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Article(id=1207658082142888882, tenantId=1146029695717560320, journalId=1205116883411038211, issueId=1207658076900008717, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1765857723984, onlineDateStr=2025-12-16, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1765857723984, onlineIssueDateStr=2025-12-16, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1765857723984, creator=13701087609, updateTime=1765857723984, updator=13701087609, issue=Issue{id=1207658076900008717, tenantId=1146029695717560320, journalId=1205116883411038211, year='2025', volume='23', issue='2', pageStart='189', pageEnd='376', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1765857722735, creator=13701087609, updateTime=1765862348176, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1207677477451833566, tenantId=1146029695717560320, journalId=1205116883411038211, issueId=1207658076900008717, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1207677477451833567, tenantId=1146029695717560320, journalId=1205116883411038211, issueId=1207658076900008717, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=318, endPage=323, ext={EN=ArticleExt(id=1207658082444878793, articleId=1207658082142888882, tenantId=1146029695717560320, journalId=1205116883411038211, language=EN, title=Research on the economy and power of explosion-proof rubber wheeler diesel engine, columnId=1207658079282373410, journalTitle=Chinese Journal of Construction Machinery, columnName=Design Manufacture and Quality Control, runingTitle=null, highlight=null, articleAbstract=

The fuel consumption meter measurement method and bench test are generally used to analyze the economy and power performance of vehicles, but the two measurement methods have high costs and complex structures. In response to this problem, back propagation (BP) neural network and regression model were selected to calculate and predict the economy and power performance of explosion-proof rubber tire vehicle diesel engines. Through comparison with experiments, the accuracy of BP neural network and regression model prediction was studied. The results show that the error between BP neural network and regression model is less than 5% when predicting fuel consumption rate and evaluating power performance, and both can be used to predict the economy and power performance of rubber tire vehicles.

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油耗仪测量法和台架实验一般用来分析车辆的经济性与动力性，但两种测量方法存在成本较高、结构复杂的问题。针对这一问题，选择反向传播（BP）神经网络和回归模型对防爆胶轮车柴油机的经济性和动力性计算与预测。通过与实验对比，研究BP神经网络与回归模型预测的准确性。结果表明：BP神经网络和回归模型在预测燃油消耗率和评估动力性时，BP神经网络与回归模型的误差均小于5%，均能用来预测胶轮车的经济性和动力性。

