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研究生： 劉柏均
Liu, Po-Chun
論文名稱： 基於自適應大規模鄰域搜尋之電動車充電最佳化策略
Electric Vehicle Charging Optimization Strategy Based on Adaptive Large Neighborhood Search
指導教授： 呂學展
Lu, Hsueh-Chan
學位類別： 碩士
Master
系所名稱： 工學院 - 測量及空間資訊學系
Department of Geomatics
論文出版年： 2026
畢業學年度： 114
語文別： 英文
論文頁數： 86
中文關鍵詞： 電動車 、車對車充電 、路徑最佳化 、自適應大規模鄰域搜尋法
外文關鍵詞： Electric Vehicles, Vehicle-to-Vehicle Charging, Routing Optimization, Adaptive Large Neighborhood Search
相關次數： 點閱：102  下載：1 
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    • 隨著電動車的普及快速成長，確保即時且可靠的充電服務，已成為都市與都會運輸系統的一項關鍵挑戰。傳統的充電策略主要仰賴固定充電站；然而，尖峰時段需求高度集中且容量有限，往往導致嚴重的排隊延遲，可能打亂旅客的行程規劃。近年來，一種車輛對車輛的新型態充電模式被提出，由電量有餘的車輛為電量不足的車輛提供充電服務，逐漸成為固定充電站充電方式深具潛力的補充方案。然而，車對車充電的一項關鍵挑戰在於如何找到合適的充電夥伴，即供給端與需求端的車輛必須先在適當的會合點交會，才能進行電能傳輸，因此需要設計一個演算法進行匹配。其次，多數既有研究未能探討同時整合固定式充電站與車對車充電共同運作的潛力。此外，許多既有的車對車充電解決方案仍聚焦於專用行動充電車的路徑規劃，而本研究所提出的方法則立基於不受固定起訖點限制的私有車輛，提供更高的服務彈性。為解決上述問題，本研究提出一套車對車充電路徑最佳化框架，結合自適應大規模鄰域搜尋與二分匹配，將車對車充電與既有的固定充電站資源加以整合，並將問題形式化為一個一對多的帶時間窗容量限制車輛途程問題。此框架的核心貢獻在於，能在處理大規模路網上多筆充電請求的同時，一併考量車輛的電池里程與時間窗限制；以自適應大規模鄰域搜尋法為基礎，框架最佳化固定充電站與供電車之間的服務分配，使單一供電車得以自其原行程繞行至鄰近的會合點，服務多輛需充電車，從而克服傳統一對一匹配在服務覆蓋上的侷限。實驗採用真實世界的充電站、停車場與路網資料進行基於模擬的驗證。實驗結果顯示，相較於既有方法，所提方法僅以少量額外的計算成本，即達成服務覆蓋率的大幅提升，並在不同的供電車數量、充電站數量與車對車充電功率下，皆穩定優於各基準方法。整體而言，所提方法展現了良好的可擴展性，並能在大規模車對車充電情境中有效降低總旅行時間成本，為未來的電動車行程規劃提供了實務上的應用潛力。

    With the rapid growth of electric vehicles, ensuring reliable charging services is a critical challenge. Traditional fixed stations often face severe queuing delays during peak hours due to limited capacity. Vehicle-to-Vehicle charging, where vehicles share surplus energy, has emerged as a promising supplementary solution. However, key operational issues remain: existing studies rarely integrate V2V with fixed stations, and most rely on dedicated mobile chargers rather than the higher flexibility of private vehicles. Furthermore, matching supply and demand vehicles at appropriate rendezvous points requires robust algorithms. To address these issues, this study proposes a V2V charging routing optimization framework that integrates private V2V charging with fixed station resources. Formulated as a one-to-many Capacitated Vehicle Routing Problem with Time Windows, the model combines Adaptive Large Neighborhood Search and bipartite matching. It optimizes service allocation by enabling a single provider vehicle to detour to rendezvous points and serve multiple EV requests, overcoming traditional one-to-one matching limitations while considering battery and time constraints. Finally, we validate the framework using real-world charging station, parking lot, and road network data. Results show that the proposed method significantly improves service coverage with marginal computational cost. Compared to baseline methods, it consistently achieves lower total travel time costs across various provider volumes and power levels, demonstrating excellent scalability and practical potential for large-scale EV route planning.

    中文摘要 I Abstract II Content III List of Tables V List of Figures VI Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 2 1.3 Problem 2 1.4 Contribution 3 1.5 Organization 4 Chapter 2 Related Work 5 2.1 Charging Infrastructure Planning and Coordination 5 2.2 Charging Vehicle Matching and Route Scheduling 11 2.3 Summary 17 Chapter 3 Problem Statement 19 Chapter 4 Methodology 29 4.1 Initial Solution Generation 30 4.1.1 Road Network Construction 30 4.1.2 Feasibility Scanning 31 4.1.3 Bipartite Matching 34 4.1.4 FCS Competition 35 4.2 Adapted Large Neighborhood Search 37 4.2.1 Solution Representation 37 4.2.2 Destroy Operator 38 4.2.3 Repair Operator 40 4.2.4 Local Search 42 4.2.5 Acceptance Criterion and Adaptive Weight Update 44 4.2.6 The AMPD Algorithm 45 Chapter 5 Experimental Evaluation 48 5.1 Experimental Data and Settings 48 5.2 Internal Experiment 51 5.2.1 Effect of Iteration and Convergence Control 52 5.2.2 Effect of Destroy Parameters 53 5.2.3 Effect of Simulated Annealing Parameters 55 5.2.4 Effect of Operator Weight Update Factor 58 5.3 External Experiment 59 5.3.1 Analysis of V2V Servicers Number 61 5.3.2 Analysis of Charging Stations Number 64 5.3.3 Analysis of V2V Charging Power 67 Chapter 6 Conclusion and Future Work 72 References 75

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