🔋 Electric Vehicle Usage Dataset — India (2019–2026)

About This Dataset

This is a synthetically generated dataset simulating real-world electric vehicle (EV) usage patterns across 7 major Indian cities from 2019 to 2026. It is designed for machine learning, data analysis, and energy policy research.

🗺️ Coverage

• Cities: Mumbai, Delhi, Bangalore, Chennai, Hyderabad, Pune, Ahmedabad
• Vehicle Types: 2-Wheeler (2W), 3-Wheeler (3W / Auto), 4-Wheeler (4W / Car)
• Date Range: January 2019 – December 2026 (projected/simulated)

🎯 Suggested Use Cases

1. Predict range anxiety risk (binary classification)
2. Forecast daily energy consumption (regression)
3. Cluster EV user behavior by income, city, and vehicle type
4. Analyze charging infrastructure gaps by city

⚠️ Note on Missing Values

328 rows contain null values across all columns and should be dropped before analysis.

📖 Data Dictionary

Column	Type	Unit	Description
user_id	string	—	Unique anonymized user identifier
city	string	—	Indian city of usage
trip_date	date	YYYY-MM-DD	Date of trip
vehicle_type	string	—	2W=Two-Wheeler, 3W=Three-Wheeler, 4W=Four-Wheeler
battery_capacity_kwh	float	kWh	Total battery capacity of the vehicle
battery_health_pct	float	%	Current battery health (0–100)
distance_km	float	km	Distance traveled in the trip
daily_trip_count	float	count	Number of trips made that day
charging_frequency_per_week	float	count	Average weekly charging sessions
charging_type	string	—	home / public_slow / public_fast
charging_duration_min	float	minutes	Time spent charging
charging_station_distance_km	float	km	Distance to nearest charging station
energy_consumed_kwh	float	kWh	Energy consumed in the trip
electricity_cost_per_kwh	float	₹/kWh	Electricity tariff at charging point
weather_condition	string	—	clear / rainy / humid / extreme_heat
traffic_density	float	0–1	Normalized traffic density score
user_income_level	string	—	low / middle / high
range_km_estimated	float	km	Estimated remaining range
range_anxiety_risk	binary	0/1	1 = High risk of range anxiety

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A dataset containing over 1,320 EV charging session records with details on energy consumption, user behavior, and vehicle attributes for AI and mobility analytics.

Gain a complete view of electric vehicle charging infrastructure across the US and Canada. This dataset provides accurate, geocoded location data for public and private EV charging stations, enabling mobility planners, energy companies, and infrastructure developers to analyze network distribution, identify coverage gaps, and plan strategic expansion.

Dataset Coverage -EV charging stations across the US and Canada -Public and private charging locations -Precise latitude & longitude coordinates -Address, city, state/province, postal code -Network/operator details (where available) -Charger type (Level 1, Level 2, DC Fast, etc.) -Open/operational status indicators -Optional building footprints and custom polygon boundaries -Built for EV & Mobility Teams

Use this dataset to: -Evaluate charging infrastructure density by region -Identify underserved corridors and high-demand zones -Support EV route planning and coverage analysis -Benchmark competing charging networks -Inform infrastructure deployment strategy -Enhance mobility, mapping, and fleet optimization platforms

Data Quality & Customization -High-accuracy geocoded locations -Custom metadata and attribute enrichment available -On-demand dataset creation based on region or network -Flexible delivery formats for GIS and analytics platforms -This EV charging station POI dataset supports data-driven decisions across mobility, energy, urban planning, and automotive ecosystems.

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Palo Alto EV Charging Station Usage Open Data provides comprehensive insights into the usage patterns of electric vehicle (EV) charging stations in the city of Palo Alto, California. This open dataset offers valuable information about the availability, utilization, and demand for EV charging infrastructure, allowing researchers, analysts, and businesses to gain a deeper understanding of the local EV market and sustainable transportation practices.

The data includes details on charging station locations, types of charging connectors available, charging session durations, energy consumption, and other relevant metrics. This information enables users to analyze trends, identify peak usage times, assess charging infrastructure performance, and develop data-driven solutions to enhance the efficiency and accessibility of EV charging services.

Researchers can leverage this dataset to study charging behavior, develop predictive models, and propose strategies for optimized charging station placement. Businesses can use the data to identify opportunities for expanding charging infrastructure and improving user experiences.

Overall, Palo Alto EV Charging Station Usage Open Data supports sustainable mobility initiatives by fostering a data-driven approach to EV infrastructure planning and management. It empowers stakeholders to make informed decisions, enhance the EV charging ecosystem, and contribute to the wider adoption of electric vehicles for a greener and more sustainable future.

EV Charging Occupancy Prediction Models Market Outlook

According to our latest research, the global EV Charging Occupancy Prediction Models market size reached USD 786 million in 2024, demonstrating robust momentum across key regions. The market is expected to expand at a CAGR of 22.4% from 2025 to 2033, culminating in a projected value of USD 4,072 million by 2033. This significant growth is driven by the rapid adoption of electric vehicles, the proliferation of smart charging infrastructure, and increasing investments in artificial intelligence and machine learning technologies to optimize EV charging station utilization and reduce waiting times.

The primary growth factor fueling the EV Charging Occupancy Prediction Models market is the accelerating transition towards electric mobility worldwide. As governments enforce stricter emission regulations and incentivize electric vehicle adoption, the number of EVs on the road has surged, leading to an exponential increase in demand for efficient charging infrastructure. However, the unpredictability of charging station availability remains a key pain point for EV users. Advanced occupancy prediction models, leveraging machine learning and deep learning, are being deployed to forecast station usage patterns, enabling operators to provide real-time occupancy information and optimize resource allocation. As a result, these models are becoming indispensable for enhancing user experience, reducing congestion, and supporting the scalability of EV charging networks.

