Genetic algorithm based speed control of hybrid electric vehicle

Hybrid electric vehicles (HEVs) have gained much more attention recently due to declining fossil resources and greenhouse effect caused by CO_x and NO_x emissions. Optimization of the parameters is a key element for the success of a HEV. Recently, Genetic Algorithm (GA) is widely applied in the optimization of HEV. Since it has conquered the deficiencies of the gradient-based optimization algorithms that require calculating the derivative of the objective function, GA is suitable for this non-linear optimization problem. The objective of this paper is to control the speed of Nonlinear Hybrid Electric Vehicle (HEV) by controlling the throttle position so as to get improved fuel economy, driving safety, reduced pollution and manufacturing cost. To control the speed, tuning of Proportional-Integral-Derivative (PID) controller is done using Genetic Algorithm (GA) Optimization method. The performance of the technique is evaluated by setting the objective function as mean square error (MSE) and is also compared with Ziegler-Nichols method.