Ali Bawab Email & Phone Number

Grenoble, Fr

Saint-Égrève, Fr

France

France

This project describes an innovative diagnostic method for Polymer Electrolyte Membrane Fuel Cells (PEMFC). The method used in this project is a magnetotomography technique in which the external magnetic field of the PEMFC is measured in the presence of an innovative ferromagnetic circuit analyzer that elaborates the magnetic signals at the sensor. The purpose of this work is to improve non-invasive diagnostic methodologies based on the FC external magnetic field measurement. Before considering the evaluation of the proposed diagnosis methodology on actual fuel cell stacks, generating a similar current distribution inside a FC emulator with similar geometry and structure is helpful for designing both numerical modeling and experimental magnetic field measurements. The main contribution of this work is represented by an innovative 3D current density emulator of a PEMFC. Based on a passivized FC structure, supplied by a current source, this emulator reproduces an equivalent current distribution in a similar geometrical domain, as the current distribution inside a Fuel Cell domain, during steady-state operation. The achieved objectives of this project are presented as follows:1) Create a Finite Element Model that describes the 2D and 3D conduction faults inside PEMFC by measuring the external magnetic field.2) Optimize the circuit analyzer geometry to obtain the optimal magnetic field measurement by the sensors.3) Build an experimental test bench to validate the numerical results obtained.4) Based on orthogonal arrays (Taguchi design), create a dataset based on the external magnetic field variations at the sensors level.5) Generate a feature extraction and classification machine-learning algorithm based on the experimental training and numerical testing datasets.6) At the end, the created algorithm diagnose the fault position inside the affected cell of the stack.

Grenoble, Auvergne-Rhône-Alpes, France

This internship is concerned with the following research areas:1. Emerging memory technologies (Memristors).2. Compact modelling of electronic devices.3. Bio-inspired neural networks.Internship Tasks:a) Read and understand the art of the compact models of the memristor device.b) Become familiar with the memristor electrical characteristics.c) Understand the synaptic devices in which they should operate.d) Implement a preliminary compact model of La2NiO4 – the implementation and verification of the suitability of already existing models of memristor for circuit simulations and correcting them using improved transition function if necessary, followed by benchmark of these models against experimental data available from electrical characterization done at IMEP-LAHC, from the La2NiO4 devices.e) Improved description of the electrical regimes – The use of memristor as synaptic-compliant devices places them in a very different electrical environment compared to data storage applications. To ensure a good description of the electrical characteristic, two subtasks will be accomplished:(i) Complementary electrical characterization of the devices in these peculiar regimes using the advanced measurement facility available at IMEP-LAHC,(ii) Compact modeling of the electric response of the device in these regimes, suitable for circuit simulation.

Doctor Of Philosophy - Phd, Electrical And Electronics Engineering

Master 2, Advanced Electronics, 14.85

Master 1, Electronics

Licentiate Degree, Electronics