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Experiments of Methanol-Gasoline SI Engine Performance and Simulation of Flexible Fuel Characteristic Field
Technical Paper
2018-01-0927
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Due to the oil crisis and the requirements of energy saving and emission reduction, the research of alternative energy sources for sustainable development has made good progress. Methanol has proven to be a very suitable alternative clean fuel. Compared with gasoline, methanol has a wide range of source and the higher oxygen content and octane number and combustion efficiency, which are beneficial for the engine performance. The effect of different proportions of methanol-gasoline mixed fuel on the performance of SI engine was studied experimentally (lower proportion and higher proportion). It was found that the engine power performance, fuel economy and exhaust emissions were related to the methanol ratio under different operating conditions. In order to adapt to different operating conditions to improve the performance of methanol-gasoline engine, an on-board flexible fuel mixed system was proposed. Based on GT-Power software the model of methanol-gasoline engine with flexible fuel characteristics was built. The best proportion of the methanol-gasoline under different operating conditions was obtained by means of simulation. In addition, the engine parameters are optimized for different operating conditions and different proportions of methanol-gasoline. The results show that compared with the original engine, the equivalent fuel consumption of the flexible hybrid fuel engine is reduced by 3% ~ 10%. And by optimizing the operating parameters the range of engine fuel economy has been further expanded. The research shows that the flexible hybrid fuel engine can accurately achieve flexible fuel operation and improve the overall performance.
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Kang, Y., Shi, X., Ni, J., and Qi, H., "Experiments of Methanol-Gasoline SI Engine Performance and Simulation of Flexible Fuel Characteristic Field," SAE Technical Paper 2018-01-0927, 2018, https://doi.org/10.4271/2018-01-0927.Data Sets - Support Documents
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