بهينه سازي متغيرهاي تنظيم موتورهاي درونسوز با استفاده از طراحي آزمايش ها

Optimization of Calibration Parameters in Internal Combustion Engines Using Design of Experiments

In this research the optimization of calibration parameters in vehicle engine is investigated by goals of minimizing fuel consumption and emissions, and maximization of engine torque. Urgent demand for decrease of fuel consumption and emissions has led to complexity of engines and power train control systems. Traditional calibration procedures are very time-consuming and expensive and calibration of modem engines with lots of increasing ECU parameters by tradttional procedures, arc against the demand of decreasing power train development duration. In this research we will introduce an applicable methodology and related experimental case study for engine calibration and determination of controllable parameters which determine the benefits of using design of experiment and statistical modeling in development of applicable models by als of decreasing the cost and duration of experiments execution. Also we will present the design of experiments and modeling with neural network in the same case study and compare it with the statistical modeling approach. An optimization algorithm has been presented for solving the multi objective problems of research based on dynamic weight factors. Two statistical and neural network models have been solved with this algorithm and the results have been compared with each other.