Heavy truck drive cycle: the stretch the battery sizing app shows, and the frozen road-load model

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README.md

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+---
+license: cc-by-4.0
+language:
+  - en
+pretty_name: Heavy truck drive cycle
+tags:
+  - time-series
+  - automotive
+  - electric-vehicles
+  - energy
+  - tabular
+size_categories:
+  - 1K<n<10K
+configs:
+  - config_name: default
+    data_files: data.csv
+---
+
+# 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](https://huggingface.co/spaces/prteek/electric-truck-battery-sizing), 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](https://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.
+
+```python
+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")))
+```

road_load.json

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+{
+ "formula": "road_load_power_w = rolling * mass_kg * g * cos(grade) * speed_mps + aero * speed_mps**3",
+ "rolling": 0.011932077183171142,
+ "aero_kg_per_m": 2.7376895505031347,
+ "g_mps2": 9.8,
+ "grade": "road_grade_pct / 100",
+ "fitted_on": "the whole 428 km log the published stretch came from, by least squares (no intercept) on the moments the truck was driving rather than braking; R^2 0.74 on a held-out 30%"
+}