Abstract With the recent development of thermoelectric materials, thermoelectric generators (TEGs) have become a technology with a huge potential in the energy recovery field. A TEG module consisted of 199 cascade couples was analyzed by a one-dimensional steady-state thermodynamic model. Meanwhile, using the thermodynamic analysis results, two discrete objective functions for the output power and efficiency of the TEG module were built on a multi-variable searching space where working conditions and geometric structures were varied. Mutation particle swarm optimization (M-PSO) was selected to conduct the optimization of the output power and efficiency of the TEG module because it converges more accurately and quickly than PSO. Under temperature differences below 40 K, the optimal output power and efficiency were 23.6 W and 4.05%, respectively. It is necessary, however, to simultaneously consider the output power and efficiency as important targets in the application of TEG technology. In this way, a weighted approach was applied to this study to establish a multi-objective function for the TEG module. Then, a multi-objective optimization was conducted using the M-PSO algorithm for the combined output power and efficiency in the same searching space. With the optimization, the output power and efficiency reached 6.69 W and 3.99%, respectively, when the weight factor for the efficiency was 0.8.

中文翻译：

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多变量空间中热电发电机的突变粒子群优化 (M-PSO)

摘要 随着近年来热电材料的发展，热电发电机（TEGs）已成为能量回收领域具有巨大潜力的技术。通过一维稳态热力学模型分析了由 199 个级联对组成的 TEG 模块。同时，利用热力学分析结果，在工作条件和几何结构变化的多变量搜索空间上，构建了 TEG 模块输出功率和效率的两个离散目标函数。选择突变粒子群优化 (M-PSO) 来对 TEG 模块的输出功率和效率进行优化，因为它比 PSO 更准确、更快速地收敛。在低于 40 K 的温差下，最佳输出功率和效率分别为 23.6 W 和 4.05%。然而，在 TEG 技术的应用中，有必要同时考虑输出功率和效率作为重要目标。通过这种方式，将加权方法应用于本研究以建立 TEG 模块的多目标函数。然后，使用 M-PSO 算法对相同搜索空间中的组合输出功率和效率进行多目标优化。通过优化，当效率的权重系数为0.8时，输出功率和效率分别达到6.69 W和3.99%。使用 M-PSO 算法对同一搜索空间中的组合输出功率和效率进行多目标优化。通过优化，当效率的权重系数为0.8时，输出功率和效率分别达到6.69 W和3.99%。使用 M-PSO 算法对同一搜索空间中的组合输出功率和效率进行多目标优化。通过优化，当效率的权重系数为0.8时，输出功率和效率分别达到6.69 W和3.99%。