Title: Tyre design based on improved vehicle multidisciplinary performance using two-step approximate optimisation with representative design indices

Authors: Fengling Gao; Dengfeng Wang; Zhixin Wu

Addresses: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130022, China; China Automotive Technology and Research Center Co., Ltd., Tianjin, 300300, China; CATARC Automotive Test Center (Tianjin) Co., Ltd., Tianjin, 300300, China ' State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130022, China ' China Automotive Technology and Research Center Co., Ltd., Tianjin, 300300, China

Abstract: To improve overall vehicle performance when using CDTire/3D model-based tyre design, a two-step approximate optimisation method that combines several representative design indices is presented. The process optimises both of the CDTire/3D model vector and scalar parameters. A vehicle road noise design index as the optimisation objective is defined using the extracted CDTire/3D model-dependent sensitive frequency subdomains, and the mean values of crucial data from vehicle ride comfort and durability simulations are used to be constraints. A sequential-updating radial basis function metamodel-based optimisation method is adopted as the solver. The proposed method is employed to develop tyres for a sport utility vehicle (SUV). Compared with typical road noise design indices-based static approximate optimisation approaches, the proposed method achieves a better overall optimisation effect. Using the developed CDTire/3D model, the road noise, ride comfort, and durability design indices of the SUV are improved by 9.8%, 6.7%, and 7.1%, respectively.

Keywords: tyre optimisation design; vehicle multidisciplinary performance improvement; CDTire/3D model; virtual proving ground; metamodel technique.

DOI: 10.1504/IJVD.2024.140431

International Journal of Vehicle Design, 2024 Vol.96 No.1, pp.1 - 21

Received: 16 Apr 2023
Accepted: 17 Jan 2024
Published online: 08 Aug 2024 *

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