Optimizing model parameters of artificial neural networks to predict vehicle emissions. (1st February 2023) - Seo, Jigu; Park, Sungwook
Abstract: This paper presents a novel approach to predict carbon dioxide (CO2 ), nitrogen oxides (NOx), and carbon monoxide (CO) emissions of diesel vehicles using artificial neural network (ANN), which offer high degrees of accuracy and practicality. Six operating parameters (velocity, engine speed, engine torque, engine coolant temperature, fuel/air ratio, and intake air mass flow) collected through on-board diagnostic interface were used as predictors of exhaust emissions. The importance of each parameter to the emission predictions were comprehensively analyzed by comparing the coefficient of determination, root mean square error, cumulative emissions, and instantaneous emission rates. The emission prediction accuracy of ANN tends to increase as more parameters were considered as model inputs at the same time. However, the level of accuracy improvement depends on the input parameters. For CO2 emissions, engine torque and fuel/air ratio were good predictors for achieving high prediction accuracy. The relative importance of intake air mass flow rate and fuel/air ratio was high for NOx and CO predictions, respectively. In addition, the emission prediction accuracy of ANN depends on the vehicle type (Euro 5, Euro 6b, Euro 6d-temp). The emission prediction accuracy of vehicles equipped with after-treatment devices (selective catalytic reduction and lean NOx trap) was lower than that of vehicles without after-treatment devices. Highlights: Artificial neural networks were developed to predict CO2, NOx, and CO emissions. On-road testing data were used to train artificial neural networks. The importance of vehicle parameters to emission predictions were quantified. Fuel/air ratio and intake air mass flow are good predictors of exhaust emissions. NOx prediction accuracy were highly dependent on vehicle types.