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任飞（1986－），男，高级工程师。E-mail：

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任飞（1986－），男，高级工程师。E-mail：

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任飞（1986－），男，高级工程师。E-mail：

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GB/T 21404—2022：内燃机发动机功率的确定和测量方法一般要求[S]. 北京：国家标准化管理委员会，2019., articleTitle=null, refAbstract=null), Reference(id=1207748681818809237, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, doi=null, pmid=null, pmcid=null, year=2017, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[11], rfOrder=10, authorNames=中国北京集团大连机车车辆有限公司, journalName=null, refType=null, unstructuredReference=中国北京集团大连机车车辆有限公司. 内燃机车用柴油机通用技术条件：GB/T 3391—2016[S]. 北京：国家标准化管理委员会，2017., articleTitle=null, refAbstract=null), Reference(id=1207748681902695318, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, doi=null, pmid=null, pmcid=null, year=2023, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[12], rfOrder=11, authorNames=安标国家矿用产品安全中心有限公司, journalName=null, refType=null, unstructuredReference=安标国家矿用产品安全中心有限公司. 煤矿用防爆柴油机无轨胶轮运输车辆通用安全技术条件：MT/T 1199—2023[S]. 北京：国家矿山安全监察局，2023., articleTitle=null, refAbstract=null)], funds=[Fund(id=1207748679579050874, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, awardId=YDZJSX2022C030, language=CN, fundingSource=中央引导地方科技发展基金资助项目(YDZJSX2022C030), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1207748671073001996, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, xref=null, ext=[AuthorCompanyExt(id=1207748671081390604, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, companyId=1207748671073001996, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1. 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School of Machinery, Jinzhong College, Jinzhong 030619, Shanxi, China), AuthorCompanyExt(id=1207748671240774175, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, companyId=1207748671223996954, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.晋中学院 机械学院，山西 晋中 030619)])], figs=[ArticleFig(id=1207748674491359930, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Fig.1, caption=Schematic diagram of a neural network, figureFileSmall=TMNP/CV3MArvGvYNd6/m9A==, figureFileBig=P2HHOKGucXnuxHI1SE0Efg==, tableContent=null), ArticleFig(id=1207748674617189057, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=图1, caption=神经网络原理图, figureFileSmall=TMNP/CV3MArvGvYNd6/m9A==, figureFileBig=P2HHOKGucXnuxHI1SE0Efg==, tableContent=null), ArticleFig(id=1207748674751406792, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Fig.2, caption=Regression model prediction results, figureFileSmall=aWDwAf0UqdROz6MXsw7F3A==, figureFileBig=3OjLOPQ0m1pSJNu++OJPiA==, tableContent=null), ArticleFig(id=1207748674843681487, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=图2, caption=预测模型预测结果, figureFileSmall=aWDwAf0UqdROz6MXsw7F3A==, figureFileBig=3OjLOPQ0m1pSJNu++OJPiA==, tableContent=null), ArticleFig(id=1207748674935956176, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Fig.3, caption=Comparison of neural network and regression model error, figureFileSmall=EFuf8JLCn35A5SqCgJ0gww==, figureFileBig=lOBkJbKICKyvQKkCViVGpQ==, tableContent=null), ArticleFig(id=1207748675045008087, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=图3, caption=神经网络与回归模型误差对比, figureFileSmall=EFuf8JLCn35A5SqCgJ0gww==, figureFileBig=lOBkJbKICKyvQKkCViVGpQ==, tableContent=null), ArticleFig(id=1207748675141477083, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Fig.4, caption=Dynamic prediction results of a regression model, figureFileSmall=uugTgTFxulOnKSBKtp9I+g==, figureFileBig=hTZ2RjYkj4gaQ6GiPrDYkA==, tableContent=null), ArticleFig(id=1207748675246334688, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=图4, caption=回归模型动力性预测结果, figureFileSmall=uugTgTFxulOnKSBKtp9I+g==, figureFileBig=hTZ2RjYkj4gaQ6GiPrDYkA==, tableContent=null), ArticleFig(id=1207748675338609381, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Fig.5, caption=Neural network vs. regression model error comparison, figureFileSmall=T69MWL9zRikxTyZfjLaczg==, figureFileBig=X9m6CoDmGRO4zGrhqqlMcA==, tableContent=null), ArticleFig(id=1207748675443466989, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=图5, caption=神经网络与回归模型误差对比, figureFileSmall=T69MWL9zRikxTyZfjLaczg==, figureFileBig=X9m6CoDmGRO4zGrhqqlMcA==, tableContent=null), ArticleFig(id=1207748675548324596, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Tab.1, caption=

Main technical indicators of DW440 dynamometer

, figureFileSmall=null, figureFileBig=null, tableContent=
参数数值
最大吸收功率/kW≥440
最大吸收扭矩/（N·m）≥2 500
最高转速/（r·min-1）5 000
转动惯量/（kg·m2）1.8
循环水量/（m3·h-1）10.5
转速测试精度/%±0.01
扭矩测试精度/%≤±0.2
), ArticleFig(id=1207748675674153722, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=表1, caption=

DW440测功机主要技术指标

, figureFileSmall=null, figureFileBig=null, tableContent=
参数数值
最大吸收功率/kW≥440
最大吸收扭矩/（N·m）≥2 500
最高转速/（r·min-1）5 000
转动惯量/（kg·m2）1.8
循环水量/（m3·h-1）10.5
转速测试精度/%±0.01
扭矩测试精度/%≤±0.2
), ArticleFig(id=1207748675783205629, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Tab.2, caption=

Main technical indicators of HZB200 fuel consumption meter

, figureFileSmall=null, figureFileBig=null, tableContent=
参数数值
流量/（kg·h-1）40~150
试验环境温度/℃23
相对湿度/%60
), ArticleFig(id=1207748675896451841, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=表2, caption=

