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Multiphysical MF-based tyre modelling and parametrisation for vehicle setup and control strategies optimisation
Vehicle System Dynamics ( IF 3.6 ) Pub Date : 2021-09-21 , DOI: 10.1080/00423114.2021.1977833
Aleksandr Sakhnevych 1
Affiliation  

ABSTRACT

Starting from the earliest phases of design of the vehicle and its control systems, the understanding of tyres is of fundamental importance to govern the overall vehicle dynamics. A properly characterised tyre–road interaction model is essential to achieve a reliable vehicle dynamics model on which more design variations can be studied directly in simulation environment optimising both cost and time. The possibility to count on computationally efficient and reliable formulations represents nowadays a great advantage, and the multiphysical Pacejka's Magic Formula (MF-evo) tyre model presented is one of the best trade-off solutions to meet the strict real-time requirements and to reproduce multiphysical variations of the tyre dynamic behaviour towards temperature, pressure and wear effects. A specific methodology has been developed to characterise and to identify the MF-evo parameters with a high grade of accuracy and reliability directly from experimental data. The proposed technique is based on a pre-processing procedure to remove non-physical outliers and to cluster the data, which allows to optimise the multidimensional parameterisation process. To the purpose of validation of the parametrisation routine, data from a motorsport case, exceptionally difficult to reproduce in simulation due particularly significant variations of the tyre dynamics during a single test, have been employed demonstrating the MF-evo model potential and robustness.



中文翻译:

用于车辆设置和控制策略优化的基于多物理场 MF 的轮胎建模和参数化

摘要

从车辆及其控制系统设计的最初阶段开始,对轮胎的了解对于控制整个车辆动力学至关重要。正确表征的轮胎-道路相互作用模型对于实现可靠的车辆动力学模型至关重要,在该模型上,可以在仿真环境中直接研究更多的设计变化,从而优化成本和时间。依靠计算高效和可靠的配方的可能性代表了当今的巨大优势,并且提出的多物理 Pacejka 的 Magic Formula (MF-evo) 轮胎模型是满足严格的实时要求和重现的最佳权衡解决方案之一轮胎动态行为对温度、压力和磨损影响的多物理变化。已经开发了一种特定的方法来直接从实验数据中以高精度和可靠性来表征和识别 MF-evo 参数。所提出的技术基于去除非物理异常值并对数据进行聚类的预处理过程,这允许优化多维参数化过程。为了验证参数化程序的目的,来自赛车案例的数据,由于在单次测试期间轮胎动力学的特别显着变化,在模拟中异常难以重现,已被用于证明 MF-evo 模型的潜力和稳健性。所提出的技术基于去除非物理异常值并对数据进行聚类的预处理过程,这允许优化多维参数化过程。为了验证参数化程序的目的,来自赛车案例的数据,由于在单次测试期间轮胎动力学的特别显着变化,在模拟中异常难以重现,已被用于证明 MF-evo 模型的潜力和稳健性。所提出的技术基于去除非物理异常值并对数据进行聚类的预处理过程,这允许优化多维参数化过程。为了验证参数化程序的目的,来自赛车案例的数据,由于在单次测试期间轮胎动力学的特别显着变化,在模拟中异常难以重现,已被用于证明 MF-evo 模型的潜力和稳健性。

更新日期:2021-09-21
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