Search Results

Nowadays, numerical modeling/simulation is an essential tool. It is used in all the design stages in order to improve quality and to reduce costs and time-to-market. As models address increasingly multiphysics and complex phenomena throughout the V-cycle process, modelers are confronted, sooner or later, with the need for model reduction. This paper describes the set-up of a virtual prototyping platform and highlights the interest of integrating energetic aspects, which are used to compute new model reduction criteria. A similar energy approach, “MORA” using “Activity”, is already used by Bond Graphists in order to obtain the “Proper Model” ([7] and [8]). The methodology presented here (PEMRA: “Power & Energy -based Model Reduction Algorithm”), with new power and energy criteria, makes it possible to obtain a simpler and more accurate reduced model than with MORA methodology, while improving the system's energy information.

Developing a knowledge-based predictive driver-aid system requires a strategy that meets the needs and goals, as well as rigorous methods. This paper presents the strategy adopted and the first results obtained in the development of the Navigation Aided Intelligent Cruise Control (N.A.I.C.C.) system. Once the vehicle is located on the route, this new copilot can: 1. define the travel direction and the vehicle speed, 2. determine the distance to the next bend and its characteristics, 3. predict the optimal speed to negotiate the next bend considering the road profile, the constraints given by the driver and the information provided by the sensors mounted on the vehicle. Then, used in the warning mode (“Driver Alarm Mode”), N.A.I.C.C. informs the driver of the danger of the situation (excessive speed) or, in the intervention mode (“Velocity Control Mode”), N.A.I.C.C. adapts the speed to the predicted reference.