基于IMPSO的双韦伯燃烧参数标定及预测

Calibration and Prediction of Combustion Parameters Using Double-Wiebe Function Method Based on IMPSO

针对柴油机放热规律的拟合,提出了基于免疫粒子群优化(IMPSO)算法的双韦伯方程标定方法.对25%负荷和100%负荷的放热规律分别进行了标定,对双韦伯方程采用改进算法和免疫算法进行多解问题优化,优化结果表明:量纲为1的决定系数R2大于0.998,最优解稳定性可以达到0.700以上;对其他工况和机型放热规律进行标定,拟合和试验结果吻合,具有良好的泛化性,满足柴油机多工况的放热规律拟合要求;选择转速、循环喷油量、进气压力和进气温度作为输入,建立了多层前馈(BP)神经网络双韦伯方程预测模型,各参数R2均大于0.950,表明预测值与校准值吻合,验证了神经网络预测建模方法的可行性.

A double-Wiebe function calibration method based on immune particle swarm optimization(IMPSO)algorithm was proposed to predict combustion parameters for a turbocharged diesel engine. The heat release at 25% and 100% loads was calibrated respectively,and an improved algorithm and immune algorithm were introduced to solve the multiple problems during fitting. The optimization results show that R~2 and the stability are improved to be more than 0.998 and 0.700,respectively. The heat release was also calibrated for other operating conditions of the diesel engine. The results show that fitting data are consistent to the experiments with a good generalization. In addition,a double-Wiebe function prediction model using back propagation(BP)neural network was established,prediction was conducted by selecting the engine speed,fuel mass per cycle,inlet pressure and temperature as input. Results show that R~2 is greater than 0.950 and the predicted value is consistent with the calibrated value,indicating that the prediction model can be used to predict the heat release law of diesel engines under multiple operating conditions.