Electric Vehicle Mobility Usage

By shrijeetverma13 · May 11, 2026

This is a synthetically generated dataset simulating real-world electric vehicle (EV) usage patterns across 7 major Indian cities from 2019 to…

The analysis reveals which cities lead in EV usage based on total distance traveled and energy consumed. A grouped bar chart has been generated comparing the top 10 cities side by side, making it easy to spot the leaders in both metrics. Data tables are also available with the detailed figures for each city.

The analysis compared 10,300 EVs across three vehicle types — Four-Wheelers, Three-Wheelers, and Two-Wheelers — on energy consumption, estimated range, and charging frequency. Two charts and summary tables were generated to visualize these differences. Energy consumption is nearly identical across all three types (~4.30–4.37 kWh), but range and charging behavior differ significantly. Four-Wheelers have by far the longest range (224.91 km), while Two-Wheelers charge most frequently (3.33 times/week).

Two line charts and supporting data tables have been generated showing how electricity costs and charging durations have trended month by month from 2019 to 2026. Overall, both metrics fluctuate across months but show no strong long-term directional trend, suggesting relatively stable pricing and charging behavior over the years.

The analysis successfully explored how user income level influences EV charging type, frequency, and electricity spending. Two interactive visualizations and three data tables were generated to break down these relationships clearly.

Three visualizations and supporting data tables were generated to show how weather conditions and traffic density affect distance traveled and energy consumption. Overall, weather has a modest effect on distance (clear skies yield the longest average trips at 26.56 km vs. 26.14 km in extreme heat), while rainy conditions drive the highest average energy use at 4.37 kWh. Traffic density has a more notable impact: trips in high-density traffic (0.6–0.8) are longest on average (27.39 km) but also most energy-intensive (4.41 kWh), while very high traffic (0.8–1.0) sees the shortest trips at 23.01 km. Interestingly, traffic density alone has virtually no linear correlation with distance (−0.001) or energy (0.002), but distance and energy are strongly correlated (0.825), meaning longer trips naturally consume more energy regardless of conditions.

The analysis successfully identified which factors most correlate with range anxiety risk. Two visualizations were generated: a horizontal bar chart showing Pearson correlations of all factors with range anxiety risk, and a grouped bar chart comparing average factor values between anxious (1) and non-anxious (0) drivers. Supporting data tables provide detailed breakdowns of each metric.

The analysis covers yearly average energy consumption, distance traveled, and energy efficiency (km/kWh) for three vehicle types — Four-Wheeler, Three-Wheeler, and Two-Wheeler — from 2019 to 2026. Three line charts were generated showing trends in energy consumption, distance, and efficiency over time. A data table summarizes yearly averages and efficiency improvement by vehicle type. Four-Wheelers showed the most improvement, gaining 3.76% efficiency (from 6.044 to 6.271 km/kWh). Three-Wheelers remained nearly flat (-0.41%), while Two-Wheelers actually declined by 4.68% in efficiency over the same period.

The correlation analysis for your key EV numeric metrics has been completed and visualized in two charts. A heatmap shows the full correlation matrix across all 9 variables (battery capacity kwh, battery health pct, distance km, charging duration min, energy consumed kwh, electricity cost per kwh, traffic density, range km estimated, and charging station distance km), with annotated coefficients color-coded from red (negative) to blue (positive). A second bar chart highlights the top 5 strongest positive and top 5 strongest negative correlations side by side for quick comparison.

Using the IQR method on battery health pct, the analysis identified very few outliers in the dataset. With Q1=83.0%, Q3=95.7%, and IQR=12.7, the valid range spans from 63.96% to 114.74%. Only 5 records (0.05% of 10,611 total) fall outside these bounds, indicating that battery health values are remarkably consistent across the dataset. Two box plots were generated comparing outlier vs non-outlier groups for battery capacity (kWh) and charging frequency per week, allowing visual comparison of these dimensions between the two groups.

The analysis examined 9 income-charging type combinations across high, middle, and low income groups and three charging types (home, public fast, public slow). Two grouped bar charts and detailed data tables were generated showing key metrics for each combination. The most cost-efficient combination is high-income users using public slow charging, with a cost-efficiency score of 24.994 (avg $7.762/kWh × 3.22 charges/week), and the lowest range anxiety risk at 22.24%. Public fast charging is consistently the quickest (around 60 min) but costs slightly more per week due to higher frequency, while home charging takes the longest (~300 min) but has comparable costs across income groups.