This work focuses on the development and validation of a data-driven model capable of predicting the maximum in-cylinder pressure during the operation of an internal combustion engine, with the least possible computational effort. The model is based on two parameters, one that represents engine load and another one the combustion phase. Experimental data from four different gasoline engines, two turbocharged Gasoline Direct Injection Spark Ignition, a Naturally Aspirated SI and a Gasoline Compression Ignition engine, was used to calibrate and validate the model. Some of these engines were equipped with technologies such as Low-Pressure Exhaust Gas Recirculation and Water Injection or a compression ignition type of combustion in the case of the GCI engine. A vast amount of engine points were explored in order to cover as much as possible of the operating range when considering automotive applications and thus confirming the broad validity of the model. The validation process was carried out using bothf mean data from each explored engine point as well as cycle-by-cycle data, focusing on onboard application and the model implementation in a combustion control strategy. The validation also includes lean operating conditions for one of the SI engines, case in which lambda had to be added as a third input variable. The main scope of this work is to analyze the robustness of the model when being applied on these particular engine operating technologies and conditions (EGR, WI, lean and GCI combustion). In all cases, the model demonstrated to be accurate within 5% when considering both mean values and cycle-by-cycle data, while retaining its simplicity and low computational weight. Additionally, a study on the minimum amount of engine points necessary for the model calibration has been conducted and it was concluded that only about 20 engine points are needed, if chosen strategically as regards engine load and combustion phase.
Development and Validation of a Virtual Sensor for Estimating the Maximum in-Cylinder Pressure of SI and GCI Engines / Scocozza G.F.; Silvagni G.; Brusa A.; Cavina N.; Ponti F.; Ravaglioli V.; De Cesare M.; Panciroli M.; Benedetti C.. - In: SAE TECHNICAL PAPER. - ISSN 0148-7191. - ELETTRONICO. - 1:2021-24(2021), pp. 0026.1-0026.15. (Intervento presentato al convegno SAE 15th International Conference on Engines and Vehicles, ICE 2021 tenutosi a ITALIA nel 2021) [10.4271/2021-24-0026].
Development and Validation of a Virtual Sensor for Estimating the Maximum in-Cylinder Pressure of SI and GCI Engines
Scocozza G. F.
;Silvagni G.;Brusa A.;Cavina N.;Ponti F.;Ravaglioli V.;
2021
Abstract
This work focuses on the development and validation of a data-driven model capable of predicting the maximum in-cylinder pressure during the operation of an internal combustion engine, with the least possible computational effort. The model is based on two parameters, one that represents engine load and another one the combustion phase. Experimental data from four different gasoline engines, two turbocharged Gasoline Direct Injection Spark Ignition, a Naturally Aspirated SI and a Gasoline Compression Ignition engine, was used to calibrate and validate the model. Some of these engines were equipped with technologies such as Low-Pressure Exhaust Gas Recirculation and Water Injection or a compression ignition type of combustion in the case of the GCI engine. A vast amount of engine points were explored in order to cover as much as possible of the operating range when considering automotive applications and thus confirming the broad validity of the model. The validation process was carried out using bothf mean data from each explored engine point as well as cycle-by-cycle data, focusing on onboard application and the model implementation in a combustion control strategy. The validation also includes lean operating conditions for one of the SI engines, case in which lambda had to be added as a third input variable. The main scope of this work is to analyze the robustness of the model when being applied on these particular engine operating technologies and conditions (EGR, WI, lean and GCI combustion). In all cases, the model demonstrated to be accurate within 5% when considering both mean values and cycle-by-cycle data, while retaining its simplicity and low computational weight. Additionally, a study on the minimum amount of engine points necessary for the model calibration has been conducted and it was concluded that only about 20 engine points are needed, if chosen strategically as regards engine load and combustion phase.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
