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
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 
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。
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.
摘要 iii
誌謝 iv
1.1 Research background and motivation 1
1.2 Problem statement 5
1.3 Research objectives 5
1.4 Research flow chart 6
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
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
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
5.1 Conclusions 78
5.2 Recommendations 79
參考文獻 1.Babaee, S., Nagpure, A. S. & DeCarolis, J. F. (2014). How much do electric drive vehicles matter to future US emissions?. Environmental science & technology, 48(3), 1382-1390.
2.Bradley, T. H. & Frank, A. A. (2009). Design, demonstrations and sustainability impact assessments for plug-in hybrid electric vehicles. Renewable and Sustainable Energy Reviews, 13(1), 115-128.
3.Cai, H., Jia, X., Chiu, A. S., Hu, X. & Xu, M. (2014). Siting public electric vehicle charging stations in Beijing using big-data informed travel patterns of the taxi fleet. Transportation Research Part D: Transport and Environment, 33, 39-46.
4.Chen, T. D., Kockelman, K. M. & Khan, M. (2013). Locating electric vehicle charging stations: Parking-based assignment method for Seattle, Washington. Transportation research record, 2385(1), 28-36.
5.Church, R. & ReVelle, C. (1974). The maximal covering location problem. In Papers of the Regional Science Association, 32(1), 101-118.
6.Davis, S. C., Diegel, S. W. & Boundy, R. G. (2009). Transportation energy data book. No. ORNL-6984, Oak Ridge National Laboratory.
7.Doll, C. & Wietschel, M. (2008). Externalities of the transport sector and the role of hydrogen in a sustainable transport vision. Energy Policy, 36(11), 4069-4078.
8.Dong, G., Ma, J., Wei, R. & Haycox, J. (2019). Electric vehicle charging point placement optimization by exploiting spatial statistics and maximal coverage location models. Transportation Research Part D: Transport and Environment, 67, 77-88.
9.Feldman, J. A. & Ballard, D. H. (1982). Connectionist models and their properties. Cognitive science, 6(3), 205-254.
10.Frade, I., Ribeiro, A., Gonçalves, G. & Antunes, A. P. (2011). Optimal location of charging stations for electric vehicles in a neighborhood in Lisbon, Portugal. Transportation Research Record, 2252(1), 91-98.
11.García-Palomares, J. C., Gutiérrez, J. & Latorre, M. (2012). Optimizing the location of stations in bike-sharing programs: A GIS approach. Applied Geography, 35(1-2), 235-246.
12.Golden, B. L., Wasil, E. A., Coy, S. P. & Dagli, C. H. (1997). Neural networks in practice: survey results. In Interfaces in Computer Science and Operations Research, 77-95. Springer, Boston, MA.
13.Granger, C. W. & Terasvirta, T. (1993). Modelling non-linear economic relationships. OUP Catalogue.
14.He, S. Y., Kuo, Y. H. & Wu, D. (2016). Incorporating institutional and spatial factors in the selection of the optimal locations of public electric vehicle charging facilities: A case study of Beijing, China. Transportation Research Part C: Emerging Technologies, 67, 131-148.
15.Hecht-Nielsen, R. (1988). Neurocomputing: picking the human brain. IEEE spectrum, 25(3), 36-41.
16.Hornik, K., Stinchcombe, M. & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5), 359-366.
17.Jain, A. K., Mao, J. & Mohiuddin, K. M. (1996). Artificial neural networks: A tutorial. Computer, 29(3), 31-44.
18.Kameda, H. & Mukai, N. (2011). Optimization of charging station placement by using taxi probe data for on-demand electrical bus system. In International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 606-615. Springer, Berlin, Heidelberg.
19.Kuby, M., Lines, L., Schultz, R., Xie, Z., Kim, J. G. & Lim, S. (2009). Optimization of hydrogen stations in Florida using the flow-refueling location model. International journal of hydrogen energy, 34(15), 6045-6064.
