Datasets:

jeyanthiii
/

sample_ev_data_csv

---
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.

	---
	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.