Development and Software in the Loop Validation of a Model-based Water Injection Combustion Controller for a GDI TC Engine

Ranuzzi, Francesco; Cavina, Nicolò; Brusa, Alessandro; Cesare, Matteo De; Panciroli, Marco

doi:10.4271/2019-01-1174

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…Despite the use of low reactivity fuels with high volatility, such as gasoline or gasoline-like bio-fuels, significantly limits the pollutants production, especially particulate matter and unburned hydrocarbons [15,[21][22][23], their different ignition dynamic strongly affects combustion stability. A lot of works report that, given a set of injection parameters, the longer ignition delay of low reactivity fuels (compared to standard diesel fuel) might lead a poor combustion of the first injection especially when GCI is run in cold operating conditions and generate a very retarded center of combustion (inefficient combustion) or misfire [19,[24][25][26]. As a result, an extremely accurate injection management, particularly pilot injections, represents the key factor to assure stable GCI combustion over the whole engine operating range.…”

Section: Introductionmentioning

confidence: 99%

Accelerometer-based SOC estimation methodology for combustion control applied to Gasoline Compression Ignition

Silvagni

Ravaglioli

Ponti

et al. 2022

J. Phys.: Conf. Ser.

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The European Community’s recent decision to suspend the marketing of cars with conventional fossil-fueled internal combustion engines from 2035 requires new solutions, based on carbon-neutral technologies, that ensure equivalent performances in terms of reliability, trip autonomy, refueling times and end-of-life disposal of components compared to those of current gasoline or diesel cars. The use of bio-fuels and hydrogen, which can be obtained by renewable energy sources, coupled with high-efficiency combustion methodologies might allow to reach the carbon neutrality of transports (net-zero carbon dioxide emissions) even using the well-known internal combustion engine technology. Bearing this in mind, experiments were carried out on compression ignited engines running on gasoline (GCI) with a high thermal efficiency which, in the future, could be easily adapted to run on a bio-fuel. Despite the well-reported benefits of GCI engines in terms of efficiency and pollutant emissions, combustion instability hinders the diffusion of these engines for industrial applications. A possible solution to stabilize GCI combustion is the use of multiple injections strategies, typically composed by 2 early injected fuel jests followed by the main injection. The heat released by the combustion of the earlier fuel jets allows to reduce the ignition delay of the main injection, directly affecting both delivered torque and center of combustion. As a result, to properly manage GCI engines, a stable and reliable combustion of the pre-injections is mandatory. In this paper, an estimation methodology of the start of combustion (SOC) position, based on the analysis of the signal coming from an accelerometer sensor mounted on the engine block, is presented (the optimal sensor positioning is also discussed). A strong correlation between the SOC calculated from the accelerometer and that obtained from the analysis of the rate of heat release (RoHR) was identified. As a result, the estimated SOC could be used to feedback an adaptive closed-loop combustion control algorithm, suitable to improve the stability of the whole combustion process.

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Section: Introductionmentioning

confidence: 99%

Accelerometer-based SOC estimation methodology for combustion control applied to Gasoline Compression Ignition

Silvagni

Ravaglioli

Ponti

et al. 2022

J. Phys.: Conf. Ser.

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“…Differently from the physical and semiphysical models [10][11][12][13][14][15], a data-driven approach could be helpful, since the processes at the basis of emission formation, such as combustion and turbulence, are quite difficult to model analytically [16] and require much time to run in virtual environments. Despite 0-D models [17][18][19][20][21] being computationally efficient, the analytical formulation of the physical phenomena can be difficult to determine when many independent variables are affecting the output. Some applications of machine learning aimed at emission modeling are already present in the literature [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of Data-Driven Approaches to Develop an Engine Surrogate Model for NOx Engine-Out Emissions under Steady-State and Transient Conditions

