Modeling and Predicting Heavy-Duty Vehicle Engine-Out and Tailpipe Nitrogen Oxide (NO(x)) Emissions Using Deep Learning

As emissions regulations for transportation become stricter, it is increasingly important to develop accurate nitrogen oxide (NO(x)) emissions models for heavy-duty vehicles. However, estimation of transient NO(x) emissions using physics-based models is challenging due to its highly dynamic nature, which arises from the complex interactions between power demand, engine operation, and exhaust aftertreatment efficiency. As an alternative to physics-based models, a multi-dimensional data-driven approach is proposed as a framework to estimate NO(x) emissions across an extensive set of representative engine and exhaust aftertreatment system operating conditions. This paper employs Deep Neural Networks (DNN) to develop two models, an engine-out NO(x) and a tailpipe NO(x) model, to predict heavy-duty vehicle NO(x) emissions. The DNN models were developed using variables that are available from On-board Diagnostics from two datasets, an engine dynamometer and a chassis dynamometer dataset. Results from trained DNN models using the engine dynamometer dataset showed that the proposed approach can predict NO(x) emissions with high accuracy, where R(2) scores are higher than 0.99 for both engine-out and tailpipe NO(x) models on cold/hot Federal Test Procedure (FTP) and Ramped Mode Cycle (RMC) data. Similarly, the engine-out and tailpipe NO(x) models using the chassis dynamometer dataset achieved R(2) scores of 0.97 and 0.93, respectively. All models developed in this study have a mean absolute error percentage of approximately 1% relative to maximum NO(x) in the datasets, which is comparable to that of physical NO(x) emissions measurement analyzers. The input feature importance studies conducted in this work indicate that high accuracy DNN models (R(2) = 0.92–0.95) could be developed by utilizing minimal significant engine and aftertreatment inputs. This study also demonstrates that DNN NO(x) emissions models can be very effective tools for fault detection in Selective Catalytic Reduction (SCR) systems.