HZB200型油耗仪主要技术指标

, figureFileSmall=null, figureFileBig=null, tableContent=
参数数值
流量/（kg·h-1）40~150
试验环境温度/℃23
相对湿度/%60
), ArticleFig(id=1207748675992920839, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Tab.3, caption=

Partial measured data of fuel consumption rate

, figureFileSmall=null, figureFileBig=null, tableContent=
序号转速/（r·min-1）扭矩/（N·m）燃油消耗率
11 000494.6275.9
21 200648.7256.4
31 400880.1225.4
41 600886.6222.8
51 800198.5218.8
), ArticleFig(id=1207748677200880393, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=表3, caption=

燃油消耗率部分实测数据

, figureFileSmall=null, figureFileBig=null, tableContent=
序号转速/（r·min-1）扭矩/（N·m）燃油消耗率
11 000494.6275.9
21 200648.7256.4
31 400880.1225.4
41 600886.6222.8
51 800198.5218.8
), ArticleFig(id=1207748677314126604, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Tab.4, caption=

BP Network structure table

, figureFileSmall=null, figureFileBig=null, tableContent=
关键参数数值
输入神经元数2
隐含层神经元3
输出神经元数1
学习算法LM
精度误差1×10-7
训练次数44
学习速度算法自适应
输入到隐层权值ωij-3.12/0.796 6
-3.416 9/20.20
1.875 7/-0.431 5
隐层阈值-3.249 3/1.582 1/-6.309
隐含层到输出层权值ωij2.280 4/-0.657 2/-4.434 4
输出层阈值-2.419 1
), ArticleFig(id=1207748677418984209, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=表4, caption=

BP网络结构表

, figureFileSmall=null, figureFileBig=null, tableContent=
关键参数数值
输入神经元数2
隐含层神经元3
输出神经元数1
学习算法LM
精度误差1×10-7
训练次数44
学习速度算法自适应
输入到隐层权值ωij-3.12/0.796 6
-3.416 9/20.20
1.875 7/-0.431 5
隐层阈值-3.249 3/1.582 1/-6.309
隐含层到输出层权值ωij2.280 4/-0.657 2/-4.434 4
输出层阈值-2.419 1
), ArticleFig(id=1207748677557396251, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Tab.5, caption=

Multiple regression model statistics

, figureFileSmall=null, figureFileBig=null, tableContent=
R2调整R2误差
0.9760.9564.04
), ArticleFig(id=1207748677641282338, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=表5, caption=

多元回归模型统计

, figureFileSmall=null, figureFileBig=null, tableContent=
R2调整R2误差
0.9760.9564.04
), ArticleFig(id=1207748677737751334, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Tab.6, caption=

Regression coefficients for multiple regression models

, figureFileSmall=null, figureFileBig=null, tableContent=
参数估计值误差tSig
常量452.8454.3518.313 30.000 411 5
转速/（r·min-1）-0.030 8170.047 06-6.548 40.001 244
扭矩/（N·m）0.148 960.211 390.704 660.512 48
转速/（r·min-1）8.09×10-51.23×10-56.5610.001 233 1
扭矩/（N·m）-9.14×10-51.40×10-4-0.653 450.542 32
), ArticleFig(id=1207748677863580462, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=表6, caption=

多元回归模型的回归系数

, figureFileSmall=null, figureFileBig=null, tableContent=
参数估计值误差tSig
常量452.8454.3518.313 30.000 411 5
转速/（r·min-1）-0.030 8170.047 06-6.548 40.001 244
扭矩/（N·m）0.148 960.211 390.704 660.512 48
转速/（r·min-1）8.09×10-51.23×10-56.5610.001 233 1
扭矩/（N·m）-9.14×10-51.40×10-4-0.653 450.542 32
), ArticleFig(id=1207748677960049456, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Tab.7, caption=

BP neural network errors

, figureFileSmall=null, figureFileBig=null, tableContent=
转速/（r·min-1）1 0001 2001 4001 6001 6011 8002 0002 2012 4002 6012 602
实验值275.9256.4239.0225.4222.8222.0218.8220.6241.4258.5250.7
BP预测值274.47256.54237.29226.65223.13221.55221.55223.94240.89259.65249.58
回归预测值276.84257.64240.19226.98220.77220.40220.31226.29238.42255.82256.24
BP误差/%0.520.050.720.550.150.201.261.510.210.440.45
回归误差/%0.340.480.500.700.910.720.692.581.231.042.21
), ArticleFig(id=1207748678048129845, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=表7, caption=

