移動式發電裝置結合V2V服務車 之最大收益路徑規劃

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童騰立(Teng-Li Tung) 查詢紙本館藏

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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)

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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

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指導教授

王啟泰(Chi-Tai Wang)

審核日期

2022-7-13

推文