Nested State and Degradation Estimation of a Satellite Battery with In-flight Data

TL;DR

This paper presents a multi-timescale Kalman filter-based algorithm for accurately estimating the state of charge and health of satellite Li-ion batteries using in-flight data, enhancing satellite operation safety.

Contribution

It introduces a novel nested Kalman filter approach combining physics-based models for improved battery state estimation in space applications.

Findings

Accurately estimates SOC and SOH from in-flight data

Demonstrates robustness with synthetic and real satellite data

Provides insights into model reliability

Abstract

Li-ion batteries are essential for the energy supply of satellites. The accurate estimation of their states is important for the reliable and safe operation in space. This paper introduces a new algorithm for the estimation of SOC and SOH. The multi-timescale algorithm combines Kalman filters and physics-based models for batteries. We use a P2D model combined with a degradation model that describes capacity fading due to SEI growth. The state estimation algorithm combines two extended Kalman filters for the two states evolving on different timescales, with one filter nested within the other one. We test the algorithm with synthetic data as well as with in-flight data from Japanese satellite REIMEI. The algorithm adequately estimates the SOC and SOH in both cases. Furthermore it gives insight into the reliability of the chosen model.