Application of neural network for air-fuel ratio identification in spark ignition engine

Abstract Machine learning (ML) models can accelerate the development of efficient internal combustion engines. This study assessed the feasibility of data-driven methods towards predicting the performance of a diesel engine modified to natural gas spark ignition, based on a limited number of experiments. As the best ML technique cannot be chosen a priori, the applicability of different ML algorithms for such an engine application was evaluated. Specifically, the performance of two widely used ML algorithms, the random forest (RF) and the artificial neural network (ANN), in forecasting engine responses related to in-cylinder combustion phenomena was compared. The results indicated that both algorithms with spark timing, mixture equivalence ratio, and engine speed as model inputs produced acceptable results with respect to predicting engine performance, combustion phasing, and engine-out emissions. Despite requiring more effort in hyperparameter optimization, the ANN model performed better than the RF model, especially for engine emissions, as evidenced by the larger R-squared, smaller root-mean-square errors, and more realistic predictions of the effects of key engine control variables on the engine performance. However, in applications where the combustion behavior knowledge is limited, it is recommended to use a RF model to quickly determine the appropriate number of model inputs. Consequently, using the RF model to define the model structure and then employing the ANN model to improve the model's predictive capability can help to rapidly build data-driven engine combustion models.

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Intelligent prediction on air intake flow of spark ignition engine by a chaos radial basis function neural network

Journal of Central South University ◽

10.1007/s11771-020-4491-y ◽

2020 ◽

Vol 27 (9) ◽

pp. 2687-2695

Author(s):

Yue-lin Li ◽

Bo-fu Liu ◽

Gang Wu ◽

Zhi-qiang Liu ◽

Jing-feng Ding ◽

...

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Spark Ignition ◽

Spark Ignition Engine ◽

Radial Basis ◽

Air Intake ◽

Intake Flow

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The Application of Artificial Neural Network in Prediction of the Performance of Spark Ignition Engine Running on Ethanol-Petrol Blends

International Journal of Engineering and Technologies ◽

10.18052/www.scipress.com/ijet.12.15 ◽

2017 ◽

Vol 12 ◽

pp. 15-31 ◽

Cited By ~ 2

Author(s):

Olisaemeka C. Nwufo ◽

Modestus Okwu ◽

Chidiebere F. Nwaiwu ◽

Johnson O. Igbokwe ◽

O. Martin I. Nwafor ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fuel Consumption ◽

Spark Ignition ◽

Combustion Efficiency ◽

Spark Ignition Engine ◽

Specific Fuel Consumption ◽

Maximum Pressure ◽

Brake Specific Fuel Consumption ◽

Artificial Neural

The performance analysis of a single cylinder spark ignition engine fuelled with ethanol – petrol blends were carried out successfully at constant load conditions. E0 (Petrol), E10 (10% Ethanol, 90% Petrol), E20 (20% Ethanol, 80% Petrol) and E30 (30% Ethanol, 70% Petrol) were used as fuel. The Engine speed, mass flow rate, combustion efficiency, maximum pressure developed, brake specific fuel consumption and Exhaust gas temperature values were measured during the experiment. Using the experimental data, a Levenberg Marquardt Artificial Neural Network algorithm and Logistic sigmoid activation transfer function with a 4–10–2 model was developed to predict the brake specific fuel consumption, maximum pressure and combustion efficiency of G200 IMEX spark ignition engine using the recorded engine speed, mass flow rate, biofuels ratio and exhaust gas temperature as input variables. The performance of the Artificial Neural Network was validated by comparing the predicted data with the experimental results. The results showed that the training algorithm of Levenberg Marquardt was sufficient enough in predicting the brake specific fuel consumption, combustion pressure and combustion efficiency of the test engine. Correlation coefficient values of 0.974, 0.996 and 0.995 were obtained for brake specific fuel consumption, combustion efficiency and pressure respectively. These correlation coefficient obtained for the output parameters are very close to one (1) showing good correlation between the Artificial Neural Network predicted results and the experimental data while the Mean Square Errors were found to be very low (0.00018825 @ epoch 10 for brake specific fuel consumption, 1.0023 @ epoch 3 for combustion efficiency and 0.0013284@ epoch 5 for in-cylinder pressure). Therefore, Artificial Neural Network toolbox called up from MATLAB proved to be a useful tool for simulation of engine parameters. Artificial Neural Network model provided accurate analysis of these complex problems and has been found to be very useful for predicting the performance of the spark ignition engine. Thus, this has proved that Artificial Neural Network model could be used for predicting performance values in internal combustion engines, in this way it would be possible to conduct time and cost efficient studies instead of long experimental ones.

