High Voltage Gain Interleaved Boost Converter with ANFIS ...joics.org/gallery/ics-1981.pdf · ANFIS...

High Voltage Gain Interleaved Boost Converter with ANFIS Based

MPPT Controller for Fuel Cell Based Electric Vehicle Applications

Manasamudram Kiran1, *Penagaluru Suresh2, Dr. Sudheer Kasa3 1PG Student, 2,3Associate Professor

Department Of EEE

S.V. College of Engineering, Tirupati-517507, India [email protected], *[email protected], [email protected]

Abstract

Because of the more energetic guidelines on Carbon gasoline emanations and efficiency,

Fuel Cell Electric Vehicles (FCEV) are ending up an increasing number of main stream in the

automobile enterprise. This paper suggests a neural framework based totally Maximum Power

Point Tracking (MPPT) controller for 1.26 kW Proton Exchange Membrane Fuel Cell (PEMFC),

giving electric vehicle powertrain via a high voltage-gain DC-DC bolster converter. The proposed

ANFIS based MPPT controller uses the Maximum Power Point (MPP) of the PEMFC. High

buying and selling repeat and high voltage advantage DC-DC converters are essential for the

catalyst of FCEV. In order to perform excessive voltage increment, a 3-arrange high voltage

expansion Interleaved Boost Converter (IBC) is in like manner predicted for FCEV device. The

interleaving framework diminishes the information cutting-edge swell and voltage weight at the

strength semiconductor devices. The introduction examination of the FCEV structure with RBFN

based MPPT controller is differentiated and the Fuzzy Logic Controller (FLC) and ANFIS in

MATLAB/Simulink arrange.

Key words_ Fuel Cell Electric Vehicle, High Voltage Gain IBC, PEMFC, MPPT, RBFN

I. INTRODUCTION

Because of the ecological contamination and limited stores of non-renewable energy

sources, car businesses are demonstrating more enthusiasm for Fuel Cell Electric Vehicles

(FCEV). The fast progressions in power gadgets and energy component advancements have

engaged the critical improvement in FCEVs [1-2]. Energy components have the benefits of clean

control age, high dependability, high productivity and low commotion [3]. Contingent upon the

sort of electrolyte substance energy units are classified into various kinds, for example, Proton

Exchange Membrane Fuel Cell (PEMFC), Alkaline Fuel Cell (AFC), Phosphoric Acid Fuel Cell

(PAFC), Solid Oxide Fuel Cell (SOFC) and Molten Carbonate Fuel Cell (MCFC). Among these,

PEMFCs are commanding the car business because of their low working temperature and the

speedy startup [4]. The yield voltage of energy component relies upon film water substance and

cell temperature. Prominently, power modules have non-straight voltage-current qualities. Thus,

Journal of Information and Computational Science

Volume 9 Issue 12 - 2019

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mailto:[email protected]

there is just a solitary one of a kind working point accessible for energy units with the most extreme

yield voltage and power. The most extreme power point following (MPPT) procedure is important

to separate the greatest power from the energy component at various working conditions. In the

writing, different MPPT strategies are accessible like bother and watch (P&O), practical swarm

enhancement (PSO), gradual conductance (INC), fluffy rationale control (FLC), sliding mode

control, neural system (NN) to follow greatest power point (MPP) [5]. Among these accessible

MPPT calculations, P&O is straightforward, mainstream and simple to execute. P&O [6] and

gradual conductance techniques produce motions at consistent state which will decrease the

proficiency of the energy component framework. To beat this issue, fluffy rationale controller and

neural system [6] calculations are acquainted with track the MPP with expanded proficiency and

precision. In this paper, spiral premise capacity arranges (RBFN) base MPPT controller is

proposed to follow the MPP of the PEMFC. The power train engineering of FCEV is appeared in

Fig. 1. A pile of PEMFC produces an unregulated low DC yield voltage. So, a lift or venture up

