2. CONTENTS
 Brief Intro.
 Ni-Cd Battery.
 Fuzzy Controller
History.
Applications
Modeling
Simulation Steps
Basics Of Fuzzy.
Membership Functions.
Conclusion
Future Scope

3. 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.

4. 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,

5. 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.

6. FUZZY CONTROLLER
A fuzzy control system or
fuzzy controller is
a control system based
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.

7. 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.

8. 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

9. MODELING
MATLAB
Simulink

10. 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.

11. 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.

12. 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.

13. 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.

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

20. 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.

21. 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.

22. Fig. Basic block diagram of charging system

23. Fig. GTO BUCK Converter

24. 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.

26. voltage
error
voltage
scope
voltage
f(u)
temperature
temp
setpoint
temp
scope
temp
error
temp
f(u)
Voltage
To Workspace 1
out
To Workspace
in
Mux 5
Mux
Mux 4
Mux
Mux 3
Mux
Mux2
Mux
Fuzzy Logic
Controller
Demux
Demux
setpoint
Fig. Simulation of Rapid Battery Charger using FCS

27. 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

28. 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.

29. Thank you!