Another significant driver is the technological advancement in artificial intelligence, data analytics, and IoT integration within the charging infrastructure ecosystem. The integration of real-time data from charging stations, vehicle telematics, weather conditions, and traffic patterns enables occupancy prediction models to deliver highly accurate forecasts. This technological synergy not only improves the reliability of predictions but also facilitates dynamic pricing, load balancing, and predictive maintenance of charging assets. Moreover, the growing collaboration between automotive OEMs, charging network operators, and AI solution providers is fostering innovation, leading to the development of hybrid and adaptive models that can cater to diverse usage scenarios and station types.

Additionally, the shift towards smart cities and sustainable urban mobility is propelling the demand for EV Charging Occupancy Prediction Models. Urban planners and municipal authorities are increasingly adopting these solutions to manage public charging infrastructure efficiently, reduce urban congestion, and promote green transportation initiatives. The integration of occupancy prediction models into city-wide mobility platforms allows for seamless coordination between public transit, parking, and charging networks. This holistic approach not only enhances the operational efficiency of urban mobility systems but also aligns with broader environmental and energy management goals, further amplifying market growth.

Regionally, Asia Pacific stands out as the fastest-growing market, driven by aggressive EV adoption policies in China, Japan, and South Korea, coupled with significant investments in smart infrastructure. North America and Europe follow closely, with mature EV markets, high digitalization rates, and strong regulatory support for clean mobility. In contrast, regions like Latin America and Middle East & Africa are in the nascent stages of market development but are expected to witness increased adoption as EV penetration rises and smart city projects gain traction. The regional dynamics underscore the importance of localized strategies and partnerships to address unique market needs and regulatory landscapes.

Model Type Analysis

The Model Type segment in the EV Charging Occupancy Prediction Models market encompasses a diverse array of predictive methodologies, including &l

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Global EV Charging Stations + EV Models (2025)

Overview

This dataset brings together information on 242,417 electric vehicle (EV) charging sites in 121 countries along with a companion table of electric vehicle models.

It includes:

• A station-level view of charging infrastructure around the world
• A city-level view that summarizes infrastructure within each city
• Country-level views with both compact and detailed indicators
• A table of EV models with key characteristics and model age as of 2025

The goal is to offer a single, consistent reference for analysis, visualization, forecasting, and research related to e-mobility, infrastructure planning, and EV adoption.

Files

File	Description	Shape (rows × columns)
charging_stations_world.csv	Global EV charging stations — one row per site	242,417 × 11
charging_stations_ml.csv	City-level view of charging infrastructure	58,642 × 17
country_summary.csv	Compact country-level overview	121 × 6
world_summary.csv	Detailed country-level infrastructure profile	121 × 12
ev_models.csv	Electric vehicle models and attributes	128 × 8

Tables and Columns

Charging stations — charging_stations_world.csv

Each row represents a single charging site.

Column	Description
id	Unique site ID (Open Charge Map identifier)
name	Station name
city	City name
state_province	State, province, or region name
country_code	ISO-2 country code (e.g. US, DE, AE)
latitude	Latitude in decimal degrees (WGS84)
longitude	Longitude in decimal degrees (WGS84)
ports	Number of charging connectors at the site
power_kw	Maximum charging power (kW) available at the site
power_class	Power band category (e.g. AC_LOW, AC_HIGH, DC_FAST_(50-149kW), DC_ULTRA_(>=150kW))
is_fast_dc	Boolean flag indicating whether the site offers fast DC charging (typically ≥ 50 kW)

This table is suitable for detailed geographic analysis, mapping, and site-level studies.

City view — charging_stations_ml.csv

Each row represents a city within a given country and state/province, together with indicators describing its charging infrastructure.

Column	Description
country_code	ISO-2 country code
state_province	State, province, or region name
city	City name
latitude	Approximate latitude of the city based on its charging sites
longitude	Approximate longitude of the city based on its charging sites
station_count	Number of charging sites in the city
port_count	Total number of charging connectors in the city
fast_station_count	Number of charging sites in the city that offer fast DC charging
fast_port_count	Total number of connectors at fast-charging sites in the city
fast_station_share	Fraction of sites in the city that are fast-charging (fast_station_count / station_count)
fast_port_share	Fraction of connectors in the city that are fast-charging (fast_port_count / port_count)
max_power_kw	Highest site power (kW) among all stations in the city
median_power_kw	Median site power (kW) for stations in the city
dc_fast_station_count	Number of sites in the city with power class DC_FAST_(50-149kW) or DC_ULTRA_(>=150kW)
dc_ultra_station_count	Number of sites in the city with power class DC_ULTRA_(>=150kW)
has_fast_dc	Indicator (1 or 0) for whether the city has at least one fast DC charging site
has_ultra_dc	Indicator (1 or 0) for whether the city has at least one ultra-fast DC charging site

This table is convenient for city-level comparisons, modeling, and clustering based on infrastructure density and fast-charging availability.

Country overview — country_summary.csv

Each row represents one country, with a concise view of its charging infrastructure.

Column	Description
country_code	ISO-2 country code
country	Country name
station_count	Total number of charging sites in the country
port_count	Total number of charging connectors in the country
fast_station_share	Share of sites in the country that are fast-charging
fast_port_share	Share of connectors in the country that belong to fast-charging sites

This table is suitable for quick charts, choropleth maps, and rankings.

Country infrastructure profile — world_summary.csv

Each row represents one country, with a more detailed description of its charging infrastructure.

Column	Description
country_code	ISO-2 country code
country	Country name
station_count	Total number...

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