Journal Article


Strategy for optimizing an F1 car’s performance based on FIA regulations

Abstract

With the introduction of the V6 engines in Formula 1, in 2014, the sport aimed to close the gap between the automotive engine and high-performance motorsport engines in the area of fuel economy. A set of very challenging engineering regulations were introduced by the FIA to restrict the power from the Internal Combustion Engine (ICE), while allowing for more power to be harvested through energy recovery systems. Although progress has been made in developing a highly efficient powertrain, the limit to which this system can be pushed to is still unknown due to a significant gap between the technological choices available and the optimal control strategy used. This study investigated an engine-powertrain model of an F1 car with real world driver data for estimating the vehicle’s full throttle performance. The work used engine and drive-cycle simulation-modeling tools to build a representative car model which complied with the 2019 FIA regulations, in conjunction with real world data to identify the most critical parameter such as the gear shift strategy and the maximum energy recovered, stored and deployed that decides which car wins the race. This work identified the different strategies used by drivers and their respective teams for achieving the best possible vehicle performance, to finish the race and win. Based on real world driver data, a comparative analysis is done between drivers finishing at 1st Place, 10th Place and the last driver to successfully complete the race. A suitable strategy is proposed in this work for all the analyzed races in order to maximize the vehicle’s performance. The work provides an insight towards the direction in which the F1 industry could possibly be progressing for 2021.

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Authors

Bopaiah, Karan
Samuel, Stephen

Oxford Brookes departments

School of Engineering, Computing and Mathematics

Dates

Year of publication: 2020
Date of RADAR deposit: 2020-02-12


Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License


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