基于PSO算法的柴油机性能多目标优化

以玉柴YC6K420LN-C31型柴油机为研究对象,基于RBF(radial basis function)神经网络算法建立发动机数据模型,采用PSO(particle swarm optimization)算法进行基于模型的多目标优化研究.研究表明:RBF神经网络建立的NOx、总碳氢化合物(THC)、CO和燃油消耗率(brake specific fuel consumption,BSFC)数据模型的决定系数R2分别为0.93、0.98、0.96和0.95,模型的预测准确度均大于90％,拟合优度和预测能力满足多目标优化的需求;采用PSO算法对发动机进行多 目标优化,将适应度目标NOx、THC、CO和BSFC的权重最终均设置为0.25,生成控制图谱并进行台架验证,在推进特性工况下总排放量和油耗相比于原机平均降低了 22.9％与5.3％.

Multi-Objective Optimization of Diesel Engine PerformanceBased on PSO Algorithm

Taking YC6K420LN-C31 diesel engine as the research object, the engine data model was established based on the RBF neural network algorithm, and the model-based multi-objective optimization research was carried out using the PSO algorithm. The results show that the decision coefficients of NO_x, THC, CO and BSFC data models based on RBF neural network are 0.93,0.98,0.96 and 0.95 respectively, the prediction accuracy of the models is greater than 90%, and the goodness of fit and prediction ability meet the needs of multi-objective optimization. The PSO algorithm was used to optimize the engine for multiple goals, and the weights of the fitness goals NO_x, THC, CO, and BSFC were finally all set to 0.25. The control map was generated and the bench verification was performed, the total emissions and fuel consumption were reduced by 22.9% and 5.3% on average compared to the original engine under the propulsion mode.