Design Document - Remote Solar Monitoring and Bateery Optimzation System

Department of Electrical Engineering,

Syed Babar Ali School of Science and Engineering,

Lahore University of Management Sciences, Lahore, Pakistan

Remote Solar Monitoring System

By

Mamoon Ismail Khalid 2014-10-0187

Mohammad Ahsan Azim 2014-10-0021

Mohammad Hamza Khawaja 2014-10-0046

A report submitted in partial fulfillment of the requirements

for the degree of

BS Electrical Engineering

Syed Babar Ali School of Science and Engineering,

Lahore University of Management Sciences

Under the supervision of

Dr./Mr. Jahangir Ikram Dr./Mr. Naveed Arshad

Designation: Associate Professor EE LUMS Designation: Associate Professor CS LUMS

Email: [email protected] Email: [email protected]

Date: May/2014

mailto:[email protected]

[email protected]%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20


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Table of Contents

Acknowledgement……………………………………………………………………………………………………………………………… 4

List of Figures ................................................................................................................................................ 3

...................................................................................................................................................................... 4

Acknowledgments ......................................................................................................................................... 5

Problem Statement ....................................................................................................................................... 6

1. Simulation Results of Proposed Design ................................................................................................ 7

2.1 Initial System Diagram .................................................................................................................. 7

2.2 Design Options within Each Project Area ..................................................................................... 9

2.3 Simulation Methodology for each Project Area ......................................................................... 14

2.4 Integrated Design Simulation ..................................................................................................... 14

3. Finalized Design .................................................................................................................................. 17

3.1 Final System Diagram .................................................................................................................. 17

4. Progress of the entire year ................................................................................................................. 22

Hardware Implementation: .................................................................................................................... 24

Switching Implementation: ..................................................................................................................... 26

5. Problems faced ................................................................................................................................... 31

Results: .................................................................................................................................................... 32

Future Work: ........................................................................................................................................... 33

References .................................................................................................................................................. 35


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List of Figures

Figure 1 Picture of the final working project ................................................................................................ 4

Figure 2-A General Building and battery monitoring system ....................................................................... 8

Figure 3 Egauge. It identifies the solar panels output, the power consumed from the grid and the

consumption of the user ............................................................................................................................... 9

Figure 4 Magnetic field measurements of a lithium iron phosphate during charge and discharge ........... 12

Figure 5 Simulink Model of the simulation ................................................................................................. 15

Figure 6 Stored Charge on the battery. (State of charge vs Time) ............................................................. 16

Figure 7 No. of charge/discharge cycles ..................................................................................................... 16

Figure 8 Battery's instantaneous capacity after considering the effect of temperature, no. of cycles and

the c-rate ..................................................................................................................................................... 17

Figure 9 a) effect on capacity with temperature at different c-rates b) our linear approximation ......... 19

Figure 10 a) Effect on the capacity with umber of cycles at different DOD. b) Linear piecewise

approximation with a DOD of 30% ............................................................................................................. 19

Figure 11 Flowchart showing the entire process associated with the project ........................................... 24

Figure 12 Schematic for switching scheme based on transistor logic ........................................................ 27

Figure 13 Switching scheme using Relays ................................................................................................... 30


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Figure 1 Picture of the final working project


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Acknowledgments

The authors wish to express sincere appreciation to Dr. Jahangir Ikram and Dr. Naveed

Arshad for their supervision during the course of this senior project and in the

preparation of this manuscript. Thanks also to Mr. Nauman Zaffar for his valuable input

about hardware implementation of the project.


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Problem Statement

Basically the goal of our project is to devise methods to solve the problems faced by

numerous renewable energy systems which include the accurate and cost effective

measurement of the state of charge of the installed batteries as well as the

production history of the solar panels and the consumption of the user.

There remains a need for a system for measuring the state-of-health of batteries

which ameliorates disadvantages of the prior art. There is a particular need for such

systems which can be adapted quickly to accommodate new types of battery.

