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 naive 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.

Pulga, L., Forte, C., Siliato, A., Giovannardi, E., Tonelli, R., Kitsopanidis, I., et al. (2023). Artificial Intelligence Strategies for the Development of Robust Virtual Sensors: An Industrial Case for Transient Particle Emissions in a High-Performance Engine. SAE INTERNATIONAL JOURNAL OF ENGINES, 17(2), 1-17 [10.4271/03-17-02-0014].

Artificial Intelligence Strategies for the Development of Robust Virtual Sensors: An Industrial Case for Transient Particle Emissions in a High-Performance Engine

Giovannardi, Emanuele
Membro del Collaboration Group
;
Bianchi, Gian Marco
Membro del Collaboration Group
2023

Abstract

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 naive 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.
2023
Pulga, L., Forte, C., Siliato, A., Giovannardi, E., Tonelli, R., Kitsopanidis, I., et al. (2023). Artificial Intelligence Strategies for the Development of Robust Virtual Sensors: An Industrial Case for Transient Particle Emissions in a High-Performance Engine. SAE INTERNATIONAL JOURNAL OF ENGINES, 17(2), 1-17 [10.4271/03-17-02-0014].
Pulga, Leonardo ; Forte, Claudio ; Siliato, Alfio ; Giovannardi, Emanuele ; Tonelli, Roberto ; Kitsopanidis, Ioannis ; Bianchi, Gian Marco
File in questo prodotto:
File Dimensione Formato  
artificial intelligence strategies postprint.pdf

Open Access dal 09/03/2024

Tipo: Postprint
Licenza: Licenza per accesso libero gratuito
Dimensione 1.62 MB
Formato Adobe PDF
1.62 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/962724
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? ND
social impact