Browsing by Author "Rabiatuladawiah Abu Hanifah"

Energy management in Electric Vehicle (EV) technology is very important as the energy source of all its system operations are solely relying on the battery. It is evident since, most auto-makers are still focused on maximizing travel distance and battery energy storage in improving the performance of the vehicle in EV. Efforts are being made to reduce the energy consumed as much as possible in EV system. As one of the auxiliary elements of the system, of Electric Power Assist Steering (EPAS) system can be controlled or manipulated in such a way that minimum energy from the battery source is being drawn during operation. This unique feature of EPAS system enables the EPAS system to be tuned with the optimal performance setting so that less power is needed for its optimum operation. The research's aim is to apply the Computational Intelligence (CI) technique; Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) to improve the controller's performance. The investigation involves an analysis of the convergence behavior of PSO and ACO toward the optimal solution and a comprehensive assessment on the current supplied to the assist motor of the EPAS system and power consumption evaluation. The test rig of the EPAS system is also designed and successfully developed in this research to be used for the testing of the proposed controller. Both simulation and experimental tests are conducted to validate the proposed controller performance. The test show about 75.22 % of power reduction with PID-PSO tuned controller and 76.87 % less power consumed when PID-ACO tuned controller is implemented in the EPAS system