Behavior PEM fuel cell for 4WD electric vehicle under different scenario consideration

Gasbaoui, Brahim; Nasri, Abdelfatah; Abdelkhalek, Othmane; Ghouili, Jamel; Ghezouani, Abdelkader

doi:10.1016/j.ijhydene.2016.08.114

Cited by 16 publications

(3 citation statements)

References 5 publications

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“…By using the DTC scheme [14], there are two hysteresis comparators receivethe error of the electromagnetic torque and flux signals. The corresponding output variables and the stator flux position sector are used to select the appropriate voltage vector from a switching table that generates pulses to control the inverter power switches [15,16].…”

Section: The Classical Direct Torque Control Dtcmentioning

confidence: 99%

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Impact of sensorless neural direct torque control in a fuel cell traction system

Aissa¹,

Hamza²,

Guettaf

et al. 2021

IJECE

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<span>Due to the reliability and relatively low cost and modest maintenance requirement of the induction machine make it one of the most widely used machines in industrial applications. The speed control is one of many problems in the traction system, researchers went to new paths instead the classical controllers as PI controller, they integrated the artificial intelligent for its yield. The classical DTC is a method of speed control by using speed sensor and PI controller, it achieves a decoupled control of the electromagnetic torque and the stator flux in the stationary frame, besides, the use of speed sensors has several drawbacks such as the fragility and the high cost, for this reason, the specialists went to propose an estimators as Kalman filter. The fuel cell is a new renewable energy, it has many applications in the traction systems as train, bus. This paper presents an improved control using DTC by integrate the neural network strategy without use speed sensor (sensorless control) to reduce overtaking and current ripple and static error in the system because the PI controller has some problems like this; and reduce the cost with use a renewable energy as fuel cell.</span>

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Section: The Classical Direct Torque Control Dtcmentioning

confidence: 99%

“…The neuron is the basic element of an artificial neural network which has a summer and an activation function [26] as shown in Figure 3 The model of a neuron is given by: For ensure best result and fast convergence it must to use the algorithm equation [27]. The main algorithm equations of the neural network as ( 13), (14).…”

Section: Neural Network Principlementioning

confidence: 99%

Impact of sensorless neural direct torque control in a fuel cell traction system

Aissa¹,

Hamza²,

Guettaf

et al. 2021

IJECE

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“…Although electric all-wheel drive vehicles have been a hot topic recently [1][2][3][4][5], the majority of the market share still vests in traditional mechanical AWD vehicles. The last few years have witnessed the evolution of the three generations of EMTC, the high fuel economy and excellent AWD performance make it widely used in many AWD cars, see Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic Hysteresis Based Dynamics Model of an Electromagnetically Controlled Torque Coupling

Feng

Chen

et al. 2019

Processes

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This paper proposes a novel computationally efficient, easy-to-implement electromagnetic hysteresis based dynamics model of a kind of intelligent electromagnetic torque controlled coupling (EMTC), which, with drag torque under consideration, first models the electromagnetic hysteresis existing in the primary clutch with the classical Preisach model, and then models the transferred torques in the three friction elements of the center coupling in the slipping and locked modes, respectively. The performance of the model is verified by simulation and experiment jointly, which lays the basis for the development of advanced control algorithm.

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Improved performance of PEM fuel cells stack feeding switched reluctance motor using multi-objective dragonfly optimizer

El-Hay

El‐Hameed

El‐Fergany

2018

Neural Comput & Applic

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Behavior PEM fuel cell for 4WD electric vehicle under different scenario consideration

Cited by 16 publications

References 5 publications

Impact of sensorless neural direct torque control in a fuel cell traction system

Impact of sensorless neural direct torque control in a fuel cell traction system

Electromagnetic Hysteresis Based Dynamics Model of an Electromagnetically Controlled Torque Coupling

Improved performance of PEM fuel cells stack feeding switched reluctance motor using multi-objective dragonfly optimizer

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