Investigation of Maximum Power Point Tracking for Thermoelectric Generators

In this paper, a thermoelectric generator (TEG) model is developed as a tool for investigating optimized maximum power point tracking (MPPT) algorithms for TEG systems within automotive exhaust heat energy recovery applications. The model comprises three main subsystems that make up the TEG system: the heat exchanger, thermoelectric material, and power conditioning unit (PCU). In this study, two MPPT algorithms known as the perturb and observe (P&O) algorithm and extremum seeking control (ESC) are investigated. A synchronous buck-boost converter is implemented as the preferred DC-DC converter topology, and together with the MPPT algorithm completes the PCU architecture. The process of developing the subsystems is discussed, and the advantage of using the MPPT controller is demonstrated. The simulation results demonstrate that the ESC algorithm implemented in combination with a synchronous buck-boost converter achieves favorable power outputs for TEG systems. The appropriateness is by virtue of greater responsiveness to changes in the system's thermal conditions and hence the electrical potential difference generated in comparison with the P&O algorithm. The MATLAB/Simulink environment is used for simulation of the TEG system and comparison of the investigated control strategies.