BP神经网络误差

, figureFileSmall=null, figureFileBig=null, tableContent=
转速/（r·min-1）1 0001 2001 4001 6001 6011 8002 0002 2012 4002 6012 602
实验值275.9256.4239.0225.4222.8222.0218.8220.6241.4258.5250.7
BP预测值274.47256.54237.29226.65223.13221.55221.55223.94240.89259.65249.58
回归预测值276.84257.64240.19226.98220.77220.40220.31226.29238.42255.82256.24
BP误差/%0.520.050.720.550.150.201.261.510.210.440.45
回归误差/%0.340.480.500.700.910.720.692.581.231.042.21
), ArticleFig(id=1207748678161376061, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Tab.8, caption=

Main technical indicators of FASM-5000

, figureFileSmall=null, figureFileBig=null, tableContent=
测量对象量程范围相对误差/%绝对误差/%
HC（0~0.2）%±30.000 4
CO（0~10）%±30.02
NO（0~0.4）%±40.002 5
CO2（0~16）%±30.3
O2（0~25）%±50.1
), ArticleFig(id=1207748678266233670, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=表8, caption=

FASM-5000主要技术指标

, figureFileSmall=null, figureFileBig=null, tableContent=
测量对象量程范围相对误差/%绝对误差/%
HC（0~0.2）%±30.000 4
CO（0~10）%±30.02
NO（0~0.4）%±40.002 5
CO2（0~16）%±30.3
O2（0~25）%±50.1
), ArticleFig(id=1207748678371091273, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Tab.9, caption=

Measured data of diesel engine power

, figureFileSmall=null, figureFileBig=null, tableContent=
序号12345
数值33.1733.6634.1733.2532.15
), ArticleFig(id=1207748678475948876, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=表9, caption=

柴油机功率部分实测数据

, figureFileSmall=null, figureFileBig=null, tableContent=
序号12345
数值33.1733.6634.1733.2532.15
), ArticleFig(id=1207748678593389393, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Tab.10, caption=

Partial data on the concentration of each gas in the exhaust gas

, figureFileSmall=null, figureFileBig=null, tableContent=
序号COHCNO
11 018.1189.1340.52
21 066.119.05370.39
31 158.739.91332.53
41 241.416.02134.68
51 340.876.94298.76
), ArticleFig(id=1207748678698246995, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=表10, caption=

尾气中各气体浓度部分数据

, figureFileSmall=null, figureFileBig=null, tableContent=
序号COHCNO
11 018.1189.1340.52
21 066.119.05370.39
31 158.739.91332.53
41 241.416.02134.68
51 340.876.94298.76
), ArticleFig(id=1207748678790521686, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Tab.11, caption=

BP network structure table

, figureFileSmall=null, figureFileBig=null, tableContent=
关键参数数值
输入神经元数3
隐含层神经元3
输出神经元数1
学习算法LM
精度误差1×10-7
训练次数50
学习速度算法自适应
输入到隐层权值0.905 6/-1.127 6/-1.564 2
0.830 3/-0.076 7/0.231 8
1.586 5/0.335 9/0.039 1
隐层阈值-2.080 7/0.133 5/-1.845 5
隐含层到输出层权值0.317 9/-0.619 3/-1.068 9
输出层阈值-0.284 5
), ArticleFig(id=1207748678891184988, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=表11, caption=

BP网络结构表

, figureFileSmall=null, figureFileBig=null, tableContent=
关键参数数值
输入神经元数3
隐含层神经元3
输出神经元数1
学习算法LM
精度误差1×10-7
训练次数50
学习速度算法自适应
输入到隐层权值0.905 6/-1.127 6/-1.564 2
0.830 3/-0.076 7/0.231 8
1.586 5/0.335 9/0.039 1
隐层阈值-2.080 7/0.133 5/-1.845 5
隐含层到输出层权值0.317 9/-0.619 3/-1.068 9
输出层阈值-0.284 5
), ArticleFig(id=1207748678991848287, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Tab.12, caption=

