采用机器学习的天然气掺氢发动机可预测燃烧模型

李弘志; 胡振宇; 陶伟俊; 黄勇成

doi:10.7652/xjtuxb202601015

您当前的位置：

首页 >

文章列表页 >

采用机器学习的天然气掺氢发动机可预测燃烧模型

专题　“双碳”背景下的污染控制与资源化利用 | 更新时间：2026-01-15

- 采用机器学习的天然气掺氢发动机可预测燃烧模型
- Predictive Combustion Model for Hydrogen-Enriched Compressed Natural Gas Engines Using Machine Learning
- 西安交通大学学报 2026年60卷第1期页码：150-161
- 作者机构：
  
  西安交通大学能源与动力工程学院，710049，西安
  比亚迪汽车工业有限公司，518118，广东深圳
- 作者简介：
  
  作者简介：李弘志（1999—），男，博士生；
  黄勇成（通信作者），男，副教授，博士生导师。
- 基金信息：
  
  内蒙古自治区科技重大专项资助项目。(21 ZD0021)
- DOI：10.7652/xjtuxb202601015
  中图分类号： TK432
- 收稿：2025-04-22，
  
  纸质出版：2026-01-10
- 稿件说明：
移动端阅览
李弘志, 胡振宇, 陶伟俊, 等. 采用机器学习的天然气掺氢发动机可预测燃烧模型[J]. 西安交通大学学报, 2026,60(1):150-161.

LI Hongzhi, HU Zhenyu, TAO Weijun, et al. Predictive Combustion Model for Hydrogen-Enriched Compressed Natural Gas Engines Using Machine Learning[J]. Journal of Xi'an Jiaotong University, 2026, 60(1): 150-161.
李弘志, 胡振宇, 陶伟俊, 等. 采用机器学习的天然气掺氢发动机可预测燃烧模型[J]. 西安交通大学学报, 2026,60(1):150-161. DOI： 10.7652/xjtuxb202601015.

LI Hongzhi, HU Zhenyu, TAO Weijun, et al. Predictive Combustion Model for Hydrogen-Enriched Compressed Natural Gas Engines Using Machine Learning[J]. Journal of Xi'an Jiaotong University, 2026, 60(1): 150-161. DOI： 10.7652/xjtuxb202601015.

摘要

针对发动机一维性能仿真软件GT-Power中缺少天然气掺氢（HCNG）燃烧基础数据的问题，同时为准确模拟HCNG特殊物化特性对发动机性能的影响，开发了一种新的机器学习驱动的可预测燃烧模型。首先基于人工神经网络（ANN）的机器学习方法开发了HCNG的层流燃烧速度和着火延迟时间计算模型，然后对上述模型进行了误差分析与验证，并将其写成子程序与GTPower软件耦合，最后根据试验数据对新开发的模型进行标定和验证，并在外特性上利用所开发的模型研究了氢气体积分数

（H

）（掺氢比）对发动机性能的影响。结果表明：训练后的ANN燃烧模型预测的层流燃烧速度和着火延迟时间与原值之间的相关系数均超过0.99；利用标定后的模型计算得到的发动机性能参数误差小于3%，燃烧相位角和爆震限制点火提前角的误差小于2°；受爆震和涡前排温的限制，最大允许的

（H

）为20%，且随着

（H

）的增大，最佳点火角度逐渐推迟，有效热效率随之下降；掺氢可加快燃烧但会增加爆震倾向，而废气再循环（EGR）技术虽然会减缓燃烧但可有效抑制爆震、提前燃烧相位，两者协同控制可提高发动机的有效热效率。

Abstract

To address the lack of fundamental combustion data for hydrogen-enriched compressed natural gas (HCNG) in engine one-dimensional performance simulation software GT-Power，and to accurately simulate the effects of HCNG's unique physicochemical properties on engine performance，a novel machine learning-driven predictive combustion model is developed.Firstly，a computational model for the laminar burning velocity and ignition delay time of HCNG is developed based on a machine learning approach utilizing an artificial neural network (ANN). Subsequently，the model undergoes error analysis and validation，and is implemented as subroutines coupled with GT-Power software.Finally，the newly developed model is calibrated and validated against experimental data.Using this model，the influence of hydrogen volume fraction

