scholarly journals High Voltage Gain Interleaved Boost Converter with Neural Network Based MPPT Controller for Fuel Cell Based Electric Vehicle Applications

2021 ◽  
Vol 9 (VI) ◽  
pp. 4728-4732
Author(s):  
Praniali Surendra Kawale
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Electric Vehicle ◽  
Proton Exchange Membrane ◽  
Radial Basis Function Network ◽  
Boost Converter ◽  
Proton Exchange ◽  
Maximum Power ◽  
Interleaved Boost Converter ◽  
Mppt Controller

As a result of the strict regulations on carbon emissions and the fuel economy, fuel cell electric vehicles (FCEV) vehicles are becoming increasingly popular in the automotive industry. This paper provides the Neural Network Maximum Power Point Tracking (MPPT) controller of the 1.26 kW Proton Exchange Membrane Fuel Cell (PEMFC), which provides electric vehicle powertrain using DC-DC power converters. The proposed neural network controls the MPPT Radial Basis Function Network (RBFN) using the PEMFC Maximum PowerPoint (MPP) tracking algorithm. High frequency switching and high DC-DC converting power are important for FCEV continuity. For maximum power gain, a three-phase power supply interleaved boost converter (IBC) is also designed for the FCEV system. The interleaving process reduces the current input pressure and electrical pressure in semiconductor electrical equipment. FCEV system performance analysis with RBFN based MPPT control compared to fuzzy logic controllers (FLC) on the MATLAB / Simulink platform.

High Step-Up Boost Converter with Neural Network Based MPPT Controller for a PEMFC Power Source Used in Vehicular Applications

2018 ◽  
Vol 19 (5) ◽  
Author(s):  
K. Jyotheeswara Reddy ◽  
N. Sudhakar ◽  
S. Saravanan ◽  
B. Chitti Babu
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Electric Vehicles ◽  
High Voltage ◽  
Boost Converter ◽  
Maximum Power ◽  
Switching Frequency ◽  
Voltage Gain ◽  
High Voltage Gain ◽  
Mppt Controller

AbstractHigh switching frequency and high voltage gain DC-DC boost converters are required for electric vehicles. In this paper, a new high step-up boost converter (HSBC) is designed for fuel cell electric vehicles (FCEV) applications. The designed converter provides the better high voltage gain compared to conventional boost converter and also reduces the input current ripples and voltage stress on power semiconductor switches. In addition to this, a neural network based maximum power point tracking (MPPT) controller is designed for the 1.26 kW proton exchange membrane fuel cell (PEMFC). Radial basis function network (RBFN) algorithm is used in the neural network controller to extract the maximum power from PEMFC at different temperature conditions. The performance analysis of the designed MPPT controller is analyzed and compared with a fuzzy logic controller (FLC) in MATLAB/Simulink environment.

scholarly journals Novel Neural network single sensor MPPT for Proton Exchange Membrane Fuel Cell

2021 ◽  
Vol 24 (1) ◽  
pp. 43-48
Author(s):  
Abdelghani Harrag
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Proton Exchange Membrane ◽  
Proton Exchange ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Single Sensor ◽  
Power Point ◽  
Exchange Membrane

This paper presents a new neural network single sensor maximum power point tracking algorithm controlling the DC-DC boost converter to guarantee the transfer of the proton exchange membrane fuel cell maximum generated power to the load. The implemented neural network single sensor controller has been developed and trained firstly in offline mode using single sensor maximum power point tracking data obtained previously; and secondly used in online mode to track the maximum output power of the fuel cell power system. Comparative simulation results prove the superiority of the proposed neural network single sensor maximum power point compared to the single sensor one especially in transit response reducing by the way the overshoot and the tracking time which leads to an overall energy losses reduction. In addition, the implemented neural network single sensor MPPT employs only one sensor which will reduce the complexity and the cost of PEM fuel cell power system. To our knowledge, this study is a pioneering work using a neural network single sensor controller as PEM fuel cell MPPT.

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