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.

翻译：远程关键基础设施的健康监测是一项复杂且昂贵的活动，这主要源于基础设施的可达性有限。巡检无人机作为普及性资产，通过提升可达性增强了关键基础设施的可靠性。然而，由于运行环境恶劣，监测其健康状况对于确保巡检任务的成功至关重要。电池是决定巡检无人机整体可靠性的关键组件，采用适当的健康管理方法有助于实现可靠且稳健的巡检。在此背景下，本文提出了一种新颖的混合概率方法，用于预测锂聚合物电池的放电终止电压。该混合方法采用误差修正架构实现，结合了基于物理的放电模型与概率误差修正模型，以量化偶然不确定性和认知不确定性。该混合概率方法的性能在一个包含不同负载条件下放电终止电压的数据集上进行了实证评估。该数据集来源于实际执行海上风力涡轮机巡检任务的不同飞行中的巡检无人机。所提方法已与多种概率方法进行了测试比较，结果表明其在概率准确性方面比最佳概率方法提升了14.8%。此外，偶然不确定性和认知不确定性提供了稳健的估计，从而增强了电池健康状态的诊断能力。