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    請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/85792

    題名: 列車最適運轉策略之模擬分析
    其他題名: Simulation Optimization of Train Speed Profiles
    作者: 鍾志成;張恩輔
    貢獻者: 淡江大學運輸管理學系
    關鍵詞: 運轉時間;能源消耗;駕駛策略;Run time;Energy consumption;Driving strategy
    日期: 2007
    上傳時間: 2013-04-10 15:33:07 (UTC+8)
    摘要: 軌道車輛之運轉時間與能源消耗存在權衡取捨的關係,過去許多研究均顯示,適當的調整駕駛策略可以節省能源消耗量,而列車的運轉時間大致隨著能源消耗量的減少而增加。由於實際運轉時,可能會依路線的條件及運轉的狀況交替運用不同駕駛策略,但文獻上對於各種駕駛策略的使用時機則較缺乏系統性的研究。因此,本篇論文根據作者先前開發的列車運轉模擬模式,針對配備具備自動列車控制(Automatic Train Control, ATC)系統的電力列車,發展科學化的尋優方法,在路線設計及列車性能已知的條件下,求解滿足一定運轉時間限制時,最節省能源的操控策略組合,並以實際列車的性能進行案例分析。研究成果可作為軌道營運機構設定及調整列車運轉策略的參考。
    It is well known that there exits a tradeoff between journey time and energy consumption for rail vehicles. Previous studies show that proper settings for driving strategies could save energy, and journey usually increases as energy consumption decreases. In real world, a train may alternate driving strategies according to the geometries of alignment and operation situations. However, systematic analysis on when each driving strategy should be adopted and how the strategies are combined are rarely found in the literature. Therefore, this paper aims at developing a scientific approach for optimizing driving strategies to minimize energy consumption under the journey time constraint. The algorithm is based on a train simulation model previously developed by the same authors. The focus is on electric trains with Automatic Train Control (ATC) where driving policies could be easily programmed in advance. A case study based on the performance of a real train is also presented in this paper to illustrate the algorithm. The results of this paper are very helpful for railway operators to set up and adjust train operation policies.
    關聯: 中華民國運輸學會96年年會暨學術論文國際研討會---追求更永續的運輸---挑戰與機會論文集=Proceedings of the 2007 International Conference and Annual Meeting of Chinese Institute of Transportation---Challenges and Opportunities Towards More Sustainable Transportation,20頁
    顯示於類別:[運輸管理學系暨研究所] 會議論文





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