Dynamic modelling of PEM fuel cell of power electric bicycle system

Kheirandish, Azadeh; Motlagh, Farid; Shafiabady, Niusha; Dahari, Mahidzal

doi:10.1016/j.ijhydene.2016.02.046

Cited by 35 publications

(13 citation statements)

References 33 publications

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“…In this regard, some manuscripts are based on artificial neural networks (ANNs) employment [80][81][82][83][84]. linear regression technique, which uses gradient descent algorithms for updating the parameters, is compared with an ANN approach, which uses Levenberge-Marquardt algorithm for training, to model a 250-W PEMFC for an electric bicycle application in [80], and is concluded that ANN model benefits from more accuracy as well as convenience in modeling. In [81], two neural structures of nonlinear auto regressive with exogenous input (NARX) and nonlinear output error (NOE) are utilized to develop a PEMFC stack voltage model and NARX is recommended for real time applications while NOE is suggested for off-line applications.…”

Section: Black Box Based Identificationmentioning

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: Black Box Based Identificationmentioning

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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“…Many other research efforts strengthen and ensure the hydrogen and fuel cells field like the study of dynamic modeling of a cell system on bicycle in the work of Kheirandish et al 2016 [13].…”

Section: Fuel Cell Vehicle Technology Description and State Of The Artmentioning

confidence: 99%

“…[13] permit to calculate the amount of reacted hydrogen and the amount of the water produced, they are illustrated respectively in Equations (7) and (8):…”

Section: Parameters Units Valuesmentioning

confidence: 99%

Flows consumption assessment study for fuel cell vehicles: Towards a popularization of FCVs technology

Abderezzak

Busawon

Binns

2017

International Journal of Hydrogen Energy

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Energy management Hydrogen consumption Car autonomyRoad profile a b s t r a c t Climate change can be caused by a major part from the high fossil fuel usage and consumption in transportation field. It contributes to the increase of pollutant emissions, which lead to serious problems on human health in addition to the environmental degradation phenomena. Hydrogen fuel cell vehicles (FCVs) are expected to have a significant impact in meeting both energy security and environmental concerns globally.Starting for the premise that public acceptance and attitudes studies were generally positive towards hydrogen and fuel cells vehicles, even if the public knows few things about this technology; authors then got the idea to present a simplified scientific work dealing with the description of the energy management and flows calculations on board FCVs. This work aims not only to the popularization of this technology but also to outreach people about its sustainable character. A variable driving profile is adopted with a total distance of 1 km with duration of 60 s. The total hydrogen amount consumed is 1,34 g km À1. Under pressure, only 5 kg of hydrogen give optimal autonomy of 700 km, which is really competitive to the conventional gasoline cars. A nice advantage is yet observed and its concerns the environmental profits.Crown

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“…Black-box approach is widely employed to model fuel cells. For instance, feedforward neural networks [5], recurrent neural networks [6], nonlinear autoregressive with exogenous input (NARX) [7], and radial basis function artificial neural networks [8] have been presented. Also, fuzzy logic control (FLC) as an adaptive neuro-fuzzy inference system (ANFIS) has been utilized in [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Simultaneously Parameter Identification and Measurement-Noise Covariance estimation of a Proton Exchange Membrane Fuel Cell

Ghaderi

Daeichian

2019

2019 6th International Conference on Control, Instrumentation and Automation (ICCIA)

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This paper proposes the online parameters identification of semi-empirical models of Proton Exchange Membrane Fuel Cell (PEMFC). The covariance of unknown measurement noise is also estimated simultaneously. The actual data are fed to Kalman (for linear-in-parameters models) or extended Kalman filter (for nonlinear ones) which have been adapted for parameter identification. These filters suffer from the fact that the noise of the measurements is unknown. In order to tackle this conundrum, the measurement-noise is simultaneously estimated, and the estimation is used in the filters. The ultimate consequence of estimating measurement-noise is error reduction which has been demonstrated by simulation results.

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Dynamic modelling of PEM fuel cell of power electric bicycle system

Cited by 35 publications

References 33 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

Flows consumption assessment study for fuel cell vehicles: Towards a popularization of FCVs technology

Simultaneously Parameter Identification and Measurement-Noise Covariance estimation of a Proton Exchange Membrane Fuel Cell

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