||The Multi-Objective Optimization of Battery Swapping Stations for Electric Scooters: Using the Artificial Neural Network Model for Demand Prediction
||Department of Transportation & Communication Management Science
Battery swapping station
Multi-objective optimization model
Artificial Neural Network
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.
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
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