基于重要工况点的柴油机逐点模型标定
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摘要
电控柴油机的标定需要综合考虑油耗、排放、进排气温度等因素,但随着国五排放法规的实施,导致标定难度成指数级增加,传统的标定手段工作量大,成本高。文中在模型标定的基础上提出了基于重要工况点的逐点模型标定方法。通过聚类分析并结合实际排放特性分析,选出NEDC循环工况中重要工况点;采用空间填充法完成对各工况点的试验设计,并通过台架试验采集发动机试验数据;在完成各响应模型搭建的基础上,通过遗传算法得到各工况点下的最优燃烧参数组合;根据三次多项式拟合完成MAP绘制;基于万有特性试验对MAP进行优化,同时进行整车转鼓排放试验。研究结果表明,该标定模型生成的MAP具有良好的燃油经济性和较好的排放性能,可以准确地预测发动机响应参数,相较于人工标定油耗降低了15%。
The calibration of the electronic control diesel engine needs to consider comprehensively factors such as fuel consumption,emission and intake and exhaust temperature. However,with the implementation of the China Ⅴ emission regulations,the difficulty of calibration is increased exponentially. The traditional calibration method needs huge work and high cost. On the basis of model calibration,a point by point model calibration method based on important working points is proposed. Through cluster analysis and analysis of the actual emission characteristics,the important working conditions in the NEDC cycle are selected. Experimental design of the working points is completed by the space filling method,and engine test data is collected by the bench test. On the basis of the response model,the optimal combustion parameters combination at each working point is obtained by genetic algorithm. MAP mapping is completed according to the cubic polynomial fitting. MAP is optimized based on the universal characteristic test,and the vehicle drum emission test is carried out simultaneously. The results show that the MAP generated by the calibration model has good fuel economy and better emission performance,which can accurately predict the engine response parameters,with the fuel consumption 15% lower than that of artificial calibration.
The calibration of the electronic control diesel engine needs to consider comprehensively factors such as fuel consumption,emission and intake and exhaust temperature. However,with the implementation of the China Ⅴ emission regulations,the difficulty of calibration is increased exponentially. The traditional calibration method needs huge work and high cost. On the basis of model calibration,a point by point model calibration method based on important working points is proposed. Through cluster analysis and analysis of the actual emission characteristics,the important working conditions in the NEDC cycle are selected. Experimental design of the working points is completed by the space filling method,and engine test data is collected by the bench test. On the basis of the response model,the optimal combustion parameters combination at each working point is obtained by genetic algorithm. MAP mapping is completed according to the cubic polynomial fitting. MAP is optimized based on the universal characteristic test,and the vehicle drum emission test is carried out simultaneously. The results show that the MAP generated by the calibration model has good fuel economy and better emission performance,which can accurately predict the engine response parameters,with the fuel consumption 15% lower than that of artificial calibration.
引文
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[8]刘敬平,李迎春,夏孝朗,等.对应于整车行驶循环的发动机简化工况点确定方法的研究[J].汽车工程,2011,33(7):563-568.
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[2] BAKALIS D P,STAMATIS A G. Data analysis and performance model calibration of a small turbojet engine[J]. Proceedings of the Institution of Mechanical Engineers Part G:Journal of Aerospace Engineering,2011,226(12):1523-1533.
[3]吴建营,杨福源,陈小讯,等.共轨柴油机基于模型标定方法应用研究[J].车用发动机,2006(3):24-27.
[4]倪计民,杜倩颖,周英杰,等. Do E在高压共轨柴油机优化设计中的应用[J].内燃机学报,2009,27(3):231-236.
[5] BRAHMI E H,DENIS-VIDAL L,CHERFI Z,et al. Statistical modeling and optimization for diesel engine calibration[C]. Proceedings of IECON 2009 35th Annual Conference of the IEEE Industrial Electronics Society[S. l.]:IEEE,2009:1770-1775.
[6] KOZAN Recep,GOKCE Mersin. Two-stage engine mapping for the calibration of carbon monoxide emission[J]. Modern Applied Science,2009,3(4):30-36.
[7]冯国胜,贾素梅,周玮.基于BP神经网络的电控单体泵柴油机标定方法[J].农业工程学报,2012,28(15):21-26.
[8]刘敬平,李迎春,夏孝朗,等.对应于整车行驶循环的发动机简化工况点确定方法的研究[J].汽车工程,2011,33(7):563-568.
[9] SHIM B J,PARK K S,KOO J M,et al. Work and speed based engine operation condition analysis for new european driving cycle[J]. Journal of Mechanical Science and Technology,2014,28(2):755-761.
[10] BEATRICE C,NAPOLITANO P,GUIDO C. Injection parameter optimization by Do E of a light-duty diesel engine fed by Bio-ethanol/RME/diesel blend[J]. Applied Energy,2014,113(6):373-384.
[11] GE H W,SHI Y,REITZ R D,et al. Optimization of a HSDI diesel engine for passenger cars using a multi-objective genetic algorithm and multi-dimensional modeling[J]. SAE International Journal of Engines,2009,2(1):691-713.