Performance Analysis of MPBC with PI and Fuzzy Logic Controllers

Applied to Solar Powered Electric Vehicle Application

RAKESH BABU BODAPATI*, R. S. SRINIVAS, P. V. RAMANA RAO

Department of Electrical and Electronics Engineering,

Acharya Nagarjuna University,

Guntur, Andhra Pradesh-522510

INDIA

*Corresponding Author

Abstract: - One of the more complicated cases is managing the energy between multiple power sources that are

utilized to power electric cars (EVs). Power management is often carried out following the load requirements of

electric vehicles. By considering the speed and current values of the motor a novel controller is modeled named

as Measurement of parameter-based controller (MPBC) which is used to obtain the smooth transition between

two passive energy sources battery and Supercapacitor (SCap). Further, the proposed MPBC is combined with

fuzzy logic (FLC) and proportional-integral (PI) controllers, forming two different hybrid controllers named

MPBC+FLC and MPBC+PI, utilized to attain proper power management. The main function of traditional

controllers FLC/PI is to generate the pulse signals to the switches present in the bidirectional converters at both

battery and SCap end. On the other hand, the MPBC is utilized to control the pulse signals based on the current

and speed values of the electric motor. Futcher's final MATLAB/Simulink model is built with the proposed

control technique with two hybrid controllers by considering different power-generating conditions of the PV

array, to know the effectiveness of the individual model.

Key-Words: - Fuzzy Logic System, Energy Management (EMGT), Proportional Integral (PI) controller, Hybrid

controller (HC), Measurement of parameter-based controller (MPBC), Electric vehicle, Battery,

Photo Voltaic (PV) energy.

Received: May 8, 2023. Revised: April 6, 2024. Accepted: May 11, 2024. Published: June 3, 2024.

1 Introduction

In the future, renewable energy (RE) sources will

replace conventional energy sources as the primary

means of generating electricity. The use of

conventional energy to produce power is getting

more expensive and hurts the environment due to

large emissions. The main alternative method for

generating energy at a low cost and with little

environmental impact is solar power generation.

Here, sufficient sunlight and irradiance can be used

as inputs to produce solar-powered electricity

without harming the ecosystem. When solar-

powered facilities generate electricity, no

appreciable emissions are emitted into the

atmosphere, indicating that they are environmentally

benign.

These days, the conventional vehicle system is

the only one that is used for a large portion of

transportation. However, depending on the input

energy storage system being used, this traditional

vehicle system-based transport system needs to be

modified. Typically, a conventional transportation

system vehicle runs on petrol or diesel, both of

which have limited supply and produce a significant

amount of exhaust gases when in use. It will be

necessary to convert to an alternative source-based

vehicle to overcome the future limitations of

conventional fuels. To reduce the use of fuel and

diesel, battery-powered electric vehicles are being

developed in place of internal combustion engine-

based vehicles. Typically, to fully charge the energy

source, we must plug the car in to charge the

battery. This element contributes to the local

electricity grid becoming overloaded once more. A

solar-powered power plant is used to create a

separate power-producing station within the car to

reduce the load on the local grid. The local

electricity grid will experience less load as a result.

Renewable energy sources, such as wind, sun, and

biomass, produce energy by absorbing input from a

wealth of resources. For instance, throughout the

day, sunshine is available, making it simple and

independent to meet rising load demands.

According to the results of the standard survey,

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conventional technologies are capable of producing

over 70% of the electricity. However, only

renewable resources are used to create a maximum

of 30% of the power. This does not bode well for

the ecosystem. The people in the society should be

educated to maximize the generation of non-

conventional power. From a transportation

perspective, this initiative will increase the use of

EVs and reduce the load on the local grid, [1].

To fulfill the current load demand, new energy

sources are of vital importance because fossil fuels

are running out quickly. Trends in global warming

can also be attributed to the use of fossil fuels, [2].

The most practical solution to this global energy

problem is to use renewable energy sources.

Electricity in the future is anticipated to be primarily

supplied by RE sources, [3], [4].

Investigation of integrated energy storage

management and real-time load evolution at grid-

connected solar electric vehicles. Without any prior

knowledge, a finite time approach with subjective

dynamics of structure inputs has been taken into

consideration. Through the combined optimization

of EV energy ordering quantity, load planning

delays, photovoltaic abundance during periods of

nearby produced renewable energy, and battery

deprivation, the goal is to lower a standard

aggregated system price. The model of one-slot

look-ahead queue stability uses the Lyapunov

optimization method (LOM) to solve the problem as

a result of repeated reformulation and adjustment of

the combined optimization challenge, [5].

