---
license: cc-by-4.0
tags:
- electric-vehicles
- telemetry
- data-engineering
- medallion-architecture
- databricks
- data-quality
- olap
size_categories:
- 100K<n<1M
configs:
- config_name: messy_stream_2024
  data_files: 1_car_telemetry_2024.csv
- config_name: charging_sessions
  data_files: 2_charging_sessions.csv
- config_name: grid_carbon
  data_files: 3_grid_carbon_hourly.csv
- config_name: archive_rare_user
  data_files: archive_2020_2024_rare_user.csv
- config_name: archive_daily_user
  data_files: archive_2020_2024_daily_user.csv
- config_name: archive_moderate_user
  data_files: archive_2020_2024_moderate_user.csv
- config_name: archive_heavy_user
  data_files: archive_2020_2024_heavy_user.csv
---

# ⚡ EV Fleet Sensor Data — Databricks Medallion Case Study

Raw source CSVs for an end-to-end **Databricks medallion architecture** (Bronze → Silver → Gold) case study:
a deliberately messy EV sensor stream is cleaned, quality-gated, and distilled into decision-grade insights —
battery health ranking, DTC risk, charger siting, and a clean-charging window.

- **Question data:** `1_car_telemetry_2024.csv` — 35,363 rows of 2024 telemetry with **planted faults**
- **Answer key:** the four `archive_2020_2024_*.csv` files — a clean ~175K-row sensor archive (2020–2024)
- The messy 2024 stream was **derived from the archive** (faults planted, seed = 7), so every fault is verifiable.
  The two sources intentionally overlap in 2024 (stream = question, archive = ground truth).

## Dataset Structure

### 🔴 Feed 1 — Car telemetry (the messy stream)

**`1_car_telemetry_2024.csv`** — hourly readings, 4 vehicles, calendar year 2024.

| Column | Type | Description |
|---|---|---|
| `timestamp` | datetime | Hour of reading |
| `vehicle_id` | string | `EV-101` … `EV-104` |
| `user_profile` | string | `rare_user` / `daily_user` / `moderate_user` / `heavy_user` |
| `district` | string | Home district: University, Downtown, Harbor, Industrial |
| `SOC` | float | State of charge, % |
| `SOH` | float | State of health (battery), % |
| `Charging_Cycles` | int | Lifetime charge cycles |
| `charging_now` | int | 0/1 — currently charging |
| `Battery_Temp` | float | Battery temperature, °C |
| `Motor_RPM` | int | Motor speed |
| `Motor_Torque` | float | Motor torque, Nm |
| `Motor_Temp` | float | Motor temperature, °C |
| `Brake_Pad_Wear` | float | Brake pad wear, % |
| `Charging_Voltage` | float | Charging voltage, V |
| `Tire_Pressure` | float | Tire pressure — ⚠️ planted unit faults (kPa vs PSI) |
| `DTC` | int | 1 = diagnostic trouble code active |
| `grid_carbon_g_per_kwh` | float | Grid carbon intensity at that hour |

**Planted fault census:** exact duplicates (419, to be dropped), kPa→PSI unit errors (fix),
out-of-bounds values (fix + flag), missing readings (flag). Silver-layer policy:
*only duplicates die — the row always survives; the faulty value is nulled.*

### 🔌 Feed 2 — Charging sessions

**`2_charging_sessions.csv`** — 484 completed sessions.

| Column | Description |
|---|---|
| `session_id`, `station_id` | Identifiers |
| `district` | Where the session happened (drives charger-siting analysis) |
| `start_time`, `end_time` | Session window |
| `energy_kwh_source_analog`, `power_kw_analog` | Energy delivered / power draw |
| `soc_start_pct`, `soc_end_pct` | Battery % before → after |
| `battery_kwh`, `vehicle_model` | Vehicle pack size and model |
| `station_temp_c` | Station temperature |
| `status` | Session status |

### ⚡ Feed 3 — Grid carbon intensity

**`3_grid_carbon_hourly.csv`** — 168 rows (7 days × 24 h): `date`, `hour`, `carbon_g_per_kwh`.
Midnight peaks near ~911 gCO₂/kWh; midday (~12:00) drops to ~319.

### 🟢 The clean archive (ground truth)

Four files, one per user profile, each with **43,800 rows** (24 h × 365 d × 5 yrs, 2020–2024):

`archive_2020_2024_{rare,daily,moderate,heavy}_user.csv` — same sensor columns as the stream
(leading index column, no timestamps/district).

| File | Vehicle | Profile |
|---|---|---|
| `archive_2020_2024_rare_user.csv` | Renault Zoe (52 kWh) | EV-101 |
| `archive_2020_2024_daily_user.csv` | Hyundai Ioniq 5 (77 kWh) | EV-103 |
| `archive_2020_2024_moderate_user.csv` | VW ID.4 (82 kWh) | EV-102 |
| `archive_2020_2024_heavy_user.csv` | Ford F-150 Lightning (131 kWh) | EV-104 |

## Example decisions this data supports

Transparent, verifiable arithmetic (no black-box model):

1. **Risk score** = `soh_decay_pct_per_year × 20 + dtc_rate_pct × 10`, thresholds 60 (act) / 30 (watch)
   → EV-102 scores **54.1** (decay 1.958 %/yr + 654 DTC events) → service first
2. **Charger siting** = `total_co2_g ÷ vehicle_days`
   → Industrial district worst at **43,311 gCO₂/vehicle-day** across 86 sessions → site the next charger there
3. **Charging window** = cleanest vs dirtiest hour
   → shift charging to **~12:00**: ~319 vs ~911 gCO₂/kWh (2.86× cleaner, ~592 g saved per kWh shifted)

## How it was used

The files are ingested as-is into a Databricks Free Edition lakehouse:

```
Bronze (raw, immutable)  →  Silver (cleaned, quality gate: 35,363 − 419 dupes = 34,944 rows)
                         →  Gold (6 decision tables: soh_trend, dtc_risk_rank, co2_per_vehicle_day,
                                  grid_carbon_by_district_hour, fleet_worry_list, final_decision)
```

## Usage

```python
from huggingface_hub import hf_hub_download
import pandas as pd

path = hf_hub_download(repo_id="jeyanthiii/sample_ev_data_csv",
                       filename="1_car_telemetry_2024.csv",
                       repo_type="dataset")
df = pd.read_csv(path)
```

## Notes

- Faults are **synthetic and intentional** (teaching/demo dataset); the archive is the reference truth.
- 2024 rows exist in both stream and archive **by design** — that overlap is what makes the quality gate provable.
- Local backup of this folder is kept at `~/Documents/work/temp/cl_demo/data/`.

## License

[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) — free to use with attribution.