MUMBAI, India, June 22 -- Intellectual Property India has published a patent application (202641068713 A) filed by P. Palpandi; Ms. Indumathi M; Mr. Madhava Rao Karri; Mr. Jigneshkumar B Pujara; Ms. Pratiksha Vinayak Gaikwad; Navaneetha Krishnan T; Polu Veeraraghava Reddy; and Ms. A. Benazir Begum on June 01, 2026, for Iot-Based Ev Charging Station With Load Scheduling.
Inventors include P. Palpandi; Ms. Indumathi M; Mr. Madhava Rao Karri; Mr. Jigneshkumar B Pujara; Ms. Pratiksha Vinayak Gaikwad; Navaneetha Krishnan T; Polu Veeraraghava Reddy; and Ms. A. Benazir Begum.
The application for the patent was published on June 12, 2026, under issue no. 24/2026.
Abstract: ABSTRACT IoT-Based EV Charging Station with Load Scheduling The present invention relates to an IoT-Based Electric Vehicle (EV) Charging Station with Load Scheduling designed to improve charging efficiency, grid stability, energy utilization, and user convenience in modern electric mobility infrastructure. The proposed system integrates Internet of Things (IoT) technology, intelligent load management algorithms, cloud computing, and real-time communication protocols to create a smart EV charging ecosystem capable of dynamically distributing electrical loads among multiple charging points. The invention employs embedded sensors, smart meters, wireless communication modules, and centralized control units to continuously monitor power consumption, charging demand, battery status, grid conditions, and energy availability. Based on collected data, the system performs adaptive load scheduling to prevent overloading, reduce peak demand, and optimize charging priority according to predefined parameters such as battery state-of-charge, user requirements, electricity tariffs, renewable energy availability, and emergency charging needs. The charging station further supports renewable energy integration including solar photovoltaic systems and battery storage units for sustainable energy utilization. A cloud-based monitoring platform enables remote supervision, predictive analytics, fault diagnosis, billing automation, and mobile application control for users and administrators. Machine learning techniques may additionally be employed to predict charging patterns and optimize energy allocation in real time. The proposed invention minimizes energy wastage, improves charging station reliability, reduces operational costs, enhances grid compatibility, and supports scalable deployment in residential, commercial, industrial, and public transportation environments. The invention thereby provides an intelligent, energy-efficient, and economically viable solution for managing large-scale EV charging infrastructure in smart cities and future sustainable transportation systems.
Disclaimer: Curated by HT Syndication.
