battery charge estimation

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TitleDevelopment of electric vehicle battery capacityestimation using neuro-fuzzy systems

Author(s) Wu, Kwok-Chiu.; .

Citation

Issue Date 2003

URL http://hdl.handle.net/10722/30799

RightsThe author retains all proprietary rights, (such as patentrights) and the right to use in future works.


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CHAPTER 1

INTRODUCTION

1.1 Backgrounds 1-11.1.1 Electric vehicles development 1-11.1.2 Overview of batteries technology in electric vehicles 1-2

1.2

Overview of battery capacity estimation approaches 1-4

1.2.1 Definition of battery capacity 1-41.2.2 Research in battery capacity estimation approaches 1-7

1.3 Project objectives 1-71.4 Thesis Outline 1-81.5 References 1-10


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1.1 BackgroundsIn a world where environmental protection and energy conservation are growing

concerns, the development of electric vehicle (EV) technology has assumed an

accelerated pace to fulfill these needs. Concerning the environment, EVs can provide

emission-free urban transportation. Even taking into account the emissions from the

power plants needed to fuel the vehicles, the use of EVs can offer still significantly

reduce global air pollution. From the energy aspect, EVs can offer a secure,

comprehensive and balanced energy option that is efficient and environmentally friendly,

such as the utilization of various kinds of the renewable energies. Furthermore, EVs will

be more intelligent to improve traffic safety and road utilization, and will have the

potential to have a great impact on energy, environment and transportation as well as hi-

tech promotion, new industry creation and economic development.

1.1.1 Electric vehicles developmentAbout 30 years ago, some countries started the rekindling of interests in EVs,

since EVs had almost vanished from the scene in the 1930s after its invention in 1834.

The major driving force for the development of EVs at that time was the energy issue

due to energy crisis in the early 1970s. The development of EVs for over 30 years was

still in research and development stage, and most of the EVs were conversion of internal

combustion engine vehicles (ICEVs). Today, major automobile manufacturers are

offering EVs for sale or lease. Most of them are the purpose-built EV, not conversion

EV.


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Nowadays, the major driving force for EVs is the environment issue, such as

mandate by California rule, rather than the previous energy issue. The major factors that

make EV affordable are the range and cost.

In the future, EVs will be commercialized. EVs will be well accepted by some

niche markets, namely the users for community transportation, the places where

electricity is cheap and ease of access, and the cities with a zero-emission mandate.

Among different EV technologies, electric propulsion and energy sources will still be

the key technologies to be addressed, and energy, environment and economy will still be

the key issues for EV commercialization [Ch1-1].

1.1.2 Overview of batteries technology in electric vehiclesThere are dozens types of batteries that can be selected as energy sources in EVs,

namely valve-regulated lead-acid (VRLA), nickel-iron (Ni-Fe), nickel-zinc (Ni-Zn),

nickel- cadmium (Ni-Cd), nickel-metal hydride (Ni-MH), zinc/chlorine (Zn/Cl2),

zinc/bromine (Zn/Br2), iron/air (Fe/Air), aluminum/air (Al/Air), zinc/air (Zn/Air),

sodium/sulfur (Na/S), sodium/nickel chloride (Na/NiCl2), lithium-aluminum/iron

monosulfide (Li-Al/FeS), lithium-aluminum/iron disulfide (Li-Al/FeS2), lithium-

polymer (Li-Po) and lithium-ion (Li-Ion). Particularly, VRLA, Ni-MH and Li-Ion

batteries have been identified for the near-term and the long-term development of EV

batteries. [Ch1-2][Ch1-4].

The lead-acid battery has been commercialized successfully for over a century.

The overall electrochemical reaction is O2H2PbSOSO2HPbOPb 24422 +++ . The

positive and negative electrodes are lead dioxide and metallic lead respectively. The


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electrolyte of the battery is the sulfuric acid solution. Recently, the valve-regulated type,

called the VRLA, has been widely accepted for EVs. It has the advantages of mature

technology, low initial cost (presently $200-400/kWh), rapid recharge capability, high

specific power (over 200W/kg), and maintenance-free operation. However, the

shortcomings of VRLA are low specific energy (about 40Wh/kg) and short cycle life

(about 500 cycles). At present, the VRLA battery is still the most popular energy source

for EV application, especially those commercially available EVs.

Besides the lead-acid battery, the Ni-MH battery is becoming mature. The Ni-

MH battery is being considered to be the near-term battery of choice for EVs. The

corresponding electrochemical reaction can be described as

2Ni(OH)MNiOOHMH ++ .The active materials of Ni-MH battery are metal

hydride for the negative electrode, nickel oxyhydroxide for the positive electrode, and

potassium hydroxide solution for the electrolyte. The metal hydride is generally of a

rare-earth alloy based on lanthanum nickel, known as an 5AB alloy, or a vanadium-

titanium-zirconium-nickel based alloy, known as an 2AB alloy. It is so attractive

because it offers high specific power (150-300 W/kg), very long cycle life (800-

2000cycles), high specific energy (about 60 Wh/kg), environmental friendliness, rapid

recharge capability, and maintenance-free operation. Up to now, the key drawback is its

very high initial cost (150-200 US$/kWh). This can be alleviated by mass production

and so the Ni-MH battery is rapidly accepted by new EVs.

The Li-Ion battery has been viewed to be the long-term battery of choice for EVs.

The corresponding electrochemical reaction is zyzyx1x OLiMCOMLiCLi ++ . The

Li-Ion battery uses lithiated carbon for the negative electrode, lithiated transition metal


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oxide ( 2x1 CoOLi , 2x1 NiOLi or 42x1 OMnLi ) for the positive electrode, and liquid

organic solution for the electrolyte. Lithium ion s are deintercalated from the negative

electrode and intercalated to the positive electrode on discharge, and vice versa on

charge. Equivalently, these ions are swinging through the electrolyte between the

negative and positive electrodes, and no metallic lithium will be deposited. The

advantages of the Li-Ion battery are high specific energy (about 130 Wh/kg), high

specific power (about 250 W/kg), and long cycle life (about 1200 cycles), good capacity

retention (little self-discharge). The disadvantages are its extremely high initial cost and

strict requirement of thermal management during charge and discharge.

Since the three batteries are regarded as the near-term and the long-term

development of EV batteries, they are selected in this research for the development of

EV battery capacity estimation approaches using neuro-fuzzy systems.

1.2 Overview of battery capacity estimation approaches1.2.1 Definition of battery capacity

EV performance highly depends on the energy capacity stored in the battery, and

the discharge current has a significant effect on this battery capacity. In literatures, there

are many terms that have been developed to describe the battery capacity. Among

them, the nominal or rated capacity NC , the state of charge (SOC), the battery available

capacity (BAC), the instantaneous discharged capacity )(tq , the battery residual

capacity (BRC) and the state of available capacity (SOAC) are representative. They are

summarized as follows [Ch1-5]:

The nominal or rated capacity NC is the quantity of electricity that the battery can


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deliver under the specified discharge current (e.g. 3-hour, 5-hour or 20-hour

discharge rates corresponding to 3/3

C , 5/5

C or 20/20

C ) and the reference

temperature (e.g. 25oC). It is determined by the mass of active material contained in

the battery that can undergo the chemical reaction before the battery can no longer

deliver the specified current before the specified cutoff voltage is reached. Generally,

the rated capacity is much higher than the battery capacity in EVs because the

average value of the discharge current in EVs is generally much higher than that

corresponding to the rated capacity.

The SOC is theoretically defined as the ratio of the remaining active material to the

total active material inside the battery that can be actually converted into electrical

energy from chemical energy. The SOC indicates the state where the battery lies in,

rather than giving the amount of ampere-hours that the battery can deliver. In other

words, the SOC only exhibits the battery discharge capability.

The BAC refers to the quantity of electricity at the fully charged state that can be

delivered at a certain discharge current and temperature before reaching the

specified cutoff voltage. Mathematically, it can be written as :

offVtVddatVtTtIfC == )(|))(),(),(( (Equation 1.1)

where aC refers to the BAC, )(tId is the discharge current, )(tT is the battery

surface temperature, )(tVd is the terminal voltage during discharge and offV is the

specified cutoff voltage. It is shown that BAC depends on the discharge current and

temperature.

The instantaneous discharged capacity )(tq refers to the quantity of electricity that

has been discharged. It is the integration of the instantaneous discharge current over


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time. It can be expressed as:

=t

d dttItq0

)()( (Equation 1.2)

The BRC refers to the quantity of electricity remained in the battery that can be

delivered at a certain discharge current and temperature before reaching the

specified cutoff voltage. Under the same discharge current and temperature, it can be

obtained by using the BAC to subtract the discharged capacity )(tq , and can be

expressed by:

)()( tqCtC ar = (Equation 1.3)

where )(tCr refers to BRC. The difficulty in the use of this equation is that the BAC

is not a constant value for the variable discharge current. It is noted that the value of

BRC can be discharged depends not only on the battery discharge capability, but

also largely on the forthcoming discharge current and temperature that the battery

will undergo. It seems that BRC is similar to SOC, actually they have no

quantitative relationship.

