A cost-effective battery management and monitoring strategy for micro-grids in India

A Cost-Effective Battery Management and

Monitoring Strategy for Micro-grids in India

by

Rakesh Kumar

B.S.E.E., Electrical and Computer Engineering

Georgia Institute of Technology, 2014

S.M. Electrical Engineering and Computer Science

Massachusetts Institute of Technology, 2016

Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of

Doctor of Philosophy at the

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

June 2019

@

Signature redacted

A u th o r ...

Department of Electrical Engineering and Computer Science May 22, 2019 Certified by... Certified by... Accepted by ... MASSACHUSETTS INSTITUTE O TECHNOLOGY

JUN

13

2019

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A A AThesis _Supervisor

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1

Professor of Electrical Engineering and Computer Science Thesis Supervisor

Signature redacted

.

...

- eLeslie A. Kolodziejski

Professor of Electrical Engineering and Computer Science Chair, Department Committee on Graduate Students

A Cost-Effective Battery Management and Monitoring

Strategy for Micro-grids in India

by

Rakesh Kumar

Submitted to the Department of Electrical Engineering and Computer Science on May 22, 2019, in partial fulfillment of the

requirements for the degree of Doctor of Philosophy

Abstract

Energy buffer cost is a key factor in rural electrification, electric vehicles, mobile de-vices, etc. Increasingly, micro-grids and off-grid home electric systems have become popular in the developing world to help meet energy demands. These systems help provide electric power to customers when the main power utility is unable to do so, such as in rural communities or newly developed townships. However, a major limi-tation of micro-grid expansion is the system cost, with the energy buffer accounting

for perhaps 50% of the cost. This work addresses such costs by addressing battery longevity through the creation of a cost-effective and easy to use battery monitor that helps off-grid system operators increase the lifetime of the battery system.

This work also explores methods of extracting battery health information from ex-isting off-grid batteries to help determine remaining battery life. To this end, a very inexpensive device capable of extracting state-of-charge (SoC) and state-of-health (SoH) is proposed and validated against data from existing battery test equipment. This work then develops an optimal depth of discharge model which can be built solely from manufacturer data. Such a model helps micro-grid operators understand the effects of different operational conditions on battery life. Finally, this work ex-perimentally studies the degradation impact of extreme battery cycling scenarios. Examples include high discharge currents or low battery voltage cut-off. These re-sults, combined with manufacturer data, lead to the synthesis of recommendations on how to size and operate the battery system to maximize lifetime using the proposed SoC/SoH monitoring device. By leveraging these optimal operating recommendations and monitoring tools, micro-grid operators may extend total battery throughputs by 35-40%, thereby reducing overall micro-grid system costs by up to 20%. For the purposes of this work, the focus will be on lead-acid batteries as they are the most common type of battery used in off-grid settings.

Thesis Supervisor: James L. Kirtley Jr.

Title: Professor of Electrical Engineering and Computer Science Thesis Supervisor: Jeffrey H. Lang

Acknowledgments

I would like to thank my advisors, Prof. James Kirtley and Prof. Jeffrey Lang. Prof.

Craig Carter also provided considerable technical assistance during the course of this work. It was great privilege to working under them, I believe that in the future I will greatly benefit from the experience gained from their mentorship and guidance.

I'd like to also thank Dr. Reja Amatya, Dr. Robert Stoner, and Dr. Jason Prapas

of the MIT Tata Center for their assistance in helping solidify short- and long-term research goals; their help and support was invaluable during the course of this thesis. I also thank my labmates for the various hiking and food trips to NH, ME, VT, CT, PA, NY, OH, CA, AZ, UT (and a few states that I'm forgetting!) I am very fortunate to have these great friendships in my life.

Last but not least, I'd like to thank my parents and brother for their unwavering support for my various endeavors and sometimes questionable adventures.

Contents

1 Introduction 17

1.1 Understanding Lead-Acid Batteries . . . . 18

1.1.1 Types of Lead-Acid Batteries . . . . 19

1.2 Understanding Lead-Acid Battery Degradation . . . . 21

1.3 M icrogrid Operation . . . . 25

1.4 Battery Charge Controllers . . . . 26

1.5 Thesis Overview . . . . 28

2 Measuring Battery State of Charge and State of Health 31 2.1 Measuring Battery State of Charge . . . . 31

2.2 Measuring Battery State of Health . . . . 37

2.3 Conclusion . . . . 43

3 Integrated SoC and SoH monitor 45 3.1 Battery Monitoring System Requirements . . . . 45

3.2 Implementation of SoC Measurement - Algorithm . . . . 48

3.3 Implementation of SoH Measurement - Algorithm . . . . 50

3.4 Initial Prototype - Breadboard . . . . 51

3.4.1 Current and Voltage Measurement . . . . 51

3.4.2 State of Health Discharge Device . . . . 54

3.4.3 Battery Charge and Discharge Setup and BMS validation . . . 59

3.5 PCB Based Monitor . . . . 64

3.7 Scaling Voltages and Currents . . . . 3.8 Conclusion . . . . 4 Battery Throughput Models and Extreme Condition Cycling

4.1 Total Battery Throughput Models . . . . 4.2 Degradation Impact of Extreme Operating Conditions . . . . 4.2.1 Battery Cycling Setup . . . . 4.2.2 High Charge Current Cycling Experiment . . . .

4.2.3 High Charge Voltage Cycling Experiment . . . .

4.2.4 Low Battery Cutoff Voltage Cycling Experiment . . . . 4.2.5 High Discharge Current Cycling Experiment . . . . 4.2.6 Low Discharge Current Cycling Experiment . . . .

4.3 Results of Extreme Condition Cycling Experiments . . . . 4.3.1 Capacity Degradation vs Cycle Count . . . . 4.3.2 Charge Efficiency vs Cycle Count . . . . 4.3.3 Energy Efficiency vs Cycle Count . . . . 4.3.4 High Temperature Cycling . . . . 4.4 Optimal Microgrid Operation Tool . . . . 4.5 Conclusion . . . .

5 Summary, Conclusions, and Future Work

5.1 Project Sum m ary . . . . 5.2 Conclusions . . . . 5.3 Future W ork . . . . Appendix A Arduino Code for PCB BMS

Appendix B BK Precision Battery Test Setup Guide B.1 Initial Download and Setup Procedure ...