20.Lewis, C. D. (1982). Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. Butterworth-Heinemann.
21.Liu, J. (2012). Electric vehicle charging infrastructure assignment and power grid impacts assessment in Beijing. Energy policy, 51, 544-557.
22.Mitropoulos, L. K. & Prevedouros, P. D. (2015). Life cycle emissions and cost model for urban light duty vehicles. Transportation Research Part D: Transport and Environment, 41, 147-159.
23.Malik, M., Dincer, I. & Rosen, M. A. (2016). Review on use of phase change materials in battery thermal management for electric and hybrid electric vehicles. International Journal of Energy Research, 40(8), 1011-1031.
24.Mak, H. Y., Rong, Y., & Shen, Z. J. M. (2013). Infrastructure planning for electric vehicles with battery swapping. Management Science, 59(7), 1557-1575.
25. Namdeo, A., Tiwary, A. & Dziurla, R. (2014). Spatial planning of public charging points using multi-dimensional analysis of early adopters of electric vehicles for a city region. Technological Forecasting and Social Change, 89, 188-200.
26.Ngatchou, P., Zarei, A. & El-Sharkawi, A. (2005). Pareto multi objective optimization. In Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems, 84-91. IEEE.
27.Nie, Y. M. & Ghamami, M. (2013). A corridor-centric approach to planning electric vehicle charging infrastructure. Transportation Research Part B: Methodological, 57, 172-190.
28.Nozick, L. K. (2001). The fixed charge facility location problem with coverage restrictions. Transportation Research Part E: Logistics and Transportation Review, 37(4), 281-296.
29.Owen, S. H. & Daskin, M. S. (1998). Strategic facility location: A review. European journal of operational research, 111(3), 423-447.
30.Parker, R., West, A. & Fertig, S. (2012). Electric vehicle charging station infrastructure community needs assessment.
31.Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D. & Tarantola, S. (2008). Global sensitivity analysis: the primer. John Wiley & Sons.
32.Schalkoff, R. J. (1992). Pattern recognition: Statistical, structural and neural approaches. New-York: John Wileys & sons.
33.Schalkoff, R. J. (1997). Artificial neural networks. McGraw-Hill Higher Education.
34.Sharda, R. (1994). Neural networks for the MS/OR analyst: An application bibliography. Interfaces, 24(2), 116-130.
35.Tu, W., Li, Q., Fang, Z., Shaw, S. L., Zhou, B. & Chang, X. (2016). Optimizing the locations of electric taxi charging stations: A spatial–temporal demand coverage approach. Transportation Research Part C: Emerging Technologies, 65, 172-189.
36.Wang, Y. W., & Wang, C. R. (2010). Locating passenger vehicle refueling stations. Transportation Research Part E: Logistics and Transportation Review, 46(5), 791-801.
37.Wang, Y. W. & Lin, C. C. (2013). Locating multiple types of recharging stations for battery-powered electric vehicle transport. Transportation Research Part E: Logistics and Transportation Review, 58, 76-87.
38.Wang, Z., Liu, P., Cui, J., Xi, Y. & Zhang, L. (2013). Research on quantitative models of electric vehicle charging stations based on principle of energy equivalence. Mathematical Problems in Engineering.
39.White, H. (1989). Learning in artificial neural networks: A statistical perspective. Neural computation, 1(4), 425-464.
40.Xu, K., Yi, P. & Kandukuri, Y. (2013). Location selection of charging stations for battery electric vehicles in an urban area. International Journal of Engineering Research and Science & Technology, 2(3), 15-23.
41.Yao, J., Zhang, X. & Murray, A. T. (2019). Location optimization of urban fire stations: Access and service coverage. Computers, Environment and Urban Systems, 73, 184-190.
  • 同意授權校內瀏覽/列印電子全文服務,於2020-08-10起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2020-08-10起公開。

  • 如您有疑問,請聯絡圖書館