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Theses and Dissertations
2012
An intelligent lead acid battery management systemfor solar and off-peak energy storageMing-Chieh ChenThe University of Toledo
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Recommended CitationChen, Ming-Chieh, "An intelligent lead acid battery management system for solar and off-peak energy storage" (2012). Theses andDissertations. Paper 285.
A Thesis
Entitled
An Intelligent Lead Acid Battery Management System for
Solar and Off-Peak Energy Storage
by
Ming-Chieh Chen
Submitted to the Graduate Faculty as partial fulfillment of the requirements for the
Master of Science Degree in Electrical Engineering
_______________________________________
Dr. Thomas A. Stuart, Committee Chair
_______________________________________
Dr. Richard Molyet, Committee Member
_______________________________________
Dr. Junghwan Kim, Committee Member
_______________________________________
Dr. Patricia R. Komuniecki, Dean
College of Graduate Studies
The University of Toledo
May 2012
Copyright 2012, Ming-Chieh Chen
This document is copyrighted material. Under copyright law, no parts of this
document may be reproduced without the expressed permission of the author
iii
An Abstract of
An Intelligent Lead Acid Battery Management System for
Solar and Off-Peak Energy Storage
by
Ming-Chieh Chen
Submitted to the Graduate Faculty as partial fulfillment of the requirements for the
Master of Science Degree in Electrical Engineering
The University of Toledo
May 2012
The development of micro-girds which combine several localized systems
into a small power network has drawn recent attention. They can operate either as a
self-contained energy network or they can be integrated into a centralized power grid.
A variety of technologies have been studied to use solar energy systems as a form of
micro-grid to enhance the reliability and performance of the system. However, the
operation of these systems is not without problems, and intermittency of the energy
from the sun is the major one. This thesis proposes a microcontroller-based solar
energy management system which combines battery management and storage
technology to address this issue. This approach uses an energy system with a solar
panel array, a maximum power point tracking (MPPT) unit, a battery management
iv
system, and a bidirectional inverter which is connected to the electric utility grid. Off-
peak energy management also is embedded in the system to further increase the
economic benefits.
v
Acknowledgements
I would like to sincerely thank my advisor Dr. Thomas A. Stuart, who asked me
to join this research project and gave me support and guidance in the course of this
research. He always patiently answers the questions I raised, and provides me with
insightful advice to cope with the problems. I also appreciate that he carefully reads
the manuscript, corrects errors, and indicates how to improve the quality of this thesis.
I would like to render my thanks to Dr. Molyet and Dr. Kim for their advice as the
committee members. I also want to thank my friends for their support and
encouragement in the areas related to my study and living in the United States.
I want to dedicate my gratitude to my parents, their love and support backs me up
to complete this study.
I also would like to acknowledge the Ohio Department of Transportation who
supported this research under agreement number, 23339, state job number 43683.
vi
Table of Contents
An Abstract of .............................................................................................................. iii
Acknowledgements ....................................................................................................... v
Table of Contents ......................................................................................................... vi
List of Figures .............................................................................................................. ix
Chapter 1 Introduction .................................................................................................. 1
1.1 Research Background and Motivation ................................................................ 1
1.2 Literature Review ................................................................................................ 4
1.3 Research Objective ............................................................................................. 6
Chapter 2 Methodology ................................................................................................ 7
2.1 The Battery Management System ....................................................................... 7
2.2 The Off-peak Energy Management System ...................................................... 11
2.3 State of Charge (SOC) Calculation ................................................................... 13
Chapter 3 System Description .................................................................................... 15
3.1 Solar Panel Array Supported by the MPPT Unit .............................................. 16
3.1.1 Solar Panel Array ....................................................................................... 16
vii
3.1.2 The MPPT Unit .......................................................................................... 17
3.2 BMS and Battery Bank ..................................................................................... 20
3.2.1 Battery Bank .............................................................................................. 20
3.2.2 BMS ........................................................................................................... 22
3.3 Central Control Unit ......................................................................................... 25
3.4 Graphical User Interface (GUI) ........................................................................ 27
3.5 Protection Unit .................................................................................................. 30
3.5.1 Hardware .................................................................................................... 30
3.5.2 Software ..................................................................................................... 31
3.6 CAN Function ................................................................................................... 31
Chapter 4 System Performance and Research Outcomes ........................................... 33
4.1 Performance of the BMS .................................................................................. 34
