Parameter Identification Method And Calibration Test For Space Solar Cells

Solar cells are the main source of power for spacecraft,and accurate calibration of their I-V characteristics can provide effective reference data for the design of spacecraft power systems.The traditional ground-based solar simulator can not completely simulate the solar illumination under real-space conditions,which makes it impossible for researchers to accurately calibrate the solar cells in the new space,and thus cannot accurately identify the unknown parameters in the equivalent mathematical model,so that the sun Battery performance evaluation,design optimization,and application are severely affected.This paper focuses on the I-V characteristic calibration technology of space solar cells and the solar cell parameter identification method.The main research contents are as follows:Firstly,according to the space environment conditions and the calibration task requirements,based on the STM32 single-chip microcomputer,the MOS tube is used to build the adjustable electronic load,and a calibration system that can automatically and accurately collect the solar cell I-V characteristic data in high altitude environment is designed.The design of the system lays the hardware foundation for the subsequent acquisition of I-V data in the solar cell model parameter identification.Secondly,an improved whale optimization algorithm is proposed for the traditional method that cannot solve the unknown parameters in the solar cell model accurately.In the initialization stage of the algorithm,the ubiquity,pseudo-randomness and regularity of the chaotic map are used to improve the uniformity of the population distribution.In the individual position update stage,the rand to best differential variation model is used to enrich the diversity of the population.The improved performance of the algorithm is verified by standard test functions and applied to the solar cell model parameter identification problem.Thirdly,an improved differential evolution algorithm is proposed for the problem that the standard differential evolution algorithm is easy to fall into local optimum.The chaotic tent mapping initialization method based on elite strategy is used to generate the initial solution with more uniform distribution and higher solution quality.The adaptive scaling factor is introduced to make the scaling factor adaptively adjust with iterative evolution,enrich the diversity of the population,and speed up the algorithm.Convergence rate;introduces evolutionary mode selection mechanism,combines the advantages of DE/rand/1/bin mode and DE/best/1/bin mode to improve the optimization efficiency of the algorithm.The optimization performance of the algorithm is verified by a standard test function and applied to the solar cell model parameter identification problem.Finally,through the experimental cabin to simulate the high-altitude environment of low temperature and low pressure,the working performance of the entire calibration platform is analyzed.Through the external field test of the calibration system,the collected I-V data identifies the unknown parameters of the solar cell model,and obtains the unknown parameters of the model with high identification accuracy.