)(hydrogen blending ratio)on engine performance is investigated under external characteristic conditions.The results indicate that：The correlation coefficients between the laminar burning velocity and ignition delay time predicted by the trained ANN combustion model and the original values b

oth exceed 0.99；the error in engine performance parameters calculated using the calibrated model is less than 3%，with errors in combustion phasing and knock-limited spark advance angle being less than 2°；limited by knock and pre-turbine exhaust temperature，the maximum allowable

)is 20%；As

)increases，the optimal spark timing is gradually delayed，and the effective thermal efficiency decreases；hydrogen enrichment accelerates combustion but increases knock tendency，while Exhaust Gas Recirculation (EGR) slows combustion but effectively suppresses knock and advanced combustion phasing，with the synergistic control of both hydrogen blending and EGR improving the engine's effective thermal efficiency.

关键词

Keywords

references

彭天铎,袁志逸,任磊,等.中国碳中和目标下交通部门低碳发展路径研究[J].汽车工程学报,2022,12 (4):351-359.

PENG Tianduo,YUAN Zhiyi,REN Lei,et al.Pathway for China's transport sector towards carbon neutrality target[J].Chinese Journal of Automotive Engineering,2022,12(4):351-359.

ZHANG Weiqi,WANG Yongjian,LONG Wuqiang,et al.Numerical investigation on knock intensity,combustion,and emissions of a heavy-duty natural gas engine with different hydrogen mixing strategies[J].International Journal of Hydrogen Energy,2024,62:551-561.

CHEN Lin,ZHANG Xiao,ZHANG Ren,et al.Effect of hydrogen direct injection on natural gas/hydrogen engine performance under high compression-ratio conditions[J].International Journal of Hydrogen Energy, 2022,47(77):33082-33093.

ALGAYYIM S J M,SALEH K,WANDEL A P,et al.Influence of natural gas and hydrogen properties on internal combustion engine performance,combustion, and emissions:a review[J].Fuel,2024,362:130844.

DE SIMIO L,IANNACCONE S,GUIDO C,et al. Natural gas/hydrogen blends for heavy-duty spark ignition engines:performance and emissions analysis[J]. International Journal of Hydrogen Energy,2024,50 (Part B):743-757.

BARATTA M,CHIRICHES S,GOEL P,et al.CFD modelling of natural gas combustion in IC engines under different EGR dilution and H 2 -doping conditions[J ] . Transportation Engineering,2020,2:100018.

吴长水,李聪,陈凌珊.天然气掺氢发动机的性能研究[J].小型内燃机与摩托车,2011,40(3):71-73.

WU Changshui,LI Cong,CHEN Lingshan.Study on performance of a hydrogen-enriched natural gas engine[J]. Small Internal Combustion Engine and Motorcycle, 2011,40(3):71-73.

董永超,马志豪,王鑫,等.掺氢比和废气再循环率对天然气发动机性能的影响[J].河南科技大学学报(自然科学版),2021 ,42(2):30-37.

DONG Yongchao,MA Zhihao,WANG Xin,et al. Effects of hydrogen ratio and EGR rate on performance of natural gas engine[J].Journal of Henan University of Science and Technology(Natural Science),2021,42 (2):30-37.

RAO A,WU Ziru,KUMAR MEHRA R,et al.Effect of hydrogen addition on combustion,performance and emission of stoichiometric compressed natural gas fueled internal combustion engine along with exhaust gas recirculation at low,half and high load conditions[J].Fuel, 2021 ,304:121358.

SHIVAPUJI A M,DASAPPA S.Quasi dimensional numerical investigation of syngas fuelled engine operation:MBT operation and parametric sensitivity analysis[J].Applied Thermal Engineering,2017,124:911-928.

SJERIC′ M,KOZARAC D,SCHUEMIE H,et al.A new quasi-dimensional flame tracking combustion model for spark ignition engines[J].Energy Conversion and Management,2018,165:263-275.