Multiple regression model statistics

, figureFileSmall=null, figureFileBig=null, tableContent=
R2调整R2误差
0.9350.911.339
), ArticleFig(id=1207748679092511590, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=表12, caption=

多元回归模型统计

, figureFileSmall=null, figureFileBig=null, tableContent=
R2调整R2误差
0.9350.911.339
), ArticleFig(id=1207748679168009067, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Tab.13, caption=

Regression coefficients for multiple regression models

, figureFileSmall=null, figureFileBig=null, tableContent=
测量对象系数误差tsig
常量51.2321.33919.5914.79×10-8
NO0.023 70.003 37.1859.48×10-5
HC-0.006 20.008 3-0.7400.48
CO8.3×10-30.000 20.3540.043
), ArticleFig(id=1207748679272866669, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=表13, caption=

多元回归模型的回归系数

, figureFileSmall=null, figureFileBig=null, tableContent=
测量对象系数误差tsig
常量51.2321.33919.5914.79×10-8
NO0.023 70.003 37.1859.48×10-5
HC-0.006 20.008 3-0.7400.48
CO8.3×10-30.000 20.3540.043
), ArticleFig(id=1207748679352558448, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=EN, label=Tab.14, caption=

BP neural network errors

, figureFileSmall=null, figureFileBig=null, tableContent=
实验值BP预测值回归预测值BP误差/%回归误差/%
33.1733.228 132.758 1-0.181.24
33.6633.376 833.516 80.840.43
34.1734.149 234.259 90.06-0.26
33.2533.114 833.271 30.41-0.06
32.1532.391 932.422 7-0.75-0.85
), ArticleFig(id=1207748679419667315, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658082142888882, language=CN, label=表14, caption=

BP神经网络误差

, figureFileSmall=null, figureFileBig=null, tableContent=
实验值BP预测值回归预测值BP误差/%回归误差/%
33.1733.228 132.758 1-0.181.24
33.6633.376 833.516 80.840.43
34.1734.149 234.259 90.06-0.26
33.2533.114 833.271 30.41-0.06
32.1532.391 932.422 7-0.75-0.85
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防爆胶轮车柴油机经济性与动力性研究
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任飞 1 , 闫政 2
中国工程机械学报 | 设计制造与质量控制 2025,23(2): 318-323
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中国工程机械学报 | 设计制造与质量控制 2025, 23(2): 318-323
防爆胶轮车柴油机经济性与动力性研究
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任飞1 , 闫政2
作者信息
  • 1.国家能源神东煤炭集团公司，陕西 神木 719315
  • 2.晋中学院 机械学院，山西 晋中 030619

    • 任飞（1986－），男，高级工程师。E-mail：

Research on the economy and power of explosion-proof rubber wheeler diesel engine
Fei REN1 , Zheng YAN2
Affiliations
  • 1. CHN Energy Shendong Coal Group, Shenmu 719315, Shaanxi, China
  • 2. School of Machinery, Jinzhong College, Jinzhong 030619, Shanxi, China
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摘要
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油耗仪测量法和台架实验一般用来分析车辆的经济性与动力性，但两种测量方法存在成本较高、结构复杂的问题。针对这一问题，选择反向传播（BP）神经网络和回归模型对防爆胶轮车柴油机的经济性和动力性计算与预测。通过与实验对比，研究BP神经网络与回归模型预测的准确性。结果表明：BP神经网络和回归模型在预测燃油消耗率和评估动力性时，BP神经网络与回归模型的误差均小于5%，均能用来预测胶轮车的经济性和动力性。

反向传播（BP）神经网络  /  回归模型  /  燃油消耗率  /  动力性

The fuel consumption meter measurement method and bench test are generally used to analyze the economy and power performance of vehicles, but the two measurement methods have high costs and complex structures. In response to this problem, back propagation (BP) neural network and regression model were selected to calculate and predict the economy and power performance of explosion-proof rubber tire vehicle diesel engines. Through comparison with experiments, the accuracy of BP neural network and regression model prediction was studied. The results show that the error between BP neural network and regression model is less than 5% when predicting fuel consumption rate and evaluating power performance, and both can be used to predict the economy and power performance of rubber tire vehicles.

