[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120536-en":3,"doc-seo-120536-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120536,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Comparison of Equivalent Circuit and Machine Learning Methods for CubeSat Battery Discharge Modeling","Study and comparison of two approaches for modeling CubeSat battery discharge: analytical equivalent-circuit modeling based on physical laws and data-driven machine learning. The modeling enables predicting consequences of disconnecting the autonomous power system and improves fault tolerance of equipment in orbit. Using orbital dataset samples covering battery and solar-panel voltage, current, and temperature, results compare transparency and robustness. Findings support an accuracy-oriented hybrid direction: analytical models as a baseline with learning-based refinement for environmental deviations.","UDC 004.314.3-047. 8:004 .85. doi: 10.32620/ reks.2025.3.16  \nIgor TURKIN, Lina VOLOBUIEVA, Andriy CHUKHRAY, Oleksandr LIUBIMOV National Aerospace University “Kharkiv Aviation Institute”, Kharkiv, Ukraine  \nCOMPARISON OF EQUIVALENT CIRCUIT AND MACHINE LEARNING METHODS FOR CUBESAT BATTERY DISCHARGE MODELING  \nThe 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’s publication. The dataset contains data on the voltage (mV), current (mA), and temperature (Celsius) 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. However, the model requires prior training on a large amount of data and is less well understood in terms of physical laws. General conclusions. The equivalent circuit approach provides high accuracy and reliability under known conditions, but it is limited when external parameters change. The machine learning approach demonstrates better overall accuracy and stability, especially under variable or unpredictable conditions, but requires a large amount of high-quality data and more complex interpretation. Thus, the most effective approach may be a hybrid one, where the analytical model serves as the basis and machine learning is used as a tool for refining or compensating for inaccuracies.  \nKeywords: CubeSat; EPS; machine learning; modelling; small satellite.  \n1. Introduction  \nDue to its attractive cost, the availability of commercially ready-made solutions, and a relatively short implementation time, CubeSat has gained popularity among space researchers. It is currently used to solve a wide range of tasks. The general overview for the current state-of-the-art SmallSat technologies [1] states the growing popularity of small satellites in general and CubeSats in particular, and shows that since 2013, the flight heritage for small spacecraft has dramatically increased and has become the main primary source of access to space for commercial, government, private, and academic.  \nThe CubeSat project was initiated in 1999 by scientists from California Polytechnic State University and Stanford University’s Space Systems Development Laboratory. Specification [2] defines a 1U (U stands for‘Unit’) CubeSat as a small satellite of standard size and shape, which is a 10 cm cube with a mass of up to 2 kg. A CubeSat can consist of several units. The current version of the specification describes the design of CubeSats up to 12U.  \n1.1. Motivation  \nAccording to various estimates, the global CubeSat market will show a GAGR over 15% in the coming years (according to CubeSat Market Research Report [https://straitsresearch.com/report/cubesat-market](https://straitsresear","cbCaiot7mxIGxh78","https://ap.wps.com/l/cbCaiot7mxIGxh78","pdf",1806144,1,14,"English","en",105,"# Introduction\n## Motivation\n## Dataset and Problem Context\n# Modeling Approaches\n## Equivalent Circuit Modeling\n## Machine Learning Modeling\n# Results and Comparison\n## Accuracy and Interpretability\n## Robustness to Environmental Changes\n# Conclusions and Future Direction\n## Hybrid Modeling Strategy","[{\"question\":\"Why model CubeSat battery discharge for power-system fault tolerance?\",\"answer\":\"Battery discharge modeling helps predict the impact of disconnecting the autonomous power system and supports fault tolerance of orbiting equipment.\"},{\"question\":\"What dataset variables are used in the study?\",\"answer\":\"The dataset includes voltage (mV), current (mA), and temperature (Celsius) for the battery and the solar panels on the satellite’s five sides.\"},{\"question\":\"How do the equivalent circuit and machine learning approaches differ in performance?\",\"answer\":\"Equivalent circuit models offer transparency but are less flexible under environmental changes or non-standard behavior; machine learning achieves more accurate predictions by capturing complex dependencies, though it requires extensive prior training.\"}]","Comparison of Equivalent Circuit and Machine Learning Methods for CubeSat Battery Discharge Modeling | PDF",1785730540,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"comparison-of-equivalent-circuit-and-machine-learning-methods-for-cubesat-battery-discharge-modeling","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/comparison-of-equivalent-circuit-and-machine-learning-methods-for-cubesat-battery-discharge-modeling/120536/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why model CubeSat battery discharge for power-system fault tolerance?","Question",{"text":75,"@type":76},"Battery discharge modeling helps predict the impact of disconnecting the autonomous power system and supports fault tolerance of orbiting equipment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset variables are used in the study?",{"text":80,"@type":76},"The dataset includes voltage (mV), current (mA), and temperature (Celsius) for the battery and the solar panels on the satellite’s five sides.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the equivalent circuit and machine learning approaches differ in performance?",{"text":84,"@type":76},"Equivalent circuit models offer transparency but are less flexible under environmental changes or non-standard behavior; 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