HILOMOT模型在柴油机快速标定试验中的应用
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摘要
基于新欧洲行驶工况(NEDC)进行某款柴油机快速标定试验,以经典多项式模型为参照研究HILOMOT模型较大样本数据的拟合能力.结果表明:较大样本时(NEDC试验共有1009个样本点),针对柴油机经济性指标(燃油消耗率)及排放指标(NO_x、soot、CO及THC排放),HILOMOT建模误差平均降低了51.9%,(最大降幅为73.0%,),数学建模质量显著优于经典多项式模型.HILOMOT模型在样本点数较多的发动机研究领域具备一定的应用优势.
Based on NEDC calibration diesel engine tests,a scientific research was done in order to find the fitting ability of the HILOMOT model to large sample compared with the polynomial model.The research result shows that for the large sample size(full factorial design,1,009 working points),HILOMOT's RMSE reduces the fuel consumption and exhaust emissions of NO_x,soot,CO and THC by an average of 51.9%,(the maximum drop is 73.0%,).HILOMOT has a much better data fitting ability than the polynomial model,and is more suitable to large samples and complicated modeling cases in engine calibration.
Based on NEDC calibration diesel engine tests,a scientific research was done in order to find the fitting ability of the HILOMOT model to large sample compared with the polynomial model.The research result shows that for the large sample size(full factorial design,1,009 working points),HILOMOT's RMSE reduces the fuel consumption and exhaust emissions of NO_x,soot,CO and THC by an average of 51.9%,(the maximum drop is 73.0%,).HILOMOT has a much better data fitting ability than the polynomial model,and is more suitable to large samples and complicated modeling cases in engine calibration.
引文
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[3]唐葆君,刘江鹏.中国新能源汽车产业发展展望[J].北京理工大学学报(社会科学版),2015,17(2):1-6.
[4]徐东辉,李岳林,解福泉,等.基于混沌RBF神经网络的汽油机瞬态工况油膜参数辨识研究[J].内燃机工程,2015,36(3):100-105.
[5]邹林君.基于Kriging模型的全局优化方法研究[D].武汉:华中科技大学机械科学与工程学院,2011.
[6]Ricardo C,Rodrigo C B,AndréC,et al.Hierarchical multi-label classification using local neural networks[J].Journal of Computer and System Sciences,2014,80(1):39-56.
[7]Erik A,Peter F,Vladislav S,et al.Hierarchical treemodel of 2D-turbulence[J].Physica D:Nonlinear Phenomena,1994,72(1):95-109.
[8]Martinez-Morales J D,Elvia P,Velaazquez C G A,et al.Modeling of internal combustion engine emissions by LOLIMOT algorithm[J].Procedia Technology,2012,3:251-258.
[9]Hafner M,Schüler M,Nelles O,et al.Fast neural networks for diesel engine control design[J].Control Engineering Practice,2000,8(11):1211-1221.
[10]Charalambos S,Vasos V,Aristodemos P.Hierarchical tree alternative path(HTAP)algorithm for congestion control in wireless sensor networks[J].Ad Hoc Networks,2013,11(1):257-272.
[11]Sivasambu M.A hierarchical model for rate-dependent poly crystals[J].International Journal of Plasticity,2009,25(5):752-767.
[12]Oliver N,Benjamin H,Tobias E,et al.Local model network toolbox for nonlinear system identification[Z].Germany:2013.
[13]计维斌,杨健,齐洪元,等.基于NEDC循环的乘用车灵活燃料优选与匹配研究[J].内燃机工程,2008,29(2):19-21.
[14]杨建.灵活燃料汽车动力总成优化匹配研究[D].上海:同济大学汽车学院,2008.
[15]朱振夏,张付军,吴滔滔,等.基于神经网络的零维预测燃烧模型及建模方法[J].内燃机学报,2015,33(2):163-170.