Review PHM Li Ion 2011

Journal of Power Sources 196 (2011) 60076014

Contents lists available at ScienceDirect

Journal of Power Sources

journa l homepage: www.e lsev ier .com

Review

A revie

JingliangCenter for Intel

a r t i c l

Article history:Received 15 FeReceived in reAccepted 31 MAvailable onlin

Keywords:PrognosticsHealth monitoLi-ion batteryEstimationPredictionRUL

Contents

1. Introd2. State of charge (SOC) estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6008

2.1. Fuzzy logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60092.2. Autoregressive moving average (ARMA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60092.3. Articial neural network (ANN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60092.4. Electrochemical impedance spectroscopy (EIS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60092.5.2.6.

3. Voltag4. Capac5. Rema

5.1.5.2.

6. ConclAcknoRefer

1. Introdu

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0378-7753/$ doi:10.1016/j.Extended Kalman lter (EKF) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6010Support vector machine (SVM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6010e estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6011ity estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6011ining-useful-life (RUL) prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6012Relevance vector machine (RVM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6012Particle lter (PF) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6012

usion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6013wledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6013ences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6013

ction

e growing awareness of climate change, anxiety ofssil fuel and the interest in electric drive, more con-e begun to consider purchasing battery-driven hybridtheir higher MPG rating and lower individual carbonnalysis of the 2009 Year-end Hybrid Market Dashboardhat the U.S. HEV market shares have increased to 2.8%

ding author. Tel.: +1 302 220 3402; fax: +1 513 556 4647.ress: [email protected] (J. Zhang).

amongall vehicles sold in2009.Given the tougheconomic situationin the last year, the U.S. hybrid sales dropped only 7.6%, comparedto 21.4% for overall vehicle sales. In California, 55,553 new HEVswere sold in 2009; in DC, every 3.7 new HEVs were purchased forevery 1000 residents in the past year. The forecast for 2010 hybridsales is still promising given the exciting introductions of PHEV andaccelerating economic recovery.

The technological breakthroughs in battery life, abuse toler-ance and drive range will eventually result in the developmentof cost-effective, long lasting Li-ion batteries. However, no mat-ter how good the Li-ion battery is, it will degrade over time dueto aging, environmental impacts and dynamic loading. Therefore,

see front matter 2011 Elsevier B.V. All rights reserved.jpowsour.2011.03.101w on prognostics and health monitoring of Li-ion battery

Zhang , Jay Leeligent Maintenance Systems, Department of Mechanical Engineering, University of Cincinnati, 560 Baldwin Hall, Cincinnati, OH 45221, United States

e i n f o

bruary 2011vised form 30 March 2011arch 2011e 27 April 2011

ring

a b s t r a c t

The functionality and reliability of Li-ion batteries as major energy storage devices have received moreandmore attention from awide spectrumof stakeholders, including federal/state policymakers, businessleaders, technical researchers, environmental groups and the general public. Failures of Li-ion batterynot only result in serious inconvenience and enormous replacement/repair costs, but also risk catas-trophic consequences such as explosion due to overheating and short circuiting. In order to preventsevere failures from occurring, and to optimize Li-ion battery maintenance schedules, breakthroughs inprognostics and healthmonitoring of Li-ion batteries,with an emphasis on fault detection, correction andremaining-useful-life prediction,must be achieved. This paper reviews various aspects of recent researchand developments in Li-ion battery prognostics and health monitoring, and summarizes the techniques,algorithms and models used for state-of-charge (SOC) estimation, current/voltage estimation, capacityestimation and remaining-useful-life (RUL) prediction.

2011 Elsevier B.V. All rights reserved.

uction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6007/ locate / jpowsour

6008 J. Zhang, J. Lee / Journal of Power Sources 196 (2011) 60076014

it is always desirable to be able to detect the underlying degrada-tion, take countermeasures to impede the developing faults andultimately prevent the catastrophic failures from occurring. Thisis how health monitoring works. Prognostics, on the other hand,deals with fsystem/comtee satisfacare two intmance. Preinformationprognosticsmonitoringbattery. Efffor Li-ion bregarding sship positio

Health mnered muchHundreds oapplicationdemic jouralgorithmsnostics cana Li-ion batdamentallyFirst of all,pack are alresulting into waveformdata collectcurrent andtery aremuexample, inelectronic dtors affectininclude aginbattery celappropriatemonitoringof Li-ion ba

This revof charge, vmethods foNote that sithe past 5 otics and prorelevance. Fsome tradities such asare also covsuch as deablywhendhas multipltion and One descripthe conditiodeterminedfailure are junsatisfactocic functionecessity osuch as ovtaminationcomplexityreports arevarious pro

to this topic with emphasis on electro-chemical analysis are [46].The subsequent sections of this paper are arranged as fol-

lows: Sections 25 explain the techniques, algorithms andmethodsapplied in SOC estimation, current/voltage estimation, capacity

tionns ofbase

te of

te ofterys thsuctimeide ting)indivacturimalcurreer-dergyed.ultithe 1ademion oakeherstemenmainctionionsctivecus msizinrge cmos

