移動式發電裝置結合V2V服務車 之最大收益路徑規劃

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：38

、訪客IP：3.15.151.214

姓名

童騰立(Teng-Li Tung) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

移動式發電裝置結合V2V服務車之最大收益路徑規劃
(Maximum Revenue Path Planning for Mobile Power Generation Devices Combined with V2V Service Vehicles)

相關論文

★ 半導體化學材料銷售策略分析-以跨國B化工公司為例	★ TFT-LCD CELL製程P檢點燈不良解析流程改善之關聯法則應用
★ 金融風暴時期因應長鞭效應的策略 –以X公司為例	★ 勞動生產力目標訂定之研究-DEA 資料包絡法應用
★ 應用田口方法導入低溫超薄ITO透明導電膜於電容式觸控面板之研究	★ 多階不等效平行機台排程與訂單決策
★ 多準則決策之應用-以雷射半導體產業為例	★ 專案管理模式進行品管圈活動-以半導體機台保養測機流程改善為例
★ 應用e8D降低不合格品之效益分析-以快速消費品製造為例	★ 供應商評選模式之建構-以塑膠射出成型機製造為例
★ 應用協同規劃預測補貨於伺服器備品存貨改善之研究－以Q代工公司為例	★ 船用五金拋光作業之生產規劃
★ 以SCOR模型探討汽車安全輔助系統供應鏈-以A公司採購作業改善為例	★ 研發補助計畫執行成效評估之研究以「工業基礎技術專案計畫」為例
★ 運用生態效益發展永續之耳機產業	★ 失效模式設計審查(DRBFM)之應用-以筆記型電腦為例

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

人們自工業革命以來，不斷地消耗著地球上的資源，伴隨而來的是環境破壞與資源短缺的問題。環境破壞造成的全球暖化成了世界各國皆關注的議題。而節能減碳是減緩全球暖化速度最直接的解決辦法，因此近年來各國在簽訂環保協議的共識下致力於減少使用會造成環境污染的石化燃料，取而代之的是將再生能源引入工業與生活中。並以提升能源使用效率與更快進行能源補給為永續發展的核心理念。
以往使用石化燃料作為動力來源的汽車也因應永續發展的議題做出改變。不少車廠推出以電力作為動力來源的電動車，近年廣受人們歡迎。電動車的優點為在行駛時不會產生二氧化碳，因此不會對空氣造成空氣汙染。但因儲存電力的電池發展尚未齊全，電池容量小、續航力不足是電動車擴大市場的阻礙。續航力不足使得電動車用戶在駕駛時，需要仰賴路邊或停車場的固定式充電樁來補充電力。在地狹人稠的都市中，固定式充電樁這類充電基礎設施因建置成本及都市規劃而難以找到可用的充電樁。因此本研究目的為利用一輛具有V2V服務的充電車搭載移動式發電的充電貨櫃，提供充電服務給需求車輛，並且有足夠電力能夠返回出發點，屬於路徑規劃的車輛路徑問題。
蟻群優化演算法原為解決旅行銷售員問題而生，但本問題之目標為尋找最大收益路徑，不會拜訪所有的需求點且服務車續航與充電貨櫃電力有限，故須將問題限制加入演算法中進行修改。本研究將情境設定為一個都會區，需求網路與需求量為已知資料，利用python撰寫蟻群優化演算法求得最佳解，以觀察演算法在路徑規劃與收益的表現。最後由電腦實驗結果得知，使用蟻群優化演算法可以解決移動式發電裝置結合V2V的最大收益路徑規劃，每一次的可行解皆有滿足限制並返回起終點。本問題的未來發展為將需求網路更接近現實，或考慮時窗限制與多服務車輛，將問題設計得更符合現實情況，藉此提供一個未來可期的綠色能源轉換、交易模式。

摘要(英)

