Early Prediction of CNG Filling Time using Artificial Intelligence for Design Optimization

The CNG fuel system is a critical component in vehicle development and typically includes large-volume gas cylinders (up to 800 liters). As these vehicles often operate over long distances daily, frequent refueling is necessary, making a short gas filling time highly desirable. Currently, CNG filling time is evaluated after prototype development to determine the refueling duration. If the filling time is excessive, design changes to the fuel system are required, leading to significant cost and time penalties and causing delays in the vehicle product development cycle. By integrating AI/ML technology, a mathematical model has been developed based on various influencing parameters. This model will predict CNG gas filling time across all commercial vehicle platforms at the initial Zero design release gateway. This early prediction will enable further optimization to improve gas filling time. The goal of this research work to optimising the filling time for various platform before physical vehicle builds.