Modeling CubeSat Storage Battery Discharge: Equivalent Circuit Versus Machine Learning Approaches

TL;DR

This paper compares analytical equivalent circuit and machine learning approaches for modeling CubeSat battery discharge, highlighting their respective advantages in transparency and accuracy for satellite power system analysis.

Contribution

It provides a comparative analysis of two modeling approaches for CubeSat batteries, guiding the choice based on transparency and adaptability to environmental variations.

Findings

Equivalent circuit model is more transparent and interpretable.

Machine learning model offers higher accuracy and adaptability.

Machine learning better captures complex dependencies in satellite data.

Abstract

The subject of the article is the study and comparison of two approaches to modelling the battery discharge of a CubeSat satellite: analytical using equivalent circuit and machine learning. The article aims to make a reasoned choice of the approach to modelling the battery discharge of a CubeSat satellite. Modelling the battery discharge of a satellite will enable the prediction of the consequences of disconnecting the autonomous power system and ensure the fault tolerance of equipment in orbit. Therefore, the selected study is relevant and promising. This study focuses on the analysis of CubeSat satellite data, based explicitly on orbital data samples of the power system, which include data available at the time of the article publication. The dataset contains data on the voltage, current, and temperature of the battery and solar panels attached to the five sides of the satellite. In this context, two approaches are considered: analytical modelling based on physical laws and machine learning, which uses empirical data to create a predictive model. Results: A comparative analysis of the modeling results reveals that the equivalent circuit approach has the advantage of transparency, as it identifies possible parameters that facilitate understanding of the relationships. However, the model is less flexible to environmental changes or non-standard satellite behavior. The machine learning model demonstrated more accurate results, as it can account for complex dependencies and adapt to actual conditions, even when they deviate from theoretical assumptions.