo_C40_Recharge',
 '2024_BYD_Dolphin_Active',
 '2024_Toyota_Vios_1',
 '2024_VinFast_VF_e34',
 '2024_Volkswagen_Polo_1',
 'BYD_ATTO_3',
 'Daycab_new_300kW',
 'Daycab_new_400kW',
 'Daycab_old_300kW',
 'Daycab_old_400kW',
 'Maruti_Swift_4cyl_2WD',
 'Nissan_Navara',
 'Renault_Clio_IV_diesel',
 'Renault_Megane_1',
 'Sleeper_new_300kW',
 'Sleeper_new_400kW',
 'Sleeper_old_300kW',
 'Sleeper_old_400kW',
 'Toyota_Corolla_Cross_Hybrid',
 'Toyota_Etios_Liva_diesel',
 'Toyota_Hilux_Double_Cab_4WD',
 'Toyota_Mirai']

camry = pt.load_model("2016_TOYOTA_Camry_4cyl_2WD")

After loading a model, we can inspect it to see what features (and units) the model expects. RouteE Powertrain models can have multiple estimators under the hood which have been trained on different feature sets. For example, there might be an estimator that takes just speed as a link feature and another that takes in speed and grade. This can be useful if you have sparse data for one feature (like grade) but still want to predict energy consumption.

camry

Model Summary
Vehicle description	2016_TOYOTA_Camry_4cyl_2WD trained July 2024
Powertrain type	ICE
Number of estimators	3
Estimator Summary
Feature	speed_mph (mph)
Distance	distance (miles)
Target	gge (gallons gasoline)
Predicted Consumption	30.279 (miles/gallons gasoline)
Real World Predicted Consumption	25.969 (miles/gallons gasoline)
Predict Method	RATE
Estimator Summary
Feature	speed_mph (mph)
Feature	grade_percent (percent)
Distance	distance (miles)
Target	gge (gallons gasoline)
Predicted Consumption	30.289 (miles/gallons gasoline)
Real World Predicted Consumption	25.977 (miles/gallons gasoline)
Predict Method	RATE
Estimator Summary
Feature	speed_mph (mph)
Feature	grade_percent (percent)
Feature	turn_angle (degrees)
Distance	distance (miles)
Target	gge (gallons gasoline)
Predicted Consumption	30.364 (miles/gallons gasoline)
Real World Predicted Consumption	26.041 (miles/gallons gasoline)
Predict Method	RATE

Now, let's predict energy consumption over a sample route. RouteE Powertrain expects the inputs to be a pandas dataframe in which each row represents a road network link. There is a sample route included with the package that we'll use for demonstration.

sample_route = pt.load_sample_route()
sample_route

	speed_mph	grade_dec	miles
0	7.632068	-0.008963	0.015469
1	6.329613	-0.047001	0.003516
2	12.248512	0.000000	0.003402
3	23.752604	-0.000463	0.019768
4	46.024926	-0.004641	0.038378
...	...	...	...
120	7.415272	-0.001339	0.006128
121	27.685268	-0.030748	0.023170
122	51.322545	-0.008490	0.028510
123	50.431920	-0.010533	0.028015
124	48.325893	-0.017581	0.040274

125 rows × 3 columns

Note that the dataframe columns don't quite match what the model is expecting. The speed_mph column matches what the model expects but it looks like it wants the grade column to be called grade_percent and it should be in the percent unit. Our current grade in represented as a decimal and so we'll have to multiply it by 100 to get it into the units the model expects. Also note that we need to rename the miles column to distance to match the model.

sample_route["grade_percent"] = sample_route.grade_dec * 100
sample_route = sample_route.drop(columns="grade_dec")
sample_route = sample_route.rename(columns={"miles": "distance"})
sample_route

	speed_mph	distance	grade_percent
0	7.632068	0.015469	-0.896343
1	6.329613	0.003516	-4.700083
2	12.248512	0.003402	0.000000
3	23.752604	0.019768	-0.046280
4	46.024926	0.038378	-0.464073
...	...	...	...
120	7.415272	0.006128	-0.133898
121	27.685268	0.023170	-3.074826
122	51.322545	0.028510	-0.848964
123	50.431920	0.028015	-1.053289
124	48.325893	0.040274	-1.758078

125 rows × 3 columns

If we just pass in the links DataFrame without any other information, the model will assume we want to use all the features and in this case will look for an internal estimator with features to match all the columns.

Based on the model summary shown above, we do have an estimator that takes in the link features speed_mph and grade_percent with a distance of distance and so it will automatically select that estimator when we predict.

camry.predict(sample_route)

	gge
0	0.001028
1	0.000252
2	0.000174
3	0.000995
4	0.001056
...	...
120	0.000411
121	0.000769
122	0.000731
123	0.000719
124	0.000886

125 rows × 1 columns

If we want to use a different estimator, we can tell the predict method to only use a subset of the features. In this case, we'll tell the model to only use speed.

camry.predict(sample_route, feature_columns=["speed_mph"])

	gge
0	0.001095
1	0.000281
2	0.000185
3	0.000967
4	0.001145
...	...
120	0.000442
121	0.001043
122	0.000851
123	0.000836
124	0.001202

125 rows × 1 columns

We can also apply some correction factors for effects that are not directly captured in the energy data the model was trained on. These include things like:

• Ambient Temperature

• Auxilary loads (i.e. Air Conditioners)

These are powertrain specific and can be included by passing the apply_real_world_adjustment paramter which is set to False by default.

camry.predict(sample_route, apply_real_world_adjustment=True)

	gge
0	0.001028
1	0.000252
2	0.000174
3	0.000995
4	0.001056
...	...
120	0.000411
121	0.000769
122	0.000731
123	0.000719
124	0.000886

125 rows × 1 columns

Note that we see slightly higher energy values when apply these factors.

Model Visualization

There are a few different functions we can visualize what a model is predicting over a range of inputs.

The first is the visualize_features function that sweeps a feature over a range and plots the results. In order to use this we first have to define what ranges the features should be considered.

feature_ranges = {
    "speed_mph": {"lower": 2, "upper": 100, "n_samples": 50},
    "grade_percent": {"lower": -20.0, "upper": 20.0, "n_samples": 50}
}

results = pt.visualize_features(camry, feature_ranges)

2025-02-25 21:30:32,388 [INFO] - Failed to extract font properties from /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: Can not load face (unknown file format; error code 0x2)

2025-02-25 21:30:33,859 [INFO] - generated new fontManager

<Figure size 640x480 with 0 Axes>

We can also look at two features simultaneously with the contour_plot function.

pt.contour_plot(camry, x_feature="speed_mph", y_feature="grade_percent", feature_ranges=feature_ranges)