The SOAC refers to the percentage of the BAC at the fully charged state for a

certain discharge current profile of the EV battery. It is used to represent the BRC in

EVs and is really the BRC of which the EV driving range is dependent. Thus, SOAC

)(tp can be written as:

aCtqtp /)(1)( = (Equation 1.4)

It is found that only the BAC, the BRC and the SOAC can reflect the actual

battery capacity under a certain condition in terms of the discharge current and

temperature. Thus, the focus of this research is on the SOAC estimation for the EV


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batteries. Since SOAC is related to both BRC and BAC.

1.2.2 Research in battery capacity estimation approachesAlthough the development of EV battery technologies is being actively

conducted, the application technology of EV batteries, namely the BRC indicator,

cannot catch up with the development pace. Since the BRC estimation is strongly related

to the driving range of EVs, an accurate calculation of the BRC is vital. Actually, this

technology is the key to commercialization and popularization of EVs.

Starting from the past decades, many battery capacity estimation approaches for

the lead-acid battery in EVs have been investigated, such as the impedance

measurement approach [Ch1-6], the mathematical model approach [Ch1-7] and neural

network (NN) modeling approach [Ch1-8]. Recent approaches for the battery capacity

estimation have been extended to the Ni-MH battery. They are the impedance approach

[Ch1-9] and the NN modeling approach [Ch1-10]. The use of neuro-fuzzy system has

ever been attempted to model the Li-Ion battery for the capacity estimation [Ch1-11].

However, these approaches cannot give out desired results and thus further

investigations are needed. Also, the battery parameters should be investigated so that the

selected parameters can truly reflect the battery capacity. Thus the accuracy of the

battery capacity estimation can be improved.

1.3 Project objectivesThe objective of this project is on the development of EV battery capacity

estimation using neuro-fuzzy systems, especially for the estimation of the SOAC, which


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refers to the BRC normalized by the BAC. For EV application, the SOAC with a per-

unit or percentage value is preferred to the BRC in watt-hours or joules.

Due to highly non-linear characteristics of EV batteries, it leads to the difficulty

in the BRC estimation. Therefore, the development of BRC indicator is hindered. Also,

it is the key to commercialization and popularization of EVs. To tackle this problem,

neuro-fuzzy system is used. Neuro-fuzzy system takes the merit of dealing with non-

linear data. This project, firstly, aims to develop SOAC estimation, which in terms of the

BRC, for lead-acid battery using NN. Secondly, it aims to develop SOAC estimation for

lead-acid battery, Ni-MH battery and Li-Ion battery using adaptive neuro-fuzzy

inference system (ANFIS) since this system has several advantages.

The development of EV battery capacity estimation approaches cannot be done

without the help of training data. Thus, the battery testing plays the key role in this

project. The battery testing and evaluation system located at the International Research

Center for Electric Vehicles in the University of Hong Kong is used for the battery

testing. All the lead-acid battery, the Ni-MH battery and the Li-Ion battery are tested

under different discharge currents and various battery surface temperatures that strive to

match the battery operating condition in EVs. The experimental data for these three

batteries are achieved and are used to construct and verify the EV battery capacity

approaches.

1.4 Thesis OutlineIn this thesis, nine chapters will be presented. Chapter 2 presents the

fundamentals of batteries applied in EVs, that is, the lead-acid battery, the Ni-MH


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battery and the Li-Ion battery. After that, the discussion of the influences of the BAC of

these three batteries is presented.

Chapter 3 is written to review the battery capacity estimation approaches. The

estimation approaches can be categorized into four parts. First is the internal resistance

and impedance related approaches. Second is the mathematical model (MM) approaches.

Third is the empirical formula approaches. And the last one is the artificial intelligent

(AI) approaches.

Chapter 4 presents the experiment on battery system. It includes the description

of the machine for battery testing and evaluation, the testing program, the testing

samples of the lead-acid battery, Ni-MH battery and Li-Ion battery, and their

corresponding charging algorithms. Also, the battery testing method is discussed in this

chapter.

Chapters 5 and 6 present the fundamentals of NN and ANFIS models used in

this project. Chapter 5 describes the fundamental of NN model, the structure of multi-

layer feed-forward NN and the training algorithm. Chapter 6 includes the fundamental

of ANFIS model. The ANFIS architecture and the hybrid learning algorithm of the

ANFIS model are delineated.

Chapters 7 and 8 are the core chapters of this thesis. These two chapters present

the proposed model for SOAC estimation, where SOAC is representing the BRC.

Chapter 7 is devoted to investigate the SOAC estimation using the NN model for the

lead-acid battery. The application of the NN model to the SOAC estimation under

different discharge currents and various battery surface temperatures is presented.

Chapter 8 firstly describes the application of the ANFIS model to the SOAC estimation


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for the lead-acid battery under different discharge currents and various battery surface

temperatures. The results of the ANFIS model are compared with that of the NN model.

Then the capacity distribution (i.e. discharged capacity and regenerative capacity) is

proposed together with the battery surface temperature are selected as the inputs of the

ANFIS model. After that, this model is then extended to the SOAC estimation

approaches for the Ni-MH and the Li-Ion batteries. Instead of straightforward

application of the model, there are modifications of the battery surface temperature in

the inputs and are presented in this chapter. The results of the comparisons between the

estimated SOACs either from the NN model or from ANFIS model with the actual

SOACs from the experimental data are shown.

Chapter 9 presents the conclusions and recommendations of this thesis. The

summary of the research work and the recommendations for further research is

suggested.

1.5 References[Ch1-1] C.C. Chan, K.T. Chau, Modern Electric Vehicle Technology. Oxford; H.K.:

Oxford University Press, 2001.

[Ch1-2] K.T. Chau, Y.S. Wong and C.C. Chan, An overview of energy sources forelectric vehicles, Energy Conversion and Management, vol. 40, no. 10,

1999, pp. 1021-1039.

[Ch1-3] K.T. Chau and Y.S. Wong, Hybridization of energy sources in electricvehicles, Energy Conversion and Management, vol. 42, no. 9, 2001, pp.

1059-1069.


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[Ch1-4] C.C. Chan, E.W.C. Lo and W.X. Shen, The overview of battery technologyin electric vehicles, Proceedings of the 16th International Electric Vehicle

Symposium, 1999, CD-ROM.

[Ch1-5] W.X. Shen, Advanced Battery Capacity Estimation Approaches for ElectricVehicles. Doctor of Philosophy dissertation, 2002.

[Ch1-6] E. Karden, P. Mauracher and F. Schoepe, Electrochemical modeling oflead-acid batteries under operating conditions of electric vehicles, Journal of

Power Sources, vol. 64, no. 1, 1997, pp. 175-180.

[Ch1-7] W.X. Shen, C.C. Chan, E.W.C. Lo and K.T. Chau, Estimation of batteryavailable capacity under variable discharge currents, Journal of Power

Sources, vol. 103, no.2, 2002, pp. 180-187.

[Ch1-8] W.X. Shen, C.C. Chan, E.W.C. Lo and K.T. Chau, A new battery availablecapacity indicator for electric vehicles using neural network, Energy

Conversion and Management, vol. 43, no. 6, 2002, pp. 817-826.

[Ch1-9] K. Bundy, M. Karlsson, G. Lindbergh and A. Lundqvist, Anelectrochemical impedance spectroscopy method for prediction of state of

charge of a nickel-metal hydride battery at open circuit and during

discharge, Journal of Power Sources, vol. 72, no. 2, 1998, pp. 118-125.

[Ch1-10] J.C. Peng, Y.B. Chen, R. Eberhart and H.H. Lee, Adaptive battery state ofcharge estimation using neural network, Proceedings of International

Electric Vehicle Symposium, 2000, CD-ROM.

[Ch1-11] Y.S. Lee, J. Wang and T.Y. Kuo, Lithium-ion battery model and fuzzyneural approach for estimating battery state-of-charge, Proceedings of


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International Electric Vehicle Symposium, 2002, CD-ROM.


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CHAPTER 2

FUNDAMENTALS OF EV BATTERIES

2.1 Introduction 2-12.2 Battery fundamentals 2-2

Lead-acid battery 2-2

Ni-MH battery 2-5

Li-Ion battery 2-8

2.3 Summary 2-102.4 References 2-11


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2.1 IntroductionThe basic element of each battery is the electrochemical cell. A number of cells

connected in series forms a battery. Since battery is the energy source in the EV, it looks

like the fuel tank of the EV, and the EV driving range is determined by the volume of

this fuel tank, i.e. the battery capacity.

