Prescribing optimal health-aware operation for urban air mobility with deep reinforcement learning

Mina Montazeri, Chetan S. Kulkarni and Olga Fink

Reliability Engineering and System Safety, 2025, vol. 259, issue C

Abstract: Urban Air Mobility (UAM) aims to expand existing transportation networks in metropolitan areas by offering short flights either to transport passengers or cargo. Electric vertical takeoff and landing aircraft powered by lithium-ion battery packs are considered promising for such applications. Efficient mission planning is crucial, maximizing the number of flights per battery charge while ensuring completion even under unforeseen events. As batteries degrade, precise mission planning becomes challenging due to uncertainties in the end-of-discharge prediction. This often leads to adding safety margins, reducing the number or duration of potential flights on one battery charge. While predicting the end of discharge can support decision-making, it remains insufficient in case of unforeseen events, such as adverse weather conditions. This necessitates health-aware real-time control to address any unexpected events and extend the time until the end of charge while taking the current degradation state into account. This paper addresses the joint problem of mission planning and health-aware real-time control of operational parameters to prescriptively control the duration of one discharge cycle of the battery pack. We propose an algorithm that proactively prescribes operational parameters to extend the discharge cycle based on the battery’s current health status while optimizing the mission. The proposed deep reinforcement learning algorithm facilitates operational parameter optimization and path planning while accounting for the degradation state, even in the presence of uncertainties. Evaluation of simulated flights of a National Aeronautics and Space Administration (NASA) conceptual multirotor aircraft model, collected from Hardware-in-the-loop experiments, demonstrates the algorithm’s near-optimal performance across various operational scenarios, allowing adaptation to changed environmental conditions. The proposed health-aware prescriptive algorithm enables a more flexible and efficient operation not only in single aircraft but also in fleet operations, increasing the overall system throughput.

Keywords: Mission planning; Prescriptive health-aware operation; Deep reinforcement learning; Lithium-ion battery; Real-time control (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:259:y:2025:i:c:s0951832025001000

DOI: 10.1016/j.ress.2025.110897

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