Often the predicament that is faced when a Renewable Energy system (Solar, wind,

biogas etc.) is installed is that the batteries that store the generated energy are

being discharged, the users are unaware of the amount of charge left and the rate of

discharge of the battery, which can often lead to shutting down of power

instantaneously (in times of increased load or prolonged usage) without any prior

warning to the user. This is a problem of particular interest not just in stationary

renewable systems but is also an area of interest for mobile storage batteries

(Electric Vehicles). Most of the state of the charge measurement systems installed in

electronics like UPS, etc. are not accurate enough and do not give a real time

measurement of the charge left /discharge rate, Load and Energy generation levels.


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1. Simulation Results of Proposed Design

2.1 Initial System Diagram

Batteries are an integral part of any solar panel installation. Numerous battery banks

may be connected to the system in order to cater the needs in case of a shutdown or

simply for means of storage of charge.

These batteries are connected to our battery monitoring system. The basic purpose of

the battery monitoring system is to effectively measure the state of charge of the

batteries. Also it ensures that the batteries are well protected from deep discharging as

well as form being over charged. This is the reason why numerous backup battery banks

are be connected so that they can be switched in order to ensure uninterrupted power

supply as well efficient usage of the batteries.

Numerous battery banks

Battery Monitoring System

Building Management System

User Interface


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Figure 2-A General Building and battery monitoring system

The Building Management system is responsible for measurement of the load

connected with the solar panels. This is an inherent problem with PV systems that

consumers are unaware of how much load can be connected to the panels without over

discharging the batteries. Hence by providing the consumers with adequate knowledge

regarding their consumption the performance and lifetime of our system can be

increased appreciatively. Furthermore the production of the panels is also measured so

that the user can identify any faults or discrepancies associated with his system and can

rectify the faults.

The web based transfer of data as the name suggests is the transmission of the

measured date from the batteries, the panels and the load to the users by means of

GSM module as well as a web based server. The end product of the user interface would

be similar to that of Egauge software based on the management of Photo Voltaic

systems. A screenshot of the Egauge is provided below[8]:


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Figure 3 Egauge. It identifies the solar panels output, the power consumed from the grid and the consumption of the user

2.2 Design Options within Each Project Area

The major aspect of the project is the measurement of the state of charge of the

batteries. Measuring the state of charge is an extremely non trivial task given the

unreliable performance of batteries in different operating conditions. Apart from this

another severe problem faced when working with batteries is that their capacity isn’t

constant but also varies with time. Hence steps had to be carried out for the

approximation of the capacity of the battery. There are several methods for measuring

the state of charge of the batteries which are explained below:


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1) Integrated Current:

The algorithm used for measuring the state of the charge (SOC) using integrated current

is a fairly simple and straight forward approach to the problem. You measure the

current flowing out of the batteries using an AMP-HOUR meter and quite simply

integrate the current to find the charge that is flowing out of the battery. [1]

Q( ) ∫ ( )

Now after the present charge in the battery is known the computation of the state of

charge is simple process. The initial charge in the battery (Qc) is given in the battery

parameters and the instantaneous charge flowing out the battery (Qi) is subtracted

from it. This will give us the amount of charge left in the battery. Hereon afterwards the

SOC can be calculated using the following:

SOC (t) = 100 (Qc - Qi (t)Qc).

However there are several problems associated with this approach. Firstly the rated

capacity of the battery needs to known. This is usually provided by the manufacturer

but as stated earlier the capacity of a battery is not constant but a variable dependant

on various parameters which need to be put into consideration.

Secondly due to the integral in the formula if the initial state of the charge has an error,

this error is translated in all the subsequent calculations of the SOC.

2) Electrolyte Specific Gravity:

Electrolyte Specific gravity is a function of the amount of acid in the battery and is

linearly proportional to the charge in the battery left. It is used for flooded batteries

(Lead Acid , Nickel-Cadmium etc) .


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The working of this method is based on the chemical compostion of the batteries .As the

state of the charge increase the electrolyte (sulfuric acid) gets broken down in Sulfate

ions which results in the flow of charge. The presence of acid ions in the water changes

the specific gravity of the battery water. This change in the specific gravity of the battery

water as a result of increase (or decrease) in the sulfate (acid) ions is used to monitor

the battery state of the charge.

