2026-07-14T19:56:22Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/146262026-07-13T13:42:01Zcom_20.500.11984_1136com_20.500.11984_14090col_20.500.11984_1141

Alcibar, Jokin Aguirre, Aitor Aizpurua Unanue, Jose Ignacio 2026-07-13T13:42:01Z 2026-07-13T13:42:01Z 2026 978-3-032-21501-7 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=202313 https://hdl.handle.net/20.500.11984/14626 The increasing deployment of inspection drones for monitoring remote and critical infrastructure presents new opportunities and challenges in asset management. These drones operate in demanding environments, where ensuring operational reliability is essential. Among the various subsystems, battery health plays a central role in determining mission success and safety. This chapter presents a physics-aware probabilistic approach for battery health management, integrating data-driven techniques with physics-based models to improve the predictability of battery performance. At the core of this methodology lies a probabilistic machine learning model that provides uncertainty quantification, enabling more informed and robust decision-making. By incorporating this uncertainty-aware perspective into battery discharge forecasting, the approach supports advanced digital maintenance strategies. The methodology is demonstrated to drone-based inspections of offshore wind energy infrastructure, highlighting its contribution to enhancing asset reliability and enabling condition-aware maintenance strategies. eng © 2026, The Author(s), under exclusive license to Springer Nature Switzerland AG Probabilistic battery health management Physics-aware machine learning Uncertainty quantification End-of-discharge voltage prediction Drone-based infrastructure inspection ODS 7 Energía asequible y no contaminante A Probabilistic Physics-Aware Battery Health Management Approach for Inspection Drone Operations