A methodology for the design optimization of rail 
vehicles with passive and active suspensions is 
presented. The methodology has the following 
features: (1) multibody dynamics is used for modelling 
and simulating complex realistic vehicle systems; 
(2) multidisciplinary optimization (MDO) methods are 
introduced to make coupled vehicle models and 
additional control systems a synergistic whole; 
(3) with genetic algorithms (GAs) and other effective 
search algorithms, the mechanical and control design 
variables can be optimized simultaneously; (4) with the 
scalarization technique, a vector optimization problem 
is converted into a scalar optimization problem. The 
proposed methodology is applied to several design 
optimization problems. First, a rail vehicle is 
optimized with respect to lateral stability. Second, 
the rail vehicle is designed so that ride quality is 
the sole design criterion. Third, the design variables 
are searched in the feasible design space so as to make 
the rail vehicle have optimal curving performance. Then, 
the rail vehicle is optimally designed for obtaining 
trade-off solutions among conflicting requirements from 
lateral stability, ride quality, and curving performance. 
Finally, the methodology is used to optimize the combined 
mechanical and control systems for vehicles with active 
suspensions. Of the results obtained, several of them 
contribute to the fields of rail vehicle dynamics and design, 
mechatronic systems, and numerical optimization. For 
automatically identifying the "critical speed" (above which 
a rail vehicle's response becomes unstable), a new approach 
combining sequential quadratic programming (SQP) with the 
Dynamic Mode Tracking (DMT) technique is proposed and 
developed. The new approach is compared with that using SQP 
alone. It is found that without DMT, several more SQP runs 
are often needed to find the critical speed because the 
relationship between mode damping and speed deviate from 
their actual shapes. In the process of optimizing the lateral 
stability of a rail vehicle model, the existence of 
sharply-discontinuous "cliffs" in the plots of critical 
speed versus suspension stiffness is identified and originally 
interpreted. In recognition of the cliff phenomenon, the 
definition of critical speed is generalized to make it a more 
practical measure of lateral stability. In the design 
optimization of a rail vehicle with respect to the lateral 
stability, vertical ride quality, and curving performance, the 
resulting Edgeworth-Pareto (EP) optimal sets clearly 
demonstrated the trade-off relation between lateral stability 
and curving performance. Moreover, the resulting EP-optimal 
sets visualize a well-known fact that a relatively weak 
coupling exists between the vertical and lateral motions of a rail 
vehicle. 
To identify effective algorithms for rail vehicle suspension
design, the GA, SQP, and Simplex algorithms are compared in
the processes of optimizing lateral stability and ride quality.
Results show that the reliability of the SQP and Simplex for
finding the global optimum decreases with an increase in number
of design variables. However, despite non-smooth objective
function surfaces with many local optimal points, the GA can
reliably find global optima. By means of GAs, important design
variables can be identified and the relative significance of
design variable sets, e.g. inertial, geometric, and suspension
(stiffness and damping) parameter sets, can be decided. When
ride quality analysis is performed in the frequency domain
based on a linear vehicle model, if SQP is used with a multibody
dynamics program, e.g. a GEM, the numerical differntiation technique
for computing gradients can be used efficiently as a link between
the multibody program and SQP.
As an application of the proposed methodology, an integrated
design approach to mechatronic vehicle systems is used to resolve
the conflicting requirements for ride comfort, suspension working
spaces, and dynamic wheel loads in the optimization of quarter-vehicle
models and half-vehicle models with active suspensions. Both 
deterministic and random track excitations and both rigid and flexible
car body cases are considered. The approach is implemented in a
GA-A'GEM-MATLAB simulation environment in such a way that the
linear mechanical vehicle models are generated in the multibody
program of A'GEM, the controllers and filters are modelled in
MATLAB, and the coupled mechanical and control subsystems are
optimized simultaneously using the GA. The numerical simulation
results are reported and discussed.