The experimental relationship between the battery charge capacity and the specific

gravity is given below:[2]

percentage of

charge

12 volt battery

voltage

24 volt battery

voltage

specific

gravity

100 12.70 25.40 1.265

95 12.64 25.25 1.257

90 12.58 25.16 1.249

85 12.52 25.04 1.241

80 12.46 24.92 1.233

75 12.40 24.80 1.225

70 12.36 24.72 1.218

65 12.32 24.64 1.211

60 12.28 24.56 1.204

55 12.24 24.48 1.197

50 12.20 24.40 1.190

40 12.12 24.24 1.176


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30 12.04 24.08 1.162

20 11.98 23.96 1.148

Table 1: Chart from the Trojan Battery company for Trojan L-16 batteries

However Specific gravity method has its disadvantages. It requires a long rest period

before is required before charge/discharge can be measured .The reason for this lies in

electrolyte diffusion dynamics. Apart from this another salient issue is that the batteries

used in PV systems are sealed gel batteries while the specific gravity method is only well

suited for flooded batteries.

3) Quantum Magnetism:

Quantum magnetism is a new technology that is developed to efficiently and accurately

measure the battery SOC. It employs the concept that different materials have different

magnetic susceptibilities. Measuring the resulting change of the magnetic field with a

sensor responding to magnetism provides linear SOC information.

The sensor employed for this method is called the Q-MAG senor and it is installed to the

batteries. [3][4]

Figure 4 Magnetic field measurements of a lithium iron phosphate during charge and discharge


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This method has a lot of advantages over other techniques. We see no rubber band

effect that is typical with the voltage method in which the weight of discharge lowers

the terminal voltage and the charge lifts it up. It can also be used to measure the SOC

while the battery is being charged (in addition to discharging cycle SOC measurement) .

The Q-MAG sensor does not need to be in contact with the battery terminals (which in

the case of high voltages is desirable) and is only limited in usage if the battery casing is

of a ferrous material (which is a rarity).

However this approach is still in the research phase and is not yet employed in large

scale applications.

4) Voltage Based Methods:

An important method of measuring the battery’s state of charge is by using its terminal

or open circuit voltage. A simple voltmeter suffices for this purpose. The battery voltage

decreases as the battery discharges to an extent dependent upon the discharging

current of the battery. Knowing the relationship of battery voltage and SOC allows the

voltmeter to be calibrated to report SOC. Hence the voltage would be used to measure

the state of the charge of the battery.

However there are some limitations associated with this concept as well. The battery

voltage varies with the change in the ambient temperature and the discharging current

and hence this deters the reliability of using voltage as a measure of the sate of charge

of the batteries. Another issue with this method occurs when disturbing the battery

with a charge or discharge. This agitation distorts the voltage and no longer represents

the true state-of-charge. To get accurate measurements, the battery needs to rest for at

least four hours to attain equilibrium; battery manufacturers recommend 24 hours.


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Adding the element of time to neutralize voltage polarization does not sit well with

batteries in active duty.

2.3 Simulation Methodology for each Project Area

Our major part of the project depends on the accurate measurement of the state

of charge of the batteries. A few of the methods used for this purpose have been

explained above along with their shortcomings. The method that suited our application

was the coulomb counting method due to its fairly low dependence on temperature as

well as its higher degree of accuracy as compared to other methods. The effect of

temperature plays a vital role on the battery’s performance as especially in our

environment the temperature fluctuates and hence this effect needs to be

compensated. Another problem associated with the battery is related to their capacity

and hence in our simulation numerous factors were incorporated for the calculation of

the instantaneous capacity of the battery.

2.4 Integrated Design Simulation

Our simulation displayed the output graph for the battery’s capacity, its number

if charge/discharge cycles and the instantaneous state of charge of the battery. In our

simulation we connected two battery banks simultaneously to the load one of which

would operate at a time. The purpose of these two banks was to ensure uninterrupted

power supply as well as to prolong the life of the battery by preventing it from over

discharging or over charging. The simulation model is illustrated below along with the

output graphs:


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Figure 5 Simulink Model of the simulation

Charge Integrator

No. of charge

cycles of the

battery

Relay switching

between

batteries

Temperature

Effect and Final

Capacity

Effect of

charge cycles

on capacity


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Output Graphs:

Figure 6 Stored Charge on the battery. (State of charge vs Time)

Figure 6 displays the constant charging and discharging of the batteries. As we have set our DOD to 30%

whenever the charge of the battery reaches 70% the relays switches the load to the second battery

bank. Furthermore it can also be observed that for the first 400 cycles the capacity of the battery

remains equal to the rated capacity due to the fact that the effect of cycle count on the negligible for

the first 400 cycles.

