EduDevCommons/Global-EV-Charging-Station-Telemetry

Global EV Charging Station Telemetry Dataset

Overview

This dataset provides a meticulously curated, end-to-end historical record of electric vehicle (EV) charging station telemetry across major global metropolitan areas. Unlike standard static station directories, this resource integrates simulated IoT hardware events with advanced power consumption metrics, duration tracking, and connector performance indicators, offering a 360-degree view of urban charging ecosystem dynamics.

Cities & Charging Hubs Covered

The dataset aggregates multi-node activity across major EV adoption markets:

• Amsterdam
• Oslo
• Berlin
• Tokyo
• London
• Stockholm
• Dubai

Data Sources & Methodology

• Telemetry Simulation Engine: Built using robust statistical modeling in Python (numpy and pandas) to emulate realistic, continuous time-series grid loads and vehicle power draws.
• Power Rating Augmentation: Incorporates standard commercial charger thresholds spanning standard Level 2 alternating current (AC) up to ultra-fast direct current (DC) high-power charging poles.
• Timestamp Architecture: Spans continuous multi-year historical logs to allow for macro-trend analysis and diurnal load-shifting research.

Timeframe

• Start Date: Historical rolling window (1 year back from current generation).
• End Date: Present.

Feature Breakdown

The dataset contains structured, clean attributes designed for analytical and machine learning workloads:

• Timestamp: Exact date and time of the charging session initiation.
• Station ID: Unique alphanumeric identifier assigned to each charging pile (ST-XXX).
• City: Metropolitan region hosting the charging station.
• Power Output (kW): Maximum charging capacity rating of the station port (e.g., 22.0, 50.0, 150.0, 350.0 kW).
• Session Duration (mins): Total active connection duration of the vehicle.
• Connector Standard: Protocol type utilized (CCS, CHAdeMO, Type2, Tesla Supercharger).
• Energy Consumed (kWh): Total delivered electrical capacity computed across the session duration.

Files Included in the Package

• Compressed Archive: ev_charging_telemetry_data.zip – Contains the master raw CSV file optimized for seamless Hugging Face data viewer integration.
• Raw CSV File: ev_charging_telemetry.csv – Uncompressed flat-file storage for direct localized ingestion.

Potential Use Cases

• Smart Grid Forecasting: Predict peak power loads and localized grid strain based on session times and city-specific distribution patterns.
• IoT Analytics: Analyze charger utilization rates, average dwell times, and hardware efficiency across different connector standards.
• Machine Learning: Train predictive models to forecast energy consumption requirements or classify session behaviors using temporal features.
• Urban Infrastructure Planning: Evaluate optimal geographical placement and power tier scaling for future EV charging stations.

License & Usage

This dataset is published under the Apache 2.0 license for educational, academic, and personal portfolio research purposes.