| 제목 |
APPLICATION OF PHYSICAL MODEL TEST-BASED LONG SHORT-TERM MEMORY ALGORITHM AS A VIRTUAL SENSOR FOR NITROGEN OXIDE PREDICTION IN DIESEL ENGINES
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| 분야 |
Engine and Emissions |
| 언어 |
English |
| 저자 |
DALHO SHIN(Konkuk University), Jo Seongin(Chonnam National University), HYUNG JUN KIM( National Institute of Environmental Research), Suhan Park( Konkuk University) |
| Key Words |
Deep learning, Long short-term memory algorithm, Intake temperature, Injection timing, Indicated specific nitrogen oxide (ISNOX), Indicated mean effective pressure (IMEP) |
| 초록 |
In this study, exhaust gas emissions are predicted using long short-term memory (LSTM) algorithm and minimum engine data, such as intake air temperature, emission gas temperature, and injection timing. Unlike existing modeling analysis methods, deep learning does not require various vehicle specifications and data, and the correlation between the measured data is derived by itself; therefore, it can serve as a virtual emission sensor. As it is difficult to analyze the correlation between the deep learning and test data from actual road cars because of the complex environment, an experimental single-cylinder diesel engine is used in this study. The intake air temperature is varied from 0 °C to 100 °C, and the injection timing is varied for nitrogen oxide measurement. Consequently, nitrogen oxide is successfully predicted with a high correlation R2 of 0.994 using minimal engine data.
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