Since the industrial revolution, people have been consuming the resources on the earth, and the problems of environmental damage and resource shortage have been accompanied by them. Global warming caused by environmental damage has become a topic of concern to countries all over the world. Energy saving and carbon reduction are the most direct solutions to slowing down the rate of global warming. Therefore, in recent years, countries have committed to reducing the use of fossil fuels that cause environmental pollution under the consensus of signing environmental protection agreements. Instead, renewable energy has been introduced into the industry and life. And to improve energy efficiency and faster energy supply as the core concept of sustainable development.
Vehicles that used fossil fuels as power sources in the past have also made changes in response to the issue of sustainable development. Many car manufacturers have launched electric vehicles that use electricity as a power source, which has been widely accepted in recent years. The advantage of electric vehicles is that they do not generate carbon dioxide while driving, so they do not cause air pollution to the air. However, due to the incomplete development of batteries for storing electricity, small battery capacity and insufficient battery life are obstacles to the expansion of the electric vehicle market. Insufficient battery life makes electric vehicle users need to rely on fixed charging stations on the roadside or in the parking lot to supplement power when driving. In densely populated cities, it is difficult to find available fixed charging stations for charging infrastructure due to construction costs and urban planning. Therefore, the purpose of this research is to use a charging vehicle with V2V service to carry a charging container for mobile power generation, provide charging services to vehicles in demand, and have enough power to return to the starting point, which belongs to the traveling salesman problem of route planning.
The ant colony optimization algorithm was originally born to solve the traveling salesman problem, but the goal of this problem is to find the maximum revenue path, it won’t visit all demand points, the battery life of the service car and the power of the charging container are limited, so the problem limit must be added to the algorithm amendments in the law. In this study, the scenario is set as a metropolitan area, the demand network and demand are known, using python to write an ant colony optimization algorithm to obtain the best solution, so as to observe the performance of the algorithm in path planning and revenue. Finally, it is known from the computer experiment results that the ant colony optimization algorithm can be used to solve the maximum revenue path planning of the mobile power generation device combined with V2V. The future development of this problem is to make the demand network closer to reality, or consider time window and multi-service vehicles, and design the problem more in line with the reality, thereby providing a promising green energy conversion and transaction model in the future.

關鍵字(中)

★ 永續發展
★ 全球暖化
★ V2V
★ 電動車
★ 車輛路徑問題

關鍵字(英)

★ Sustainable Development
★ Global Warming
★ V2V
★ Electric Vehicle
★ Vehicle Routing Problem

論文目次

摘要 i
Abstract ii
目錄 iv
圖目錄 vi
表目錄 vii
第一章研究問題 1
1.1 全球暖化與永續發展 1
1.2 研究動機 4
1.3 研究問題 7
第二章文獻探討 10
2.1 電動車 10
2.2 V2V與移動式發電 12
2.2.1 V2V 14
2.2.2 移動式發電 16
2.3 啟發式演算法 17
第三章研究方法 21
3.1 問題分析 21
3.2 研究方法介紹 22
3.3 蟻群優化演算法架構及流程 23
第四章電腦實驗 31
4.1 資料收集 31
4.2 資料分析 33
第五章結論與建議 40
5.1 研究總結 40
5.2 後續研究 40
參考文獻 42
中文文獻 42
英文文獻 42
附錄 50