B.2 Configuring BK Precision Battery Test Software . . . . B.3 Output Data of BK Precision Battery Test Software . . . .

72 73 77 78 82 83 86 87 88 88 89 89 90 91 93 94 95 100 103 104 105 107 109 137 137 138 143

Appendix C Battery Monitor and SoH Device Bill of Materials 147

Appendix D MATLAB Code and Data Repositories - Extreme Cycling

Scenarios 151

D.1 MATLAB Code - Optimal Operation Tool ... 151 D.2 MATLAB Code - Parallelized Battery Capacity Extraction ... 156 D.3 Data Repository for Battery Lifetime Testing ... 164

List of Figures

1-1 Construction diagram of lead-acid battery, illustration taken from [1] 19

1-2 Cut-Away of VRLA battery, picture taken from [2] . . . . 20

1-3 Cycle service life of 12 V - 10 AH lead-acid battery from EXP12100

datasheet [3] . . . . 22 1-4 Lead electrode plates with sulfation, pictures from [41: (a) Electrodes of

new lead-acid battery (b) Minor sulfate build-up on lead-acid battery electrodes (c) Electrodes on degraded lead-acid battery . . . . 23 1-5 Corroded positive plate of car starter battery at end of service life,

picture from [5] . . . . 24

1-6 Conceptual block diagram of a microgrid [6] . . . . 26 1-7 Cost breakdown of a microgrid in India, taken from microgrid

invest-m ent guide [7] . . . . 27 1-8 Typical three-step charge profile for battery charging, figure from

lead-acid battery lecture [8] . . . . 28

2-1 Plot of Actual Battery Capacity (AH) vs. Discharge Current (A) for a 10 AH battery [3] . . . . 33

2-2 State of charge vs. open circuit voltage for four lead-acid batteries, data taken from [9] . . . . 34

2-3 Diagram of sample neural network to perform battery SoC estimation, architecture topology taken from [10] . . . . 36

2-4 Electrical battery model proposed in [11] . . . . 38 2-5 Simplified battery model proposed in [12] . . . . 39

2-6 RC battery model tuned with EKF-based SoH model in [13] . . . . . 40

2-7 "Coup de fouet" at beginning of the (a) discharge phase and (b) charg-ing phase, diagram from [14] . . . . 40

3-1 Picture of Arduino Mega2560 platform [15] . . . . 47

3-2 Schematic of proposed SoC and SoH monitoring system . . . . 47

3-3 Capture of INA219 Arduino breakout board . . . . 49

3-4 First iteration of BMS - with component annotations . . . . 51

3-5 Voltage divider circuit to reduce battery voltage for voltage measurement 52 3-6 Constant current discharge circuit for lead-acid battery. R1 computed using (3.1). . . . . 53

3-7 Internal schematic of TIP141 Darlington pair BJT, taken from TIP141 datasheet [16] . . . . 55

3-8 Bare aluminum heat sink used for mounting SoH discharge device . 56 3-9 8 A discharge device used to measure 10 AH battery SoH . . . . 57

3-10 Protoboard based battery monitoring system with full SoC and SoH monitoring capability - Annotated with component descriptions . . . 58

3-11 Correlation between ambient temperature and cycle-life of lead-acid batteries, figure taken from [17] . . . . 59

3-12 Load and supply setup used for BMS validation testing . . . . 61

3-13 Annotated electromechanical relay for switching SoC and SoH mea-surem ent m odes . . . . 61

3-14 Comparison of battery current - Arduino vs BK Precision. Lab equip-ment data sign adjusted to indicate negative battery current during charging. . . . . 62

3-15 Comparison of battery voltage - Arduino vs BK Precision . . . . 63

3-16 Comparison of C/i discharge by BK Precision load vs SoH discharge device . . . . 64

3-17 Low resistance connection between the current sensor, relay, and volt-age regulator . . . . 66

3-18 Grouping of 12C devices on BMS - INA219 current sensor, ADS1115 ADC, DS1307 real-time clock, MCP4725 DAC . . . . 67 3-19 Battery SoC level indicator LEDs . . . . 68 3-20 Full BMS including battery, PCB based monitoring system and SoH

discharge device . . . . 68 3-21 Serial information display from BMS - measuring SoC . . . . 69 3-22 Serial information display from BMS - measuring SoH . . . . 71

3-23 Serial information display from BMS - measuring SoH - end of

dis-charge test . . . . 72

4-1 Cycle Service Life of 12 V - 120 AH Lead-Acid Battery, from Century 12 V - 120 AH datasheet [18] . . . . 79

4-2 Curve fit of number of cycles vs. DoD limits for differing states of health 80 4-3 Total AH throughput vs. DoD limits for differing states of health for

120 AH lead-acid battery [181 . . . . 81

4-4 Three channel battery cycling setup using BK Precision electronic load and supplies . . . . 84 4-5 BK Precision battery cycling software used for battery cycle life

exper-im ents . . . . 85

4-6 Three new batteries loaded into temperature controlled chamber . . . 86

4-7 Battery cycler setup loaded with BK Precision test software simulta-neously controlling 3 channels . . . . 87

4-8 Capacity fade of different extreme cycling scenarios compared with a

control cycling scenario. Performed at 25 'C ambient temperature. . . 90

4-9 Charge efficiency of different extreme cycling scenarios compared with a control cycling scenario. Performed at 25 'C ambient temperature. 92

4-10 Energy efficiency of different extreme cycling scenarios compared with a control cycling scenario. Performed at 25 'C ambient temperature. 93

4-11 Capacity fade of different extreme cycling scenarios compared with a

4-12 Charge efficiency of different extreme cycling scenarios compared with a control cycling scenario. Performed at 45 'C ambient temperature. 96

4-13 Energy efficiency of different extreme cycling scenarios compared with a control cycling scenario. Performed at 45 'C ambient temperature. 96

4-14 25 year system operating cost for daily load of 3.33 AH vs battery DoD limit. Optimal DoD operating point highlighted by red circle. Number of batteries required at each step in parenthesis. . . . . 99

