文献类型：期刊文献

英文题名：A comprehensive review of thermoelectric generation optimization by statistical approach: Taguchi method, analysis of variance (ANOVA), and response surface methodology (RSM)

作者：Chen, Wei-Hsin[1,2,3];Uribe, Manuel Carrera[1,4];Kwon, Eilhann E.[5];Lin, Kun-Yi Andrew[6,7];Park, Young-Kwon[8];Ding, Lu[9];Saw, Lip Huat[10]

机构：[1]Natl Cheng Kung Univ, Dept Aeronaut & Astronaut, Tainan 701, Taiwan;[2]Tunghai Univ, Res Ctr Smart Sustainable Circular Econ, Taichung 407, Taiwan;[3]Natl Chin Yi Univ Technol, Dept Mech Engn, Taichung 411, Taiwan;[4]Natl Cheng Kung Univ, Int Master Degree Program Energy Engn, Tainan 701, Taiwan;[5]Hanyang Univ, Dept Earth Resources & Environm Engn, Seoul 04763, South Korea;[6]Natl Chung Hsing Univ, Dept Environm Engn & Innovat, Kuo Kuang Rd, Taichung 250, Taiwan;[7]Natl Chung Hsing Univ, Dev Ctr Sustainable Agr, Kuo Kuang Rd, Taichung 250, Taiwan;[8]Univ Seoul, Sch Environm Engn, Seoul 02504, South Korea;[9]East China Univ Sci & Technol, Inst Clean Coal Technol, Shanghai 200237, Peoples R China;[10]UTAR, Lee Kong Chian Fac Engn & Sci, Kajang 43000, Malaysia

年份：2022

卷号：169

外文期刊名：RENEWABLE & SUSTAINABLE ENERGY REVIEWS

收录：;EI(收录号：20223712705056);WOS:【SCI-EXPANDED(收录号:WOS:000862176100006)】；

基金：The authors acknowledge financial support from the Ministry of Science and Technology, Taiwan, R.O.C., under the grant numbers MOST 110-2221-E-006-145-MY3 and MOST 109-2622-E-006-006-CC1. This research is also supported in part by Higher Education Sprout Project, Ministry of Education to the Headquarters of University Advancement at National Cheng Kung University (NCKU) .

语种：英文

外文关键词：Waste heat recovery; Thermoelectric generator; Statistical optimization; Taguchi method; Analysis of variance (ANOVA); Response Surface Methodology (RSM)

摘要：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 tech-nology. 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 charac-teristics 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 ther-moelectric generators utilized the Taguchi method; thus, the Taguchi method remains the most popular statis-tical tool for TEG analysis. Finally, the perspectives and challenges of optimizing thermoelectric generators using statistical approaches are underlined.