Tesfato/Electric-Vehicle-Specs-Dataset-2025

⚡ Electric Vehicle Specs Dataset 2025

This dataset provides a comprehensive collection of specifications and performance metrics for modern electric vehicles (EVs), scraped from EV-Database.org.

It supports use in:

• —🔍 Data science
• —🧠 Machine learning
• —📈 Market analysis
• —♻️ Sustainability studies
• —🚗 EV adoption research

📊 Core Attributes

Each row represents a specific EV model with attributes across:

🏷 Brand & Classification

• —Brand & Model: Manufacturer and nameplate
• —Car Body Type: Hatchback, SUV, sedan, etc.
• —Segment: Compact, midsize, executive, etc.

🔋 Battery & Range

• —Battery Capacity (kWh): Gross energy capacity
• —Battery Type & Cell Count
• —Efficiency (Wh/km): Power usage
• —Range (km): Estimated full-charge range

⚡ Charging Specs

• —Fast Charging Power (kW)
• —Port Type: CCS, CHAdeMO, etc.

🚀 Performance

• —Top Speed (km/h)
• —0–100 km/h Acceleration (s)
• —Torque (Nm)

🧳 Practical Specs

• —Towing Capacity (kg)
• —Cargo Volume (L)
• —Seats

📐 Dimensions

• —Length, Width, Height (mm)

⚙️ Technical Info

• —Drivetrain: AWD, RWD, FWD
• —Source URL for reference (scraping origin)

🧼 Data Quality & Cleaning Notes

• —All numeric fields cleaned and converted to proper formats (int, float)
• —Some missing values in technical specs (like towing capacity, battery cell count)

💡 Use Cases

• —EV trend analysis & visualization
• —Vehicle comparison dashboards
• —Feature engineering for ML models
• —Price/power prediction tasks
• —Clean energy and EV adoption research

📄 Source

• —Scraped from: https://ev-database.org
• —Creator: Urvish Ahir

📜 License

Released under CC BY 4.0 — credit the source when using.

📊 Also Available on Kaggle


👍 If you found this dataset useful or interesting, please consider dropping an upvote on Kaggle — it really helps! ⚡

👤 My Kaggle Profile: Urvish Ahir

🧪 Quick Usage in Python

from datasets import load_dataset

ds = load_dataset("urvishahir/Electric-Vehicle-Specs-Dataset-2025")
df = ds["train"].to_pandas()
df.head()