The choice of HEVs in transportation networks

is becoming more attractive and important due to

their increased energy consumption. Because of its

eco-friendly design and support for the smart grid

idea, HEVs are experiencing rapid growth. There

are variations in HEV types because of the

differences in ESS across various control

techniques. This makes it harder to choose a suitable

control approach for HEV applications. An

extensive analysis of the key ESS data about HEVs

and feasible optimization topologies based on

various control schemes and vehicle tools, [6].

In this case, the research and analysis involve

transforming the conventional vehicle system into

an autonomous electric vehicle (EV). Reach out to

several ESS devices, such as lead-acid and lithium-

ion (Li-ion) batteries, during this procedure. Three

driving cycles that match the conditions of moving

in have been used in MATLAB/Simulations. These

cycles include a highway with a climb up a

mountain a city, and a highway. All requirements

are based on highways in the Vale do Paraíba

Paulista region, [7], [8].

This study observes the current ESS in EVs and

HEVs, which consists of a battery and a

supercapacitor, to reduce its power density scarcity.

Because there are two ESSs, energy management

needs to be implemented for the HESS. The best

energy management strategy is created by taking

into account Pontryagin's minimal principle, which

instantly distributes the required impulsion power to

the two ESS during vehicle propulsion and also

quickly distributes the regenerative braking energy

to the two ESS, [9].

The main obstacle to optimal energy

management for HESS-based EVs is the

development of supervisory control techniques. A

multi-objective optimization model is developed to

enhance the power exchange between the

supercapacitor and battery. This method handles

problems in an approachable and optimal manner,

[10], [11].

For an EV powered by supercapacitors, a real-time

combined speed control and power flow supervisory

system is developed using a nonlinear control

system approach. Given the relationship between

energy management and HESS sizing, this work

uses a controller design for HESS sizing to find the

ideal HESS size to serve an EV. The controller uses

the HESS selectively to reduce power consumption

and traces the vehicle's set speed with uniformly

exponential stability to decrease battery stress. It is

necessary to use a composite controller by using the

physical source of the vehicle's power requirement.

To determine the use of the controller and HESS

sizing system, a typical urban dynamometer driving

program is used to imitate the driving cycle of a

full-size EV, [12]. Finding 2n−1 stage rearrangeable

Banyan-type networks that are not isomorphic to

one another is the objective of this work. This is

achieved by building substitute networks and

evaluating how well they can be rearranged using

the satisfiability problem. The limited scalability of

this strategy is a drawback because of the huge

number of candidates. To eliminate this issue, it is

shown that the possibilities can be reduced to a

smaller class of networks called pure banyan

networks. This is achieved through the use of

network isomorphism analysis, [13], [14]. This

study intends to evaluate the potential for fuel cell

electric vehicle (FCEV) adoption in Morocco and to

provide insight into fuel cell vehicles by thoroughly

evaluating the Moroccan hydrogen roadmap.

To determine the crucial success factor for

increasing FCEV adoption in the Kingdom, a

SWOT analysis was also carried out, [15], [16].

Based on an analysis of the development status of a

BESS, the study presented application scenarios,

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such as the reduction of power output fluctuations,

acceptance of output plans at the side of renewable

energy generation, power grid frequency

adjustment, power flow optimization at the side of

power transmission, and a distributed and mobile

energy storage system at the side of power

distribution, [17], [18], [19], [20].

2 Energy Storage System of Existed

Model

In the current study, a single energy source that

combines solar energy with built-in power

generation was utilized to power the vehicle

independently of the local power grid. With this

setup, the EV can simultaneously charge the battery

and generate power using the solar panel when there

is sunlight. It indicates that the battery will supply

the necessary power to the load for continuous

operation when driving at night.

2.1 Battery Model

The model-based design needs only one or two

iterations of changing and re-verifying the power

system design. The battery is used in the wind

power EV system to store the energy from the grid

supply and also provide the energy to load.

2.1.1 Internal Structure of the Battery

C1 R2 R0

Main Branch Psrasitic Branch

Fig. 1: Equivalent circuit of the battery

The battery's physical model is developed by

taking into account several variables, including

temperature, voltage, and current rating values.

Figure 1 depicts the battery's electrical circuit

model.

Equation (1) provides an expression for the main

branch voltage.

0(273 )(1 )

m m E

E E K SOC



   

(1)

The terminal resistance R0 is expressed in equation

(2).

