本研究為一電動車之多電源系統最佳化能量管理與多能源平台實做驗證之研究。結合不同電力源之特性，找尋最佳化之能量管理策略，以解決目前電動車續航力不足、充電時間過長、電池循環壽命不高等問題。本研究可分為三個部分： (1)最佳化能量管理控制技術、(2)多能源主動式能量分配系統、 (3)多能源電動車機電整合技術與系統驗證平台。
本研究主要選定三種電力源進行能量分配與控制管理，分別為：燃料電池、超級電容、鋰電池三種電力源。首先透過全域搜尋法則(Global Search Algorithm)分析出最佳化參數，作為最佳化控制策略調控之依據。其中以鋰電池為主要之電力來源；超級電容作為需求功率大時提供瞬間大電流的輔助，如此可減少鋰電池之損害並提高電池壽命；燃料電池則作為延距(增程)之能量源，主要用於發電提供給其他電池回充，增加續航力的同時亦可當作一輔助電力源。最後透過最佳化能量管理技術，分為四大模式：純電動模式(EV)、混合模式(Hybrid)、延距模式(RE)及超級電容輔助模式(SC-Power Assist)。
本研究架構的配置包含上述三種電池外，亦有一邏輯控制器用以分析各電池狀態、不同行車型態之負載，藉此找出最佳化能量控制策略。另外在各電力源輸入端及輸出端皆搭載一塊自行設計之主動式能量分配電路板，用以接收由邏輯控制器回傳之參數訊號，進而調控各電力源之限流值並切換充放電模式，以達到多能源最佳化控制之目的。其中電路板包含一直流/直流轉換器(DC/DC converter)用於調控限制電流值、一個電阻用於控制輸出電壓、十二顆電容用以濾波並抵銷主動式電能分配系統動態響應速度不足之問題，並透過模組化製程以減少元件誤差，使得轉換效率可達94.6%以上。
最終結合最佳化能量管理控制技術與多能源主動式能量分配系統，在軟硬體整合的應用下，建置一多能源電動車系統驗證平台。可模擬不同之行車型態，使得各電池無論是處在不同的狀況(SOC值)或是不同的負載(行車型態)下，皆可即時調控電流、切換模式並找出最佳的切換點，讓各電池保持在最佳操作狀態。相較於傳統電動車，更減少耗能並提升續航力，改善目前電動車發展所遇瓶頸。

This research aims at developing an active energy management system for an experimental platform of the multi-energy-source electric vehicle (EV). The main purposes are to deal with the shortages of long charging time, short battery life cycles, and insufficient mileage of EVs. Hence, this research were separated into three segments: (1)optimal energy management control technologies, (2) active power distribution hardware, and (3) performance verification on an experimental platform.
The selected green energy sources for EVs are fuel cells, supercapacitors and lithium batteries. By a global search method, the optimal control parameters were derived. The fuel cell were determined to be the range extension source, the batteries were the main energy provider, while the supercapacitors was the high-power-assist device. The energy management was with four modes: EV mode, hybrid mode, range-extension (RE) mode, and the supercapacitor-power assist mode.
For the active power distribution hardware, a self-designed control board was integrated at the input and (or) output of each energy source. It consists of a DC/DC converter to regulate the output current (power), a variable resistance to control the commanded voltage for the regulated current, and twelve electric capacitors for the current filter as well as for the compensation of slow dynamics of IC circuit.
The energy management control was coded on the Matlab/Simulink environment, and was consequently downloaded to a rapid-prototyping controller, where the inputs are the traction motor power,lithium batteries (SOC),supercapacitors(SOC),residual hydrogen content and the outputs are the regulated current commands of three energy sources. Experimental results show that under various battery state-of-charge (SOC), and time-variant outload, the active power module provide the proper energy management online. The implementation on a real EV will be conducted in the future.

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