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Strategy for Optimizing an F1 Car’s Performance Based on FIA Regulations
ISSN: 2641-9637, e-ISSN: 2641-9645
Published April 14, 2020 by SAE International in United States
Citation: Bopaiah, K. and Samuel, S., "Strategy for Optimizing an F1 Car’s Performance Based on FIA Regulations," SAE Int. J. Adv. & Curr. Prac. in Mobility 2(5):2516-2530, 2020, https://doi.org/10.4271/2020-01-0545.
The aim of the present work is to propose a control strategy for maximizing the performance of a Formula One (F1) car through numerical simulation for 2021 regulations taking 2019 regulations as a benchmark. This study has developed an engine-powertrain model of an F1 car with real world driver data for estimating the vehicle’s full throttle performance. The maximum possible energy recovered, stored and deployed by the Energy Recovery System (ERS) was estimated for the first 10 circuits in the 2019 FIA Formula One World Championship® Race Calendar. A 1.6L V6 Internal Combustion Engine (ICE), as well as, a full vehicle was modelled according to the 2019 Federation Internationale de l'Automobile (FIA) Formula One technical regulations using GT-Suite software. The model was validated against the experimental data. The data for validation was extracted from On- Board videos using Optical Character Recognition (OCR) and FIA regulations. Strategy for qualifying has been developed for all 10 circuits and compared. Strategies used by different drivers on different circuits are analysed and the optimum strategy for maximizing the Power Unit’s (PU) performance is proposed.
Findings show that the data acquired from FIA Formula One on-board videos from the F1 TV App is highly accurate in conjunction to the official lap time with an error of less than 1%. The full car model is able to meet the Target Vehicle Speed at the required gear number and follow the FIA regulations at all times. Factors affecting the performance of the PU such as Average vehicle speed and the Motor Generator Unit-Kinetic (MGU-K) deployment speed have been found and quantified. Driver to Driver variation in the MGU-K deployment speed for maximizing performance during qualification has been quantified. It was found that the winning drivers had higher Average vehicle speed and MGU-K deployment speed which resulted in better performance from the PU. The maximum possible energy recovered per lap by the MGU-K is analysed for both the qualification and race laps. It was found that the energy recovered per lap by the MGU-K is higher in race laps as compared to qualification laps because of increase in the vehicle mass due to fuel weight. Lastly, Strategies have been proposed to maximise the performance of the PU for qualification and race.