Multi-objective and multi-parameter optimization of two-stage thermoelectric generator in electrically series and parallel configurations through NSGA-II
Multi-objective and multi-parameter optimization of two-stage thermoelectric generator in electrically series and parallel configurations through NSGA-II
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DOI：
10.1016/j.energy.2015.08.044
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发表时间：
2015-11-01
期刊：
影响因子：
9
通讯作者：
Arora, Rajesh
中科院分区：
文献类型：
--
作者：
Arora, Ranjana;Kaushik, S. C.;Arora, Rajesh
热电发电机（TEG）的输入参数的适当考虑是必不可少的设计具有上级性能标准，如高热效率和功率输出的设备。在此基础上，采用多目标进化算法，即非支配排序遗传算法-II（NSGA-II），在MATLAB的Simulink环境下，对串联和并联两级外可逆TEG进行了仿真研究。同时优化所提出的系统最大化的功率输出（P），热效率（eta）和生态功能（E），考虑工作电流（I），在顶部的“n”和底部级的热电元件的数量“m”，热侧的温度T-h和冷侧的温度T-c作为设计变量。本工作探讨了各种性能参数/设计变量的最优值从帕累托边界的三重和双重目标函数，并通过使用三个决策技术，即模糊，香农和TOPSIS，最佳的解决方案被选中。与目前的研究，它已被证明，多目标优化给出了理想的和得到的解决方案，称为偏差指数之间的差异要小得多，相比双/单目标优化。通过多目标优化实现了TEG的优化设计，在功率、效率和生态功能之间建立了适当的平衡，使三者同时得到改善。(C)2015爱思唯尔有限公司版权所有。
Appropriate consideration of input parameters of a thermoelectric generator (TEG) is essential to design devices with superior performance criteria such as high thermal efficiency and power output. In this view, multi-objective evolutionary algorithm namely non-dominated sorting genetic algorithm-II (NSGA-II) is applied to two-stage exoreversible TEG in electrically series and electrically parallel configurations in matrix laboratory (MATLAB) simulink environment. Simultaneous optimization of proposed system for maximizing power output (P), thermal efficiency (eta) and ecological function (E) is done with considering working electric current(I), number of thermoelectric elements in top 'n' and bottom stage 'm', temperature of hot side T-h and cold side T-c as design variables. The present work explores various optimal values of performance parameters/design variables from Pareto frontier of triple and dual objective functions and by using three decision making techniques viz. fuzzy, Shannon and TOPSIS, best solution is selected. With the current study, it has been demonstrated that multi-objective optimization gives much lower difference between ideal and obtained solution, termed as deviation index, as compared to the dual/single objective optimization. The optimal design of TEG achieved by multi-objective optimization formulates an appropriate balance between power, efficiency and ecological function, in order that all the three are improved concurrently. (C) 2015 Elsevier Ltd. All rights reserved.