本文開發一創新智慧型混合熱管理系統(Intelligent Hybrid Thermal Management System, IHTMS)暨最佳化控制，應用於雙電力電動車之燃料電池/鋰電池模組。本研究首先於Matlab/Simulink平台進行實體動態建模後，以仿生演算法之粒子群優化(Particle Swarm Optimization, PSO)開發最佳熱管理控制策略。目標是縮短電動車初始啟動過程中的低效率溫度期，並將質子交換膜燃料電池(Proton Exchange Membrane Fuel Cell, PEMFC)和鋰電池溫度保持並穩定在最佳效率區間，以提高電動車的行駛里程和功率輸出。系統建模部分，透過控制導向(Control-Oriented)模型與兩控制體積模組，建構了8個 IHTMS 子系統。其中並建構與設計單鋰電池實驗以得到實體參數導入模型。在熱管理控制部分，以仿生演算PSO控制策略，設定兩輸入為燃料電池和鋰電池溫度；兩輸出為總質量流率(水泵電壓)和流量比例分配(比例閥電壓)。目標函數為實際與最佳操作溫度誤差絕對值之和。另使用4模式規則庫(Rule-Based, RB)控制策略為對照組。此外亦開發使用 PSO 初始條件(initial) 的 RB(PSOi-RB)作為實際應用參考。最後將IHTMS在兩行車型態:WLTC和NEDC測試。結果顯示:在WLTC行車型態中，將PSO和PSOi-RB與RB策略進行比較，PEMFC的最佳溫度上升時間分別減少13.655%和9.505%；鋰電池為8.77% 和 4.385%。在NEDC行車型態，PEMFC的最佳溫度上升時間分別減少8.908%和7.318%，而鋰電池的最佳溫度上升時間分別減少5.226%和3.136%。PEMFC的平均溫度誤差改善分別為19.759%及11.023%；鋰電池平均溫度誤差改善分別為57.027%及3.67%。 NEDC行車型態，PEMFC的平均溫度誤差改善分別為18.879%和9.551%； 鋰電池平均溫度誤差改善分別為29.144%及20.221%。在未來的工作中，IHTMS將整合到混合能源電動車中進行實驗驗證。

In this study, an innovative Intelligent Hybrid Thermal Management System (IHTMS) was developed and optimized for application in dual-power electric vehicles (EVs), specifically for Proton Exchange Membrane Fuel Cell (PEMFC)/Lithium Battery modules. Initially, physical dynamic modeling was carried out on the Matlab/Simulink platform, followed by the development of optimal thermal management control strategies utilizing Particle Swarm Optimization (PSO), a bio-inspired algorithm. The primary aim of this research was to minimize the duration of low-efficiency temperatures during the initial start-up phase of EVs and to stabilize the temperatures of PEMFCs and lithium batteries within their optimal efficiency ranges, thereby enhancing the driving range and power output of EVs. In terms of system modeling, a control-oriented model and two control volume modules were employed to construct eight IHTMS subsystems. Single lithium battery experiments were conducted to obtain physical parameters for integration into the model. In the thermal management control segment, the bio-inspired PSO control strategy utilized two inputs: the temperatures of the fuel cell and lithium battery; and two outputs: the total mass flow rate (pump voltage) and flow rate distribution ratio (proportional valve voltage). The objective function was defined as the sum of the absolute values of the difference between actual and optimal operating temperatures. Additionally, a four-mode Rule-Based (RB) control strategy was designed as a baseline case. Furthermore, an RB incorporating initial conditions derived from PSO (PSOi-RB) was developed for practical application. Lastly, the IHTMS was tested under two driving patterns: WLTC and NEDC. The results indicate that under the WLTC driving cycle, when comparing PSO and PSOi-RB with RB strategies, the time to reach optimal temperature for the PEMFC decreased by 13.655% and 9.505%, respectively, and for the lithium battery by 8.77% and 4.385%. Under the NEDC driving cycle, the time to reach optimal temperature for the PEMFC decreased by 8.908% and 7.318%, respectively, and for the lithium battery by 5.226% and 3.136%. The improvements in the average temperature errors for the PEMFC were 19.759% and 11.023%; for the lithium battery, they were 57.027% and 3.67%. For the NEDC driving cycle, the improvements in average temperature errors for the PEMFC were 18.879% and 9.551%; for the lithium battery, they were 29.144% and 20.221%. Future work will involve integrating the IHTMS into a hybrid-energy EV for experimental verification.

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