Abstract: Increasing demands for lower carbon dioxide emissions and fuel consumption drive technological developments for car manufacturers. One trend that has shown success for reducing fuel consumption in Spark Ignited engines is downsizing, where the engine size is reduced to save fuel and a turbocharger is added to maintain the power output. Even though many downsized turocharged engines matches the larger engines in terms of power and fuel consumption, they still cannot match the natural aspirated engine in the transient torque response. Recent hardware improvements have facilitated the use of Variable Geometry Turbochargers (VGT) for spark ignited engines, which can improve the transient torque response. In the present techniques the optimal control of the valve timing and VGT are preferred for a fast torque response. Optimal open-loop control signals are found by maximizing the torque integral. Here, a control strategy to improve the combustion efficiency of Spark Ignited engines is presented. To reduce fuel consumption and to achieve high performance, multi-objective genetic optimization algorithm is proposed. The NSGA (Non-dominated Sorting Genetic Algorithm) reduces the computational complexity based on a certain number of decision variables and a given population of solutions. The efficiency of the proposed multi-objective genetic optimization control scheme is checked through simulation experiments.
Keywords: Spark Ignited Engines, Downsizing, Turbocharger, VGT, VVT, NSGA.