基于AMESim和LS-SVM的高压共轨系统建模与仿真

Study of the Modeling and Simulation of High Pressure Common Rail Based on LS-SVM and AMESim

为了提高高压共轨压力预测模型的精确性,采用AMESim软件建立了柴油机高压共轨仿真模型.利用灰色关联分析方法对共轨压力影响因素进行理论分析计算,并确定了高压共轨压力预测模型的输入输出变量;然后利用最小二乘支持向量机对共轨压力与主要的影响因素之间的数值关系进行了智能拟合,并利用自适应粒子群算法优化了最小二乘支持向量机的初始参数.通过20个预测样本的检测,最小二乘支持向量机模型的最大预测误差为0.079 1,平均相对误差降至0.039 6,其性能明显优于BP神经网络.

To increase the precision of high pressure common rail forecast model, the modeling of high pressure common rail diesel engine based on AMESim was introduced. On this basis, grey relational theoretical analysis was used to analyze the multi-parameter system and calculation to determine the input and output variables of the predictive model. Adaptive weighted Particle Swarm Optimization algorithm was applied to the optimization of initial parameters of least square support vector machine. Through the examination of 20 forecasting samples, the maximal error of the forecast model is 0.079 1, and the average relative error is reduced to 0. 039 6 by the least square support vector machine, which is far superior to commonly used empirical formula and neural network.