  This paper presents an interaction-aware energy management optimization
framework for Formula 1 racing. The considered scenario involves two agents and
a drag reduction model. Strategic interactions between the agents are captured
by a Stackelberg game formulated as a bilevel program. To address the
computational challenges associated with bilevel optimization, the problem is
reformulated as a single-level nonlinear program employing the
Karush-Kuhn-Tucker conditions. The proposed framework contributes towards the
development of new energy management and allocation strategies, caused by the
presence of another agent. For instance, it provides valuable insights on how
to redistribute the energy in order to optimally exploit the wake effect,
showcasing a notable difference with the behavior studied in previous works.
Robust energy allocations can be identified to reduce the lap time loss
associated with unexpected choices of the other agent. It allows to recognize
the boundary conditions for the interaction to become relevant, impacting the
system's behavior, and to assess if overtaking is possible and beneficial.
Overall, the framework provides a comprehensive approach for a two-agent
Formula 1 racing problem with strategic interactions, offering physically
intuitive and practical results.
