Electric Vehicle Charging Dataset

Charging Session Metrics

• Charging Duration & Timing:
  The dataset logs the start and end time for each charging session, allowing for the calculation of the total charging duration. Analysis can explore patterns based on time-of-day and day-of-week trends, such as peak charging periods or weekend preferences.
  • Energy Consumption (kWh):
    Detailed records of energy consumed during each charging session provide a clear picture of vehicle energy usage over time, essential for understanding the energy demands of different EV models.

Environmental and Vehicle Data

• Ambient Temperature:
  The temperature during the charging session is recorded to assess environmental factors on charging efficiency and energy consumption.
  • Vehicle Age & Distance Driven:
    Vehicle age and distance traveled since the last charge provide insight into how wear and driving patterns affect charging behavior over time.

Advanced Behavioral Insights

• Charger Type:
  Data includes the type of charger used (e.g., Level 1, Level 2, or DC Fast Charger), enabling analysis of which charger types are preferred and how they impact the duration and cost of charging sessions.
  • User Classification:
    Users are classified based on driving habits, such as commuters or long-distance travelers, providing further segmentation for analysis of EV usage.

New Features and Predictive Applications

• Time-of-Use Pricing and Rebates:
  This dataset can be expanded with new features like time-of-use pricing, EV-to-grid interaction, and user incentives such as rebates, which can enhance predictive modeling of EV charging trends.
• Predictive Modeling Potential:
  By incorporating these data points, the dataset becomes a robust tool for researchers, city planners, and energy companies to predict charging station demand, optimize charging infrastructure, and develop smart grid solutions.

Conclusion

The Electric Vehicle Charging Behavior Dataset is a rich resource for those looking to analyze EV charging trends, user behavior, and energy consumption. With applications ranging from infrastructure planning to predictive modeling, this dataset offers significant value to the EV industry, researchers, and energy companies alike.