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For many years durability tests engineers have worked in the sense of improving the tests that, at first, were performed using public roads with high time consumption and low reproducibility. Proving grounds were specially designed to reproduce the most important efforts to the body and chassis systems, but time problem was still there. Time and cost reduction allied to the needs of quality, reliability and reproducibility improvement led the engineers to develop methods and equipments to reproduce the durability tests in the lab. In this way the road simulators appear as a powerful tool able to perform durability tests with high reliability, self-controlled and with very low time compared to the road tests. At this scenery bench tests were also created to components and systems mainly used to anticipate problems before a whole vehicle test.

One of the principal goals of modern vehicle control systems is to ensure passenger safety during dangerous maneuvers. Their effectiveness relies on providing appropriate parameter inputs. Tire-road contact forces are among the most important because they provide helpful information that could be used to mitigate vehicle instabilities. Unfortunately, measuring these forces requires expensive instrumentation and is not suitable for commercial vehicles. Thus, accurately estimating them is a crucial task. In this work, two estimation approaches are compared, an observer method and a neural network learning technique. Both predict the lateral and longitudinal tire-road contact forces. The observer approach takes into account system nonlinearities and estimates the stochastic states by using an extended Kalman filter technique to perform data fusion based on the popular bicycle model.

Currently the durability test of FIAT vehicles powertrain suspension system is performed in pattern roads that reproduces conditions which the vehicle is submitted by costumer during product life cycle. The test done in these roads is time consuming and expensive. Experimental Engineers, for quite some time, have endeavored in doing automotive components fatigue tests in the lab. These environments provide more controlled test conditions and enable a less time consuming test. This work analyzes, over one of the three powertrain system attachment points of a passenger vehicle, differences that are found between a test performed in pattern roads and a test performed in a 6DOF road simulator. As conclusion, presents alternatives to perform the test of these components in lab using a 6DOF road simulator.