Development of an Engine Calibration Model Using Gaussian Process Regression

Abstract

To enhance the calibration efficiency, reduce the fuel consumption and improve the emission performance of the engine, a calibration method using Gaussian Process Regression (GPR) is proposed in this work. First, the design of experiment (DoE) is constructed by using the Space-filling method, and the engine bench sampling test is implemented according to results of DoE. Then, the square exponential covariance function is selected through the comparison of four covariance functions, and the corresponding hyper-parameters are optimized by using Newton gradient algorithm. Finally, the GPR model of the engine is established and its calibration performance is validated by the experimental data. The comparison shows that the performance of the developed GPR model is superior to the Polynomial model and Neural Network model, whose coefficient of determination (R2) of Fuel Consumption (FC), NOx emission (NOx) and Soot emission (Soot) are up to 0.9980, 0.9326 and 0.9247. The case study demonstrates that the virtual calibration optimization based on GPR model improves the fuel consumption performance greatly, while taking NOx and Soot emission indicators into account.