  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.