4.2 SOC Estimation ................................................................................................ 35
4.3 Graphical User Interface (GUI) ........................................................................ 37
4.4 Data Analysis .................................................................................................... 38
Chapter 5 Conclusion and Direction of Future research ............................................. 40
Reference .................................................................................................................... 43
Appendix ..................................................................................................................... 46
viii
A. Introduction to Lead Acid Batteries ................................................................... 46
B. Thin-film Solar Cells .......................................................................................... 47
C. The Maximum Power Point Tracking (MPPT) Method .................................... 48
ix
List of Figures
Figure 1-1 Diagram of Residential Grid Connected PV System .................................. 2
Figure 1-2 Hourly Average Residential Load Profile ................................................... 3
Figure 2-1 Block Diagram of the BMS......................................................................... 9
Figure 2-2 Flowchart of BMS Algorithm ................................................................... 10
Figure 2-3 Off-peak Storage Strategy ......................................................................... 12
Figure 2-4 Example of SOC versus OCV Chart ......................................................... 14
Figure 3-1 Solar Array with Energy Storage Battery .................................................. 16
Figure 3-2 The 1KW Roof Mounted Solar Array ....................................................... 17
Figure 3-3 System Schematic ..................................................................................... 19
Figure 3-4 Schematic of Boost Converter .................................................................. 19
Figure 3-5 The MPPT Unit ......................................................................................... 20
Figure 3-6 Lead Acid Battery ..................................................................................... 22
Figure 3-7 Configuration of the ECU Module ............................................................ 23
Figure 3-8 Configuration of the ECU Module ............................................................ 23
Figure 3-9 Battery Bank with one BMS Local Module .............................................. 24
x
Figure 3-10 The Complete Battery Bank Installation ................................................. 25
Figure 3-11 Central Module and PC with ABC150 Remote Control Panel ............... 26
Figure 3-12 ABC-150 Bi-direction Inverter ............................................................... 26
Figure 3-13 Diagram of Inverter, Central Module, CAN Interface, MPPT Module,
ECU Module, and EQU Module ......................................................... 27
Figure 3-14 Page Showing the Initial Settings ........................................................... 28
Figure 3-15 Main Page................................................................................................ 28
Figure 3-16 Dispatch Function Page........................................................................... 29
Figure 4-1 Basic Schematic of the System ................................................................. 33
Figure 4-2 Time Diagram of Vmax and Vmin with BMS .......................................... 34
Figure 4-3 Time Diagram of Vmax and Vmin without BMS ..................................... 35
Figure 4-4 Plot of Operation Current and SOC Curve Using OCV Method .............. 36
Figure 4-5 Plot of Battery Bank Operation Current and SOC Curve when Integrating
OCV Method with Coulomb Counting Method ....................................... 36
Figure 4-6 GUI and Data Plots for the Solar Battery System ..................................... 37
Figure 4-7 Time Chart of Solar Voltage and Battery Bank Voltage .......................... 38
Figure 4-8 Time Diagram of Load Simulation and Output Current Provided from
Solar and Battery Bank ............................................................................. 39
xi
Figure A-1 Structure of a Lead Acid Battery.............................................................. 46
Figure B-1 The Sketch of Solar Cell........................................................................... 48
Figure C-1 The Max Power Point of the I-V Curve and the P-V Curve .................... 49
1
Chapter One
Introduction
1.1 Research Background and Motivation
With diminishing petroleum energy resources, developing renewable energy
through wind, sun, and bio-fuel becomes imperative. Taking into account both
technology and installation cost, solar and wind energy have been assessed as the best
candidates for integration into the incumbent power systems. For stand-alone power
system applications, micro-girds which combine several localized systems into a
small power network have drawn recent attention. Figure 1-1 shows a residential
stand-alone solar system operating either as a self-contained energy network or as
part of a centralized power grid or micro-grid network.
However, attempts to substitute wind and solar power for power derived from
fossil fuels face many challenges. One major disadvantage of wind and solar power
systems is the intermittency of the energy supply. The velocity of wind changes often,
2
and solar energy delivery varies depending on the level of sunlight. Therefore, the
level of energy generated fluctuates from minute to minute, daily, and with the
seasons.
Figure 1-1 Diagram of Residential Grid Connected PV System [1]
Energy sources that are characterized by intermittent power influx into the
public utility grid can disturb the grid operation. Therefore, a grid with fossil or hydro
energy sources must be able to accommodate the power variation from the renewable
energy sources. This is why solar and wind systems complicate the operation of
electricity dispatch systems which must continuously maintain a certain level of
power to match the load.
3
As shown in Figure 1-2, the load curve for a power system is not constant, and
it has a slowly varying energy consumption pattern over time. To provide the energy
required during a peak period, power companies tend to supply additional power by
using smaller gas turbine generators. This is a common practice to provide the extra
energy to meet the demand during the peak time, but at a higher cost.
