Modular thermoelectric generation arrays reconfiguration under heterogeneous temperature distribution via improved cooperation search algorithm: Modelling, design and HIL validation

This paper innovatively proposes a modular thermoelectric generation (TEG) arrays reconfiguration technique based on an improved cooperation search algorithm (ICSA), which aims to mitigate the troublesome effects of heterogeneous temperature distribution (HTD) conditions and fully exploit their power generation potential. Firstly, the original TEG array is divided into three blocks, upon which only two switch matrices are required to implement various reconfiguration, while its number of switches and construction costs can be significantly reduced. Secondly, the ratio of output power and voltage range (namely voltage imbalance factor (VIF)) between TEG columns is regarded as a fitness function to simultaneously maximize output power and minimize voltage imbalance. Furthermore, in order to solve this model, ICSA is designed to effectively and efficiently seek the global optimum. Meanwhile, three traditional discrete meta-heuristic algorithms are employed for comparison (e.g., genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) algorithm). Besides, to validate the problem formulation and solver design, both symmetric (15×15) and asymmetric (15×20) TEG arrays are tested under three typical HTD conditions, i.e., outer, centre, and random. Lastly, simulation results on MATLAB verify that ICSA can rapidly smooth the output power curves and dramatically enhance generation efficiency of TEG arrays. Specifically, the maximum output power can be increased at most by 93.77 W with a decrease of 164.02 V of VIF under symmetric case, and 146.45 W with a decrease of 111.92 V of VIF under asymmetric scenario, respectively.