DC-DC converter is required to support and control the PEMFC yield voltage. Lift converter is

broadly utilized as a front-end control conditioner for the energy unit. For low control applications,

the ordinary lift converter Is applied as a energy electronic interface while for excessive manipulate

packages assist converter likely may not be best resulting from its low cutting-edge handling

potential and warm administration problems [7]. To beat these problems numerous high voltage

advantage DC-DC converters are deliberate in the writing. In [8], a quadratic lift converter made

out of two lift converters is proposed to accomplish high voltage gain. Be that as it may, utilizing

of two lift converters may lessen the general effectiveness of the framework. A fell 2-stage

interleaved DC-DC support converter is proposed in [9]. In any case, this topology experiences

poor dependability and less productivity. In [10], a lift converter with voltage multiplier cell is

proposed to accomplish high voltage gain, yet the voltage increase of single multiplier cell isn't

much enough to drive the powertrain of FCEV. Detached converters with coupled inductors or

high recurrence transformers are proposed to accomplish high voltage gain in [11].

The high voltage increase is accomplished by modifying the transformer turns proportion

[12]. Be that as it may, these separated converters are progressively costly contrasted with non-

disconnected DC-DC converters. Thus, this paper proposes a high voltage increase three-stage

non-disengaged interleaved support converter (IBC) for energy unit applications to accomplish

low exchanging pressure and high voltage gain. Interleaving system expands the unwavering

quality of the energy unit and gives high control capacity.



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Fig 1. Conventional configuration of fuel cell fed BLDC motor driven electric vehicle.

The yield voltage of the proposed converter is given to the electric engine through an inverter for

impetus of the vehicle. The electric engine assumes a significant job in FCEVs. A sufficient engine

extensively diminishes the expense and size of the fuel cell. In past, most of automakers are utilized

DC engines for electric vehicle applications. Antagonistically, DC engines have high upkeep cost

and low proficiency because of the brushes and turning gadgets.

At present, perpetual magnet BLDC engine is for the most part utilizing in FCEV

applications because of straightforward control, high unwavering quality and high roughness.

Fig.2 demonstrates the proposed BLDC engine driven FCEV framework with three-stage high

voltage gain IBC. It comprises of a 1.26 kW PEMFC, three-stage high voltage gain IBC, voltage

source inverter (VSI) and a BLDC engine. The three-stage IBC works as an interface among

PEMFC and VSI. RBFN based MPPT calculation is intended to remove the greatest power from

the energy component. Three-stage IBC supplies capacity to the BLDC engine through VSI. The

switches of the VSI are constrained by utilizing electronic recompense of BLDC engine. The

engine shaft is associated with vehicle wheels for the drive. The remainder of the paper is

composed as pursues.

PEMFC demonstrating is talked about in Section II; displaying of the proposed converter

is canvassed in Section III; MPPT and BLDC engine control methods are depicted in Section IV;

reproductions and results are examined in Section V and the ends condensed in Section VI.



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Fig 2. Proposed configuration of fuel cell fed BLDC motor driven electric vehicle.

II. FUEL Cell MODELING

A power gadget is an electrochemical contraption that changes over hydrogen fuel into

power. The commitments to the vitality part are air and fuel and these are changed over into water

and power through an engineered reaction. A lone vitality segment includes two terminals (anode

and cathode) and an electrolyte. The electrolyte separates the positive and negative charged

particles of the hydrogen fuel. Exactly when the hydrogen and oxygen are supported into the cell,

control is created at the yield of the cell inside seeing an electrolyte. Power gadget makes just

warmth and water as the wastage of the creation response. The cell voltage of PEMFC is given as

VFC = ENernst − Vact − Vohm − Vcon

(1)

Where ENernst is the open-circuit (or reversible) thermodynamic voltage and is given as

ENernst=1.229-8.5*10−4(T-298.15) +4.308*10−5T (ln)+0.5ln (P02)

(2)

Where T is absolute temperature (K), PO2 and PH2 are oxygen and hydrogen partial pressures

(atm) respectively. Activation voltage Vact is the combination of both anode and cathode

activation overvoltage and is expressed as

Vact = −[δ1 + δ2Tln (C02) + δ4Tln (IFC)]

(3)

Where i d (i= 1,2,3,4) is empirical coefficient for each cell and CO2 is the dissolved oxygen

concentration at the liquid/gas interface and is calculated by using the following expression



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C02 =P02

(5.08∗106)∗exp (−498

T)

(4)

Ohmic overvoltage Vohm is expressed as

Vohm = IFC(Rc + RM)

(5)

Where RM is the electron flow equivalent resistance and RC is the proton resistance. RC is

considered as constant.

