RouteE-Powertrain Model Library
Pre-trained mesoscopic vehicle energy prediction models: given link-level driving conditions (speed, road grade, turn angle, …) they predict how much energy a specific vehicle consumes traversing that link. They are the model catalog behind routee-powertrain and are consumed by routing engines such as routee-compass to compute energy-aware routes.
- 282 models covering 74 vehicle configurations across 25 makes
- Powertrains: ICE, HEV, BEV, PHEV (both charge-depleting and charge-sustaining modes), and generic Class-8 heavy duty
- Format: ONNX (random forest, 266 models) and joblib (NGBoost probabilistic, 16 models)
- Maintained by the National Laboratory of the Rockies
Quickstart
pip install routee.powertrain
import pandas as pd
import routee.powertrain as pt
# This repo is the default registry — no configuration needed.
print(pt.query_available_models(make="tesla", model="model 3"))
model = pt.load_model("tesla/model_3_bev/2022/rf_c3326385") # version optional -> latest
links_df = pd.DataFrame({
"distance": [0.1, 0.2], # miles
"speed_mph": [30, 55], # mph
"grade_percent": [-2.0, 1.0], # percent
})
model.predict(links_df)
# kwh
# 0 0.005089
# 1 0.064848
print(model) prints the full input contract — every feature with its units, the
distance column, the target, and the predict method.
Downloads go through huggingface_hub into the shared HF cache, so repeat loads are
offline. Everything here is public; no token is required.
Addressing a model
Each model lives at a path that is its identifier:
v2/<make>/<vehicle_slug>/<year>/<config_slug>/v<N>/
metadata.json # full model card data: contract, errors, provenance, digest
model.onnx # or a .joblib blob for NGBoost estimators
v2/index.json # machine-readable catalog of every model in the repo
vehicle_slug=<model>_<powertrain_family>(e.g.camry_ice,bolt_bev). Both PHEV modes share one vehicle slug; the mode lives in the config slug.config_slug=<architecture>_<variant?>_<feature_hash>— the same vehicle trained with a different feature set or variant is a different config, not a different version.v<N>is a registry coordinate. Omit it and you get the latest.
Every path segment is derived from the model's own metadata, so a path and its
metadata.json can never disagree — the loader raises if they do.
Catalog
ICE — 30 vehicles, 96 models
| Path prefix | Description | Target | Configs |
|---|---|---|---|
audi/a3_ice/2016 |
2016_AUDI_A3_4cyl_2WD trained July 2024 | gde | rf_b80965c8, rf_c3326385, rf_db8522fb |
bmw/328d_ice/2016 |
2016_BMW_328d_4cyl_2WD trained July 2024 | gde | rf_b80965c8, rf_c3326385, rf_db8522fb |
chevrolet/colorado_diesel_ice/2020 |
2020 Chevrolet Colorado 2WD Diesel | gde | ngb_stochastic_02107a97, ngb_stochastic_940b80b8, ngb_stochastic_aaa9554f, ngb_stochastic_db8522fb, rf_b80965c8, rf_c3326385, rf_db8522fb |
chevrolet/malibu_ice/2016 |
2016_CHEVROLET_Malibu_4cyl_2WD trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
fiat/panda_mild_hybrid_ice/2021 |
2021_Fiat_Panda_Mild_Hybrid trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
ford/escape_ice/2016 |
2016_FORD_Escape_4cyl_2WD trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
ford/explorer_ice/2016 |
2016_FORD_Explorer_4cyl_2WD trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
ford/focus_ice/2012 |
2012_Ford_Focus trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
ford/fusion_ice/2012 |
2012_Ford_Fusion trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
generic_transit/40_foot_diesel_ice/2020-2025 |
Test Vehicle | gallons | rf_793469d3 |
honda/n-box_g_ice/2021 |