Figure 1-2 Hourly Average Residential Load Profile (Source: Southern California
Edison Territory 2008)
On the other hand, surplus energy at lower cost is available during the low
load periods, because the power plants are operating below full capacity. One way to
solve this problem is to store energy during the low load period and then release this
4
energy during the peak load period. This enables the power company to cut costs and
pass part of the benefit to the electric power customers. An off-peak energy storage
system also can cushion the solar or wind energy power variation.
As renewable energy becomes widespread, a robust power dispatching system
is a must. The purpose of this study is to propose an approach to tackle these
emerging issues and make a contribution to real world applications.
1.2 Literature Review
There are many articles in the literature about renewable energy applications
in different areas [2-4]. Of these, solar energy technology is the most appropriate for
residential applications because of physical size and safety issues.
To solve the intermittency problem, an energy storage component is needed to
stabilize the energy output, and many types of energy storage systems have been
proposed in the literature. Primary technologies include batteries, flywheels, pumped-
hydro, and compressed air. Other storage systems include thermal energy storage
systems such as underground thermal energy storage and ice storage systems [5]. At
the present time, batteries appear to be the best option because of economic
considerations [6-8]. Many types of batteries are available in the market, such as
lithium ion, zinc bromine, nickel-cadmium, sodium-sulfur, sodium-nickel chloride
5
and lead-acid system [9]. However, the advantages of low maintenance requirements
and cost effectiveness indicate that lead acid batteries are still the best choice,
especially for residential applications [8] [10].
Compared with other battery technologies, the significant drawback of lead
acid battery is the relatively short service life [8]. The factors affecting the lead acid
battery service life include acid stratification when deep discharge occurs with water
loss, and hydrogen evolution and corrosion when the battery is overcharged [11].
Acid stratification reduces the available battery capacity, and it also affects the
voltage/current performance characteristic. Water loss and hydrogen evolution along
with corrosion are primary causes of aging [8]. When using a valve-regulated lead–
acid (VRLA) type of battery under in a photovoltaic (PV) system application, the acid
stratification phenomena is not an issue. To equalize the voltage levels of the series
connected cells, a low trickle current can be used [12]. However, instead of using a
trickle charge to equalize battery cell voltages, a battery management system (BMS)
with equalization is a better option.
However, reducing the intermittency of the solar energy is not the only task
that a battery storage system can fulfill. When combined with off-peak energy storage,
a higher level of cost-effectiveness also can be achieved [13-14].
6
1.3 Research Objective
This thesis proposes an intelligent battery management system (BMS) that is
operated by the microcontroller. The system includes a solar cell array, DC-DC
converter with maximum power point tracking (MPPT), a bi-directional inverter
connected to the grid, and a graphical user interface (GUI) to monitor operation.
To verify the system operation and performance, all the data in this research
was generated from this engineering prototype rather than by computer simulation.
The results demonstrate that this system is a promising architecture for a cost
effective, high efficiency installation.
In the Chapter 2, several methodologies are described to present the core
strategy and technology. Chapter 3 describes each component in the system as well as
their tasks. Chapter 4 presents the system performance and Chapter 5 provides the
conclusion indicates the direction for future research.
7
Chapter 2
Methodology
2.1 The Battery Management System
The internal electrochemistry, and physical construction, and operating
parameters are the main factors that affect a battery’s service life. Therefore, one of
the tasks of battery management systems (BMS) in photovoltaic (PV) applications is
to allow the batteries to operate under conditions that will not reduce the service life
[15-16]. The other main functions of a BMS are monitoring and protection. The
monitoring function measures the levels of current and voltage in the solar panels and
battery bank. The BMS also helps the battery to work in a more efficient way. The
battery performance will deteriorate when substantial differences occur in the voltage
levels among individual cells. Therefore, identifying the cells with high or low
voltage levels is necessary to trigger the equalization process to balance the cell
voltages. A well-balanced battery pack will significantly extend the service life and
provide maximum capacity.
8
The Equalization Unit (EQU) is a device to balance the voltage levels among
the battery cells. Generally, there are two types of equalizer (EQU) in use, the
discharge only D type and the charge/discharge C/D type. The C/D type can reduce
the operation time by about 50% as compared to the D type [17], and it is more
efficient. Although the C/D EQU used here can only handle a few cells at a time, it
can charge low cells and discharge those with high voltages.
Figure 2-1 shows the block diagram of the BMS. After initialization, the
central module sends a voltage measurement request via a Controller Area Network
(CAN) to the ECU modules to measure the voltage level of each cell. The
equalization decision command from the previous round is also sent to the ECUs after
the voltage measurements are done.
The equalization decision depends on the differences between the maximum,
minimum and average values of the cell voltages. If the difference between Vmax and
Vavg is larger than the value of Vavg – Vmin and above the tolerance value, the
system will discharge the cell with Vmax. If the difference between Vmin and Vavg
is larger than the value of Vmax – Vavg and tolerance value, the system will charge
the cell with Vmin. This charge/discharge process will continue until the target all
9
cells come close to the average voltage level. The flow chart of the BMS algorithm is
shown as Figure 2-2.
