INVESTIGATION THE MODELING OF GENETIC ALGORITHM BASED VEHICLE POWERTRAIN SYSTEM OPTIMIZATION

摘要

Vehicle powertrain system has many key parameters to obtain optimum system dynamics under real vehicle condition. In parallel to new technological developments, driving comfort is investigated with the new methodologies to provide passenger satisfaction. The clutch system is of the importance for vehicle powertrain system with torque transmission controlling and vibration damping properties. A vehicle clutch system is subjected to high dynamic loads and vibrations under operational conditions that need further system analysis. Optimization algorithms are the cost and time effective methodologies during system analysis prior to real production phases. In this study, the medium segment vehicle powertrain system is analyzed with the genetic algorithm methodology integrated with 1-D modeling. The powertrain system was modeled with 1-D set-up, then the genetic algorithm was run with multiple loops to provide optimum vibration level with the clutch damper spring stiffness and clutch disc inertia. In conclusion, the results give an assumption of using the genetic algorithm in the 1-D vehicle system modeling. Inertia and stiffness parameters are the key factor in using vibration analysis for system dynamics. In the study, optimum analysis outputs of clutch damper inertia and stiffness values have illustrated the effects and feasibility of the genetic algorithm integrated 1-D modeling with the eliminating of many real vehicle road testing.