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.

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