As EV adoption accelerates, charging infrastructure operators face a critical challenge: ensuring stations are profitable, reliable, and optimally utilized.
This project analyzes the operational performance and financial sustainability of an EV charging network using SQL and Python.
Charging network operators often lack visibility into which stations are underperforming, when demand peaks, and where costs erode profitability.
This project answers:
- Which stations generate the most revenue?
- Are chargers being utilized efficiently?
- Which stations operate at a profit or loss?
- When does charging demand peak?
- How does EV adoption influence charging revenue?
- Charging demand peaks during evening hours, reflecting residential charging behavior
- Fast chargers generate significantly higher revenue than standard chargers
- Some stations operate at negative gross margins due to high electricity costs and low utilization
- Cities with higher EV adoption show stronger charging revenue, though infrastructure distribution varies
- Python — Pandas, data cleaning and preprocessing
- SQL (MySQL) — Schema design, KPI computation, operational analytics
8 relational tables: stations, charging_sessions, charger_units,
electricity_costs, maintenance_logs, city_demo, tariff_plan, customers
Revenue: Revenue per station, per charger, per session — by city and charger type
Demand: Peak hour charging patterns, sessions per charger
Efficiency: Charger utilization rate, downtime percentage
Profitability: Electricity cost, maintenance cost, and gross margin per station
Strategic: Tariff pricing efficiency, EV adoption vs charging revenue correlation
Raw Data → Python Data Cleaning → SQL Data Modeling → KPI Calculation → Insights
- Interactive Power BI dashboard for operational monitoring
- Predictive analytics for demand forecasting
- Customer behavior analysis