Advances in Satellite Power Systems and Battery Technology

The field of satellite power systems and battery technology is rapidly evolving, with a focus on improving reliability, efficiency, and fault detection. Researchers are exploring new approaches to modeling and analyzing battery discharge, including the use of machine learning and equivalent circuit models. Additionally, there is a growing interest in understanding the performance of retired batteries and developing strategies for their reuse. Notable papers in this area include:

  • A study on smart fault detection in satellite electrical power systems, which achieved over 99% accuracy in identifying faults across multiple subsystems.
  • A comparative analysis of equivalent circuit and machine learning approaches to modeling CubeSat battery discharge, which demonstrated the advantages of machine learning in accounting for complex dependencies and adapting to actual conditions.

Sources

Identifiability Analysis of a Pseudo-Two-Dimensional Model & Single Particle Model-Aided Parameter Estimation

Smart fault detection in satellite electrical power system

Influence of Cell Position on the Capacity of Retired Batteries: Experimental and Statistical Studies

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

Reliability-Based Fault Analysis and Modeling of Satellite Electrical Power Subsystems Using Fault Tree and Simulation Tools

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