A reconfigurable architecture for maximizing energy harvesting of thermoelectric generators in non-stationary conditions

Abstract

The use of Thermoelectric Generators (TEGs) has proliferated across a multitude of applications for energy harvesting. As more modules are employed to recover greater amounts of energy, the temperature mismatch between them increases. This results in each module operating at a distinct maximum power point, thereby reducing the overall system efficiency. Furthermore, in dynamic applications such as automotive scenarios, the temperatures of the thermoelectric generators are not constant, and the maximum power point accordingly shifts. A fixed architecture is unable to cope with these fluctuating situations. Therefore, this paper introduces a reconfigurable architecture capable of harnessing maximum energy at any given moment, improving energy recovery compared to a fixed architecture. Optimization techniques, lean methodologies, and clustering approaches are employed to efficiently design the reconfigurable TEG, which enables modification of the electrical connections inside the TEG modules and the number of Maximum Power Point Tracking (MPPT) modules. A use case is presented where the reconfigurable TEG is compared with fixed, yet optimized, TEG configurations under mixed driving modes. In this specific case, the results demonstrate that the reconfigurable TEG achieves enhanced performance in dynamic environments with two MPPTs under mixed scenarios, reaching an efficiency of 96.3% and a 0.29% improvement in energy recovery.