<scp>PSO</scp>
            optimized
            <scp>PI</scp>
            controlled
            <scp>DC‐DC</scp>
            buck converter‐based proton‐exchange membrane fuel cell emulator for testing of
            <scp>MPPT</scp>
            algorithm and battery charger controller

Premkumar, K.; Vishnupriya, M.; Thamizhselvan, T.; Padmanaban, Sanjeevikumar; Manikandan, B.V.

doi:10.1002/2050-7038.12754

Cited by 19 publications

(11 citation statements)

References 25 publications

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“…which has a low implementation cost, as shown in (14). In this case, model ( 2) can be expressed as:…”

Section: Unconstrained Nonlinear Optimizationmentioning

confidence: 99%

“…The RMSE of the Diffusive Global model is 0.3648 V, for the parameters adjusted by ES, 0.7961 V, and for the Gaussian approach it is 0.3567 V. Therefore, the diffusive approach and the Gaussian model have similar predictions of the output voltage. For its low implementation cost, the exponential function of the Gaussian model can be used as shown in the approximation (14) with n = 265. Finally, Figure 8 shows the validation of both approaches, where the RMSE of the diffusive global model and the parameters adjusted by ES were 2.3273 V and 2.3116 V, respectively.…”

Section: Comparison Of Gaussian Model With the Parameter Identification By Means Of Evolution Strategymentioning

confidence: 99%

“…For different applications, an FC simulation or hardware emulation is necessary for prototype design, testing, and fault diagnosis, reducing the overall cost [11]. Existing FC simulators and emulators are based on curve fitting, cell equivalent-impedance model, and employment of artificial intelligence algorithms using a massive amount of data from a real FC battery [12][13][14][15]. Therefore, several models that describe the phenomena occurring within fuel cells have been developed [16,17].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Modeling of a Proton-Exchange Membrane Fuel Cell Using a Gaussian Approach

González-Castaño

Lorente-Leyva

Alpala³

et al. 2021

Membranes

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This paper proposes a Gaussian approach for the proton-exchange membrane fuel cell (PEMFC) model that estimates its voltage behavior from the operating current value. A multi-parametric Gaussian model and an unconstrained optimization formulation based on a conventional non-linear least squares optimizer is mainly considered. The model is tested using experimental data from the Ballard Nexa 1.2 kW fuel cell (FC). This methodology offers a promising approach for static and current-voltage, characteristic of the three regions of operation. A statistical study is developed to evaluate the effectiveness and superiority of the proposed FC Gaussian model compared with the Diffusive Global model and the Evolution Strategy. In addition, an approximation to the exponential function for a Gaussian model simplification can be used in systems that require real-time emulators or complex long-time simulations.

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“…which has a low implementation cost, as shown in (14). In this case, model ( 2) can be expressed as:…”

Section: Unconstrained Nonlinear Optimizationmentioning

confidence: 99%

Section: Comparison Of Gaussian Model With the Parameter Identification By Means Of Evolution Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Modeling of a Proton-Exchange Membrane Fuel Cell Using a Gaussian Approach

González-Castaño

Lorente-Leyva

Alpala³

et al. 2021

Membranes

View full text Add to dashboard Cite

show abstract

“…This is yet to be developed. Besides, this study 8 proposed a Particle Swarm Optimized-Proportional Integral (PSO-PI) controlled DC-DC for testing the battery charging controller and the MPPT algorithm. The results revealed that the efficiency of the proposed method is improved.…”

Section: Introductionmentioning

confidence: 99%

MPPT mechanism based on novel hybrid particle swarm optimization and salp swarm optimization algorithm for battery charging through simulink

Dagal

Akın

Akboy

2022

Sci Rep

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In this paper, a battery charging model is developed for solar PV system applications. As a means of photovoltaic power controlling system, buck-boost converter with a Maximum Power Point Tracking (MPPT) mechanism is developed in this paper for maximum efficiency. This paper proposed a novel combined technique of hybrid Particle Swarm Optimisation (PSO) and Salp Swarm Optimization (SSO) models to perform Maximum Power Point Tracking mechanisms and obtain a higher efficiency for battery charging. In order to retrieve the maximum power from the PV array, the Maximum Power Point Tracking mechanism is observed which reaches the maximum efficiency and the maximum power is fed through the buck-boost converter into the load. The buck-boost converter steps up the voltage to essential magnitude. The energy drawn from the PV array is used for the battery charging by means of an isolated buck converter since the buck-boost converter is not directly connected to the battery. The Fractional Order Proportional Integral Derivative (FOPID) controller handles the isolated buck converter and battery to enhance the efficiency obtained through the Maximum Power Point Tracking mechanism. The simulation results show higher steady efficiency by using the hybrid PSOSSO algorithm in all stages. The battery is charged without losing the efficiency obtained from the hybrid PSOSSO algorithm-based Maximum Power Point Tracking mechanism. The higher efficiency was obtained as 99.99% at Standard Test Conditions (STC) and 99.52% at PV partial shading conditions (PSCs) by using the new hybrid algorithm.

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“…An emulator was developed to check Maximum Power Point operation of PEMFC using PI controller whose gain was optimized using PSO strategy in Ref. 19. From simulation and experimental results, it was concluded that the proposed emulator was the better choice to check any optimum power tracking controller performance.…”

Section: Introductionmentioning

confidence: 99%

A variable step size fuzzy logic controller based maximum power point tracking controller for proton exchange membrane fuel cell powered resonant pulse width modulation high step up converter with multicarrier sinusoidal pulse width modulation inverter fed induction motor

Karthikeyan¹,

Sundararaju²,

Palanisamy

2021

Int Trans Electr Energ Syst

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Summary A variable step size Fuzzy logic based MPPT algorithm is proposed to ensure maximum power extraction from 1.26 kW Proton Exchange Membrane Fuel Cell (PEMFC). The Resonant PWM High Step up Converter (RPHC) is designed with soft switching capability to boost PEMFC output voltage and ensured optimum power tracking during dynamic operating condition by triggering RPHC switches with pulses generated from MPPT controller. The proposed variable step size FLC controller results are compared with conventional P&O and IC algorithm. To run a three‐phase induction motor, three‐phase Voltage source inverter connected at the output port of step up converter whose switching pattern is given by Multicarrier Sinusoidal Pulse Width Modulation (MSPWM) controller which reduces THD in output voltage and current. A detailed simulation analysis is done with MATLAB/Simulink environment and discussed the results. The hardware prototype model is implemented to validate the simulation results.

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PSO optimized PI controlled DC‐DC buck converter‐based proton‐exchange membrane fuel cell emulator for testing of MPPT algorithm and battery charger controller

Cited by 19 publications

References 25 publications

Dynamic Modeling of a Proton-Exchange Membrane Fuel Cell Using a Gaussian Approach

Dynamic Modeling of a Proton-Exchange Membrane Fuel Cell Using a Gaussian Approach

MPPT mechanism based on novel hybrid particle swarm optimization and salp swarm optimization algorithm for battery charging through simulink

A variable step size fuzzy logic controller based maximum power point tracking controller for proton exchange membrane fuel cell powered resonant pulse width modulation high step up converter with multicarrier sinusoidal pulse width modulation inverter fed induction motor

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