Article(id=1311999949886292188, tenantId=1146029695717560320, journalId=1311385739772133495, issueId=1311999647825101743, articleNumber=null, orderNo=null, doi=10.6041/j.issn.1000-1298.2025.12.014, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1757260800000, receivedDateStr=2025-09-08, revisedDate=1758729600000, revisedDateStr=2025-09-25, acceptedDate=null, acceptedDateStr=null, onlineDate=1790734763832, onlineDateStr=2026-09-30, pubDate=1766592000000, pubDateStr=2025-12-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790734763832, onlineIssueDateStr=2026-09-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790734763832, creator=13701087609, updateTime=1790734763832, updator=13701087609, issue=Issue{id=1311999647825101743, tenantId=1146029695717560320, journalId=1311385739772133495, year='2025', volume='56', issue='12', pageStart='1', pageEnd='818', issueExtLink='null', onlineDate='null', pubDate='1766592000000', pubDateStr='2025-12-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790734691815, creator='13701087609', updateTime=1790738411040, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1312015247465083866, tenantId=1146029695717560320, journalId=1311385739772133495, issueId=1311999647825101743, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1312015247465083867, tenantId=1146029695717560320, journalId=1311385739772133495, issueId=1311999647825101743, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=158, endPage=169, ext={EN=ArticleExt(id=1311999950083424477, articleId=1311999949886292188, tenantId=1146029695717560320, journalId=1311385739772133495, language=EN, title=Evaluation Method of Tractor Dynamic Models Based on Time Response Error and Grey Relational Analysis, columnId=1311999648647185329, journalTitle=Transactions of the Chinese Society for Agricultural Machinery, columnName=Special Column of Key Technologies for Intelligent Agricultural Power Equipment, runingTitle=null, highlight=null, articleAbstract=

Aiming to address the issues of missing accuracy evaluation for tractor dynamic models and the lack of systematic, standardized assessment methods, an accuracy evaluation approach was proposed based on time-response error and grey relational analysis, grounded in the verification, validation, and accreditation (VV&A) theory of model simulation systems. A hierarchical validation index system comprising phase error, amplitude error, and shape error was constructed, and the grey relational degree method was applied for segmental evaluation of dynamic response curves. Four sets of idealized test cases with defined error components were designed and analyzed by using both the proposed method and traditional numerical error analysis techniques. The results demonstrated that the proposed method effectively decoupled and accurately identified multiple error characteristics in each test case, precisely reflecting the sources and distribution of errors, while its comprehensive evaluation accuracy outperformed traditional methods. Furthermore, taking a self-developed longitudinal dynamic model of a power-shift tractor as an application case, real vehicle data were collected under five road conditions: tilled field, untilled field, stubble field, concrete surface, and mixed road surface. The proposed method was used to compare and analyze simulation results against real vehicle data under various working conditions. The results indicated that the mean comprehensive accuracy score of the model was 90.95. Under high-speed and complex road conditions, scores for various error characteristics decreased significantly, with comprehensive results in some extreme conditions falling below 88, reflecting certain limitations of the model under different working conditions, which aligned with objective reality and verified the effectiveness and comprehensiveness of the proposed evaluation method.

, authors=Liquan LU1, Zhongxiang ZHU1, 2, Zhenghe SONG1, 2, Zhiqiang ZHAI1, 2, Jianhua WANG3, Chuanchuan ZHANG3, authorsList=Liquan LU, Zhongxiang ZHU, Zhenghe SONG, Zhiqiang ZHAI, Jianhua WANG, Chuanchuan ZHANG, authorCompany=null, correspAuthors=Zhongxiang ZHU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1311999966860640630, articleId=1311999949886292188, tenantId=1146029695717560320, journalId=1311385739772133495, language=CN, title=基于时间响应误差与灰色关联度的拖拉机动力学模型评价方法, columnId=1311999648798180275, journalTitle=农业机械学报, columnName=智能农业动力装备关键技术专栏, runingTitle=null, highlight=null, articleAbstract=

针对拖拉机动力学模型准确性评价环节缺失以及系统化、标准化评价手段缺乏等问题,基于模型仿真系统的校核、验证与验收理论,提出了一种基于时间响应误差与灰色关联度的模型准确性评价方法,构建了包含相位误差、幅值误差和形状误差的分层验证指标体系,并采用灰色关联度法对动态响应曲线进行分段评估。设计了4组包含明确误差成分的理想算例,采用本文方法与传统数值误差分析方法进行对比,结果表明,本文方法能够对各算例多重误差特征进行有效解耦和准确识别,精确反映误差来源与分布的同时,其综合评价结果准确性优于传统方法。在此基础上以自主构建的动力换挡拖拉机纵向动力学模型为应用实例,采用所提方法评价不同工况下的模型准确性。结果显示,模型准确性综合结果均值为90.95,在高速及复杂路面等条件下,各误差特征评分明显降低,部分极端工况综合结果小于88,反映出所建模型针对不同工况存在一定局限,符合客观事实,验证了所提评价方法的有效性和全面性。

, authors=路力权1, 朱忠祥1, 2, 宋正河1, 2, 翟志强1, 2, 王建华3, 张钏钏3, authorsList=路力权, 朱忠祥, 宋正河, 翟志强, 王建华, 张钏钏, authorCompany=null, correspAuthors=朱忠祥, authorNote=

路力权(1997—),男,博士生,主要从事车辆电液控制及智能化技术研究,E-mail:

