  Health monitoring of remote critical infrastructure is a complex and
expensive activity due to the limited infrastructure accessibility. Inspection
drones are ubiquitous assets that enhance the reliability of critical
infrastructures through improved accessibility. However, due to the harsh
operation environment, it is crucial to monitor their health to ensure
successful inspection operations. The battery is a key component that
determines the overall reliability of the inspection drones and, with an
appropriate health management approach, contributes to reliable and robust
inspections. In this context, this paper presents a novel hybrid probabilistic
approach for battery end-of-discharge (EOD) voltage prediction of Li-Po
batteries. The hybridization is achieved in an error-correction configuration,
which combines physics-based discharge and probabilistic error-correction
models to quantify the aleatoric and epistemic uncertainty. The performance of
the hybrid probabilistic methodology was empirically evaluated on a dataset
comprising EOD voltage under varying load conditions. The dataset was obtained
from real inspection drones operated on different flights, focused on offshore
wind turbine inspections. The proposed approach has been tested with different
probabilistic methods and demonstrates 14.8% improved performance in
probabilistic accuracy compared to the best probabilistic method. In addition,
aleatoric and epistemic uncertainties provide robust estimations to enhance the
diagnosis of battery health-states.