2021_Honda_N-Box_G trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
hyundai/elantra_ice/2016 |
2016_HYUNDAI_Elantra_4cyl_2WD trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
maruti/dzire_vdi_ice/2017 |
2017_Maruti_Dzire_VDI trained July 2024 | gde | rf_b80965c8, rf_c3326385, rf_db8522fb |
maruti/swift_ice/2018 |
Maruti_Swift_4cyl_2WD trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
mazda/3_i-stop_ice/2010 |
2010_Mazda_3_i-Stop trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
mitsubishi/pajero_sport_ice/2023 |
2023_Mitsubishi_Pajero_Sport trained July 2024 | gde | rf_b80965c8, rf_c3326385, rf_db8522fb |
nissan/navara_ice/2020 |
Nissan_Navara trained July 2024 | gde | rf_b80965c8, rf_c3326385, rf_db8522fb |
peugeot/3008_ice/2021 |
2021_Peugot_3008 trained July 2024 | gde | rf_b80965c8, rf_c3326385, rf_db8522fb |
renault/clio_iv_diesel_ice/2016 |
Renault_Clio_IV_diesel trained July 2024 | gde | rf_b80965c8, rf_c3326385, rf_db8522fb |
renault/megane_1.5_dci_authentique_ice/2016 |
Renault_Megane_1.5_dCi_Authentique trained July 2024 | gde | rf_b80965c8, rf_c3326385, rf_db8522fb |
toyota/avanza_e_j_mt_ice/2022 |
2022_Toyota_Avanza_E_J_MT trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
toyota/camry_ice/2016 |
2016_TOYOTA_Camry_4cyl_2WD trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
toyota/corolla_ice/2016 |
2016 Toyota Corolla 4cyl 2WD | gge | ngb_stochastic_02107a97, ngb_stochastic_940b80b8, ngb_stochastic_aaa9554f, ngb_stochastic_db8522fb, rf_b80965c8, rf_c3326385, rf_db8522fb |
toyota/etios_liva_diesel_ice/2015 |
Toyota_Etios_Liva_diesel trained July 2024 | gde | rf_b80965c8, rf_c3326385, rf_db8522fb |
toyota/highlander_3.5_l_ice/2017 |
2017_Toyota_Highlander_3.5_L trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
toyota/hilux_double_cab_ice/2020 |
Toyota_Hilux_Double_Cab_4WD trained July 2024 | gde | rf_b80965c8, rf_c3326385, rf_db8522fb |
toyota/vios_1.5_g_ice/2024 |
2024_Toyota_Vios_1.5_G trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
volkswagen/golf_1.5tsi_ice/2020 |
2020_VW_Golf_1.5TSI trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
volkswagen/golf_2.0tdi_ice/2020 |
2020_VW_Golf_2.0TDI trained July 2024 | gde | rf_b80965c8, rf_c3326385, rf_db8522fb |
volkswagen/polo_1.0_mpi_ice/2024 |
2024_Volkswagen_Polo_1.0_MPI trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
HEV — 9 vehicles, 31 models
| Path prefix | Description | Target | Configs |
|---|---|---|---|
ford/c-max_hev/2016 |
2016_FORD_C-MAX_HEV trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
hyundai/tucson_fuel_cell_hev/2016 |
2016_Hyundai_Tucson_Fuel_Cell trained July 2024 | kg_h2 | rf_b80965c8, rf_c3326385, rf_db8522fb |
kia/optima_hev/2016 |
2016_KIA_Optima_Hybrid trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
toyota/corolla_cross_hev/2022 |
Toyota_Corolla_Cross_Hybrid trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
toyota/highlander_hev/2016 |
2016_TOYOTA_Highlander_Hybrid trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
toyota/mirai_hev/2021 |
Toyota_Mirai trained July 2024 | kg_h2 | rf_b80965c8, rf_c3326385, rf_db8522fb |
toyota/prius_two_hev/2016 |
2016_Toyota_Prius_Two_FWD trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
toyota/rav4_hybrid_le_hev/2022 |
2022 Toyota RAV4 Hybrid LE | gge | ngb_stochastic_02107a97, ngb_stochastic_940b80b8, ngb_stochastic_aaa9554f, ngb_stochastic_db8522fb, rf_b80965c8, rf_c3326385, rf_db8522fb |
toyota/yaris_hybrid_mid_hev/2022 |
2022_Toyota_Yaris_Hybrid_Mid trained July 2024 | gge | rf_b80965c8, rf_c3326385, rf_db8522fb |
BEV — 22 vehicles, 77 models
