铁道运输与经济2026，Vol.48Issue(5)：73-83,11.DOI:10.16668/j.cnki.issn.1003-1421.20250828003
基于列车牵引运行仿真的碳排放量预测方法
Carbon Emission Prediction Method Based on Train Traction Operation Simulation
摘要
Abstract
The carbon emissions from railway transportation are primarily derived from carbon emissions during train traction operations.Based on the force analysis during train operation,a virtual simulation software for power traction was developed.By setting different road conditions,a normalized prediction model for carbon emissions during traction operation was established.The actual operation's carbon emissions of the FXD1-J type electric multiple unit train on the case line LC were compared with those calculated by the virtual simulation and prediction models.It was found that the actual operation's carbon emission of the FXD1-J type electric multiple unit train on the entire LC line was 4.53 tCO2e;the carbon emission calculated by the simulation software was 1.7%lower than that of the actual operation,and the carbon emission calculated by the prediction model was 6.5%lower than that of the actual operation.The results obtained from both virtual simulation and prediction model indicate that the impact of line conditions on carbon emissions is consistent with that observed in actual operation,suggesting that both calculation methods can accurately calculate the carbon emissions from traction operation.However,the simulation software requires the input of detailed line and train attribute data,and it is applicable for completed railways,while the prediction model is more applicable for feasibility studies or preliminary design.The research results provide methodological support for the optimization of low-carbon railway lines.关键词
动力牵引/碳排放/能源消耗/虚拟仿真软件/归一化预测模型Key words
Power Traction/Carbon Emission/Energy Consumption/Virtual Simulation Software/Normalized Prediction Model分类
交通工程引用本文复制引用
蒋曦辕,苗丁洁,刘北胜,韩京芮,聂梦凡,李雅琪,冉墨文..基于列车牵引运行仿真的碳排放量预测方法[J].铁道运输与经济,2026,48(5):73-83,11.基金项目
国家自然科学基金铁路基础研究联合基金项目(U2268208) （U2268208）
中国国家铁路集团有限公司科技研究开发计划课题(J2024Z406) （J2024Z406）