B-1 Initial control panel for BK Precision battery test software . . . . 138

B-2 Empty cycler control panel for BK Precision battery test software . . 139

B-3 Cycler control panel for BK Precision battery test software - High-lighted connection panel in red . . . . 140 B-4 Cycler control panel for BK Precision battery test software -

High-lighted sequence program panel in orange . . . . 141 B-5 Cycler control panel for BK Precision battery test software -

High-lighted step program panel in green . . . . 142 B-6 Cycler control panel for BK Precision battery test software -

High-lighted datalog program panel in blue . . . . 144

B-7 Example CSV output from BK Precision Battery Test Software . . . 145

C-1 Annotated capture of PCB based BMS . . . . 149

List of Tables

2.1 Comparison of total throughput SoH estimation vs. actual SoH as measured by lab battery test equipment . . . .

3.1 Summary of specifications for proposed BMS . . . .

4.1 Number of cycles required to attain various states of health at differing DoD limits for 12 V - 120 AH battery [18] . . . . 4.2 Curve fit coefficients for Figure 4-2 . . . . 4.3 Cost breakdown for highlighted points from Figure 4-14. Projected cost over 25 years. . . . .

C.1 Bill of materials for battery monitoring system . . . . C.2 Bill of materials for SoH measurement device . . . .

42 75 78 81 99 148 148

Chapter 1

Introduction

Microgrids play an important role in delivering energy to rural communities spread across the world [19]. Typical microgrids consist of generators, loads, and distributed energy storage elements [20]. Generators (photovoltaic (PV) cells, diesel engines, etc.) serve to pull energy from a source and supply various loads (lights, refrigera-tors, fan units, etc.) distributed throughout the microgrid. However, a microgrid that relies on non-continuous sources of energy such as PV cells requires energy storage elements to act as an energy buffer when generators cannot supply microgrid load demands [21]. Currently, lead-acid batteries are the primary energy storage elements due to their ease of transportation and low cost per unit energy [22]. However, one issue faced by microgrid owners is reduced battery lifetime. In some situations, the battery lasted only half of the expected lifetime. Or to avoid premature degradation from deep discharging, operators greatly oversize the battery storage capacity to meet load requirements. Typical microgrid lead-acid batteries last approximately 5-6 years, however lead-acid batteries employed in rural microgrids tend to have a shorter lifes-pan on the order of 2-3 years. The current solution is to replace the batteries every few years, but this may be impractical as off-grid systems are located in remote loca-tions and the act of replacing batteries comes with a substantial cost. Alternatively, the microgrid operator vastly over sizes the battery to avoid deep cycling, which adds a significant cost to the microgrid installation. This thesis focuses on reducing energy storage cost by improving the lifetime of rural microgrid lead-acid batteries through

real-time monitoring and improved operation. The majority of this work can be used to improve lead acid battery lifetime in many other applications as well.

This work is broken down into five primary objectives:

1. develop a flexible real-time system to monitor the State of Health (SoH) and

State of Charge (SoC) of a lead-acid battery during operation;

2. propose methods to quantify the impact of battery depth of discharge on total ampere-hour (AH) throughput;

3. explore extreme battery operating conditions and their respective impact on

battery cycle life;

4. develop an optimization framework to make suggestions on optimal battery size and depth of discharge for a particular microgrid scenario; and

5. release all battery cycle data to the public for future work in the battery space.

1.1

Understanding Lead-Acid Batteries

Lead-acid batteries are attractive to rural applications due to their lower cost per unit capacity when compared to other battery technologies [23]. Lead-acid batteries typically have a positive electrode (cathode) made from lead dioxide, PbO2, and a

negative electrode (anode) made of pure lead [6]. These plates are then submerged in sulfuric acid which acts as the electrolyte. During discharge, the positive and negative electrodes become coated with lead sulfate, PbSO4. The electrolyte loses its dissolved

sulfates (SOj-) and becomes primarily water. The discharge chemical reaction at the anode, the negative electrode plate, can be stated as

Pb(s) + SO~ 2 ) -+ PbSO4(s) + 2e (1.1)

PbO₂(s) + 4H+(aq) + 2e- + SO~-q -+ PbSO4(s) + 2H20(l)

The overall lead-acid chemical reaction, combining (1.1) and (1.2) can be stated as

Pb(,) + PbO2(,) + 4H*(aq) + 2SO-(aq) -* 2PbSO₄(₈) + 2 H₂0(l (1.3)

During charging, lead oxide is formed at the cathode, pure lead is formed at the anode and sulfuric acid once again forms in the electrolyte [24]. However, if the battery is over-discharged or left inactive for prolonged periods of time, lead sulfate will harden onto the plates and cannot be readily removed during charging. Under normal operation, the residual lead sulfate crystal is not harmful to battery capacity. However, if left for a long period of time in a deeply discharged state, the lead sulfate will begin to harden, reducing the battery's active material, capacity, and subsequent performance. This build-up of hardened lead sulfate is the primary mechanism behind battery capacity reduction.

Electrode connected

Lu _{pongy lead pIates Electrode connected}

to lead dioxide .+

spong lead

lead dioxide

... ..

Sulfuric Banks of lead and

----acid lead dioxide plates .B

Figure 1-1: Construction diagram of lead-acid battery, illustration taken from [1]

1.1.1 Types of Lead-Acid Batteries

There exist a variety of lead-acid battery constructions and compositions to serve different purposes. A typical lead-acid battery used in a microgrid setting is a valve (1.2)

regulated lead acid (VRLA) battery [2]. During the charging process, H2 gas is

released, as seen by running (1.2) backwards. An excess buildup of H2 gas may

damage the physical structure of the battery and it is essential to vent the gas to the external atmosphere. Here, the valve in a VRLA battery opens up and releases the built up gas to avoid over-pressurization of the system. A construction diagram of a VRLA battery can be seen in Figure 1-2.

Va ve I d Pillair Pi IlarSeal Hand Ie PositivePlate

NegativePlate *Ont iner

Figure 1-2: Cut-Away of VRLA battery, picture taken from [2]

Within the general category of VRLA batteries, there are two subcategories of VRLA battery constructions, a gel cell battery and an absorbent glass mat (AGM) battery. In a gel cell battery, the sulfuric acid is mixed with a thick gel agent in order to increase the shock and temperature resistance of the battery. This construction type also makes the battery resistant to spills when compared to a regular flooded battery. These types of batteries can be found in mobility uses such as wheelchairs,

all-terrain vehicles, etc. In AGM battery constructions, the acid is absorbed into thin fiberglass mats, making the acid easily available for quick reactions with the electrode plates. In turn, this reduces the internal resistance of the battery, allowing for larger amounts of current to be supplied by the battery.

