Tailpipe NOx Emissions Modeling of a Heavy-Duty Diesel Truck Using Deep Learning Methods

This study develops deep learning (DL) long–short-term memory (LSTM) models to predict tailpipe nitrogen oxides (NOx) emissions using real-driving on-road data from a heavy-duty Class 8 truck. The dataset comprises over 4 million data points collected across 11,000 km of driving under diverse road, weather, and load conditions. The effects of dataset size, model complexity, and input feature set o…