In the realm of satellite technology, the reliable operation of lithium-ion (Li-ion) batteries is paramount for consistent energy supply. A team of researchers from various institutions, including the University of Stuttgart, the German Aerospace Center (DLR), and the Japan Aerospace Exploration Agency (JAXA), has developed a novel algorithm to enhance the estimation of battery states, which is crucial for the safe and efficient functioning of satellites in space.
The research, published in the Journal of The Electrochemical Society, introduces a multi-timescale algorithm that combines Kalman filters and physics-based models to accurately estimate the State of Charge (SOC) and State of Health (SOH) of satellite batteries. The algorithm employs a P2D (Pseudo-Two-Dimensional) model, which is a detailed physics-based model describing the electrochemical processes within the battery. This model is coupled with a degradation model that accounts for capacity fading due to the growth of the Solid Electrolyte Interphase (SEI) layer, a known cause of battery degradation.
The innovative aspect of this algorithm lies in its nested structure, featuring two extended Kalman filters that operate on different timescales. One filter is nested within the other, allowing for more accurate and reliable state estimations. The researchers tested the algorithm using both synthetic data and real in-flight data from the Japanese satellite REIMEI. The results demonstrated that the algorithm effectively estimates the SOC and SOH in both scenarios, providing valuable insights into the reliability of the chosen battery model.
For the energy sector, particularly in space applications, this research offers practical implications. Accurate estimation of battery states is essential for predicting battery life, optimizing energy usage, and ensuring the safety and reliability of satellite operations. By implementing this advanced algorithm, satellite operators can enhance their battery management systems, leading to improved mission performance and reduced risks of power-related failures. Furthermore, the insights gained from this research can be applied to other energy storage systems, contributing to advancements in battery technology across various industries.
This article is based on research available at arXiv.