Macroscopic Model of Solid Oxide Fuel Cell Stack for Integrating in a Generator Simulation

International audienceThis paper presents a macroscopic model of solid oxide fuel cell (SOFC) with the aim to perform a simulation of the whole generator. Three sub-models have been developed to take into fluidic, thermal and electrical phenomena. The fluidic sub-model is based on an equivalent circuit based on electrical analogy. Pressure drops in channels are modelled by resistances and the fluid accumulation in the volume is modelled by capacitor. Each electrode compartment (channel+electrode) is represented by two resistances and one capacitor. We have used this model to calculate the pressure at the catalytic sites and gas flows at fuel cell input and output. The electrical response is based on the classical Nernst potential equation, activation, ohmic and concentration overvoltages. The thermal modelling is based on a (2D) nodal network. Two aspects are studied in this article (conduction and the convection heat transfer). Results have been validated on a 5 cell stack

Prognostics of proton exchange membrane fuel cell stack in a particle filtering framework including characterization disturbances and voltage recovery.

International audienceIn the perspective of decreasing polluting emissions and developing alternative energies, fuel cells, and more precisely Proton Exchange Membrane Fuel Cells (PEMFC), represent a promising solution. Even if this technology is close to being competitive, it still suffers from too short life duration. As a consequence, prognostic seems to be a great solution to anticipate PEMFC stacks degradation. However, a PEMFC implies multiphysics and multiscale phenomena making the construction of an aging model only based on physics very complex. One solution consists in using a hybrid approach for prognostics combining the use of models and available data. Among these hybrid approaches, particle filtering methods seem to be really appropriate as they offer the possibility to compute models with time varying parameters and to update them all along the prognostics process. But to be efficient, not only should the prognostics system take into account the aging of the stack but also external events influencing this aging. Indeed, some acquisition techniques introduce disturbances in the fuel cell behavior and a voltage recovery can be observed at the end of the characterization process. This paper proposes to tackle this problem. First, PEMFC fuel cells and their complexities are introduced. Then, the impact of characterization of the fuel cell behavior is described. Empirical models are built and introduced in both learning and prediction phases of the prognostics model by combining three particle filters. The new prognostic framework is used to perform remaining useful life estimates and the whole proposition is illustrated with a long term experiment data set of a PEMFC in constant load solicitation and stable operating conditions. Estimates can be given with an error less than 5% for life durations of more than 1000 hours. Finally, the results are compared to a previous work to show that introducing a disturbance modeling can dramatically reduce the uncertainty coming with the predictions

Prognostics and Health Management of PEMFC - state of the art and remaining challenges.

International audienceFuel Cell systems (FC) represent a promising alternative energy source. However, even if this technology is close to being dustrial deployment: FC still must be optimized, particularly by increasing their limited lifespan. This involves a better understanding of wearing processes and requires emulating the behavior of the whole system. Furthermore, a new area of science and technology emerges: Prognostics and Health Management (PHM) appears to be of great interest to face the problems of health assessment and life prediction of FCs. According to this, the aim of this paper is to present the current state of the art on PHM of FCs, more precisely of Proton-Exchange Membrane Fuel Cells (PEMFC) stack. PHM discipline is described in order to depict the processing layers that allow early deviations detection, avoiding faults, deciding mitigation actions, and thereby increasing the useful life of FCs. On this basis, a taxonomy of existing works on PHM of PEMFC is given, highlighting open problems to be addressed. The whole enables getting a better understanding of remaining challenging issues in this area

Towards an ageing model of a PEMFC for prognostics purpose

International audienceOne of the main constraints of Proton Exchange Membrane Fuel Cell (PEMFC) in its breakthrough in the industrial world is its life duration [1], which is too short and not enough managed. From this point of view, the development of Prognostics and Health Management (PHM) technology [2] appears to be a relevant discipline; the estimation of the Remaining Useful Life (RUL) of a fuel cell system enables deciding for actions in order to try to mitigate the degradation and ensure longer life duration and availability. This paper addresses this topic and aims at proposing a basic FC ageing model [3][4] that will serve for prognostics issues. Here, the proposed ageing model is based on the combination of a static model with a dynamical one. A physical based model is considered built on a Butler-Volmer law where the voltage is function of the current density and of fuel cell's physical parameters. This analytical static model allows the separation of cathode and anode contribution. The dynamical model is based on an electrical equivalence; on which different internal components can be distinguished. This model is expressed in a state-space representation and defines the voltage depending on the current. Moreover, the model also provides the possibility of modelling the ageing of the Polymer Exchange Membrane Fuel Cell. Indeed, different internal parameters can be represented using time-dependent functions. The limits of this model and its suitability for prognostics are discussed. The model validation is based on actual long-duration experimental tests

ELECTRICAL ANALOGY MODELLING OF PEFC SYSTEM FED BY A COMPRESSOR

14International audienceThe PEFC generator for automotive application is expected to have a low cost, a low weight and a low size, to compete with more and more efficient combustion engine. To reach this aim, the complete system has to be taken into account, not only the stack itself, but also the fluid circuit ancillaries. A model is developed with the aim in view to be integrated in the simulation of an electrical vehicle power train. As many components have to be modelled, a macroscopic approach has been chosen. A general scheme of the system is proposed, which structure is representative of an embedded generator, i.e. few sensors, few actuators. Gaseous hydrogen is stored in a tank. The anode output is closed by a solenoid valve. It is opened when the fuel cell voltage reaches a minimum threshold, allowing flushes of the channels. Air is provided by a compressor which flow is controlled by the motor speed. The modelling of the fuel cell electrical response is developed, based on semi-empirical approach. The decrease of the output voltage which can be attributed to the anodic dead end operation is taken into account. The fluidic behaviour of the gas circuits has been dealt through an electrical analogy to facilitate the implementation in a usual electrical engineering software (Matlab/Simulink®). Each component of volume V and fluidic resistance Rf is represented by a RC cell. In a formal approach, the flow is related to the current and the pressure is related to the voltage. The model is validated with experimentations carried out on a low power test bench (100W). Then, it is simulated on a load cycle, compatible with vehicle application dynamics

Remaining useful life estimates of a PEM fuel cell stack by including characterization-induced disturbances in a particle filter model.