Figure 2-1 Block Diagram of the BMS
CANLocal Selection
Charge /DischargeOperation
CANLocal Selection
Charge /DischargeOperation
DataData
EQUModule
Communication
Central Module ECU Module #3
CANLocal Selection
Charge /DischargeOperation
Data
EQUModule
ECU Module #2
CANLocal Selection
Charge /DischargeOperation
Data
EQUModule
ECU Module #1
10
Start
Initializeation
Vcharged_cell<Vavg
OrVdischarged_cell>Vavg
Vavg – Vmin > 0.05Vavg – Vmin > Vmax - Vavg
Vmax – Vavg > 0.05
Keep same process in same battery
Charge Vmin batteryl
Discharge Vmax batteryl
Yes
Yes
Yes
No
No
No
Reset Target cell selection and
equalization process
Calculate and identify measurement result
(Vavg, Vmax, Vmin and cells number)
Send cell selection and equalization process type
selection
Measure batteries voltage
Figure 2-2 Flowchart of the BMS Algorithm
11
2.2 The Off-peak Energy Management System
The solar energy system in this research also is intended to provide stored
energy during high electric power demand periods. The strategy of the ―Off-Peak
Energy Management System‖ is to store energy during low load periods and release
energy during high demand periods. Figure 2-3 shows the energy allocation decision
below chart for the system. The time when energy is to be released is from 6 to 8 a.m.
in the morning and from 5 to 7p.m. in the evening when electric power demand is
highest.
During the day time, the system stores the energy generated from the solar
panels in the battery bank for consumption in the evening. At midnight, the system
also charges the battery by importing cheap energy from public utility power grid.
This provides the base energy for the next day’s load, and it also helps retard the
sulfation process in the lead-acid battery, which occurs if the battery stays in a low
state of charge (SOC) for too long.
12
Start
Get Time data
5 ≤ Hour < 6 Vmax > 7.5v
System stand by
Release energy to grid in 10A until Vmax < 7.5V
6 ≤ Hour < 8Stop releasing energy process
Vmin < 6V
Release energy to grid in 23A
8 ≤ Hour < 17 Vmax >7.5V
Absorb energy from solar panel
Release energy to grid in 10A until Vmax < 7.5V
17 ≤ Hour < 19 Vmin < 5.7VStop releasing energy process
Releasing energy to grid in 30A
19 ≤ Hour < 22 Vmax > 7.5VRelease energy to grid in 10A
until Vmax < 7.5V
System stand by
22 ≤ Hour < 3 Vmax > 7.5VStop absorb energy
from grid
Absorb energy from grid with step current 31A, 18A,
8A until Vmax = 7.5V
3≤ Hour<5 Vmax > 7.5v
System stand by
Release energy to grid in 10A until Vmax < 7.5V
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No No
No No
No No
No No
No No
NoNo
NoNo
Figure 2-3 Off-peak Storage Strategy
13
2.3 State of Charge (SOC) Calculation
SOC is the percentage of energy capacity still available inside the battery. The
conventional method to calculate the value of SOC is by comparing the open circuit
voltage (OCV) with the battery cell performance chart. Figure 2-4 shows a typical
SOC versus OCV chart. This is a practical method, but it may fail to deliver accurate
readings. This is because the battery voltage is higher than the actual OCV value
during the charge period and is lower during the discharging period. This error
increases with current, and the OCV will not completely stabilize until several hours
after the current reaches zero.
Therefore, in order to obtain an accurate SOC value during the
charge/discharge period, the ―coulomb counting method‖ also must be used. The
OCV method generates the initial SOC value before the charge/discharge operation.
The boundary to select OCV or coulomb counting is ± 0.5A, because of the current
sensor measurement offset.
The pseudo code for SOC calculation is as follows:
If operation current -0.5A < I < 0.5A
Then SOCOCV = Slope * OCV – parameter value (2.1)
If operation current I > 0.5A or I < -0.5A
14
Then SOCCC = SOCini + ΔSOC (2.2)
and ΔSOC =
=
∫
(2.3)
Where SOCOCV = state of charge value from open circuit voltage method
SOCCC = state of charge value from coulomb counting method
= total coulombs of electric charge
Cap = the capacity of battery or battery bank
Figure 2-4 Example of SOC versus OCV Chart
15
Chapter Three
System Description
The configuration of the proposed system for this research is shown in Figure
3-1. The solar energy source and the energy storage are connected to the DC link
which leads to a bi-directional inverter linked to the power grid and local loads. The
details about the solar cell and the lead acid battery are included in the Appendix.