, correspAuthorsNote=
朱忠祥(1976—),男,教授,博士生导师,主要从事车辆电子控制及智能化技术研究,E-mail:
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路力权(1997—),男,博士生,主要从事车辆电液控制及智能化技术研究,E-mail:

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路力权(1997—),男,博士生,主要从事车辆电液控制及智能化技术研究,E-mail:

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Study on gear design method and control strategy of power-shift transmission[D]. Chongqing: Chongqing University, 2020. (in Chinese), articleTitle=Study on gear design method and control strategy of power-shift transmission, refAbstract=null), Reference(id=1311999980102058453, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999949886292188, doi=null, pmid=null, pmcid=null, year=2019, volume=140, issue=null, pageStart=274, pageEnd=291, url=null, language=null, rfNumber=[6], rfOrder=10, authorNames=LI Baogang, SUN Dongye, HU Minghui, journalName=Mechanism and Machine Theory, refType=null, unstructuredReference=LI Baogang, SUN Dongye, HU Minghui, et al. Coordinated control of gear shifting process with multiple clutches for power-shift transmission[J]. 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Dynamic modeling and vibration noise analysis of tractor power shift transmission box[D]. Changchun: Jilin University, 2021. 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Research on the key technologies of virtual test of tractor powershift transmission[D]. Luoyang: Henan University of Science and Technology, 2021. 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Research on tractor PST co-simulation technology based on FMI[D]. Luoyang: Henan University of Science and Technology, 2023. 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Dynamic modeling and vibration characterization of high-horsepower tractor under plowing operation[D]. Zibo: Shandong University of Technology, 2024. 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Time series partitioning results for shape error analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
名称时间序列结果
St起步加速序列St1
St匀速行驶序列St2
To起步加速序列To1
To匀速行驶序列To2
起步加速序列
匀速行驶序列
起步加速序列
匀速行驶序列
), ArticleFig(id=1311999975098253758, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999949886292188, language=CN, label=表1, caption=

形状误差分析时间序列划分结果

, figureFileSmall=null, figureFileBig=null, tableContent=
名称时间序列结果
St起步加速序列St1
St匀速行驶序列St2
To起步加速序列To1
To匀速行驶序列To2
起步加速序列
匀速行驶序列
起步加速序列
匀速行驶序列
), ArticleFig(id=1311999975177945535, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999949886292188, language=EN, label=Tab. 2, caption=

Comparison of case study accuracy evaluation results

, figureFileSmall=null, figureFileBig=null, tableContent=
算例ephEphemaEmaγEshERMSERMSEmp
10100.000.013 486.620.137 184.7788.551.567 486.938 33
22086.670.002 597.570.158 082.4489.311.631 686.403 33
31888.000.018 181.920.021 497.6289.410.601 294.990 00
42484.000.014 485.660.147 683.6384.481.565 486.955 00
), ArticleFig(id=1311999975408632256, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999949886292188, language=CN, label=表2, caption=

算例准确性评价结果对比

, figureFileSmall=null, figureFileBig=null, tableContent=
算例ephEphemaEmaγEshERMSERMSEmp
10100.000.013 486.620.137 184.7788.551.567 486.938 33
22086.670.002 597.570.158 082.4489.311.631 686.403 33
31888.000.018 181.920.021 497.6289.410.601 294.990 00
42484.000.014 485.660.147 683.6384.481.565 486.955 00
), ArticleFig(id=1311999975509295553, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999949886292188, language=EN, label=Tab. 3, caption=

Test road surfaces and travel speeds

, figureFileSmall=null, figureFileBig=null, tableContent=
路面条件路面长度/m速度/(km·h-1)
未耕路面1808,10,12
已耕路面1805,7,9
茬地2208,10,12
水泥路面65015,20,25
混合路面120(7°水泥坡地)12,16,20
50(水泥平地)
90(9°硬质土坡)
290(硬质平土地)
), ArticleFig(id=1311999975811285442, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999949886292188, language=CN, label=表3, caption=

试验路面与行驶速度

, figureFileSmall=null, figureFileBig=null, tableContent=
路面条件路面长度/m速度/(km·h-1)
未耕路面1808,10,12
已耕路面1805,7,9
茬地2208,10,12
水泥路面65015,20,25
混合路面120(7°水泥坡地)12,16,20
50(水泥平地)
90(9°硬质土坡)
290(硬质平土地)
), ArticleFig(id=1311999975895171523, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999949886292188, language=EN, label=Tab. 4, caption=

Basic parameters of simulation model

, figureFileSmall=null, figureFileBig=null, tableContent=
参数数值
拖拉机整车质量/kg12 400
轴距/mm3 005
驱动轮直径/mm2 072
驱动轮断面宽度/mm666
发动机初始转速/(r·min-1)800
滚阻系数(依次对应表4各路况)0.06、0.18、0.08、0.04、0.03~0.04
主变速箱Ⅰ挡传动比1.80
主变速箱Ⅱ挡传动比1.47
主变速箱Ⅲ挡传动比1.23
主变速箱Ⅳ挡传动比1.09
主变速箱Ⅴ挡传动比0.91
副变速箱LL挡传动比3.21
副变速箱L挡传动比1.83
副变速箱M挡传动比1.00
副变速箱H挡传动比0.65
中央减速传动比4.55
轮边减速传动比4.40
动力换挡输入轴转动惯量/(kg·m2)0.31
动力换挡输出轴转动惯量/(kg·m2)0.20
换向中间轴转动惯量/(kg·m2)0.28
变速箱输出轴转动惯量/(kg·m2)0.30
), ArticleFig(id=1311999975983251908, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999949886292188, language=CN, label=表4, caption=