Figure 7 No. of charge/discharge cycles

Figure 7 illustrates the count of the discharge cycles. Whenever the battery’s state of charge reaches 70

% we count that as a complete discharge cycle. Thus due to the constant charging and discharging of the

batteries the number of cycles also progressively increases.


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Figure 8 Battery's instantaneous capacity after considering the effect of temperature, no. of cycles and the c-rate

Figure 8 further explains the effect of all the above mentioned factors on the instantaneous capacity of

the battery. After approximately 400 cycles a decrease in the capacity can be seen. Also the successive

drops in the capacity are related to the constant discharge current or the c-rate of the battery. In

addition the two notable peaks in the capacity are due to our temperature blocks where the ambient

temperature is incremented by 5 .

3. Finalized Design

3.1 Final System Diagram

In our simulation we have connected two battery banks with the load. The state of

charge of these two batteries needs to be measured. However the instantaneous

capacity of these batteries changes and this also has to be put into consideration. The

capacity of the battery is adversely affected by the number of charge/ discharge cycles

of the battery or by the life count of the battery. The life of a battery is defined as the

number charge/discharge cycles a battery can perform before its capacity falls to 80% of

its rated capacity.

Another significant factor is the temperature. At higher temperatures the chemical

activities such as the redox reactions speed up resulting in an increase in the capacity of

the battery. Hence temperature ranging from 25 to 40 is optimum for the battery.


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At temperatures lower than this the performance of the battery deteriorates

considerably.

Lastly the effect of c-rate is also taken into account for as it has an adverse effect on the

performance of the battery. C-rate is defined as the rate of discharge of current. Hence

for a 45Ah battery a c-rate of 0.1 means that the battery would provide us with 4.5A

current for duration of 10 hours. As the battery’s c-rate progressively increases, the

capacity of the battery is reduced as large current is being withdrawn constantly from

the battery.

Furthermore, the capacity of the battery is also linked to the depth of discharge (DOD)

at which the battery is being operated. The Depth of Discharge is defined as the amount

of charge being withdrawn from the battery. Depth of Discharge is the complementary

function as compared to the state of charge of the battery. A higher DOD means that

large currents are being withdrawn from the battery and hence due to this deep

charging the capacity is greatly affected. The significance of calculating the capacity is to

minimize the error as this instantaneous capacity is used for the state of charge

computation.

Considering these effects we have put a limit on the DOD of our batteries. Hence we

operate our batteries at a DOD of 30%. Whenever the State of Charge (SOC) equals 70%

the battery banks are switched. The purpose of this is to avoid the deep discharging of

the batteries and to prolong the number of discharge and charge cycles a battery can

perform.

The effect of temperature, number of cycles and the c-rate are illustrated in the

following graphs provided to us by the manufacturer:


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Figure 9 a) effect on capacity with temperature at different c-rates b) our linear approximation

It can be observed from Figure 9 that as the c-rate is adversely related to the capacity of the battery.

Hence as the c-rate goes from 0.1c to 2.0c the capacity of the battery diminishes to about 10% of its

rated value. This is precisely why for our simulation we have used a c-rate of 0.1c but different c-rates

can also be accommodated in our simulation if a low capacity but a high c-rate is demanded by the user.

Figure 10 a) Effect on the capacity with umber of cycles at different DOD. b) Linear piecewise approximation with a DOD of 30%

Figure 10 shows the relationship between the number of cycles and the capacity of the battery at

numerous DOD rates.


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Design of an amp hour meter is mostly based on the design of an ammeter. A shunt is

used to measure current and it must be inserted in the negative leg of the battery

input/output terminal. A shunt is actually a very low resistance which produces voltages

across its terminal proportional to the current flowing through it. The voltage that

occurs across the shunt is put through an op-Amp In order to scale it to +5/-5 volts. The

output of the op-Amp becomes input to an Analog to digital converter. The output of

the ADC is fed to the micro controller where the blocks would be implemented.

