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Artificial Intelligence Strategies for the Development of Robust Virtual Sensors: An Industrial Case for Transient Particle Emissions in a High-Performance Engine
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Journal Article
Artificial Intelligence Strategies for the Development of Robust Virtual Sensors: An Industrial Case for Transient Particle Emissions in a High-Performance Engine
2024
Overview
The use of data-driven algorithms for the integration or substitution of current
production sensors is becoming a consolidated trend in research and development
in the automotive field. Due to the large number of variables and scenarios to
consider; however, it is of paramount importance to define a consistent
methodology accounting for uncertainty evaluations and preprocessing steps, that
are often overlooked in naïve implementations. Among the potential applications,
the use of virtual sensors for the analysis of solid emissions in transient
cycles is particularly appealing for industrial applications, considering the
new legislations scenario and the fact that, to our best knowledge, no robust
models have been previously developed. In the present work, the authors present
a detailed overview of the problematics arising in the development of a virtual
sensor, with particular focus on the transient particulate number (diameter
<10 nm) emissions, overcome by leveraging data-driven algorithms and a
profound knowledge of the underlying physical limitations. The workflow has been
tested and validated using a complete dataset composed of more than 30 full
driving cycles obtained from industrial experimentations, underlying the
importance of each step and its possible variations. The final results show that
a reliable model for transient particulate number emissions is possible and the
accuracy reached is compatible with the intrinsic cycle to cycle variability of
the phenomenon, while ensuring control over the quality of the predicted values,
in order to provide valuable insight for the actions to perform.
Publisher
SAE International,SAE International, a Pennsylvania Not-for Profit
Subject
