Universite de technologie de Belfort-Montbeliard

About: Universite de technologie de Belfort-Montbeliard is a education organization based out in Belfort, France. It is known for research contribution in the topics: Coating & Microstructure. The organization has 1087 authors who have published 2593 publications receiving 47819 citations.

Cascade control of the moto-compressor of a PEM fuel cell via second order sliding mode

Abstract: This paper presents a cascade control of the moto-compressor of a Polymer Electrolyte Membrane Fuel Cell (PEMFC). The control objective is to optimize the net power by maintaining the oxygen excess ratio between 2 and 2.4. The proposed control strategy is based on two cascaded super twisting second order sliding mode controllers (Fig.1), which regulate the moto-compressor supplying air to the cathode side of the fuel cell. Simulation results show that the proposed controller has a good transient performance under load variations and parametric uncertainties.

Towards a Multilevel Simulation Approach Based on Holonic Multiagent Systems

Abstract: Simulation is an appropriate approach for studying complex systems that are inacessible through direct observations and measurements. In a simulation involving a great number of interacting entities, it is difficult to create a reliable and tractable abstraction of the real reference system. One of the involved problems is amount of computational resources required to handle microscopic simulation of large number of entities. One solution is to use macroscopic models. However, this type of models may be at hand unavailable or not reliable, or it doesn’t allow observations of individual behaviours. In this paper a multilevel simulation model is proposed to dynamically adapt the level of simulated behaviours while being as faithful as possible to the reference model. Our approach is based on Holonic Multi-Agent Systems and provides a generic scheduling model for multilevel simulations.

Predictive Energy Management for Hybrid Vehicles Based on Driving Cycle Recognition

Abstract: This article presents a novel approach to design an optimum energy management for a series plug-in hybrid electric vehicle based on driving cycle recognition. Thanks to an intensive study of driver habits, an important part of cycles is predictable. Optimized energy management can be developed for representative cycles via DP. Those results can be applied to the same cycle with slight adaptations thereafter with quality factors above 98%.