Abstract In our previous work [1], an ensemble of Convolutional Neural Network (CNN)
models was used to predict engine-out emissions of Carbon Monoxide (CO), Unburned …

Abstract Analysis-driven pre-calibration of a modern automotive engine is extremely
valuable in significantly reducing hardware investments and accelerating engine designs …

The transportation sector contributes significantly to emissions and air pollution globally.
Emission models of modern vehicles are important tools to estimate the impact of …

In this paper, a methodology based on data-driven models is developed to predict the NOx
emissions of an internal combustion engine using, as inputs, a set of ECU channels …

Accurate and reliable prediction of exhaust emissions is crucial for combustion optimization
control and environmental protection. This study proposes a novel ensemble 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 …

On-board diagnostics (OBD) data contain valuable information including real-world
measurements of vehicle powertrain parameters. These data can be used to gain a richer …

With global warming, and internal combustion engine emissions as the main global non-
industrial emissions, how to further optimize the power performance and emissions of …

Deep learning has been used to predict engine phenomena that are otherwise difficult to
predict using conventional modeling approaches. Previous studies using deep learning for …

With the advent of hybrid electric vehicles, computer-based vehicle simulation becomes
more useful to the engineer and designer trying to optimize the complex combination of …