RM =ρm L

A

(6)

Where L is membrane thickness (cm), A denotes active area of membrane (cm2) and m r is the

membrane specific resistivity (Ω-cm) and is given as

ρm =181.6[1+0.03J+0.062(

T

303)2(J)2.5]

[G−0.634−3J]exp [4.18(1−303

T)]

(7)

Where G is water content of the membrane and J is current density and is expressed as

J =IFC

A

(8)

Finally, the concentration overvoltage Vcon can be calculated from the following expression

Vcon = −RT

nF ln [1-

J

Jmax]

(9)

Where F is Faraday’s constant, R is universal gas constant and Jmax is maximum greatest current

thickness. A DC-DC converter is associated with the yield of the power device to keep up a

consistent voltage over the DC connect.

III. THREE-PHASE HIGH VOLTAGE GAIN IBC

The proposed converter includes three switches (S1, S2 and S3) and three diodes (D1, D2

and D3). L1, L2 and L3 are the filtering inductors of stage 1, arrange 2 and stage 3 independently.

VFC is the information voltage, VO is yield voltage and R is the stack resistor. The going with

suppositions are considered for the examination of proposed high voltage gain IBC: I. Inductors

of all the three phases are believed to be immaculate (L1= L2 =L3=L). ii. Isolating capacitors C1

and C2 are considered as same (C1 = C2=C). iii. The proposed converter reliably works in

Continuous Conduction Mode (CCM). iv. The voltage and current swells over the capacitor and

inductor are believed to be close to nothing. The switches S1, S2 and S3 are turned ON by utilizing

two entry beats which are 180˚ stage moved. One portal heartbeat is given to the switch S2 and

another gateway beat with 180˚ stage move is given to both the switches S1 and S3.



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(a)

(b)

(c)

Fig.3. Modes of operation of 3-phase high voltage gain IBC.

Mode-1 (to ≤ t ≤ t1):

During this mode, all the three switches S1, S2 and S3 are switched ON and all the three

diodes D1, D2, and D3 are reverse biased as shown in Fig. 3(a). The input voltage source

VFC charges the inductors L1, L2 and L3. The current through these inductors I1, I2 and

I3 increased linearly with a slope of (VFC/L). The input capacitor Cin is disconnected from

the load as well as from the supply. The output capacitors C1 and C2 supplies energy to

the load resistor and the voltage of output capacitors VC1 and VC2 decreases with a slope

of (VO/RC).

Mode-2 (t1 ≤ t ≤ t2):

In this mode, the switch S2 is switched ON and the switches S1 and S3 are switched OFF.

The diodes

D1 and D3 are forward biased and the diode D2 is reverse biased as shown in Fig. 3(b).

The current through the inductors L1 and L3 decreased with a slope of (VFC –VCin)/L and

(VFC – VC2)/L respectively. The current through the inductor L2 increases with a slope

of (VFC/L). The capacitor C1 supplies the energy to the load and the capacitors C2 and

Cin are charged by the input voltage VFC.

Mode-3 (t3 ≤ t ≤ t4):

This mode is similar to mode-1. All the three switches S1, S2 and S3 are switched ON and

all the three diodes D1, D2 and D3 are switched OFF.



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Mode-4 (t4 ≤ t ≤ t5):

In this mode, the switch S2 is switched OFF and the switches S1 and S3 are switched ON.

The diodes D1 and D3 are reverse biased and the diode D2 is conducting as shown in Fig.

3(c). The input voltage source VFC charges the inductors L1 and L3 and the current

through these inductors increases with a slope of (VFC/L). The current through the inductor

L2 decreases with a slope of (VFC+VCin-VC1)/L. The capacitors C2 and Cin supplies

energy to the load. Capacitor C1 gets charged by the input voltage VFC.

