• 전자공학회논문지 IE
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• v.48 no.2
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• pp.19-25
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• 2011

This paper presented neural network modeling method using vehicle data to predict fuel consumption. To acquire data for training and testing the proposed neural network, medium-class gasoline vehicle drove at downtown and parameters measured include speed, engine rpm, throttle position sensor (TPS), and mass air flow (MAF) as input data, and fuel consumption as target data from OBD-II port. Multi layer perception network was used for nonlinear mapping between the input and the output data. It was observed that the neural network model can predict the vehicle quite well with mean squared error was $1.306{\times}10^{-6}$ for the fuel consumption.

• Journal of ILASS-Korea
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• v.9 no.2
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• pp.9-17
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• 2004

The performance characteristics of lean burn system in gasoline engine are mainly affected by the air-fuel mixture in cylinder, gas exchange process of manifold system, exhaust emission of engine, and the electronic engine control system. In order to obtain the effect of performance factors on the optimum conditions of lean burn engine, this study deal with the behavior of mixture formation, gas flow characteristics of air, flow and evaporation analysis of spray droplet in cylinder, vaporization and burning characteristics of lean mixture in the engine, and the control performance of electronic engine control system. The optimum flow conditions were investigated with the swirl and tumble flows in the combustion chamber with swirl control valve. The performance characteristics and optimum condition of flow field in intake system were analyzed by the investigation of inlet flow of air and combustion stabilization on cylinder.