MUMBAI, India, Jan. 9 -- Intellectual Property India has published a patent application (202541134293 A) filed by Karpagam Academy Of Higher Education; Karpagam Institute Of Technology; C Sasthi Kumar; and Mohaneswaran S, Coimbatore, Tamil Nadu, on Dec. 31, 2025, for 'predicting the location of electric vehicle charging.'
Inventor(s) include C Sasthi Kumar; and Mohaneswaran S.
The application for the patent was published on Jan. 9, under issue no. 02/2026.
According to the abstract released by the Intellectual Property India: "The rapid expansion of Electric Vehicle (EV) adoption in the United States has created an urgent need for a robust, scalable, and intelligently planned EV charging infrastructure. Traditional station deployment methods often rely on static demographic estimates or limited traffic analyses, leading to uneven network distribution, congestion, and inefficient resource utilization. This project proposes a comprehensive, data-driven methodology for predicting optimal EV charging station locations using advanced machine learning, geospatial analytics, and renewable energy assessment techniques. The system integrates diverse datasets-including EV ownership records, mobility and traffic flow patterns, population density, existing charging infrastructure, land-use characteristics, government incentives, and renewable energy availability-to generate high-resolution spatial predictions of future charging demand. The proposed framework employs machine learning models to analyze temporal trends in EV usage, forecast future growth, and identify emerging high-demand zones. Geospatial techniques such as spatial clustering, heatmap generation, accessibility modeling, and grid proximity analysis enable precise evaluation of site suitability. An optimization engine further refines candidate locations by balancing multiple objectives, including accessibility, installation cost, grid capacity, environmental impact, and potential for renewable energy integration. The system also incorporates a renewable feasibility module that identifies opportunities for solar- or wind-powered charging stations to support sustainable and eco-friendly infrastructure development. A GIS-based visualization dashboard provides actionable insights to policymakers, utility companies, and private developers, enabling informed decision-making in infrastructure planning. Additionally, an automated feedback loop continuously updates model parameters using real-time charger usage data and evolving EV adoption patterns, ensuring long-term adaptability and accuracy. Overall, this method delivers a scalable, intelligent, and sustainability-oriented solution for EV charging infrastructure optimization. By leveraging advanced analytics and renewable energy considerations, the framework supports efficient nationwide EV expansion, reduces environmental impact, enhances accessibility, and contributes to a reliable and future-ready electric transportation ecosystem in the United States."
Disclaimer: Curated by HT Syndication.
