Florence Druart

Robust optimal control strategies for a hybrid fuel cell power management system

In this paper several optimal control strategies are proposed for the power management subsystem of a hybrid fuel cell/supercapacitor power generation system. The control strategies are based on different control configurations involving the power converters associated to the hybrid source. Given certain desired performances, Linear Matrix Inequalities methods are used to solve the controller design problem that is written as an optimization problem with inequalities constraints. The solution to the optimization problem yields a simple PID controller with H∞ desired performance. For the several control strategies proposed, robustness is a primary issue. Time simulations and robustness analysis shows the effectiveness of the proposed strategies when compared with the classic control strategies used for this type of hybrid power generation system.

On the Robust Control of DC-DC Converters: Application to a Hybrid Power Generation System

Abstract In this paper a complete robust control synthesis is performed for a hybrid power generation structure composed by a Fuel Cell and a Supercapacitor. The control strategies are applied to the DC-DC boost power converters associated to each power source. Multivariable PI control with H ∞ performance, H ∞ full and reduced order controllers are designed and compared. The multivariable PI controller is designed through an optimization procedure based on solving some Linear Matrix Inequalities. A μ-analysis and frequency/time response performances results shows the advantages of the different proposed control strategies.

Diagnosis of PEM fuel cell stack based on magnetic fields measurements

Abstract Non-uniform current density distribution in PEM fuel cell results in local over-heating, accelerated ageing, and lower power output. This paper proposes a diagnosis approach for PEM fuel cell system based on monitoring the current density distribution inside the stack. A magnetic sensor network has been used to provide an image of the current density distribution. The diagnosis method has three steps: residuals generation, residual analysis to obtain symptoms and decision by classifying these symptoms. The proposed approach has been applied on virtual measurements and validated on experimental measurements under performance degradation due to long term functioning of the PEM fuel cell stack and under low air stoichiometric ratio due to an actuator fault. Results show that the proposed method is a remarkable tool for diagnosis and taking compensatory actions.