EV Map Analyzer
Data-driven EV charging infrastructure planning
Created on 5th January 2026
•
EV Map Analyzer
Data-driven EV charging infrastructure planning
The problem EV Map Analyzer solves
The Problem It Solves
EV charging infrastructure planning today is largely manual, fragmented, and reactive. Decisions are often based on limited data, leading to poorly placed stations, low utilization, grid stress, and unequal access—especially outside major cities.
What People Can Use It For
- Plan optimal EV charging locations using real demand, mobility, and grid data
- Decide charger type and capacity instead of guessing standard setups
- Identify coverage gaps in urban, rural, and highway networks
- Prioritize investments under government schemes and PPPs
- Support policy targets with measurable, data-backed deployment plans
How It Makes Work Easier & Safer
- Replaces manual surveys with automated, scalable analysis
- Reduces financial risk by avoiding underused or overloaded stations
- Prevents grid stress by factoring electrical capacity constraints
- Enables transparent and explainable decisions for audits and approvals
- Improves equitable access by highlighting underserved areas
Challenges we ran into
🚧 Challenges I Ran Into
-
No ML model, but meaningful prediction was still required
Instead of using machine learning, we designed a cost-based, rule-driven scoring algorithm (O(n²)) that evaluates grid cells using demand proxies (population density, proximity to stations, income, and accessibility). This ensured transparency, explainability, and faster iteration without needing large labeled datasets. -
Handling complex spatial interactions on the client side
Calculating polygon intersections, grid overlays, and distance-based penalties in real time caused performance issues. We optimized this by precomputing grid boundaries, reducing redraw cycles, and batching calculations to keep interactions smooth. -
Inconsistent and noisy CSV datasets
Uploaded CSV files often had missing headers, mixed formats, or invalid coordinates. We solved this by adding strict validation, schema checks, and fallback defaults during parsing with PapaParse. -
Balancing accuracy vs responsiveness
High-resolution grids improved insights but slowed rendering. We introduced configurable grid sizes so users can trade off precision for performance depending on their use case. -
Map state persistence across interactions
Redrawing polygons or reloading data would reset analysis state. This was resolved by client-side caching using localStorage, ensuring a seamless analysis workflow.
Key takeaway:
Even without ML, a transparent cost-based algorithm combined with geospatial analysis can deliver practical, explainable decision support for EV infrastructure planning.
Technologies used