 

0 00 0

(1 )(1 )R R A SOC  

(2)

The main branch resistance R1 is expressed in

equation (3).

1 10 ln( )R R SOC

(3)

The main branch capacitance C1 is expressed in the

equation (4).

1 1 1

/CR





(4)

The main branch resistance R2 is expressed in

equation (5).

 

2 20 22 m

exp (1 )

1 exp I / *

A SOC

RR AI





(5)

The extracted charge of the battery is expressed in

equation (6).

I ( )

e einit

Q Q dt



  



(6)

The normal current can be assessed by using the

following equation (7).

( 1)

avg





(7)

2.2 Super Capacitor Model

The ultracapacitor differs from a typical capacitor in

that it is constructed with a dielectric made of two

plates and can store a certain quantity of energy.

Because UC uses multilayer porous electrodes,

which increase the layer's surface area more than a

regular capacitor does, UC can store 100–1000

times more energy than a normal capacitor.

Fig. 2: General first-order model of the

Supercapacitor

The general practical ultracapacitor is

represented with a Figure 2, in which Rs, L, C, and

Rp are connected in series and parallel. In this case,

losses during the charging and discharging phases

are caused by the series Rs. However, Rp also

results in losses when the capacitor that is connected

in parallel with it discharges. Practically from a

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high-power applications point of view, the Rp value

is neglected because the Rp value is much higher

than the Rs value.

Resr R1

Rp C2

R3 Rn

Fig. 3: Detailed model of the Supercapacitor

Based on the multilayer capacitor technology

the SCap is derived, and its structure resembles the

distributed network of the transmission line shown

in Figure 3 so to perform the theoretical calculation

of the SCap voltage-dependent capacitance model is

taken as the reference.

The classical and simplified model of the SCap

is represented in Figure 4. in which Rp is connected

as a parallel resistance and Resr is connected as a

series resistance C will be the variable capacitance

Co is the constant capacitance value as treated. The

total capacitance value of the SCap depends upon

the voltage of the SCap, again which can be

expressed in terms of variable capacitance and

constant capacitance.

0cell C

C C kV

(8)

Resr

Fig. 4: A simplified model of the Ultracapacitor

Co/3

Resr

kVC/3

Rline

SUPER-CAPACITOR

Fig. 5: Derived Equivalent Model of 6v Series

Bmod0140-E048 Supercapacitor

Here Resr is the equivalent series resistance,

which relates the energy losses during charge and

discharging periods. Figure 5 Three different cells

are considered with voltage level 2.7V each and the

required total voltage level is 6V after three cells

series connection. So, the capacitance for three cells

in the model is given as:

1 2 3 18

1 1 1 1

total

cell cell cell cell

C C C C



  

(9)

 

total cell C

C C C kV  

(10)

Equivalent resistance in series is given as:

 

R 3 R

ESR sr



(11)

Apply Kirchhoff's voltage law (KVL) to the circuit

in Figure 5 then:

 

line ESR T

total

R R i idt V

  



(12)

The above equation can be expressed in differential

equation form as:

 

line ESR T

total

R R q V

dt C

  

(13)

Put

qC

Then voltage value of the SCap will be,

  

0()

2 ( )

line ESR C C T

dv t

R R C kV v t V

   

(14)

  

c T c

line ESR c

dv V v

dt R R C kv





(15)

The voltage across internal resistance will be found

from:

( ) ( )

ESR T

total

R i t i t dt V





(16)

At the instant of switching, t = 0+;

( ) ( )

ESR

total

R i t i t dt V





(17)

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Multiply by Ctotal and take a derivative:

()

R ( ) 0

total ESR di t

C i t

dt 

(18)

Multiply by RESR and note that

( ) ( )

v t Ri t

()

R ( ) 0

total ESR r

dv t

C v t

dt 

(19)

And the solution of,

() ESR total t

v t ke

(20)

Hence, the voltage across the terminal of the SCap

is:

( ) ( ) ( )

t r c

v t v t v t

(21)

and for discharging a UC is:

 

  

line sr

dvc

dt R C kvc





(22)

and the terminal voltage of the supercapacitor in

discharging is:

( ) ( ) ( )

t c r

v t v t v t

(23)

3 Description of Existed Model

Controllers

3.1 Fuzzy Logic Controller (FLC)

The rule-based fuzzifier interface and fuzzifier are

two crucial FLC phases. In addition to the MPBC

controller, another controller included in the

suggested control architecture is the FLC. Three

blocks are essential for producing output signals in

these, which are then utilized to generate the

controlling signal for the converter switches which

is clear from Figure 6. Here, the fuzzification block

receives the change in error and error value. The

fuzzy inference uses this block as its input.

