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研究生:胡暟倫
研究生(外文):HU, KAI LUN
論文名稱:基於深度學習之多線圈無線電能傳輸平台自由定位方法之研究
論文名稱(外文):Study on Free-Positioning of Multi-Coil Wireless Power Transfer Platform Based on Deep Learning
指導教授:陳財榮陳財榮引用關係阮昱霖
指導教授(外文):CHEN, TSAIR-RONGJUAN, YU-LIN
口試委員:顏義和陳德超
口試委員(外文):YAN, YIH-HERCHEN, TE-CHAU
口試日期:2024-07-03
學位類別:碩士
校院名稱:國立彰化師範大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:中文
論文頁數:104
中文關鍵詞:E類放大器無線電能傳輸深度學習自由定位
外文關鍵詞:Class-EWPTDeep learningFree-positioning
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無線電能傳輸技術在工業生產、電子設備、醫療、交通等各種領域被廣泛應用。為能實現高效率電能傳輸且避免傳輸能量的損耗,線圈之間必須互相對準才能進行有效充電,造成使用者受到約束,無法自由自在於任何位置為裝置充電。為實現充電位置不受約束且確保有效電能傳輸,本研究提出一具有自由定位功能的無線電能傳輸,針對需要刻意對準才能充電之問題進行改善。
本文研製一額定輸出功率為20 W之多線圈無線電能傳輸平台,針對自由定位方法進行研究。線圈方面,依照Qi標準使用具有良好耦合的六邊形線圈,以一層線圈緊密排列,改善多層線圈充電板之開關及線圈數量較多的問題;電路方面,利用E類放大器(Class-E)、LCC-S諧振補償架構以及全橋整流器,功率開關數量相較全橋換流器少且損耗較小的優點。最後,透過深度學習技術,利用輸入電流來預測二次側線圈的位置。進一步由回授輸出功率,從而尋找較適合的供電線圈組合,有效避免元件誤差或人為判定的疏失。由實驗結果顯示使用DNN(Deep Neural Network, DNN)模型具有96.19%的準確度,整體電路效率皆達到70%以上,時間上僅需2秒以內可準確判定線圈位置,足以證實本文所提出自由定位方法確實可行。


Wireless power transfer is widely using in various fields such as medical, electric vehicles, portable and wearable devices. It is necessary to align specific positions for high efficiency charging that means users are limited to charge their devices at any location. In order to address the issue of deliberate alignment requirements, this study proposes a WPT system with free positioning ability.
In this study, free positioning techniques are investigated and a WPT platform with a rated output power of 20 W is developed. Owing to solve the problem using multiple layers’ coils, which multiple switches and coils make cost increase, and regard the coil arrangement, a single-layer hexagonal coil with high coupling coefficient layout is employed according to the Qi standard, allowing for dense coil array. The circuit topology has Class-E amplifiers, LCC-S resonant compensation structures, and full-bridge rectifier, including fewer switches and lower losses. Finally, in order to find the best combination of coils and avoiding component errors or human judgment errors and finding the best coupling of coils, the free positioning method in this study employs deep learning to predict the approximate position of the secondary coil based on input current data. The precise position is determining by feedback power. Experimental results demonstrate that using a Deep Neural Network(DNN) model achieves an accuracy of 96.19%. The overall circuit efficiency exceeds 70%, and coil positioning can be accurately determined within 2 seconds, validating the feasibility of the proposed free positioning method.

目錄
摘要 I
Abstract II
圖目錄 V
表目錄 IX
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 4
1.3 論文架構 6
第二章 文獻探討 7
2.1 無線電能傳輸 7
2.2 無線電能傳輸與自由定位 9
2.3 深度學習法 16
第三章 系統架構與設計 22
3.1 系統架構 22
3.2 單發射線圈架構及電路設計 27
3.3 多發射線圈架構及電路設計 43
3.4 二次側線圈位置判定策略 54
第四章 實驗結果 62
4.1 實體電路參數設計 62
4.2 深度學習分類結果 71
4.3 實驗波形與效率量測 74
第五章 結論與建議 89
5.1 結論 89
5.2 建議 92
參考文獻 93