仿真模型基本参数

, figureFileSmall=null, figureFileBig=null, tableContent=
参数数值
拖拉机整车质量/kg12 400
轴距/mm3 005
驱动轮直径/mm2 072
驱动轮断面宽度/mm666
发动机初始转速/(r·min-1)800
滚阻系数(依次对应表4各路况)0.06、0.18、0.08、0.04、0.03~0.04
主变速箱Ⅰ挡传动比1.80
主变速箱Ⅱ挡传动比1.47
主变速箱Ⅲ挡传动比1.23
主变速箱Ⅳ挡传动比1.09
主变速箱Ⅴ挡传动比0.91
副变速箱LL挡传动比3.21
副变速箱L挡传动比1.83
副变速箱M挡传动比1.00
副变速箱H挡传动比0.65
中央减速传动比4.55
轮边减速传动比4.40
动力换挡输入轴转动惯量/(kg·m2)0.31
动力换挡输出轴转动惯量/(kg·m2)0.20
换向中间轴转动惯量/(kg·m2)0.28
变速箱输出轴转动惯量/(kg·m2)0.30
), ArticleFig(id=1311999976310407621, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999949886292188, language=EN, label=Tab. 5, caption=

Scores of response parameters and model accuracy results in stubble field at 10 km/h and on mixed road at 12 km/h

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试验条件参量ephEphemaEmaγEshEmp
茬地10 km/hv0100.000.003 596.500.933 293.3295.9391.21
δ4892.000.071 576.170.787 889.3984.62
ω10100.000.004 395.700.907 790.7794.59
ω20100.000.004 995.100.904 190.4194.21
ω30100.000.005 494.600.906 590.6594.10
混合路面12 km/hv0100.000.008 691.400.944 094.4094.3290.88
δ1199.330.101 366.230.821 691.0882.79
ω10100.000.011 188.900.908 690.8691.90
ω20100.000.011 188.900.910 991.0992.00
ω30100.000.011 388.700.908 590.8591.82
), ArticleFig(id=1311999976654340550, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999949886292188, language=CN, label=表5, caption=

茬地、速度10 km/h和混合路面、速度12 km/h各响应参量评分及模型精度结果

, figureFileSmall=null, figureFileBig=null, tableContent=
试验条件参量ephEphemaEmaγEshEmp
茬地10 km/hv0100.000.003 596.500.933 293.3295.9391.21
δ4892.000.071 576.170.787 889.3984.62
ω10100.000.004 395.700.907 790.7794.59
ω20100.000.004 995.100.904 190.4194.21
ω30100.000.005 494.600.906 590.6594.10
混合路面12 km/hv0100.000.008 691.400.944 094.4094.3290.88
δ1199.330.101 366.230.821 691.0882.79
ω10100.000.011 188.900.908 690.8691.90
ω20100.000.011 188.900.910 991.0992.00
ω30100.000.011 388.700.908 590.8591.82
), ArticleFig(id=1311999976734032327, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999949886292188, language=EN, label=Tab. 6, caption=

Calculated model accuracy under various road surface and speed conditions

, figureFileSmall=null, figureFileBig=null, tableContent=
路面条件速度/(km·h-1)第1组第2组第3组第4组第5组第6组平均值
未耕路面891.6890.6792.1992.4992.3391.1291.50
1090.7891.5391.1192.4090.5290.56
1291.6592.0590.6090.8092.0892.42
已耕路面588.3990.6690.1490.9392.2891.7690.93
790.1890.9891.8291.0290.5191.75
990.3290.5091.7790.6491.7591.29
茬地891.1190.3990.6590.1391.2790.6991.05
1091.2190.2491.3392.3090.7892.31
1291.3690.5291.4790.9790.7391.51
混合路面1290.8890.5290.1390.0690.3190.9590.60
1690.1591.2791.6791.0290.6691.17
2091.6490.3489.9690.1490.1989.72
水泥路面1591.0090.2590.1891.9790.6390.9890.68
2090.0390.4090.4590.3591.0591.67
2591.3690.3890.6291.5889.1090.29
), ArticleFig(id=1311999977077965256, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999949886292188, language=CN, label=表6, caption=

各路面与速度条件下模型精度计算结果

, figureFileSmall=null, figureFileBig=null, tableContent=
路面条件速度/(km·h-1)第1组第2组第3组第4组第5组第6组平均值
未耕路面891.6890.6792.1992.4992.3391.1291.50
1090.7891.5391.1192.4090.5290.56
1291.6592.0590.6090.8092.0892.42
已耕路面588.3990.6690.1490.9392.2891.7690.93
790.1890.9891.8291.0290.5191.75
990.3290.5091.7790.6491.7591.29
茬地891.1190.3990.6590.1391.2790.6991.05
1091.2190.2491.3392.3090.7892.31
1291.3690.5291.4790.9790.7391.51
混合路面1290.8890.5290.1390.0690.3190.9590.60
1690.1591.2791.6791.0290.6691.17
2091.6490.3489.9690.1490.1989.72
水泥路面1591.0090.2590.1891.9790.6390.9890.68
2090.0390.4090.4590.3591.0591.67
2591.3690.3890.6291.5889.1090.29
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基于时间响应误差与灰色关联度的拖拉机动力学模型评价方法
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路力权 1 , 朱忠祥 1, 2 , 宋正河 1, 2 , 翟志强 1, 2 , 王建华 3 , 张钏钏 3
农业机械学报 | 智能农业动力装备关键技术专栏 2025,56(12): 158-169
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农业机械学报 |智能农业动力装备关键技术专栏 2025 , 56 (12) : 158 -169
基于时间响应误差与灰色关联度的拖拉机动力学模型评价方法
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路力权1 , 朱忠祥1, 2 , 宋正河1, 2, 翟志强1, 2, 王建华3, 张钏钏3
作者信息
  • 1.中国农业大学工学院,北京 100083
  • 2.智能农业动力装备全国重点实验室,洛阳 471039
  • 3.洛阳拖拉机研究所有限公司,洛阳 471039
通讯作者:
朱忠祥(1976—),男,教授,博士生导师,主要从事车辆电子控制及智能化技术研究,E-mail:
作者简介:

