Reducing the sampling frequency for the control of the switched reluctance machine

This paper presents two solutions for dramatically reduce the sampling frequency and therefore the CPU demand while keeping the same performance in terms of torque ripple and efficiency on a SRM. The problem of a low sampling frequency with a regular control is first highlighted. Then, two different solutions are proposed for the self switching's function. Such solutions tries to magnetize the stator phase at an accurate instant in order to reduce the inherent torque ripple. Simulations results on a 8/6 SRM corroborate the validity of the proposed solutions and show the improvements of its performance.Peer ReviewedPostprint (author’s final draft

Industrial applications of the Kalman filter:a review

International audienc

Reducing the sampling frequency for the control of the switched reluctance machine

This paper presents two solutions for dramatically reduce the sampling frequency and therefore the CPU demand while keeping the same performance in terms of torque ripple and efficiency on a SRM. The problem of a low sampling frequency with a regular control is first highlighted. Then, two different solutions are proposed for the self switching's function. Such solutions tries to magnetize the stator phase at an accurate instant in order to reduce the inherent torque ripple. Simulations results on a 8/6 SRM corroborate the validity of the proposed solutions and show the improvements of its performance.Peer Reviewe

Fuel Cells Remaining Useful Life estimation using an Extended Kalman Filter

International audienceFuel cells, especially the proton exchange membrane fuel cell, are promising energy converters that can be usedfor various applications (transportation, mobile and stationary applications ). Although, they have a limited lifespan due todegradation mechanisms (chemical and mechanical stresses) that are not completely understood. Consequently, researches have been conducted to estimate the Remaining Useful Life of such devices, in order to take mitigation actions, extending the life of this electrochemical converter. The aim of the presented work is to propose an observer based method for prognostic of Proton Exchange Membrane Fuel Cell. An Extended Kalman Filter estimates the State of Health and its derivative using a singleempirical degradation model, in any operating conditions. Those estimations are used to predict the future State of Health. Oncethe prediction equalizes a defined threshold, the fuel cell is considered out of use and the Remaining Useful Life is given.This method is validated by simulation and then used on a set of experimental data resulting from a long term test on a 5-cellstack operated under a constant current solicitation

Aging Tolerant Control of Proton Exchange Membrane Fuel Cell : a Model-Based Approach

International audienc

Remaining Useful Life Prediction and Uncertainty Quantification of Proton Exchange Membrane Fuel Cell Under Variable Load

International audienceAlthough, the proton exchange membrane fuel cell is a promising clean and efficient energy converter that can be used to power an entire building in electricity and heat in a combined manner, it suffers from a limited lifespan due to degradation mechanisms. As a consequence, in the past years, researches have been conducted to estimate the state of health and now the remaining useful life (RUL) in order to extend the life of such devices. However, the developed methods are unable to perform prognostics with an online uncertainty quantification due to the computational cost. This paper aims at tackling this issue by proposing an observer-based prognostic algorithm. An extended Kalman filter estimates the actual state of health and the dynamic of the degradation with the associated uncertainty. An inverse first-order reliability method is used to extrapolate the state of health until a threshold is reached, for which the RUL is given with a 90% confidence interval. The global method is validated using a simulation model built from degradation data. Finally, the algorithm is tested on a dataset coming from a long-term experimental test on an eight-cell fuel cell stack subjected to a variable power profile

System-on-Chip FPGA Devices for Complex Electrical Energy Systems Control

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Dynamical modeling of Proton Exchange Membrane Fuel Cell and parameters identification

International audienceProton Exchange Membrane Fuel Cell (PEMFC) is a complex multi-physical system in which phenomenon from several fields are coupled (e.g. electro-chemical, electrical, thermal, and hydraulic). This promising energy converter needs to be optimized to become competitive. This is the reason why many researches have been conducted for its modeling. The optimization of such a converter requires the study of the whole system through simulation. Thus the Bond Graph theory is well suited. In this paper, a dynamical model of a PEMFC stack using the Bond Graph formalism is presented and a procedure to estimate the model parameters is proposed. The global model is validated thanks to experimental data from an 8-cell fuel cell stack

Fuel Cell Remaining Useful Life Prediction and Uncertainty Quantification Under an Automotive Profile

International audienceBeing a very efficient and clean energy converter, a proton exchange membrane fuel cell may be utilized topower an electrical vehicle efficiently. Nevertheless, degradation mechanisms affect the lifespan of this electrochemical converter. Consequently, the estimation of the State of Health and Remaining Useful Life have been the subject of numerous researches in the past years. However, most of the methods available considering fuel cell prognostic do not allow the uncertainty quantification of the estimation that can be implemented online considering the calculation cost. As a novelty, the present article depicts a prognostic algorithm based on an Extended Kalman Filter. This observer estimates the State of Health, the speed of the degradation and also provides the estimation uncertainty. Then, an Inverse First Order Reliability Method computes the Remaining Useful Life with a 90% confidence interval based on the estimation of the observer. This method is applied on a 175 hours data set subsequent of an experiment on an 8-cells fuel cell stack that was subjected to an automotive power profile

Fuel Cell Remaining Useful Life Prediction and Uncertainty Quantification Under an Automotive Profile

International audienceBeing a very efficient and clean energy converter, a proton exchange membrane fuel cell may be utilized topower an electrical vehicle efficiently. Nevertheless, degradation mechanisms affect the lifespan of this electrochemical converter. Consequently, the estimation of the State of Health and Remaining Useful Life have been the subject of numerous researches in the past years. However, most of the methods available considering fuel cell prognostic do not allow the uncertainty quantification of the estimation that can be implemented online considering the calculation cost. As a novelty, the present article depicts a prognostic algorithm based on an Extended Kalman Filter. This observer estimates the State of Health, the speed of the degradation and also provides the estimation uncertainty. Then, an Inverse First Order Reliability Method computes the Remaining Useful Life with a 90% confidence interval based on the estimation of the observer. This method is applied on a 175 hours data set subsequent of an experiment on an 8-cells fuel cell stack that was subjected to an automotive power profile