electricsheepafrica/nigerian_transport_and_logistics_ev_charging

# Nigeria Transport & Logistics – EV Charging Data

EV charging sessions: power, energy, SOC, temperature and battery health.

- **[category]** Emerging & Advanced
- **[rows]** ~160,000
- **[formats]** CSV + Parquet (snappy)
- **[geography]** Nigeria (major cities, corridors, ports, airports)

## Schema

| column | dtype |

|---|---| | session_id | object | | vehicle_id | object | | station_id | object | | start_time | object | | end_time | object | | power_kw | float64 | | energy_kwh | float64 | | soc_start | float64 | | soc_end | float64 | | ambient_temp_c | float64 | | battery_health | float64 |

## Usage

```python
import pandas as pd
df = pd.read_parquet('data/nigerian_transport_and_logistics_ev_charging/nigerian_transport_and_logistics_ev_charging.parquet')
df.head()
```

```python
from datasets import load_dataset
ds = load_dataset('electricsheepafrica/nigerian_transport_and_logistics_ev_charging')
ds
```

## Notes

- Nigeria-specific parameters (fleets, roads, traffic, fuel prices)
- Time-of-day traffic effects and seasonal impacts where applicable
- Physical plausibility checks embedded during generation