Engine Emission Prediction Based on Extrapolated Gaussian Process Regression Method

doi:10.16183/j.cnki.jsjtu.2021.231

Abstract

Abstract:

Aimed at improving the prediction accuracy of engine emissions under driving conditions which are not covered by the training set, an extrapolated Gaussian process regression (GPR) method is proposed. First, the training set data is fed to the GPR model for pre-training, and then a wide-area input set is constructed by uniform sampling between plus/minus three standard deviations, and the hyperparameters are optimized for the goal of minimizing the prediction variance of the input set. The test results on a direct injection gasoline engine show that the mean absolute error of emission prediction using the extrapolated GPR is 0.53411, which is 24.27% lower than that of using the traditional GPR and 36.32% lower than that of using the back propagation (BP) neural network, which signifies the effectiveness of the proposed method in terms of reducing test costs and improving the accuracy of emission prediction during real driving.

Key words: engine, emission, Gaussian process regression (GPR), prediction, extrapolation

CLC Number:

TK411
TP181

WANG Ziyao, GUO Fengxiang, CHEN Li. Engine Emission Prediction Based on Extrapolated Gaussian Process Regression Method[J]. Journal of Shanghai Jiao Tong University, 2022, 56(5): 604-610.

Figures/Tables 7

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设备名称	型号	功能
排放转毂	AIP-ECDM 48寸	两驱汽车的整车排放测试
PEMS	MEXA-7200	采集并测量NO_x、CH₄、总烃(THC)和CO₂等排放量