Time-optimal Energy Management of the Formula 1 Power Unit with Active Battery Path Constraints

Duhr, Pol; Schaller, Maximilian; Arzilli, Luca; Cerofolini, Alberto; Onder, Christopher H.

doi:10.23919/ecc54610.2021.9655192

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…This is significant in the context of circuit racing in general and F1 in particular, confirming that we must take this effect into account. More information on this can be found in [53], [54].…”

Section: F Single-lap Solutionmentioning

confidence: 99%

Minimum-Race-Time Energy Allocation Strategies for the Hybrid-Electric Formula 1 Power Unit

Duhr¹,

Buccheri²,

Balerna³

et al. 2023

IEEE Trans. Veh. Technol.

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The hybrid-electric powertrain currently used in Formula 1 race cars draws its energy from the car's fuel tank and battery. The usable battery size is limited, and refueling during a race is forbidden by the regulations of the Formula 1 race series. From a strategic point of view, lap-by-lap targets for the fuel and battery consumption must be chosen and imposed on the energy management controller of the car. This task is non-trivial due to the influence of the on-board fuel mass on the achievable lap time, as well as the cross-couplings between the electric and the combustion part of the powertrain. A systematic approach is thus required to compute the energy allocation strategy that minimizes the total race time. In this paper, we devise an optimization framework in the form of a non-linear program, yielding the optimal battery and fuel consumption targets for each lap of the race. The approach is based on maps that capture the achievable lap time as a function of car mass and allocated battery and fuel energy. These maps are generated beforehand with a model-based single-lap optimization framework and fitted using artificial neural network techniques. To showcase the approach, we present three case studies: First, we compare the optimal strategy to a heuristic method. The improvement of 2 s over the entire race is substantial, given that the difference only lies in the energy allocation, but not in the overall consumption. It underlines the importance of optimizing the energy allocation. Second, we leverage the framework to compute the optimal fuel load at the beginning of the race. Finally, we apply the developed non-linear program in a shrinking-horizon fashion. Our simulation results show that the resulting model predictive controller correctly reacts to disturbances that frequently occur during a race.

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Section: F Single-lap Solutionmentioning

confidence: 99%