Polymer electrolyte membrane fuel cell fault diagnosis and sensor abnormality identification using sensor selection method

Mao, Lei; Jackson, Lisa M.; Huang, Weiguo; Li, Zhinong; Davies, Ben

doi:10.1016/j.jpowsour.2019.227394

Cited by 27 publications

(17 citation statements)

References 39 publications

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“…Through accurate fault diagnosis, relevant personnel can take corresponding measures to prevent more serious faults. At present, many scholars have done a lot of research on fault diagnosis methods of the PEMFC system [1,[3][4][5][6][7][8][9][10][11][12][13][14]. The proposed methods can be mainly categorized into two types [4]-model-based methods [3,[5][6][7][8][9][10] and data-driven methods [1,4,[11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification

2021

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Data-driven diagnosis methods for faults of proton exchange membrane fuel cell (PEMFC) systems can diagnose faults through the state variable data collected during the operation of the PEMFC system. However, the state variable data collected from the PEMFC system during the stack switching between different operating points can easily cause false alarms, such that the practical value of the diagnosis system is reduced. To overcome this problem, a fault diagnosis method for PEMFC systems based on steady-state identification is proposed in this paper. The support vector data description (SVDD) and relevance vector machine (RVM) optimized by the artificial bee colony (ABC) are used for the steady-state identification and fault diagnosis. The density-based spatial clustering of applications with noise (DBSCAN) and linear least squares fitting (LLSF) are used to identify the abnormal data in datasets and estimate change rates of the system state variables respectively. The proposed method can automatically identify the state variable data collected from the PEMFC system during the stack switching between different operating points, so that the diagnosis accuracy can be improved and false alarms can be reduced. The proposed method has a certain practical value and can provide a reference for further study.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification

2021

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“…In particular, the proton exchange membrane fuel cell (PEMFC) has attracted the attention of researchers in the last few decades due to its characteristics as low operating temperature, low noise, quick start-up capability, light mass, and high-power density [2,4,6,7]. The PEMFCs have recently passed the test phase and have slightly reached the commercialization stage due to the impressive research effort [8].…”

Section: Introductionmentioning

confidence: 99%

“…The PEMFCs have recently passed the test phase and have slightly reached the commercialization stage due to the impressive research effort [8]. However, the two biggest limitations preventing the PEMFC system from further commercialization are its reliability and durability [7].…”

Section: Introductionmentioning

confidence: 99%

“…A lot of studies on PEMFC performance have been carried out, since three-dimensional simulation models to more detailed measurement techniques, such as electrochemical impedance spectroscopy [9,10]. To have a PEMFC diagnosis, the fault detection method has been used commonly to guarantee correct and safe operation in the PEMFC system [7,11,12]. However, to achieve such a diagnosis, several sensors have been used to measure different parameters like the mass flow, oxygen pressure, hydrogen pressure, compressor velocity, electrical current, water pressure, voltage, and temperature of the stack [11,13,14].…”

Section: Introductionmentioning

confidence: 99%

“…However, in real applications, the reliability of sensors during the system operation is variable. Thus, inaccurate sensor measurements can provide misleading results in PEMFC fault diagnosis, which can end in failures and damages of the PEMFC system [7,11]. To solve this problem, novel methods have been proposed to reduce errors in PEMFC fault diagnosis [9,10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of an Oxygen Pressure Estimator Using the Immersion and Invariance Method for a Particular PEMFC System

2020

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The fault detection method has been used usually to give a diagnosis of the performance and efficiency in the proton exchange membrane fuel cell (PEMFC) systems. To be able to use this method a lot of sensors are implemented in the PEMFC to measure different parameters like pressure, temperature, voltage, and electrical current. However, despite the high reliability of the sensors, they can fail or give erroneous measurements. To address this problem, an efficient solution to replace the sensors must be found. For this reason, in this work, the immersion and invariance method is proposed to develop an oxygen pressure estimator based on the voltage, electrical current density, and temperature measurements. The estimator stability region is calculated by applying Lyapunov’s Theorem and constraints to achieve stability are established for the oxygen pressure, electrical current density, and temperature. Under these estimator requirements, oxygen pressure measurements of high reliability are obtained to fault diagnosis without the need to use an oxygen sensor.

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A Review of Polymer Electrolyte Fuel Cells Fault Diagnosis: Progress and Perspectives

Zhou,

Jervis

2023

Chemistry Methods

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Polymer electrolyte fuel cells (PEFCs) are regarded as a substitution for the combustion engine with high energy conversion efficiency and zero CO2 emissions. Stable system operation requires control within a relatively narrow range of operating conditions to achieve the optimal output, leading to faults that can easily cause accelerated degradation when operating conditions deviate from the control targets. Performance recovery of the system can be realized through early fault diagnosis; therefore, accurate and effective diagnostic characterisation is vital for long‐term serving. A review of off‐line and on‐line techniques applied to the fault diagnosis of fuel cells is presented in this work. Off‐line approaches include electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), galvanostatic charge (GSC), visualisation‐based and image‐based techniques; the on‐line methods can be divided into model‐based, data‐driven, signal‐based and hybrid methods. Since each methodology has advantages and drawbacks, its effectiveness is analysed, and limitations are highlighted.

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Polymer electrolyte membrane fuel cell fault diagnosis and sensor abnormality identification using sensor selection method

Cited by 27 publications

References 39 publications

Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification

Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification

Development of an Oxygen Pressure Estimator Using the Immersion and Invariance Method for a Particular PEMFC System

A Review of Polymer Electrolyte Fuel Cells Fault Diagnosis: Progress and Perspectives

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