RAPID BATTERY CHARGER

USING

FUZZY CONTROLLER

-PRIYA SRIVASTAVA

Sharda University

E.I.E 8th SEM.

CONTENTS

Brief Intro. Ni-Cd Battery. Fuzzy Controller

History.ApplicationsModelingSimulation StepsBasics Of Fuzzy.Membership Functions.ConclusionFuture Scope

BRIEF

Rapid Battery Charger Using

Fuzzy Controller is,

modern technology which are being utilized

these days;

based on Fuzzy Logic,

which is quite different from

classical Boolean logic.

Fuzzy logic is widely used in

machine control.

NI-CD BATTERY

•using nickel oxide hydroxide and

•metallic cadmium as electrodes.

The nickel–cadmium battery (NiCd

battery or NiCad battery) is a type

of rechargeable battery

•but without doing any damage to them.

The main objective for the development of

rapid battery charger was to charge the Ni-Cd

batteries quickly,

Since the behavior of Ni-Cd batteries at very high

charging rates was not available,

• so there was need to obtain them through experimentation.

• Based on the upper limit of the charging current as fixed at 8C i.e. 4A, since batteries with capacity C=500 mAh were the target batteries.

Based on the rigorous experimentation with the Ni-

Cd batteries,

• it was observed that the two input variables used to control the charging rate (Ct) are absolute temperature of the batteries (T) and its temperature gradient (dT/dt).

• Universe of discourse for a variable is defined as its working range.

FUZZY CONTROLLERA fuzzy control

system or fuzzy

controller is a control system bas

ed on fuzzy logic—

•a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1,

•in contrast to classical or digital logic, which operates on discrete values of either 1 or 0.

HISTORY

Fuzzy logic was first proposed by Lotfi A.

Zadeh.

He elaborated on his ideas in a 1973 paper

that introduced the concept of "linguistic

variables",

which equates to a variable defined as a

fuzzy set.

Applications:

Research and development is also continuing on fuzzy

applications in software,

as opposed to firmware, design,

•so-called adaptive "genetic" software systems, with the ultimate goal of building "self-learning" fuzzy-control systems.

including fuzzy expert systems and integration

of fuzzy logic with neural-network and

MODELING

MATLAB

Simulink

MATLAB (Matrix Laboratory) is a numerical computing environment and fourth-generation programming

language.

Developed by MathWorks, MATLAB allows matrix manipulations,• plotting of functions and data,

implementation of algorithms, • creation of user interfaces, and interfacing

with programs written in other languages,• including C, C++, Java, • and Fortran.

Simulink,• developed by MathWorks,• is a data flow graphical programming language

tool for modeling,• simulating and analyzing multidomain dynamic

systems.• Its primary interface is a graphical block

diagramming tool and a customizable set of block libraries.

Simulink is widely used in control theory and digital signal

processing for multidomain simulation and Model-Based Design.

BASICS OF FUZZY CONTROLLER

• A Fuzzifier, which converts input data into suitable linguistic values;

• a fuzzy rule base, which consists of a database with the necessary linguistic definitions and the control rule set;

• a fuzzy inference engine which simulating a human decision process, that infers the fuzzy control action from the knowledge of the control rules and finally linguistic variable definitions;

• a Defuzzifier, which yields a nonfuzzy control action from an inferred fuzzy control action.

Membership Functions

Fuzzy sets must be defined for each input and output

variable.

As shown in Figure , four fuzzy Subsets (ZERO,

SMALL, MEDUM, HIGH) have been chosen for

charge current while only two fuzzy subsets (SMALL,

HIGH),

• have been selected for the Battery temperature and voltage changes in order to smooth the control action.

This & Above Figures are the Membership Functions of Rapid Battery Charger.

The first step in the fuzzy controller

definition is to select input and output

variables.

Block diagram of the fuzzy controller

structure show that we have two input variable (battery temperature and output voltage)

While the only output variable is charge

current as an external signal to switch duty-

cycle.

Fuzzy controller is simulated in fuzzy

toolbox of MATLAB software.

SIMULATION STEPS

MATLAB simulation toolbox is strong

graphical software for analyzing of control systems.

The system contains three important

blocks, fuzzy controller,

BUCK converter and the battery.

The basic scheme of a general-purpose

fuzzy controlled battery charger is shown in Figure.

Fig. Basic block diagram of charging system

Fig. GTO BUCK Converter

Derivation Of Control Rules

Fuzzy control rules are obtained from the analysis of

the system behavior.

In their formulation it must be considered that using different control laws

depending on the operating conditions can greatly

improve the battery charger performances.

The improved performances are the dynamic response and

robustness.

voltage error

voltagescope

voltage

f(u)temperature

tempsetpoint

tempscope

temperror

temp

f(u)

Voltage

To Workspace 1

out

To Workspace

in

Mux 5

Mux

Mux 4

Mux

Mux 3

Mux

Mux2

Mux

Fuzzy LogicController

Demux

Demux

setpoint

Fig. Simulation of Rapid Battery Charger using FCS

Conclusion:

As a final result, it is shown that fuzzy controller provides

a safe and stable charge process with optimized time and acceptable temperature

variations.

This fast and safe method is used to charge a set of Ni-Cd batteries and the charge time is 100 min and temperature

during charge process doesn't exceed from 40°C

This system can be used to charge batteries with different characteristics because of it's

independence to state variables and system model

Future Scope

The suggested framework can be extended to increase the

flexibility of the search

by incorporating additional parameters so that the search for optimal solution could be

executed in terms of number of membership functions for each

variable,

the type of membership function and the number of

iterations &

possibly trying variants of PSO algorithm for identifying fuzzy systems with an objective to improve their performance

further.

Thank you!