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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.

Since 2014, the F\'ed\'eration Internationale de l'Automobile has prescribed a parallel hybrid powertrain for the Formula 1 race cars. The complex low-level interactions between the thermal and the electrical part represent a non-trivial and challenging system to be controlled online. We present a novel controller architecture composed of a supervisory controller for the energy management, a feedforward cylinder deactivation controller, and a track region-dependent low-level nonlinear model predictive controller to optimize the engine actuators. Except for the nonlinear model predictive controller, the proposed controller subsystems are computationally inexpensive and are real time capable. The framework is tested and validated in a simulation environment for several realistic scenarios disturbed by driver actions or grip conditions on the track. In particular, we analyze how the control architecture deals with an unexpected gearshift trajectory during an acceleration phase. Further, we demonstrate how an increased maximum velocity trajectory impacts the online low-level controller. Our results show a suboptimality over an entire lap with respect to the benchmark solution of 49 ms and 64 ms, respectively, which we deem acceptable. Compared to the same control architecture with full knowledge of the disturbances, the suboptimality amounted to only 2 ms and 17 ms. For all case studies we show that the cylinder deactivation capability decreases the suboptimality by 7 to 8 ms.