熱電發電機 (TEG) 可以直接將熱能轉化為電能。 然而，它的效率很低，因此有必要優化 TE 系統以最大化輸出功率。 許多回顧文章都關注這項技術。 然而，還沒有透過統計方法對 TEG 優化進行的全面回顧。 本研究通過田口方法、方差分析 (ANOVA) 和響應面方法 (RSM) 回顧了熱電發電機優化，以確定該技術的主要優化結果和趨勢。 確定了三個優化路徑：操作條件、幾何配置和熱電發電機 (TEG) 的 TE 材料。 儘管 TEG 系統不應該具有“一體適用”的特徵組合，但有些基於先前研究結果的趨勢已經確定。 對於每條優化路徑，對 TEG 系統影響最顯著的關鍵參數是工作條件下的熱源溫度和幾何配置下的 TE 晶粒高度。 但是，對於 TE 材料，沒有明確公認的參數。 因此，這些結果表明，優化 TEG 系統的熱源條件將產生最佳結果，優化 TE 模塊中的 TE 晶粒高度將進一步改進系統。 大約 70% 的優化熱電發電機的研究使用了田口方法； 因此，田口方法仍然是最流行的 TEG 分析統計工具。 此外，還強調了使用統計方法優化熱電發電機的前景和挑戰。
此外，本研究還介紹了市售 TEG 的優化，這些 TEG 透過模擬自然風速的強制對流進行冷卻，並在低質量廢熱溫度下運行，以進一步提高 TEG 性能。 兩種市售的 TEG 通過田口方法進行了優化。 田口方法採用三個參數和三個水平來實現，形成一個 L9 正交陣列，其中目標函數是最大輸出功率，並且是從阻抗匹配方法中找到的。 優化的參數是熱側溫度、散熱器尺寸和風速。 方差分析 (ANOVA) 與田口正交陣列一起用於結果的統計分析。 優化結果表明，熱端溫度是對輸出功率影響最大的參數。 此外，散熱器尺寸的變化對輸出功率的提高有顯著影響，而風速的影響則沒有那麼顯著。 此外，還進行了參數之間的相互作用分析。 田口方法產生了令人滿意的優化結果。 優化後的系統在 140°C 熱側溫度、型號 K402 散熱器和 3.7 m/s 風速下產生 0.65 W 的功率。 結果表明，與熱側溫度和散熱器尺寸相比，空氣速度對輸出功率的影響最小。 結果還表明，高度具有較低 TE 晶粒高度的 TEG 產生最佳功率輸出，而具有較高 TE 晶粒高度的 TEG 在較高負載電阻下產生更好的性能。

The thermoelectric generator (TEG) can directly convert heat to electricity. However, its efficiency is low, so optimizing TE systems to maximize output power is necessary. Many review papers have focused on this technology. However, there has not been a comprehensive review of TEG optimization by a statistical approach. This study reviews thermoelectric generator optimization by the Taguchi method, analysis of variance (ANOVA), and the response surface methodology (RSM) to identify the major optimization findings and tendencies for this technology. Three optimization paths are identified: operating conditions, geometrical configuration, and TE materials for thermoelectric generators (TEGs). Although there is no “one-size-fits-all” combination of characteristics that a TEG system should have, some tendencies based on the results of previous studies have been identified. The key parameters that show the most significant effect on the TEG system for each optimization path are the heat source temperature for the operating conditions and the TE leg height for the geometrical configuration. However, there are no distinctly recognized parameters for TE materials. Thus, these results show that optimizing the heat source conditions of a TEG system will yield the best possible results, and optimizing the TE leg height in the TE module would further improve the system. About 70% of the studies optimizing thermoelectric generators utilized the Taguchi method; thus, the Taguchi method remains the most popular statistical tool for TEG analysis. In addition, the perspectives and challenges of optimizing thermoelectric generators using statistical approaches are underlined.
Furthermore, this study presents the optimization of commercially available TEGs that are cooled by forced convection simulating naturally occurring wind speeds, and that operate under low quality waste heat temperatures. Two commercially available TEGs are optimized via the Taguchi method. The Taguchi method was implemented with three parameters and three levels making an L9 orthogonal array, where the objective function is the maximum output power which value is found by approaching the impedance matching curve. The optimized parameters were the hot side temperature, the heat sink size, and the wind airspeed. Analysis of Variance (ANOVA) is used alongside the Taguchi orthogonal array for the statistical analysis of the results. The optimization results show that the hot side temperature is the most influential parameter to the output power. In addition, the change in the heat sink size shows to have a significant influence on the improvement of the output power, whereas the impact of the wind speed was not as significant. In addition, interaction analysis between the parameters is performed. The Taguchi method yielded satisfactory optimization results. The optimized system yielded 0.65 W of power at 140°C hot side temperature, Model K402 heat sink, and 3.7 m/s wind speed. The results showed that the influence of the air velocity is minimal to the output power in comparison to the hot side temperature and the size of the heat sink. The results also show that the TEG with a smaller TE leg height yielded the best power output, whereas the TEG with the taller TE leg yielded better performance at higher load resistance.

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