  Citation: Yang, L.; Gobbi, M.; Mastinu, G.; Previati, G.; Ballo, F. Multi-Disciplinary Optimisation of Road Vehicle Chassis Subsystems. Energies 2022, 15, 2172. https:// doi.org/10.3390/en15062172 Academic Editor: Pablo García-Triviño and Carlos Andrés García-Vázquez Received: 7 February 2022 Accepted: 11 March 2022 Published: 16 March 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). energies Article Multi-Disciplinary Optimisation of Road Vehicle Chassis Subsystems † Liunan Yang 1,2,3 , Massimiliano Gobbi 1, * , Gianpiero Mastinu 1 , Giorgio Previati 1 and Federico Ballo 1 1 Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy; liunan.yang@polimi.it (L.Y.); gianpiero.mastinu@polimi.it (G.M.); giorgio.previati@polimi.it (G.P.); federicomaria.ballo@polimi.it (F.B.) 2 Chongqing Changan Automobile Company, Chongqing 401120, China 3 College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China * Correspondence: massimiliano.gobbi@polimi.it † This paper is an extended version of the work presented by the same authors at the ASME 2019 International Design Engineering Technical Conferences, Anaheim, CA, USA, 18–21 August, 2019; Paper no.: DETC2019-97308. Abstract: Two vehicle chassis design tasks were solved by decomposition-based multi-disciplinary optimisation (MDO) methods, namely collaborative optimisation (CO) and analytical target cascad- ing (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 1. Introduction In the automotive field, multi-disciplinary design problems typically involve several groups of experts. They are responsible for different performances and for designing different subsystems that constitute the vehicle. The expert groups must interact during the development process. Some groups are responsible for the design (e.g., the body, the powertrain, the suspension system, etc.), while other groups are responsible for different performance metrics (e.g., handling, safety, noise, vibration, and harshness (NVH), etc.). Traditionally, the design process and the assessment of performance are divided into parallel phases with intermediate synchronisation phases (usually “design review meeting”) between the groups. Although the traditional approach leads to a feasible design, it may not be the optimal choice. The purpose of MDO is to obtain the optimal solution by taking several disciplines into account simultaneously. In this way, the design groups can work in parallel and au- tonomously [1,2]. The most common optimisation approach to handle the computationally demanding simulation models involves the use of metamodels [1,3]. Metamodels need to be created by the design groups before the optimisation process, and they offer an easy way of distributing the design work. The single-level optimisation method, AiO, in combination with metamodels, is the most straightforward way of implementing multi-disciplinary Energies 2022, 15, 2172. https://doi.org/10.3390/en15062172 https://www.mdpi.com/journal/energies