基于离子电流的汽油HCCI发动机燃烧相位传感方法

Ion Current-Based Method for Gasoline HCCI Combustion Phase Detecting

为了实现HCCI发动机闭环反馈控制,提出了一种采用递归神经网络算法在线检测燃烧相位CA10和CA50的方法.该方法首先提取每个循环的离子电流信号曲线的峰值位置、始点位置、终点位置和拐点位置,将这4个特征信息、发动机转速及4个控制参数进行归一化处理,输入到Elman神经网络,然后计算出燃烧相位CA10或CA50.以基于全可变气门机构的汽油HCCI发动机为对象,选取了台架试验中6个典型的HCCI动态变负荷过程数据作为训练样本,以转速为2 000 r/min和2 500 r/min下的2个动态变负荷数据为测试样本.测试结果标明,该方法对HCCI动态过程的燃烧相位CA10预测误差小于0.8oCA,对CA50预测误差小于0.9oCA;该方法与BP网络和RBF网络相比,具有更低的误差和更强的泛化能力.

An dynamic recurrent neural networks （ DRNN ）-based approach to the on-line detection of combustion phase CA10 and CA50 was presented for close-loop feedback control of HCCI gasoline engine. Some characteristic values of the ion current signal, including the position of peak, the start point, the end point and the inflection point, were extracted from the ion current signal of every cycle. Then the normalized parameters including control parameters of valves events and the engine speed were inputted into an Elman network, which outputs the combustion phase CA10 and CA50. The study was based on HCCI gasoline engine with a fully variable valve actuating system, and 6 sets of typical dynamic process data via varying load were used as training sets, the other two dynamic data of which the speed was 2 000 r/min and 2 500 r/min, were used as test sets. The results show that the error of detecting HCCI combustion phase CA10 is less than 0.8℃A, the error of detecting HCCI dynamic process combustion phase CA50 is less than 0.9℃A. Compared with BP network and RBF network, Elman network has lower error and stronger generalization ability.