圖目錄
圖2-1 無線電能傳輸基本架構 8
圖2-2 線圈排列方式 11
圖2-3 基本諧振補償架構 14
圖2-4 LCL及LCC諧振補償架構 14
圖2-5 E類功率放大器 16
圖2-6 DNN演算法 18
圖2-7 LSTM演算法 19
圖2-8 CNN演算法 20
圖3-1 系統架構圖 23
圖3-2 系統電路圖 23
圖3-3 控制系統架構圖 24
圖3-4 一次側電路架構 25
圖3-5 二次側電路架構 25
圖3-6 深度學習資料擷取動作流程 26
圖3-7 一、二次側線圈設計 28
圖3-8 Maxwell有限元素分析軟體繪製線圈等角圖 29
圖3-9 一對一線圈磁場分析前視圖 29
圖3-10一次側線圈排列示意圖 30
圖3-11 一、二次側線圈之間的三種關係 30
圖3-12 典型Class-E電路 32
圖3-13 Class-E電路動作波型圖 33
圖3-14 典型Class-E電路輸出阻抗改變 33
圖3-15 Class E參數設計架構 34
圖3-16 無關負載的Class-E電路輸出阻抗改變 35
圖3-17 LCC-S諧振補償結構等效模型阻抗分析 36
圖3-18 全橋整流器架構及整流電壓波形 38
圖3-19 一對一線圈錯位程度 42
圖3-20 y軸距離與耦合係數關係 42
圖3-21外部中斷重置PWM起始位置 44
圖3-22六邊形線圈磁場方向 45
圖3-23多個六邊形線圈排列磁場方向 45
圖3-24 七對一線圈等角圖 46
圖3-25 七對一線圈渦流分析磁場前視圖 .46
圖3-26 LCC-S諧振補償二對一結構等效電路模型 47
圖3-27 二對一線圈距離與耦合係數關係 50
圖3-28 二對一線圈y軸錯位程度 51
圖3-29 二對一線圈最大錯位程度 52
圖3-30 二次側線圈位置示意圖 53
圖3-31 耦合係數對輸入電流關係圖 55
圖3-32 多層密集層架構 56
圖3-33 訓練資料的擷取圖例 57
圖3-34 資料實測的Label範例 58
圖3-35 深度學習參數及架構 59
圖3-36 偵測系統流程圖 61
圖4-1 系統實體電路圖 63
圖4-2 無線電能傳輸線圈變壓器實體圖 63
圖4-3 線圈繞製平台 64
圖4-4 無線電能傳輸線圈電路等效模型 65
圖4-5 無線電能傳輸線圈測量示意圖 65
圖4-6 十組一次側實驗平台架構 68
圖4-7 功率開關驅動電路 69
圖4-8 電流感測電路 70
圖4-9 電壓感測電路 71
圖4-10 實際測試之Label抓取 72
圖4-11 深度學習成效 73
圖4-12 功率開關的驅動訊號波形 75
圖4-13 一對一線圈位置示意圖 76
圖4-14 一對一Class-E開關訊號與旁路電容之波形 76
圖4-15 一對一線圈電壓、電流波形 77
圖4-16 二對一線圈位置示意圖 78
圖4-17 二對一Class-E開關訊號與旁路電容之波形 78
圖4-18 一次側線圈電壓與二次側線圈電壓、電流波形 79
圖4-19 一次側線圈電流與二次側線圈電壓、電流波形 79
圖4-20各列之所有位置效率 80
圖4-21 透過I2C協定對微控制器傳送指令 81
圖4-22 透過I2C協定對微控制器回傳資料 81
圖4-23 透過UART協定與RF射頻模組對微控制器溝通 82
圖4-24 預測結果為一個之線圈位置示意圖 83
圖4-25 預測結果為一個之系統運作波形圖 83
圖4-26 預測結果為兩個之線圈位置示意圖 84
圖4-27 預測結果為兩個之系統運作波形圖 85
圖4-28 預測結果為三個之線圈位置示意圖 86
圖4-29 預測結果為三個之系統運作波形圖 86

表目錄
表1-1 無線電能傳輸方法比較 3
表2-1 不同諧振補償架構之特性 14
表3-1 Qi標準線圈設計範例 28
表3-2 Maxwell軟體分析之線圈自互感值、耦合係數 28
表3-3 深度學習之輸入資料與標籤範例 57
表3-4 深度學習之標籤範例 58
表3-5 深度學習輸入與輸出範例一 60
表3-6 深度學習輸入與輸出範例二 60
表4-1 整體電路系統元件規格 64
表4-2實際繞製之無線電能傳輸線圈參數 66
表4-3諧振補償電路元件參數 67
表4-4 Class-E元件參數設計 69
表4-5 電流感測電路參數設計 70
表4-6 電壓感測電路參數設計 71
表4-7 深度學習訓練成果 74
表4-8 預測結果為一個之功率及效率 84
表4-9 預測結果為兩個之功率及效率 85
表4-10 預測結果為三個之功率及效率 87
表4-11 有無使用本文偵測方法之系統功率 88
表4-12 有無使用本文偵測方法之系統效率 88

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