Another type of battery construction is a Starter, Lighter, Ignition (SLI) battery. These batteries are used with starter motors, in-car ignition systems, etc. Their construction incorporates a large number of thin electrode plates which allows for a larger exposed surface area for the lead-acid reaction. For microgrid usage, SLI batteries are not used as most microgrid loads are not short, impulse type loads, but rather more longer and sustained loads.

The final type of battery that is commonly used in a microgrid setting is the deep cycle battery. Deep cycle batteries are capable of being cycled to much lower depths of discharge than a typical VRLA or SLI battery. Deep cycle batteries have thicker electrode plates that allow for large amounts of lead sulfate build-up under normal operation. However, since the electrode plates are thicker, fewer number of plates can be used, thereby reducing the overall plate surface area available for reaction. As a result, deep cycle batteries cannot deliver high peak currents like a VRLA or SLI battery.

1.2

Understanding Lead-Acid Battery Degradation

In a lead-acid battery, there are a number of aging processes which degrade the performance of the system [5], namely:

* anodic electrode material loss;

9 irreversible formation of lead sulfate on the electrodes; e loss of water from the electrolyte; and

-J

120

100

S80 _ _

60

100% DOD 50%DOD 30%DOD

40

Ambient Temperat re:25*C (77F)

20

0

200 400 600 800 1000 1200

Number of Cycles (Times)

Figure 1-3: Cycle service life of 12 V - 10 AH lead-acid battery from EXP12100

datasheet [3]

References [25], [26], [27] further explore the exact chemical processes and reactions which lead to cell degradation.

Typical lead-acid battery degradation can be split into two components, shelf-life degradation and cycle degradation [28]. Shelf-shelf-life degradation is a straight line depreciation of the battery capacity in time. Shelf life is defined as the length of time a battery may be stored without being too degraded for use, normally 60% of rated capacity. At 60% capacity, the battery becomes unable to meet typical load demands within a single cycle. A sealed lead-acid battery can generally sit at room temperature with no charging for up to a year. However, if the battery is stored beyond a year or so, the capacity will reduce due to self-discharge through internal resistances. If the battery is left in a discharged state, it may suffer from premature capacity fade.

The second form of degradation is cycle degradation. This form of degradation is related to how the battery is operated during regular usage, a function of the loading, depth of discharge (DoD) limit, and temperature to name a few variables. Figure

1-3 illustrates the impact of DoD limits on cycle life. For example, if this particular

battery is consistently discharged to 100% DoD, the battery will approximately only have 200-250 cycles before its end-of-life, rated at 60% of initial capacity. If, however, the battery is discharged to 50% DoD, 400-500 cycles can be expected from the same battery. These types of figures are released by battery manufacturers through empirical battery testing at different DoD cutoff levels. In terms of rural microgrid applications, if the end users are discharging the batteries to a deep DoD, the number of cycles achieved by the battery before end-of-life will be diminished, as seen in Figure 1-3.

(a)

_(b)

_(c)

Figure 1-4: Lead electrode plates with sulfation, pictures from [4]: (a) Electrodes of new lead-acid battery (b) Minor sulfate build-up on lead-acid battery electrodes (c)

Electrodes on degraded lead-acid battery

This DoD based cycle degradation is due to the slow, but irreversible process of lead sulfate build-up on the electrodes, as seen in Figure 1-4. The lead acid reaction shown in (1.3) is not a fully reversible reaction. Over time, there will be a steady residue of PbSO4 deposited on the lead electrodes as the battery undergoes normal

operation. This leads to a reduction of specific gravity of the electrolytic solution and the effective reaction area of the lead electrodes. As a result, the lead-acid battery can no longer support the same charge delivery capacity as a new battery and the effective battery capacity is reduced. Some charge controller algorithms may institute a "cleaning cycle" which pulses the charging current to force more lead sulfate back into the electrolyte solution. However, this comes at the cost of premature electrode corrosion, which negatively affects battery life. Section 1.4 discusses the typical charge controller algorithm used to charge a lead-acid battery.

2j

Deeper DoD accelerates the degradation process due to an increased amount of lead sulfate on the lead electrodes [29]. Although, during the charging state, most of the lead sulfate goes back into the electrolyte solution, at deep DoD, a higher concentration of lead sulfate crystals are 'left behind' on the lead electrodes when compared to the sulfate deposition at shallower DoD cycles. This leads to premature degradation of effective battery capacity. Here, having a SoC monitor will allow the microgrid operator to avoid deep discharge cycles, prolonging battery life.

Figure 1-5: Corroded positive plate of car starter battery at end of service life, picture from [5]

Another form of battery degradation is positive electrode corrosion, as seen in Figure 1-5. This phenomenon is discussed in [5], which details the exact chemistry

behind electrode corrosion; a simple explanation is presented here. The likely reason behind positive electrode corrosion is due to the movement of lead ions in the lead electrode across the PbSO4 layer into the electrolytic solution forming lead hydroxide (PbOH-) on the electrode surface. Then, the negatively charged lead hydroxide allows

for sites that create mobile surface protons on the lead electrodes, which results in ionic current flow and eventual electrochemical corrosion of the positive electrode. This form of degradation is primarily seen in high current applications such as car starter batteries or other SLI batteries. Thus, it can be inferred that battery failures in higher current applications may be a result of positive plate electrode corrosion in addition to the other factors as discussed above.

1.3

Microgrid Operation

As mentioned previously, microgrids are widely deployed in rural communities where guaranteed electricity access from the primary grid is non-stable or non-existent. This instability of energy access may be a result of an improperly maintained grid, dam-aged infrastructure from a natural disaster, or insufficient supply for a particular local demand profile. These systems can be defined as an intentional electric power sys-tem island with distributed resources and loads [19]. Distributed resources include examples such as a generator, photovoltaic panels, wind turbines, bio-gas reactors, and energy storage elements. Loads may range from cell phone chargers, home light-ing, televisions, etc. for small rural-scale microgrids, to industrial machinery and plant operations for large-scale microgrids. A block diagram of a typical microgrid is shown in Figure 1-6. Many smaller microgrids may not have all of the components illustrated in Figure 1-6 (such as the generator) and may rely solely on the battery and PV panel as an energy source.

