Nonlinear model predictive control methodology for efficiency and durability improvement in a fuel cell power system

The main contribution of this work is the improvement of the efficiency of a PEMFC power system while guaranteeing conditions that also improve its durability. Adopting the NMPC scheme with the distributed parameter model and the nonlinear observer, the efficiency of the PEMFC-based system can be maximized guaranteeing at the same time the appropriate internal gas concentration profiles to avoid global and local hydrogen and oxygen starvation and proper membrane humidification.Peer ReviewedPostprint (author's final draft

ANOVA Method Applied to PEMFC Ageing Forecasting Using an Echo State Network

International audienceAccording to the International Energy Agency, an increase of the requests of energy of 40% could arise in the next decades, mainly due to the emergence of developing countries. The problem with the nowaday energy system is the use of fossil energy, which is limited and attempt to disappear in the near future. Thus an energy transition has to begin in order to replace the fossil fuels and anticipate their disappearance. Consequently, in recent years, the promotion and development of renewable energy have been realized. One of this renewable energy, the energy vector hydrogen, appears to be a promising solution, mainly due to interesting performance of Fuel Cells (FC) systems and hydrogen abundance on Earth (it is still important to underline that the hydrogen does not exist in natural form). However, this research area is still subject to scientific and technological bottlenecks. One of these major bottlenecks preventing the industrialization of FC systems is it limited useful lifetime. It is therefore important to develop reliable tools for the diagnosis and prognosis of FC system in order to optimize its efficiency. The aim of this article is to present the results of a sensibility analysis applied to a prognosis tools called Echo State Network

Predicting the Remaining Useful Lifetime of a Proton Exchange Membrane Fuel Cell using an Echo State Network

International audienceOne remaining technological bottleneck to develop industrial Fuel Cell (FC) applications resides in the system limited useful lifetime. Consequently, it's important to develop failure diagnostic and prognostic tools enabling the optimization of FC. The Prognostic and Heath Management (PHM) is a discipline involved in the process of industrial maintenance. The objective, in PHM, is to estimate the Remaining Useful Life (RUL) of a system by predicting its future behavior. The RUL enables to predict the moment when a fault could occur on a system. It also allows identifying the relevant part of the system where a fault could happen. Then, a preventive maintenance could be performed to avoid non-reversible degradations. Three main prognosis approaches can be distinguished: model-based, data-based and hybrid methods. Data-based methods such as Artificial Neural Network (ANN), aim to estimate the ageing behavior of the process without specific knowledges related to the physical system phenomenon. Nevertheless, the deployment of such an approach can be a tedious work, mainly due to the trial and error algorithm method, which represents a real problem for industrial applications where real-time complying algorithms must be developed. Among the various methods of this area, the tool chosen here is called Echo State Network (ESN). An ESN consists in the use of a dynamical neurons reservoir where the training step consists in performing a linear regression. The computation time of this algorithm is thus shorter while keeping the same modeling capability of a Recurrent Neural Network (RNN). Created in 2001 by H. Jaeger, an ESN proposes a better human brain paradigm than traditional ANN, and are based on a reservoir of neurons randomly connected to each other. The aim of this paper is to study the application of ESN as a prognostics system enabling the estimation of the Remaining Useful Life of a Proton Exchange Membrane Fuel Cell using an iterative predictive structure, which is the most common approach performing a one-step prediction. This estimation output value is used in the next step as one of the input regressor and these operations can be repeated until the desired prediction horizon. The results obtained thanks to this method exhibits good prediction and they will be detailed in this paper

Nonlinear predictive control for durability enhancement and efficiency improvement in a fuel cell power system

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this work, a nonlinear model predictive control (NMPC) strategy is proposed to improve the efficiency and enhance the durability of a proton exchange membrane fuel cell (PEMFC) power system. The PEMFC controller is based on a distributed parameters model that describes the nonlinear dynamics of the system, considering spatial variations along the gas channels. Parasitic power from different system auxiliaries is considered, including the main parasitic losses which are those of the compressor. A nonlinear observer is implemented, based on the discretised model of the PEMFC, to estimate the internal states. This information is included in the cost function of the controller to enhance the durability of the system by means of avoiding local starvation and inappropriate water vapour concentrations. Simulation results are presented to show the performance of the proposed controller over a given case study in an automotive application (New European Driving Cycle). With the aim of representing the most relevant phenomena that affects the PEMFC voltage, the simulation model includes a two-phase water model and the effects of liquid water on the catalyst active area. The control model is a simplified version that does not consider two-phase water dynamics.Peer ReviewedPostprint (author's final draft

