How fuzzy logic can improve PEM fuel cell MPPT performances?

Harrag, Abdelghani; Messalti, Sabir

doi:10.1016/j.ijhydene.2017.04.093

Cited by 97 publications

(31 citation statements)

References 12 publications

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“…Hence, MPPT controllers are necessary for both PV and PEMFC systems to extract the maximum power. In the literature, some researchers have presented various MPPT techniques for both PV and PEMFC systems such as perturb & observation (P&O) [15,16], incremental conductance (IC) [17,18], fuzzy logic controller (FLC) [19][20][21], sliding mode controller (SMC) [22,23], particle swarm optimization (PSO) [24,25], hysteresis controller (HC) [26], etc. The output voltages of the PV and PEMFC systems are given to the DC-DC boost converters to boost the voltage.…”

Section: Nomenclaturementioning

confidence: 99%

Design and Analysis of a Hybrid PV-PEMFCSystem with MPPT Controllerfor a Three-Phase Grid-Connected System

Reddy

Sudhakar

2018

JGE

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The renewable energy sources (RES) are stochastic in nature and requires an energy storage system to reduce power fluctuations. In this work, a three-phase grid connected hybrid renewable energy system (HRES) is designed with solar PV and fuel cell. A new radial basis function network (RBFN) based maximum power point tracking (MPPT) controller strategy is designed for fuel cell and a fuzzy logic controller (FLC) is used for solar PV to extract the maximum power at different PEMFC temperatures and solar irradiation levels. In addition to this, a high step-up DC-DC boost converter (HSBC) is designed for fuel cell in order to provide high step-up voltage. The proposed hybrid energy system is designed by considering a 1.26kW proton exchange membrane fuel cell (PEMFC) with HSBC and a 950W solar PV system with conventional boost converter. The proposed system is simulated in MATLAB/Simulink platform to analyse the performance of the system.

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Section: Nomenclaturementioning

confidence: 99%

Design and Analysis of a Hybrid PV-PEMFCSystem with MPPT Controllerfor a Three-Phase Grid-Connected System

Reddy

Sudhakar

2018

JGE

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“…In general, a typical PEMFC stack displays a nonlinear output power because of internal limitations and operating parameters which include the temperature, hydrogen and oxygen partial pressures, humidity levels, gas speed and stoichiometry, and membrane water content, leading to a reduced system performance [1,12,13]. It is crucial to extract as much power as possible from the stack knowing that at any operating condition, there is only one Maximum Power Point.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Rule-Based and Particle Swarm Optimisation MPPT Techniques for a Fuel Cell Stack

Luta

Raji

2019

Energies

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The negative environmental impact and the rapidly declining reserve of fossil fuel-based energy sources for electricity generation is a big challenge to finding sustainable alternatives. This scenario is complicated by the ever-increasing world population growth demanding a higher standard of living. A fuel cell system is able to generate electricity and water with higher energy efficiency while producing near-zero emissions. A common fuel cell stack displays a nonlinear power characteristic as a result of internal limitations and operating parameters such as temperature, hydrogen and oxygen partial pressures and humidity levels, leading to a reduced overall system performance. It is therefore important to extract as much power as possible from the stack, thus hindering excessive fuel use. This study considers and compares two Maximum Power Point Tracking (MPPT) approaches; one based on the Mamdani Fuzzy Inference System and the other on the Particle Swarm Optimisation (PSO) algorithm to maintain the output power of a fuel cell stack extremely close to its maximum. To ensure that, the power converter interfaced to the fuel cell unit must be able to continuously self-modify its parameters, hence changing its voltage and current depending upon the Maximum Power Point position. While various methods exist for Maximum Power Point tracker design, this paper analyses the response characteristics of a Mamdani Fuzzy Inference Engine and the Particle Swarm Optimisation technique. The investigation was conducted on a 53 kW Proton Exchange Membrane Fuel Cell interfaced to a DC-to-DC boost converter supplying 1.2 kV from a 625 V input DC voltage. The modelling was accomplished using a Matlab/Simulink environment. The results showed that the MPPT controller based on the PSO algorithm presented better tracking efficiency as compared to the Mamdani controller. Furthermore, the rise time of the PSO controller was slightly shorter than the Mamdani controller and the overshoot of the PSO controller was 2% lower than that of the Mamdani controller.

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“…However, some oscillations are accrued in the electrical characteristics, which may cause serious life-shortening and severe cell deterioration. In [24], chattering reduction of 70.93% is achieved using an advanced variable step-size fuzzy logic control (FLC). Kim [25] and Samosir [26] used a dynamic evolution control (DEC) to achieve an accurate duty cycle control for energy storage in a hybrid fuel cell system.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Implementation of a Super Twisting Algorithm for PEM Fuel Cell Power System

et al. 2019

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Proton exchange membrane fuel cell (PEMFC) topology is becoming one of the most reliable and promising alternative resource of energy for a wide range of applications. However, efficiency improvement and lifespan extension are needed to overcome the limited market of fuel cell technologies. In this paper, an efficient approach based on a super-twising algorithm (STA) is proposed for the PEMFC system. The control objective is to lengthen the fuel cell lifetime by improving its power quality, as well as to keep the system operating at an optimal and efficient power point. The algorithm adjusts the PEMFC operating point to the optimum power by tuning the duty cycle of the boost converter. The closed-loop system includes the Heliocentris hy-ExpertTM PEMFC, DC–DC boost converter, DSPACE DS1104, dedicated PC, and a programmable electronic load. The practical implementation of the proposed STA on a hardware setup is performed using a dSPACE real-time digital control platform. The data acquisition and the control system are conducted together with the dSPACE 1104 controller board. To demonstrate the performance of the proposed algorithm, experimental results are compared with 1-order sliding mode control (SMC) under different load resistance. The obtained results demonstrate the validity of the proposed control scheme by ensuring at least 72% of the maximum power produced by PEMFC. In addition, it is proven that the STA ensures all the fundamental properties of the 1-order SMC, as well as providing chattering reduction of 91%, which will ameliorate as a consequence the fuel cell lifetime.

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How fuzzy logic can improve PEM fuel cell MPPT performances?

Cited by 97 publications

References 12 publications

Design and Analysis of a Hybrid PV-PEMFCSystem with MPPT Controllerfor a Three-Phase Grid-Connected System

Design and Analysis of a Hybrid PV-PEMFCSystem with MPPT Controllerfor a Three-Phase Grid-Connected System

Fuzzy Rule-Based and Particle Swarm Optimisation MPPT Techniques for a Fuel Cell Stack

Real-Time Implementation of a Super Twisting Algorithm for PEM Fuel Cell Power System

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