Explanation of the Simulation:

Source Block

We have simulated the batteries in our simulation with function blocks that have a

charging current of 8A while a discharging current of 9A. The disparity amongst these

figures is due to the fact that batteries discharge in a shorter period of time.

Integrator Block

The next block is the current integration block where the equation

Q( ) ∫ ( ) is computed. This tells us the amount of charge that is present in the

battery. The rest of the simulation is based on the computation of the capacity of the

battery from which the state of charge needs to be obtained.

Cycle Count Block

The second block is called the cycle count block and it calculates the number of

discharge cycles of the battery. A discharge cycle has been defined when the SOC

reaches 70% of its value or when it has to be recharged again.


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Relay Block

As mentioned previously two battery banks are connected with each operating at a DOD

of 30%. Hence when battery A reaches a SOC of 70 % it is disconnected from the load

and is recharged while Battery B is consequently connected to the load so that load

receives uninterrupted power supply.

Cycle count vs Capacity Block

Furthermore, after the charge cycles of each battery have been calculated we move into

the block where the relationship between the capacity of the battery and the number of

cycles is devised by using piecewise linear approximation of the graphs provided by the

manufacturer. This block incorporates the effect on the capacity by the no. of discharge

cycles and the DOD of the battery.

Temperature & C-Rate vs. Capacity Block

Lastly the dependence of the capacity on the ambient temperature and the c-rate has to

be considered. As mentioned previously an increase in temperature increases the

capacity while c-rate has an inverse relationship with the capacity of the battery. These

relationships are formulated in this block by using the graphs illustrate previously. In

order to model the variation of temperature we have used a function block where the

temperature remains constant at 25 for majority of time but at significant intervals it

increases by 5 resulting in an increase in the capacity of the battery.

The final capacity that is calculated from these blocks is our instantaneous capacity

which is fed back to all blocks for calculation of instantaneous capacity as well as the

state of charge of the battery.


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4. Progress of the entire year

6th September

Submission of the project proposal

6th October

Order the components required for implementation and understand all the designs and research related to the project

6th Novemeber

Deciding the best technique for implementation of the project especially for calculating the state of charge of the installed batteries

6th December

Have a running simulation for all the parameters involved including State of Charge and the production levels.


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21st January

Initial Harware implementation and interfacing with smaller lithium ion batteries

21st February

Used the DAQ Card and ensured proper interfacing of the software simulation in order to ensure accurate data processing.

21st March

Designed the overall switching scheme between the installed batteries using transistor logic which introduced numerous glitches.

21st April

Rectified the switching scheme using an entirely different approach. Calculated the power input and output of the batteries instanatenously and plotted the results on an application similar to EGAUGE

28th April

Open house for the projects. Presented the idea as well completely functional project to members of reputed organizations and faculty members.


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Hardware Implementation:

Figure 11 Flowchart showing the entire process associated with the project

First of our proposed tasks was to calculate the charging and discharging currents

of the batteries. However detecting currents and feeding it into digital circuitry is never

a trivial task. We achieved this by using shunt resistors of negligible resistance and

connected them in series with the load. The voltage across these resistors was then

detected and fed into the DAQ card which then was processed using the Simulink model

developed earlier. The temperature feature was incorporated by using


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LM35(temperature sensor) whose output voltage was fed in the DAQ card and the

temperature was calibrated with the output voltage levels.

The voltage acquired was converted into current using simply Ohm’s law and

then the current was integrated over sampling time in order to obtain the instantaneous

charge of the batteries. The charge of the batteries was then compared and then DAQ

output was used to implement the switching scheme of the batteries which is described

below.

The implementation of proper switching between the batteries was an integral

part of our project. The basic concept of switching was that as soon as the SOC of

battery 1 drops to below 70% of its instantaneous capacity the battery should be

disconnected from the load and connected to the charging source while battery 2

should provide power to the load. The number of battery banks could be increased

which was facilitated by our code but the number was kept to 2 simply due to cost

constraints. The switching ensured uninterrupted power supply to the load as well

increased efficiencies for the batteries. The response time for the switching was also

calculated to be less than 0.1 second ensuring that the user did not notice any adverse

effects of switching.

