内燃机工程2026，Vol.47Issue(2)：47-57,11.DOI:10.13949/j.cnki.nrjgc.2026.02.006
基于多算法与XGBoost融合的柴油机瞬态排放预测
Transient Emission Prediction for Diesel Engines Based on Multiple Algorithms and XGBoost Fusion
摘要
Abstract
To address the limitation of single algorithm,a novel hybrid model combining multiple machine learning algorithms,including artificial neural network(ANN),support vector machine(SVM),nonlinear autoregressive with exogenous inputs(NARX),long short-term memory networks(LSTM),gated recurrent unit(GRU),transformer and temporal convolutional networks(TCN),with extreme gradient boosting(XGBoost)was proposed to predict transient emission from diesel engines.Additionally,random forest algorithm was used to select best input variables of the hybrid model,and particle swarm optimization(PSO)and genetic algorithm(GA)algorithms were employed to determine the optimal hyperparameters.The results indicate that the hybrid model integrates all advantages of different machine learning algorithms and achieves more precise prediction.Meanwhile,the hybrid model demonstrates excellent prediction performance on both the training dataset and validation dataset,with R2 values exceeding 0.980 and 0.967,respectively.In the test dataset,the R2 value of the hybrid model is above 0.930,which demonstrates good generalization.关键词
柴油机/排放特性预测/多算法混合模型/机器学习算法/极端梯度提升Key words
diesel engine/emission characteristic prediction/multi-algorithm hybrid model/machine learning algorithm/extreme grandient boosting(XGBoost)分类
能源科技引用本文复制引用
王浩锦,何晓乐,严浩,陈鑫,周嘉豪,王满,曹荣雪,廖健雄..基于多算法与XGBoost融合的柴油机瞬态排放预测[J].内燃机工程,2026,47(2):47-57,11.基金项目
流体及动力机械教育部重点实验室(西华大学)开放课题项目(LTDL2025017) （西华大学）
国家级大学生创新创业训练计划项目(202510623015) Open Research Subject of Key Laboratory of Fluid and Power Machinery(Xihua University),Ministry of Education(LTDL2025017) （202510623015）
National Innovation and Entrepreneurship Training Program for College Students(202510623015) （202510623015）