Fig. 2.1 Basic principle of batteries

Figure 2.1 shows the basic components of the electrochemical cell. Both the

positive electrode (P ) and negative electrode (N ) are immersed in the electrolyte ( E ).

During discharge, the negative electrode performs oxidation reaction which drives

electrons to the external circuit, while the positive electrode carries out reduction which

accepts electrons from the external circuit. In this process, chemical energy is released

as electrical power. During charge, the process is reversed so that electrons are injected

into the negative electrode to perform reduction while the positive electrode releases


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electrons to carry out oxidation and thus the chemical energy in the form of active

materials is stored in the battery [Ch2-1], [Ch2-2].

In this project, the development of EV battery capacity estimation using neuro-

fuzzy systems is focused on the lead-acid battery, the Ni-MH battery and the Li-Ion

battery. Thus, the fundamentals of these batteries are presented below.

2.2 Battery fundamentalsLead-acid battery

The lead-acid battery is among the oldest known rechargeable battery couples.

This is the most widely manufactured and used rechargeable battery couple throughout

the world. The wide use of the lead-acid is mainly due to its mature technology and low

price. Nevertheless, new designs and fabrication processes are still being introduced at

significant rates to improve its performance of EV batteries.

The lead-acid battery has specific energy of 35Wh/kg, energy density of 70Wh/l,

and specific density of 200W/kg. The main components of the lead-acid battery are the

metallic lead as the negative electrode, lead dioxide as the positive electrode, and the

electrolyte is a sulphuric acid solution. At the negative electrode, the electrode reaction

is:

-++++ eHPbSOSOHPb 22442 (Equation 2.1)

At the positive electrode, the electrode reaction is:

OHPbSOeHSOHPbO 24422 222 ++++-+

(Equation 2.2)

And the overall electrochemical reactions are:

OHPbSOSOHPbOPb 24422 222 +++ (Equation 2.3)


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On discharge, both lead and lead dioxide are converted into lead sulphate. On charge,

the reactions are reversed. From the above electrochemical reaction, it is found that the

electrolyte, sulphuric acid, participates in the electrochemical reactions and its

concentration changes with the SOC.

The open-circuit voltage of the lead-acid battery depends on both the acid

concentration and the temperature. And it is independent of the amount of lead, lead

dioxide or lead sulphate present in the cell. According to the Nernst equation, the open-

circuit voltage, )84.0(0 x+=E V, where x is the acid concentration in kg/l. Since the

open-circuit voltage depends on the acid concentration, this leads to the disadvantage

that the discharge voltage does not remain constant even at low discharge rates.

The lead-acid battery has the nominal voltage of 2V at room temperature. The

specified cutoff voltage at moderate discharge rates is fixed at 1.8V and can be as low as

1.0V at extremely high rates at low temperatures. This specified cutoff voltage is set in

order to prevent the battery from overdischarge. The cell voltage of the lead-acid battery

should be maintained lower than the gassing voltage (typically 2.35V - 2.45V) and it can

be done by controlling the charging current. Further charging of the battery will result in

overcharge reactions which are the electrolytic decomposition of water. The overall

reaction can be presented as:

222 22 OHOH + (Equation 2.4)

It is noted that the cell begins to gas when overcharge and the equation is irreversible.

In the sealed type lead-acid battery, a special porous separator is so employed in

the cell that the evolved oxygen is transferred from the negative electrode to the positive

electrode and then re-combines with hydrogen to form water. Therefore, it provides a


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definite advantage of maintenance-free operation. Moreover, the immobilization of the

gelled electrolyte or absorbed electrolyte with absorptive glass mat separators can allow

the battery to operate in different orientations without spillage. A well-accepted sealed

type lead-acid battery is so-called the VRLA battery [Ch2-1], [Ch2-2].

The influences of the BAC in lead-acid battery can be categorized into (a)

discharge current, (b) temperature, and (c) aging [Ch2-2], [Ch2-3].

For the influence of BAC caused by the discharge current, a high discharge

current can significantly reduce the BAC due to three reasons [Ch2-4]. First, a high

discharge current will increase the current density for a given battery which leads a

higher activation overvoltage. Second, the electrochemical reactions take place mostly

on the surface of the electrodes and a high discharge current can cause the active

material at the bulk electrolyte not to be able to diffuse into the pores of the electrode

immediately. Third, a high discharge current will result in the rapid formation of

sulphate and this increases the internal resistance of the battery, leading to the resistant

overvoltage. In this research, variable discharge current is used instead of constant

discharge current because of EV operation. The variable discharge current can be

termed as discharge current profile. Notice that BACs under various discharge current

profiles are different.

For the influence of BAC caused by the temperature, the increase in the battery

temperature results in an increase in the BAC. It is because there is a reduction in the

viscosity and resistance of the electrolyte and the diffusion rate of active material to the

reaction site is increased. As a result, the battery can reach the specified cutoff voltage

for a longer time, and more BAC can be delivered. It is not practical to measure the


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internal temperature of the battery and so only battery surface temperature is considered

in this research.

For the influence of BAC caused by aging, BAC gradually decreases in value

with the aging effect. Aging effect is the change of active materials and the transport of

substances inside a battery after repeated charge-discharge cycles. In lead-acid battery,

the aging processes are the re-crystallization of the active material at the negative

electrode, disintegration of the active material and the grid corrosion at the positive

electrode. All these processes will cause the reduction of the active material quantity and

these processes are irreversible.

In summary, the lead-acid can maintain its prime position for more than a

century, and is still the attractive energy source for EVs [Ch2-1], there are a number of

advantages including proven technology and mature manufacturing, good high-rate

performance that suits EV applications, high cell voltage, low cost, and available in a

variety of sizes and designs. However, the lead-acid battery still suffers from a number

of disadvantages and needs continual development. The disadvantages are relatively

high self-discharge rate, relatively short cycle-life, relatively low specific energy and

energy density, and it is unsuitable for long-term storage because of electrode corrosion

by sulphation.

Ni-MH battery

The Ni-MH battery has been available on the market for over a decade. Its

characteristics are similar to those of the Ni-Cd battery. The advantages of Ni-MH

battery over Ni-Cd battery are that the Ni-MH battery has a higher specific energy than


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Ni-Cd battery. And the main factor is that, the Ni-MH battery employs hydrogen

incorporated in a metal hydride for the active negative electrode material instead of the

cadmium, which is toxic and carcinogenic [Ch2-1][Ch2-4].

At present, the Ni-MH battery has the specific energy of 65Wh/kg, energy

density of 150Wh/l and specific power of 200W/kg. The active materials of Ni-MH

battery are the nickel oxyhydroxide for the positive electrode and the hydrogen in the

form of a metal hydride for the negative electrode. The electrolyte of the Ni-MH battery

is aqueous potassium hydroxide. The main electrochemical reactions of the Ni-MH

battery can be expressed as:

--

+++ OHOHNieOHNiOOH 22 )( (Equation 2.5)

at the positive electrode and

--

+++ eOHMOHMH 2 (Equation 2.6)

at the negative electrode. The overall electrochemical reactions of discharge and charge

are:

2)(OHNiMNiOOHMH ++ (Equation 2.7)

From the above equations, the metal hydride in the negative electrode is oxidized to

form the metal alloy and the nickel oxyhydroxide in the positive electrode is reduced to

the nickel hydroxide on discharge. On the other hand, hydrogen is stored in the metal

alloy in the negative electrode and the nickel hydroxide is oxidized to the nickel

oxyhydroxide in the positive electrode on charge. Notice that the electrolyte does not

involve in the electrochemical reactions much and it provides only a media for ion-

flowing. So the overall electrolyte concentration remains unchanged during both charge

and discharge.


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The hydrogen storage metal alloy in the Ni-MH battery should be formulated to

obtain a material that is stable over a large number of cycles. There are two major types

of these metal alloys being used for the Ni-MH battery. These are the rare-earth alloys

consisting of titanium and zirconium, known as the AB2, and alloys based around

lanthanum nickel, known as the AB5. The AB2 alloys typically have a higher capacity

than the AB5 alloys. However, the trend is to use the AB5 alloys because of better charge

retention and stability characteristics.

