PEM fuel cell prognostics using particle filter with model parameter adaptation

Kimotho, James Kuria; Meyer, Tobias; Sextro, Walter

doi:10.1109/icphm.2014.7036406

Cited by 50 publications

(35 citation statements)

References 7 publications

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“…The degradation of a PEMFC stack can be assessed from different measurements. The stack voltage (or power) is the most commonly used indicator in literature [4,5,6,7]. Since this indicator can be continuously monitored in PEMFC applications, it is a good candidate for online prognostic purposes.…”

Section: Introductionmentioning

confidence: 99%

An ensemble of models for integrating dependent sources of information for the prognosis of the remaining useful life of Proton Exchange Membrane Fuel Cells

Zhang

Baraldi

Cadet

et al. 2019

Mechanical Systems and Signal Processing

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Section: Introductionmentioning

confidence: 99%

An ensemble of models for integrating dependent sources of information for the prognosis of the remaining useful life of Proton Exchange Membrane Fuel Cells

Zhang

Baraldi

Cadet

et al. 2019

Mechanical Systems and Signal Processing

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“…Finally, another prognostic method developed in [13], used the particular filter. Authors use the adaptive parameter to adapt to the trend of degradation in the different ageing stages of PEMFCs.…”

Section: A Short Review On Fuel Cell Prognosismentioning

confidence: 99%

Proton Exchange Membrane Fuel Cell Model for Prognosis

Detti

Jemeï

Steiner

2018

2018 IEEE Vehicle Power and Propulsion Conference (VPPC)

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The deployment at a large-scale of fuel cells will only occur if it is sufficiently competitive with other conventional energy generation solutions. Indeed, it is essential that these new technologies can provide acceptable performance and guarantee continuity of service for users. This involves the deployment of a robust, fault tolerant and ageing resilient systems. Fuel cell systems, by their technology, have behaviors that are difficult to apprehend. The nonlinear nature of the phenomena, the reversible or non-reversible character of the degradations, and the interactions between components and parameters make the modeling of aging a difficult task [2]. It is however necessary to develop a complete ageing model of a Fuel Cell system depending on the operating conditions in order to extend the lifetime of the system by setting appropriate control. Fault and degradation tolerant control, or by setting predictive maintenance. In this work, we present a mathematical model that will be used for fuel cell system prognosis, part of a global fault resilient approach. The model developed has been validated on experimental tests. Keywords-proton exchange membrane fuel cell; prognosis; fuel cell ageing, fuel cell model.

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“…연구가 활발히 이루어지고 있다 [26][27] . 이 장에서는 그 중에서 가장 많은 연구가 이루어지고 있는 데이터 중 심 모델과 물리 기반 모델의 조합방법에 대한 내용을 소개하고자 한다 [15] .…”

Section: 하나의 모델만 이용했을 때에 발생하는 단점을 보 완하기 위해 앞서 소개된 모델들을 조합하여 각 모델 이 지니unclassified

Prognostics and Health Management for Battery Remaining Useful Life Prediction Based on Electrochemistry Model: A Tutorial

Choi¹,

Kim²

2017

The Journal of Korean Institute of Communications and Informati

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Prognostics and health management(PHM) is actively utilized by industry as an essential technology focusing on accurately monitoring the health state of a system and predicting the remaining useful life(RUL). An effective PHM is expected to reduce maintenance costs as well as improve safety of system by preventing failure in advance. With these advantages, PHM can be applied to the battery system which is a core element to provide electricity for devices with mobility, since battery faults could lead to operational downtime, performance degradation, and even catastrophic loss of human life by unexpected explosion due to non-linear characteristics of battery. In this paper we mainly review a recent progress on various models for predicting RUL of battery with high accuracy satisfying the given confidence interval level. Moreover, performance evaluation metrics for battery prognostics are presented in detail to show the strength of these metrics compared to the traditional ones used in the existing forecasting applications.

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PEM fuel cell prognostics using particle filter with model parameter adaptation

Cited by 50 publications

References 7 publications

An ensemble of models for integrating dependent sources of information for the prognosis of the remaining useful life of Proton Exchange Membrane Fuel Cells

An ensemble of models for integrating dependent sources of information for the prognosis of the remaining useful life of Proton Exchange Membrane Fuel Cells

Proton Exchange Membrane Fuel Cell Model for Prognosis

Prognostics and Health Management for Battery Remaining Useful Life Prediction Based on Electrochemistry Model: A Tutorial

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