MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641085333 A) filed by Madhankumar C; Sidhant Mallick; Dr. A. Vetrivel; A. M. Hemalatha; Dr. G. Kasiraman; J. Pavalam; Vishnu R; Ajay J; and Niranjana K. S. on July 13, 2026, for Ai-Driven Predictive Battery Thermal Management System Using Digital Twin, Phase Change Materials, And Adaptive Multi-Channel Liquid Cooling For Electric Vehicles.
Inventors include Sidhant Mallick; Dr. A. Vetrivel; A. M. Hemalatha; Dr. G. Kasiraman; J. Pavalam; Vishnu R; Ajay J; and Niranjana K. S..
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: AI-Driven Predictive Battery Thermal Management System Using Digital Twin, Phase Change Materials, and Adaptive Multi- Channel Liquid Cooling for Electric Vehicles Abstract The present invention discloses an AI-Driven Predictive Battery Thermal Management System (AI-PBTMS) that integrates Digital Twin technology, Phase Change Materials (PCM), and Adaptive Multi- Channel Liquid Cooling to enhance the safety, efficiency, and lifespan of lithium-ion battery packs used in electric vehicles. Conventional battery thermal management systems primarily rely on reactive cooling mechanisms that respond only after excessive temperature rise, resulting in thermal imbalance, accelerated battery degradation, reduced charging efficiency, and increased risk of thermal runaway. The proposed invention overcomes these limitations by employing artificial intelligence and real-time digital twin simulation to predict thermal behavior, optimize cooling strategies, and autonomously regulate battery temperature under varying operating conditions. The system comprises a network of temperature, voltage, current, pressure, and coolant flow sensors integrated with an AI-based predictive analytics engine and a virtual digital twin model of the battery pack. The digital twin continuously replicates the real-time thermal and electrochemical state of the battery, while machine learning algorithms forecast heat generation, cell aging, charging conditions, and thermal anomalies before they occur. Based on these predictions, an adaptive controller dynamically regulates multi-channel liquid cooling circuits, coolant flow rates, micro-pumps, electronic valves, and phase change material activation to maintain uniform temperature distribution across individual battery cells with minimal energy consumption. The invention further incorporates cloud-enabled diagnostics, edge computing, and self-learning optimization algorithms that continuously improve cooling efficiency using historical operating data and real-time sensor feedback. The integrated thermal management framework significantly reduces temperature gradients, prevents thermal runaway, extends battery life, improves fast-charging capability, enhances driving range, and lowers overall energy consumption. The proposed system is applicable to electric cars, buses, trucks, autonomous vehicles, energy storage systems, aerospace batteries, and high-performance electric mobility platforms, providing a reliable, intelligent, and sustainable solution for next-generation battery thermal management.
Disclaimer: Curated by HT Syndication.