The Ni-MH battery has the open circuit voltage between 1.2V to 1.4V at fully

charge state, as determined by the choice of alloy. The nominal value is usually taken to

be 1.2V for simplicity. The specified cutoff voltage is 1.0V for each cell according to

the battery manufacturer. Further discharge beyond this point may lead to the abrupt

drop of the terminal voltage, which signifies the exhaustion of the BAC value. The Ni-

MH battery has a high discharge-rate capability and is resilient with regard to

overcharge, overdischarge and cell reversal. During overcharge, oxygen is generated at

the nickel positive and is reduced back to water at the alloy negative. During

overdischarge, the potential of the nickel electrode drifts into the hydrogen evolution

region; the hydrogen generated is re-absorbed at the alloy negative electrode and hence

prevents serious build-up of pressure [Ch2-2].

The influence of BAC in the Ni-MH battery is also the discharge current,

temperature and aging. For the influence by the discharge current, when a higher the

discharge rate is used, the lower the BAC value is measured. For the influence by the

temperature, the BAC of the Ni-MH battery increases with the temperature raises within

a certain range. The BAC is reduced at extremely low and high temperature [Ch2-5]. As


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the activities of the active materials and the self-discharge rate are increased with the

increasing temperature, the self-discharge becomes dominant and leads to the reduction

of BAC at extremely high temperature. For the influence by aging, BAC is gradually

decreased because the metal alloy in negative electrode is deteriorated to form the metal

hydride irreversibly by the repeated charge-discharge processes.

Nowadays, Ni-MH battery is still under continual development. The advantages

based on present technology can be summarized as: high specific energy and energy

density (65Wh/kg and 150Wh/l), non-toxic materials (no cadmium), tolerance to

overcharge and overdischarge, long cycle life, flat discharge profile, and rapid recharge

capability. But it suffers from high initial cost. Also it may have a memory effect and be

exothermic on charge. The Ni-MH battery has been regarded as an important near-term

choice for EV battery.

Li-Ion battery

Li-Ion battery is identified as the long-term development of EV battery. It is now

considered to be the most promising rechargeable battery of the future. Although still in

the stage of development, the Li-Ion battery has already gained acceptance for EV

applications.

The Li-Ion battery uses a lithiated transition metal intercalation oxide (Li1-xMyOz)

for the positive electrode, a lithiated carbon intercalation material (LixC) for the negative

electrode instead of metallic lithium and a liquid organic solution or a solid polymer for

the electrolyte. During discharge and charge, the lithium ions are swinging through the


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electrolyte between the positive and negative electrodes. The general electrochemical

reactions are described as:

zyxxzy OMLiCLiCOLiM -++ 1 (Equation 2.8)

On charge, lithium ions are released from the positive electrode, migrate via the

electrolyte and are taken up by the negative electrode. On discharge, the process is

reversed. Notice that there are several possible positive electrode materials including

Li1-xCoO2, Li1-xNiO2 and Li1-xMn2O4, which have the advantages of stability in air, high

voltage and reversibility for the lithium intercalation reaction.

The Li-Ion battery at fully charge state has the open circuit voltage ranging from

4.0V to 4.2V for each cell, which is determined by the choice of positive electrode

materials. For simplicity, 4.0V is usually taken to be the nominal voltage [Ch2-1], [Ch2-

2]. The battery manufacturer suggests that the specified cutoff voltage is 3.0V. It should

take more precaution in handling the Li-Ion battery. On overcharge of the Li-Ion battery,

the charged positive electrode will decompose with liberation of oxygen gas, which

results in a significant loss in capacity. So restricted charging rate and accurate

electronic control of charging voltage is needed.

Same as the Ni-MH battery, the BAC of the Li-Ion battery is influenced by the

discharge current, temperature and aging. For the influence by the discharge rate, the

higher discharge current can cause the BAC to be lower [Ch2-6], but not as significant

as the lead-acid battery. For the influence by the temperature, the temperature can

significantly affect the BAC. The BAC values increase with the elevated temperature. It

should be noted that Li-Ion battery contains organic solvents, which are flammable. On

overheating (above ~100oC), the active materials may react with the electrolyte and


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produce more heat, results in burning of the cell [Ch2-6]. For the influence by aging, the

repeated charge-discharge cycles can deteriorate the negative electrode (the graphite)

gradually, thus the BAC is reduced [Ch2-7].

In summary, the LixC/Li1-xNiO2 type has the specific energy of 120Wh/kg,

energy density of 200Wh/l and specific power of 260W/kg. The cobalt-based type has

higher specific energy and energy density, but with a higher cost and a significant

increase of the self-discharge rate. The manganese-based type has the lowest cost and its

specific energy and energy density lie between those of the cobalt-based and nickel-

based types. Thus the development of Li-Ion battery will ultimately move to the

manganese-based type. The general advantages of Li-Ion battery are highest cell voltage

(as high as 4V), safest design of lithium batteries (absence of metallic lithium) high

specific energy and energy density (90-130Wh/kg and 140-200Wh/l), and long cycle life

(about 1000 cycles). However, it still suffers from relatively high self-discharge rate (as

high as 10% per month) [Ch2-1].

2.3 SummaryIn this chapter, we have briefly come across the fundamentals of the lead-acid,

the Ni-MH, and the Li-Ion batteries. Table 2.1 has summarized the influence of BAC

due to discharge rate, temperature and aging. This gives us the fundamental ideas to the

development of the EV battery capacity estimation approaches using neuro-fuzzy

systems.


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Table 2.1 Summary of the relation of discharge current, temperature and aging to the

BAC for the three EV batteries.

Batteries Discharge current

related to BAC

Temperature

related to BAC

Aging related to

BAC

Lead-acid Significant Significant Gradually decreasein BAC

Ni-MH Moderate Moderate Gradually decreasein BAC

Li-Ion Moderate Moderate Gradually decreasein BAC

2.4 References[Ch2-1] C.C. Chan, K.T. Chau,Modern Electric Vehicle Technology. Oxford; H.K.:

Oxford University Press, 2001.

[Ch2-2] D.AJ. Rand, R. Woods and R.M. Dell, Batteries for electric vehicles.Research Studies Press Ltd., 1998.

[Ch2-3] D. Berndt,Maintenance-Free Batteries. Research Studies Press Ltd., 1997.[Ch2-4] W.X. Shen, Advanced Battery Capacity Estimation Approaches for Electric

Vehicles. Doctor of Philosophy dissertation, 2002.

[Ch2-5] P. Gifford, J. Adams, D. Corrigan and S. Venkatesan, Development ofadvanced nickel-metal hydride batteries for electric and hybrid vehicles,

Journal of Power Sources, vol. 80, no. 1-2, 1999, pp. 157-163.

[Ch2-6] M. Broussely, M Perelle, J. McDowall, G. Sarre and J. Martaeng, Lithiumion: the next generation of long life batteries characteristics, life predictions,

and integration into telecommunication systems, Telecommunications


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Energy Conference, 2000. INTELEC. Twentysecond International, pp. 194-

201.

[Ch2-7] M. Broussely, S. Herreyre, P. Biensan, P. Kasztejna, K. Nechev and R. J.Staniewicz, Aging mechanism in Li Ion cells and calendar life predictions,

Journal of Power Sources, vol. 97-98, 2001, pp. 13-21.


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CHAPTER 3

BATTERY CAPACITY ESTIMATION

APPROACHES A REVIEW

3.1 Introduction 3-13.2 Overview of the battery capacity estimation approaches 3-1

3.2.1 Resistance and impedance related approaches 3-23.2.2 Mathematical model approaches 3-63.2.3 Empirical formula approaches 3-83.2.4 Artificial intelligent approaches 3-10

3.3 Summary 3-153.4 References 3-16


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3.1 IntroductionThis chapter reviews the battery capacity estimation approaches for EV batteries.

Various approaches are described. Although the focus of this project is on the SOAC,

which is actually the normalized value of BRC, namely the ratio of BRC to BAC, the

previous works on SOC and BAC estimation approaches are also discussed.

There are many kinds of capacity estimation approaches to improve the battery

capacity estimation. They can be categorized into four major groups, namely the internal

resistance and impedance related approaches, mathematical model (MM) approaches,

the empirical formula approaches and the artificial intelligent (AI) approaches.

The organization of this chapter is as follows. First, the overview of the battery

capacity estimation approaches is presented. Then, the summary of this chapter is

described. Finally, the references used in this chapter are given.

3.2 Overview of the battery capacity estimation approachesThe determination of battery capacity may be a problem of more or less

complexity depending on the battery type and on the application in which the battery is

used. Thus, many researchers have used different methods to determine the battery

capacity. Some researchers proposed to use the methods like the specific gravity (SG)

and the fully stabilized open circuit voltage. But both have their limitations and are not

practical for EVs. For SG, it can only be used if the electrolyte is liquid and accessible.

Moreover, the electrolyte must be homogeneous when the measurement is made, that

means the battery has to rest after a charge or discharge [Ch3-1]. For the fully stabilized

open circuit voltage, the measurement is reliable only after a rest of at least 10 hours.


