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Braking has a strong effect on a vehicle's front suspension loads when the vehicle is driven over a pothole. The suspension loads of a vehicle braking while going over a pothole are also affected by vehicle design, vehicle weight and speed. In this study a simplified suspension model is presented, which is then validated by the simulation of a vehicle model. The simplified suspension model provides an efficient approach to evaluate effects of braking on wheel rebound into potholes, which determines the magnitude of impact loads when the tires hit the pothole edge. The vehicle model is used not only to validate the simplified suspension model, but also to provide the information of wheel center loads in addition to the wheel position and velocity. The analysis using the vehicle model agrees with pothole test results. The study reveals how vehicle braking affects the wheel center longitudinal forces during the pothole impact.

Front road wheel toe dynamics directly affects tire wear and steering wheel vibration, which in turn negatively impacts customer satisfaction. Though static toe can be preset in assembly plants, the front road wheels can vibrate around steering axes or kingpin axes due to tire mass unbalance and nonuniformity. The frequency of the vibration depends on the wheel size and vehicle speed, while the amplitude of the vibration is not only dictated by the tire forces, but also by suspension and steering parameters. This paper presents a study on the sensitivities of the front road wheel toe dynamics to the parameters of a short-long-arm suspension (SLA) and a parallelogram steering system. These parameters includes hard point shift, steering gear compliance, gear friction, control arm bushing rates, friction in control arm ball joints, and compliance in tie rod outboard joints.

Evaluation of electric steering (EPAS) system performance using vehicle specific load conditions is important for steering system design validation and vehicle steering performance tuning. Using real-time vehicle dynamics mathematical models is one approach for generating steering loads in steering hardware-in-the-loop (HIL) testing. However achieving a good correlation of simplified mathematical models with real vehicle dynamics is a challenge. Using rack force models from measured steering tie rod forces or from simulations using a high-fidelity vehicle dynamics model is an effective data-driven modelling method for testing EPAS systems under vehicle specific load conditions. Rack force models are identified from physical measurements or validated vehicle simulations of selected steering test maneuvers. The rack force models have been applied in steering system performance evaluation, benchmarking, and steering model validation.

For powertrain mounts design and durability evaluation, directly measuring all the engine and transmission mount loads is a costly and time-consuming option. Analytical or semi-analytical approaches have been used for estimating maximum powertrain mount loads at the early design stage and also for calculating the dynamic loads when prototype test data is available. After reviewing various semi-analytical approaches or hybrid approaches using MATLAB® [1], this paper introduces a hybrid method using ADAMS® [2] with input of measured engine and transmission accelerations as well as drive shaft or half shaft loads. In this hybrid method the engine is modeled as a rigid body supported by the base structure (vehicle subframe, frame or body) through powertrain mounts with measured nonlinear properties. The method has been studied in various vehicle prototypes, in which the mount loads were measured for analysis and test correlation.

Distinguishing physical modes from mathematical modes in the modal analysis of complex systems, such as full vehicle structures, is a difficult and time-consuming process. The major tools frequently used are stabilization diagrams, mode indicator functions, or modal participation factors. When closely spaced modes are to be identified, the stabilization diagrams and mode indicator functions are no longer effective. Even the reciprocities of mode shapes and modal participation factors cannot be well satisfied to indicate whether a mode is a physical one, when measurement errors are large. To overcome these difficulties, an optimization procedure is developed, whereby physical modes can be sorted out in a given frequency range while the error between measured and synthesized frequency responses is minimized. An optimal subset selection algorithm is used in this procedure.

Subjective steering feel tuning and objective verification tests are conducted on vehicle prototypes that are a subset of the total number of buildable combinations of body style, drivetrain and tires. Limited development time, high prototype vehicle cost, and hence limited number of available prototypes are factors that affect the ability to tune and verify all the possible configurations. A new model-based process and a toolset have been developed to enhance the existing steering development process such that steering tuning efficiency and performance robustness can be improved. The innovative method utilizes the existing vehicle dynamics simulation and/or physical test data in conjunction with steering system control models, and provides users with simple interfaces which can be used by either CAE or development engineers to perform virtual tuning of the vehicle steering feel to meet performance targets.