For the prototype the load we used was a 16W Energy Saver. A 150 W inverter

was connected in our circuit in order to provide AC supply to the load. The load was

generic and any other equipment could be connected but due to limited capacity of the

batteries we decided to use energy savers due to their favorable response in steady

state conditions.


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Switching Implementation:

Our first approach towards switching between batteries was based upon

transistor logic and their gate driving circuitry. The DAQ outputs for active switching

were fed into the gate drivers whose low and high outputs were used for the turning on

and off of the power mosfets used. The inherent anti parallel diodes of the mosfets

were taken care of by power diodes connected with all the transistors. The schematic of

this approach is provided below.


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Figure 12 Schematic for switching scheme based on transistor logic

The transistor logic introduced a few glitches in our scheme which included the

clamping of the load voltage to around 8 volts. Although this could be acceptable for the

sake of our projects but we still tried to devise other methods to overcome this

obstacle. The underlying problem was that at low frequencies the gate drivers are not

able to persistently keep the transistors on. Hence the transistors even when in the on

Charging

Source

Battery 1

Battery 1

Gate Driving Circuit

Electrical Load

Emergency Shutdown switch


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state had impedance and restricted the flow of current resulting in a substantial voltage

drop across the mosfets. One of the methods that we tried to implement was charge

pumps.

A charge pump is a kind of DC to DC converter that uses capacitors as energy

storage elements to create either a higher or lower voltage power source. Charge

pumps use some form of switching devices to control the connection of voltages to the

capacitor. For instance, a two-stage cycle can be used to generate a higher pulsed

voltage from a lower-voltage supply. In the first stage of the cycle, a capacitor is

connected across the supply, charging it to that same voltage. In the second stage of the

cycle, the circuit is reconfigured so that the capacitor is in series with the supply to the

load. Ignoring leakage effects, this effectively provides double the supply voltage to the

load (the sum of the original supply and the capacitor). The charge pumps seemed to

beneficial for our purposes as they could ensure constant current to the gates of the

transistors keeping them persistently on and avoiding any appreciable voltage drop

across the transistor. However numerous problems are associated with the practical

implementation of charge pumps. These include excessive dependence on noise as well

as voltage levels of the circuit. Hence we also surveyed for another method which was

primarily based on the application of relays.

http://en.wikipedia.org/wiki/DC_to_DC_converter

http://en.wikipedia.org/wiki/Capacitor

http://en.wikipedia.org/wiki/Voltage

http://en.wikipedia.org/wiki/Leakage_%28electronics%29


29

The relay logic mainly consisted of two relays; one for the charging side of the

batteries while the other for the discharging side of the batteries. The digital output of

our logic circuit was fed into the relay for the energizing/de-energizing of the relay coil.

This relay then provided separate paths for the charging and discharging of the

batteries. The schematic of this scheme is illustrated below:


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Figure 13 Switching scheme using Relays

The relay logic provided us with fruitful results as proper switching between

batteries was obtained. The DC source for the charging of batteries was WAPDA

however solar panels were also tested with the batteries which worked perfectly.

However the use of panels at this moment was not feasible as 1 24V panel was not

sufficient enough to provide large charging currents required by the batteries.

Relay(Discharging)

Electrical Load

Battery 1

Battery 2

Relay

(Charging)


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At the output of the relay a 150W inverter was connected in order to provide us

with 220V AC line voltage to the load. A 16 W energy saver was selected due to

distinctive peaks associated with these devices at the start and then smooth steady

state behavior. However we did test our system with other prototype loads which

include tungsten film bulbs, DC loads such as DC fans as well as 12 V motors.

Lastly the most important phase of our project was to display all the data in a

visually appealing manner to the user. This data included the effective state of charge of

the batteries, the power drawn by the load, the input power of the battery through the

charging circuit and the power output of the load. The next step was to record all this

real time data and display it instantaneously to the user. Also the control of the

application and the usage of the batteries were handed to the user who had the control

over the threshold limit of the Depth of Discharge of the batteries. According to the

demand posed by the load the user could set his depth of discharge threshold of each

battery in order to ensure that the load continues to receive uninterrupted power

supply.