This research was intended to build a battery energy management system that
fits into a solar power system that is practical and useful in the real world. The whole
system consists of three sub-systems: the first is the solar panel array with the MPPT
unit which is the main energy source; the second sub-system includes a BMS and a
battery bank to optimize the energy storage capacity; the third is the central control
module and the AeroVironment ABC-150 grid connected inverter. The central
module is the brain of the system, and the ABC-150 inverter interfaces to the external
16
power grid and the load. Communication among those sub-systems uses a Controlled
Area Network (CAN) serial link for data transmission.
Figure 3-1 Solar Array with Energy Storage Battery. [18]
3.1 Solar Panel Array Supported by the MPPT Unit
3.1.1 Solar Panel Array
The CdTe thin-film solar array consists of 24 model FS-40 solar panels from
First Solar, as shown in Figure 3-2. The power output for the solar panels is about 40
watts each. The array features eight parallel-connected strings, where each string has
three panels connected in series. This provides a maximum rated power output of
about 960W, at an operating point of 165Vdc and 6Adc.
DC to DC
converter with
Peak Power
Tracking
Solar ArrayBi-directional
Inverter
Storage Battery
Local Loads
Vdc1 Vdc2 Vac
Electric GridDC Link
= direction of energy flow
MPPT
17
Figure 3-2 The 1KW Roof Mounted Solar Array
3.1.2 The MPPT Unit
Figure 3-3 shows the schematic of the MPPT unit which was developed on a
previous project by Qiang Wu and Qiang Mei [19]. The main power component of
the MPPT is the DC-DC boost converter shown in Figure 3-4. The converter is able
to boost the output voltage by transferring energy stored in the inductor to the load.
The inductor stores energy while the switch, M, is on, and releases the energy to the
load while the switch is off. The boost converter voltage transfer equation is shown
below [20].
(3.1)
Where D = duty cycle
18
Based on to this equation, the value of the input voltage is related to the duty
cycle (D). The battery bank voltage (Vout) is almost constant, so the duty cycle varies
to control the voltage level of the solar array (Vin). The duty cycle of the boost
converter is controlled by a pulse width modulation (PWM) signal which is generated
by the TL954 PWM regulator chip. The PWM output is determined by a current or
voltage set point. The voltage set point is limited at a certain level when the battery
voltage value is too high. During normal operation the current set point is provided by
the MPPT control algorithm from the Infineon C515 microcontroller.
The C515 microcontroller monitors the voltage and current from the PV array
and generates a reference signal, Isref. This is based on the MPPT control algorithm
in order to operate the TL954 PWM controller. By comparing the source current Is
with Isref, the TL594 outputs a signal to adjust the duty cycle of the boost converter
so that the input current matches the peak power point. Figure 3-5 shows the MPPT
unit hardware.
19
Figure 3-3 System Schematic [19]
Figure3-4 Schematic of Boost Converter [19]
L
M
D1R1
C1
D
RloadCV
345uH
Vs
+-
.0167ohm
Is
Is
594
Controller
Microcontroller
System Unit
Isref
Is
Vs
Vo
I3
+
-
Adjust Isref to get Ps=VsIs=Max
Vs
Is
Isref
LEM
Drive
Circuit
CAN bus To Other Control System
Units and Inverter Control
Output Voltage
Feedback SignalGate Trigger
Signal
Input Current
Feedback Signal
Input Current
Sampling
Signal
Input Voltage
Sampling
Signal
Duty Cycle
Control System
Solar 60V
G Go
Battery
86V
Grid
Ib
LEM
I3
Vo
LE
M
Ib
Inverter
20
Figure 3-5 The MPPT Unit
3.2 BMS and Battery Bank
The battery bank can store electricity energy that is delivered from the solar
array or the power grid. It is an indispensable unit when the solar energy system is
disconnected from the grid or for off-peak energy storage.
3.2.1 Battery Bank
The lead acid battery is one of the most common types of rechargeable
batteries in the world, and it is also a low cost and mature product in terms of
manufacturing technology. It is widely used in a vast number of applications such as
motor starting, backup power supplies, un-interruptible power supplies and energy
storage systems. There are three major types of lead acid battery in the market for
21
different application purposes, namely, the starter battery, the marine battery, and the
deep cycle battery.
A battery bank is a collection of inter-connected batteries that can provide
power during outages or low production by renewable energy sources. Heavy
discharge in the evening and charging in the daytime is the general characteristic of
most off-peak solar energy storage systems. Among the three major types of lead acid
battery, the deep cycle battery best matches a solar energy system. Since the deep
cycle battery features thick plates it can provide a heavy discharge for several hour,
down to a low SOC.
The 6V-200AH sealed lead acid battery made by Crown Battery Company
and shown in Figure 3-6 is used in this research [21]. 14 of these lead acid batteries
are wired together in series to deliver about 80Vdc.
