In electric vehicle technology, battery energy conservation is paramount due to the dependency of all system operations on the available battery. The proportional, integral and derivative (PID) controller parameters in the electric power assisted steering system for electric vehicle need to be tuned with the optimal performance setting so that less current is needed for its operation. This proposed two methods under the umbrella of swarm-intelligence technique namely particle swarm optimization (PSO) and ant colony optimization (ACO) in order to reduce current consumption and to improve controller performance. The investigation involves an analysis on the convergence behavior of both techniques in search for accurate controller parameters. A comprehensive assessment on the assist current supplied to the assist motor of the system is also presented. Investigation reveals that the proposed controllers, PID-PSO and PID-ACO are able to reduce the assist current supplied to the assist motor as compared to the conventional PID controller. This study also demonstrate the feasibility of applying both swarm-intelligence tuning method in terms of reduced time taken to tune the PID controller as compared to the conventional tuning method.

With the massive interest in electric vehicle technology, all different types of vehicles are moving toward green awareness, including the motorcycle. As time progresses, the investigations on the motorcycle developed to an even more complex model as the model need to be able to include the dynamics of the motorcycle at high speed. Relatively, few works of the literature found on an electric motorcycle (MC) modeling. Therefore, this paper aims to develop an E-MC model that represents a realistic model of the motorcycle with both kinematics and dynamics of the motorcycle incorporated in the model. The developed model is then tested for the speed tracking and the range travelled to evaluate the performance. Two different driving cycles that commonly applied in the commercial motorcycle evaluation test are used as the driving profiles in the simulation, namely, the Worldwide Motorcycle Test Cycle and New European Driving Cycle profiles. The results show an evident ability for the developed model of the E-MC to track the speed profile. It is also noted that the distance travelled by the E-MC model can be effectively determined.

Electric power assist steering (EPAS) system provides an important significance in enhancing the driving performance of a vehicle with its energy-conserving features. This paper presents a hybrid PID (Proportional-Integral-Derivative) and particle swarm optimization (PSO) based control scheme to minimize energy consumption for EPAS. This single objective optimization scheme is realized using the PSO technique in searching for best gain parameters of the PID controller. The fast tuning feature of this optimum PID controller produced high-quality solutions. Simulation results show the performance and effectiveness of the hybrid PSO-PID based controller as opposed to the conventional PID controller.