MUMBAI, India, Aug. 31 -- Intellectual Property India has published a patent application (202641099020 A) filed by St. Joseph'S College Of Engineering; and Ms. Gomathi S on August 17, 2026, for Coulomb Counting-Based State Of Charge Estimation And Adaptive Range Prediction For Electric Vehicles.
Inventors include St. Joseph'S College Of Engineering; Ms. Gomathi S; Vinith Gibbston K; and Vimalraaj R.
The application for the patent was published on August 28, 2026, under issue no. 35/2026.
Abstract: Electric vehicles (EVs) are rapidly emerging as a sustainable transportation solution due to their high efficiency and reduced environmental impact. However, one of the major challenges faced by EV users is the accurate estimation of battery State of Charge (SOC) and remaining driving range. Inaccurate prediction may lead to range anxiety and inefficient battery utilization. To address this issue, this project presents a real-time battery monitoring and adaptive range prediction system for electric vehicles using the Coulomb Counting method and ESP32 microcontroller. The proposed system continuously monitors battery voltage and current using voltage and current sensors connected to a lithium-ion battery pack and DC motor load. The measured current data is processed using the Coulomb Counting technique to estimate the battery SOC in real time. Based on the estimated SOC, remaining battery energy and driving range are adaptively predicted according to energy consumption conditions. A 16×2 LCD display is used to visualize important battery parameters such as voltage, current, SOC, power consumption, remaining energy, and predicted range. A MATLAB/Simulink model is developed to validate the proposed methodology under battery discharge conditions. The simulation results demonstrate gradual reduction in SOC, remaining energy, and driving range, closely matching the behavior observed in the hardware prototype. The experimental implementation confirms that the proposed system provides a simple, low-cost, and efficient solution for real-time EV battery monitoring and adaptive range estimation. The developed prototype can be further enhanced using IoT-based monitoring, cloud data logging, and advanced battery management algorithms for future smart electric vehicle applications.
Disclaimer: Curated by HT Syndication.
