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系統識別號 U0026-2007202018561000
論文名稱(中文) 電動機車電池交換站多目標最佳化之研究-以類神經網路建立需求預測模型
論文名稱(英文) The Multi-Objective Optimization of Battery Swapping Stations for Electric Scooters: Using the Artificial Neural Network Model for Demand Prediction
校院名稱 成功大學
系所名稱(中) 交通管理科學系
系所名稱(英) Department of Transportation & Communication Management Science
學年度 108
學期 2
出版年 109
研究生(中文) 盧俊杰
研究生(英文) Jyun-Jie Lu
電子信箱 ji432598@gmail.com
學號 R56071240
學位類別 碩士
語文別 英文
論文頁數 85頁
口試委員 指導教授-魏健宏
口試委員-沈宗緯
口試委員-王瑩瑋
口試委員-陳正杰
中文關鍵字 電動機車  電池交換站  多目標最佳化模型  類神經網路 
英文關鍵字 Electric scooter  Battery swapping station  Multi-objective optimization model  Artificial Neural Network 
學科別分類
中文摘要 在近半個世紀以來,人類都以化石燃料為交通運輸主要的動力來源,其大量排放的溫室氣體導致全球暖化的問題不斷加劇。因此,為了打造以永續發展為目標的綠色運輸環境,發展對環境更加友善的電動車成為世界各國一致的目標。相較於燃油車,電動車在行駛時並不會有溫室氣體的排放、能源轉換效率較高、電力來源更加多樣化。
根據交通部2019年統計資料顯示,在臺灣每百人中就持有93.1輛機車,換句話說,臺灣民眾最主要的私人運具為機車。因此,本研究以電動機車作為欲探討之研究標的。雖然電動機車能為環境帶來極大的效益,但是臺灣邁向電動機車普及化還有非常大的距離,主要受制於其續航力尚無法負擔長途行駛,因此,電力補充設施的選址更顯得重要。目前主流的電力補充設施分為電池交換站與充電站,然而,電池交換站將會是臺灣未來發展的主要方向。因此,本研究將探討,同時滿足業者與消費者需求的電池交換站位置與設施規劃之最佳化。
本研究建構一多目標最佳化模型,以最大化電池交換站之設施使用量、消費者需求覆蓋率,在多種預算的限制下,同時以兩個不同的目標優化電池交換站的選址與設施規劃。本研究利用影響設施使用率之不同因素(如人口、區域類型、交通狀況等)作為預測之變數,並且利用類神經網路建立預測模型。在後續章節中,進一步探討不同現實條件下的兩種延伸模型之結果與分析。研究成果顯示: (1)類神經網路模型在設施使用率預測上具有90%準確性;(2)在較寬鬆的預算限制下,模型容易納入過多設施使用率較低之站點,以滿足更高的需求覆蓋率;以及(3)設施設立在三角窗、經銷商與交通狀況較壅塞的地段對設施使用率有較顯著的正向影響,而設立在巷弄內或路側路段則會有較顯著的負向影響。本研究之研究成果可以提供政府或業者進行設施使用率預測與設點位置最佳化的決策依據。
英文摘要 During the past half-century, people utilize fossil fuels as the main activation power of transport vehicles. The large amount of Greenhouse gases (GHG) emitted exacerbates the problems of global warming and climate change. Therefore, in order to create a green transportation environment with a goal of sustainable development, the development of electric vehicles that are eco-friendlier has become unanimous for all countries in the world. Compared with fuel vehicles, electric vehicles will not emit GHGs when driving, have better energy conversion efficiency and produce more diverse power sources.
Pursuant to the statistics of the Ministry of Transportation and Communications in 2019, every 100 people in Taiwan possess 93.1 scooters. In other words, the most popular private vehicle in Taiwan is the scooter. Thus, this study aims at electric scooters (ESs) as the research target. Although ESs can benefit the environment, the government confronts a huge challenge to popularize ESs. The major reason is that ESs cannot afford long-distance driving. Therefore, charging facilities are crucial. The coverage of charging facilities plays a key role for consumers to purchase ESs. Nowadays, the mainstream charging facilities on the market are divided into battery swapping facilities and recharging facilities. However, battery swapping facility will be the primary direction for the development of ESs in the future. Hence, this study discusses the optimization of the location and facility allocation of battery swapping stations while satisfying the various needs of both commercial operators and consumers.
This study builds a multi-objective optimization model to maximize the usage amount of facilities and demand coverage of users. We will optimize the location and facilities allocation with two different objectives while limiting several fixed budgets. This study considers various factors affecting facility usage rate (e.g., population, location characteristics, traffic status) as input variables for predicting, and uses Artificial Neural Network (ANN) to build the prediction model. In addition, the results and analysis of the two extension models under different realistic conditions will be discussed. The empirical study results indicate that: (1) Proposed ANN model is 90% accurate in predicting facility usage rate, (2) under loose budget constraints, proposed optimization model will easily incorporate too many locations with low usage rate to meet higher demand coverage, and (3) a station located in a street corner, distributor or high level of traffic status area will have a more significant positive impact on facility usage rate, while in an alley or roadside will have a more significant negative impact. The results of this study can provide a basis for the government or commercial operators to predict the usage rate of facilities and optimize the site location.
論文目次 ABSTRACT i
摘要 iii
誌謝 iv
TABLE OF CONTENTS vi
LIST OF FIGURES viii
LIST OF TABLES ix
CHAPTER 1 INTRODUCTION 1
1.1 Research background and motivation 1
1.2 Problem statement 5
1.3 Research objectives 5
1.4 Research flow chart 6
CHAPTER 2 LITERATURE REVIEW 9
2.1 Electric scooters (ES) 9
2.1.1 Development of ESs in Taiwan 11
2.1.2 ESs market status in Taiwan 13
2.1.3 Business model of ESs leading brands in Taiwan 15
2.1.4 Charging stations for ESs 18
2.2 Location model 18
2.2.1 Location problems 19
2.2.2 Application of location models in charging stations 22
2.2.3 Maximum coverage location model 24
2.3 Artificial Neural Network (ANN) 27
2.4 Multi-objective optimization approach 30
2.5 Summary of literature review 32
CHAPTER 3 RESEARCH METHODOLOGY 34
3.1 Research structure 34
3.2 Assumptions 35
3.3 Proposed optimization model formulation 36
3.4 Proposed prediction model 39
3.4.1 ANN prediction model 39
3.4.2 Multilayer perceptron 40
3.5 Research framework of methodologies 43
3.6 Summary of research methodology 44
CHAPTER 4 EMPIRICAL STUDIES 45
4.1 Data collection 45
4.2 Variables and parameters setting 50
4.3 Results analysis of ANN model 51
4.4 Results analysis of optimization model 58
4.5 Model extension 69
4.5.1 Hybrid optimization model with location set covering problem 69
4.5.2 Various weight considerations of optimization model 75
CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS 78
5.1 Conclusions 78
5.2 Recommendations 79
REFERENCES 81
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