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系統識別號 U0026-1108202011215000
論文名稱(中文) 基於人工智慧方法於質子交換膜燃料電池壽命預測之驗證
論文名稱(英文) Validation of Proton Exchange Membrane Fuel Cell Lifetime Prediction Based on Artificial Intelligent Methodology
校院名稱 成功大學
系所名稱(中) 能源工程國際碩士學位學程
系所名稱(英) International Master Degree Program on Energy Engineering
學年度 108
學期 2
出版年 109
研究生(中文) 謝欣頤
研究生(英文) Hsin-Yi Hsieh
學號 P06071028
學位類別 碩士
語文別 中文
論文頁數 57頁
口試委員 指導教授-賴維祥
口試委員-王振源
口試委員-陳震宇
中文關鍵字 燃料電池  老化現象  壽命預測  深度學習  時間序列 
英文關鍵字 Proton Exchange Membrane Fuel Cell  Aging Phenomenon  Lifetime Prediction  Deep Learning  Time Series 
學科別分類
中文摘要 在2022年世界燃料電池的市場規模估值8,643百萬美元,相對於2016年2,894百萬美元,預期成長17.36%,再生能源的利用成為世界能源主流。因此,具有低噪音、低操作溫度、零CO2、NOX排放、能源使用效率高與快速啟動的燃料電池就成為最具潛能的能源選擇。然而,膜電極組(Membrane Electrode Assembly, MEA)使用後會產生衰退現象,除了會影響燃料電池的表現性能和效率,亦會影響燃料電池的使用壽命。由於長時間老化衰退的測試非常耗時,本研究將致力於提出快速評估對膜電極組老化的預測方法,此方法將有利綠能產業蓬勃發展。
本研究將著重於透過機器學習來預測燃料電池的衰退對其壽命和性能的,透過機器學習的壽命預測方式,以較少的時間和有限燃料有效的預測燃料電池壽命。在本研究中,配合質子交換膜最佳化的操作參數,導入以LSTM等演算法,藉此達到預測燃料電池壽命之驗證。本研究初步的模型對測試資料有良好的預測效果,目前階段針對五級石墨燃料電池堆在氫氣流量6 L/min,加濕溫度於攝氏80度,經過性能測試,於機器學習中導入重要老化特徵,誤差率(Mean Absolute Error, MAE)可以在 2%以內。本研究結果驗證機器學習可有效的應用於燃料電池壽命預測。
英文摘要 The use of renewable energy has conceptually become the world's mainstream. Therefore, fuel cells with low noise, low operating temperature, zero CO2, NOx emissions, high energy efficiency and fast start-up have become the most potential energy options. However, membrane electrode assembly will have a degradation phenomenon after use. In addition to affecting the performance and efficiency of the fuel cell, it will also affect the lifetime of the fuel cell. Since the long-term test is time-consuming, this study proposes a rapid prediction method for the aging of the membrane electrode assembly. This method will be a vigorous development of the green energy industry.

This study focuses on predicting the degradation of fuel cell lifetime and performance through machine learning. Machine learning can effectively predict the lifetime of fuel cell with less time and limited fuel. The optimized operating parameters of the PEMFC and LSTM algorithms are introduced to achieve verification of predicted fuel cell lifetime. The preliminary model of this study has a good prediction effect on the test data. Five-stage graphite fuel cell stack is used in this study, the hydrogen flow rate is 7 L/min, the humidification temperature is 80 oC, after long-term testing, degradation data is introduced in machine learning. Error rate (mean absolute error, MAE) can be within 2%. The results of this study verify that machine learning can be effectively applied to fuel cell lifetime prediction.
論文目次 中文摘要 I
英文摘要 II
誌謝 VI
目 錄 VIII
表目錄 XII
圖目錄 XIII
符號 XV
第 1 章 緒論 1
1-1 前言 1
1-2 研究動機及目的 3
1-3 文獻回顧 4
第 2 章 基礎理論 8
2-1 燃料電池介紹 8
2-2 質子交換膜燃料電池基本構造 8
2-2-1 質子交換膜 9
2-2-2 電極觸媒層 10
2-2-3 氣體擴散層 10
2-2-4 氣密墊片 10
2-2-5 石墨流道板 11
2-2-6 集電板 11
2-2-7 端板 12
2-3 質子交換膜燃料電池工作原理 12
2-4 機器學習介紹 14
第 3 章 實驗設備 16
3-1 燃料電池測試機台 16
3-2 電腦性能/規格 19
3-3 燃料電池堆規格介紹 19
3-3-1 膜電極組 21
3-3-2 石墨流道板 22
3-3-3 電木端板 24
3-3-4 氣密墊圈 24
第 4 章 實驗方法 27
4-1 極化曲線 27
4-1-1 活化極化 28
4-1-2 歐姆極化 29
4-1-3 濃度極化 30
4-2 演算法模型 32
4-2-1 機器學習 33
4-2-2 類神經網路 33
4-2-3 激勵函數 33
4-2-4 損失函數 35
4-2-5 梯度下降 36
4-2-6 遞迴神經網路 36
4-2-7 長短期記憶模型 38
4-2-8 LSTM的輸入資料維度 39
4-2-9 批標準化 40
4-2-10 正則化 40
4-2-11 擬合 41
4-2-12 開發環境 Anaconda 41
4-3 實驗流程 42
4-3-1 實驗流程 42
4-3-2 基於長短期記憶與遞歸神經網路之預測模型 44
第 5 章 結果與討論 45
5-1 膜電極組活化與再現性測試 45
5-2 燃料氣體最佳化條件測試 47
5-3 100小時電壓衰退實驗 48
5-4 基於LSTM演算法之預測結果 50
5-4-1 在不同Time Step的狀態下 50
5-4-2 LSTM之設定條件 53
第 6 章 結論 54
6-1 結論 54
6-2 未來工作 54
參考文獻 55
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