A. ANALYSIS OF THE CONVERTER

To enhance the examination of the converter, inductors, capacitors and strength

semiconductor gadgets are believed to be flawless and the converter working in CCM. The static

voltage benefit (M) of the DC-DC converter is obtained with the aid of applying volt-2nd stability

on inductors L1, L2 and L3. By making use of volt second parity to the inductor L1, we get.

VL1 = VFC(t1 − t0) + (VFC − VCin)(t2 − t1) + VFC(t3 − t2) + VFC = 0

(10)

From Eq. (10), input capacitor voltage VCin is acquired as

VCin =VFC

(1−D) (11)

By applying volt-second balance to the inductor L2, we get

VL2 = VFC(t1 − t0) + (VFC)(t2 − t1) + VFC(t3 − t2) + (VFC + VCin − VC1) = 0

(12)

By applying volt-2d balance to the inductor L2, we get

VC1 =VFC

(1−D)+VCin (13)



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Fig.4. Steady-state waveforms of 3-Phase high voltage gain

From Eq. (11) and Eq. (13) we get

VC1 =2VFC

(1−D) (14)

By making use of volt-2nd balance to the inductor L3, we get

VL3 = VFC(t1 − t0)+(VFC−VC2)(t2−t1)+VFC(t3−t2)+VFC(t4−t3)=0 (15)

From Eq. (15), capacitor C2 voltage is acquired as

VC2=

VFC(1−D)

(16)

The output voltage of the converter is acquired via using the Eq. (17)

vo = VC1 + VC2 − VFC

(17)

From Eqs. (14) (16) and (17), the converter static voltage benefit M is acquired as

M=V0

VFC=

(2+D)

(1−D) (18)

.

The switches S1 and S3 are switched OFF in mode-2 and stays switched ON in all of the

different modes. From Fig. 3(b), the voltage stress of switches S1 and S2 may be expressed as

VS1 = VCin =VFC

(1−D) (19)

VS2 = VC1 − VCin =VFC

(1−D) (20)

The transfer S2 is switched OFF in simplest mode-4. From Fig. Three(c), the voltage

pressure of transfer S3 is expressed as



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VS3 = VC2 =VFC

(1−D) (21)

In the same way the voltage stress of the diodes D1, D2 and D3 can be derived and they expressed

as

VD1 = −VC1=−2VFC

(1−D) (22)

VD2 = VCin − VC2=−VFC

(1−D) (23)

VD3 = VC2= VFC

(1−D) (24)

Inductance L is designed by using the usage of the input current ripple (ΔI). The maximum

enter present day ripple is believed as 20% of the enter contemporary. The value of the input

inductor is calculated by the use of the Eq. (25).

L=L1=L2=L3=DVFC

∆Ifs (25)

Similarly, the input and output capacitors are designed by means of the use of the voltage

ripples throughout the enter and output capacitors. The voltage ripple (ΔV) is taken into

consideration as 10% of the enter voltage

Cin =Vo

R∆Cinfs (26)

C=C1 = C2 =DVo

R∆Vfs (27)

IV. CONTROLLER DESIGN

Two control procedures are utilized for the proposed arrangement. One is for to follow

the most extreme intensity of the energy component and another is for BLDC engine activity.

A. RBFN BASED MPPT CONTROLLER

MPPT is needful for energy component framework to remove the most extreme power

from it at various temperature conditions. For the proposed setup RBFN based MPPT controller is

created and the outcomes are contrasted and FLC. RBFN is a sort of feedforward neural system

model and has both directed and solo learning stages. RBFN commonly involves three layers: a

data layer, a covered layer and a yield layer as appeared in Fig.6. The shrouded layer comprises of

non-direct spiral premise actuation work though the yield layer is straight one . The hubs in the

info layer are utilized to transmit the contributions to the concealed layer. The net info and yield

of the information neuron are spoken to as

xi(1)

(n) = neti(1) (28)

yi(1)

(n) =fi(1)

[neti(1)

(n)] = neti(1)

(n) i=1,2 (29)



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Where xi (1) is info layer, yi (1) is shrouded layer and neti (1) is entirety of the information layer.