MA Fanhua,LI Shun,ZHAO Jianbiao,et al.A fractal-based quasi-dimensional combustion model for SI engines fuelled by hydrogen enriched compressed natural gas[J].International Journal of Hydrogen Energy,2012,37(12):9892-9901 .

PERINI F,PALTRINIERI F,MATTARELLI E.A quasi-dimensional combustion model for performance and emissions of SI engines running on hydrogen-methane blends[J].International Journal of Hydrogen Energy,2010,35(10):4687-4701 .

KREBS S,BIET C.Predictive model of a premixed,lean hydrogen combustion for internal combustion engines[J]. Transportation Engineering,2021 ,5:100086.

李昭.基于机器学习的C 1 ~C 4 烷烃燃料层流燃烧速度预测[D ] .西安:长安大学,2021.

RAO A,CHEN Tianhao,LIU Yongzhen,et al.Computational analysis of performances for a hydrogen enriched compressed natural gas engine'by advanced machine learning algorithms[J].Fuel,2023,347:128244.

FARHAN M,CHEN Tianhao,SHAHID M I,et al. Effect of exhaust gas recirculation on knock of HCNG fueled spark ignition engine and prediction of knock intensity by improved particle swarm optimization-back propagation neural network[J].Applied Thermal Engineering,2025 ,264:125439.

FARHAN M,CHEN Tianhao,RAO A,et al.Comparative knock analysis of HCNG fueled spark ignition engine using different heat transfer models and prediction of knock intensity by artificial neural network fitting tool[J].Energy,2024,304:132135.

AN Jian,HE Guoqiang,LUO Kaihong,et al.Artificial neural network based chemical mechanisms for computationally efficient modeling of hydrogen/carbon monoxide/kerosene combustion[J]. International Journal of Hydrogen Energy,2020,45 (53):29594-29605.

SHIVAPUJI A M,DASAPPA S.Analysis of thermodynamic scope engine simulation model empirical coefficients:suitability assessment and tuning of conventional hydrocarbon fuel coefficients for bio syngas[J]. International Journal of Hydrogen Energy,2017,42 (26):16834-16854.

张文嘉.基于 K-k-ε 流动模型与可预测燃烧模型的点燃式发动机性能模拟和开发[D ] .西安:西安交通大学,2018.

FOGLA N,BYBEE M,MIRZAEIAN M,et al.Development of a K-k-ε phenomenological model to predict in-cylinder turbulence[J ] .SAE International Journal of Engines,2017,10(2):562-575.

LIVENGOOD J C,WU P C.Correlation of autoignition phenomena in internal combustion engines and rapid compression machines[J].Symposium (International)on Combustion,1955,5(1):347-356.

SMITH G P,GOLDEN D M,FRENKLACH M,et al.GRI-Mech 3.0[EB/OL].[2025-03-02].http://combustion.berkeley.edu/gri-mech/version30/text30.html.

ZHOU Chongwen,LI Yang,O'CONNOR E,et al.A comprehensive experimental and modeling study of isobutene oxidation[J]. Combustion and Flame, 2016,167:353-379.

BERWAL P,SOLAGAR S,KUMAR S.Experimental investigations on laminar burning velocity variation of CH 4 +H 2 +air mixtures at elevated temperatures[J ] . International Journal of Hydrogen Energy, 2022, 47 (37):16686-16697.

HUANG J,HILL P G,BUSHE W K,et al.Shocktube study of methane ignition under engine-relevant conditions:experiments and modeling[J].Combustion and Flame,2004,136(1/2):25-42.

HANN S.A quasi-dimensional SI burn rate model for carbon-neutral fuels[M]. Wiesbaden, Germany:Springer Fachmedien Wiesbaden,2021 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

机器学习驱动天然气掺氢发动机可预测燃烧模型的开发与验证

采用机器学习的大规模并行程序I/O性能建模与预测方法

利用人工神经网络的偏光下热解炭织构类型识别

级环境下离心压气机扩压器叶片气动优化设计

混合电解槽阵列协同的风光耦合制氢系统双层多目标优化