Perovskite battery heat transfer model based on steady-state heat transfer

Abstract

The invention relates to the field of thermal simulation of perovskite batteries, in particular to a perovskite battery heat transfer model based on steady-state heat transfer. According to the technical scheme, compared with the prior art, the method has the advantages that on the basis of the second law of thermodynamics, the overall structure and the structure and material characteristics of each layer of the perovskite cell are combined, an overall heat transfer model and a layered heat transfer mode of the perovskite cell are analyzed and considered, and the perovskite cell is optimized on the premise that environmental factors are known. The overall temperature and the temperature of each layer of the perovskite cell are accurately predicted in a numerical simulation mode, so that the temperature of the perovskite cell is accurately predicted, and the non-negligible influence on the economical efficiency of component power generation is achieved.

Description

technical field [0001] The invention relates to the thermal simulation field of perovskite batteries, in particular to a formal perovskite battery heat transfer model based on steady-state heat transfer. Background technique [0002] Perovskite solar cells are considered to be the most promising photovoltaic power generation technology due to their high conversion efficiency and low cost. However, due to its fixed bandgap, it can only use about 58% of the solar spectral energy in the bandgap range of 300-800nm, which limits the efficiency of the battery; at the same time, the spectral energy outside the bandgap range will increase the heat load of the battery, resulting in a decrease in battery efficiency. . Therefore, the influence of temperature on power generation prediction is often ignored, or only empirical formulas are used, and the corresponding value is added to the ambient temperature as an approximation of the battery temperature. Such prediction effect is not id...

AI Technical Summary

Problems solved by technology

However, due to its fixed bandgap, it can only use about 58% of the solar spectral energy in the bandgap range of 300-800nm, which limits the efficiency of the battery; at the same time, the spectral energy outside the bandgap range will increase the thermal load of the battery, resulting in a decrease in battery efficiency.

Therefore, the influence of temperature on power generation prediction is often neglected, or only empirical formulas are used to increase the corresponding value on the basis of ambient temperature as an approximation of battery temperature. Such prediction effect is not ideal.

Images