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研究生: 林祐偉
Lin, Yu-Wei
論文名稱: 基於教與學優化演算法之適應性階層式模糊控制應用於鋰電-超級電容混合電力系統
Adaptive Hierarchical Fuzzy Control for Battery-Supercapacitor Hybrid Powertrain Using Teaching-Learning-Based Optimization Algorithm
指導教授: 陳瑄易
Chen, Syuan-Yi
口試委員: 李政道
Lee, Tsung-Dao
魏榮宗
Wai, Rong-Jong
陳瑄易
Chen, Syuan-Yi
口試日期: 2022/01/25
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2022
畢業學年度: 110
語文別: 中文
論文頁數: 112
中文關鍵詞: 教與學演算法最小等效能耗法傳統模糊邏輯控制策略混合電力系統直流-直流轉換器
英文關鍵詞: Teaching-learning-based optimization algorithm, equivalent consumption minimization strategy, traditional fuzzy logic control strategy, hybrid power system, DC-DC converter
DOI URL: http://doi.org/10.6345/NTNU202201756
論文種類: 學術論文
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  • 電源轉換器需具備穩定電力輸出品質與良好的即時功率調節能力,而電源轉換器的並聯操作雖然可以提高輸出功率,但並聯式轉換器必須透過均流技術來確保模組間的輸出電流相同。本論文針對鋰三元電池模組與超級電容搭配成複合式電力系統並提出最佳化能量管理策略,利用傳統模糊邏輯控制策略、最小等效能耗策略,以及教與學優化演算法控制策略,以雙向直流-直流轉換器與數位訊號控制器實現混合電力系統最佳化能量管理之硬體架構。此架構根據目前負載端需求功率及超級電容之殘電量即時對複合式電力系統進行能量分配最佳化。本論文選用WMTC全球機車測試型態與新歐洲WLTP測試型態作為能耗比較基準, 最後經由實驗測試傳統模糊邏輯控制策略、最小等效能耗策略以及教與學優化演算法控制策略應用於並聯式直流-直流轉換器之耗能結果。藉由兩種行車型態測試與比較,本論文所提出之教與學優化演算法控制策略確實能達到最佳的能源使用效率,獲得最節能之控制效果。

    Power converters must have the ability to maintain stable power output and good real-time power regulation. This thesis proposed an optimal energy management strategy for a hybrid power system with a lithium-ion triple battery and a supercapacitor. A traditional fuzzy logic control strategy, an equivalent consumption minimization strategy (ECMS) strategy, and a teaching-learning-based optimization strategy, are developed to control a bidirectional DC-DC converter for the optimal energy management of a hybrid power system. The energy distribution of the hybrid power system is optimized in real time according to the demand power and the residual power of the supercapacitor. In this thesis, the WMTC global motorcycle test cycle and the new European WLTP test cycle are chosen to test for comparing energy consumptions of different control strategies. Finally, it is experimentally proven that the traditional fuzzy logic control strategy, the ECMS strategy, and the teaching-learning-based optimization strategy can control the parallel DC-DC converter system. Moreover, the teaching-learning-based optimization strategy consumes the least amount of energy, which obtains the best energy usage efficiency.

    致謝 i 摘要 ii ABSTRACT iii 目錄 iv 表目錄 vii 圖目錄 viii 第一章 緒論 1 1.1研究背景與動機 1 1.2文獻探討 3 1.3研究目的 6 1.4研究架構 7 第二章 鋰電-超級電容混合電力系統 8 2.1直流-直流升/降壓轉換器穩態分析 8 2.2雙向直流-直流轉換器模式分析 13 2.3直流-直流轉換器並聯控制 16 2.4 PID控制系統 20 第三章 基於模糊控制之複合式電力系統能量管理策 22 3.1 基本規則控制策略 23 3.2 傳統模糊邏輯控制策略 27 3.3 階層式模糊邏輯控制策略 30 第四章 基於教與學優化演算法之混合電力系統能量管理策略 33 4.1最小等效能耗法控制策略 33 4.2教與學優化演算法控制策略 37 4.2.1教與學優化演算法之數學模型 38 4.2.2教與學優化演算法流程 40 4.2.3教與學優化演算法之能量管理控制策略 42 第五章 混合電力系統之能量管理控制模擬與討論 44 5.1車輛動態模型 44 5.1.1行車型態介紹 46 5.2基本規則庫控制策略之模擬結果 49 5.3傳統模糊邏輯控制策略之模擬結果 52 5.4階層式模糊邏輯控制策略之模擬結果 55 5.5最小等效能耗控制策略之模擬結果 58 5.6教與學優化演算法控制策略之模擬結果 61 5.7模擬結果之能耗比較 64 第六章 鋰電-超級電容混合電力系統實驗與論 69 6.1實驗平台說明 69 6.2 WMTC行車型態實驗結果 72 6.2.1基本規則庫控制策略之實驗結果 72 6.2.2傳統模糊邏輯控制策略之實驗結果 73 6.2.3階層式模糊邏輯控制策略之實驗結果 75 6.2.4最小等效能耗策略之實驗結果 77 6.2.5教與學優化演算法控制策略之實驗結果 79 6.3 WLTP Class1行車型態實驗結果 82 6.3.1基本規則庫控制策略之實驗結果 82 6.3.2傳統模糊邏輯控制策略之實驗結果 83 6.3.3階層式模糊邏輯控制策略之實驗結果 85 6.3.4最小等效能耗策略之實驗結果 87 6.3.5教與學優化演算法控制策略之實驗結果 89 6.4 WLTP Class2行車型態實驗結果 90 6.4.1基本規則庫控制策略之實驗結果 91 6.4.2傳統模糊邏輯控制策略之實驗結果 92 6.4.3階層式模糊邏輯控制策略之實驗結果 94 6.4.4最小等效能耗策略之實驗結果 96 6.4.5教與學優化演算法控制策略之實驗結果 97 6.5均流控制實驗結果 99 6.6實驗結果之能耗比較 100 第七章 結論與未來展望 105 7.1結論 105 7.2未來展望 105 參考文獻 107

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