| Path prefix | Description | Target | Configs |
|---|---|---|---|
bmw/ix_xdrive40_bev/2021 |
2021_BMW_iX_xDrive40 trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb |
byd/atto_3_bev/2022 |
BYD_ATTO_3 trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb |
byd/dolphin_active_bev/2024 |
2024_BYD_Dolphin_Active trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb |
chevrolet/bolt_bev/2017 |
2017_CHEVROLET_Bolt trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb |
chevrolet/bolt_bev/2020 |
2020_Chevrolet_Bolt_EV_0F_110F_steady trained July 2025 | kwh | rf_steady_thermal_856e8a60, rf_steady_thermal_ab1db342, rf_transient_thermal_856e8a60, rf_transient_thermal_ab1db342 |
chevrolet/spark_bev/2016 |
2016_CHEVROLET_Spark_EV trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb |
cupra/born_bev/2021 |
2021_Cupra_Born trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb |
ford/f-150_lightning_bev/2022 |
2022_Ford_F-150_Lightning_4WD trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb |
generic_transit/40_foot_battery_electric_bev/2020-2025 |
BEB Vehicle | kWhs | rf_793469d3 |
mini/cooper_se_hardtop_2_door_bev/2022 |
2022_MINI_Cooper_SE_Hardtop_2_door trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb |
mitsubishi/i-miev_bev/2016 |
2016_MITSUBISHI_i-MiEV trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb |
nissan/leaf_24_kwh_bev/2016 |
2016_Leaf_24_kWh trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb |
nissan/leaf_30_kwh_bev/2016 |
2016_Nissan_Leaf_30_kWh trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb, rf_steady_thermal_856e8a60, rf_steady_thermal_ab1db342, rf_transient_thermal_856e8a60, rf_transient_thermal_ab1db342 |
polestar/2_long_range_bev/2023 |
2023_Polestar_2_Long_range_Dual_motor trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb |
renault/megane_e-tech_bev/2022 |
2022_Renault_Megane_E-Tech trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb |
renault/zoe_ze50_r135_bev/2022 |
2022_Renault_Zoe_ZE50_R135 trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb |
tesla/model_3_bev/2022 |
2022_Tesla_Model_3_RWD trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb, rf_steady_thermal_856e8a60, rf_steady_thermal_ab1db342, rf_transient_thermal_856e8a60, rf_transient_thermal_ab1db342 |
tesla/model_s60_bev/2016 |
2016_TESLA_Model_S60_2WD trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb |
tesla/model_y_bev/2022 |
2022 Tesla Model Y RWD | ess_kwh_out_ach, kwh | ngb_stochastic_02107a97, ngb_stochastic_940b80b8, ngb_stochastic_aaa9554f, ngb_stochastic_db8522fb, rf_b80965c8, rf_c3326385, rf_db8522fb |
vinfast/vf_e34_bev/2024 |
2024_VinFast_VF_e34 trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb |
volvo/c40_recharge_bev/2023 |
2023_Volvo_C40_Recharge trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb |
volvo/xc40_recharge_bev/2022 |
2022_Volvo_XC40_Recharge_twin trained July 2024 | kwh | rf_b80965c8, rf_c3326385, rf_db8522fb |
PHEV_EV_MODE — 5 vehicles, 15 models
| Path prefix | Description | Target | Configs |
|---|---|---|---|
bmw/i3_rex_phev/2016 |
2016_BMW_i3_REx_PHEV_Charge_Depleting trained July 2024 | kwh | rf_charge_depleting_b80965c8, rf_charge_depleting_c3326385, rf_charge_depleting_db8522fb |
chevrolet/volt_phev/2016 |
2016_CHEVROLET_Volt_Charge_Depleting trained July 2024 | kwh | rf_charge_depleting_b80965c8, rf_charge_depleting_c3326385, rf_charge_depleting_db8522fb |
ford/c-max_phev/2016 |
2016_FORD_C-MAX_(PHEV)_Charge_Depleting trained July 2024 | kwh | rf_charge_depleting_b80965c8, rf_charge_depleting_c3326385, rf_charge_depleting_db8522fb |
hyundai/sonata_phev/2016 |