back propagation (BP) neural network  /  regression model  /  fuel consumption rate  /  dynamics
任飞, 闫政. 防爆胶轮车柴油机经济性与动力性研究. 中国工程机械学报, 2025 , 23 (2) : 318 -323 .
Fei REN, Zheng YAN. Research on the economy and power of explosion-proof rubber wheeler diesel engine[J]. Chinese Journal of Construction Machinery, 2025 , 23 (2) : 318 -323 .
正文
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经济性和动力性是衡量车辆优劣的两个最重要的指标。燃油消耗量是评价燃油经济性的关键指标，快捷而准确地测量汽车的燃油消耗量，成为有效、准确地评价汽车燃油经济性能的重要手段。不断改进的油耗测量方法对燃油消耗量的评价具有非常重要的影响。
张庆洪[1]利用径向基函数（radial basis function，RBF）神经网络进行汽车油耗的测量，通过与油耗仪测量数据对比，验证了径向基神经网络测量油耗的正确性；苗会等[2]利用AMESim搭建了柴油机油耗测量系统并对其进行优化；成文浩等[3]利用多元回归模型对胶轮车进行了动力性评估，证明了动力性预测回归模型的正确性；刘树成等[4]对车用大功率柴油机与液力变矩器动态匹配的影响因素进行了分析。
Cesur等[5]利用数字孪生的方法去预测柴油机的燃油消耗量；Amer[6]采用神经网络技术建立了基于神经网络的燃油消耗成本估计模型，利用发动机尺寸、距离、速度以及乘客来预测油耗；Yao等[7]利用随机森林模型对于车辆的燃油消耗量进行了预测。
综上所述，已有研究人员证明神经网络与回归模型预测车辆的经济性和动力性是可行的，但针对胶轮车同时用两种模型来预测并比较其优劣还未见报道。本文采用反向传播（back propagation，BP）神经网络和回归模型预测胶轮车的动力性和经济性，并与实验进行对比，来获得更精确的模型。
1　测试装置
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使用江苏启测测功器有限公司生产的DW440测功机和HZB200型油耗仪对防爆柴油机车辆进行燃油经济性和动力性试验，试验标准为GB 1105.1~1105.3—2022《内燃机台架性能试验方法》、GB/T 33192—2016《内燃机车用柴油机通用技术条件》、MT/T 1199—2023煤矿用防爆柴油机无轨胶轮运输车辆通用安全技术条件等。
DW440测功机主要技术指标见表1。
HZB200型油耗仪主要技术指标见表2。
2　预测模型
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考虑到汽车发动机属于强非线性系统，本章将研究建立神经网络油耗估计模型与多元回归油耗模型，以提高油耗的估计精度。
2.1　BP神经网络模型搭建
BP神经网络采用误差逆传播算法，算法的基本思想由信号的正向传播与误差的反向传播两部分构成，其原理如图1所示。
设神经网络的输入层、隐含层及输出层的节点数分别为2、3、1。xi为输入层的输入，ωij为节点i与节点j的连接权值，fi(·)为激活函数，y为输出层的输出。正向传播时，输入样本从输入层传入，经过各隐层逐步处理，传给输出层。若输出层的实际输出与期望的输出不相等，则转到误差的反向传播过程。
BP神经网络学习算法主要包括动量BP算法、弹性BP算法、变梯度算法和LM算法等。对包含较多权值的网络而言，LM算法的收敛速度最快，且适合于高精度的网络，因此，本文选择LM算法。
隐层神经元个数的确定使用了经验公式如下：
式中：m为输入层神经元个数；n为输出层神经元个数；a为[1，15]之间的常数。因此，隐层神经元的个数应在4~18之间。为得到最优的BP模型，构建了最优隐含层的BP模型，最终确定最优隐含层为3个。
2.2　回归模型搭建
回归模型是研究自变量与因变量之间线性相关情况的模型，如果回归分析模型中因变量和自变量包含2个或者2个以上，且变量之间存在关系，则称之为多元回归模型。
设随机变量y与一般变量（x1，x2，…，xk）的回归模型为
式中：β0，β1，…，βk为k+1个未知参数；μ为随机误差项。
3　燃油消耗率预测
