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系統識別號 U0026-1408201700120000
論文名稱(中文) 鳳凰立方衛星電力系統之電力行為估測
論文名稱(英文) Estimation of Power Behavior in PHOENIX’s Electrical Power Subsystem
校院名稱 成功大學
系所名稱(中) 電機工程學系
系所名稱(英) Department of Electrical Engineering
學年度 105
學期 2
出版年 106
研究生(中文) 葉家亨
研究生(英文) Chia-Heng Yeh
學號 N26044197
學位類別 碩士
語文別 英文
論文頁數 80頁
口試委員 指導教授-莊智清
口試委員-苗君易
口試委員-詹紹勳
口試委員-余國瑞
口試委員-壽賀年
中文關鍵字 立方衛星  電力系統  模糊邏輯  電池電壓估測  太陽能發電 
英文關鍵字 CubeSat  Electrical Power System  Fuzzy Logic  Solar Power Generation  Battery Voltage Estimation 
學科別分類
中文摘要 近十幾年來,對於太空科技的探索與小衛星的研究越趨盛行。進而如此,國立成功大學參與了國際合作的QB50計畫並研製、開發2U大小的立方衛星-鳳凰(PHOENIX)。鳳凰立方衛星於民國106年四月18日發射至國際太空站,且於五月17日彈射出太空站,目前順利運行且成功與歸仁地面站建立通聯。此計畫的其中目標之一為針對低熱層的太空離子量測,因此一個穩定且可靠的電力系統是必需的,而為了防止超負荷的事件發生及最大化衛星任務,量好的掌握衛星的電力情況是勢在必行的。
執行任何任務前必須確保電力是足以承擔的。因此,本篇論文提出一個新穎的方法估測電力系統的電力行為。鑒於電力系統的架構,本篇論文分為三部份依續探討: 發電、儲能、耗能。發電的部份藉由建立一組電力產生模型來估算發電的多寡,主要根據軌道的參數與太陽能板建立起此模型預估的基準,另外,再藉由諸多的修正因子更加趨近於真實情況。鋰離子電池經常被使用於衛星,作為儲能的利器及太陽能板與負載間的緩衝器;然而,時間與操作環境常成為左右電池表現的因素,於是,此篇論文開發展一個演算法結合模糊邏輯來預測電池電壓的變化情況。電力的損耗根據任務的規劃而有所不同,而分析各種可能的情境有助於預測各子系統的操作情形。
驗證此篇論文所提出的相關方法,首先關於電力產生模型的部份,利用相關衛星的歷史資料驗證其相關數據。而為了驗證所提出的演算法的部份,相關的軟硬體設備整合成一個測試環境擷取相關參數。因為衛星任務的需求,此篇論文的所提出的演算法相較於大多數的研究最大不同之處為估測電池的電壓,而不是電池容量或健康狀態。任務規劃者亦可透過本篇的論文的相關成果,達成良好的任務規劃與評估。
英文摘要 Research in space technology and small satellite development has become more predominant in recent years. The 2U CubeSat project, PHOENIX, was developed at NCKU as a part of the QB50 mission and was launched to the International Space Station (ISS) on April 18, 2017 by Atlas V and deployed from ISS on May 17, 2017. It is currently still operational and communicates with NCKU Guiren campus ground station several times every day. PHOENIX consists of several subsystems, the most critical one of which is the Electrical Power Subsystem (EPS), which provides, stores, distributes, and controls the satellite’s electrical power.
Prior to executing any missions, it is necessary to ensure that the power margin is sufficient. Therefore, this thesis proposes a new method to estimate the power behavior of the EPS to avoid overloading and maximize mission performance. In the light of the EPS architecture, there are three parts comprising the estimation: power generation, storage and power consumption. A power generation model builds the basic assessment based on the orbit and solar cells parameters and then compensates for any errors via some correction factors. The storage device, the lithium-ion cells, is like a bridge between the solar cells and loads, which degrades the performance due to the lifetime and the environment, so a proposed algorithm combined with fuzzy logic is used estimate the variations in the battery voltage. Power consumption depends on mission planning. Thus, in this thesis, each scenario is analyzed in order to predict the behavior of each subsystem.
With a view to verifying the power generation model, this thesis employs the data from the 2U RAIKO CubeSats. Moreover, the verification environment was constructed to retrieve the parameters of the lithium-ion battery exploited in the estimation algorithm. On account of PHOENIX’s operation, this innovative method provides predictive capacity for the mission planners and appraises the battery voltage rather than its capacity via the proposed power generation model and the scenario power consumption analyses.
論文目次 摘要 I
Abstract III
Acknowledgements V
Contents VI
List of Tables VIII
List of Figures IX
List of Abbreviation XI
Chapter 1 Introduction 1
1.1 Overview 1
1.2 Literature Review 3
1.3 Thesis Contributions 5
1.4 Thesis Organization 5
Chapter 2 PHOENIX EPS 7
2.1 QB50 Mission 7
2.2 PHOENIX CubeSat 9
2.2.1 Subsystem and Payload 11
2.2.2 Operation Mode 14
2.3 Electrical Power Subsystem 17
2.3.1 Design Criteria 17
2.3.2 EPS Architecture 18
Chapter 3 Power Generation and Consumption 21
3.1 Power Generation Model 21
3.1.1 Photovoltaic Electrical Characteristics 22
3.1.2 Simulation and Environment Parameters 25
3.1.3 Albedo 28
3.1.4 Battery Charge Regulator (BCR) 30
3.2 Power Consumption 33
3.2.1 Power Condition Module (PCM) 34
3.2.2 Scenarios Analysis 35
Chapter 4 Energy Storage 37
4.1 Lithium-ion Battery Characteristic 37
4.2 Voltage Estimation Algorithm 41
4.2.1 Algorithm Overview 42
4.2.2 Application of Fuzzy Logic 44
4.2.3 Estimation in Eclipse 45
4.2.4 Estimation in Sunlight 50
Chapter 5 Testing and Verification 54
5.1 Power Generation Estimation 54
5.2 Testing Configuration 62
5.2.1 Hardware and Testing Environment 62
5.2.2 Software Architecture for Measuring Parameters 65
5.2.3 Software Architecture for Verifying the Estimation Algorithm 67
5.3 Verification of Battery Voltage Estimation Algorithm 69
5.3.1 Testing Script One 69
5.3.2 Testing Script Two 71
Chapter 6 Conclusions and Future Work 75
6.1 Conclusion 75
6.2 Future Work 76
Reference 78
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