22
Figure 3-6 Lead Acid Battery
3.2.2 BMS
The BMS contains a central and one or more local modules, and each local
has two functional units: an electronic control unit (ECU) and an electronic
equalization unit (EQU), as shown in Figure 3-7 and Figure 3-8 respectively. The
ECU measures the cell voltages and sets the EQU to equalize the voltage levels
among the individual cells. The BMS in this research project has three local modules
in total; two modules manage six lead acid battery cells, and the other manages the
remaining two battery cells.
23
Figure 3-7 Configuration of the ECU Module [22]
Figure 3-8 Configuration of the ECU Module [22]
24
To ensure accurate cell voltage measurements by the ECU every ten seconds,
the charge and discharge circuits should turn off during the measurement so the
equalization current does not affect the voltage. To select the target cell for
equalization, the ECU sets the relays in the EQU board as needed. A resistor is used
to discharge a cell and a small charger is used to charge a cell, i.e., the C/D type EQU
[17] for each local module cans either charge or discharge the cells. Figure 3-9 shows
8 of the 6V batteries (cells) and one local module. Figure 3-10 shows the complete
battery bank pack.
Figure 3-9 Battery Bank with one BMS Local Module
25
Figure 3-10 The Complete Battery Bank Installation
3.3 Central Control Unit
The central unit controls the local module and the ABC-150 inverter. The
central module in Figure 3-11 collects the cell voltage data from the three local
modules and sends the equalization commands back to those modules. The ABC-150
in Figure 3-12 is a bi-directional, computer-controlled inverter which is used to
control the magnitude direction of the current to or from the power grid. The inverter
links to a PC via an RS-232 cable for remote control. The current control command is
given by the off-peak storage algorithm in the MPPT unit via a CAN interface to the
central module for processing the control software in PC. The block diagram to
26
describe the relationships between the ABC-150, MPPS, central module, ECU
module and EQU module is shown in Figure 3-13.
Figure 3-11 Central Module and PC with ABC150 Remote Control Panel
Figure 3-12 ABC-150 Bi-direction Inverter
27
Figure 3-13 Diagram of Inverter, Central Module, CAN Interface, MPPT
Module, ECU Module, and EQU Module
3.4 Graphical User Interface (GUI)
The GUI is a software visualization tool to offer a convenient way to monitor
the real-time system information and to observe the data changes over time. The GUI
consists of three function pages that show the GUI windows. The first page for the
initial settings is shown in Figure 3-14. The second page describes the system status
and has the dispatch function buttons as shown in Figure 3-15. The Third page in
Figure 3-16 is the dispatch function page that allows the user to dispatch power to and
from the grid.
CAN
USART
Local SelectionCharge /
DischargeOperation
ABC-150 Inverter
CANLocal Selection
Charge /DischargeOperation
Data
DataEQU
Module
CAN Bus
Central Module ECU Module
CANCurrent Select
DataGUI
Monitor
MPPT Module
Current Operation
28
Figure 3-14 Page Showing the Initial Settings
Figure 3-15 Main Page
29
Figure 3-16 Dispatch Function Page
In the graph windows the plots start from left to right; the yellow line
represents the electrical current in the grid; the red line refers to the current delivered
from the solar array; the green line indicates the current from the battery; and the blue
line shows the value of the state of charge (SOC).
The values of the system parameters are shown in the display boxes. For
example, the display in Figure 3-15 shows the max and min cell voltage for each local
and the locals’ EQU status. This page also includes a button for the dispatch function,
and clicking this button allows the GUI to display the third page.
The third page in Figure 3-16 displays the dispatch functions such as the
ABC-150 charge or discharge action and the current value and operation duration.
30
The user dispatch command has a higher priority that will over ride the programmed
algorithm. This dispatch function offers different strategies to allow the power
company dispatcher to control the charge or discharge rates as needed. Clicking the
―Confirm‖ button makes the display return to the system status page
3.5 Protection Unit
The charge phase runs the risk of overcharging which can damage the battery
or lead to a fire in extreme cases. A sealed VRLA type battery re-combines hydrogen
and oxygen produced inside the battery, and the volume of gas products expands
when the temperature goes up, which usually takes place when the battery is
overcharged. A sulfation process also occurs when batteries are discharged for a
prolonged period. For safe battery operation and service life extension, a variety of
protection functions are embedded in the system.
3.5.1 Hardware
Thermal switches are used to control the connection between the battery bank
and the inverter. The normally closed thermal switches are connected in series with a
relay coil. If the temperature of one of the battery cells is too high (overheating), one
of the switches opens to trip the relay.