In a microgrid, sufficient energy storage is essential for times when normal energy generation resources are not capable of meeting load demands. Then, the energy storage element, such as a battery, can deliver the difference in energy between the generator supply and load demand. Most remote microgrids employ lead-acid

batter-PV/WU&dI Other Charger Battery DC load DC Loads Control Inverter

-> -rter AC Load -- Poo, AC Loads Control

Diesel-powered Genrator

Figure 1-6: Conceptual block diagram of a microgrid [6]

ies as their primary energy storage element. This is largely due to the cost per AH of lead-acid batteries when compared to similarly sized lithium ion systems. How-ever, batteries comprise a significant portion of the overall system cost, as seen in Figure 1-7. In some microgrid settings, the battery accounts for 50% of the overall system cost.

1.4

Battery Charge Controllers

In order to prevent gassing or premature degradation of the lead-acid battery, proper charging techniques should be followed. A typical charge controller profile can be seen in Figure 1-8. The first stage is called the 'bulk charging' stage. Here, the battery is charged at maximum current until approximately 80% SoC is reached (or in certain charge controllers, the battery reaches a pre-defined voltage). The 80% SoC set point is recommended so as to prevent over-charging and subsequent H2 gas release. Then,

the charger is switched to constant voltage charging mode. Here, the battery voltage is maintained at a specified level and the charge current accepted by the battery begins to reduce. This charging stage prevents gassing of the lead-acid battery and it mitigates the potential for overcharging. Finally, the charger enters a trickle charging

Without battery

Cd cwnected Off-gud

With battery

Grid connected

System size bin (kW) *ttery *Soft cost

Figure 1-7: Cost breakdown of a guide [7]

I

System size bin (kW)

ur hardww

*

*

Ii

microgrid in India, taken from microgrid investment

mode where the battery is maintained at a recommended float voltage.

Another feature that must be present for any lead-acid battery charger is high temperature disconnect. Charging the battery increases cell temperatures, which in-turn causes the battery voltage to decrease as lead-acid batteries have a negative temperature coefficient. Over time, as the cell begins to heat, the apparent battery voltage reduces. If the charge controller does not detect cell temperature and adjust the output voltage appropriately, the battery may be severely overcharged, leading to premature degradation.

Voltage

Bulk

Absorption

Pulse

10

U

Up

0

Current

Time

Figure 1-8: Typical three-step charge profile for battery charging, figure from

lead-acid battery lecture [8]

1.5

Thesis Overview

The reminder of this thesis is divided into four chapters as follows. Chapter 2 dis-cusses methods to measure battery state of charge (SoC) and state of health (SoH). In the process, it discusses advantages and pitfalls of various SoC and SoH measure-ment methods. Chapter 3 proposes an integrated battery monitoring system (BMS) that has the ability to be retro-fitted to any existing microgrid or incorporated into a new grid installation. The chapter first presents the SoC measurement method and compares its accuracy against dedicated battery test equipment. The chapter contin-ues on to present two novel SoH measurement methods and compares the methods against battery test equipment. The chapter concludes with a discussion on user interface design for the proposed BMS and how the BMS can be scaled for larger microgrid application. Finally, Chapter 4 presents a method to quantify impact of

battery depth of discharge on total AH throughput. The chapter then discusses the impact of extreme stress operation on battery capacity fade. The chapter concludes with a presentation of an optimization framework to minimize overall battery cost

for a microgrid given certain cost and load demand parameters. This optimization tool can be leveraged by microgrid operators to help reduce overall system cost and potentially pass on the savings to the end consumer.

Chapter 2

Measuring Battery State of Charge

and State of Health

This chapter discusses various methods of measuring the state of charge (SoC) and state of health (SoH) of a battery. As discussed in Chapter 1, over- or under-charging prematurely degrades total battery lifetime. If microgrid operators wish to extend battery lifetimes, measuring SoC in real-time during operation is essential for proper control of their system. For example, if a battery is at 20% SoC, high loads should not be further placed on the battery in-order to avoid deep discharging the battery. Conversely, if a battery is at 95% SoC, the charge controller should cutoff charging to avoid over charging the battery.

In addition to battery SoC, battery SoH is essential in predicting how much usable capacity is left in a battery. For example, if a battery system is initially rated to deliver

100 AH, but has suffered capacity degradation, the battery may only be able to deliver 65 AH. Knowing this reduction of capacity is essential for microgrid operators as it

impacts the quality of service for their end customers.

2.1

Measuring Battery State of Charge

A simple way to measure SoC is to integrate the battery current to determine the

to+rIb(t) _dt

SoC(t) = 100% *

[

where Qrated is the total battery capacity and I is the battery current.

However, this method is prone to offset errors and drift errors. As such, this method requires regular re-calibration, i.e. resetting the initial SoC to 100% when the charge controller determines that the battery is at full charge. Nominal full charge conditions are reached when a battery has reached its float charge voltage with the accepted charge current less than 10% of its constant current charge mode value. If the cycling periodically takes the battery back up to full charge, such

as experienced in electric vehicle or microgrid applications, sensible re-calibration points are available when the battery reaches a fully-charged state. Typical microgrid systems deployed in the field are designed with a maximum of 2-3 days of autonomy, meaning a maximum of 2-3 days can go by where the battery does not reach a fully-charged state. Therefore, every 2-3 days, at most, there will be a point where the SoC integrator can be reset back to 100%, removing drift and offset errors.

Another factor that must be considered is that Qated in (2.1) varies with battery loading. For example, a 10 AH battery will deliver less than 10 AH of capacity at a

C/i or a C/5 load. This reduction in capacity delivery is illustrated in Figure 2-1.

For the purposes of this thesis, a 'C/N load' refers to the loading required to fully discharge the battery to 0% SoC in N hours.