路力权(1997—),男,博士生,主要从事车辆电液控制及智能化技术研究,E-mail:

Evaluation Method of Tractor Dynamic Models Based on Time Response Error and Grey Relational Analysis
Liquan LU1 , Zhongxiang ZHU1, 2 , Zhenghe SONG1, 2, Zhiqiang ZHAI1, 2, Jianhua WANG3, Chuanchuan ZHANG3
Affiliations
  • 1.College of Engineering, China Agricultural University, Beijing 100083, China
  • 2.State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471039, China
  • 3.Luoyang Tractor Research Institute Ltd., Luoyang 471039, China
出版时间: 2025-12-25 doi: 10.6041/j.issn.1000-1298.2025.12.014
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针对拖拉机动力学模型准确性评价环节缺失以及系统化、标准化评价手段缺乏等问题,基于模型仿真系统的校核、验证与验收理论,提出了一种基于时间响应误差与灰色关联度的模型准确性评价方法,构建了包含相位误差、幅值误差和形状误差的分层验证指标体系,并采用灰色关联度法对动态响应曲线进行分段评估。设计了4组包含明确误差成分的理想算例,采用本文方法与传统数值误差分析方法进行对比,结果表明,本文方法能够对各算例多重误差特征进行有效解耦和准确识别,精确反映误差来源与分布的同时,其综合评价结果准确性优于传统方法。在此基础上以自主构建的动力换挡拖拉机纵向动力学模型为应用实例,采用所提方法评价不同工况下的模型准确性。结果显示,模型准确性综合结果均值为90.95,在高速及复杂路面等条件下,各误差特征评分明显降低,部分极端工况综合结果小于88,反映出所建模型针对不同工况存在一定局限,符合客观事实,验证了所提评价方法的有效性和全面性。

拖拉机动力学模型  /  评价方法  /  时间响应误差  /  灰色关联度

Aiming to address the issues of missing accuracy evaluation for tractor dynamic models and the lack of systematic, standardized assessment methods, an accuracy evaluation approach was proposed based on time-response error and grey relational analysis, grounded in the verification, validation, and accreditation (VV&A) theory of model simulation systems. A hierarchical validation index system comprising phase error, amplitude error, and shape error was constructed, and the grey relational degree method was applied for segmental evaluation of dynamic response curves. Four sets of idealized test cases with defined error components were designed and analyzed by using both the proposed method and traditional numerical error analysis techniques. The results demonstrated that the proposed method effectively decoupled and accurately identified multiple error characteristics in each test case, precisely reflecting the sources and distribution of errors, while its comprehensive evaluation accuracy outperformed traditional methods. Furthermore, taking a self-developed longitudinal dynamic model of a power-shift tractor as an application case, real vehicle data were collected under five road conditions: tilled field, untilled field, stubble field, concrete surface, and mixed road surface. The proposed method was used to compare and analyze simulation results against real vehicle data under various working conditions. The results indicated that the mean comprehensive accuracy score of the model was 90.95. Under high-speed and complex road conditions, scores for various error characteristics decreased significantly, with comprehensive results in some extreme conditions falling below 88, reflecting certain limitations of the model under different working conditions, which aligned with objective reality and verified the effectiveness and comprehensiveness of the proposed evaluation method.

