Performance Optimization of a Formula Student Racing Car Using the IPG CarMaker, Part 1: Lap Time Convergence and Sensitivity Analysis

It is increasingly common for simulation and AI tools to aid in the vehicle design process. The IPG CarMaker uses a multibody vehicle model and a learning algorithm for the virtual driver. The goal is to discover the behavior of the learning algorithm from the point of view of reliability and conver...

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Main Authors: Dominik Takács, Ambrus Zelei
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Engineering Proceedings
Subjects:
Online Access:https://www.mdpi.com/2673-4591/79/1/86
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author Dominik Takács
Ambrus Zelei
author_facet Dominik Takács
Ambrus Zelei
author_sort Dominik Takács
collection DOAJ
description It is increasingly common for simulation and AI tools to aid in the vehicle design process. The IPG CarMaker uses a multibody vehicle model and a learning algorithm for the virtual driver. The goal is to discover the behavior of the learning algorithm from the point of view of reliability and convergence. Simulations demonstrate that the lap time converges reliably. We also report that small changes in the vehicle parameters induce small changes in the simulated lap time, i.e., the lap time is a differentiable function of the vehicle parameters. Part 2 of this paper explains the aerodynamics and Drag Reduction System optimization.
format Article
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institution Kabale University
issn 2673-4591
language English
publishDate 2024-11-01
publisher MDPI AG
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series Engineering Proceedings
spelling doaj-art-fe3ea36de9b84a69bb052461e02c337c2024-12-27T14:24:42ZengMDPI AGEngineering Proceedings2673-45912024-11-017918610.3390/engproc2024079086Performance Optimization of a Formula Student Racing Car Using the IPG CarMaker, Part 1: Lap Time Convergence and Sensitivity AnalysisDominik Takács0Ambrus Zelei1Department of Whole Vehicle Engineering, Audi Hungaria Faculty of Automotive Engineering, Széchenyi István University in Győr, 9026 Győr, HungaryDepartment of Whole Vehicle Engineering, Audi Hungaria Faculty of Automotive Engineering, Széchenyi István University in Győr, 9026 Győr, HungaryIt is increasingly common for simulation and AI tools to aid in the vehicle design process. The IPG CarMaker uses a multibody vehicle model and a learning algorithm for the virtual driver. The goal is to discover the behavior of the learning algorithm from the point of view of reliability and convergence. Simulations demonstrate that the lap time converges reliably. We also report that small changes in the vehicle parameters induce small changes in the simulated lap time, i.e., the lap time is a differentiable function of the vehicle parameters. Part 2 of this paper explains the aerodynamics and Drag Reduction System optimization.https://www.mdpi.com/2673-4591/79/1/86lap time simulationoptimizationIPG CarMaker
spellingShingle Dominik Takács
Ambrus Zelei
Performance Optimization of a Formula Student Racing Car Using the IPG CarMaker, Part 1: Lap Time Convergence and Sensitivity Analysis
Engineering Proceedings
lap time simulation
optimization
IPG CarMaker
title Performance Optimization of a Formula Student Racing Car Using the IPG CarMaker, Part 1: Lap Time Convergence and Sensitivity Analysis
title_full Performance Optimization of a Formula Student Racing Car Using the IPG CarMaker, Part 1: Lap Time Convergence and Sensitivity Analysis
title_fullStr Performance Optimization of a Formula Student Racing Car Using the IPG CarMaker, Part 1: Lap Time Convergence and Sensitivity Analysis
title_full_unstemmed Performance Optimization of a Formula Student Racing Car Using the IPG CarMaker, Part 1: Lap Time Convergence and Sensitivity Analysis
title_short Performance Optimization of a Formula Student Racing Car Using the IPG CarMaker, Part 1: Lap Time Convergence and Sensitivity Analysis
title_sort performance optimization of a formula student racing car using the ipg carmaker part 1 lap time convergence and sensitivity analysis
topic lap time simulation
optimization
IPG CarMaker
url https://www.mdpi.com/2673-4591/79/1/86
work_keys_str_mv AT dominiktakacs performanceoptimizationofaformulastudentracingcarusingtheipgcarmakerpart1laptimeconvergenceandsensitivityanalysis
AT ambruszelei performanceoptimizationofaformulastudentracingcarusingtheipgcarmakerpart1laptimeconvergenceandsensitivityanalysis