1

i is thch rhe timion ored aThesore, tversiinal

thusof S

and

1

isng enacityply

le, wrge c].kingcircuault propagation/degradation and predicts how soon aponent will fail, or reach a level that cannot guaran-tory performance. Health monitoring and prognosticsegral parts in realizing near-zero-breakdown perfor-ferably, prognostics is conducted rst, with or withoutextracted from health monitoring. For cases in whichcannot accurately predict remaining-useful-life, healthis relied on to disclose the health status of a Li-ion

ective health monitoring and prognostics functionalityatteries will adequately address customers concernsafety issues and help companies establish early leader-ns in the eld.onitoring and prognostics for machinery has gar-attention in the research community in recent years.f papers in this eld, including theories and practicals, appear every year in conference proceedings, aca-nals and technical reports. The most popular models,and technologies for machinery diagnostics and prog-be found in a reviewgiven by Jardine et al. [2]. However,tery, which features electro-chemical behaviors, is fun-different from a mechanical system in various aspects.the electro-chemical reactions inside a Li-ion batterymost inaccessible using common sensor technologies,the scarcityofdata foranalysis. Secondly, in comparisontype machinery data, the most available monitoring

ed from Li-ion battery is value-typed such as voltage,temperature. Lastly, theoperationprolesof Li-ionbat-chmore dynamic than those ofmechanical systems. Fora PHEV the Li-ion battery is subject to drive behaviors,evice usages, driving environments, etc. Other fac-g the performance and degradation of Li-ion batteriesg-dependent capacity loss, capacity imbalance among

ls, self-discharge, etc. Therefore, the development ofmethodologies andalgorithms for Li-ionbatteryhealthand prognosticsmust take into account the uniquenessttery system.iew summarizes different methods to predict the stateoltage, current and capacity, as well as techniques andr predicting remaining useful life, of Li-ion batteries.nce Li-ion batteries have come into popular use in onlyr 6 years, the literature that directly addresses diagnos-gnostics aspects are limited in terms of quantity andor the purpose of establishing a broader perspectiveional diagnostic techniques for other types of batter-lead-acid batteries and nickel metal hydride batteriesered in this review. It should also be noted that termsgradation, health and failure are used interchange-iscussinghealthmonitoring. The reason is that failuree denitions in various situations and both degrada-health are related to failure in one way or another.tion of failure assumes that failure only depends onn variables. Once the condition variable exceeds a pre-threshold, a failure has occurred. Other denitions ofudged by criteria such as performance (performing atry level), functionality (incapable of conducting spe-n) and availability (machine breaks down) [2]. Thef studying the mechanisms of specic failure modes,er-heating, over-charging, chemical and metal con-, has attracted more and more attention. Due to theof experiment design and critical safety issues, fewavailable in the literature up to now. The progress ofjects can be found in [3]. Some published papers related

estimaand codrawn

2. Sta

Staall batassessemationusablewill gubalancwheremanufan optthe ocand ovthe enextend

A msince tous acdenitvant stin undinvolvand rethis sedenitperspewill foempha

Chably the

SOC =

whereapproarent, tindicatmeasuature.Therefcontroin termoccur,versionciency

SOC =

wherecharginal capwas simexampdischaSOC [9

Ma(openand remaining-useful-life prediction, including the proseachmethod; and thenal section contains conclusionsd on this review.

charge (SOC) estimation

charge estimation has always been a big concern fordriven devices. An accurate SOC estimation not onlye reliability of products, but also provides critical infor-h as the remaining useful energy and/or the remaining. Moreover, an efcient and accurate SOC estimationhe design of charging/discharging strategies (cell-levelwhich is of great importance in high current applicationidual cells are likely to have different capacities due toe variation, natural aging, degradation, etc. In this case,charging/discharging strategy will effectively preventnce of abnormities such as overcharging, overheatingischarge from happening. As a result, the reliability ofstorage device will be enhanced and the product life

ude of SOC estimation methods has been introduced980s, the adoption of which has been gradual in vari-ic research and industrial applications. However, thef SOC has yet to be agreed upon by all of the rele-oldersthis ambiguity has resulted in much confusionanding the meaning and usefulness of SOC and itst in further analytical tasks, such as capacity estimationing-useful-life prediction. To address this confusion,starts with itemizing and explaining some commonof SOC, which hopefully will give readers a broaderof the issues. The SOC estimation methods introducedainly on models, algorithms and technologies withoutg the denition of SOC.ounting or current integration shown in (2.1) is proba-t classical SOC calculation method.