INTERFACE ENGINEFUZZYFIER DEFUZ

ZIFIER

COMPUTATED

ERROR

DC-DC CONVERTER

RULE BASE

REFERENCE

VOLTAE

Fig. 6: Block diagram representation of FLC

3.2 PI Controller

P K e edt





(24)

After applying the Laplace transformation:

( ) 1 ( )

P s Kp E s



















(25)

Let

sinet





input

sin sin

P K t tdt







(26)

sin cos

P K t t













(27)

( ) ,sin Tan ()

P K t







   

  

 

   

(28)

Ti/S

Integral, I

Proportional,P PLANT

G(s)

C(s)

R(s)

Fig. 7: Traditional block diagram representation of

PI controller

PI controller with gain and plant blocks including

error signal block is represent in Figure 7.

3.2.1 Implementation of the PI Controller

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1/CS

Ve V1 Vout

-Vout

Fig. 8: Real-time implication model of PI controller

e out

V V V V

R CS

RRCS





(29)

 

out

V R C

VRCS





(30)

1 1 1 1

out e e

R CS R

V V V

RCS RCS R RCS

    

(31)

1 1 2

out e e

V V V dt

R R R C

 

(32)

After comparing the above expression with the

standard PI controller equation:



(33)

T R C

(34)

Real time block implementation of the PI controller

is shown in Figure 8, which includes different

resistance, capacitor and Operational amplifiers.

4 Energy Management Strategy with

Proposed Control Technique

This proposes a novel control method, SCap, based

on the electric motor's speed and current values, to

enable the battery to switch between its charging

and discharging stages. Two distinct controllers are

used to implement the suggested control technique:

the typical controllers FLC and PI are employed to

produce the pulse signals that are sent to the

converter. Additionally, such signals are controlled

by the MPBC controller by the electric motor's

speed and current levels. Ultimately, the approach to

fulfill the suggested technique is provided by the

combination of MPBC+PI and MPBC+FLC.

The PV array, battery, SCap, two bidirectional

converters, Boost converter, and the electric motor

shown in Figure 9 comprise the primary circuit of

the suggested model. In this case, the battery is used

to supply the EV with backup power when solar

power generation isn't available, while the SCap is

used to fulfill the peak power requirement. In

contrast, PV arrays are used, depending on the load

placed on the EV, to meet the requirement for EVs

as well as for battery and SCap charging. The two

bidirectional converters' regulated signals are all

based on the proposed control technique, which

consists of two separate controller combinations

known as MPBC plus PI. Based on the actual and

reference voltage levels of the converter, the PI or

FLC controller in this instance generates the pulse

signal to the converter at the battery and SCap end.

However, MPBC operates based on the EM's

current and speed values. This is achieved through

the use of math functions, which produce three

distinct signal types depending on the EM's current

and speed values. To provide the converter at the

battery and SCap ends with regulated pulse signals,

MPBC, PI, and FLC work together to provide the

EV with the appropriate power supply based on the

applied load.

PV Array with

input Temperture

and Irradiance

Battery

DC-DC Boost

Converter

DC-DC Buck-

Boost Converter

Works under

Buck Mode

Electric

Drive

MPBC Controller

with input Speed

and Current of

Electric Motor

PI/FLC Controller Generates

pulse to Buck- Boost

converter based on applied

actual and Referece voltagaes

DC-DC Buck-

Boost Converter

Works under

Buck Mode

SuperCapcitor

Fig. 9: Main circuit with a Proposed control

technique

Here, three distinct load scenarios are taken into

account, and the controlled signals to the EV are

created accordingly. This will occur by the

suggested controlled technique, which combines

FLC, PI, and MPBC.

The EM's speed and generation-related current

are described by the math functions.

1. The MPBC system creates a signal and sets Y1

to 1 and Y2 and Y3 to 0 when the speed reaches

or exceeds 1500 rpm and the current remains

below 6A. As a result, the supercapacitor

(SCap) and the converter at the battery end

function in a buck mode, making it easier to

charge the battery and SCap using solar energy.

Current flows from the PV array to the load,

SCap (for a short while), and batteries during

this procedure. In addition, the SCap releases an

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equivalent amount of energy to support the

motor if it encounters a high load during startup.