To help address the reduction in capacity at higher C discharge rates, Peukert's law can be used [30]. For a 1 A discharge rate, Peukert's Law is states that

CP = I₁t (2.2)

where C, is the battery capacity at 1 A discharge rate (AH); T is the battery discharge current (A); t is the time required to discharge the battery (hours); and k is the Peukert constant.

Re-writing (2.2), one can solve for the effective capacity, Qactuat, at a particular

10 * Raw Data Fitted Curve 8 7 al CL * 5* 4 3 0 5 10 15 20 25 30 Current (A)

Figure 2-1: Plot of Actual Battery Capacity (AH) vs. Discharge Current (A) for a

10 AH battery [3]

Qactuai = C

( c)

IbH

where Qactual is the effective capacity (AH) at a discharge current Ib; H is the rated

discharge time (hours); Ib is the battery discharge current (A); C is the rated capacity

(AH) at discharge current Ib; and k is the Peukert constant.

Now, instead of using Qated in (2.1), Qactual from (2.3) better represents the battery

capacity at a given battery discharge current, b. However, in order to accurately

track of Qactuai, the Peukert's constant, k, must be adjusted based on battery health.

Peukert's constant serves as a de-rating factor for battery capacity calculation. For

example, if Peukert's constant is equal to 1, the AH capacity delivered by the battery

would be independent of the battery current. However, for a real battery, the constant is greater than 1, representing a reduction in battery capacity as the discharge current increases. For the purposes of this work, Peukert's constant can be thought of as a parameter that encapsulates various losses associated with discharging a battery at higher currents. An adjustment of Peukert's constant can be performed whenever the

110 100 -90 80 70-0 50 40 30-11.7 11.9 12.1 12.3 12.5 12.7 12.9 13.1 13.3 Open cirnuk vmg. N

Figure 2-2: State of charge vs. open circuit voltage for four lead-acid batteries, data taken from [9]

health of the battery is measured. Rearranging (2.3), the value of k can be directly computed using

log (Qactu)

k = 1 + C (2.4)

log s)

when the battery is discharged to extract SoH, as Ib,

Qactuai,

and k can be determined. Then, this updated value of k can be used to perform SoC estimation. SoH discharge tests, among other methods of extracting SoH, are discussed in Section 2.2.

Reference [9] discusses using the open-circuit voltage measurement of a battery to estimate its SoC. Seen in Figure 2-2, there is a nearly linear relationship between open-circuit voltage and SoC. However, to measure the open-circuit voltage of a lead-acid battery, the cells must be disconnected from the load to allow the voltage to settle, often for four to six hours. This method can be used for systems in which the battery may be fully disconnected from the load for long periods, but it is inaccurate for scenarios when the battery is required to be in operation a majority of the time, like in a microgrid setting.

In [9], a linear model is presented which establishes a relationship between present battery SoC value, previously measured battery SoC value, and intermediate battery

voltage and current measurements. This model is presented as:

AQ(i) = 00 + 1U(i) + 321(i) +

f

Q(i) = Q(i - 1) + AQ(i)

where

Q(i)

The coefficients &, . .. ,/#3 can be determined from running least-mean-square error calculations on battery data. Note, however, the / values do not describe any physical parameters of the battery. This model can be used to estimate SoC for various battery types at differing SoH, but is only valid if the parameters are derived from reference data of a new battery. This method may yield inaccurate results if the battery being modeled and the reference battery type, used to generate the original # values, are different.

Impedance spectroscopy can also be used to measure SoC in addition to SoH. For SoC measurement, a combination of multi-frequency complex impedance mea-surement and fuzzy logic may be used [31] [32]. In their work, the authors made impedance measurements at three different frequencies and defined fuzzy logic rules to correlate impedance data with SoC and charge cycle number. However, this method relied on previous work to correlate the battery circuit model with the SoC of a NiMH battery. This work does not detail how the correlation was done or how to extend the correlation information to different types of batteries. Correlation work was shown to be accurate only for batteries that have undergone at least 100 cycles and have not exceeded 600 cycles. As such, this method cannot be used to effectively model to SoC for new batteries. In addition, the fuzzy logic model must be retrained for every battery to be modeled. If large battery banks need to be tested, the resources required could be computationally and monetarily expensive. Reference [9] also notes-that impedance spectroscopy is influenced by temperature, making the measurement unreliable at fluctuating battery temperatures.

Layer 2 Xk k x2(f)x2) 2k 2 2 1 b b b

Figure 2-3: Diagram of sample neural network to perform battery SoC estimation, architecture topology taken from [10]

discuss employing an artificial neural network (ANN) to estimate battery SoC. A neural network (NN) can be used for a variety of classification methods, from image recognition to text processing, but the specific structure of a neural network is es-sential to ensuring low estimation error. An example NN structure can be seen in Figure 2-3. Here, the inputs consist of various features measured from the battery. For the purposes of [10], the input features were battery current at time [t], voltage at time [t], current at time [t - 1], and voltage at time [t - 1]. These features were then

provided to the network for training in-order to adjust the weights of the neurons to minimize SoC estimation error. The inclusion of previous time points is essential for

NN based SoC estimation; otherwise the network would simply associate a battery

voltage value to a SoC.

One drawback of a NN based SoC estimator is the reliance on large and varied

datasets. Battery manufacturers are not willing to release cycle data to the general academic community and any data required for NN training must be generated by the researcher. Also, for a small (10 AH) lead-acid battery, cycling at the quickest possible discharge rate, C/1, a single cycle takes approximately four hours as a manufacturer recommended charging procedure would take three hours to complete. However, for a microgrid usecase, the battery must be exercised at different loading conditions to mimic realistic load variations. Essentially, a real world load will not always be at C/1, but rather a variety of load profiles. To fully degrade a small 10 AH lead-acid battery at the fastest possible discharge rate would require approximately 35-40 days. However, in a microgrid setting, battery capacities may be upwards of

100 - 200 AH. Also, unless the test temperatures are varied to account for battery temperature fluctuations, the network would not capture the effect of temperature on SoC measurements. Due to prohibitively large data collection time scales for these larger batteries, a NN based estimation approach becomes unrealistic.

2.2

Measuring Battery State of Health

For this work, state of health (SoH) is defined as

SoH = * 100% (2.6)

Qrated

where Qrated is the rated AH capacity of a battery at a given discharge load, and QMAX is the actual AH capacity of a battery at the same discharge load.

