Intelligent health states recognition of fuel cell by cell voltage consistency under typical operating parameters

Pang, Ran; Zhang, Caizhi; Dai, Haifeng; Bai, Yunfeng; Hao, Dong; Chen, Jinrui; Zhang, Bin

doi:10.1016/j.apenergy.2021.117735

Cited by 18 publications

(7 citation statements)

References 49 publications

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“…Consequently, impedance spectra serve as effective input parameters for predicting the state of health of FC. However, so far, authors have not defined or mathematically expressed the definition of SOH in FC, but rather presented several parameters, as will be explained further on, to benchmark the actual state with past states [58,59]. Additionally, non-invasive methods have been explored, involving the measurement of the magnetic field generated by induced current changes within a stack to detect faults in a PEMFC stack.…”

Section: State Of Health (Soh)mentioning

confidence: 99%

“…The general aspect of FC State of Health is presented in Figure 8. explained further on, to benchmark the actual state with past states [58,59]. Additionally, non-invasive methods have been explored, involving the measurement of the magnetic field generated by induced current changes within a stack to detect faults in a PEMFC stack.…”

Section: State Of Health (Soh)mentioning

confidence: 99%

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A Comprehensive Review on Condition Monitoring and Fault Diagnosis in Fuel Cell Systems: Challenges and Issues

Andrade,

Laadjal,

Alcaso

et al. 2024

Energies

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The complexity of Fuel Cell (FC) systems demands a profound and sustained understanding of the various phenomena occurring inside of it. Thus far, FCs, especially Proton Exchange Membrane Fuel Cells (PEMFCs), have been recognized as being among the most promising technologies for reducing Green House Gas (GHG) emissions because they can convert the chemical energy bonded to hydrogen and oxygen into electricity and heat. However, their efficiency remains limited. To enhance their efficiency, two distinct factors are suggested. First, the quality of materials plays a significant role in the development of more robust and efficient FCs. Second, the ability to identify, mitigate, and reduce the occurrence of faults through the use of robust control algorithms is crucial. Therefore, more focused on the second point, this paper compiles, distinguishes, and analyzes several publications from the past 25 years related to faults and their diagnostic techniques in FCs. Furthermore, the paper presents various schemes outlining different symptoms, their causes, and corresponding fault algorithms.

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Section: State Of Health (Soh)mentioning

confidence: 99%

Section: State Of Health (Soh)mentioning

confidence: 99%

A Comprehensive Review on Condition Monitoring and Fault Diagnosis in Fuel Cell Systems: Challenges and Issues

Andrade,

Laadjal,

Alcaso

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…Neural networks are the most common methods for supervised learning [3,4]. Fuzzy mathematics [5] and decision trees [6] are both satisfactory classification methods. Many researchers have improved the multi-objective particle swarm optimization (PSO) algorithm [7] and the Bayesian classifier [8] to achieve higher accuracy of classification.…”

Section: Caizhi Zhang and Weifeng Huang Have Contributed Equally To T...mentioning

confidence: 99%

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

et al. 2023

Self Cite

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Clustering is an unsupervised learning technology, and it groups information (observations or datasets) according to similarity measures. Developing clustering algorithms is a hot topic in recent years, and this area develops rapidly with the increasing complexity of data and the volume of datasets. In this paper, the concept of clustering is introduced, and the clustering technologies are analyzed from traditional and modern perspectives. First, this paper summarizes the principles, advantages, and disadvantages of 20 traditional clustering algorithms and 4 modern algorithms. Then, the core elements of clustering are presented, such as similarity measures and evaluation index. Considering that data processing is often applied in vehicle engineering, finally, some specific applications of clustering algorithms in vehicles are listed and the future development of clustering in the era of big data is highlighted. The purpose of this review is to make a comprehensive survey that helps readers learn various clustering algorithms and choose the appropriate methods to use, especially in vehicles.

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“…It is noted that Equations ( 12) and ( 13) are converted from the third order Padé approximation of the transfer functions for Equation (1). 43 Furthermore, it gives a line combination relationship between the output variables y and the system states vector x ¼ x 1 , x 2 , x 3 ½ T , and the coefficient matrices A, B, C are shown below.…”

Section: The Simplified Solid-phase Diffusionsmentioning

confidence: 99%

“…As global warming intensifies, several countries including China have adopted some energy transition policies to promote carbon neutrality, which makes the battery energy storage systems (BESS) develop rapidly in electric vehicles (EVs), 1 smart grid technologies, 2 and renewable energy-based power generation. 3 Lithiumion batteries (LIBs), as one of the most promising BESS, have received wide attention in the development of traffic electrification because of their advantages in the application of EVs.…”

Section: Introductionmentioning

confidence: 99%

Low‐complexity state of charge and anode potential prediction for lithium‐ion batteries using a simplified electrochemical model‐based observer under variable load condition

Pang

Geng

et al. 2022

Intl J of Energy Research

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The accurate knowledge of the physics-based state of charge (SOC) and anode potential for lithium-ion batteries (LIBs) plays an essential role in the driving range prediction and charge strategy optimization of electric vehicles (EVs). However, the SOC estimation based on empirical equivalent circuit models and the lack of anode potential information makes it challenging in developing advanced battery management systems for EVs. For this reason, this paper proposes a low-complexity SOC and anode potential prediction method for LIBs using a simplified electrochemical model (SEM)-based observer under variable load condition. First, based on the Padé approximation and volume average method, a reduced-order SEM is proposed and verified. Then, a lowcomplexity proportional-integral-differential observer framework incorporating the SEM is developed to obtain the physics-based SOC and anode potential.

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Intelligent health states recognition of fuel cell by cell voltage consistency under typical operating parameters

Cited by 18 publications

References 49 publications

A Comprehensive Review on Condition Monitoring and Fault Diagnosis in Fuel Cell Systems: Challenges and Issues

A Comprehensive Review on Condition Monitoring and Fault Diagnosis in Fuel Cell Systems: Challenges and Issues

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

Low‐complexity state of charge and anode potential prediction for lithium‐ion batteries using a simplified electrochemical model‐based observer under variable load condition

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