During the satellite’s operations, simulation tools perform an important role in ensuring the space mission success. In this sense, the models implemented in the context of an operational satellite simulator that enables analysis of health status and maintenance during operations shall reflect the current satellite behavior with high fidelity. Moreover, it is complicated to obtain all analytical models of a satellite’s disciplines, considering its aging. This paper proposes an Artificial Neural Network (ANN) to reproduce the battery voltage behavior of a large sun-synchronous remote sensing satellite, the CBERS-4, currently in operation. Using the genetic algorithm to find the best network architecture of ANN, the neural model for this application presented an error of less than 1%, demonstrating its feasibility to obtain a high fidelity model for an operational simulator enabling extend analyses. The paper addresses advanced techniques aligned with the space industry’s future, increasing the ability to analyze a large amount of data and improve the space system’s operation.

在卫星运行期间，仿真工具在确保太空任务成功方面起着重要作用。从这个意义上讲，在可操作的卫星模拟器的环境下实现的模型能够在运行过程中分析健康状况和维护状况，应以高保真度反映当前的卫星行为。此外，考虑到卫星的老化情况，要获得有关该卫星学科的所有分析模型都非常复杂。本文提出了一个人工神经网络（ANN）来重现正在运行的大型太阳同步遥感卫星CBERS-4的电池电压行为。通过使用遗传算法找到ANN的最佳网络架构，此应用的神经模型的误差小于1％，证明了为可扩展分析的操作模拟器获得高保真度模型的可行性。本文介绍了与航天工业的未来相适应的先进技术，从而提高了分析大量数据的能力并改善了航天系统的运行。