tractors dynamics model  /  evaluation method  /  time response error  /  grey relational analysis
路力权, 朱忠祥, 宋正河, 翟志强, 王建华, 张钏钏. 基于时间响应误差与灰色关联度的拖拉机动力学模型评价方法. 农业机械学报, 2025 , 56 (12) : 158 -169 . DOI: 10.6041/j.issn.1000-1298.2025.12.014
Liquan LU, Zhongxiang ZHU, Zhenghe SONG, Zhiqiang ZHAI, Jianhua WANG, Chuanchuan ZHANG. Evaluation Method of Tractor Dynamic Models Based on Time Response Error and Grey Relational Analysis[J]. Transactions of the Chinese Society for Agricultural Machinery, 2025 , 56 (12) : 158 -169 . DOI: 10.6041/j.issn.1000-1298.2025.12.014
随着计算机模拟技术的逐步发展,基于系统动力学模型的数字化仿真已成为拖拉机产品开发和性能验证的核心手段,其显著缩短了开发周期,降低了研发成本[1]。然而,实现这一过程的关键在于确保所构建的拖拉机系统动力学模型具有高度的准确性。目前该领域研究过程大多可总结为“构建模型,使用模型”,尤其是基于动力学模型仿真开展的拖拉机控制与测试[2-3],缺少了对模型准确性客观评价的关键环节[4-8]。同时,在涉及模型准确性评价时不同研究采用的验证指标与验证方法存在显著差异[7-9],难以进行对比,缺乏普适性框架。
在拖拉机动力学领域,面向模型准确性评价理论与方法层面的研究严重缺失,多是以单一评价结果说明所建模型是否准确而非重点关注评价手段的科学性与合理性,且多以仿真与实际结果在数值层面的误差表征模型准确性,梁玉辉[9]构建了动力换挡变速箱传动模型,通过对比啮合力传递仿真结果与理论值最大值误差与平均值误差验证了所建模型准确性。文献[10-11]通过求解仿真与试验结果间的经验模态分解值对所构建的动力换挡虚拟传动系统进行了准确性验证。黄鹏[12]通过计算运输工况与犁耕作业工况下仿真结果与试验结果相对误差,验证了所构建的拖拉机动力学模型的准确性。上述基于单一误差指标的模型准确性评价方法虽可在一定程度上描述模型准确性,体现模型反映实车响应的能力,但仿真与试验结果曲线在整个时间域的误差特征总是包含多个层面如相位、幅值、形状等,这些误差特征往往同时存在且相互影响,仅通过数值层面的分析对比得到的模型准确性结论难以清晰展现误差来源与分布,从而导致评价全面性不足,甚至掩盖了模型部分非线性瞬态工况下关键动力学特性的失真。而在其他领域,针对模型仿真系统的准确性评价工作已广泛开展,且已形成较为成熟的理论基础,其中,基于仿真系统的校核、验证与验收(VV&A)理论提出于上世纪60年代[13-14],是复杂模型准确性评价的重要手段之一,目前已充分应用到军事国防[15]、航空航天[16]以及工业制造[17]等领域,而在车辆领域应用相对较少,且主要面向道路车辆。詹振飞[18]提出了一种改进时间响应误差评估法,对汽车安全动态系统模型的一元动态响应误差问题进行了分析,郑凯等[19]在此基础上融合层次分析法实现了对模型多元动态响应误差的评估。王堃[20]基于文献[19]提出的方法验证了车辆AEB预制动工况和碰撞工况仿真模型的准确性。王世川[21]基于动态时间规整方法,通过计算相位误差与幅度误差,定量分析了车辆操纵稳定性模型的精确程度。孙浩[22]以离差平方和表征测试值与仿真值偏差,计算了稳态回转工况、方向盘中心区工况、蛇形工况下的车辆操纵稳定性模型准确性。李伟等[23]对所建立的某型号履带车辆的紧急制动动力学模型进行了VV&A验证,结果表明所建模型在反映履带车辆行驶及紧急制动动力学行为方面能够满足工程分析需要。徐亮等[24]总结了车辆动力学模型校核和验证发展历程与现状,提出了一套验证指标体系,使车辆动力学模型校核和验证标准化、规范化。
综上所述,VV&A理论虽已发展较为成熟,但其在拖拉机动力学模型领域尚未得到应用。本文结合VV&A理论,提出一种基于时间响应误差与灰色关联度的模型准确性评价方法,描述该方法实现原理与计算流程,最终通过算例分析和实例应用体现所提方法的优越性、有效性以及全面性。
拖拉机动力学模型是一个响应多元、模态复杂的综合系统,本研究将模型准确性定义为以实际车辆动力学多元响应结果为参考的模型仿真结果全时域误差综合量化值。基于该定义,模型准确性的验证应对比模型仿真结果与实车动力学响应结果在整个时间域上的一致性,其特征主要包括相位误差、幅值误差以及形状误差。以拖拉机从静止状态开始经起步加速至稳速行驶过程为例,如图1所示。图1a2组结果曲线在加速过程速度变化趋势与稳态速度一致的前提下,响应时间误差可作为相位误差的主要表现形式,图1b2组结果曲线加速过程速度变化趋势与响应时间一致前提下,稳态速度误差可作为幅值误差的具体表现形式,图1c中在响应时间与稳态速度相同前提下,加速过程速度变化趋势误差可作为形状误差的具体表现形式。除上述误差表现形式之外,相位误差还包括车速起步延迟、发动机转速下降或回升时刻偏差、离合器接合完成时刻差异以及滑移率峰值出现时间等;幅值误差还体现在发动机转速下降和回升幅度、离合器传递转矩峰值、最大加速度幅值等;形状误差还包括加速度曲线波动趋势、速度曲线拐点位置、发动机转速和转矩变化轨迹、轮胎滑移率变化规律等。这些特征误差往往同时存在于模型仿真结果与实车响应结果对比中且相互影响。采用基于传统数值对比的误差分析方法将存在较大的局限性,仅能通过单一化标量指标对系统响应进行全局性度量,而无法解耦并量化动态过程中同时存在的各项特征误差,从而导致模型验证的完备性缺失。采用基于时间响应误差与灰色关联度的模型精度评价方法,实现对相位、幅值与形状误差的独立辨识与综合量化。