One proposed method for determining SoH is to measure the AC impedance of the battery. References [35] and [36] show correlation between battery conductance and its SoH. Though the performance at low to medium SoH were well captured

by a single frequency conductance measurement, accuracies at higher SoH were, on

average, had measurement errors of 12-16%. Reference [37] addresses this issue by taking impedance measurements at three different frequencies (5, 70, 1000 Hz) to generate the spectral response of the battery's AC impedance. Then, this data is used to fit a simplified battery circuit model. Reference [11] expands upon this work and

I I

-S0 Reuies RTEmfliU.s RTwiwjLIJ

Batt

Figure 2-4: Electrical battery model proposed in [11]

proposes various circuit and battery models to account for dynamic battery behavior. Though the work was performed with nickel metal hydride (NiMH) batteries, the method can be adapted to fit lead-acid batteries. One drawback of impedance based SoH measurement is that battery impedance is a function of how the battery was cycled. For example, if the battery was cycled at a high discharge rate (>1C), the measured impedance would be different than that for a battery cycled at a lower

discharge rate (<0.1C - 0.05C).

For small battery modeling experiments, [11] can be used to accurately represent a battery, but for large systems, it is computationally intensive to compute the full model fit. Reference [12] proposes a simplified equivalent circuit model allowing for quick computation of battery dynamics. However, for robust SoH monitoring, a unique model for every potential battery would be required.

Inference networks (fuzzy logic) have also been proposed to generate SoH mod-els [38] [39]. Fuzzy logic is a system that maps measured inputs to desired outputs. The system has predefined rules which govern the mapping of the input data to a qual-itative output as opposed to a quantqual-itative value. In the above references, researchers have used multi-frequency impedance data from differing batteries to generate SoH estimator model for lead-acid batteries. However, the references do not discuss how the model must change to account for real-world battery degradation. One drawback of using fuzzy logic systems in rural microgrids is the need to potentially re-train

C1

R2 P RD

R1+

N

Figure 4: Lead-acid battery third order model.

where:

"

Em was the main branch voltage,

"

R

was the main branch resistance,

"

C

was the main branch capacitance,

"

R

was the main branch resistance,

"

I (Vpn) was the Parasitic branch current,

"

Ro was the Terminal resistance.

Figure 2-5: Simplified battery model proposed in [12]

the fuzzy inference system depending on the battery used. In rural applications, it is essential that the SoH characterization model is able to adjust dynamically while using minimal compute resources, in-order to reduce monitoring cost.

References [13] and [40] propose an extended Kalman-filter-based (EKF) approach to estimate SoH and SoC. Using the open-circuit voltage of a lead-acid battery, the authors model the underlying dynamic behavior of the battery using two capacitors (to model bulk and surface capacitance) and three resistors (to model terminal, sur-face, and end-of-discharge resistances), as shown in Figure 2-6. Like the fuzzy logic based model, the EKF based model tends to be computationally intensive to com-pute and is not practical for rural applications where cost-effective comcom-pute power is scarce. Although classification can be performed relatively easily, the fuzzy logic model must be trained with field data for every potentially new battery which incurs a non-negligible computation penalty.

I Rt

VO

S lb

Csurface Vcs Ck t Veb

T

Figure 2-6: RC battery model tuned with EKF-based SoH model in [13]

fouet" method [41]. Literally translated from French as 'whiplash', there is an initial voltage dip when discharging a lead-acid battery (or voltage spike when charging) that may be used to predict SoH, as shown in Figure 2-7. However, [14] presents a study on the "coup de fouet" method and determined that it cannot be used to diagnose SoH for irregular loads such as those seen in PV based systems.

2 15 2.10- 2.05-206 1.95 20 1.90' 204 M #We 2002 21 V 1.75 1.70 0 tow 0D ( 4) 0 () 7.00.144021026600 360043200 60400 66480 (a) Time(9)

I

- 2 -2 ' 0 tO Ter (S) . 14400 28800 43200 72000 860 Tim. (9)

(a) discharge phase and (b) charging

Research has also been conducted on using the sample entropy to help determine the SoH of a lead-acid battery [42]. Sample entropy can be thought of as the proba-bility that two sequential data points are similar. Therefore, a lower sample entropy value indicates a data series that is more self-similar than a data series with high sample entropy. References [42] uses this idea to measure the sample entropy of a lead-acid battery to generate a SoH metric. In this work, the argument presented is

Awl

that a healthy battery will have a relatively smooth sample entropy curve whereas a degraded battery will have abrupt changes in voltage when going through normal operation. By measuring sample entropy of a battery, capacity degradation as a func-tion of cycle count can be observed. One limitafunc-tion of this method is that it cannot be used to identify degradation without pre-existing data on how entropy changes as a function of SoH. This would involve testing every battery that potentially could be used in the microgrid setting under a pre-determined set of load conditions to measure the change in sample entropy as a function of SoH. Also, during battery cycling experiments conducted through the course of this work, no sharp drops or irregularities in battery voltage were observed from degraded batteries.

One can also employ the definition of SoH itself to determine remaining battery capacity. To determine the actual capacity of a battery under load, one can simply load the battery with a pre-determined discharge current load. Then, the user can measure how much time is required for the battery to reach its cut-off voltage. Then, the total AH delivered by the battery can be compared to the rated capacity from the battery datasheet. This method offers the advantage of being able to measure SoH for any battery at any point in time in its service cycle. This means this discharge based SoH measurement method can be retrofitted onto an existing microgrid battery, a key requirement of microgrid operators. However, this method does require a full battery discharge. Also, the battery cannot be discharged unless it is fully charged and able to be disconnected for at least one full discharge cycle.