31
The MPPT unit also features an output voltage limit protection circuit to limit
the energy from the solar array; this is to prevent an excessive battery voltage.
3.5.2 Software
The microcontroller receives voltage data from the measurement devices and
feeds the data to the control algorithm. The limit value is set in the MPPT algorithm
to stop the charge and discharge processing when the voltage value limit is exceeded.
3.6 CAN Function
The data communication between microcontrollers in the three modules
(Central, MPPT and ECU) is via the CAN bus. The CAN protocol is widely used in
the auto industry. It uses a differential signal technique that can provide high noise
immunity to electromagnetic interference, and the protocol also provides an error
frame and an error detection mechanism to handle corrupted messages.
The CAN protocol is a multi-master system where several nodes can use the
bus. The Infineon C515C microcontroller in each of the control modules have 15
CAN Message Objects, which are groups of registers. Each message can contain up
to 8 bytes of data, and the data is accepted between two nodes only when the two
message ID values are matched to each other. The ID value also defines the priority
of the message; the lower the value, the higher priority.
32
CAN is used to communicate between the microcontrollers in the Central,
MPPT and ECU modules. The communication cycle is as fallows. The MPPT unit
sends a start signal to the Central unit. The Central unit sends a request to the ECU
module to measure its cell data, and it then sends the data back to the Central. The
Central then sends the equalizer settings to the ECU, and it also sends cell data and
equalizer information to the MPPT unit for display on the GUI window.
33
Chapter 4
System Performance and Research Outcomes
To study the performance of the proposed system in this research project, an
installation connecting the solar array and the energy storage battery was developed.
For the sake of convenience in running and observing the operating details of this
system, the normal 24 hours cycle was reduced to 8 hours. Figure 4-1 illustrates a
basic schematic of the system. The SOC estimation, graphical user interface and
experimental data analysis are presented later in this chapter.
Figure 4-1 Basic Schematic of the System
34
Vmax Vmin
4.1 Performance of the BMS
Figure 4-2 and Figure 4-3 are plots of the maximum and the minimum cell
voltages vs. time. Figure 4-2 is the data plot with BMS equalization, and Figure 4-3 is
the plot without BMS equalization. Comparison of these two diagrams indicates the
significant advantage in equalizing the cell voltage levels. Both operations were run
during the solar energy charge mode. With the BMS, the values of Vmax and Vmin
in Figure 4-2 are nearly constant and very close at an average cell voltage of about
6.3V. Without the BMS, the maximum gap between Vmax and Vmin is more than 2V
with substantial variations in the voltage levels as shown in Figure 4-3.
Figure 4-2 Vmax and Vmin of the Cell vs Time with the BMS
Vmax Vmin
35
Figure 4-3 Vmax and Vmin of the cell vs Time without the BMS
4.2 SOC Estimation
As mentioned in the previous chapter, in this system the SOC is estimated by
combining the OCV method and the coulomb counting (CC) method. Figure 4-4
shows the diagram of the SOC plot when only the OCV method is in use, and Figure
4-5 shows the plot after integration with the CC method. In both diagrams, the green
line is the current from the battery; and the blue line is the SOC. Comparison between
SOC values indicated by the red circles in these two diagrams indicates that without
CC, the SOC has a significant error discontinuity of about 6% at the transition from
charge to discharge.
36
Figure 4-4 Plot of Battery Current and SOC Curve using OCV Method Only
Figure 4-5 Plot of Battery Current and SOC When Integrating the OCV Method with
Coulomb Counting Method
After the coulomb counting method is implemented, the uneven curve of the
SOC is considerably smoother. Based on the OCV method, the SOC calculation
procedure is to reset the SOC when the current magnitude is less than ±0.5A. The red
rectangle in Figure 4-5 shows the SOC value from the coulomb counting method is
very close to the corrected value base on the OCV.
37
4.3 Graphical User Interface (GUI)
Figure 4-6 shows the GUI display. The graph windows plot the data
continuously vs. time. The yellow line is the grid current; the red line represents the
current from solar array; the green line stands for the current from the battery; and the
blue line is for the SOC. In the data block panel, the data is updated every 10 seconds.
The system also saves the operational data in an Excel file so the user can study and
analyze the system performance.
Figure 4-6 GUI Display for the Solar Battery System
38
4.4 Data Analysis
Figure 4-7 shows the voltage variations vs. time for the solar array and the
battery bank. This diagram demonstrates the improvement if a battery storage unit is
integrated into the solar energy system. The voltage generated by the solar array is
not stable due to the erratic solar irradiance due to clouds. Thus, the role of the battery
and its management system is to buffer the voltage variation and to provide a stable
voltage output. Furthermore, the battery bank can provide extra energy to compensate
for insufficiencies in the solar energy. Figure 4-8 shows an example of inverter,
battery and MPPT currents with a 23A load.
