An artificial neural network (ANN) was trained on chassis dynamometer data and used to predict the oxides of nitrogen (NO x), carbon dioxide (CO2), hydrocarbons (HC), and carbon monoxide (CO) emitted from heavy-duty diesel vehicles. Axle speed, torque, their derivatives in different time steps, and two novel variables that defined speed variability over 150 seconds were defined as the inputs for the ANN. The novel variables were used to assist in predicting off-cycle emissions. Each species was considered individually as an output of the ANN. The ANN was trained on the Highway cycle and applied to the City/Suburban Heavy Vehicle Route (CSHVR) and Urban Dynamometer Driving Schedule (UDDS) with four different sets of inputs to predict the emissions for these vehicles. The research showed acceptable prediction results for the ANN, even for the one trained with only eight inputs of speed, torque, their first and second derivatives at one second, and two variables related to the speed pattern over the last 150 seconds. However, off-cycle operation (leading to high NO x emissions) was still difficult to model. The results showed an average accuracy of 0.97 for CO2, 0.89 for NO x, 0.70 for CO, and 0.48 for HC over the course of the CSHVR, Highway, and UDDS.
A RBFNN & GACMOO-Based Working State Optimization Control Study on Heavy-Duty Diesel Engine Working in Plateau Environment
