Efficient energy valley optimization approach for reconfiguring thermoelectric generator system under non-uniform heat distribution

A thermoelectric generator (TEG) suffers from low conversion efficiency, especially when it has been operated under non-uniform temperature distribution. Dynamic reconfiguration is an effective method to enhance the TEG overall efficiency although it requires many electrical switches that make the process more complex. This paper proposes a new approach of energy valley optimizer (EVO) to optimally reconfigure the TEG array operated at non-uniform temperature distribution and enhance the harvested electrical energy. The selection of EVO is due to fast convergence rate, parameter-free, and avoiding stuck in local optima. Minimization of absolute error between the TEG system current at maximum power and the actual one is the proposed fitness function. The analysis is performed on small 9 × 9 TEG array and large 15 × 15 TEG array. Eight temperature distribution patterns of non-uniform row, non-uniform column, short wide, long wide, diagonal, external, internal, and random are analyzed. The proposed EVO is assessed via comparing to reported immune genetic algorithm (IIGA) and other programmed approaches of osprey optimization algorithm (OOA), chef-based optimization algorithm (CBOA), artificial hummingbird algorithm (AHA), and northern goshawk optimizer (NGO). Moreover, statistical investigation is implemented via performing Kruskal-Wallis test, Friedman test, ANOVA test, and Wilcoxon rank test. The fetched results revealed that, the best enhanced power from small 9 × 9 array is 9.143% compared to the original arrangement occurred at long wide temperature distribution. Regarding large 15 × 15 TEG array, the best enhanced power happened during diagonal pattern with 3.359%. The results confirmed the preference of the proposed EVO in solving the TEG system reconfiguration problem with great efficiency.