This dataset provides detailed information about electric vehicle (EV) charging behaviour, including vehicle-specific details, charging session data, and environmental factors. It is designed to help researchers and data analysts study EV usage patterns, charging efficiency, cost optimization, and the impact of external variables on EV charging.

Dataset Features:

• Vehicle Model: The make and model of the electric vehicle (e.g., "Hyundai Kona," "BMW i3").
• Battery Capacity (kWh): Total battery capacity of the vehicle in kilowatt-hours (kWh).
• Charging Station Location: City or area where the charging session took place (e.g., "Houston," "Los Angeles").
• Charging Start Time: Timestamp indicating the start of the charging session.
• Charging End Time: Timestamp indicating the end of the charging session.
• Energy Consumed (kWh): The amount of energy consumed during the session, measured in kilowatt-hours.
• Charging Duration (hours): Total time spent charging during the session, measured in hours.
• Charging Rate (kW): Average charging rate during the session, measured in kilowatts.
• Charging Cost (USD): The total cost of the charging session in U.S. dollars.
• Time of Day: The time period during which the charging occurred (e.g., "Morning," "Evening").
• Day of Week: The day of the week when the charging session took place (e.g., "Tuesday," "Wednesday").
• State of Charge (Start %): Battery charge percentage at the start of the charging session.
• State of Charge (End %): Battery charge percentage at the end of the charging session.
• Distance Driven (since last charge) (km): Distance driven by the vehicle since the previous charging session, measured in kilometres.
• Temperature (°C): Ambient temperature during the charging session, measured in degrees Celsius.
• Vehicle Age (years): The age of the electric vehicle in years.
• Charger Type: Type of charging station used (e.g., "Level 1," "Level 2," "DC Fast Charger").
• User Type: The driving profile of the vehicle user (e.g., "Casual Driver," "Commuter," "Long-Distance Traveler").

Distribution:

Usage:

This dataset is ideal for applications such as:

• EV Charging Analytics: Analyzing charging behaviour, session costs, and efficiency based on various factors like vehicle type and charger type.
• Predictive Modeling: Developing models to predict charging costs, energy consumption, or optimal charging times based on external conditions.
• Sustainability Studies: Exploring patterns in energy usage and environmental factors to enhance sustainability in electric vehicle infrastructure.
• Consumer Behavior Research: Understanding different user types and their charging preferences.

Coverage:

This dataset is synthetic and anonymized, providing a safe and accessible resource for experimentation and learning without exposing real-world data.

License:

CC0 (Public Domain)

Who Can Use It:

• Researchers and Educators: For studying and teaching EV-related data analysis and charging infrastructure optimization.
• Data Science Enthusiasts: To practice data manipulation, visualization, and predictive modelling in the EV domain.
• Energy Sector Professionals: For analyzing charging behaviour and improving infrastructure to meet user needs.