參考文獻

中文文獻
[1] 台大風險中心（2022）。台灣行不行──各國電動車政策大評比。天下雜誌。網站：https://csr.cw.com.tw/article/42336（上網日期：2022年3月9日）。
[2] 王亨（2021）。全球電動車產值爆發成長 2025年銷量將突破1600萬台。自由時報。網站：https://ec.ltn.com.tw/article/paper/1480660（上網日期：2022年2月17日）。
[3] 交通部公路總局（2022）。統計查詢網。網站：https://stat.thb.gov.tw/hb01/webMain.aspx?sys=100&funid=11200（上網日期：2022年3月9日）。
[4] Woody, T.（2020）。藏在海底下的「定時碳炸彈」，可能會讓暖化更加惡化。網站: https://www.natgeomedia.com/environment/article/content-10276.html（上網日期：2021年12月7日）。
[5] Greenpeace.（2021）。聯合國氣候大會落幕，結果如何？減碳協議是成功還是失敗？網站：https://www.greenpeace.org/taiwan/update/28276/（上網日期：2022年3月9日）。
英文文獻
[6] Afshar, S., Macedo, P., Mohamed, F. & Disfani, V. (2020). A Literature Review on Mobile Charging Station Technology for Electric Vehicles. 2020 IEEE Transportation Electrification Conference & Expo, 1184-1190.
[7] Andwari, A. M., Pesiridis, A., Rajoo, S., Martinez-Botas, R., & Esfahanian, V. (2017). A review of Battery Electric Vehicle technology and readiness levels. Renewable and Sustainable Energy Reviews, 78, 414-430.
[8] APTIV. (2021). BEV, PHEV or HEV: The Differences Affect the Architecture. Retrieved from https://www.aptiv.com/en/insights/article/bev-phev-or-hev-the-differences-affect-the-architecture (Accessed: Nov 25, 2021).
[9] Cai, Y., Qi, Y., Chen, H., Cai, H. & Hejlesen, O. (2018). Quantum Fireworks Evolutionary Algorithm for Vehicle Routing Problem in Supply Chain with Multiple Time Windows, 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, 383-388.
[10] Chang, C., Cho, Y. J., & Lan, Y. H. (2005). A modified nested partitions meta-heuristics for the traveling salesman problem. Transportation Planning Journal, 34(4), 549-574.
[11] Chaudhry, I. A. (2010). Minimizing flow time for the worker assignment problem in identical parallel machine models using GA. The International Journal of Advanced Manufacturing Technology, 48, 747-760.
[12] Chauhan, V. & Gupta, A. (2018). Scheduling Mobile Charging Stations for Electric Vehicle Charging, 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), 131-136.
[13] Chellaswamy, C., Balaji, L., & Kaliraja, T. (2020). Renewable energy based automatic recharging mechanism for full electric vehicle. Engineering Science and Technology, an International Journal Volume 23, 3, 555-564.
[14] Chellaswamy, C., Ramesh, R. (2017). Future renewable energy option for recharging full electric vehicles, Renewable and Sustainable Energy Reviews, 76, 824-838.
[15] Clinton, B. C., Steinberg, D. C. (2019). Providing the Spark: Impact of financial incentives on battery electric vehicle adoption, Journal of Environmental Economics and Management, 98.
[16] Cordeau, J. F., Gendreau, M., Laporte, G., Potvin, J. Y., & Semet, F. (2002). A guide to vehicle routing heuristics. Journal of the Operational Research society, 53(5), 512-522.
[17] Cui, S., Yao, B., Chen, G., Zhu, C., Yu, B. (2020). The mobile charging service problem with time windows and multiple mode service. Energy.
[18] Cui, S., Zhao, H., Zhang, C. (2018). Multiple Types of Plug-In Charging Facilities’ Location-Routing Problem with Time Windows for Mobile Charging Vehicles. Sustainability, 10(8).
[19] Department of Scientific Computing - Florida State University (2019). TSP Data for the Traveling Salesperson Problem. Retrieved from https://people.sc.fsu.edu/~jburkardt/datasets/tsp/tsp.html (Accessed: Jun 16, 2022)
[20] Dimitropoulos, A., Rietveld, P. & Van Ommeren, J. N. (2013). Consumer valuation of changes in driving range: A meta-analysis. Transportation Research Part A: Policy and Practice, 55, 27-45.
[21] Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), 29-41.
[22] ecoRI (2019). Global Warming Has Concrete Problem When It Comes to CO2. Retrieved from https://www.ecori.org/climate-change/2019/10/4/global-warming-has-a-co2ncrete-problem (Accessed: Oct 10, 2021).
[23] Edmonds, E. (2016). Despite vehicle advances, break downs at record high. 2016, Retrieved from https://newsroom.aaa.com/2016/07/despite-vehicle-advances-break-downs-atrecord-high/ (Accessed Feb 16, 2022).
[24] evadoption. (2020). EV Charging Stations Statistics. Retrieved from https://evadoption.com/ev-charging-stations-statistics/ (Accessed: Dec 22, 2021).
