PlugPal

Your AI copilot for EV charging infrastructure

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Inspiration

As EV adoption continues to rise, the gap between demand and infrastructure is becoming harder to ignore. Chargers are often installed based on outdated assumptions, political preferences, or incomplete data. The result is underused stations, missed funding opportunities, and slowed adoption—especially in dense urban areas.

PlugPal was built to close that gap: a tool that brings speed, data, and clarity to charger siting, one of the most overlooked but essential parts of electrification.

What it does

PlugPal is a geospatial intelligence tool that helps cities and developers identify optimal EV charging locations.

The MVP combines infrastructure, amenity, and accessibility data from five major cities leading in EV adoption (Toronto, NYC, London, Oslo, and Shanghai) and lets users:

  • Adjustable sliders to prioritize variables (e.g., nearby amenities, highway traffic, proximity to residents)
  • Dynamic map-based interface with multiple data layers: grid, roads, amenities, population, land use, and more
  • Generate a ranked list of top-performing charger locations per metropolitan area
  • Access a Site Analysis dashboard for each site, including planning notes and forward-looking readiness metrics (EV adoption growth, grid modernization, policy support)

How I built it

  • Vite + React + TypeScript for core app framework
  • Tailwind CSS for styling
  • Radix UI for accessible components
  • React Router for navigation
  • React Hook Form + Zod for form and slider input validation
  • Recharts for metric visualizations
  • Lucide-react + Sonner + Vaul + CMDK for icons, notifications, modals, and command menus
  • @tanstack/react-query for async state and data fetching
  • Preprocessed GeoJSON and CSV overlays for city infrastructure and road networks data

Challenges I ran into

  • Designing a scoring model that balances user-defined preferences with real-world infrastructure constraints
  • Optimizing map performance when rendering multi-layer overlays across large metropolitan areas
  • Creating a dashboard experience that communicates complex geospatial insights to both technical and non-technical users

Accomplishments that I’m proud of

  • Built a fully functional, interactive MVP supporting five international cities
  • Implemented a scalable site scoring system using structured geospatial overlays
  • Designed a modular Site Analysis dashboard structure that can expand with future features like zoning, construction feasibility, cost overlays, and more
  • Delivered a user experience that makes complex infrastructure data usable for planners, developers, and city decision-makers

What I learned

  • The importance of designing tools that communicate why a location is optimal—not just where
  • How to structure scoring logic so it's transparent, tunable, and adaptable to new criteria
  • That infrastructure tools need to balance performance, usability, and domain flexibility to truly support public and private planning workflows

What’s next for PlugPal

  • Add zoning overlays, construction feasibility filters, and construction constraints (e.g. cost modeling)
  • Build an AI-powered memo/report generator for grant applications and council documentation
  • Expand dataset coverage to support more cities
  • Launch as a SaaS platform with tiered access for cities, consultants, and private developers

By turning data into decisions, PlugPal helps planners move faster and smarter on EV infrastructure.

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