本研究提出了以遺傳演算法進行熱電式能源系統之新穎式最佳化設計方法。當考慮熱電式的致冷系統時，參數最佳化後的目的是要將該系統的致冷能力或是冷凍性能係數(COP)提到最大，因此在本文的研究中考慮了兩種熱電致冷系統結構—單層式以及雙層式的熱電元件—分別對其參數進行最佳化設計。單層式的熱電致冷系統考慮的參數包括接腳長度、接腳截面積、接腳個數；而對於雙層式的熱電致冷系統，考慮的參數包括供給予上、下兩層元件的電流、接腳長度、接腳截面積、接腳個數，同時雙層式的熱電致冷系統又考慮三種不同的電源配接形式—電源串聯、電源並聯、以及電源個別供給。本研究同時提出新的數學模型，用以處理熱電系統中材料參數隨溫度變化的現象，以及處理在雙層結構中，上層與下層間的接觸熱阻與擴散熱阻的計算；且提出運算流程，用以將熱電式能源系統之數學理論模型與遺傳演算法結合運算；不論是單層式或是雙層式的熱電元件，均能引入限制條件進入最佳化過程中，例如製程限制、材料體積之限制。本文所提出的演算法計算模式，不僅可以處理複雜的數學模型運算、進行更趨近於工程目標的設計，而在參數最佳化後的結果顯示，熱電式致冷系統的致冷能力或是冷凍性能係數表現，均優於以傳統方式進行最佳化設計之結果。

This work presented a novel method based on genetic algorithms (GAs) to optimize thermoelectric energy systems. The objective of the optimization is on maximizing the cooling capacity or maximizing the coefficient of performance (COP) of thermoelectric cooling (TEC) systems. Two kinds of arrangements, including single- and two-stage TEC systems, have been studied. While optimizing a single-stage TEC system, structural parameters – including the thermocouple length, the thermocouple cross-section area and the number of thermocouple – were taken as the variables. While optimizing a two-stage TEC system, parameters – including the applied electrical current, the thermocouple length, the thermocouple cross-section area and the number of thermocouples – were considered. Two-stage TEC systems can be further categorized into three types, which are with two stages electrically connected in parallel, in series and in separate. A new mathematical modelling was also proposed to deal with the temperature-dependent material properties and to include the effects of contact and spreading thermal resistances between the two stages. For both single- and two-stage TEC systems, this study developed the design flowchart and programs that combine the mathematical modelling with GAs’ technique. All kinds of design constraints–space constraints and all others–can be considered and modeled during the optimization. The results indicate that the cooling capacity or COP can be increased by optimizing the parameters of TEC systems. This study also demonstrates that the new approach based on GAs can be used effectively to optimize the thermoelectric energy systems, and this method exhibits highly potential in handling complex design problems.

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