M. Gobbi

Optimization and integration of ground vehicle systems

This article deals with the optimal design of ground vehicles and their subsystems, with particular reference to ‘active’ safety and comfort. A review of state-of-the-art optimization methods for solving vehicle system design problems, including the integration of electronic controls, is given, thus further encouraging the use of such methods as standard tools for automotive engineers. Particular attention is devoted to the class of methods pertaining to complex system design optimization, as well as approaches for the optimal design of complex systems under uncertainty. Some examples of design optimizations are given in the fields of vehicle system dynamics, powertrain/internal combustion engine design, active safety and ride comfort, vehicle system design and lightweight structures, advanced automotive electronics, and smart vehicles.

On the Optimal Design of Composite Material Tubular Helical Springs

A method is presented for the design of helical springs, with particular reference to those made from composite material and having a hollow circular section. Given the technical specifications, (e.g. stiffness, maximum deflection, ...), the method allows to define the spring geometrical and mechanical parameters in order to get the best compromise among spring performances (minimum mass, maximum strength, ...), with constraints on local and global stability, on resonance frequency, ... The method is based on Multi-Objective Programming (a branch of Operations Research), which provides a theoretically correct way for defining the values of many design variables when many objectives (performance indexes) have to be taken into account. Mathematical models are developed for describing the mechanical behaviour of the spring. The models have been validated with satisfactory results. The design solutions coming from the application of the method suggest the best parameter setting for obtaining the desired spring performances.

On the optimal design of railway passenger vehicles

Abstract A method is presented for the concept design of railway passenger vehicles. The method requires adequate mathematical modelling for describing quantitatively the many relationships between vehicle parameters and vehicle performance indexes. The main purpose of the method is defining the layout of vehicles (i.e. length, number of wheelsets, etc.) in order to obtain the lowest possible life cycle cost (LCC). The method is based on multiobjective programming (MOP), a branch of operations research. By the proposed method, the optimal design of railway passenger vehicles can be performed in a theoretically correct and rigorous way. Genetic algorithms (GAs) are used to find the numerical solution to the problem. The optimal design of urban, suburban and intercity (IC) passenger vehicles is performed in order to obtain the best compromise between conflicting requirements such as maximum payload, minimum tare weight and axle load, minimum track deterioration, maximum ride comfort, etc. It is shown that, with respect to modern vehicles, a major improvement in LCC would be gained if shorter vehicles and newly designed wheelsets and/or bogies could be adopted. Should this happen, new families of passenger vehicles with two or three axles would be the optimal solutions to be built.

A PARAMETER IDENTIFICATION METHOD FOR ROAD ACCIDENTS RECONSTRUCTION

The paper deals with the mathematical reconstruction of road accidents. Often, many of the relevant parameters for an accurate simulation are not known. The aim of the paper is to introduce and validate a new method to identify these (many) relevant parameters, such as exact impact location, tyre-road friction, ... The velocity of the two vehicles before the impact are computed by applying the principle of conservation of momentum and angular momentum and by estimating the kinetic energy losses. The identification process is based on a Global Approximation technique. Up to 17 numerical values pertaining to both running conditions before impact and parameters can be defined accurately and in a relatively short time. The definition of the 17 numerical values is made on the basis of the known data coming from the positions of the vehicles at rest and from tyre marks (if present and measured). The method has undergone a preliminary validation. Two case studies are presented which show the effectiveness of the identification method.Copyright

Optimal and Robust Design of Ground Vehicle Systems

A new approach for the design of vehicle subsystems is addressed in the paper. The new approach is based not only on the theory of multi-objective optimisation but also on robust design. The method is characterised both by the optimisation of the objective functions (corresponding to system performance indices) and by the reduction (or minimisation) of the sensitivity (variance) of the performance indices to stochastic perturbations. Such variances are computed (very quickly) by means of an original procedure based on the global approximation of the objective functions. Additionally, with respect to the mentioned features, the new approach is based on both a special study to explore all of the feasible design solutions, and on a global sensitivity procedure to analyse (in a stochastic context) the influence of each design variable on each objective function. Pareto-optimal design solutions for different levels of “robustness” can be computed in a very short time. The optimisation method has been tested on a relatively simple problem and applied with successful results to a complex design problem related to vehicle design.Copyright