2. The MPBC, controller emits a signal when the

speed is between 1400 and 1499 rpm and the

current is between 6 and 8 amps. Y2 is then set

to 1, and Y1 and Y3 are set to 0. In this case, the

supercapacitor (SCap) rapidly supplies the

motor with the required peak power while the

converter at the battery end stays inactive. In

boost mode, the converter at the SCap functions.

There is no current flowing to the battery and

just a brief current flowing to the load from the

PV array and SCap. This suggests that during

this time, the battery does not charge or

discharge.

3. When the EM's speed falls below 1400 rpm and

its current exceeds 8 amps, the MPBC will

produce a signal of one for Y3 and zero for the

other two functions. Because of this, the

converter at the battery and SCap ends operated

in boost mode, allowing current to flow to the

load from the battery, SCap, and PV array. This

shows that, up until the point of charging, the

battery helps the solar array and the SCap under

high-load situations.

4.1 Modes of Operation of the Main Circuit

Model

Depending on the load placed on the EV, the main

circuit operates in one of three modes. The various

modes of operation of the main circuit about the

applied load are depicted in Figure 10, Figure 11

and Figure 12.

PV Array with

input Temperture

and Irradiance

Battery

DC-DC Boost

Converter

DC-DC Buck-

Boost Converter

Works under

Buck Mode

Electric

Drive

MPBC Controller

with input Speed

and Current of

Electric Motor

PI/FLC Controller Generates

pulse to Buck- Boost

converter based on applied

actual and Referece voltagaes

DC-DC Buck-

Boost Converter

Works under

Boost Mode

SuperCapcitor

Super Capacitor

Discharging

Battery Charging

Fig. 10: The main circuit with load and battery

power meet from the PV array and SCap short time.

The EV's main circuit under typical load

circumstances is seen in Figure 10. In this instance,

the battery can be charged by the PV array in

addition to providing electrical power to the load.

This suggests that controlled pulse signals are

generated to the converter at the battery end to

operate under the buck mode of operation,

depending on the speed and current values of the

electric vehicle. Here, the converter at the PV array's

end operates exclusively in boost mode. This

demonstrates that for mathematical function Y1, the

MPBC controller will be able to generate signals as

1, and for the other two functions, as zero. In

addition, the PV array is supported by the SCap

when the electric motor is operating at maximum

power.

PV Array with

input Temperture

and Irradiance

Battery

DC-DC Boost

Converter

DC-DC Buck-

Boost Converter

Works under

Buck Mode

Electric

Drive

MPBC Controller

with input Speed

and Current of

Electric Motor

PI/FLC Controller Generates

pulse to Buck- Boost

converter based on applied

actual and Referece voltagaes

DC-DC Buck-

Boost Converter

Works under

Boost Mode

SuperCapcitor

Super Capacitor

Discharging

Battery No Charging and

No Discharging

Fig. 11: The main circuit with load power meet from

the PV array and SCap for a short time

Because of the rated load placed on the EM,

Figure 11 depicts the main circuit power flow from

the PV array to the EV only. In this instance, the

MPBC controller uses the suggested control

approach to regulate the pulse signals produced by

the PI or FLC. As a result, for the remaining math

functions, the MPBC will develop the Y2 function

as 1 and 0. The battery is neither charged nor

discharged when operating in this mode since the

converter at the battery is in the no-working zone. In

addition, the PV array is supported by the SCap

when the electric motor is operating at maximum

power.

PV Array with

input Temperture

and Irradiance

Battery

DC-DC Boost

Converter

DC-DC Buck-

Boost Converter

Works under

Buck Mode

Electric

Drive

MPBC Controller

with input Speed

and Current of

Electric Motor

PI/FLC Controller Generates

pulse to Buck- Boost

converter based on applied

actual and Referece voltagaes

DC-DC Buck-

Boost Converter

Works under

Boost Mode

SuperCapcitor

Super Capacitor

Discharging

Battery Discharging

Fig. 12: The main circuit with load power meet from

the PV array and battery

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The main circuit current flow from the PV array

plus batteries to the load is shown in Figure 12. This

demonstrates the high load that the EV is subjected

to because the battery supports the PV array by

supplying more power to the load as needed.

Furthermore, the PI or FLC-generated pulses are

controlled and supplied to the converter at the

battery end to perform the boost operation only

because the MPBC controller can generate the

signal as 1 for math function Y3 and zero for the

remaining two functions, Y1, Y2. In addition, the

SCap operates in boost mode on the electric motor

and converter to support the PV array at peak power

conditions.