et al. 2022

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In this paper, a methodology based on data-driven models is developed to predict the NOx emissions of an internal combustion engine using, as inputs, a set of ECU channels representing the main engine actuations. Several regressors derived from the machine learning and deep learning algorithms are tested and compared in terms of prediction accuracy and computational efficiency to assess the most suitable for the aim of this work. Six Real Driving Emission (RDE) cycles performed at the roll bench were used for the model training, while another two RDE cycles and a steady-state map of NOx emissions were used to test the model under dynamic and stationary conditions, respectively. The models considered include Polynomial Regressor (PR), Support Vector Regressor (SVR), Random Forest Regressor (RF), Light Gradient Boosting Regressor (LightGBR) and Feed-Forward Neural Network (ANN). Ensemble methods such as Random Forest and LightGBR proved to have similar performances in terms of prediction accuracy, with LightGBR requiring a much lower training time. Afterwards, LightGBR predictions are compared with experimental NOx measurements in steady-state conditions and during two RDE cycles. Coefficient of determination (R2), normalized root mean squared error (nRMSE) and mean average percentage error (MAPE) are the main metrics used. The NOx emissions predicted by the LightGBR show good coherence with the experimental test set, both with the steady-state NOx map (R2 = 0.91 and MAPE = 6.42%) and with the RDE cycles (R2 = 0.95 and nRMSE = 0.04).

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“…During the last decades, the increasingly stringent limits on the pollutants emitted by a vehicle have pushed the automotive industry to design more clean and efficient powertrains. The introduction of new technologies for turbocharging [1], direct injection in downsized spark-ignition engines [2,3] and knock mitigation [4,5] has been effective for the improvement of engine performance in terms of efficiency and pollutants released in the atmosphere and it is currently one the most common solution adopted by Original Equipment Manufacturers (OEMs) together with electrification. It is also well known that the achievement of the simultaneous reduction of all the main pollutants in light-duty conventional combustions systems, Particulate Matter (PM), Nitrogen Oxides (NOx), Unburn Hydrocarbons (UHC) and Carbon Monoxide (CO), throughout the operative engine map, is quite challenging, especially considering the current homologation procedures.…”

Section: Introductionmentioning

confidence: 99%

1D-3D coupled approach for the evaluation of the in-cylinder conditions for Gasoline Compression Ignition Combustion

Viscione

Bianchi

Ravaglioli

et al. 2022

J. Phys.: Conf. Ser.

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Nowadays, progressive improvements of engine performance must be performed to reduce fuel consumption, which directly affects the amount of CO2 released in the atmosphere. For this purpose, considering modern technologies in the automotive scenario, Gasoline Compression Ignition (GCI) combustion might represent one promising solution, since it experiences high thermal efficiency of Compression Ignited (CI) engines and pollutant emission mitigation. This paper shows the first step of a project aimed at reproducing the combustion behavior of a Diesel engine running with GCI combustion by means of CFD simulations. In particular, this work presents a methodology used to reconstruct the mixing process inside the cylinder before the combustion event, since those engines are dramatically sensitive to the global and local mixture quality. Firstly, a reverse-engineering procedure aimed at generating the CAD model of the engine was performed. Afterwards, the discharge coefficients of the intake and exhaust valves through specifically designed 3D CFD simulations were determined, which was necessary due to the customized intake/exhaust line. Eventually, to reasonably reconstruct the in-cylinder state, the Rate of Heat Release (RoHR) curve, calculated from the analysis of the in-cylinder pressure signal running the engine in GCI mode, was imposed in GT-Power by means of a combination of Wiebe functions with the purpose of generating representative trends of pressure, temperature, and mass flow to properly define the domains of the CFD model.

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Development and Software in the Loop Validation of a Model-based Water Injection Combustion Controller for a GDI TC Engine

Cited by 8 publications

References 14 publications

Accelerometer-based SOC estimation methodology for combustion control applied to Gasoline Compression Ignition

Accelerometer-based SOC estimation methodology for combustion control applied to Gasoline Compression Ignition

Comparative Evaluation of Data-Driven Approaches to Develop an Engine Surrogate Model for NOx Engine-Out Emissions under Steady-State and Transient Conditions

1D-3D coupled approach for the evaluation of the in-cylinder conditions for Gasoline Compression Ignition Combustion

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