Loris Ventura

Dottorato in Energetica , 34o ciclo (2018-2021)

Dottorato concluso nel 2022

Tesi:

Development and assessment of model-based and sensor-based algorithms for combustion and emission control in diesel engines (Abstract)

Tutori:

Roberto Finesso Stefano Alberto Malan Stefano D'Ambrosio

Presentazione della ricerca:

Poster

Profilo

Argomento di ricerca

Model- and sensor-based algorithms for air-path, combustion and emission control in diesel engines

Interessi di ricerca

Clean Mobility and Innovative Powertrains

Biografia

Diesel powertrains will remain an indispensable energy carrier to meet the CO2 emission targets in the short-term, especially for light-duty and heavy-duty applications. Innovative solutions are needed in order to increase thermal efficiency and decrease engine-out and tailpipe emissions (especially NOx, PM), so as to meet the more and more stringentregulations. Among the different technologies which have been recently proposed, a significant contribution will be given by the development of advanced techniques for the control (sensor-based or model-based) of in-cylinder combustion and pollutant formation processes. This has been made possible by the recent advances in the performance of modern ECUs.
The research goal is to develop and assess model-based and sensor-based algorithms for air path, combustion and emission control in diesel engines. This requires to address the following problems:
  • Find appropriate black-box model structure: family and order. The model complexity has to be as small as possible.
  • Development of test procedures: how to extract as much information as possible, in a reasonable amount of time, from the real system in order to build the model.
  • EGR mass flow estimation: obtaining the HP and LP EGR mass flows given the intake O2 concentration and LAMBDA measurements.
  • Control variable selection: correlations between variables involved in the combustion process and the pollutant emission (NOx).
The adoption of model based control systems will offer several advantages over the traditional map-based approach, both on terms of calibration effort (maps are expensive to build) and engine performance increase and emission reduction. Self-adaptive features for the models and the control system can be added exploiting the informations coming from the available sensors. This method well suits control systems realized through maching learning algorithms.

Didattica

Insegnamenti

Corso di laurea magistrale

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Corso di laurea di 1° livello

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