Part Two: Neural Network Controller for Hydrogen-CNG Powered Vehicle

Background: The control system of the vehicle regulates parameters like fuel flow control, vehicle speed control, tracking, etc. Objective: The main objective of the paper is to monitor and determine an efficient, and automated control system for an H-CNG-powered vehicle. Using neural networks and machine learning, we would develop an algorithm for the controller to regulate the speed of the car with the help of variables involved during the runtime of the vehicle. Methods: Initially, Generating a dataset with the help of formulation and computation for training. Further, analysing different supervised machine learning algorithms and training the Artificial Neural Network (ANN) using the generated dataset to predict and track the gains of the H-CNG vehicle accurately. Results: Analysis of the gains of the H-CNG vehicle are presented to understand the precision of the trained Neural Network. Conclusion: The final verdict of the paper is that the Neural Network is successful in tracking the gains of the H-CNG vehicle with the help of the dataset presented for training using the Random Forest Regression technique for machine learning.