Goal:
Help utilities, municipalities, and developers design EV charging infrastructure that meets future demand, maximizes utilization, and balances grid load — ensuring ROI and sustainability.
Impact Keywords:
Approach:
LunarTech Lab built an AI-powered planning and operations suite to forecast EV charging needs, optimize station placement, and manage real-time load balance.
Mobility & Demand Modeling:
Integrated GPS and traffic data, demographic density, and EV ownership projections to map high-demand charging zones.
Charger Placement Optimization:
Applied geospatial ML algorithms to identify optimal station locations minimizing both user travel time and grid strain.
Dynamic Pricing Engine:
Designed an AI-driven pricing model that adjusts tariffs based on demand, time of day, and grid load, maximizing usage and ROI.
Grid Load Simulation:
Simulated load impact across substations using predictive models to avoid hotspots and optimize renewable usage.
Regulatory & Emission Reporting:
Automated CO₂ and sustainability reports aligned with government EV adoption and clean-mobility policies.
Summary:
By combining behavioral mobility data, demand forecasting, and grid intelligence, LunarTech Lab’s system helps governments and utilities accelerate EV adoption — while keeping infrastructure efficient, profitable, and sustainable.