Treating the battery as a total AH throughput device, SoH can also be approxi-mated by tracking the lifetime AH discharged from the battery. Looking at Figure 1-3, the total AH of the battery is approximately 2100 AH. This arises from the obser-vation that if the battery is taken to 100% DoD, 210 cycles can be completed before end-of-life, whereas if the battery is taken to 50% DoD, 425 cycles can be completed before end-of-life. Also, assuming the battery can produce 10 AH per cycle, the lifetime throughput is 2100 AH. Of course this is a rough estimate of total battery throughput and is dependent on a variety of factors such as temperature, discharge rate, etc. Then, the number of AHs discharged from the battery is tracked in real-time

Table 2.1: Comparison of total throughput sured by lab battery test equipment

and SoH can be estimated as follows:

SoH estimation vs. actual SoH as

mea-Estimated SoH = 100% 1 - Total AHdischarged

Total _{AHcomputed I} (2.7) where Total AHdischarged is the real-time track of AH discharged from the battery

and Total AHcomputed is the lifetime AH throughput of the battery. For the 10 AH battery used in experiments, the Total AHomputed is 2100 AH.

To illustrate the SoH measurement capability of this method, a 10 AH battery was cycled to end-of-life (defined as having less than 60% of initial capacity). The battery was cycled for 30 cycles at a C/1 discharge rate at 35 'C and then one cycle at a C/5 discharge rate at 25 'C. The 30 cycles were used to age the battery and the one cycle at C/5 discharge rate was used to measure battery capacity. The comparison between the estimated SoH using the total throughput method and actual battery SoH is presented in Table 2.1. For the purposes of prediction, output CSV files from cycle experiments were processed to compute a running counter of Total AHischarged.

Then, using (2.7), real-time SoH was estimated and compared to the actual battery SoH at that point in the experiment.

Cycle Procedure Measured SoH Estimated SoH

30 cycles ( 35 *C

1 cycle 9 25 C 74 % SoH 82 % SoH

30 cycles A 35 'C

1 cycle L 25 'C 69.17 % SoH 77 % SoH

30 cycles 9 35 *C

1 cycle L 25 'C 62.98 % SoH 68 % SoH 30 cycles 35 'C

1 cycle 4 25 'C 57 % SoH 59 % SoH 30 cycles A 35 'C

As seen, the real-time SoH prediction has an error of 5-7%, but is able to provide microgrid operators a good indication of battery SoH at the expense of a dedicated

AH counter (involves minimal compute and cost overhead). One major problem

associated with 'on-the-fly' SoH estimation, as discussed above, is the requirement of monitoring the battery from initial installation. To over come this issue, the total

AH throughput estimation may be used in conjunction with the controlled discharge

SoH measurement method. When first installed the battery monitor may perform a SoH discharge test to determine battery SoH, leveraging (2.6). Then, the SoH value may be used with the lifetime battery AH throughput value to estimate how many AHs the battery has already produced. Finally, the total AH throughput can be used as normal, following (2.7).

2.3

Conclusion

This chapter presents a discussion on different methods of measuring battery state of charge and state of health. Each method has its own advantages and disadvantages depending on the use-case. For the purposes of this thesis, a coulomb-counting based SoC estimator is used. This method was chosen for its ease of computation allowing for use of a cost-effective and open-source Arduino platform. As discussed in Sec-tion 2.1, to use Peukert's equaSec-tion, the exact Peukert's constant for the battery will be recomputed after every SoH discharge test using (2.4).

For the purposes of this thesis, a combination of SoH discharge measurement and total AH throughput based SoH measurement was used for determine SoH. These methods allow the operator to retrofit the battery monitor onto an existing system and measure SoH as opposed to being restricted to only new off-grid installations. The specifications for the battery monitoring system (BMS) will be discussed in Chapter 3.

Chapter 3

Integrated SoC and SoH monitor

This chapter details the physical construction and testing of the proposed battery monitoring system (BMS). SoC and SoH measurement algorithms are also presented, and a detailed description of various measurement pitfalls are discussed. Then, this chapter transitions to a discussion of the various states of prototyping and the ca-pabilities of the proposed battery monitor. Finally, this chapter concludes with a discussion on components that need to be scaled in order to accommodate differing microgrid voltage and current requirements.

3.1

Battery Monitoring System Requirements

In order to effectively monitor battery operation and current state, several require-ments must be met. These requirerequire-ments include:

" measure instantaneous current flowing in and out of the battery;

* measure instantaneous voltage of the battery;

" measure battery pack temperature;

" measure ambient temperature;

e track the SoC of the battery;

" measure the SoH of the battery;

" disconnect the battery from the charge controller to perform a SoH test; " track the total AH discharged from the battery; and

* be relatively inexpensive (<$50).

These requirements served as the basis for BMS development. For initial prototyping and design flexibility, the BMS was based on an Arduino platform. Arduino pro-gramming is very similar to that of C/C++ and the native developer environment abstracts away the requirement of manually setting individual register bits, unlike other micro-controller platforms. The platform has built-in serial communication ca-pability allowing for real-time monitoring of different signals and variables for ease of debug. The Arduino platform also supports Inter-Integrated Circuit (12C) [43] and Serial Peripheral Interface (SPI) [44] communication protocols. This capability allows the BMS to be upgraded if necessary as I2C and SPI are standardized interface pro-tocols for device-to-device communication. Another benefit of the Arduino platform is readily available code libraries. This allows for rapid prototyping as pre-existing libraries can be leveraged when designing BMS algorithms.

For the purposes of this thesis, an Arduino Mega2560 [15] was used as the main controller, as seen in Figure 3-1. Initially, an Arduino Uno [45] was used for pro-totyping given its lower price point. However, the flash memory size of the Uno proved to be insufficient and the Mega2560 was used instead. An added benefit of the Mega2560 platform is the 4x larger programmable read only memory (PROM) space compared to the Uno. This allows for the operator to store more information about the battery state in PROM in case of unexpected BMS power loss. This capa-bility is essential for storing lifetime AH discharged from the battery, further details discussed in Section 3.3.

The final implementation schematic is illustrated in Figure 3-2. Here, the two colors represent two BMS operational states. The green path represents when the

Figure 3-1: Picture of Arduino Mega2560 platform [15]

battery is connected to the microgrid. The BMS will be in this operational state for the majority of time. Here, a current sensor is used to perform real-time battery SoC estimation. The blue path represents the state when the battery is disconnected from the main microgrid charge controller and a SoH discharge test is active. As previously mentioned, in Section 2.2, the SoH discharge test will likely be performed on a monthly basis when the battery is taken off-line for regular maintenance.

SoH MEASUREMENT

SWITCH

IBATTERY

ENERGY +

SOURCE LOADS Vpcontroier _VBATTERY

RBIAS