[25] Fahima, K. S., Subhashini, M., Pavithra, M., Priyadharhini, R. and Vetrichelvi, G. (2021). Wind Energy Based on Board Charging System for Electric Vehicles, IJRESM, 4, 5, 51–53.
[26] Ford. (2022). 2022 FORD F-150 LIGHTNING. Retrieved from https://www.ford.com/trucks/f150/f150-lightning/2022/ (Accessed: Jan 19, 2022).
[27] Funke, S. Á., Sprei, F., Gnann, T., & Plötz, P. (2019). How much charging infrastructure do electric vehicles need? A review of the evidence and international comparison, Transportation Research Part D: Transport and Environment, 77, 224-242.
[28] Gaton, B. (2018). The ICE age is over: Why battery cars will beat hybrids and fuel cells. Retrieved from https://thedriven.io/2018/11/14/the-ice-age-is-over-why-battery-cars-will-beat-hybrids-and-fuel-cells/ (Accessed: Dec 5, 2021).
[29] Geng, X., Chen, Z., Yang, W., Shi, D., & Zhao, K. (2011). Solving the traveling salesman problem based on an adaptive simulated annealing algorithm with greedy search. Applied Soft Computing, 11(4), 3680-3689.
[30] Ghafurian, S., & Javadian, N. (2011). An ant colony algorithm for solving fixed destination multi-depot multiple traveling salesmen problems. Applied Soft Computing, 11(1), 1256-1262.
[31] Goldbarg, E. F. G., Goldbarg, M. C., & Souza, G. R. d.(2008). Particle Swarm Optimization Algorithm for the Traveling Salesman Problem. In(Ed.), Traveling Salesman Problem. IntechOpen.
[32] Huang, S., He, L., Gu, Y., Wood, K., & Benjaafar, S. (2015). Design of a Mobile Charging Service for Electric Vehicles in an Urban Environment, IEEE Transactions on Intelligent Transportation Systems, 16, 2, 787-798.
[33] International Energy Agency (IEA). (2021). Global EV Outlook 2021.
[34] Idaho National Lab (INL). (2017). Plugged in: How Americans charge their electric vehicles. Retrieved from https://avt.inl.gov/sites/default/files/pdf/arra/PluggedInSummaryReport.pdf (Accessed Feb 16, 2022).
[35] Intergovernmental Panel on Climate Change (IPCC). (2021). Climate change widespread, rapid, and intensifying. Retrieved from https://www.ipcc.ch/2021/08/09/ar6-wg1-20210809-pr/ (Accessed: Oct 12, 2021).
[36] Intergovernmental Panel on Climate Change (IPCC). (2021). Climate Change 2021: The Physical Science Basis.
[37] Jaradat, A. S., Matalkeh, B., & Diabat, W. (2019). Solving Traveling Salesman Problem using Firefly algorithm and K-means Clustering. 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), 586-589.
[38] Kempton, W., & Tomić, J. (2005). Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy, Journal of Power Sources, 144, 1, 280-294.
[39] Kester, J., Noel, L., Zarazua de Rubens, G., & Sovacool, B. K. (2018). Policy mechanisms to accelerate electric vehicle adoption: A qualitative review from the Nordic region, Renewable and Sustainable Energy Reviews, 94, 719-731.
[40] Kim, O. T. T., Tran, N. H., Nguyen, V., Kang, S. M., & Hong, C. S. (2018). Cooperative between V2C and V2V charging: Less range anxiety and more charged EVs, 2018 International Conference on Information Networking (ICOIN), 679-683.
[41] Kumar, R. R., & Alok, K. (2020). Adoption of electric vehicle: A literature review and prospects for sustainability, Journal of Cleaner Production, 253.
[42] Laporte, G. (1992). The vehicle routing problem: An overview of exact and approximate algorithms. European journal of operational research, 59(3), 345-358.
[43] Letmathe, P., & Suares, M. (2017). A consumer-oriented total cost of ownership model for different vehicle types in Germany. Transportation Research Part D: Transport and Environment, 57, 314-335.
[44] Lin, J., Zhou, W., & Wolfson, O. (2016). Electric vehicle routing problem. Transportation research procedia, 12, 508-521.
[45] Liu, Q., Li, J., Sun, X., Wang, J, Ning, Y., & Zheng, W. (2018). Towards an efficient and real-time scheduling platform for mobile charging vehicles. In: International conference on algorithms and architectures for parallel processing. Springer; 402–416.
[46] Lo, C. C., & Hsu, C. C. (1998). An annealing framework with learning memory, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 28, 5, 648-661.