时间响应误差评估(Error assessment of response time histories)方法能够有效区分相位误差与幅值误差,具有良好的响应量特征误差识别能力。然而,该方法以2组结果斜率曲线幅值误差表征原始结果形状误差,在实际应用中存在一定局限性。原因为:①拖拉机在行驶过程中受随机路面激励影响,使得纵向动力学响应结果存在连续突变,加之实车试验中的响应量观测结果因采集设备的原因常受噪声干扰,上述因素导致斜率计算存在不连续或不稳定的现象,仅通过斜率曲线无法全面反映原始曲线形状,使得形状误差验证的准确性难以保证。②根据拖拉机行驶状态的不同,可将试验结果划分为稳态阶段(如匀速行驶)与非稳态阶段(如起步加速),分别对应图1中的30~50 s阶段和10~30 s阶段,由图1可知非稳态阶段的形状误差对响应结果曲线的整体形状误差影响更大,因此,在整个时间域上采用统一标准求解形状误差可能会降低结果准确性。为此,本文提出采用灰色关联度分析的方法对仿真与试验结果序列的形状误差进行分段处理,以提高形状误差分析的准确性。
通过时间响应误差方法对模型相位误差与幅值误差进行评价。对仿真与试验结果序列进行插值处理,使其包含相同数据量n。仿真与试验结果序列相关系数ρ计算式为
式中
      ti、si——试验与仿真结果序列第i点数据
      、——试验与仿真结果数据均值
      n——试验结果与仿真结果数据量
以相关系数ρ描述两组序列之间的线性相关程度,相关系数计算结果随序列在时间域上的移动而改变,将2组序列相关系数最大值所对应的仿真结果时移步数称为相位误差,即
式中
      eph——相位误差
      ntmax——相关系数最大值所对应的仿真结果时移步数
时间响应误差相位评分计算式为
式中
      ——可接受的相位误差上限,取整个试验时步的10%
为消除相位对2组序列幅值误差分析的影响,将仿真结果Soriginal移动ntmax时步后的新序列Sts与试验结果Toriginal进行对比,求解幅值误差。此外,为消除形状对幅值误差的影响,采用动态时间规整(DTW)的方法对Sts、Toriginal进行局部非线性时间匹配处理,以快速对齐2条曲线波峰、波谷。同上对Sts、Toriginal进行插值处理,得到数据量N,构造一个2组时间序列间的N维距离平方矩阵D,矩阵中各元素值可表示为
假设存在1条通过矩阵D的时间规整路径W={w1,w2,…,wk},N≤k≤2N-1,其中wt=(Dij)t,W满足以下约束条件:①边界条件:w1=D11、wk=DNN。②连续性:给定wt=Dij、wt+1=DUV,则有u-i≤1、v-j≤1。③单调性:给定wt=Dij、wt+1=DUV,则有u-i≥0、v-j≥0。动态时间规整的本质是寻找矩阵D中从D11到DNN代价最小的路径,即
对已消除相位误差与形状误差影响后的仿真结果与试验结果时间序列Snew、Tnew求解幅值误差ema,即
式中
      ‖·‖1——1-范数
时间响应误差幅值评分计算式为
式中
      ——可接受的幅值误差上限,取0.1
灰色关联分析方法是通过2组时间序列曲线之间的几何形状评价两者相关性,如图2所示。拖拉机纵向动力学模型仿真结果与实车试验结果形状误差的评价应对整车不同运动状态,包括起步加速与匀速行驶进行分段考虑。为进一步提高形状误差分析准确性,同时对原始结果及其斜率曲线开展灰色关联分析,并赋予两者计算结果合理的权重,以得到综合评价结果。
为避免相位误差的影响,对移动时步后的仿真结果序列Sts与原始试验结果序列Toriginal(后文简写为St、To)以及两者斜率、(后文简写为、)进行形状误差分析。假设上述4组时间序列插值后数据量都为N,根据拖拉机行驶状态对插值后的4组序列进行统一划分,得到整车起步加速阶段与匀速行驶阶段数据量分别为N1、N2,则有N=N1+N2,得到8组时间序列结果,如表1所示。
仿真与试验结果St、To灰色关联度表示为
式中
      ξ1、ξ2——原始序列、斜率曲线灰色关联度对应权重
      η1、η2——起步加速阶段、匀速行驶阶段灰色关联度结果对应权重
      γ1、γ2、、——St1与To1、St2与To2、与、与灰色关联度结果
根据层次分析方法的1~9标度法[19]分析原始序列与斜率结果,起步加速与匀速行驶阶段灰色关联度对形状误差求解的重要程度,经判断原始序列误差结果与起步加速阶段误差结果是影响形状误差的主要因素。因此,得出ξ1∶ξ2=5∶1、η1∶η2=7∶1,由此构造2组权重判断矩阵为
根据归一化标准计算得各权重系数为
以St1与To1为例,灰色关联度计算式为
其中
同理,可分别计算得灰色关联度γ2、、,进而形状评分可表示为
式中
      ——可接受的形状误差上限,取1
通过时间响应误差与灰色关联度分析的方法,分别得到了仿真与试验结果序列的相位评分Eph、幅值评分Ema以及形状评分Esh,根据1~9标度法分析3者对模型精度综合评价结果影响的显著性,得出对应的权重比例为1∶2∶2,综合评分可表示为
计算综合评分,确定模型精度总体流程如图3所示。
拖拉机实车响应结果与动力学模型仿真结果在实际工作过程中的相位误差、幅值误差以及形状误差真实值难以直接获取,导致采用本文方法在对所建模型进行准确性评价时,虽可通过与实车结果对比计算得到3种特征误差量化值,但由于没有真实值作为参照,导致其评价有效性难以体现。采用本文方法与传统数值误差分析方法对同一拖拉机动力学模型进行准确性评价时虽然计算结果不同,但由于无法获取其误差真实值,因此本文方法的优越性也难以明确体现。综合上述原因,设计构造了4组理想算例,分别采用本文所提方法与均方根误差分析方法进行准确性评价,通过对比其评价结果说明本文方法的优越性和有效性。
