Daniel Hissel

From Modeling to Control of a PEM Fuel Cell Using Energetic Macroscopic Representation

This paper presents a methodology to design the control part of a proton exchange membrane fuel cell (FC) stack. The objective is to control the FC voltage. This methodology is based on an energetic macroscopic representation (EMR) of the FC and leads to a so-called maximal control structure (MCS). The MCS is a step-by-step inversion of the EMR (inversion-model-based control structure). The control design process is based on an explicit definition of the problem. Basically, for instance, the tuning inputs, the system objectives, or constraints are highlighted to organize the control. Moreover, the MCS shows the places where sensors are required and controllers are requested. Unfortunately, the MCS is only a theoretical control structure. Consequently, a realistic structure needs some simplifications, leading to a so-called practical control structure. The FC model is first presented and experimentally validated. The designed control structure is then simulated, and the results are discussed.

Degradations analysis and aging modeling for health assessment and prognostics of PEMFC

Applying prognostics to Proton Exchange Membrane Fuel Cell (PEMFC) stacks is a good solution to help taking actions extending their lifetime. However, it requires a great understanding of the degradation mechanisms and failures occurring within the stack. This task is not simple when applied to a PEMFC due to the different levels (stack - cells - components), the different scales and the multiple causes that lead to degradation. To overcome this problem, this work proposes a methodology dedicated to the setting of a framework and a modeling of the aging for prognostics. This methodology is based on a deep literature review and degradation analyses of PEMFC stacks. This analysis allows defining a proper vocabulary dedicated to PEMFC׳s prognostics and health management and a clear limited framework to perform prognostics. Then the degradations review is used to select critical components within the stack, and to define their critical failure mechanisms thanks the proposal of new fault trees. The impact of these critical components and mechanisms on the power loss during aging is included to the model for prognostics. This model is finally validated on four datasets with different mission profiles both for health assessment and prognostics.

Experimental validation of a type-2 fuzzy logic controller for energy management in hybrid electrical vehicles

The aim of this paper is to present experimental validation results of an energy management system for hybrid electrical vehicles based on type-2 fuzzy logic. The energy management system (EMS) is designed by extracting knowledge from several experts using surveys. The consideration of interval type-2 fuzzy sets enables modeling the uncertainty in the answers of the experts. The validation of the EMS is performed on a real-scale heavy duty vehicle equipped with different energy sources such as batteries, fuel cell system and ultracapacitors. Experimental results are strong evidence that type-2 fuzzy logic is wide adapted for performing the energy management in hybrid electrical vehicles.

Joint Particle Filters Prognostics for Proton Exchange Membrane Fuel Cell Power Prediction at Constant Current Solicitation

Proton Exchange Membrane Fuel Cells (PEMFC) are promising energy converters, but still suffer from a short life duration. Applying Prognostics and Health Management seems to be a great solution to overcome that issue. But developing prognostics to anticipate and try to avoid failures is a critical challenge. To tackle this problem, a hybrid prognostics approach is proposed. It aims at predicting the power aging of a PEMFC stack working at a constant operating condition and a constant current solicitation. The main difficulties to overcome are the lack of adapted modeling of the aging for prognostics, and the occurrence of disturbances creating recovery phenomena through aging. Consequently, this work proposes a new empirical model for power aging that takes into account these recoveries based on different features extracted from the data. These models are used in a joint particle filter framework directly initialized by an automatic parameter estimate process. When sufficient data are available, the prognostics can give accurate behavior predictions compared to experimentation. Remaining useful life estimates can be given with an error smaller than 5% for a horizon of 500 hours on a life duration of 1750 hours, which is clearly long enough for decision making.

