Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel Cell systems

Polverino, Pierpaolo; Frisk, Erik; Jung, Daniel; Krysander, Mattias; Pianese, Cesare

doi:10.1016/j.jpowsour.2017.04.089

Cited by 52 publications

(17 citation statements)

References 39 publications

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“…When a device in the EV runs outside of standard conditions, a fault occurs. This flaw will lead to a loss of dynamic stability or costly breakdown 263,264 . Therefore, it is critical to monitor the operation to ensure the safety and stability of vehicles.…”

Section: Issues and Challengesmentioning

confidence: 99%

Optimization and cutting‐edge design of fuel‐cell hybrid electric vehicles

Luo

et al. 2021

Int J Energy Res

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Summary The transportation sector consumes a large amount of fossil fuels consequently exacerbating the global environmental and energy crisis. Fuel‐cell hybrid electric vehicles (FCHEVs) are promising alternatives in the continuous transition to clean energy. This article summarizes the recent advances pertaining to the optimization and cutting‐edge design of fuel‐cell hybrid electric vehicles, especially the fuel cell + battery hybrid topology, and discusses current technological bottlenecks hindering the commercialization of FCHEVs. The development of HEVs, markets, environmental and economic benefits, components, topologies, energy management strategies, degradation mechanisms, and safety standards of FCHEVs are reviewed. Proton exchange membrane fuel cells constitute the mainstream and most mature fuel cell technology for automobile applications. Battery hybridization is currently favored among the available FCHEV topological designs to improve the dynamic response and recover the braking energy. Energy management strategies encompassing logic rule‐based simple methods, intelligent control methods, global optimization strategies, and local optimization strategies are described, and issues and challenges encountering FCHEVs are discussed. In addition to promoting the construction of hydrogen supply facilities, future efforts are expected to focus on solving problems such as the high cost, durability of fuel cells, cold start, lifetime of batteries, security and comfort, system optimization, energy management systems, integration, and diagnosis of faults. This review serves as a reference and guide for future technological development and commercialization of FCHEVs. Highlights Advances of the optimization and cutting‐edge design of FCHEVs are reviewed. Battery hybridization is currently favored among the available topological designs. Benefits, components, topologies, and energy management strategies are described. Markets, degradation mechanisms, and safety standards of FCHEVs are introduced. Technological bottlenecks hindering the commercialization of FCHEVs are discussed.

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Section: Issues and Challengesmentioning

confidence: 99%

Optimization and cutting‐edge design of fuel‐cell hybrid electric vehicles

Luo

et al. 2021

Int J Energy Res

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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%

“…It can be seen from [1,[3][4][5][6][7][8][9][10][11][12][13][14] that the model-based fault diagnosis method can be used to diagnose and isolate the different faults for fuel cell systems. However, because of the complex structure of PEMFC systems, it is difficult to obtain the parameters of some key components or materials.…”

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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“…Currently, various diagnostic approaches are being used [13,14] which can broadly be split into data-based and modelbased methods. In model-based approaches [15][16][17][18], the model is the outcome of the comprehensive understanding of the stack and its inner phenomena which are expressed as to relations of different natures (electrochemical, thermodynamic, thermal, electrical and fluidic). In this approach, an on-line comparison between the real behavior of the stack, and the simulation of the dynamic model is conducted.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Isolation for Proton Exchange Membrane Fuel Cell Using Impedance Measurements and Multiphysics Modeling

et al. 2020

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This study proposes a fault detection and isolation tool for proton exchange membrane fuel cell (PEMFC) operating in embedded applications. A model-based approach, taking partially into account degradation phenomena, is proposed in order to increase the robustness of the tool regarding transient operations and stack ageing. The considered faults are the abnormal operating conditions that can decrease the fuel cell lifetime. The fault detection approach is based on residual generation using both voltage and high frequency resistance measurements and thus combining the advantages of knowledge-based model and electrochemical impedance spectroscopy (EIS) diagnosis approaches. To that end, a multi-physics fuel cell model has been used. This model computes not only the stack voltage but also the high frequency resistance in dynamic conditions. Additionally, the model is modified to take into account the ageing of the fuel cell. Validation is carried out on experimental characterizations during 1,000 h ageing. The results on a new fuel cell stack show a score of 91% for fault isolation. However, without any adaptation, this score drops dramatically as the stack ages. Finally, thanks to ageing modeling and to the proposed adaptation of the detection/isolation procedure, the diagnosis performance remains reliable during fuel cell stack ageing.

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Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel Cell systems

Cited by 52 publications

References 39 publications

Optimization and cutting‐edge design of fuel‐cell hybrid electric vehicles

Optimization and cutting‐edge design of fuel‐cell hybrid electric vehicles

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

Fault Detection and Isolation for Proton Exchange Membrane Fuel Cell Using Impedance Measurements and Multiphysics Modeling

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