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Both are impractical since EVs may not rest for such a long period of time for capacity

measurement.

Conventionally, the practical battery capacity estimation for EVs can be

categorized into four groups. The first group is based on internal resistance and

impedance, the second group is based on the mathematical model approach, the third

group is based on the empirical formula and the fourth group is based on the artificial

intelligent.

3.2.1 Internal resistance and impedance related approachesThe internal resistance and impedance related approaches have been widely used

for a decade. These approaches can be grouped into two major parts. One is the direct

measurement of the battery impedance, which is the measurement of the electrolyte

resistivity. The other one is the indirect measurement of the battery impedance or

internal resistance.

For the direct measurement of the electrolyte resistivity, it is valid to lead-acid

battery because of the characteristics of the electrolyte in lead-acid battery and the liquid

form electrolyte. The SOC is determined since it varies with the electrolyte resistivity.

Gayol et al. [Ch3-2] suggested the use of electrochemical senor to measure the

electrolyte resistivity so that it can be used as the real time determination of SOC in the

lead-acid batteries. But the problem of gas bubbles generated from the battery electrodes

can disturb sensors and generate error readings.

On the other hand, J.M. Charlesworth [Ch3-3] proposed to use a 10 MHz AT cut

quartz crystal immersed in solutions of 0-59 wt% sulphuric acid for the purpose of


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determining the SOC during battery operation. However, this method experienced

problem with the formation of deposits on the crystal face following periods of

immersion in the battery electrolyte. This problem can be solved by adding a membrane

filter. And this method can give an effective SOC sensor for lead-acid battery.

For the indirect measurement of the battery impedance or internal resistance

methods, they use the relationship between the SOC and the battery impedance or

battery internal resistance and claim the SOC is the function that contains the battery

impedance or internal resistance.

For approaches related to the battery internal resistance [Ch3-4], the terminal

voltage and constant discharge current were measured and internal resistance for the

lead-acid battery was calculated from the equivalent circuit as shown in Figure 3.1.

Then the BRC was estimated by the new estimation equation proposed by the author.

The new estimation approach can give a better result compare with the former

estimation approach. And the BRC can be estimated more precisely even though the

BRC is very small.

Fig. 3.1 Equivalent circuit of battery proposed by Sato et al.

As shown in Figure 3.2, the internal resistance of the Li-Ion battery is a function

of SOC and T. R is determined by a series of pulses of constant current applying to the


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battery and monitoring the voltage response. The percentage error of this SOC

estimation is about 3% [Ch3-5].

Fig.3.2 Resistive battery model

For the approaches related to the battery impedance [Ch3-6]-[Ch3-14], the

terminal voltage (response) is measured when a small amplitude ac signal (stimulus) is

injected into the battery, and hence, the impedance is calculated by the ratio of the

response to the stimulus. After the impedance spectrum with different frequencies and

SOC has been made, the researchers have used different approaches to correlate the

battery impedance with the SOC. Beya et al. [Ch3-6] suggested the use of nonlinear

function for the calculation of battery capacity of lead-acid battery. The percentage error

of this suggested method is around 2-3%.

Some researchers proposed to use fuzzy logic methodology to analyze the

impedance spectrum of the batteries. Fennie et al. [Ch3-7]-[Ch3-8] suggested a three-

inputs, one-output fuzzy logic system. The inputs were impedance at 10.3Hz, impedance


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at 103Hz and the phase angle of impedance at 10.3Hz. The output of the fuzzy logic

model was the SOC. The researchers have tried the fuzzy logic model with 3

membership functions [Ch3-7] and 11 membership functions [Ch3-8] respectively.

Different testing data are used to test the fuzzy logic models. Both of them can give out

the percentage error with 5%. Singh et al. [Ch3-9] suggested using three-inputs, one-

output model. This model was similar to what Fennie had used. The impedance at 10Hz,

impedance at 100Hz and phase angle at 10Hz were selected as the inputs of the model.

Of course, the output was the SOC of the battery. Three membership functions had been

employed in the fuzzy logic model and the average error of the model was found to be

1.4%. This fuzzy logic methodology to the SOC determination was extended to the Ni-

MH battery of EVs. Salkind et al. [Ch3-10]-[Ch3-11] proposed a two-inputs, one-input

model for the SOC estimation. The two inputs were the cycle number and the

capacitance 2C , where 2C is a function of frequency. Four membership functions had

been employed in this fuzzy logic model and the percentage error for the estimation

result was within 10%.

Other than fuzzy logic methodology, Bundy et al. [Ch3-12] used the partial least

square (PLS) regression to analyze the impedance spectrum of the Ni-MH battery so as

to predict the battery capacity. The percentage error for this method was 7%.

Shalini et al. [Ch3-13] proposed the use of non-linear least square fitting

procedure to analyze the impedance parameters of the Li-Ion battery for estimation of

the SOC. They concluded the use of impedance parameters can provide a helpful way in

predicting the SOC


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3.2.2 Mathematical model approachesOther than the battery internal resistance or impedance related approaches, some

researchers suggested the use of mathematical model for the battery capacity estimation.

These mathematical models could analyze the battery characteristics of the battery

discharge.

Salameh et al. [Ch3-15] suggested the use of mathematical model with nonlinear

components to estimation the BAC of lead-acid battery. This model obtained the

parameters from the steady state behavior of the battery. Constant discharge current was

used to discharge the battery. They claimed that the model accurately depicted the

performance of a lead-acid battery with temperature compensation.

Several mathematical models have been proposed to reflect the dynamic

behavior of the battery in EVs. Torikai et al. [Ch3-16] proposed a mathematical model

for the battery voltage of lead-acid battery and can be expressed as:

)),((),( ff tigtv =

(Equation 3.1)

where ),( ftv

is the estimated battery voltage, )(ti is the measured battery current and f

is the unknown BAC. Then the battery voltage was approximated by a polynomial on

battery current. The nonlinear least square estimation was used to find model parameters.

Finally, the BAC can be found by iteration.

Ma et al. [Ch3-17] developed a dynamic mathematical model of the battery

voltage of lead-acid battery. And the least square estimate was used for the estimation of

BAC.

Shen et al. [Ch3-18] presented a mathematical model for the battery voltage of

lead-acid battery and can be represented as:


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)()())(

)(1()()()( 10 titRC

tqtLogtVtVtVN

--+= b (Equation 3.2)

Based on this model, the BAC estimation under variable discharge currents was

developed and implemented by the real-time identification of the model parameters. The

average percentage error for all tests was around 3%.

The mathematical model has been extended to the battery capacity estimation for

the Ni-MH battery and the lithium batteries. Gu et al. [Ch3-19] considered several

conditions to buildup a mathematical model for the SOC estimation of the Ni-MH

battery. However, this method neglected the thermal effect of the Ni-MH battery, which

is very important at discharge process of the Ni-MH battery.

Basecq et al. [Ch3-20] suggested the mathematical model for battery voltage of

Li-Ion battery with the consideration of reversible and non-reversible effects, the

equation can be shown as:

)');'(()');'(();(),()()( 0 tttIVtttIVtIAhIRAhVtV nrevrev


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performance of the model and he claimed a very accurate SOC estimation may be

achieved.

3.2.3 Empirical formula approachesThe empirical formula can give an expression with the relation between the

battery capacity and the battery parameters.

There are many empirical expression have been proposed for the battery capacity

and discharge current. The most widely acceptable expression is the Peukert Equation

[Ch3-23]; the available capacity aC can be expressed as:

1-=n

d

aI

KC (Equation 3.4)

wheredI is the discharge current, n and K are constant and depend on temperature, the

concentration of the electrolyte, and the structure of the battery. Usually n is between 1

and 2. This equation is not valid for low discharge currents, that means when

ad CI ,0 , which is physically absurd and is only suitable for the constant

discharging current. Later, Baikie et al introduce the temperature into the (Equation 3.4)

and the Peukert equation becomes:

)1(

)1(-

+=

n

d

ta

I

atKC (Equation 3.5)

This equation is often used in EVs to estimate the battery remaining capacity.

At low discharge rate, Blood et al. [Ch3-24] suggested the empirical equation

with od CmIC loglog += , where m is the empirical constant and oC is the capacity at

zero discharge rate and can be found in discharge curve.


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Other than the Peukert equation, some researchers suggested to use the

coulometric counter method. The coulometric counter is defined as:

=t

dc dttIC0

)( (Equation 3.6)

The coulometric counter measured the amount of Ah taken out of or put into a battery,

and can be thought as an indirect indication of really used capacity. However, the total

initial available capacity, namely the BAC, cant be known before the battery was

discharged. Thus, this method was modified by adding the correction factor or taking

the open circuit voltage into account. This made the coulometric counter possible to

determine the BAC [Ch3-25]. The counter is further modified with the new formula as

follows:

-=t

regdd CdttIICc0

' )()(a (Equation 3.7)

where )( dIa is determined by statistical analysis and regC is the regenerated Ah [Ch3-

26]-[Ch3-27].

