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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪英超 | zh_TW |
| dc.contributor.advisor | Ying-Chao Hung | en |
| dc.contributor.author | 林霈辰 | zh_TW |
| dc.contributor.author | Pei-Chen Lin | en |
| dc.date.accessioned | 2024-08-14T16:54:54Z | - |
| dc.date.available | 2024-08-15 | - |
| dc.date.copyright | 2024-08-14 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-07-31 | - |
| dc.identifier.citation | International Energy Agency. Global ev outlook 2024, 2024. Licence: CC BY 4.0.
Rick Wolbertus, Maarten Kroesen, Robert van den Hoed, and Caspar Chorus. Fully charged: An empirical study into the factors that influence connection times at ev charging stations. Energy Policy, 123:1–7, 2018. Shahid Hussain, Yun-Su Kim, Subhasis Thakur, and John G. Breslin. Optimization of waiting time for electric vehicles using a fuzzy inference system. IEEE Transactions on Intelligent Transportation Systems, 23(9):15396–15407, 2022. Jun-Li Lu, Mi-Yen Yeh, Yu-Ching Hsu, Shun-Neng Yang, Chai-Hien Gan, and Ming-Syan Chen. Operating electric taxi fleets: A new dispatching strategy with charging plans. In 2012 IEEE International Electric Vehicle Conference, pages 1–8, 2012. Hua Qin and Wensheng Zhang. Charging scheduling with minimal waiting in a network of electric vehicles and charging stations. In Proceedings of the Eighth ACM International Workshop on Vehicular Inter-Networking, VANET ’11, page 51–60, New York, NY, USA, 2011. Association for Computing Machinery. Rui Chen, Xinwu Qian, Lixin Miao, and Satish V Ukkusuri. Optimal charging facility location and capacity for electric vehicles considering route choice and charging time equilibrium. Computers & Operations Research, 113:104776, 2020. Ying-Chao Hung, Horace PakHai Lok, and George Michailidis. Optimal routing for electric vehicle charging systems with stochastic demand: A heavy traffic approximation approach. European Journal of Operational Research, 299(2):526-541, 2022. Mazin Abed Mohammed, Mohd Khanapi Abd Ghani, Raed Ibraheem Hamed, Salama A. Mostafa, Dheyaa Ahmed Ibrahim, Humam Khaled Jameel, and Ahmed Hamed Alallah. Solving vehicle routing problem by using improved k-nearest neighbor algorithm for best solution. Journal of Computational Science, 21:232–240, 2017. Søren Asmussen. Queueing simulation in heavy traffic. Mathematics of Operations Research, 17(1):84–111, 1992. WARD WHITT. Approximations for the gi/g/m queue. Production and Operations Management, 2(2):114–161, 1993. Norm Matloff. Introduction to discrete-event simulation and the simpy language. Davis, CA. Dept of Computer Science. University of California at Davis. Retrieved on August, 2(2009):1–33, 2008. Ying-Chao Hung and George Michailidis. Optimal routing for electric vehicle service systems. European Journal of Operational Research, 247(2):515–524, 2015. Matthew Andrews and Lisa Zhang. Energy-delay tradeoffs in a load-balanced router. In 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pages 1705–1712, Oct 2012. Reuven Y Rubinstein and Dirk P Kroese. Simulation and the Monte Carlo method. John Wiley & Sons, 2016. Nicholas Metropolis and Stanislaw Ulam. The monte carlo method. Journal of the American statistical association, 44(247):335–341, 1949. Ying-Chao Hung, George Michailidis, and Shih-Chung Chuang. Estimation and monitoring of traffic intensities with application to control of stochastic systems. Applied Stochastic Models in Business and Industry, 30(2):200–217, 2014. Stuart Coles, Joanna Bawa, Lesley Trenner, and Pat Dorazio. An introduction to statistical modeling of extreme values, volume 208. Springer, 2001. D. Kraft. A Software Package for