2016_HYUNDAI_Sonata_PHEV_Charge_Depleting trained July 2024 | kwh | rf_charge_depleting_b80965c8, rf_charge_depleting_c3326385, rf_charge_depleting_db8522fb |
toyota/prius_prime_phev/2017 |
2017_Prius_Prime_Charge_Depleting trained July 2024 | kwh | rf_charge_depleting_b80965c8, rf_charge_depleting_c3326385, rf_charge_depleting_db8522fb |
PHEV_HEV_MODE — 5 vehicles, 15 models
| Path prefix | Description | Target | Configs |
|---|---|---|---|
bmw/i3_rex_phev/2016 |
2016_BMW_i3_REx_PHEV_Charge_Sustaining trained July 2024 | gge | rf_charge_sustaining_b80965c8, rf_charge_sustaining_c3326385, rf_charge_sustaining_db8522fb |
chevrolet/volt_phev/2016 |
2016_CHEVROLET_Volt_Charge_Sustaining trained July 2024 | gge | rf_charge_sustaining_b80965c8, rf_charge_sustaining_c3326385, rf_charge_sustaining_db8522fb |
ford/c-max_phev/2016 |
2016_FORD_C-MAX_(PHEV)_Charge_Sustaining trained July 2024 | gge | rf_charge_sustaining_b80965c8, rf_charge_sustaining_c3326385, rf_charge_sustaining_db8522fb |
hyundai/sonata_phev/2016 |
2016_HYUNDAI_Sonata_PHEV_Charge_Sustaining trained July 2024 | gge | rf_charge_sustaining_b80965c8, rf_charge_sustaining_c3326385, rf_charge_sustaining_db8522fb |
toyota/prius_prime_phev/2017 |
2017_Prius_Prime_Charge_Sustaining trained July 2024 | gge | rf_charge_sustaining_b80965c8, rf_charge_sustaining_c3326385, rf_charge_sustaining_db8522fb |
HEAVY_DUTY — 8 vehicles, 48 models
| Path prefix | Description | Target | Configs |
|---|---|---|---|
generic_heavy_duty/class_8_daycab_300kw_heavy_duty/2000-2010 |
Daycab_old_300kW | gde | rf_0ae6c8e2, rf_4bf282c6, rf_a1728df5, rf_b80965c8, rf_c3326385, rf_db8522fb |
generic_heavy_duty/class_8_daycab_300kw_heavy_duty/2010-2020 |
Daycab_new_300kW | gde | rf_0ae6c8e2, rf_4bf282c6, rf_a1728df5, rf_b80965c8, rf_c3326385, rf_db8522fb |
generic_heavy_duty/class_8_daycab_400kw_heavy_duty/2000-2010 |
Daycab_old_400kW | gde | rf_0ae6c8e2, rf_4bf282c6, rf_a1728df5, rf_b80965c8, rf_c3326385, rf_db8522fb |
generic_heavy_duty/class_8_daycab_400kw_heavy_duty/2010-2020 |
Daycab_new_400kW | gde | rf_0ae6c8e2, rf_4bf282c6, rf_a1728df5, rf_b80965c8, rf_c3326385, rf_db8522fb |
generic_heavy_duty/class_8_sleeper_300kw_heavy_duty/2000-2010 |
Sleeper_old_300kW | gde | rf_0ae6c8e2, rf_4bf282c6, rf_a1728df5, rf_b80965c8, rf_c3326385, rf_db8522fb |
generic_heavy_duty/class_8_sleeper_300kw_heavy_duty/2010-2020 |
Sleeper_new_300kW | gde | rf_0ae6c8e2, rf_4bf282c6, rf_a1728df5, rf_b80965c8, rf_c3326385, rf_db8522fb |
generic_heavy_duty/class_8_sleeper_400kw_heavy_duty/2000-2010 |
Sleeper_old_400kW | gde | rf_0ae6c8e2, rf_4bf282c6, rf_a1728df5, rf_b80965c8, rf_c3326385, rf_db8522fb |
generic_heavy_duty/class_8_sleeper_400kw_heavy_duty/2010-2020 |
Sleeper_new_400kW | gde | rf_0ae6c8e2, rf_4bf282c6, rf_a1728df5, rf_b80965c8, rf_c3326385, rf_db8522fb |
Inputs and outputs
Inputs — one row per road-network link, as a pandas DataFrame:
| Column | Units | Typical range |
|---|---|---|
distance |
miles | — |
speed_mph |
mph | 0 – 120 |
grade_percent |
percent | -20 – 20 |
turn_angle |
degrees | -180 – 180 |
mass_lbs |
pounds | heavy duty only |
ambient_temp_f |
degrees Fahrenheit | thermal models |
Outputs — energy consumed on each link, in the units the vehicle's fuel implies:
| Target | Units | Used by |
|---|---|---|
gge |
gallons gasoline | gasoline ICE, HEV, PHEV (CS) |
gde |
gallons diesel | diesel ICE, heavy duty |
kwh |
kilowatt-hours | BEV, PHEV (CD) |
kg_h2 |
kilograms hydrogen | fuel-cell vehicles |
Most models are trained on an energy rate (energy per mile) and multiply by
distance at predict time; the contract in each model states which.
