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Driven by the necessity to reduce costs and improve products quality the automotive industry replaced the design method known as "trial and error" by those grounded on mathematical and physical theory. In this context, a longitudinal performance test was made by BAJA SAE UFMG team, in order to acquire vehicular performance data that will be used to validate computer models. The methodology consists of sensors and data acquisition system research, validation, fixation and installation in the vehicle, test and process of acquired data. From these steps, correlated data were acquired from magnitudes such as angular velocity in transmission shafts, global longitudinal acceleration and velocity, travel of break and throttle pedals and pressure inside of master cylinder. These results developed the knowledge about vehicular dynamic allowing the improvement of futures prototypes.

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.

The analysis of engine’s performance, gas emissions and combustion parameters is critical in the development of internal combustion engines. The combustion parameters analysis provide important information to speed up real-time engine’s operation in order to shorter the process of engine’s map calibration. The real-time analysis of these parameters allows the detection of anomalies, such as the prediction of knocking event. From the measurement of the In-cylinder pressure curve and the use of a one-zone combustion model is possible to evaluate the heat release rate, mass burned fraction and average In-cylinder gas temperature. Aiming to expand the amount of real-time data available, such as unburned and burned gases temperature and volume, radius and velocity of turbulent spherical flame and turbulence factor, this paper presents a hybrid combustion model, being composed by coupling a two-zone model to a one-zone model.