5 Simulation Results and Analysis

Fig. 13: Electric motor speed curve representation

under various load scenarios using PI and FLC

Figure 13 shows the speed curve response of

EM under various loads. Here, three distinct loads

are delivered to the EV at three separate times to

confirm the efficacy of the suggested control

method. When a normal load is applied to the EM

for 0.2 seconds, the motor's speed decreases to about

1500 rpm. When a rated load is applied for 0.4

seconds, the motor's speed decreases to 1400 rpm.

Finally, when a 0.6-second extra rated load is

applied, the motor's speed decreases to even less

than 1400 rpm. While MPBC+FLC took 0.075

seconds, 0.08 seconds, and 0.28 seconds to attain

stability in accordance with the applied loads in

order, MPBC+PI took 0.09 seconds, 0.1 seconds,

and 0.30 seconds. The results from the two

controller outputs showed that MPBC plus FLC

performed better.

Fig. 14: Electric motor's back EMF under various

load scenarios using PI and FLC

The electric motor's back EMF curve under

various load scenarios is displayed in Figure 14.

This will follow the speed curve: at 0.2 seconds, the

curve appears to be decreasing and eventually

achieves the steady state; at 0.4 seconds, the EM's

rated load condition causes the back emf to fall for a

while before reaching the steady state once more.

Eventually, at 0.6 seconds, the motor's overrated

load causes the rear emf value to drop significantly

once more. This process will continue for some time

before returning to its steady-state value, all because

of the suggested control strategy.

Fig. 15: Electric motor current changes related to

load.

Figure 15 shows the electric motor current

under varying load levels. The motor draws a

significant amount of current at first, but after it

reaches a steady state, that value drops to nominal,

meaning the motor is operating without a load.

Different types of loads are applied to the EM at 0.2,

0.4, and 0.6 seconds, which causes the motor to

draw more current in proportion to the imposed

load—6A, 8A, and more than 8A.

Fig. 16: Load changes representation on the Electric

Motor

The load curve representation on the EM is

displayed in Figure 16. The motor has a heavy load

before reaching a steady state, therefore EM will

require more current than at regular intervals. Figure

12 makes it clear that the motor reaches a steady

state at 0.1 seconds, at which point no load will

manifest itself in the motor. To assess the

WSEAS TRANSACTIONS on SYSTEMS and CONTROL

DOI: 10.37394/23203.2024.19. 18

Rakesh Babu Bodapati, R. S. Srinivas, P. V. Ramana Rao

E-ISSN: 2224-2856

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Volume 19, 2024

effectiveness of the suggested control method,

varying loads are applied for 0.2, 0.4, and 0.6

seconds. As a result, the motor's speed reduces and

the current drawn by the EM increases in

combination.

Table 1. Performance comparison between MPBC

plus PI and MPBC plus FLC based on the speed

value

S.No

Controller

Name

Time Taken to reach Steady state in

sec

Mode1

Mode2

Mode3

MPBC+PI

0.09

0.1

0.30

MPBC+FLC

0.075

0.08

0.28

Table 1 represents the comparison analysis

between MPBC plus PI and FLC based on the speed

curve settling value. And which clear MPB plus

FLC has given better performance compared to

MPBC plus PI.

6 Conclusion

This work examines a control mechanism that

modifies the battery and SCap concerning the

motor's speed. Three different math functions that

are independently realized based on the motor's

speed and current values are combined to create the

MPBC. The proposed MPBC controller is combined

with a traditional PI controller and FLC to create a

hybrid controller to fulfil the primary goal of the

project. The MFBC regulated the pulse signals that

corresponded to the motor speed, while the PI

controller FLC generated the switching signals

needed by the converter. Ultimately, a smooth

transition between battery and SCap is achieved

by the demands of electric vehicles by using the

suggested control approach. To carry out the same

function as MFB with PI controller, another hybrid

controller called MPBC with FLC is devised. When

applying the afterload and starting, a comparison

study is carried out. The final section contains a

tabulation and presentation of the findings for all

two techniques. Ultimately, the performance of the

MPBC plus FLC was superior to that of the other

hybrid controller, MPBC+PI.

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Contribution of Individual Authors to the

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Policy)

The authors equally contributed in the present

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problem to the final findings and solution.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflict of Interest

The authors have no conflicts of interest to declare.

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DOI: 10.37394/23203.2024.19. 18

Rakesh Babu Bodapati, R. S. Srinivas, P. V. Ramana Rao

E-ISSN: 2224-2856

176

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