Title: Design optimisation of vehicle suspension systems using artificial intelligent techniques

Authors: Vivek D. Kalyankar; Ajinkya V. Musale

Addresses: Mechanical Engineering Department, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat 395007, India ' TATA Consulting Engineers Ltd., Pune, Maharashtra, India

Abstract: Suspension system plays important role in automobiles and to some extent it is treated as backbone of vehicles. Design of suspension systems present challenges because of different conflicting criteria's and hence, optimum design of its parameters is essential to get better ride comfort. Important design parameters involved in suspension systems are un-sprung mass, sprung mass, tire stiffness, spring stiffness, suspension damping coefficient, etc.; and obtaining optimum design combination of all these parameters is only possible with the use of appropriate optimisation techniques. This article presents the summary of various optimisation techniques used by previous researchers for design optimisation of suspension systems. It is observed that, despite having various evolutionary optimisation techniques, most of the earlier work was surrounded with traditional methods and genetic algorithm. Hence, a better performing algorithm compared to those, is demonstrated here to prove, uses of appropriate algorithm will help to improve the performance of suspension systems. A swarm based artificial bee colony algorithm is considered here to achieve optimum design and it is demonstrated with two examples having different road conditions. Results obtained shows considerable improvement in the design of suspension system thereby achieving a better ride comfort when compared with the results of previous researchers.

Keywords: design optimisation; ABC algorithm; vehicle model; degree of freedom; DOF; suspension system; ride comfort.

DOI: 10.1504/IJOR.2020.105442

International Journal of Operational Research, 2020 Vol.37 No.3, pp.324 - 344

Received: 06 Jul 2016
Accepted: 27 Mar 2017
Published online: 02 Mar 2020 *

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