基于支持向量机的氢混天然气发动机性能预测

Performance Prediction of Hydrogen-enriched Compressed Natural Gas Engine Based on Support Vector Machine

为提高氢混天然气(HCNG)发动机的标定效率,精确预测发动机参数,对一台氢气体积分数为20%的HCNG燃料发动机进行试验研究和性能预测分析。基于高转速低负载工况稳态标定试验数据,采用支持向量机(SVM)方法建立发动机参数关联模型,并利用不同寻优算法为模型寻找最优参数,以提高各项参数的预测精度。结果显示:若发动机运行于最大扭矩点火正时,则等效天然气比消耗(BSFC)最小,NO_(x)比排放(BSNO_(x))也处于较理想的水平,尤其在增加氢气比例时,这些外特性有更加显著的提升;SVM模型可以较好地描述发动机输入参数与输出参数之前的非线性关系,自变量与因变量之间的相关性较强(决定系数R^(2)均大于0.97),模型的预测精度较高,利用遗传算法得出的最优预测模型具有较高的泛化能力,扭矩、BSFC、BSNO_(x)的平均绝对百分比误差分别仅为1.23%、1.98%、5.43%。

A hydrogen-enriched compressed natural gas(HCNG)fuel engine with hydrogen volume fraction of 20%is experimentally studied and its performance is analyzed to improve the calibration efficiency of HCNG fuel engine and accurately predict the engine parameters.An engine parameter association model is established by using support vector machine(SVM)based on the steady-state experimental calibration data under high speed and low load conditions,and the different optimization algorithms are used to improve the prediction accuracy of engine parameters.Results show that the engine has the minimum brake specific fuel consumption(BSFC)and ideal brake specific NO_(x) consumption level(BSNO_(x))at the minimum advance for best torque.The external characteristics is obviously improved when increasing the hydrogen ratio especially.The SVM model can describe the nonlinear relationship between the input and output parameters of the engine,and has high prediction accuracy and strong correlation between independent variables and dependent variables(all coefficients of determination R^(2) are all greater than 0.97).The optimal prediction model derived using the genetic algorithm has a high generalization capability,with mean absolute percentage errors of only 1.23%,1.98%,and 5.43%for torque,BSFC,and BSNO_(x),respectively.