Multi-objective and multi-parameter optimization of two-stage thermoelectric generator in electrically series and parallel configurations through NSGA-II

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 (η) 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 Th and cold side Tc 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.