西南交通大学学报2026，Vol.61Issue(2)：299-307,9.DOI:10.3969/j.issn.0258-2724.20240101
基于高斯过程回归的高压共轨燃油系统多次喷射喷油量预测
Injection Quantity Prediction of High-Pressure Common Rail Systems under Multiple Injections Based on Gaussian Process Regression
摘要
Abstract
In high-pressure common rail systems under multiple injections,the pressure waves induced by the pilot injection cause fluctuations in the main injection quantity,thereby reducing in-cylinder combustion efficiency and increasing pollutant emissions.A data-driven prediction model for the main injection quantity based on Gaussian process regression(GPR)was proposed to achieve the accurate control of injection quantity under multiple injections.First,D-optimal design and a second-order response surface method were employed to build a response surface model for the main injection quantity by utilizing rail pressure,pilot-injection pulse width,pilot-main injection interval,and main-injection pulse width as factors.Analysis of variance indicates that the four operating parameters all have extremely significant effects on the main injection quantity.Then,based on a self-developed multi-physics coupled digital simulation platform,a dataset containing 528 operating conditions was constructed,and the model was trained.On this basis,several combinations of mean functions(zero,constant,linear,and quadratic polynomials)and different kernel functions(SEiso,RQard,and Matérn)were systematically compared,and the linear mean function combined with the rational quadratic kernel function was identified as the optimal configuration.Results show that in test conditions,the mean absolute percentage error(MAPE)of main injection quantity predicted by the GPR-based model is 0.347％and the coefficient of determination R2 is 0.999 6,with predictions at different main-pulse widths and pilot-main intervals clustered closely around the regression line.In non-test conditions,the model can still accurately reproduce the fluctuation law of the main injection quantity with varying pilot-main intervals,and features lower error and higher consistency than BP,GR,and SVR models.The proposed GPR-based data-driven model under multiple injections is proved to have both high prediction accuracy and sound generalization capability,providing model support for the precise control of high-pressure common rail systems under multiple injections.关键词
柴油机/多次喷射/GPR数据驱动模型/喷油量/高压共轨燃油系统Key words
diesel engine/multiple injections/GPR-based data-driven model/injection quantity/high-pressure common rail system分类
能源科技引用本文复制引用
赵建辉,蓝中泽,卢相东,杨津韬..基于高斯过程回归的高压共轨燃油系统多次喷射喷油量预测[J].西南交通大学学报,2026,61(2):299-307,9.基金项目
国家重点研发计划(2021YFE0114600) （2021YFE0114600）