Each hub in the shrouded layer executes as Gaussian capacity. The Gaussian capacity is utilized

as a participation work in the RBFN.

Fig.6. RBFN structure.

netj(2)= -(X-Mj)

TEj(X-Mj) (30)

yj(2)(n) = fj

(2) [netj(2)(n)]xp[netj

(2)(n)] ,j=1,2,......

(31)

Where Mj and Σj are mean and standard deviation of the Gaussian capacity separately. The yield

layer has

single hub k, produces the straight control signal

netk(3) =Ejwjyj(2) (32)

yk(3) =f(3)k [net k(3)(n)] =net(3)k(n) (33)

Where wj is the connective weight lattice among yield and concealed layer. In this paper current

and voltage of energy unit are taken as contributions to the RBFN controller and it produces

obligation cycle (D) as the yield as appeared in Fig. 7.

Fig.7. RBFN based MPPT architecture for the fuel cell.



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B. ELECTRONIC COMMUTATION

The control sign to the switches of the VSI are gotten from the BLDC engine electronic

substitution. Three lobby sensors are utilized to produce three corridor sign contingents upon the

engine rotor position for every interim of 60˚. These created lobby signs are changed over into

changing heartbeats to the VSI by utilizing a decoder circuit. The exchanging conditions of VSI

are recorded in Table 1.

TABLE 1. Switching states for electronic commutation of BLDC motor

V. Adaptive Neuro-Fuzzy Inference System

An adaptable neuro-cushioned assurance arranges or adaptable structure based fleecy

pondering system (ANFIS) is a kind of false neural shape that depends on Takagi–Sugeno quiet

enrollment coordinate. The strategy was made in the mid-Nineteen Nineties. Since it makes each

neural frameworks and padded clarification measures, it can get the upsides of both in a lone

structure. Its enlistment structure thinks about to a machine of padded IF–THEN determinations

which have learning ability to off base nonlinear cutoff focuses. Along these lines, ANFIS is

analyzed to be a completed estimator. For utilizing the ANFIS as to a couple of recognition an

evidently decent and most ideal way, you can utilize the top-notch parameters got by methods for

characteristic calculation. ANFIS: Artificial Neuro-Fuzzy Inference Systems

1. ANFIS are a class of adaptable structures which can be all round that truly matters

foggy to delicate fame frameworks.

2. ANFIS adapt to Sugeno e Tsukamoto padded models.

3. ANFIS utilizations a mutt getting the hold of figuring.

In the circle of electronic thinking neuro-padded translates blends of trickery neural

structures and woolen assistance. Neuro-pleasant hybridization gets a handle on a cream sharp



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shape that synergizes these two methods by method for joining the human-like contemplating style

cushioned systems with the getting to know and connectionist shape of neural structures. Neuro-

cushioned hybridization is all around named as Fuzzy Neural Network (FNN) or Neuro-Fuzzy

System (NFS) in the made paintings. Neuro-cushioned structure (the greater well-known term is

utilized from this time ahead) wires the human-like considering style warm frameworks the usage

of agreeable sets and a semantic version which includes a approach of IF-THEN cushioned

benchmarks. The crucial idea of neuro-soft systems is that they're paying little thoughts to what

you resemble at it approximates with the capability to request interpretable IF-THEN runs the

display.

The possibility of neuro-sensitive structures wires two conflicting requirements in open to

acting: interpretability versus exactness. All round that in reality topics, one of the properties wins.

The neuro-woolen in cushioned indicating research area is restrained into two zones: semantic

touchy showing this is revolved round interpretability, by using and massive the Mamdani display;

and right feathery demonstrating that relies upon upon exactness, in a general sense the Takagi-

Sugeno-Kang (TSK) layout.