Other than the above empirical formula, Pang et al. [Ch3-28] related the

measured battery voltage of lead-acid battery to the other battery parameters. As a result,

the dynamic empirical model can be used and the SOC was estimated by state

estimation. Alzieu et al. [Ch3-29] used an empirical formula of BRC with the

parameters of ampere-hour, battery voltage, rate of discharge, temperature coefficients,

pause time and aging effect for the lead-acid battery. These parameters were

investigated and thus the BRC could be found. This method was implemented in a

gauge and a battery capacity indicator had been made.


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This empirical formula approach had been extended to other batteries. Gaston et

al. [Ch3-30] suggested the empirical formula using the cell pressure as the parameter for

the NiH2battery. The expression can be expressed as:

cp

PnFCPnSOC

DD

-=

/

)((Equation 3.8)

where FCPn is the normalized fully charge pressure, Pn is the instantaneous

normalized pressure and cp DD / can be obtained from the statistical analysis. They

claimed the percentage error for this method is 3.2%.

Jung et al. [Chh3-31] gave a practical empirical formula for which SOC was

related to rated, used and charged capacity, capacity compensation factor, self-discharge

effect and aging effect. This equation was modified by including the temperature effect,

standing time, number of cycles, battery rated capacity, and discharge and charge

current. The SOC accuracy for this empirical formula was around 3% and this SOC

display could give direct information to the driver on instrument cluster of the vehicle.

3.2.4 Artificial intelligent approachesRecently, many researchers have raised the topic of battery capacity estimation

using artificial intelligent (AI). There are a dozen of AI approaches, in this review; the

focuses are on the NN model approach, the fuzzy logic model approach and the fuzzy

neural model approach.

For the battery capacity estimation using the NN model, the experimental data of

the EVs batteries are used as the training sample. The NN model which has the multi-

layer structure is trained by the training data until the predefined error has been reached

or number of epoch has been met. After training, the NN model has been obtained.


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[Ch3-32]-[Ch3-36]. Yamazaki et al. [Ch3-32] suggested a four-input, ten-output NN

model. The inputs were the battery surface temperature, battery terminal voltage,

discharge current and the battery impedance, whereas the ten output neurons indicated a

state between 0% and 100% in steps of 10%. A single testing pattern was used in the

experiment of battery testing. The corresponding percentage error of this model was

within 10%. The NN model is shown in Figure 3.3.

Fig.3.3 The three-layer feed-forward NN proposed by Yamazaki et al.

Peng et al. [Ch3-33] proposed a NN model with four input neurons, five neurons

in the hidden layer and a single output of BAC. The inputs were the discharge current,

discharged capacity, battery surface temperature and minimum battery voltage. The


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percentage error for this BAC estimation was around 3%. The proposed network is

shown as below:

Input layer Hidden layer Output layer

BAC

I

Ah used

Temp

Vmin

Fig.3.4 NN model proposed by Peng et al.

Shen et al. [Ch3-34]-[Ch3-35] had also presented the NN model for BAC

estimation. A three-layer NN model has been proposed. Two input neurons

corresponding to the average discharge current and the battery surface temperature. Six

neurons in the hidden layer and the output was the BAC. The percentage error of this

model was around 5%.

Input layer Hidden layer Output layer

dI

sT

aC

Fig.3.5 NN model proposed by Shen et al.


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Strabnick et al. [Ch3-36] implemented a recurrent NN model for the SOC

estimation. As shown in Figure 3, the neural network, together with a state space model,

calculated the concentration states and the charge state every 30 seconds. The different

resistances were decisive for the clamp voltage at different moments. These moments

were given by analysis the estimation errors. Since the model adjusted its weights during

runtime, ageing is included. With the charge Q and the maximum charge, which was

given by the adaptive estimated surface, a precise BAC estimation was realized.

-

-

Z-1

Z-1

Z-1

UH(k1(t))

UJ(Q)

U2(t)-Ue(t)

Uk(t)-U

e(t)-U

H(k

1(t))

Uk(t)Error 1

Error 2

k1(t)

k2(t)

T(t)

I(t)

Q(t)

Inputs

Time delay

State Weights Neurons

Fig.3.6 Proposed recurrent NN model by Strabnick et al.

For the battery capacity estimation using the fuzzy logic model, Sun et al. [Ch3-

37] proposed the fuzzy logic model and was shown in Figure 3.7. Discharge current and

battery terminal voltage were selected as the inputs of the model and the output was the


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BAC. This BAC estimation approach implemented in the system can effectively prevent

the overdischarge for the lead-acid battery.

Fig.3.7 Proposed fuzzy logic model by Sun et al.

The fuzzy neural system has been adopted by researchers; Lee et al. [Ch3-38]

suggested a fuzzy neural system. It was a four-layered structure with three inputs and

one output. Three inputs were the battery terminal voltage, discharge current and the

battery surface temperature. The corresponding output of this model is SOC. This model

had the average percentage error of 5.5% for 7 testing data. The schematic diagram of

the neural fuzzy system can be shown in Figure 3.8.

Fig. 3.8 Fuzzy neural model proposed by Lee et al.


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3.3 SummaryThis chapter gives a brief review of the battery capacity estimation approaches.

All the internal resistance and impedance related approaches, mathematical model (MM)

approaches, the empirical formula approaches and the artificial intelligent (AI)

approaches have been reviewed. The corresponding battery capacity estimation

approaches with different battery types have been summarized in Table 3.1.

Table 3.1 Summary of the battery capacity estimation approach

Approach Purpose Battery types References

Electrolyte resistivity

measurements

SOC estimation Lead-acid battery [Ch3-2]-

[Ch3-3]

Internal resistance BRC estimation Lead-acid battery [Ch3-4]

Impedance SOC estimation Lead-acid battery &

Ni-MH battery &

Li-Ion battery

[Ch3-5]-

[Ch3-13]

MM BAC estimation Lead-acid battery [Ch3-15]-

[Ch3-18]

BRC estimation Li-Ion battery [Ch3-20]

SOC estimation Ni-MH battery &

Li-Po battery

[Ch3-19],

[Ch3-21]-

[Ch3-22]

Empirical formula BAC estimation Lead-acid battery [Ch3-23],

[Ch3-25]

BRC estimation Lead-acid battery [Ch3-24],

[Ch3-29]


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SOC estimation Lead-acid battery &

NiH2 battery &

Ni-MH battery

[Ch3-26]-

[Ch3-28],

[Ch3-30],

[Ch3-31]

NN model SOC estimation Lead-acid battery [Ch3-32]

BAC estimation Lead-acid battery &

Ni-MH battery

[Ch3-33]-

[Ch3-36]

Fuzzy logic model BAC estimation Lead-acid battery [Ch3-37]

Fuzzy neural model SOC estimation Li-Ion battery [Ch3-38]

From the above estimation approaches, it can be observed that the NN model and

fuzzy logic model are very useful for correlating the battery parameters to the battery

capacity. Since the highly non-linear characteristics of EV batteries, the NN model or

fuzzy logic model play an important role to incorporate inexact information about the

batteries into usable form. Thus more and more researchers have done their researches

on relating the battery parameters to the battery capacity using the NN model or fuzzy

logic model or fuzzy neural model.

3.4 References[Ch3-1] R.T. Barton and P.J. Mitchell, Estimation of the residual capacity of

maintenance-free lead-acid batteries. Part 1. Identification of a parameter for

the prediction of state-of-charge, Journal of Power Sources, vol. 27, no.4,

1989, pp.287-295.


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[Ch3-2] A. Gayol, J. Marcos, X.R. Novoa, C.M. Penalver and M.C. Perez,

Resistivity measurements in lead-acid batteries, Proceedings of the 18th


[Ch3-3] J.M. Charlesworth , Determination of the state-of-charge of a lead-acid

battery using impedance of the quartz crystal oscillator, Journal of

Electrochimica Acta, vol. 41, no. 10, 1996, pp. 1721-1726.

[Ch3-4] S. Sato and A. Kawamura, A new estimation method of state of charge

using terminal voltage and internal resistance for lead acid battery,

Proceedings of the Power Conversion Conference, vol. 2, 2002, pp. 565-570.

[Ch3-5] V.H. Johnson and A.A. Pesaran, Temperature-dependent battery models for

high-power lithium-ion batteries, Proceedings of the 17th International


[Ch3-6] K.B. Beya and G. Maggetto, Impedance-based state of charge indicator for

EV & HEV batteries, Proceedings of the 17th International Electric Vehicle

Symposium, 2000, CD-ROM.

