Author
Listed:
- Alessandro Brusa
(Department of Industrial Engineering, School of Engineering and Architecture, University of Bologna, 40126 Bologna, Italy)
- Nicolò Cavina
(Department of Industrial Engineering, School of Engineering and Architecture, University of Bologna, 40126 Bologna, Italy)
- Nahuel Rojo
(Department of Industrial Engineering, School of Engineering and Architecture, University of Bologna, 40126 Bologna, Italy)
- Jacopo Mecagni
(Department of Industrial Engineering, School of Engineering and Architecture, University of Bologna, 40126 Bologna, Italy)
- Enrico Corti
(Department of Industrial Engineering, School of Engineering and Architecture, University of Bologna, 40126 Bologna, Italy)
- Davide Moro
(Department of Industrial Engineering, School of Engineering and Architecture, University of Bologna, 40126 Bologna, Italy)
- Matteo Cucchi
(Ferrari S.p.A., 41053 Maranello MO, Italy)
- Nicola Silvestri
(Ferrari S.p.A., 41053 Maranello MO, Italy)
Abstract
This work focuses on the implementation of innovative adaptive strategies and a closed-loop chain in a piston-damage-based combustion controller. In the previous paper (Part 1), implemented models and the open loop algorithm are described and validated by reproducing some vehicle maneuvers at the engine test cell. Such controller is further improved by implementing self-learning algorithms based on the analytical formulations of knock and the combustion model, to update the fuel Research Octane Number (RON) and the relationship between the combustion phase and the spark timing in real-time. These strategies are based on the availability of an on-board indicating system for the estimation of both the knock intensity and the combustion phase index. The equations used to develop the adaptive strategies are described in detail. A closed-loop chain is then added, and the complete controller is finally implemented in a Rapid Control Prototyping (RCP) device. The controller is validated with specific tests defined to verify the robustness and the accuracy of the adaptive strategies. Results of the online validation process are presented in the last part of the paper and the accuracy of the complete controller is finally demonstrated. Indeed, error between the cyclic and the target combustion phase index is within the range ±0.5 Crank Angle degrees (°CA), while the error between the measured and the calculated maximum in-cylinder pressure is included in the range ±5 bar, even when fuel RON or spark advance map is changing.
Suggested Citation
Alessandro Brusa & Nicolò Cavina & Nahuel Rojo & Jacopo Mecagni & Enrico Corti & Davide Moro & Matteo Cucchi & Nicola Silvestri, 2021.
"Development and Experimental Validation of an Adaptive, Piston-Damage-Based Combustion Control System for SI Engines: Part 2—Implementation of Adaptive Strategies,"
Energies, MDPI, vol. 14(17), pages 1-21, August.
Handle:
RePEc:gam:jeners:v:14:y:2021:i:17:p:5342-:d:623642
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Citations
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Cited by:
- Karol Tucki & Olga Orynycz & Leszek Mieszkalski & Joao Gilberto Mendes dos Reis & Jonas Matijošius & Michał Wocial & Ivan Kuric & Simone Pascuzzi, 2023.
"Analysis of the Influence of the Spark Plug on Exhaust Gas Composition,"
Energies, MDPI, vol. 16(11), pages 1-25, May.
- Alessandro Brusa & Emanuele Giovannardi & Massimo Barichello & Nicolò Cavina, 2022.
"Comparative Evaluation of Data-Driven Approaches to Develop an Engine Surrogate Model for NOx Engine-Out Emissions under Steady-State and Transient Conditions,"
Energies, MDPI, vol. 15(21), pages 1-22, October.
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