[47] Marinakis, Y., Marinaki, M., & Dounias, G.D. (2011). Honey bees mating optimization algorithm for the Euclidean traveling salesman problem. Inf. Sci., 181, 4684-4698.
[48] Meisel, S., & Merfeld, T. (2018). Economic incentives for the adoption of electric vehicles: A classification and review of e-vehicle services. Transportation Research Part D: Transport and Environment, 65, 264-287.
[49] Moghaddam, V., Ahmad, I., Habibi, D., & Masoum, A.S. M. (2021). Dispatch management of portable charging stations in electric vehicle networks, eTransportation, 8.
[50] Nian, V., Hari, M.P. & Yuan, J., (2019). A new business model for encouraging the adoption of electric vehicles in the absence of policy support. Applied energy, 235, 1106-1117.
[51] Omar, N., Daowd, M., Hegazy, O., Al Sakka, M., Coosemans, T. van den Bossche, P., & Van Mierlo, J. (2012). Assessment of lithium-ion capacitor for using in battery electric vehicle and hybrid electric vehicle applications, Electrochimica Acta, 86, 305-315.
[52] Palmer, K., Tate, J.E., Wadud, Z. & Nellthorp, J., (2018). Total cost of ownership and market share for hybrid and electric vehicles in the UK, US and Japan. Applied energy, 209, 108-119.
[53] Qin, H., Su, X., Ren, T., & Luo, Z. (2021). A review on the electric vehicle routing problems: Variants and algorithms. Frontiers of Engineering Management, 8(3), 370-389.
[54] Quak, H., Nesterova, N., & van Rooijen, T. (2016). Possibilities and barriers for using electric-powered vehicles in city logistics practice. Transportation Research Procedia, 12, 157-169.
[55] Reinelt, G. (1991). TSPLIB—A traveling salesman problem library. ORSA journal on computing, 3(4), 376-384.
[56] Rotthier, B., Van Maerhem, T., Blockx, P., Van den Bossche, P., & J. Cappelle. (2013). Home charging of electric vehicles in Belgium, 2013 World Electric Vehicle Symposium and Exhibition (EVS27), 1-6.
[57] Skippon, S.M., Kinnear, N., Lloyd, L., & Stannard, J. (2016). How experience of use influences massmarket drivers’ willingness to consider a battery electric vehicle: a randomised controlled trial. Transportation Research Part A: Policy and Practice, 92, 26-42.
[58] Smith, M., Conte, J., & Guss, S. (2016). Understanding Academic Patrons’ Data Needs through Virtual Reference Transcripts: Preliminary Findings from New York University Libraries. IASSIST Quarterly, 40(1), 20.
[59] Tamai, G. (2019). What Are the Hurdles to Full Vehicle Electrification? [Technology Leaders], IEEE Electrification Magazine, 7, 1, 5-11.
[60] Tang, P., He, F., Lin, X., Li, M. (2020). Online-to-offline mobile charging system for electric vehicles: Strategic planning and online operation, Transportation Research Part D: Transport and Environment, 87.
[61] United Nation (UN). (2021). The Sustainable Development Goals Report 2021.
[62] United Nations Framework Convention on Climate Change (UNFCCC). (2021). Glasgow Climate Pact.
[63] Vempalli, S. K., Deepa, K. & Prabhakar. G. (2018). A Novel V2V Charging Method Addressing the Last Mile Connectivity, 2018 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), 1-6.
[64] Wang, M., Ismail, M., Zhang, R., Shen, X., Serpedin, E. & Qaraqe, K. (2016). Spatio-Temporal Coordinated V2V Energy Swapping Strategy for Mobile PEVs, IEEE Transactions on Smart Grid, 9, 3, 1566-1579.
[65] Wolbertus, R., Maarten, K., Robert van den, H., & Caspar, G.C. (2018). Policy effects on charging behaviour of electric vehicle owners and on purchase intentions of prospective owners: Natural and stated choice experiments. Transportation Research. Part D: Transport and Environment, 62, 283-297.
[66] Wu, P. (2019). Which battery-charging technology and insurance contract is preferred in the electric vehicle sharing business?. Transportation Research Part A: Policy and Practice, Elsevier, 124(C), 537-548.
[67] Xos. (2021). All-in-One Infrastructure System for Rapid Electric Fleet Deployment. Retrieved from https://xostrucks.com/energysolutions#energysolutions-intro-anchor (Accessed: Jan 28, 2022).

指導教授

王啟泰(Chi-Tai Wang)

審核日期

2022-7-13

推文