Performance evaluation of fuel cell fed electric vehicle system with reconfigured quadratic boost converter

Kumar, K.; Tiwari, Ramji; Varaprasad, P Venkata; Babu, Challa; Reddy, K. Jyotheeswara

doi:10.1016/j.ijhydene.2020.11.272

Cited by 26 publications

(4 citation statements)

References 21 publications

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“…From Equations ( 25) and ( 36)- (38), the current stress across the diodes, and switch is determined as follows:…”

Section: Proposed Converter Under Continuous Conduction Mode Of Opera...mentioning

confidence: 99%

A novel on high voltage conversion ratio DC‐DC converter for EV‐fed fuel cell application with hybrid MPPT controller

Eswaraiah,

Balakrishna

2023

Circuit Theory & Apps

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SummaryAs of now, nonrenewable energy sources are not used for automotive industrial applications because their disadvantages are high installation costs, more maintenance costs, and less adaptability all over the places in the world. Also, these sources produce highly flammable gasses in the environment. So, in this work, a Proton Exchange Membrane Fuel Stack (PEMFS) is used as an input source for the proposed system. The PEMFS generated voltage value is very low which is improved by integrating the proposed Single Switch High Voltage Conversion Ratio (SSHVCR) DC‐DC converter. The properties of this SSHVCR converter are less component utilization for reducing the ripple ratio, more flexibility, less voltage stress, and high voltage conversion ratio. In the second objective, a Differential Step‐Grey Wolf Optimization (DSGWO) with Adaptive Fuzzy Logic (AFL) controller is used for generating switching pulses to the DC‐DC converter. The merits of DSGWO‐AFL controller are quick dynamic response, moderate design complexity, good adaptability, and needed few number iterations for identifying the MPP of PEMFS. The overall proposed system is studied by applying the MATLAB/Simulink tool at rapid changes in the operating temperature of the fuel cell stack.

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“…From Equations ( 25) and ( 36)- (38), the current stress across the diodes, and switch is determined as follows:…”

Section: Proposed Converter Under Continuous Conduction Mode Of Opera...mentioning

confidence: 99%

A novel on high voltage conversion ratio DC‐DC converter for EV‐fed fuel cell application with hybrid MPPT controller

Eswaraiah,

Balakrishna

2023

Circuit Theory & Apps

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“…To control the boost converter with high static and dynamic performances, we represent the system of state (23) in the form of a closed loop. From this state equation we designed a control system based on the linear quadratic regulator technique which allows the DC-DC converter to operate without disturbances and oscillations and which gives great stability for different characteristics (current and voltage).…”

Section: System Descriptionmentioning

confidence: 99%

Improvement of the linear quadratic regulator control applied to a DC-DC boost converter driving a permanent magnet direct current motor

Bouchahed,

Assabaa,

Draidi

et al. 2023

IJECE

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This article discusses a new robust control technique that enables the DC-DC boost converter driving a permanent magnet direct current (PMDC) motor to operate in high static and dynamic performances. The new technique is based on the design of a both linear quadratic regulator (LQR) and linear quadratic regulator-proportional integral (LQR-PI) type controllers, which have the advantage of eliminating oscillations, overshoots and fluctuations on different characteristics in steady-state system operation. In order to increase the output voltage, the LQR regulator is combined with a first-order system represented in the form of a closed-loop transfer function, the latter raising the output voltage to 24 volts, this voltage is enough to drive the permanent magnet direct current motor. The contribution of this paper is the creation of a robust control system represented in the form of a hybrid corrector able to regulate steady-state and transient disturbances and oscillations as well as to increase DC-DC boost converter output voltage for the PMDC motor to operate at rated voltage. The results of the three control techniques are validated by MATLAB Simulink.

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“…Elman Neural Network. In 1990, ElmanNN was proposed based on the BP neural network (BPNN) by Jeffery L. ElmanNN is a typical dynamic recurrent neural network, which consists of the input layer, hidden layer, and connection layer (state layer) as well as output layer [24]. Figure 6 shows the basic structure of ElmanNN.…”

Section: System Structure and Proposed Algorithmmentioning

confidence: 99%

A Firefly Algorithm and Elite Ant System-Trained Elman Neural Network for MPPT Algorithm of PV Array

Zhang

Wang

et al. 2022

International Journal of Photoenergy

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This article proposes a novel MPPT algorithm based on the firefly algorithm and elite ant system-trained Elman neural network (FA-EAS-ElmanNN). First, the position of fireflies is randomly initialized by the firefly algorithm (FA), meanwhile the firefly individuals with higher attractiveness degree value are selected as the optimal solution. Second, the extra pheromones are artificially released to boost the positive feedback effect and convergence rate of the elite ant system (EAS). Third, the weight and threshold of the Elman neural network (ElmanNN) are updated by the FA and EAS. Also, the trained ElamnNN is employed to acquire the maximum voltage of the photovoltaic (PV) array. At last, the PID controller and PWM technology are adapted to regulate the switch time of the boost converter. Furthermore, MATLAB/Simulink is adopted to acquire the datasets of irradiance, temperature, and maximum voltage and validate the reliability and superiority of the proposed algorithm under complex atmospheric conditions. The tracking characteristic, response speed, and efficiency of the proposed MPPT algorithm are contrasted with the particle swarm optimization (PSO), ant colony optimization (ACO), ACO-artificial neural network (ACO-ANN), and PSO-RBF neural network (PSO-RBNFNN) algorithm via simulation. The efficiency of the FA-EAS-ElmanNN algorithm is 99.73%, compared with the ACO-ANN, PSO-RBFNN, PSO, and ACO algorithm, which is increased by 0.49%, 0.58%, 1.2% %, and 1.5%, respectively. Additionally, the experimental setup is built to demonstrate the tracking characteristic of the proposed MPPT algorithm.

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Performance evaluation of fuel cell fed electric vehicle system with reconfigured quadratic boost converter

Cited by 26 publications

References 21 publications

A novel on high voltage conversion ratio DC‐DC converter for EV‐fed fuel cell application with hybrid MPPT controller

A novel on high voltage conversion ratio DC‐DC converter for EV‐fed fuel cell application with hybrid MPPT controller

Improvement of the linear quadratic regulator control applied to a DC-DC boost converter driving a permanent magnet direct current motor

A Firefly Algorithm and Elite Ant System-Trained Elman Neural Network for MPPT Algorithm of PV Array

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