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

Internal Combustion Engines (ICEs) will remain the main propulsion system for vehicles in the next 10 to 30 years in both conventional and hybrid architectures. In particular, with reference to diesel engines, innovative solutions are needed in order to increase thermal efficiency and decrease pollutant emissions, especially NOx and PM, in order to meet the law regulations. This aim requires to improve combustion by means of new highly efficient and advanced combustion processes and systems, new sensing  methodologies and innovative control techniques. The research deals with the development and assessment of algorithms for the model-based control of combustion: predictive models, capable of simulating combustion and pollutant formation processes in ICEs with a low computational effort will be used. The developed algorithms allows to identify the optimal, in terms of engine performance increase and emission reduction, engine set-up (injection parameters, exhaust gas recirculation ratio, boost pressure) starting from pollutant emissions and engine performance targets, avoiding heavy and expensive engine calibration experimental activity and the use of the traditional map-based approaches. Refinement of the control algorithms, in order to improve their performance and robustness, by adding self-adaptive features to the models exploiting the information obtained from available sensors, is also pursued such as their assessment on the engine by means of rapid prototyping tests.

ERC Sector:

  • PE7_1 Control engineering
  • PE7_3 Simulation engineering and modelling

Keywords:

  • Systems modeling
  • Diesel engines
  • Automatic Control
  • System identification

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