A Temperature Difference Estimation Method for Thermoelectric Modules Based on an Improved Thermal Network and Parameter Identification
Accurate estimation of the cold- and hot-side temperature difference in thermoelectric generator (TEG) modules is critical for online performance assessment and reliability prediction in industrial waste heat recovery systems. Conventional equivalent thermal networks neglect the nonlinear effects of convective and radiative heat dissipation, which are particularly pronounced under natural convection, leading to substantial errors in temperature difference estimation. This study proposes an improved equivalent thermal network that incorporates nonlinear convective and radiative branches to capture the temperature-dependent heat dissipation characteristics of TEG modules. A multi-objective parameter identification framework is developed, employing the estimation errors of hot-side, cold-side, and heat sink temperatures as objective functions. The convexity properties of the objective functions are analyzed, and the non-dominated sorting genetic algorithm II (NSGA-II) is applied to extract the thermal parameters that are difficult to determine theoretically. Experimental validation under natural convection conditions, where nonlinear heat dissipation is most significant, demonstrates that the proposed method achieves high-precision extraction of TEG module thermal parameters and accurately estimates the dynamic variations of the cold- and hot-side temperature difference. The method exhibits strong adaptability and extensibility, as it can be readily adapted to forced convection environments by substituting the corresponding convective thermal resistance expression. This work provides a robust tool for enhancing the performance prediction and reliability evaluation of thermoelectric power generation systems.