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利用BP神经网络以及回归模型预测燃油消耗率。车辆的油耗与速度、加速度、转弯半径、爬坡度以及滚阻系数等关键参数都存在相关性，都可将这些因素转化为发动机的转速和转矩。因此选取发动机转速、转矩两个参数作为神经网络的输入量，利用BP神经网络和回归模型对燃油消耗率进行预测。利用所测得的转矩、转速预测车辆的燃油消耗率，部分数据见表3。
3.1　BP神经网络预测
利用Matlab软件中内置的BP神经网络模型进行燃油消耗率的预测，以探讨燃油消耗率与转矩转速的关系，BP神经网络模型数据见表4所示。
3.2　回归模型预测
利用Matlab软件对燃油消耗率和转矩转速进行回归分析，以探讨燃油消耗率与转矩转速的关系。燃油消耗率的多元回归模型统计和回归系数见表5~表6。
针对发动机转矩与转速与车辆燃油消耗量存在较强的非线性关系，可得燃油经济性的评估模型为
式中：Ne为发动机转速；T为扭矩。将A1~A5代入到式（3）中得
BP神经网络油耗预测模型与实验数据的曲线重合度较高，如图2所示，误差最大出现在样本点8处，误差达到1.51%，最大误差小于5%。BP神经网络能够较为准确地预测车辆的燃油消耗率。回归模型与实验数据的曲线重合度较高，误差最大出现在样本点8处，误差达到2.58%，最大误差小于5%。因此，回归模型可以用来预测车辆的燃油消耗率。
神经网络与回归模型与实验数据的误差对比情况如图3所示。BP神经网络和回归模型的预测误差见表7。
由图3、表7可知，BP神经网络与回归模型的预测结果相近，且误差均小于5%，但通过对比两者的预测误差，BP神经网络的预测效果要优于回归模型的预测效果。
4　动力性预测
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尾气测量所用仪器为FASM-5000汽车排气分析仪，主要技术参数见表8。利用BP神经网络以及回归模型预测车辆动力性，车辆动力性取决于排放尾气中CO、HC、NO的气体浓度，本文采用实验所测得的各气体浓度来预测车辆的动力性，具体数据见表9~表10。
4.1　BP神经网络预测
利用Matlab软件中内置的BP神经网络模型对最大底盘输出功率与尾气中各气体浓度进行预测，以探讨燃油消耗率与转矩转速的关系，BP神经网络模型数据见表11。
4.2　回归模型预测
对最大底盘输出功率与尾气中各气体浓度进行回归分析，研究底盘输出功率与尾气中各气体浓度的关系。多元回归模型统计结果见表12，回归系数见表13。
针对尾气中各气体浓度与车辆输出功率存在线性关系，可得动力性评估模型为
式中：N为NO浓度；C为CO浓度。
将表13的数据代入式（5）可得
BP神经网络预测结果和实验数据的对比，误差最大值为0.84%，小于5%，表明利用BP神经网络预测胶轮车的动力性具有较高的一致性。
回归模型动力性预测结果和实验数据的对比如图4所示。回归模型的预测值与实验数据有较高的重合度，见表14，误差最大值为1.24%，小于5%，这说明可以利用回归模型预测胶轮车的动力性。
神经网络以及回归模型与实验数据的误差均小于5%，如图5所示，两者均能较为准确地预测车辆的动力性。
5　结语
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本文利用BP神经网络以及回归模型对于胶轮车防爆柴油机的动力性以及经济性进行预测，通过对比BP神经网络的预测值，与回归模型的预测值与实验数据的误差来对比两种模型的优劣。
BP神经网络与回归模型在预测燃油消耗率时误差均小于5%，因此在经济性预测时，两者均可以使用。在评估动力性时，BP神经网络与回归模型的误差均小于5%，均能用来预测胶轮车的动力性。
基金
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  • 中央引导地方科技发展基金资助项目(YDZJSX2022C030)
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中央引导地方科技发展基金资助项目(YDZJSX2022C030)
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    1.国家能源神东煤炭集团公司，陕西 神木 719315
    2.晋中学院 机械学院，山西 晋中 030619
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
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