EMR Modeling of Mobypost

International audienceThe development of high-confidence, real-time capable simulation models for virtual and hardware-in-the-loop testing offers new opportunities for the automotive industry in terms of virtual product development and production to reduce the time-to-market of the fuel cell electric vehicles (FCEVs) at lower costs. However, the challenge is the combination of the hybridization and electrifications in the FCEVs leading to significantly increased vehicle variants and increased system complexity. In this paper, a unified organization of digital models is applied with the objective to seamlessly integrate virtual and real testing as proposed in the PANDA project is presented. This tool is than applied to the postal delivery FCEV developed during the Mobypost project and its components at the vehicle level. The model can reflect that the method developed during the PANDA project is capable to describe the Mobypost vehicle with its multi-domains, including electric, magnetic, mechanical and chemical. Besides, by simulating a complex road environment, the proportion of power and energy between battery and fuel cell can also be provided for the optimization of the FCEVs parameters

Hybrid fuel cell system degradation modeling methods: A comprehensive review

International audienceLast years, the fuel cell has become well-known as an efficient and clean energy converter being a potential alternative to internal combustion engines. However, despite being very promising, the durability of those systems is still a bottleneck. Most of the time, a fuel cell is integrated in a hybrid system which considers the fuel cell stack, the battery, and the balance of plant. To keep improving the durability of such a system, diagnostic and prognostic tools are of particular importance and to implement such tools, modeling the system is a mandatory step. The purpose of this paper is to propose a critical review of the existing methods to model all elements of a hybrid fuel cell system according to operating conditions and degradation. In this review, interactions and major degradation mechanisms occurring at all components will be presented and the physicsbased models, data-driven and hybrid models of these components reviewed. Finally, methods will be discussed, and advantages and drawbacks will be summarized

Degradation prediction of PEM fuel cell based on artificial intelligence

In the last years, Proton Exchange Membrane Fuel Cells (PEMFC) became a promising energy converter for both transportation and stationary applications. However, durability of fuel cells still needs to be improved to achieve a widespread deployment. Degradation mechanisms and aging laws are not yet fully understood. Therefore, long-term durability tests are necessary to get more information. Moreover, degradation models are requested to estimate the remaining useful life of the system and take adequate corrective actions to optimize durability and availability. This paper presents in a first part the results of a longterm durability test performed on an open cathode fuel cell system operated during 5000 h under specific operating conditions including start/stop and variable ambient temperature. Performance evolution and degradation mechanisms are then analyzed to understand influence of operating conditions and how to extend the durability. In a second part of the paper, the results are used to build a degradation model based on echo state neuralnetwork in order to predict the performance evolution. Results of the degradation prediction are very promising as the normalized root mean square error remains very low with a prediction time over 2000 h

Analyse et Modélisation de la dégradation d'une PEMFC à l'aide d'intelligence artificielle

SYMPOSIUM DE GENIE ELECTRIQUE, SGE 2020, NANTES, FRANCE, 30-/06/2020 - 02/07/2020Aujourd'hui la durabilité des piles à combustible est encore trop limitée pour concurrencer les moteurs thermiques et la compréhension des mécanismes de dégradation n'est toujours pas entièrement maitrisée. C'est pourquoi des expérimentations de longue durée sont nécessaires afin d'approfondir les connaissances. Par ailleurs, des modèles de dégradation sont requis pour estimer et prédire l'évolution de ce vieillissement dans le but d'optimiser les lois de contrôle et les prises de décisions correctives de manière à prolonger la durée de vie du système. Ce papier présente dans un premier temps une expérimentation de 5000 heures réalisée sur un système PEMFC (Proton Exchange Membrane Fuel Cell) à cathode ouverte dans des conditions proches de la réalité. L'influence des conditions opératoires sur la dégradation est ensuite analysée. Dans un second temps, cette dégradation est modélisée à l'aide d'un réseau de neurones à réservoir. Les résultats montrent que le modèle proposé est capable de prédire l'évolution des performances sur plus de 2000 heures avec une erreur faible

Long term durability test of open-cathode fuel cell system under actual operating conditions

Proton exchange membrane fuel cells are promising energy conversion solutions for both stationary and transportation applications. However, for the latter application, durability still needs to be improved in order to compete with internal combustion engine. Therefore, many researches have been carried out to improve their design, materials, reliability and energy management. A good understanding of the degradation mechanisms is one of the keys to a better durability. However, these degradation mechanisms are not yet fully understood. The purpose of this paper is to present a long term durability test performed on a commercial 1 kW open-cathode proton exchange membrane fuel cell system during more than 5000 h under specific operating conditions that aim at reproducing driving cycles, including electrical solicitations, idle, start-up, shutdown, and environmental temperature conditions. This study presents voltage, efficiency and electrochemical impedance spectroscopy measurements evolution all along the system operating duration. Results are used to investigate and understand effects of ambient temperature on the PEMFC system performance and degradation. Analyses could be used in further studies to improve energy management and extend the system durability

Modélisation de batterie utilisant des données d'usage réel à travers l'algorithme d'optimisation Big-Bang Big-Crunch

National audienc

A method to estimate battery SOH indicators based on vehicle operating data only

Batteries are multi-physical systems and during actual operating conditions they are submitted to variable ambient operating conditions which can affect the dynamic behavior and the degradation. Therefore, a good understanding of the dynamic behavior and the degradation laws under actual operating conditions is the key to a durability improvement and to the development of better energy management strategies. The purpose of the proposed study is to use an experimental database issuedfrom a three years monitoring of a ten postal vehicle fleet to model the batteries with respect to operating conditions. Based on an electrical circuit model, an optimization algorithm and a Kalman filter, the scientific contribution is to propose a simple but efficient method, using vehicle operating data only, to estimate on-board the state of charge and state of health indicators linked to internal resistance and available capacity. The proposed model presents a very good accuracy and state of health indicators estimations show promising results. In the future, the proposed method could be applied on-board to estimate and analyze the state of health during the entire battery lifetime in order to provide an accurate state of charge estimation and to contribute to a better understanding of the degradation laws

Battery Modeling Using Real Driving Cycle and Big-Bang Big-Crunch Algorithm

International audienceThe purpose of this study is to model battery packs integrated in a ten fuel cell hybrid electric vehicle fleet developed among the European project Mobypost dedicated to postal delivery applications. This project led to create and feeda big database as the vehicles were deeply monitored. Thanks to this database and Big-Bang Big-Crunch optimization algorithm this paper proposes a method to model battery using real driving cycle data in few minutes with a NRMSE less than 0.02

Battery aging study using field use data

International audienceRegarded as the future of the automotive industry, electric cars are not far from a worldwide deployment. However some drawbacks still slow down this deployment such as batteries lifetime and autonomy which have to be increased in order to compete conventional internal combustion engine vehicles (ICE). Therefore, a well understanding of the battery aging phenomena is mandatory regarding the development of new hybrid achitectures and their energy management. In order to study those phenomena and understand the causes, ten fuel cell hybrid electric vehicles developed for postal delivery purpose among the european Mobypost project have been monitored during three years. This experimentation led to create a rich database. Using this database the remaining capacity of an entire fleet of vehicule have been calculated. Consequently the capacity fades could be estimated and illustrated in function of the temperature and the batteries cycle number, through a non linear regression with an error NRMSE of 0.129