本研究中依過往的混合動力平台強化一種七模式的車輛混合動力平台機電整合系統，用來評估各種混合動力組合的性能測試。平台設計方面選擇了三種動力源來提供混合動力，第一個動力源是1.5kW輪轂電動馬達，第二個動力源是125c.c.汽油引擎，第三個動力源是空氣引擎，利用三電磁離合器使動力源可獨立運作或進行混合動力輸出。電子磁粉制動器模擬道路負載。Matlab/Simulink軟體接收測量的信號並執行控制命令，通過三個可控離合器的開與關狀態控制，可以執行三種單動力模式、三種雙動力模式和一種三動力模式共七種操作模式。運用混合動力平台測得引擎/馬達效率並於Matlab/Simulink軟體建置雙動力混合動力系統實驗性能分析，選定歐盟規範之ECE40 (Four Cycles)行車型態進行測試，並以人工蜂群演算法搜尋雙動力源之最佳動力分配比例，達到最小等效油耗。模擬最佳化演算法並與規則庫控制相比節能效益。研究結果顯示：第一此平台可以進行七種動力模式測試，證明了混合動力組合的高度靈活性。將來可以應用於其他動力源；第二相較於規則庫控制，仿生最佳化演算法具有較佳的節能效益，採用人工蜂群演算法於雙動力引擎/馬達系統中之能耗改善為28.5%。

This study developed a mechatronics platform for a seven-mode vehicle-oriented powertrain system. It is used for flexibly arranging various power or energy sources to be combined for various hybrid powertrains. In this study, three power sources were chosen for providing hybrid power. The first source is a 1.5kW hub motor, the second one is a 125 c.c. spark ignition engine, and the third source is an air engine, With the help of e-clutches, different power sources can be combined into hybrid powertrain or separated from it. A magnetic powder brake emulates the road load. Matlab/Simulink package receives the measured signals and sends the control commands to actuators. Through the on and off state control of three controllable e-clutches, three single-source modes, three dual-source modes, and one three-source mode, a total seven-mode can be conducted. Use the hybrid platform to measure engine/motor efficiency and Matlab/Simulink software builds a dual-power hybrid system experimental performance analysis, We selected the EU-standard ECE40 (four cycles) model for testing, and then used artificial bee colony algorithm (ABC) to search for the best power distribution ratio of the dual engine/motor power source, to reach the minimum equivalent fuel consumption. Simulated optimization algorithm was compared with rule-based for the evaluation of energy-saving benefits. The research results show that : first, the platform can test seven power modes, it proving the high flexibility of the hybrid combination. In the future, it can be applied to other power sources. Second, compared with rule-based control, the bionic optimization algorithm has better energy-saving benefits. And use with ABC the energy consumption compared with that of the rule-based control can be improved by 28.5%.

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