Figure 4-7 Solar Voltage and Battery Bank Voltage vs Time
39
Figure 4-8 Time Diagram of Load Simulation and Output Current Supply from Solar
and Battery Bank
As Figure 4-8 shows, the current generated from solar array cannot always
meet the energy demand, but the battery bank unit will provide the system with extra
current to make up the difference.
40
Chapter 5
Conclusion and Directions for Future Research
The goal of the research in this thesis was to develop a simple and lower cost
system for coping with the intermittency of a solar energy supply, and to help manage
the erratic power demand. This task required the coordination the solar energy supply,
energy storage, and power dispatching.
An energy management system was developed and studied to evaluate the
system performance. The system also used an intelligent battery management system
to monitor and equalize the battery cell voltage levels is order to maximize the
capacity and service life of the battery bank. This also helps protect the battery from
damage due to overcharge and undercharge. Off-peak energy management also was
used to store energy storage from the utility during low load periods and to release
energy during the high demand periods.
The outcomes of this research demonstrate that this system can reduce power
variation and potential instability by controlling the power output. It presents a
41
practical system which can reduce the energy used from the grid, and it is easy to
implement. The system is composed of various sub-systems such as the MPPT, the
BMS and the off-peak storage algorithm.
The system simulated 24 hour operation by using an 8 hour cycle to study
the feasibility and performance of the proposed system. The outcome demonstrates
that this system can reduce the power cost by generating uniformly distributed output
power, providing a practical installation which can reduce the power supplied from
the public grid.
As seen in this research, a comprehensive GUI is important because it enables
the user to easily observe the system status and conduct data analysis. Availability of
remote control can enhance the system’s safety and offer convenience in monitoring
system’s status at a distance. It can extend the scope of applications ranging from
residential to larger installations, such as solar farms. Moreover, the control units
used do not have to be tailor-made for this research since they are generic products
developed in earlier projects.
Although this installation verified the feasibility, its maximum output was
limited to slightly less than 1KW. This power level is much too small to make any
economic predications or to estimate the cost recovery period. Therefore the next
42
logical step would be to develop a larger system of perhaps 100KW, and do an
economic study that included savings in utility costs and the sale of renewable energy
credits.
43
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46
Appendix
A. Introduction to Lead Acid Batteries
Lead acid batteries have been used in many different application fields for more
than 100 years, and the structure of a typical battery is shown in Figure A-1.
Figure A-1 Structure of a Lead Acid Battery [23]
47
The valve-regulated version of the lead acid (VRLA) battery was developed to
avoid the water and sulfuric acid loss in the flooded-electrolyte version. VRLA
batteries use an immobilized electrolyte which is absorbed by a glass-mat (AGM) or
gel. The benefit of the immobilization is that the liquid electrolyte does not evaporate.
As a consequence, there is a marked reduction in the amounts of hydrogen and
oxygen that leave the cell [24].
B. Thin-film Solar Cells
A solar cell or photovoltaic (PV) cell is a solid-state device which coverts light
energy into electrical energy. A PV cell is something like the p-n junction diode
which is made of two different semi-conducting materials: a p-doped layer and an n-
doped layer. When sunlight reaches the solar panels composed of PV cells, photons
of light are absorbed by the cell and photon energy is imparted as electrons at the p-n
junction. If photons possess sufficient energy, electrons will be excited from the state
of valance band to that of conduction band. This transition allows an electron-hole
pair to form, with the hole migrates towards the positive contact and the electron
migrates towards the negative contact, depending on the structure of the PV cell. If a
load is connected across the positive and negative contacts, the electron current will
flow through the load as shown in Figure B-1.
48
Figure B-1 The Sketch of Solar Cell [25]
C. The Maximum Power Point Tracking (MPPT) Method
A photovoltaic (PV) array under uniform solar irradiance displays a current-
voltage (I-V) operating characteristics as shown in Fig C-1. There is a unique point
on the I-V curve that is the maximum power point (Imp, Vmp) where the array
generates the maximum power output, Pmax. If the solar array is connected directly to
the batteries, the operating voltage will be constrained to a level equal to the present
battery voltage and this will prevent the array from achieving maximal power.
Therefore, to increase the efficiency of the solar array a technique called Maximum
Power Point Tracking (MPPT) is used.
49
A power converter using MPPT relies on a control algorithm to adjust the
operating point current to deliver the maximum output power. Some of the existing
types of MPPT algorithms are, ―perturb and observe (P&O)‖, ―constant voltage and
current‖, ―pilot cell‖, ―incremental conductance‖, ―parasitic capacitance‖, and
―model-based MPPT‖ algorithms [26].
Figure C-1 The Max Power Point of the I-V Curve and the P-V Curve [27]