结合图1所示拖拉机运行过程中3种特征误差形式,构造理想化算例1~4组,如图4所示。其中,算例1存在幅值误差和形状误差,其相位误差为0,即开始加速与速度到达稳态的时间一致,但加速过程以及最终稳态速度不同,即
式中,在10 s≤t<30 s阶段分别采用Sigmoid函数与Tanh函数以实现不同的加速过程,在t≥30 s阶段采用不同的稳态值表现稳态速度差异。
同上,算例2存在相位误差和形状误差,幅值误差为0,即
算例3存在相位误差和幅值误差,形状误差为0,即
算例4 3种误差特征同时存在,即
为了更接近实际测量情况,在式(19)~(26)基础上均加入服从零均值、方差为σ2=0.12的独立同分布高斯白噪声。采用融合时间响应误差与灰色关联度的方法和均方根误差分析方法对以上4个算例进行准确性评价,其计算结果如表2所示,其中RMSE为均方根误差,RMSEmp为基于均方根误差的综合评分,计算式为
式中
      rmax——均方根误差最大值
      r——均方根误差
由表2可以看出,本文所提方法能够实现对相位、幅值、形状3种特征误差较为准确的解耦和识别。其中,算例1同时包含幅值误差与形状误差,相位误差为0,计算结果显示相位误差评分为100,幅值误差与形状误差评分分别为86.62、84.77,相对较低,符合其原始误差构成。算例2仅包含相位误差与形状误差,因此计算结果2种误差评分分别为86.67、82.44,而幅值误差评分结果为97.57,由于稳态速度的数值差异是幅值误差的主要来源,并不能完全等效于幅值误差,因此算例2中依然存在其他来源的幅值误差,其评分相对较高但并未达到100的原因。同理,算例3不包含形状误差因此其形状评分为97.62,未达到100的原因同上,其余2种误差评价分别为88.00与81.92相对较低,而算例4同时包含3种误差因此其各误差评分均低于90。从准确性综合评价结果E来看,仅包含2种误差特征的算例1~3评分均分布在88~90之间相对较高,而同时包含3种误差特征的算例4综合评分仅有84.48,这一结果也符合各理想算例构造的客观事实。相比之下,采用均方根误差分析的方法虽可在一定程度上反映各算例的准确性,但仍存在一定局限性,一方面难以通过单维数据体现出多个特征误差的分布,另一方面相较于本文所提方法计算结果,均方根误差综合评价结果准确性也相对较低,例如算例3准确性评分为94.99远高于算例1、2,而同时包含3种特征误差的算例4计算结果与算例1、2结果接近均保持在86~87之间,与各算例初始误差构造结果并不相符。综合上述分析可以看出,本文所提方法相对于传统误差评价方法具有较高的优越性和全面性。
以某配备4区段5挡动力换挡变速箱的拖拉机为评价对象,构建整车及传动系统纵向动力学模型
式中
      Te——发动机转矩,N·m
      ai,j——多项式系数
      α——油门开度
      Ie、I1、I2、I3、I4、Iw——发动机输出轴、动力换挡输入轴、动力换挡输出轴、换向中间轴、变速箱输出轴以及轮边半轴等效转动惯量,kg·m2
      Te、T0、Tw、Tv——发动机、中央传动、轮边半轴传递转矩及轮胎所受阻力矩,N·m
      ωe、ωw——发动机、轮边半轴角速度,rad/s
      im、if、id、i0、iw——主变速箱传动比(1~5挡对应传动比i1~i5)、前进挡传动比、副变速箱传动比(LL、L、M、H区段对应传动比iLL、iL、iM、iH)、中央传动、轮边减速传动比
      Ce、C0——发动机输出轴、中央传动等效转动阻尼系数,N·s/m
      θ0、θw——后轴、驱动轮角位移,rad
      K0——中央传动等效扭转刚度,N·m/rad
      m——整车质量,kg
      a——车辆纵向加速度,m/s2
      g——重力加速度,取9.8 m/s2
      f——滚动阻力系数,取值参照文献[25]
      β——坡角,rad
      Fz——牵引阻力,N
在构建轮胎-土壤模型时,一方面考虑到动力换挡拖拉机配备斜交轮胎,且行驶路面以田间路面为主。另一方面若模型所需辨识参数过多且在实际作业环境中难以对其进行准确测量,则可能在验证环节引入额外误差。因此,经对比选择典型经验模型Wismer模型[26-27]构建轮胎牵引力、垂向载荷、滑转率及土壤参数之间的关系
其中
式中
      Fq——牵引力,N
      W——轮胎所受垂向载荷,N
      δ——滑转率      CI——土壤圆锥指数
      b——轮胎断面宽度,m
      d——轮胎直径,m
开展不同工况条件的动力换挡拖拉机运输试验。试验地点为河南省洛阳市崔沟村,试验数据采集设备包括MS-6111型组合导航系统(GPS)、HWT905型六轴角度传感器(陀螺仪)、PEAK002022型分析仪(CAN分析仪)和上位机,试验用车以及传感器设备安装如图5所示。GPS测量拖拉机实际车速,陀螺仪测量整车俯仰角,动力换挡变速箱各轴转速、主\副变速箱挡位以及油门开度通过CAN分析仪读取。
分别于5种路面条件下开展试验,每种路面对应3种稳态行驶速度,在拖拉机全生命周期内使用频率较高的速度区间内取值,参照文献[5],每种速度进行6组试验,共90组试验,具体信息见表3。各路面条件下行驶轨迹及试验现场如图6所示。
搭建动力换挡拖拉机Matlab/Simulink动力学仿真平台。为提高模型验证准确性,规避路面条件与整车行驶状态等因素干扰,试验采用开环仿真方式,将实车试验主/副变速箱挡位以及油门开度观测值作为仿真试验输入条件,同时将标定过的整车俯仰角换算为路面坡度进行输入。在仿真过程中添加车速、轮速、变速箱各轴转速监测模块,对比仿真结果与实车试验结果以验证模型精度。仿真采用ode5求解器,Fixed-step固定步长类型,设置步长为0.01 s,仿真模型其他基本参数见表4。对应实车试验条件开展仿真试验共90组,以茬地、速度10 km/h和混合路面、速度12 km/h为例,对比仿真试验结果与实车试验结果如图7、8所示。图7d、7e中结果曲线一致原因为当前副变速箱挡位对应传动比为1,因此换向轴与变速箱输出轴转速相同。
