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As the electronic engine technologies advances, the engine management tool has drawled attention for being one of the main efficiency improvement methodologies and for being applied on Diesel and Otto cycle engines. With the creation of hybrid vehicles there comes the need to manage several engines simultaneously in order to optimize the energy consumption and to reduce the waste emission, among other improvements. To accomplish this objectives are necessary to adapt this system to the driver's needs and to improve its controls. In order to doing so, we propose an intelligent approach for controlling this managing system using of artificial and computational intelligence techniques such as Bayesian Nets, Neural networks and Genetic Algorithms.

We propose a demonstration of a new analytical method of failure diagnosis for diesel engines. This method is able to help the end user to solve problems in the vehicle using failure detection without having deep technical knowledge in automotive engineering, and it gives Product Engineering the opportunity to update DFMEA directly by field failure results detected by the system diagnosis. The ability to detect failures by this method is based on the component failures rate in the field, its mileage and symptoms of the engine failure that may be perceived as leaks, noise and smoke, among others. This method is developed using statistical calculation of probabilities through Bayesian networks, in addition to applying the knowledge of experts in the field of quality, diesel engines, artificial intelligence and vehicle diagnostics.

Internal combustion engine calibration teaching by Stand Alone System. This paper illustrates a teaching methodology for technical students of internal combustion engine calibration, by stand alone engine control unit with variable ignition and fuel injection time. Using a system named HIS (Stand alone Electronic Control Unit), to change the engine parameters, as fuel injection time and ignition time, the students can optimize fuel consumption, performance and exhaust emission. The tests are developed using the DOE (design of experiments) technique of artificial intelligence.

From simple collisions to major tragedies, car accidents happen every day. Automobile industry has been investing a lot in security systems (e.g., airbags, ABS brakes, vehicle proximity warning systems). However, drivers are still the ones who have to act in order to avoid collisions, by using the brakes or maneuvering the car. The purpose of this project is to develop an anti-collision logic that can be used on automobile vehicles to avoid collisions with both static and moving objects by interfering on the vehicle behavior. The control system is based on a fuzzy modeled controller using the MATLAB and Simulink tools from Math Works. The prototype chosen to test the logic was the Robotino, a mobile robot system made by Festo Didactic, and used for educational, training and research purposes. Robotino is able to integrate to Simulink tool natively, and provides out-of-the-box sensors and actuators, so the developers are able to focus the efforts on the software itself.

The FMEA methodology, widely used in projects through the application of DFMEA (Design FMEA), and processes for implementing the PFMEA (Process FMEA), represents a great opportunity to be used in Project Management, recording the experiences and lessons learned in current or past projects in order to prevent the recurrence of project failure. In this paper we are proposing the improvement of RFMEA (Risk FMEA) to prevent the automotive program mistakes. The main goal of the application of FMEA methodology on project management is to generate an organized way to document the experiences of existing projects and to transform the tacit knowledge of the team that develops projects into explicit knowledge, as well as, prevent future projects from existing critical failure modes. Due to globalization and the consequent need to launch products in the shortest possible time, project management has been essential for business survival.