International audienceProton Exchange Membrane Fuel Cells (PEMFC) are available for a wide variety of applications such as transportation, micro-cogeneration or powering of portable devices. However, even if this technology becomes close to competitiveness, it still suffers from too short life duration to pretend to a large scale deployment. In a perspective of a longer lifetime, prognostics aims at tracking and anticipating degradation and failure, and thereby enables deciding mitigation actions to increase life duration. Yet, the complexity of degradation phenomena in PEMFC can make prognostic implementation really tough. Indeed, a PEMFC implies multiphysics and multiscale phenomena making the construction of a physics-based aging model very complex. Moreover, prognostics should also take into account external events influencing the aging. Among them, characterization techniques such as electrochemical impedance spectroscopies and polarization curves introduce disturbances in the stack behavior, and a voltage recovery is observed at the end of characterizations process. It means that irreversible degradation and reversible decrease of performances have to be considered. This work proposes to tackle this problem by setting a prognostics system that includes disturbances' effects. We propose a hybrid prognostics approach by combining the use of empirical models and available data. In an evolving system like a fuel stack, a particle filtering framework seems to be really appropriate for life prediction as it offers the possibility to compute models with time varying parameters and to update them all along the prognostics process. Moreover, it offers a great adaptability to include characterization effects and allows giving prediction with a quantified uncertainty. The logic of the work is the following. First, it is shown that simple empirical models only taking into account the aging are very limited in terms of prognostics performances. Then, some features describing the impact of characterization on the stack behavior and aging are extracted and a more complete prognostics model is built. Finally, the new prognostic framework is used to perform remaining useful life estimation and the whole proposition is illustrated with a long term experiment data set in constant current solicitation and stable operating conditions

PHM of Proton-Exchange Membrane Fuel Cells - A review.

International audienceFuel Cell (FC) systems are promising power-generation sources that are more and more presented as a good alternative to current energy converters such as internal combustion engines. They suffer however from insufficient durability for stationary and transport applications, and lifetime may be improved. A greater understanding of underlying wearing processes is needed in order to improve this technology. However, FCs are in essence multi-physics and multi-scales systems (from the cells to the whole power system), which makes a modeling step of behaviors and degradation very difficult, even impossible. Thereby, data-driven Prognostic and Health Management (PHM) principles (as defined in condition-basedmaintenance scheme CBM) appear to be of great interest to face with the problems of health assessment and life prediction of FCs. According to all this, the aim of this paper is to present the current state of the art on PHM for FCs. Developments emphasize on PHM of the Proton-Exchange Membrane Fuel Cells (PEMFC) stack. The paper is organized so that important aspects like "behavior and losses FCs", "observation techniques", and "advanced PHM techniques" are addressed. Also, a taxonomy of existing works on PHM of PEMFC is given accordingly to the processing layers of CBM. The whole enables PHM practitioners as well as FCs experts to get a better understanding of remaining challenging issues

Prognostics of PEM fuel cell in a particle filtering framework.

International audienceProton Exchange Membrane Fuel Cells (PEMFC) suffer from a limited lifespan, which impedes their uses at a large scale. From this point of view, prognostics appears to be a promising activity since the estimation of the Remaining Useful Life (RUL) before a failure occurs allows deciding from mitigation actions at the right time when needed. Prognostics is however not a trivial task: 1) underlying degradation mechanisms cannot be easily measured and modeled, 2) health prediction must be performed with a long enough time horizon to allow reaction. The aim of this paper is to face these problems by proposing a prognostics framework that enables avoiding assumptions on the PEMFC behavior, while ensuring good accuracy on RUL estimates. Developments are based on a particle filtering approach that enables including non-observable states (degradation through time) into physical models. RUL estimates are obtained by considering successive probability distributions of degrading states. The method is applied on 2 data sets, where 3 models of the voltage drop are tested to compare predictions. Results are obtained with an accuracy of 90 hours around the real RUL value (for a 1000 hours lifespan), clearly showing the significance of the proposed approach

Cost-minimization predictive energy management of a postal-delivery fuel cell electric vehicle with intelligent battery State-of-Charge Planner

Fuel cell electric vehicles have earned substantial attentions in recent decades due to their high-efficiency and zero-emission features, while the high operating costs remain the major barrier towards their large-scale commercialization. In such context, this paper aims to devise an energy management strategy for an urban postal-delivery fuel cell electric vehicle for operating cost mitigation. First, a data-driven dual-loop spatial-domain battery state-of-charge reference estimator is designed to guide battery energy depletion, which is trained by real-world driving data collected in postal delivery missions. Then, a fuzzy C-means clustering enhanced Markov speed predictor is constructed to project the upcoming velocity. Lastly, combining the state-of-charge reference and the forecasted speed, a model predictive control-based cost-optimization energy management strategy is established to mitigate vehicle operating costs imposed by energy consumption and power-source degradations. Validation results have shown that 1) the proposed strategy could mitigate the operating cost by 4.43% and 7.30% in average versus benchmark strategies, denoting its superiority in term of cost-reduction and 2) the computation burden per step of the proposed strategy is averaged at 0.123ms, less than the sampling time interval 1s, proving its potential of real-time applications

Modélisation orientée vers le système Pile à Combustible

National audienceModélisation orientée vers le système Pile à Combustibl