依据前文所提模型准确性评价方法与流程,分别计算仿真与实车试验实际车速v、滑转率δ、动力换挡输出轴转速ω1、换向中间轴转速ω2以及变速箱输出轴转速ω3结果序列的相位、幅值误差,灰色关联度形状误差,根据式(18)计算多元动力学响应结果的综合评分,基于各综合评分结果求解当前条件下模型准确性计算方法,如图9所示。
将多元动力学响应结果综合评分转换为模型精度,建立模型
式中
      mp——模型精度
      Ev、Eδ、Eω1、Eω2、Eω3——车速、滑转率、动力换挡输出轴、换向中间轴以及变速箱输出轴转速对应综合评分结果
      σ1~σ5——实际车速、滑转率、动力换挡输出轴、换向中间轴以及变速箱输出轴转速对应权重系数
根据实际车速与变速箱输出轴转速2项参数结果计算得实车试验滑转率,同时,受整车状态、环境条件、传感器采样误差等多个因素影响,仿真试验与实车试验滑转率结果存在基础误差。依据1~9标度法原理,适度降低滑转率评价的重要程度,保持其他响应特征参数精度的重要性一致且与滑转率权重系数比值为9∶4,从而得到判断矩阵为
根据归一化标准计算得到各权重为
结合图3、9计算茬地、速度10 km/h和混合路面、速度12 km/h条件下各响应参量评分及模型准确性评价结果如表5所示。
根据表5可知,2种试验条件下的拖拉机纵向动力学模型准确性均大于90,其中,除滑转率以外的其他响应结果序列相位误差均为0,说明本文所建动力学模型在响应时间上与实际车辆相比误差较小。对比2种路面条件下的其他评价结果,混合路面各参数的特征误差评分均低于茬地,其中幅值误差差异尤为显著,最大差距达到9.94,同时,模型准确性的综合评价结果相较于茬地降低0.33。说明所建动力学模型仿真结果与实车响应结果稳态速度一致性较差,反映模型在速度较高以及路况复杂的情况下输出动力不足,以及同一输入条件下的模型发动机转速匹配较试验结果存在误差。同理,对90组实车试验及其对应的仿真试验结果进行上述分析与计算,得到不同路面与速度条件下模型准确性结果及平均值如表6所示。
由表6可知,不同路面与速度条件下拖拉机纵向动力学模型精度结果中有86组大于90,计算所有试验模型准确性评价结果平均值为90.95,说明所建模型具有一定准确性。然而,其综合结果在速度较高或路况复杂时整体呈下降趋势,以致出现多组结果小于88的情况,这一现象产生一方面由于随着行驶速度的增加或环境条件的恶化,车辆所受来自路面的激励响应更为剧烈,整车行驶状态波动明显动力学响应特征更加复杂,从而导致模型准确性降低,另一方面也说明所建模型本身在复杂工况下仿真结果较实际车辆存在一定误差,具有一定局限性,此结果可与表5结果相互印证,可体现出本文所提方法的有效性和全面性。
综合上述分析,提出的多维度模型准确性评价方法通过量化分析不同工况下的误差特征,能够灵敏地反映行驶条件变化对模型准确性的影响,如高速工况下,模型精度下降,存在精度低于88的情况;复杂路面条件下,综合评价结果较平整路面降低,幅值误差显著增大等。基于量化数据的评价方法不仅能够精确表征模型在不同路面条件和不同速度区间下的适应性,更能通过误差分解明确揭示模型对于较为极端工况(高速、复杂路面)的局限性,可为模型工程应用提供可靠依据。
(1)提出了一种基于时间响应误差与灰色关联度的拖拉机动力学模型准确性评价方法,构建了包含相位误差、幅值误差和形状误差的分层验证指标体系,阐述了其实现原理,明确了其评价流程。
(2)设计构造了4组包含明确误差成分的理想算例,采用本文所提方法与传统数值误差分析方法进行对比,结果表明本文方法能够对算例所包含的多个特征误差进行有效解耦和准确识别,且综合评价准确性显著高于传统方法,体现了其优越性和有效性。
(3)以构建的动力换挡拖拉机纵向动力学模型为应用实例,通过本文方法对其准确性进行评价,结果表明所建模型准确性平均值为90.95,但在部分极端工况下准确性显著降低,存在小于88的情况,说明了所建模型针对不同工况存在一定局限性,同时也体现了该评价方法的全面性。
  • 国家自然科学基金项目(52572505)
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2025年第56卷第12期
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doi: 10.6041/j.issn.1000-1298.2025.12.014
  • 接收时间:2025-09-08
  • 首发时间:2026-09-30
  • 出版时间:2025-12-25
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  • 收稿日期:2025-09-08
  • 修回日期:2025-09-25
基金
国家自然科学基金项目(52572505)
作者信息
    1.中国农业大学工学院,北京 100083
    2.智能农业动力装备全国重点实验室,洛阳 471039
    3.洛阳拖拉机研究所有限公司,洛阳 471039

通讯作者:

朱忠祥(1976—),男,教授,博士生导师,主要从事车辆电子控制及智能化技术研究,E-mail:
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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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