Multi-Disciplinary Optimisation of Road Vehicle Chassis Subsystems

Abstract

Two vehicle chassis design tasks were solved by decomposition-based multi-disciplinary optimisation (MDO) methods, namely collaborative optimisation (CO) and analytical target cascading (ATC). A passive suspension system was optimised by applying both CO and ATC. Multiple parameters of the spring and damper were selected as design variables. The discomfort, road holding, and total mass of the spring–damper combination were the objective functions. An electric vehicle (EV) powertrain design problem was considered as the second test case. Energy consumption and gradeability were optimised by including the design of the electric motor and the battery pack layout. The standard single-level all-in-one (AiO) multi-objective optimisation method was compared with ATC and CO methods. AiO methods showed some limitations in terms of efficiency and accuracy. ATC proved to be the best choice for the design problems presented in this paper, since it provided solutions with good accuracy in a very efficient way. The proposed investigation on MDO methods can be useful for designers, to choose the proper optimisation approach, while solving complex vehicle design problems.

Keywords

• multi-disciplinary optimisation
• analytical target cascading
• collaborative optimisation
• passive suspension
• electric vehicle powertrain