Real-world adjustment
Predictions are scaled by a powertrain-level factor that corrects laboratory/simulated consumption toward observed real-world consumption:
| Powertrain | Factor |
|---|---|
| ICE | 1.166 |
| HEV | 1.1252 |
| BEV | 1.3958 |
| PHEV (EV) | 1.3958 |
| PHEV (HEV) | 1.1252 |
| Heavy duty | 1.0 |
Set apply_real_world_adjustment=False when training, or divide it back out, if you
want the unadjusted estimate.
Using the ONNX files directly
You do not need the Python package. Every ONNX graph is self-describing: the
positional input/output contract is embedded in metadata_props, so a consumer holding
only the .onnx file can reconstruct the exact column order.
metadata_props key |
Value |
|---|---|
routee_input_columns |
JSON array of {name, units, dtype}, positional input order |
routee_output_columns |
JSON array of {name, units, dtype}, positional output order |
routee_predict_method |
"rate" or "raw" |
routee_distance_column |
name of the distance column |
import onnxruntime as ort, json
from huggingface_hub import hf_hub_download
path = hf_hub_download(
"nreinicke/routee-powertrain-model-library",
"v2/toyota/camry_ice/2016/rf_c3326385/v1/model.onnx",
)
sess = ort.InferenceSession(path)
meta = sess.get_modelmeta().custom_metadata_map
cols = [c["name"] for c in json.loads(meta["routee_input_columns"])] # ['speed_mph', 'grade_percent']
Feed features in that order. Getting the order wrong does not raise — it silently
returns wrong energy. With predict_method == "rate", multiply the output by distance
and apply the real-world factor yourself.
Evaluation
Every model carries its own hold-out test errors under errors in metadata.json
(RMSE, normalized RMSE, weighted relative percent difference, and net error, at both
link and trip aggregation). Across the 280 models that report trip-level errors:
| Powertrain | Models | Median trip wRPD | p90 | Max |
|---|---|---|---|---|
| ICE | 95 | 0.09 | 0.11 | 0.27 |
| HEV | 31 | 0.12 | 0.15 | 0.33 |
| Heavy duty | 48 | 0.12 | 0.16 | 0.19 |
| BEV | 76 | 0.18 | 0.23 | 0.72 |
| PHEV (EV) | 15 | 0.25 | 0.30 | 0.32 |
| PHEV (HEV) | 15 | 0.16 | 0.48 | 0.48 |
| All | 280 | 0.13 | 0.24 | 0.72 |
Weighted RPD is reported as a fraction, so 0.13 ≈ 13% typical trip-level error. Median absolute net error — total predicted energy vs. total actual across the test set — is 1.5% (p90 4.3%), which is the metric that matters for fleet- or corridor-level aggregates. Link-level errors are naturally larger (median wRPD 0.40).
Training data
Models are trained on link-aggregated drive-cycle data: high-frequency GPS or telematics traces map-matched to a road network and aggregated per link, paired with energy consumption that is either vehicle-reported/measured or simulated with a powertrain model such as NLR FASTSim.
You can train your own models on the same footing and publish them into a registry of this shape — see the training example and publishing a model.
Reproducibility
Content identity. Every model carries a
model_digest(sha256:…) computed at train time over its identity, contract, estimator bytes, and training provenance, plus anestimator_sha256over the exact binary. A corrupt binary raises on load. Resolve a digest back to a path withpt.query_available_models(model_digest="sha256:…").Pinning. Set
ROUTEE_HF_REVISIONto a commit sha to freeze the entire library — every model and the index — to an exact state:export ROUTEE_HF_REVISION=<commit-sha>
License
BSD 3-Clause, matching the routee-powertrain package.
Citation
@software{routee_powertrain,
title = {RouteE-Powertrain},
author = {{National Laboratory of the Rockies}},
url = {https://github.com/NatLabRockies/routee-powertrain},
note = {Model library: https://huggingface.co/nreinicke/routee-powertrain-model-library}
}
Links
- 📦 routee-powertrain on GitHub
- 📖 Documentation
- 🐍 PyPI
- 🧭 routee-compass — energy-aware routing engine