Watching out for fuzzification, cushioned inducing and defuzzification via multi-layers

feed-forward connectionist frameworks. It must be raised that interpretability of the Mamdani-

type neuro-agreeable structures may be misplaced. To refresh the interpretability of neuro-

cushioned systems, sure value determinations should be taken, wherein important bits of

interpretability of neuro-agreeable systems are in like manner separated. A propelling appraisal

line watches out for the fact’s movement mining case, wherein neuro-cushioned structures are

successively resuscitated with new pushing towards fashions on asking for and on-the-fly. Thusly,

shape restores don't just merge a recursive distinction in model parameters, but moreover a unique

development and pruning of version with a selected intense goal to oversee thought flow and

continuously changing framework direct sufficiently and to maintain the frameworks/fashions

"within the gift style" at something factor.

ANFIS MPPT controller

ANFIS is the combination of both neural networks and fuzzy inference sytem. Thus, it offers the

benefits of inference mechanism of fuzzy system and learning ability of neural network. Fig.8

shows the typical ANFIS structure of the Sugeno fuzzy system. It has two inputs (X and Y) and

single output (F) with 5 layers. The fuzzy if-then rules of the Sugeno fuzzy system are given as

follows:

Rule 1: If X is A1 and Y is B1, then F1 ¼ p1X þ q1Y þ r1

Rule 2: If X is A2 and Y is B2, then F1 ¼ p2X þ q2Y þ r2

In Fig.8, square specifies an adaptive node, whereas a circle specifies a fixed node. The purpose

of each layer in the ANFIS



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Layer 1: In this layer the input values are converted to fuzzy values. Each node in fuzzification

layer is an adaptive node with a node function given as

O1,i =µAi(X) ,for i= 1,2

(34)

O1,i = µBi-2 (Y) ,for i = 3, 4 (35)

Fig.8 ANFIS architecture.

where O1;i is membership value for the inputs X and Y, m is membership function. The subscripted

i and 1 indicates the node and layer numbers respectively.

Layer 2: This layer is called as product layer. Each node in this layer is fixed node. The output

of this layer is product of every incoming signals. The output of each node indicates the firing

strength of each rule.

O2,i = wi = µAi(X)µBi(Y) i = 1,2

(36)

Layer 3: The third layer in Sugeno fuzzy system is normalization layer. Each node in this layer

is a fixed node. This layer normalizes the firing strength by dividing rule's firing strength with

sum of firing strengths of all the rules.

𝑂3𝑖 = 𝑤𝑖 =𝑤1

𝑤1+𝑤2 (37)

Layer 4: It is defuzzification layer. Each node in this layer is an adaptive node with node

function given as:

O4,i = wifi =wi ( piX + qiY + ri ) i =1, 2

(38)

Layer 5: It is output layer and has only a fixed node. It calculates the overall output value by

summing all the incoming signals.



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O𝑜5 = ∑ 𝑊𝑖𝐹𝑖𝑖 =∑ 𝑊𝑖𝐹𝑖𝑖

∑ 𝑊𝑖𝑖 (39)

Input variables to the ANFIS controller are PEMFC voltage (VFC) and current (IFC) and the

output is duty cycle (K).

Input_1

Input_2

Output

VI.SIMULATION RESULTS

The performance of the proposed BLDC motor driven FCEV system is analyzed by using

the MATLAB/Simulink platform. To analyze the dynamic response of the FCEV system, sudden

changes in the temperature of the fuel cell is considered as follows: T= 300˚K for a period of 0 to

0.3sec, T= 340˚K for a period of 0.3 sec to 0.6 sec and T= 320˚K for a period of 0.6sec to 0.9 sec

as shown in Fig. 9. For the different temperatures the output current, voltage and power waveforms

of the fuel cell are as shown in Fig. 10. Fuel cell generates a power of 1080W for 0 to 0.3 sec,

970W for 0.3sec to 0.6 sec and 1220W for 0.6sec to 0.9sec.Fig. 11 shows The DC link current,

voltage and power by using the FLC base MPPT technique. It generates a power of 775W, 1140W

and 900W for the temperatures of 300˚K, 340˚K and 320˚K respectively.