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Linear Parameter-Varying Lean Burn Air-Fuel Ratio Control for a Spark Ignition Engine

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.2745849 ◽

2007 ◽

Vol 129 (4) ◽

pp. 404-414 ◽

Cited By ~ 38

Author(s):

Feng Zhang ◽

Karolos M. Grigoriadis ◽

Matthew A. Franchek ◽

Imad H. Makki

Keyword(s):

Time Delay ◽

Spark Ignition ◽

Spark Ignition Engine ◽

Linear Parameter ◽

Lean Burn ◽

Linear Parameter Varying ◽

Variable Time Delay ◽

Variable Time ◽

Fuel Ratio ◽

Parameter Varying

Maximization of the fuel economy of the lean burn spark ignition (SI) engine strongly depends on precise air-fuel ratio control. A great challenge associated with the air-fuel ratio feedback control is the large variable time delay in the exhaust system. In this paper, a systematic development of an air-fuel ratio controller based on post lean NOx trap (LNT) oxygen sensor feedback using linear parameter-varying (LPV) control is presented. Satisfactory stability and disturbance rejection performance is obtained in the face of the variable time delay. The LPV controller is simplified to an explicit parameterized gain scheduled lead-lag controller form for the ease of implementation. A Ford F-150 truck with a V8 4.6 l lean burn engine was used to demonstrate the LPV air-fuel ratio control design. Both simulation and experimental results demonstrate that the designed controller regulates the tailpipe air-fuel ratio to the preset reference for the full engine operating range.

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Application of Rapid Optical Measurement to Detect the Fluctuations of the Air-Fuel Ratio and Temperature of a Spark Ignition Engine

10.4271/910499 ◽

1991 ◽

Cited By ~ 11

Author(s):

K. Sohma ◽

T. Yukitake ◽

S. Azuhata ◽

Y. Takaku

Keyword(s):

Optical Measurement ◽

Spark Ignition ◽

Spark Ignition Engine ◽

Fuel Ratio

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Estimation of operational parameters for a direct injection turbocharged spark ignition engine by using regression analysis and artificial neural network

Thermal Science ◽

10.2298/tsci160302151t ◽

2017 ◽

Vol 21 (1 Part B) ◽

pp. 401-412 ◽

Cited By ~ 4

Author(s):

Erdi Tosun ◽

Kadir Aydin ◽

Simona Merola ◽

Adrian Irimescu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Analysis ◽

Catalytic Converter ◽

Direct Injection ◽

Spark Ignition ◽

Spark Ignition Engine ◽

Back Propagation Algorithm ◽

Brake Mean Effective Pressure ◽

Artificial Neural

This study was aimed at estimating the variation of several engine control parameters within the rotational speed-load map, using regression analysis and artificial neural network techniques. Duration of injection, specific fuel consumption, exhaust gas at turbine inlet, and within the catalytic converter brick were chosen as the output parameters for the models, while engine speed and brake mean effective pressure were selected as independent variables for prediction. Measurements were performed on a turbocharged direct injection spark ignition engine fueled with gasoline. A three-layer feed-forward structure and back-propagation algorithm was used for training the artificial neural network. It was concluded that this technique is capable of predicting engine parameters with better accuracy than linear and non-linear regression techniques.

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rediction of spark ignition engine performance responses fueled with fusel oil/gasoline blends by artificial neural network

International Journal of Automotive Engineering and Technologies ◽

10.18245/ijaet.797720 ◽

2021 ◽

Vol 10 (2) ◽

pp. 111-117

Author(s):

Kenan KUZULUGİL ◽

Zeynep HASIRCI ◽

İsmail ÇAVDAR

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Engine Performance ◽

Spark Ignition ◽

Spark Ignition Engine ◽

Fusel Oil ◽

Artificial Neural

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Measurement of the air-to-fuel ratio inside a passive pre-chamber of a fired spark-ignition engine

Automotive and Engine Technology ◽

10.1007/s41104-020-00067-w ◽

2020 ◽

Vol 5 (3-4) ◽

pp. 147-157

Author(s):

Nicolas Wippermann ◽

Olaf Thiele ◽

Olaf Toedter ◽

Thomas Koch

Keyword(s):

Spark Ignition ◽

Spark Ignition Engine ◽

Engine Operation ◽

Ratio Measurement ◽

Fuel Ratio ◽

Mixture Preparation ◽

Fuel Delivery ◽

Absorption Of Light ◽

Mass Flows ◽

Motored Engine

Abstract This paper investigates the local air-to-fuel ratio measurement within the pre-chamber of a spark-ignition engine by determining the absorption of light from hydrocarbons using an infrared sensor. The measurement was performed during fired and motored engine operation points and compared to the more common exhaust lambda measurements. The experiment provided data to compare the mixture preparation in a hot and cold environment of pre-chamber and main combustion chamber. The experiment also gives an indication regarding the possible use of a pre-chamber sensor in a motored engine at higher boost pressures and fuel mass flows, operation points that would overheat the sensor in a fired engine. The work also includes the analysis of the fuel delivery into the pre-chamber of a direct and indirect injection engine. Furthermore, pressure and temperature measurement within the pre-chamber provides information about the critical sensor environment and helps to understand the gas exchange between the two volumes.

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