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

Jouin, Marine; Gouriveau, Rafael; Hissel, Daniel; Péra, Marie‐Cécile; Zerhouni, Noureddine

doi:10.1109/icphm.2014.7036363

Cited by 26 publications

(16 citation statements)

References 13 publications

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“…Each cell has an active area, nominal current density, and maximal current density of 100 cm 2 , 0.70, and 1 A/cm 2 , respectively. The details of the datasets can be found in the literature 27,28 . Test facilities allow normal or accelerated aging of FC stacks under constant and/or variable operating conditions while controlling and collecting information on health monitoring data such as power loads, temperatures, hydrogen, and air stoichiometry rates.…”

Section: Materials and Existing Methodsmentioning

confidence: 99%

“…The details of the datasets can be found in the literature. 27,28 Test facilities allow normal or accelerated aging of FC stacks under constant and/or variable operating conditions while controlling and collecting information on health monitoring data such as power loads, temperatures, hydrogen, and air stoichiometry rates. The test conditions of current for FC1 and FC2 are stationary and dynamic, respectively.…”

Section: Two Pemfc Stacksmentioning

confidence: 99%

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Ensemble model for the degradation prediction of proton exchange membrane fuel cell stacks

Wang

Huang

Mamo

et al. 2020

Quality & Reliability Eng

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Proton exchange membrane fuel cell (PEMFC) stacks are widely used in mobile and portable applications due to their clean and efficient model of operation. We propose an ensemble model based on a stacked long short‐term memory model that combines three machine‐learning models, including long short‐term memory with attention mechanism, support vector regression, and random forest regression, to improve the degradation prediction of a PEMFC stack. The prediction intervals can be derived using the dropout technique. The proposed model is compared with some existing models using two PEMFC stacks. The results show that the proposed model outperforms the other models in terms of mean absolute percentage error and root mean square error. Regarding the remaining useful life prediction, the proposed model with the sliding window approach can provide better results.

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Section: Materials and Existing Methodsmentioning

confidence: 99%

Section: Two Pemfc Stacksmentioning

confidence: 99%

Ensemble model for the degradation prediction of proton exchange membrane fuel cell stacks

Wang

Huang

Mamo

et al. 2020

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…Model-based method which have been successfully applied to PEMFC RUL estimation, was never applied to an automotive dataset where the dynamics are fast. In addition, the uncertainty quantification issue is adressed in [11] by mean of a Particle Filter which is challenging to implement online [14]. As an extension of a previous work [12], the method presented in this paper ( Fig.…”

Section: A Generalities and Methodsmentioning

confidence: 99%

Fuel cell remaining useful life prediction and uncertainty quantification under an automotive profile

Bressel

Hilairet

Hissel

et al. 2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

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Proton exchange membrane fuel cell is a clean and efficient energy converter that can be use to power 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 in the literature dealing with fuel cell prognostic do not allow the uncertainty quantification of the estimation that can be implemented online due to the computational cost. As a novelty, this paper presents 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 dataset coming from a experimental test on a 8-cells fuel cell stack subjected to an automotive power profile.

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“…Empirical and simplified physical models are proposed in Refs for modeling the degradation process. Extended Kalman filtering and Particle Filtering (PF) are two popular approaches for remaining useful life prediction based on the physical models. Data‐driven approaches are also widely used for prognostics of PEMFC systems.…”

Section: Introductionmentioning

confidence: 99%

Prognostics of a multistack PEMFC system with multiagent modeling

Liu

Zio

2018

Energy Science & Engineering

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Current research on prognostics of PEMFC mainly focuses on one stack. However, in practice, the PEMFC stacks are assembled together to provide higher and more reliable power. In this paper, an original multiagent approach is proposed for predicting the remaining useful life‐times of all stacks in a multistack PEMFC system. The predictions are updated dynamically by particle filtering when new stack loads are collected. The major contribution of the paper is not on degradation modeling of a single PEMFC, but on the cooperative prognostics of multiple PEMFC stacks in the same power system. A case study concerning a synthetic combined heat and power system with three PEMFC stacks is considered and the results show the effectiveness of the proposed approach.

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

Cited by 26 publications

References 13 publications

Ensemble model for the degradation prediction of proton exchange membrane fuel cell stacks

Ensemble model for the degradation prediction of proton exchange membrane fuel cell stacks

Fuel cell remaining useful life prediction and uncertainty quantification under an automotive profile

Prognostics of a multistack PEMFC system with multiagent modeling

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