煤层气发动机混合气充量系数模型的辨识

Identification of Volumetric Efficiency Model for Gaseous Mixture of a Coal-bed Gas Engine

将充量系数作为预混合点燃式煤层气发动机转速和进气歧管压力的函数,根据实验数据,应用系统辨识方法,建立了基于多项式、BP神经网络和自适应神经网络模糊推理系统(ANFIS)的混合气充量系数模型,比较了各种模型的建模效果。为了验证模型的有效性,将所建充量系数模型分别嵌入煤层气发动机平均值模型,对平均值模型的估计值和实验数据进行了比较。检验结果表明,充量系数的非参数模型比多项式模型具有更高的预测精度,适合作为系统仿真的子模型。

The volumetric efficiency of gaseous mixture （VEGM） is regarded as a function of speed and intake manifold absolute pressure of a pre-mixed spark ignition coal-bed gas engine. Three models of VEGM were developed based on polynomial, BP neural network and the adaptive neural fuzzy inference system （ANFIS）, respectively, by using the system identification method and the experimental data. The modeling efficiencies of various models was compared. In order to validate the models, the volumetric efficiency models were embedded into the mean value model of the coal-bed gas engine respectively. The estimated output of the mean value model was compared with the experiments data. The results show that non-parametric models of the volumetric efficiency are more accurate than the parametric model for prediction and more suitable for system simulation as a sub-model.