prteek/drive-cycle

Heavy truck drive cycle

3,831 seconds of driving logged once a second from a diesel heavy truck (15 to 32 tonnes): 58 km over about 14 hours, with three stops of half an hour or more in between. It is the data behind the electric truck battery sizing app, which replays it as if the truck were electric to see what battery and charger it would need. Code: github.com/prteek/real-world-vehicle-simulation.

Rows are logged only while the truck is running: a jump in time_s larger than a second is a stop.

column	content
time_s	seconds since the start of the log (gaps are stops)
speed_kph	vehicle speed [km/h]; 255 means no reading
engine_torque_nm	net engine torque [N·m]; negative when the engine is driven
overall_gear_ratio	overall torque ratio from the engine to the transmission output
transmission_output_speed_rpm	transmission output shaft speed [rpm]
accelerator_pedal_pct	accelerator pedal position [%]
vehicle_mass_kg	estimated vehicle mass [kg]
road_grade_pct	road grade [%], averaged

Power at the wheels is engine torque × overall gear ratio × transmission output speed. Brake power is not logged; the code estimates it as what is left after road load, grade and inertia whenever the driver is off the accelerator and slowing down.

Road load (road_load.json)

The road-load model, frozen: power [W] = rolling × mass × g × cos(grade) × speed + aero × speed³ (speed in m/s, grade = road_grade_pct / 100). It was fitted by least squares on the whole 428 km log this stretch came from, on the moments the truck was driving rather than braking (R² 0.74 on a held-out 30%); the stretch alone is too uniform in speed to separate the two terms.

import json, pandas as pd
from huggingface_hub import hf_hub_download

log = pd.read_csv(hf_hub_download("prteek/drive-cycle", "data.csv", repo_type="dataset"))
road_load = json.load(open(hf_hub_download("prteek/drive-cycle", "road_load.json", repo_type="dataset")))