Speed Control of Hybrid Electric Vehicle Using Optimization Algorithm

Abstract: Hybrid electric vehicle (HEV) is getting more attention recently because it has no emission of toxic gases which leads to global warming. Now a days, Particle swarm Optimization Gravity Search Algorithm (PSOGSA) is widely used for optimization, because of its faster convergence rate to the optimal minimum value than other optimization techniques. The main aim of this paper is to control the speed of non-linear hybrid electric vehicle by controlling the throttle position so as to get driving safety, improved fuel economy, reduced manufacturing cost and pollution. To control the speed of hybrid electric vehicle tuning of PID controller is done using particle swarm optimization gravity search algorithm. The performance of the technique is determined by taking mean square error(MSE) as a fitness function. The comparative study is carried out to identify the suitable optimization technique for controlling the various parameters of PID for HEV. The results obtained by tuning of PID with PSOGSA technique compared with other techniques such as Zeigler-Nichols tuning method and standard PID tuning.

Keywords: Hybrid Electric Vehicle, Gravity search algorithm (GSA), PID controller, Particle Swarm optimization (PSO), Particle Swarm optimization Gravity search algorithm (PSOGSA), Zeigler-Nichols (ZN).