MUMBAI, India, Feb. 6 -- Intellectual Property India has published a patent application (202541121524 A) filed by Peri Institute Of Technology, Chennai, Tamil Nadu, on Dec. 4, 2025, for 'energy-constrained optimized vehicle routing system using ga, sa, aco and pso for urban ev logistics.'
Inventor(s) include Balaji R; Nagalakshmi S V; Pavithra S; Diwakar S; Chukka Gayatri Preethi; Gopika R; and Heerashini S P.
The application for the patent was published on Feb. 6, under issue no. 06/2026.
According to the abstract released by the Intellectual Property India: "The significant issue with the urban logistics of Electric Vehicles (EVs) is the short range of electric vehicles, which restricts traveling long distances without the need to recharge the battery as often. This drawback renders the planning of efficient routes, the scheduling of deliveries, and energy-sensitive decision-making processes crucial to efficient fleet operation in the present day. To address these limitations, the invention proposes a smart optimization framework which is tailor-made with respect to the Electric Vehicle Constrained Delivery Routing Problem (EVCDRP). The proposed system is a combination of four highly developed metaheuristic algorithms, including Simulated Annealing (SA), Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) to produce optimized, battery-constrained delivery paths of EV fleet-based logistics. In contrast to traditional methods of Vehicle Routing Problem (VRP), the framework will make sure that all the calculated paths are feasible under battery capacity of each vehicle, to avoid failure in the middle of the route and unnecessary energy usage. The system also has a user-friendly visualization component that is designed specifically to serve large urban areas and allow the logistics operators to read and act on the results of tlie optimized routing. This boosts efficiency in delivery, minimizes downtimes in operations and helps to make the operations more sustainable and environmental friendly. Examining the four algorithms experimentally it was found that the Genetic Algorithm (GA) generated the most optimized output and this generated shorter routes and better battery performance. Through the use of the advantages of GA in the integrated system, the invention will greatly enhance the optimization of routes of EV fleets, which will, in the end, facilitate smarter decisions and encourage green urban logistics."
Disclaimer: Curated by HT Syndication.
