  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 cru-cial, 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 po-tential 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 opera-tional parameters
to prescriptively control the duration of one discharge cycle of the battery
pack. We pro-pose 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 learn-ing
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 NASA concep-tual multirotor
aircraft model, collected from Hardware-in-the-loop experiments, demonstrates
the algo-rithm's near-optimal performance across various operational scenarios,
allowing adaptation to changed en-vironmental conditions.
