Research on knock recognition of coal-based naphtha homogeneous charge compression ignition engine based on combined feature extraction and classification. (15th September 2021) - Lu, An; Zhang, Chunhua; Ren, Yunzheng; Li, Yangyang; Li, Songfeng; Yin, Peng
Highlights: Knock recognition of coal-based naphtha HCCI engines was investigated. With occurrence of knock, HRRmax, PRRmax, and PRAmax are greatly increased. EMD and SampEn were used to recognize engine knock based on in-cylinder pressure. At Tin of 60 °C, the best and average classification accuracy reach 99.67% and 91.35%. The proposed method is a good choice for knock recognition. Abstract: The purpose of this study is to investigate combustion characteristics and knock recognition of HCCI engines fueled with coal-based naphtha by analyzing in-cylinder pressure. The in-cylinder pressures of knock and normal combustion at two different intake temperatures ( Tin ) of 60 °C and 90 °C were collected on a modified diesel engine, characteristic vector was constructed from the in-cylinder pressure signal based on empirical mode decomposition (EMD) and sample entropy (SampEn), and a support vector machine (SVM) classifier was used to recognize knock. The results show that in-cylinder pressure, heat release rate ( HRR ), in-cylinder temperature, pressure rise rate ( PRR ) and pressure rise acceleration ( PRA ) change steadily in normal combustion. With occurrence of knock, they have a sharp rise and huge fluctuations. HRRmax, PRRmax, and PRAmax are greatly increased, and the corresponding phase is significantly advanced, especially in high-temperature stage. When Tin is 90 °C, PRAmax reaches 1.174 MPa/°CA 2, which is an increase of 854.47% compared to normal combustion. However, the coefficient of variation for peak pressure ( COVPmax ) of two Tin s show completely opposite results. At Tin of 60 °C, COVPmax increases from 3.95% to 4.71%, while at Tin of 90 °C, COVPmax reduces from 3.08% to 1.52%. Moreover, knock recognition achieves the best classification result at Tin of 60 °C. The average classification accuracy reaches 91.35%, and the best classification accuracy reaches 99.67%. This shows that feature extraction method based on EMD and SampEn has excellent classification performance for knock recognition of coal-based naphtha HCCI engines.