Improving accuracy of long-term prognostics of PEMFC stack to estimate remaining useful life

Proton Exchange Membrane Fuel cells (PEMFC) are energy systems that facilitate electrochemical reactions to create electrical energy from chemical energy of hydrogen. PEMFC are promising source of renewable energy that can operate on low temperature and have the advantages of high power density and low pollutant emissions. However, PEMFC technology is still in the developing phase, and its large-scale industrial deployment requires increasing the life span of fuel cells and decreasing their exploitation costs. In this context, Prognostics and Health Management of fuel cells is an emerging field, which aims at identifying degradation at early stages and estimating the Remaining Useful Life (RUL) for life cycle management. Indeed, due to prognostics capability, the accurate estimates of RUL enables safe operation of the equipment and timely decisions to prolong its life span. This paper contributes data-driven prognostics of PEMFC by an ensemble of constraint based Summation Wavelet-Extreme Learning Machine (SW-ELM) algorithm to improve accuracy and robustness of long-term prognostics. The SW-ELM is used for ensemble modeling due to its enhanced applicability for real applications as compared to conventional data-driven algorithms. The proposed prognostics model is validated on run-to-failure data of PEMFC stack, which had the life span of 1750 hours. The results confirm capability of the prognostics model to achieve accurate RUL estimates.

Fuel Cells Remaining Useful Lifetime Forecasting Using Echo State Network

The Hydrogen energy vector is one of the possible solutions to overcome future energy crisis announced by the International Energy Agency. However, various bottleneck, whether technological or societal, slow the industrial interest for this technology and therefore the mass production of fuel cells. Among these locks that may be mentioned one relating to the still limited useful lifetime of the fuel cells. To improve this lifetime, one of the existing approaches is to use the discipline of PHM (for Prognostics and Health Management). This discipline aims to improve the efficiency of control and maintenance operations on the system by using diagnostic or prognostics algorithms. This article covers the prognostics aspect of PHM applied to a PEMFC using an algorithm based on a tool from the reservoir computing discipline to predict the Remaining Useful Lifetime.

Prognostics of Proton Exchange Membrane Fuel Cell stack in a particle filtering framework including characterization disturbances and voltage recovery

In 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.

ANOVA method applied to proton exchange membrane fuel cell ageing forecasting using an echo state network

This paper presents the results of a sensibility analysis applied to a Reservoir Computing method. The method ANOVA has been chosen in order to analyse the influence of the different parameters of a new kind of Neural Network paradigm: the Echo State Network. The three sets of parameters used correspond to the more influential parameters in the case of the fuel cell voltage ageing forecasting. The simulations are then compared with experimental data obtained after a 1700 h long duration test. The results obtained, with a Mean Average Percentage Error of less than 5%, prove that Echo State Network is an interesting and promising tool.

Diagnostic & health management of fuel cell systems: Issues and solutions

Abstract Continuous depletion of the crude oil and gradual increase in the oil price have emphasized the need of a suitable alternative to our century-old oil-based economy. A clean and efficient power supply device based on a renewable energy source has to be available to face this issue. Among the different technological alternatives, fuel cell power generation becomes a more and more interesting and promising solution for both automotive industry and stationary power plants. However, many technological hurdles have still to be overcome before seeing the development of industrial and competitive products in these fields. Among them, one of the major issues to be solved is their insufficient reliability and durability for stationary and transport applications. To reach this aim, efficient diagnostic and state-of-health estimation methodologies should be available, able also to operate real-time and with limited number of additional physical sensors. This paper describes the state-of-the-art and the motivations regarding these research issues. It presents also selected recent developments and experimentations in this area.

Estimation of the lead-acid battery initial state of charge with experimental validation

This paper presents a novel method for the estimation of the lead-acid battery initial state of charge (SOC) using the battery impedance measurement. The initial SOC is a crucial value used in the commonly used Coulomb counter. An experimental test bench for battery characterization and impedance measurement is built using CompactRio and Labview software. A very simple electrical circuit is proposed and designed allowing the determination of the initial SOC. Mathematical formulas are derived allowing obtaining a good estimation of the initial SOC. The simulation results are validated experimentally.