Prognostics-Informed Battery Reconfiguration in a Multi-Battery Small UAS Energy System

TL;DR

This paper introduces a prognostics-informed MDP model for managing multiple batteries in small UAS, aiming to enhance safety and resilience by optimizing reconfiguration strategies based on battery health predictions.

Contribution

It develops a novel MDP-based framework that incorporates battery prognostics for dynamic reconfiguration in sUAS, improving safety and operational reliability.

Findings

Battery dynamics characterized through experiments and simulations.

Trade-offs between system complexity, weight, and resilience analyzed.

Case studies demonstrate improved safety with prognostics-informed reconfiguration.

Abstract

Batteries have been identified as one most likely small UAS (sUAS) components to fail in flight. sUAS safety will therefore be improved with redundant or backup batteries. This paper presents a prognostics-informed Markov Decision Process (MDP) model for managing multi-battery reconfiguration for sUAS missions. Typical lithium polymer (Lipo) battery properties are experimentally characterized and used in Monte Carlo simulations to establish battery dynamics in sUAS flights of varying duration. Case studies illustrate the trade off between multi-battery system increased complexity/weight and resilience to non-ideal battery performance.