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铁路机车能耗与机车运用水平关系探讨 被引量:5

Relationship between Railway Locomotive Energy Consumption and Locomotive Operation Level
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摘要 机车是铁路运输的基本动力,机车能耗与机车运用水平的有机结合,才能实现机车能耗过程的有效控制,满足机车精细化管理的要求。为挖掘机车能耗情况与机车运用水平两者之间的关联关系,从机车运用工作量和机车运用效率角度出发,考虑机车运用工作量、机车牵引运用效率、机车时间运用效率、机车产量运用效率4个方面,选择9个综合指标及14个分项指标构建影响因素指标体系,运用灰色关联法定量分析各因素与机车能耗间的关联度,并通过构建机车能耗总量、牵引总重吨公里的多元线性回归模型,进而得到机车单耗测算模型,科学指导铁路运输企业采取合理措施控制机车能耗。 Locomotives are the basic power of railway transportation. The organic combination of locomotive energy consumption and locomotive operation level can effectively control the locomotive energy consumption process and meet the requirements of the refined management of locomotives. To explore the relationship between locomotive energy consumption and locomotive operation level, this paper constructed the influencing factor index system in view of locomotive operation workload and locomotive operation efficiency and considered the operation workload, traction operation efficiency, time utilization efficiency,and operation efficiency of locomotives. Nine comprehensive indices and 14 sub-indices were selected in the system. The grey correlation method was used to quantitatively analyze the correlation between various factors and locomotive energy consumption. According to the calculation formula of locomotive unit consumption, the calculation model of locomotive unit consumption was obtained by building the multivariate linear regression model of total locomotive energy consumption and total traction weight ton km.
作者 游艳雯 权诗琦 周学军 王菲 YOU Yanwen;QUAN Shiqi;ZHOU Xuejun;WANG Fei(Transportation&Economics Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China;Planning and Statistics Department,China Railway Beijing Group Co.,Ltd.,Beijing 100860,China)
出处 《铁道货运》 2021年第12期60-65,共6页 Railway Freight Transport
基金 中国国家铁路集团有限公司科技研究开发计划课题(K2020J011) 中国铁道科学研究院集团有限公司科研项目(2020YJ027,2019YJ081)。
关键词 铁路 机车能耗 机车运用 灰色关联度 多元线性回归 Railway Locomotive Energy Consumption Locomotive Operation Grey Relational Degree Multivariate Linear Regression
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