5. Problems faced

Hardware implementation was the most important challenge we faced throughout our

project. Some of the problems associated with the hardware implementation are as

follows:

The real time behavior of batteries is not a well specified quantity which changes

due to numerous factors and displays various anomalies. All these irregularities

needed to be incorporated.


32

Interfacing the analog circuitry with the digital part is always a non-trivial task

especially due to the fact that we had to sense the current. Different ground

terminals of analog and digital also posed problems for us.

Most importantly the transistor logic that we used for the switching scheme had

too many glitches which forced us to opt for another approach. The clamping of

voltage and the significant losses were the undesirable features that forced us to

use relays.

Lastly, after the extraction of all data the setup of e-gauge like software was a

daunting task due to construction of a dedicated storage unit as the extraction of

real time data from MATLAB. Generally MATLAB provides data only after the

simulation has run its course but did devise a method of extracting instantaneous

data and display it simultaneously to the user.

Results:

Our project primarily helped us to achieve greater efficiencies associated with

batteries. By incorporating an efficient switching scheme and calculating

instantaneous capacity we were able to deal with one of the most unreliable

technologies affiliated with renewable energy systems. With our switching

algorithm we were able to increase the efficiencies of batteries by approximately

30%. Battery state of charge, input and output power was also accurately

calculated and displayed instantaneously which is only currently available in state

of the art solar monitoring technologies such as EGAUGE.


33

Future Work:

Implementation of an android application for the display and record of physical

data.

Set up of a web server to be accessed throughout LUMS based on similar lines as

EGAUGE

A publication also in the works for the efficient usage of batteries in renewable

systems


34


35

References A Reliable Approa’ch to Charge-Pumping Measurements in MOS Transistors GUIDO GROESENEKEN, HERMAN E. MAES, NICOLAS BELTRAN, AND ROGER F. DE KEERSMAECKER

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Arlington, VA June 25-27, 2001

[2].Other Power, “Battery metering”,

http://otherpower.com/otherpower_battery_metering.html

[3]. “How to Measure State-of-charge” , Battery University ,

http://batteryuniversity.com/learn/article/how_to_measure_state_of_charge

[4]. “Impedance Spectroscopy Checks Battery Capacity in 15 Seconds”,Battery University

,http://batteryuniversity.com/learn/article/impedance_spectroscopy_checks_battery_c

apacity_in_15_seconds

[5]. “Technical note on “How is IR measured” by

CellTrak”,http://www.celltraksystems.com/downloads/How%20is%20IR%20measured.pd

f

[6].Battery University ,“Rapid Testing Portable Batteries

“,http://batteryuniversity.com/learn/article/rapid_testing_portable_batteries

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page1.asp

[8]. EGAUGE, www.Egauge.net/Devices .

[9].Cadex ,SPECTRO™ TEST PLATFORM FOR LEAD ACID BATTERIES ,

http://www.cadex.com/technology/our-technologies/spectro

[10]. Cell Track Integrates ,http://www.celltraksystems.com/technology.html

http://otherpower.com/otherpower_battery_metering.html

http://batteryuniversity.com/learn/article/how_to_measure_state_of_charge

http://batteryuniversity.com/learn/article/impedance_spectroscopy_checks_battery_capacity_in_15_seconds

http://batteryuniversity.com/learn/article/impedance_spectroscopy_checks_battery_capacity_in_15_seconds

http://www.celltraksystems.com/downloads/How%20is%20IR%20measured.pdf

http://www.celltraksystems.com/downloads/How%20is%20IR%20measured.pdf

http://batteryuniversity.com/learn/article/rapid_testing_portable_batteries

http://www.buchmann.ca/article22-page1.asp

http://www.buchmann.ca/article22-page1.asp

http://www.egauge.net/Devices

http://www.cadex.com/technology/our-technologies/spectro

http://www.celltraksystems.com/technology.html

Documents

Design Document - Remote Solar Monitoring and Bateery Optimzation System