臺灣博碩士論文加值系統

影響質子交換膜燃料電池的性能參數甚多其中包含陽極氣體加濕溫度、電池操作溫度、陰極氣體加濕溫度、陽極氣體計量比、陰極氣體計量比等，如何能夠快速且有效率調整不同之操作條件以適應環境的變化與需求，並獲得燃料電池最佳性能參數組合提高燃料電池效率，是為極具研究及應用價值。其中燃料電池氣體傳輸現象影響電池性能甚鉅，因此如何使氣體的傳輸能均勻分佈於反應面積增加燃料使用率，並且考慮較低的氣體流動壓力損失進而減少反應氣體之成本消耗，電池流道設計遂成一重要課題。
因此，本論文研究自行開發一組碎形樹枝狀仿生流道構型，並運用工程設計原理中實驗設計最佳化方法，以有效率且系統化的設計分析，提高質子交換膜燃料電池性能。首先運用部分因子實驗設計法篩選分析出重要因子及交互作用效應，將可提高實驗效率及降低實驗成本，再經由田口直交表進行穩健參數設計，並結合類神經網路建構非線性模型，以作為遺傳演算法(GA)尋優的適應函數。本研究提出此一部分因子實驗設計法與田口-類神經網路建構模式，期望在多個操作變數條件下，符合最大S/N之適應值，而能迅速反應燃料電池性能之最佳品質特性。

There are many factors affect the performance of Proton Exchange Membrane Fuel Cells, such as the operating temperature of fuel cell, the anode and cathode humidification temperature and the flow rate of reactant. How to quickly and efficiently adjust the operating conditions in order to adapt to environmental changes is an important issue. This research of the fuel cells can get the best combination of parameters to improve the efficiency of the performance of the fuel cells, which is extremely valuable research and application. The effect of gas transmission phenomenon is important on the fuel cell, how to uniform distribution of gas transmission to increase fuel utilization and reduce the gas pressure loss in the less consumption, so the gas channels design of the fuel cell is an important issue.
Thus the development fractional tree-liked channel is main goal in this work. We are devoted to the study of enhancing the performance of proton exchange membrane fuel cell (PEMFC) by using design of experiment that is an efficient and systematic approach for optimization. At first, we must carry out a screen experiment by using a fractional factorial design for finding out the main factor and interaction effects. Hence, not only the efficiency of experiment will be improved, but also the cost will be reduced. A non-linear model can be made through the Taguchi method combined with artificial neural network in order to supply a fitness function to genetic algorithms (GA). This approach proposed in the present study has been verified to obtain the optimum design to operating parameters on the performance of PEMFC.

誌謝 ii
摘要 iii
ABSTRACT iv
目錄 v
表目錄 ix
圖目錄 xi
符號說明 xiv
1. 緒論 1
1.1 研究背景 1
1.2 文獻回顧 3
1.3 研究動機與目的 6
1.4 仿生流道設計 7
1.5 論文架構 12
2. 燃料電池之簡介 13
2.1 燃料電池組成元件 13
2.1.1 質子交換膜(Proton Exchange Membrane, PEM) 13
2.1.2 觸媒層(Catalyst Layer, CL) 13
2.1.3 氣體擴散層(Gas Diffusion Layer, GDL) 14
2.1.4 雙極板(Bipolar Plates) 14
2.2 燃料電池基本工作原理 14
2.3 燃料電池分類 16
2.4 燃料電池性能極化曲線 20
3. 實驗系統與量測方法 23
3.1 燃料電池測試組件 23
3.1.1 膜電極組(Membrane Electrode Assembly, MEA) 25
3.1.2 氣體擴散層(Gas-Diffusion Layer, GDL) 25
3.1.3 氣密墊片(Gasket) 26
3.1.4 流道雙極板(Bipolar Plate) 27
3.1.5 集電板(Collector Plate) 29
3.1.6 端板(End Plate) 29
3.2 實驗設備 30
3.2.1 氣體供輸系統 32
3.2.2 流量控制系統 32
3.2.3 溫度控制系統 33
3.2.4 增濕系統 34
3.2.5 電子負載系統 34
3.3 燃料電池測試系統操作程序 35
3.3.1 電池與測試機台連接 35
3.3.2 測試系統開機步驟 35
3.3.3 測試系統關機步驟 36
3.3.4 實驗裝備操作步驟 37
3.3.5 氣密測試 37
3.3.6 單電池活化試驗 38
4. 實驗設計最佳化方法 39
4.1 實驗設計法 39
4.1.1 部分因子實驗法 40
4.1.2 田口直交表(Taguchi’s Orthogonal Arrays)實驗設計法 43
4.1.2.1 直交表與點線圖建構 43
4.1.2.2 田口參數設計分析 45
4.1.2.3 田口直交表實驗步驟 47
4.1.3 變異數分析(Analysis of Variance, ANOVA) 47
4.2 類神經網路 50
4.2.1 類神經網路的基本架構 51
4.2.2 類神經網路學習法則 53
4.2.3 類神經網路運作過程 54
4.2.4 倒傳遞類神經網路理論與架構 54
4.2.5 倒傳遞類神經網路流程 56
4.2.6 徑向基底類神經網路理論與架構 57
4.2.7 類神經網路之活化函數 59
4.3 基因演算法 62
4.3.1 適應函數定義(Fitness Function) 65
4.3.2 運算機制 65
5. 結果與討論 67
5.1 直行仿生流道 67
5.1.1 部分因子實驗法 68
5.1.2 田口直交表實驗 71
5.1.3 徑向基底類神經網路模型建立 80
5.1.4 基因演算法求解最佳值 86
5.1.5 小結 89
5.2 蛇型仿生流道 90
5.2.1 部分因子實驗法 90
5.2.2 田口直交表實驗 94
5.2.3 倒傳遞類神經網路模型建立 100
5.2.4 基因演算法求解最佳值 104
5.2.5 小結 108
6. 結果與建議 110
6.1 結論 110
6.2 建議 111
參考文獻 112
自傳 116

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