Fuel Cells prognostics using Echo State Network

International audienceOne remaining technological bottleneck to develop industrial Fuel Cell (FC) applications resides in the system limited useful lifetime. Consequently, it is important to develop failure diagnostic and prognostic tools enabling the optimization of the FC. Among all the existing prognostics approaches, datamining methods such as artificial neural networks aim at estimating the process' behavior without huge knowledge about the underlying physical phenomena. Nevertheless, this kind of approach needs huge learning dataset. Also, the deployment of such an approach can be long (trial and error method), which represents a real problem for industrial applications where realtime complying algorithms must be developed. According to this, the aim of this paper is to study the application of a reservoir computing tool (the Echo State Network) as a prognostics system enabling the estimation of the Remaining Useful Life of a Proton Exchange Membrane Fuel Cell. Developments emphasize on the prediction of the mean voltage cells of a degrading FC. Accuracy and time consumption of the approach are studied, as well as sensitivity of several parameters of the ESN. Results appear to be very promising

Proton exchange membrane fuel cell operation and degradation in short-circuit

This paper presents an experimental study dealing with operation and degradation during an electrical short circuit of a proton exchange membrane fuel cell stack. The physical quantities in the fuel cell (electrical voltage and current, gas stoichiometry, pressures, temperatures and gas humidity) are studied before, during and after the failure. After a short circuit occurs, a high peak of current appears but decreases to stabilize in a much lower value. The voltage drops in all the cells and even some cells presents reversal potentials. The degradation is quantified by using electrochemical impedance spectroscopy

Proton exchange membrane fuel cell degradation prediction based on Adaptive Neuro-Fuzzy Inference Systems .

International audienceThis paper studies the prediction of the output voltage reduction caused by degradation during nominal operating condition of a PEM fuel cell stack. It proposes a methodology based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) which use as input the measures of the fuel cell output voltage during operation. The paper presents the architecture of the ANFIS and studies the selection of its parameters. As the output voltage cannot be represented as a periodical signal, the paper proposes to predict its temporal variation which is then used to construct the prediction of the output voltage. The paper also proposes to split this signal in two components: normal operation and external perturbations. The second component cannot be predicted and then it is not used to train the ANFIS. The performance of the prediction is evaluated on the output voltage of two fuel cells during a long term operation (1000 hours). Validation results suggest that the proposed technique is well adapted to predict degradation in fuel cell systems

Analyse des mécanismes de dégradation dans un système pile à combustible

International audienceLes systèmes pile à combustible (SPàC) sont considérés comme une solution viable et une alternative prometteuse autant pour des applications embarquées que stationnaires. Cela dit, ces systèmes doivent répondre à des critères essentiels à leur large développement, à savoir, coût, durabilité et fiabilité. Le présent travail se focalise sur l’aspect fiabilité du système pile à combustible. En effet, une meilleure compréhension des mécanismes de dégradation dans le SPàC permettra de développer les stratégies nécessaires à la réduction des dégradations au sein du SPàC et augmenter sa durée de vie utile. Une analyse des mécanismes de dégradation et leurs effets au niveau du SPàC a été faite dans le but de construire un arbre de défaillances le plus complet possible. Le SPàC étudié comprend le stack (membrane, couche catalytique, plaques bipolaires, couche de diffusion des gaz) le système d’alimentation en air (compresseur, capteurs, régulateurs, électrovannes), le système d’alimentation en hydrogène (capteurs, régulateurs, électrovannes) et le système de refroidissement (pompe de circulation, capteurs, électrovannes, régulateurs). Cette étude permettra de déduire les lois de propagation des défauts dans le SPàC qui permettront une meilleure estimation de sa durée de vie

Robust control of the PEM fuel cell air-feed system via sub-optimal second order sliding mode

International audienceThis paper is focused on the control of air-feed system of Polymer Electrolyte Membrane Fuel Cell (PEMFC). This system regulates the air entering in the cathode side of the fuel cell. The control objective is to maintain optimum net power output by regulating the oxygen excess ratio in its operating range, through the air compressor. This requires controllers with a fast response time in order to avoid oxygen starvation during load changes. The problem is addressed using a robust nonlinear second order sliding mode controller in cascaded structure. The controller is based on sub-optimal algorithm, which is known for its robustness under disturbances and uncertainties. The controller performance is validated through Hardware- In-Loop (HIL) simulation based on a commercial twin screw air compressor and a real time fuel cell emulation system. The simulation results show that the controller is robust and has a good transient performance under load variations and parametric uncertainties