Development of a fuzzy logic model for the prediction of spark-ignition engine performance and emission for gasoline–ethanol blends

Abstract Due to the enormous of fossil fuels and the ensuing increase in automobiles, an unprecedented scenario has arisen with pollution levels that are out of human control. In this study, a fuzzy logic model is developed to predict how well a spark-ignition engine running on gasoline and ethanol mixes would operate. A test engine was operated on pure gasoline and gasoline–ethanol fuel mixtures in a range of ratios at varying engine speeds. In order to estimate outputs such as brake-specific fuel consumption (BSFC), brake thermal efficiency, nitrogen oxides (NOx), hydrocarbon emissions, and carbon monoxide, a fuzzy logic model, a sort of logic model application, has been developed using experimental data. The developed fuzzy logic model’s output was compared to the results of the trials to see how well it performed. The output parameters were indicated, including braking power, thermal, volumetric, and mechanical efficiency. The input parameters were engine speed and ethanol mixes. Regression coefficients were nearly equal for training and testing data. According to the study, a superior method for accurately forecasting engine performance is the fuzzy logic model. To eliminate proportionality signs from equations, regression analysis is used. It is accurate to develop mathematical relations based on dimensional analysis. Based on the root mean square errors, BSFC is a minimum of 6.12 and brake power is a maximum of 8.16; lower than 2% of errors occur on average.