[Ch3-7] S. Arey, V.R. Gaddam, P Singh, Z.J. Yang, C. Fennie Jr. and D.E. Reisner,

Fuzzy logic-enabled battery state-of-charge meters , Proceedings of the

16th International Electric Vehicle Symposium, 1999, CD-ROM.

[Ch3-8] P. Singh, S. Damodar, C. Fennie Jr. and D.E. Reisner, Fuzzy logic-based

determination of Pb-acid battery SOC by impedance interrogation methods,

Proceedings of the 17th International Electric Vehicle Symposium, 2000,

CD-ROM.


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[Ch3-9] P. Singh, R. LaFollette, X. Wang and D.E. Reisner, Fuzzy logic method to

determination SOC/SOH in Pb-acid batteries-assessment of robustness,


CD-ROM.

[Ch3-10] P. Singh, C. Fennie Jr., D.E. Reisner, A.J. Salkind, A fuzzy logic approach

to state-of-charge determination in high performance batteries with

applications to electric vehicles, Proceedings of the 15th International


[Ch3-11] A.J. Salkind, C. Fennie, P. Singh, T. Atwater and D.E. Reisner,

Determination of state-of-charge and state-of-health of batteries by fuzzy

logic methodology, Journal of Power Sources, vol. 80, no. 1-2, 1999, pp.

293-300.

[Ch3-12] K. Bundy, M. Karlsson, G. Lindbergh and A. Lundqvist, An

electrochemical impedance spectroscopy method for prediction of the state

of charge of a nickel-metal hydride battery at open circuit and during

discharge, Journal of Power Sources, vol. 72, no. 2, 1998, pp. 118-125.

[Ch3-13] R. Shalini, N. Munichandraiah and A. K. Shukla, A review of state-of-

charge indication of batteries by means of a.c. impedance measurements,

Journal of Power Sources, vol. 87, no. 1-2, 2000,pp. 12-20.

[Ch3-14] E. Barsoukov, J.H. Kim, C.O. Yoon and H. Lee, Universal battery

parameterization to yield a non-linear equivalent circuit valid for battery

simulation at arbitrary load, Journal of Power Sources, vol. 83, no. 1-2,

1999, pp. 61-70.


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[Ch3-15] Z.M. Salameh; M.A. Casacca, W.A. Lynch, A mathematical model for lead-

acid batteries, IEEE Transactions on Energy Conversion, vol. 7, no. 1, 1992,

pp. 93 -98.

[Ch3-16] T. Torikai, T. Takesue, Y. Toyota, K. Nakano, Research and development

of model-based battery state of charge indicator, Proceedings of the

International Conference on Industrial Electronics, Control, Instrumentation,

and Automation, 1992, vol.2, pp. 996 -1001.

[Ch3-17] R. Ma, L. Sun, H. Tian, The identify of dynamic model and the self-tuning

prediction of SOC for EV battery, Proceedings of the 18th International


[Ch3-18] W.X. Shen, C.C. Chan, E.W.C. Lo and K.T. Chau, Estimation of battery

available capacity under variable discharge currents, Journal of Power

Sources, vol. 103, no.2, 2002, pp. 180-187.

[Ch3-19] W.B. Gu, C.Y. Wang, S.M. Li, M.M. Geng and B.Y. Liaw, Modeling

discharge and charge characteristics of nickelmetal hydride batteries,

Journal of Electrochimica Acta, vol. 44, no. 25, 1999, pp. 4525-4541.

[Ch3-20] J. Basecq, H. Yuan, J.Y. Zhao, C. Ades, Li-Ion battery modeling and state

of charge measurement, Proceedings of the 17th International Electric

Vehicle Symposium, 2000, CD-ROM.

[Ch3-21] G.L. Plett, LiPB dynamic cell models for Kalman-filter SOC estimation,


CD-ROM.


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[Ch3-22] G.L. Plett, Kalman-filter SOC estimation for LiPB HEV cells, Proceedings

of the 19th International Electric Vehicle Symposium, 2002, CD-ROM.

[Ch3-23] W. Peukert, An equation for relating capacity to discharge rate, Electrotech

Z., 18, 287, 1897.

[Ch3-24] P.J. Blood, S. Sotiropoulos, An electrochemical technique for state of

charge (SOC) probing of positive lead-acid battery plates, Journal of Power

Sources, vol. 110, no. 1, 2002, pp.96-106.

[Ch3-25] J.H. Aylor, A. Thieme and B.W. Johnson, A battery state-of-charge

indicator for electric wheelchairs, IEEE Transactions on Industrial

Electronics, vol. 39, no. 5, 1992, pp.398-409.

[Ch3-26] O. Caumont, P. Le Moigne, X. Muneret, P. Lenain and C. Rombaut, An

optimized state of charge algorithm for lead-acid batteries in electric

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CHAPTER 4

EXPERIMENTAL SETUP FOR BATTERY

TESTING

4.1 Introduction 4-1

4.2 Battery testing and evaluation system 4-2

4.3 Batteries used in the tests 4-6

4.4 Battery charging 4-8

4.5 Battery testing 4-10

4.6 Data usage 4-16

4.7 Summary 4-17

4.8 References 4-17


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4.1 Introduction

Battery testing plays an important role in evaluating the performance of

batteries, especially for those used in EVs. The most effective way of testing the EV

battery should be in the EV itself. However, many parameters (e.g. temperature and

discharge current) that are related to the EV battery performance are difficult to

obtain from actual EV driving and may vary simultaneously because of the external

environment.

This chapter is organized as follows, in section 4.2, a battery testing and

evaluation system is described. In section 4.3, the introduction of batteries used in

the tests, namely the lead-acid battery, the Ni-MH battery, and the Li-Ion battery, are

presented. They are tested and the data acquired are used for the development of

capacity estimation approaches using neuro-fuzzy systems. In section 4.4, the

charging methods of the lead-acid battery, the Ni-MH battery, and Li-Ion battery are

presented. In section 4.5, the battery testing methods for the lead-acid battery, the

Ni-MH battery, and the Li-Ion battery are described. The representation of the data

is discussed in section 4.6. Eventually, the summary and the references are included

in section 4.7 and 4.8 respectively.

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4.2 Battery testing and evaluation system

The battery testing can be performed in the battery testing and evaluation

system (BTES). With this system, the batteries can be tested under different charge

or discharge currents at the predefined temperatures. Figure 4.1 shows the schematic

diagram of the BTES, consisting of four main parts [Ch4-1]:

Computer with data

acquisition program

Temperature controlled chamber

Programmable electronic load

Power

supply

Programmable charger

Power flow Signal flow

Batteryunder test

+

Fig. 4.1 Schematic diagram of BTES.

The four main parts of the BTES consist of 1.) a programmable charger, 2.) a

programmable electronic load, 3.) a temperature controlled chamber, and 4.) a

computer control and data acquisition subsystem. Brief description of the four main

parts is as follows:

1.) A programmable charger can perform any charging algorithms such as

constant-voltage charging, constant-current charging, multistage variable-

voltage variable-current charging and even pulse charging. An example of the

constant voltage, constant current charge (12V, 9A) with the voltage limitation

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of 12V and the temperature limitation of 45C implemented by the BTES

program is shown in Table 4.1.

Table 4.1 Program of constant voltage, constant current charge in the BTES program

Step Procedure Nominal value Limit Registration

1 SET Ah = 0.0 STANDARD

2 CHA 12.0 V

9.0 A

>12 V

>45C

10 min.

3 STO

N.B.: Ah refers to the ampere-hour counter to record the capacity that has been

charged to the battery. At start, the counter is set to be zero. STANDARD is to

define the parameters to be measured and stored in the computer. The value in

Registration is to define the sampling interval. CHA is the charge command.

The values in Limit give the limitations of the charger to stop charging when

either of them is reached. STO is to stop charge and end the program.

2.) A programmable electronic load can perform various discharging algorithms,

including constant-current discharge; varying-current discharge and EV

discharge current profiles. Table 4.2-3 give the examples of the constant

current (30A) and EV discharge current profile (ECE) discharges implemented

by the program in the BTES, where cutoff voltage limitation is 10.8 V and the

temperature limitation is 45oC.

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Table 4.2 Program of constant current discharge in the BTES



2 DCH 30.0 A 45C

1 min.

3 STO

N.B: DCH is the discharge command.

