Genetic Optimization for Engine Combustion System Calibration: A Case Study of Optimization Performance Sensitivity to Algorithm Search Parameters

Abstract

Abstract The use of genetic optimization algorithms (GOA) has been shown to significantly reduce the resource intensity of engine calibration, motivating investigation into the development of these methods. The objective of this work was to quantify the sensitivity of GOA performance to the algorithm search parameter values, in a case study of engine calibration. A GOA was used to calibrate four combustion system control parameters for a direct-injection gasoline engine at a single operating condition, with an optimization goal to minimize brake-specific fuel consumption (BSFC) for a specified engine-out NOx concentration limit. The calibration process was repeated for two NOx limit values and a wide range of values for five GOA search parameters, including the number of genes, mutation rate, and convergence criteria. Results indicated GOA performance is very sensitive to algorithm search parameter values, with converged calibrations yielding BSFC values from 1 to 14% higher than the global minimum value and the number of iterations required to converge ranging from 10 to 3000. Broadly, GOA performance sensitivity was found to increase as the NOx limit was decreased from 4500 to 1000 ppm. GOA performance was the most sensitive to the number of genes and the gene mutation rate, whereas sensitivity to convergence criteria values was minimal. Identification of one set of algorithm search parameter values which universally maximized GOA performance was not possible as ideal values depended strongly on engine behavior, NOx limit, and the maximum level of error acceptable to the user.