The DC link output current, voltage and power using proposed RBFN based MPPT

controller are shown in the Fig. 12. The proposed controller gives 780W for the temperature of

300˚K, 1150W for 340˚K and 910W for the temperature of 320˚K. In Fig. 13, the performance of

the RBFN based MPPT controller for fuel cell is compared with fuzzy logic based MPPT

controller. From Fig. 13, it is observed that proposed controller generates the high DC link power

than the FLC. The comparative analysis of FLC and RBFN controllers are listed in Table 3. The

starting and steady-state characteristics of the BLDC motor at different temperatures of the fuel



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cell are as shown in the Fig. 14. The motor parameters such as stator current (Isa), back EMF (E),

electromagnetic torque (Te) and load torque (TL) are presented at dynamic temperature conditions

of the fuel cell. The BLDC motor has a speed of 3300 rpm for 0 to 0.3sec, 2400 rpm for 0.3sec to

0.6sec and 3700 rpm for 0.6sec to o.9sec. The torque of the BLDC motor remains constant for

varying speed conditions.

The DC link output current, voltage and power using proposed ANFIS based MPPT

controller are shown in the Fig. 18. The proposed controller gives 790W for the temperature of

300˚K, 1160W for 340˚K and 920W for the temperature of 320˚K. In Fig. 17, the performance of

the ANFIS based MPPT controller for fuel cell is compared with fuzzy logic and RBFN based

MPPT controller. From Fig. 20, it is observed that proposed controller generates the high DC link

power than the RBFN and FLC. The comparative analysis of FLC, RBFN and ANFIS controllers

are listed in Table 2.

Fuzzy logic Simulation results:

Fig.9.Temperature changes in PEMFC

system.

Fig.10.Fuel cell output current, voltage and

power at different temperatures

Fig.11.DC link output current, voltage and

power at different temperatures using FLC.

Fig.12.BLDC parameters using FLC.



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RBFN simulation results:

Fig.13.Fuel cell output current, voltage and

power at different temperatures using RBFN

Fig.14. DC link output current, voltage and

power at different temperatures using

RBFN.

Fig.15.BLDC parameters using RBFN Extension

Simulation results by using ANFIS Controller

Fig.16.Temperature changes in PEMFC

system.

Fig.17. Fuel cell output current, voltage and

power at different temperatures by using

ANFIS controller



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Fig.18.DC link output current, voltage and

power at different temperatures using

ANFIS.

Fig.19.BLDC parameters using ANFIS

Fig.20.Comparison of power by using fuzzy controller, RBFN Controller, ANFIS controller

Table.2. Comparison of DC link power with both RBFN and Fuzzy based MPPT controllers

Parameter

Period (sec) 0 to 0.3 0.3 to0.6 0.6 to 0.9 0 to 0.3 0.3 to0.6 0.6 to 0.9 0 to 0.3 0.3 to0.6 0.6 to 0.9

Fuel cell temperature (˚K) 300 340 320 300 340 320 300 340 320

DC link power (W) 775 1140 900 780 1150 910 790 1160 920

Time taken to reach MPP (sec) 0.12 0.42 0.7 0.09 0.38 0.68 0.04 0.33 0.65

1.26 kW PEMFC with fuzzy 1.26 kW PEMFC with RBFN 1.26 kW PEMFC with ANFIS

VII. CONCLUSION

In this paper, a three-arrange excessive voltage gain DC-DC converter is proposed for

FCEV packages. The proposed converter has dwindled the energy section enter current swells and

the voltage weight on the power semiconductor switches. The ANFIS based MPPT has maximum

brilliant strength from the energy phase at extraordinary temperatures. The proposed MPPT

framework is differentiated and the FLC MPPT controller. The reenactment effects monitor that

the ANFIS based MPPT controller has pursued the maximum high-quality electricity point

snappier whilst diverged from the smooth technique of reasoning controller. Also, fantastic

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90

500

1000

1500

2000

2500

3000

time(s)

pow

er(

w)

with fuzzy controller

with RBFN controller

with ANFIS controller



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execution traits of the BLDC motor, as an instance, electromagnetic torque, speed and returned

EMF are broke down at exceptional temperatures of the energy unit system.

REFERENCES

[1] Penagaluru Suresh, and T. Gowri Manohar. "Review on Renewable Source Integrated

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