Table 4.3 Program of EV discharge current profile discharge in the BTES



2 TABLE Datafile (ECE) 45C

1 sec

3 STO

N.B. TABLE in the program commands the electronic load to perform each

value in the row stored in the Datafile (ECE as an example). The Datafile is

in .txt format. The rows of the Datafile are:

10sec; - 10.0;

30sec; - 90.0;

3sec; + 15.0;

6sec; - 85.0;

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where the values in the first column are the discharge or charge duration, the

minus and positive values in the second column represent to discharge and

charge currents, respectively.

3.) A temperature controlled chamber can provide any predefined air temperature

ranging from 20C to 50C for battery testing [Ch4-2].

4.) A computer control and data acquisition subsystem can generate the control

signals to feed the programmable charger and the programmable electronic

load, while it can automatically acquire all necessary experimental data. The

sampling time of data acquisition can be preset as in the form of seconds,

minutes or hours, depending on the requirements of users.

Figure 4.2 shows the experimental setup of the BTES, which is located at the

International Research Center for Electric Vehicles, the University of Hong Kong.

Fig. 4.2 Experimental setup of BTES.

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4.3 Batteries used in the tests

Three batteries are used in the battery testing. They are the lead-acid battery,

the Ni-MH battery, and the Li-Ion battery. The lead-acid battery with the rated

voltage of 12 V is used for exemplification, whose rated capacity is 40 Ah at the 20-

hour discharge rate, namely, = 40 Ah. The Ni-MH battery with the rated voltage

of 12 V is used for exemplification, whose rated capacity is 45 Ah at the 3-hour

discharge rate, namely, = 45Ah. The Li-Ion battery with the rated voltage of

4.2V is used for exemplification, whose rated capacity is 15 Ah at the 5-hour

discharge rate, namely = 15Ah. Table 4.4 summarizes the specifications of these

three batteries, which are extracted from the data sheet of their respective

manufacturing companies. Figures 4.3, 4.5, 4.6 shows the three batteries used in the

tests.

20C

3C

5C

Table 4.4 Major characteristics of the batteries under tests

Battery type Lead-acid Ni-MH Li-Ion

Manufacturer Sonnenschein Gold Peak Maote

Rated capacity (Ah) 40 45 15

Rated voltage (V) 12 12 4.2

Charging voltage limit (V) 14.4 15.0 4.25

Charging current limit (A) 20 N/A 10

Normal charging current (A) 8 9 3

Charging factor 1.1 1.1 1.0

Maximum discharge current (A) 400 225 15

Operating temperature (C) -30-50 0-55 N/A

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Fig. 4.3 Lead-acid battery

Fig. 4.4 Ni-MH battery

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Fig. 4.5 Li-Ion battery

4.4 Battery charging

When a battery is run out of its energy storage, it must need to be recharged.

Battery charging process is the reversal of the discharge process, which means the

electrochemical reaction in charging is the reverse to that in discharge. The degree of

battery charging is the key to the battery discharge performance, inadequate charge

will result in reduced BAC. The aim of this project is to make a comparison of

BACs at different discharge conditions so that we can develop the battery estimation

approaches. To make the comparison meaningful, the battery should be discharged

from a same state. Since it is difficult to determine precisely when a battery is fully

charged, a certain degree of overcharge can be applied to ensure that the battery is

full-charged. However, some batteries (e.g. Li-Ion battery) cannot be overcharged

for safety purpose. Overcharge state is adopted as the initial state only for the BAC

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tests throughout the experimentation of the lead-acid battery and the Ni-MH battery.

For the Li-Ion battery, it is charged to its fully-charged state as the initial state.

There are many charging methods for different application purposes, they are

the constant voltage, constant current, constant current-constant voltage, constant

current-constant voltage-constant current, multi-current and the pulsed current

charging with or without a short depolarization current.

For the lead-acid battery, its charging method is the application of constant

current-constant voltage-constant current. In this method, charging is carried out

under the constant current followed by a crossover to the constant voltage and then

to the constant current again. The first constant current is set to around 20% of the

rated capacity, and is held constant until the cell voltage in the battery rises to the

slight gas voltage of about 2.4 V/cell. Such voltage is maintained at this level and

the current decreases exponentially until it reaches at a preset small current. Then,

this small current is used to charge the battery again to ensure the each cell in the

battery to be fully charged, which is usually called equalizing charge. The total

charging time is either controlled by the temperature limitation or selected to return

of a predetermined percentage of the previous discharged capacity, for example,

105%-110% or 5-10% overcharge. For the lead-acid battery used in this project, the

constant current, gassing voltage, equalizing charge current and temperature

limitation are set to the 8 A, 14.4 V, 1 A and 45C, respectively.

For the Ni-MH battery, its charging method is the application of constant

current. In this method, constant current charging is carried out, its value is around

20% of the rated capacity until the temperature limitation or the predetermined

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percentage of the previous discharged capacity is reached. The values of the

temperature limitation, predetermined percentage and charge current are respectively

set to 45C, 110% and around 3-hour discharge rate, namely 9 A, for the Ni-MH

battery used in this project.

For the Li-Ion battery, its charging method is the application of constant

current-constant voltage. In this method, predefined voltage and current are applied

to the battery until the temperature limitation or the predetermined percentage of the

previous discharged capacity or predefined period of time is reached. The values of

the temperature limitation, predetermined percentage, predefined period of time,

charge current and charge voltage are respectively set to 45C, 100%, 20 hours, 3A

and 4.2V, for the Li-Ion battery used in this project.

4.5 Battery testing

In this project, the battery testing has two main parts: 1.) constant current

discharge, 2.) variable current discharge. Constant current discharge mainly

investigates the influence of the constant discharge current and the battery surface

temperature on the BAC. Whereas variable current discharge investigates the

influence of the variable discharge current (i.e. the discharge current profile) and the

battery surface temperature on the BAC.

For constant current discharge, the discharge currents between 8A and 40A

are used for testing the lead-acid battery. Whereas the discharge currents between

9A and 40A are used for testing the Ni-MH battery.

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For variable discharge current (i.e. the discharge current profile) for the three

batteries, the profiles include the climbing-hill discharge current (CDC) corresponds

to the EV hill-climbing, the fast discharge current (FDC) corresponds to the EV

highway driving, the normal discharge current (NDC) corresponds to the EV normal

driving and the small discharge current (SDC) corresponds to the EV low-speed

urban driving. These profiles are used to describe the typical operation of an EV.

Figures 4.6-4.9 show the corresponding profiles. Other than these profiles, the

standard EV driving cycle based current discharges are used to test the battery. They

are the US federal urban driving schedule (FUDS), the US federal highway driving

schedule (FHDS), the standard European test schedule (ECE) and the Japanese mode

10.15 (JM10.15). For safety reasons, some profiles have been scaled down in order

not to over the rated values of the batteries. Figures 4.10-4.13 show each profiles

respectively.

Different battery surface temperatures between 10C and 30C are used so as

to make the environment of the battery testing alike the actual situation inside the

vehicle chassis.

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20

22

24

26

28

30

32

34

36

0 1000 2000 3000 4000 5000

Time (s)

Dischargecurrent(A)

Fig. 4.6 CDC profile.

15

16

17

18

19

20

21

22

23

0 1000 2000 3000 4000 5000 6000 7000 8000

Time (s)

D

ischargecurrent(A)

Fig. 4.7 FDC profile.

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10

11

12

13

14

15

16

17

0 4000 8000 12000

Time (s)

Dischargecur

rent(A)

Fig. 4.8 NDC profile.

8

8.5

9

9.5

10

10.5

11

11.5

0 4000 8000 12000 16000

Time (s)

D

ischargecurrent(A)

Fig. 4.9 SDC profile.

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0 100 200 300 400 500 600 700 800

-150

-100

-50

0

50

100

150

200

Time (s)

Dischargecurrent(A)

Fig. 4.10 FUDS based current profile.

0 200 400 600 800 1000 1200 1400

-100

-50

0

50

100

150

200

Time (s)

Dischargecurrent(A

)

Fig. 4.11 FHDS based current profile.

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0 200 400 600 800 1000 1200-150

-100

-50

0

50

100

150

200

250

Time (s)

Dischargecurrent

(A)

Fig. 4.12 ECE based current profile.

0 100 200 300 400 500 600 700-40

-20

0

20

40

60

80

100

120

Time (s)

Dischargecurrent(A)

Fig. 4.13 JM10.15 based current profile.

.

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With above-mentioned constant discharge current and variable discharge

current, BAC tests for the lead-acid battery, the Ni-MH battery, and the Li-Ion

battery are carried out on the BTES. The lead-acid battery, the Ni-MH battery, and

the Li-Ion battery at the fully-charged state ( = 1) are discharged by using those

discharge current profiles until the respective specified cutoff voltages of 10.8V,

10.0V, and 3.0V are reached ( = 0). The experimental data are automatically

recorded in the d

battery charge estimation

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