Sequential Quadratic Programming. Deutsche Forschungs- und Versuchsanstalt für Luft- und Raumfahrt Köln: Forschungsbericht. Wiss. Berichtswesen d. DFVLR, 1988. Erhan Cinlar. Introduction to stochastic processes. Courier Corporation, 2013. Andrew R Conn, Nicholas IM Gould, and Philippe L Toint. Trust region methods. SIAM, 2000. Kolmogorov An. Sulla determinazione empirica di una legge di distribuzione. Giorn Dell’inst Ital Degli Att, 4:89–91, 1933. Nickolay Smirnov. Table for estimating the goodness of fit of empirical distributions. The annals of mathematical statistics, 19(2):279–281, 1948. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94140 | - |
| dc.description.abstract | 本研究提出了一個以資料驅動的途程策略方法，結合統計管制圖的概念，建構出一個最佳途程策略，旨在最小化從發出充電需求到完成充電所需的整體時間。我們採用了最鄰近K充電站的途程策略（Nearest-K RoutingPolicy），並利用EWMA管制圖來監控車流量的顯著變動，使得在節省計算成本的同時，能靈活因應車流量的變化。研究結果顯示，當輸入流量產生重大變化時，使用最鄰近K充電站的途程策略來調整最佳途程策略，能有效降低系統的平均交通時間和等待時間，提升充電系統的整體效率。整體而言，本研究提出的結合最鄰近K充電站途程策略與EWMA管制圖的方法，為應對隨時間變動的輸入參數，提供了一種高效的電動車充電系統途程策略。 | zh_TW |
| dc.description.abstract | This study proposes a data-driven routing strategy combined with the concept of statistical control charts to construct an optimal routing strategy, aiming to minimize the overall time from initiating a charging request to completing the charge. We adopted the Nearest-K Routing Policy and used the EWMA control chart to monitor significant changes in traffic flow, allowing for flexible adaptation to traffic changes while saving computational costs. The research results show that when there are significant changes in the input flow, adjusting the optimal routing strategy using the Nearest-K Routing Policy can effectively reduce the system’s average travel time and waiting time, enhancing the overall efficiency of the charging system. Overall, the proposed method,which combines the Nearest-K Routing Policy with the EWMA control chart, provides an efficient routing strategy for electric vehicle charging systems with time-varying input parameters. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-14T16:54:54Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-14T16:54:54Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員審定書 i
致謝 ii 摘要 iii Abstract iv 目次 v 圖次 vii 表次 ix 第一章研究動機與目的 1 第二章系統介紹與途程策略 3 2.1符號簡介 3 2.2車輛充電服務系統介紹及其定義 4 2.3最佳途程策略 6 2.3.1基於分區的隨機途程策略(PBRR) 6 2.3.2在交通繁忙狀態下對於PBRR策略之影響 8 第三章時變參數下的因應策略 10 3.1根據輸入參數建構途程策略 10 3.1.1最鄰近K充電站途程策略（Nearest-K Routing Policy） 12 3.1.2使用EWMA控制圖來估計及監控系統輸入參數的變化 21 3.2策略步驟與流程圖 25 第四章模擬結果與分析 27 4.1充電站單位容量配置 27 4.2依時變參數來變動策略下的成效29 4.2.1以均勻配置分配充電容量之模擬結果 36 4.2.2充電站相對距離擴大對於系統表現的影響 36 4.3不同ARL對於系統的影響 39 4.3.1切換頻率上限p=0.05下的模擬結果 39 4.3.2控制界線下限p=0.005下的模擬結果 41 4.3.3總結 43 第五章結論與未來建議45 5.1結論 45 5.2未來建議方向 46 參考文獻 47 | - |
| dc.language.iso | zh_TW | - |
| dc.title | 基於時變參數的電動車充電系統最佳途程規劃研究 | zh_TW |
| dc.title | Optimal Routing of Electric Vehicle Charging Systems with Time-Varying Input Parameters | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 洪一薰;黃奎隆;喻奉天 | zh_TW |
| dc.contributor.oralexamcommittee | I-Hsuan Hong;Kwei-Long Huang;Vincent F. Yu | en |
| dc.subject.keyword | 時變參數,電動車,途程策略,EWMA管制圖,最鄰近K充電站策略, | zh_TW |
| dc.subject.keyword | Time-Varying input parameters,Electric vechile,Routing policy,EWMA control chart,Nearest-K Routing Policy, | en |
| dc.relation.page | 49 | - |
| dc.identifier.doi | 10.6342/NTU202402815 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-08-02 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 工業工程學研究所 | - |
| dc.date.embargo-lift | 2029-07-31 | - |
| 顯示於系所單位： | 工業工程學研究所 | |
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| ntu-112-2.pdf 目前未授權公開取用 | 5.17 MB | Adobe PDF | 檢視/開啟 |
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