Dynamic fuzzy cognitive network approach for modelling and control of PEM fuel cell for power electric bicycle system

Kheirandish, Azadeh; Motlagh, Farid; Shafiabady, Niusha; Dahari, Mahidzal; Wahab, Ahmad Khairi Abdul

doi:10.1016/j.apenergy.2017.05.084

Cited by 40 publications

(11 citation statements)

References 41 publications

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“…They are designed to investigate various phenomena, such as polarization influences, catalyst employment, water management, and so forth, and have different spatial dimensions. As opposed to the white box models, black box models are obtained by means of observations and do not go through the details of physical relationships inside the PEMFC [29][30][31][32][33][34][35]. Since the computational effort of black box models is very low, they are very interesting for online applications like vehicles.…”

Section: Fig 2 Pemfc Models Categoriesmentioning

confidence: 99%

Overview and benchmark analysis of fuel cell parameters estimation for energy management purposes

Kandidayeni

Macías

Amamou

et al. 2018

Journal of Power Sources

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Proton exchange membrane fuel cells (PEMFCs) have become the center of attention for energy conversion in many areas such as automotive industry, where they confront a high dynamic behavior resulting in their characteristics variation. In order to ensure appropriate modeling of PEMFCs, accurate parameters estimation is in demand. However, parameter estimation of PEMFC models is highly challenging due to their multivariate, nonlinear, and complex essence. This paper comprehensively reviews PEMFC models parameters estimation methods with a specific view to online identification algorithms, which are considered as the basis of global energy management strategy design, to estimate the linear and nonlinear parameters of a PEMFC model in real time. In this respect, different PEMFC models with different categories and purposes are discussed first. Subsequently, a thorough investigation of PEMFC parameter estimation methods in the literature is conducted in terms of applicability. Three potential algorithms for online applications, Recursive Least Square (RLS), Kalman filter, and extended Kalman filter (EKF), which has escaped the attention in previous works, have been then utilized to identify the parameters of two well-known semi-empirical models in the literature, Squadrito et. al and Amphlett et. al. Ultimately, the achieved results and future challenges are discussed.

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Section: Fig 2 Pemfc Models Categoriesmentioning

confidence: 99%

Overview and benchmark analysis of fuel cell parameters estimation for energy management purposes

Kandidayeni

Macías

Amamou

et al. 2018

Journal of Power Sources

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“…IT2FPID presented a better performance in terms of transient response. In [24], a fuzzy cognitive map (FCM) was used to model an electric bicycle powered by a fuel cell. The Hebbian algorithm was proposed for the FCM to self-learn from its own data.…”

Section: Related Workmentioning

confidence: 99%

PEM Fuel Cell Voltage Neural Control Based on Hydrogen Pressure Regulation

et al. 2019

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Fuel cells are promising devices to transform chemical energy into electricity; their behavior is described by principles of electrochemistry and thermodynamics, which are often difficult to model mathematically. One alternative to overcome this issue is the use of modeling methods based on artificial intelligence techniques. In this paper is proposed a hybrid scheme to model and control fuel cell systems using neural networks. Several feature selection algorithms were tested for dimensionality reduction, aiming to eliminate non-significant variables with respect to the control objective. Principal component analysis (PCA) obtained better results than other algorithms. Based on these variables, an inverse neural network model was developed to emulate and control the fuel cell output voltage under transient conditions. The results showed that fuel cell performance does not only depend on the supply of the reactants. A single neuro-proportional–integral–derivative (neuro-PID) controller is not able to stabilize the output voltage without the support of an inverse model control that includes the impact of the other variables on the fuel cell performance. This practical data-driven approach is reliably able to reduce the cost of the control system by the elimination of non-significant measures.

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“…The results demonstrated that type-2 FLC can be widely adopted for performing energy management. In the work of Kheirandish et al [20], a dynamic fuzzy cognitive network is proposed to describe the behaviour of a fuel cell electric bicycle. However, such FL-based systems are established based on human cognition, and their performance is largely limited by empirical knowledge.…”

Section: Introductionmentioning

confidence: 99%

Back-to-Back Competitive Learning Mechanism for Fuzzy Logic Based Supervisory Control System of Hybrid Electric Vehicles

Zhou

Williams

et al. 2020

IEEE Trans. Ind. Electron.

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Dynamic fuzzy cognitive network approach for modelling and control of PEM fuel cell for power electric bicycle system

Cited by 40 publications

References 41 publications

Overview and benchmark analysis of fuel cell parameters estimation for energy management purposes

Overview and benchmark analysis of fuel cell parameters estimation for energy management purposes

PEM Fuel Cell Voltage Neural Control Based on Hydrogen Pressure Regulation

Back-to-Back Competitive Learning Mechanism for Fuzzy Logic Based Supervisory Control System of Hybrid Electric Vehicles

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