目前電池能量密度不足問題，行駛里程距離經常受到限制。研究三動力源複合動力源之最佳功率分配與切換時機最佳化。採用Matlab/Simulink建立三動力源整車複合動力搭載引擎、驅動馬達、起動發電機模組(Integrated Starter Generator,ISG )、動力鋰電池、煞車回充、傳動系統、能量管理策略及行車型態，並且使用最小等效油耗(Equivalent Consumption Minimization Strategy)之控制策略與巢狀式五項變數(需求功率、殘電量、轉速、引擎功率及馬達功率)之全域搜尋(Global Sreach Algorithm,GSA)，將等效最小等效函數之動力分配崁入整車系統中。另搭配全域搜尋五項變數找出最佳模式切換時機，進一步在不同操作條件下尋找整車可行駛最大里程之最佳操作點。模擬行車型態為NEDC與FTP-75。在NEDC工況中，使用ECMS可改善26.32 %，再將最佳能量源理論和模式切換最佳化整合可改善30.52 %；FTP-75工況中，使用ECMS可改善17.22 %，再將ECMS和模型切換最佳化整合可改善19.81 %

The mileage of hybrid electric vehicles (HEV) is limited by the low energy density of batteries. Therefore, this study was conducted to determine the optimal power distributionand to optimize the mode-switching of a three-power source hybrid power system. Matlab/Simulink was employed to develop a three-power source hybrid vehicle with an engine, propulsion motor, integrated starter generator (ISG), lithium battery, regenerative braking, drive system, energy management system, and driving pattern. Furthermore, an equivalent consumption minimization strategy and global search algorithm (GSA) with five nested variables (power required, state of charge, revolutions per minute, engine power, and motor power) were employed to embed the power distribution system of minimum equivalent function into the overall vehicle system. Separately, the five-variable GSA was applied to determine the optimal time for switching modes as well as the optimal operating point for determining the ideal mileage under various operating conditions. The driving cycles were simulated according to the New European Driving Cycle NEDC and Federal Test Procedure 75 (FTP-75). Under the NEDC, the driving cycle was improved by 26.32% by using the electrical control and energy management system (ECMS) alone; integrating the ECMS with the optimal energy source and optimized mode-switching configuration resulted in a 30.52% improvement. Under FTP-75, the driving cycle was improved by 17.22% by using the ECMS alone, whereas integrating the ECMS and optimized mode-switching configuration resulted in a 19.81% improvement.

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