i dtCn

(2.1)

e current; Cn is the nominal capacity; t is the time. Thisequires dynamic measurement of the cell/battery cur-e integral of which is considered to provide a directf available capacity. The nominal capacity, however, ist a constant discharge rate under controlled temper-e conditions seldom occur in real-world applications.he use of nominal capacity as the total capacity remainsal [7]. Besides, due to its reliance on integration, errorsmeasurements accumulate and large SOC errors mayrequiringa recalibrationat regular intervals [8]. AnotherOC calculation considers the effect of coulombic ef-is given as follows:

i dtCn

(2.2)

the coulombic efciency dened as the ratio betweenergy and discharging energy required to restore origi-. is equal or less than 1. Still another version of SOCin a linear relationship with a preset voltage level. Forhen minimum discharge voltage was reached duringycle, the battery was considered fully discharged at 0%

extensive and costly tables comparing SOC and OCVit voltage) under different temperatures has long been

J. Zhang, J. Lee / Journal of Power Sources 196 (2011) 60076014 6009

the common practice of battery manufacturers for preparing man-ufacturers recommended datasheets. Once sufcient tables weremade and stored appropriately, the inference of SOC from OCVbecame very straightforward. Guiheen et al. disclosed anothersimilar SOCtionships ofmethod, howell and is tracy and efdrawbackotions seldomdiscrepanci

Insteadadvanced Sgressive mKalman ltacademic jobetter evalthese approalgorithmsestimationare discusse

2.1. Fuzzy l

Instead otion, fuzzyambiguitymentationinputoutpoutputs, rea

Salkindmate SOC udata both fbattery. Inoutput. The3 differentcase, the mcapacitanceber. Again, tcapacitancecapacitancemargin of eparametersserved as intesting andand high inthese appro

2.2. Autore

Autoregstatistics mstudy the ufuture valuean autoregrARMA mod

xta + tp

i=1

where x is tnoise; p is A

In orderKozlowski [and used m

this model, such as electrolyte resistance, charge transfer resis-tance and double-layer capacitance, were extracted and fed into asecond-order ARMAmodel to compute the SOC. ARMA is basically aregressionmodelwithnoiseadded. Theaccuracyof anARMAmodel

s onsed ie hisupd

ep-ah

tici

ANNitatioelingousnd scomeodelit layc neewithssityghts,ther[4], bnce amateancetern, twoetermtherentrge vd wr mas un

ed frnumat w

ethodOner the

ectro

has bctionssibleapp) shoain frmporm thuse

the fhrouancetimatwetive incy rn oftheestimation method which looked for recording rela-the magnitude of ramp-peak current and SOC [9]. Thiswever, requires extensive testing and table making asakenmore as a supplementalmeans to assure the accu-fectiveness of traditional SOCOCV tables. One majorf these table-makingefforts is that the real-world condi-match exactly those recorded, resulting in signicant

es between the estimated SOC and true SOC [10].of relying exclusively on SOCOCV tables, moreOC estimation methods based on fuzzy logic, autore-oving average, articial neural network, extendeder and support vector machine have been proposed inurnals, conference proceedings and led patents. To

uate and explain the advantage and disadvantage ofaches in SOC estimation, brief descriptions of theseare given rst. The detailed approaches in tackling SOCfollow. Finally, the pros and cons of these approachesd.

ogic

f pursuing only absolutely clean and precise informa-logic methods allow a certain level of uncertainty andin processing incomplete and noisy data. The imple-of fuzzy logic method consists of 4 parts: rule-basedut relationship, membership function for inputs andsoning and defuzzication of outputs.et al. [11] applied the fuzzy logic methodology to esti-sing EIS (electro-chemical impedance spectroscopy)rom Li/SO2 battery and NiMH (nickel metal hydride)the Li/SO2 battery case, the model had 3 inputs and 1three inputs were imaginary impedances measured atfrequenciesthe output was SOC. In the NiMH batteryodel had 2 inputs and 1 output. The two inputs were(an element in assumedbattery circuit) and cycle num-heOutputwas SOC. The empirical relationship betweenand imaginary impedance was established to facilitateestimation. The reported accuracy was within a 10%rror. In [4], only a subset of electro-chemical model(34) were found to be very important and thereforeputs for the Fuzzy logic model in order to speed up EISreduce redundancy in data collection.However, the sizestrument cost for EIS is likely to inhibit the extension ofaches to on-line application.

gressive moving average (ARMA)

ressive moving average (ARMA) model is a popularodel applied to time series and index-based data tonderlying patterns of a system and/or predict near-s in a series. ARMAmodels are composed of two parts:essive (AR) part and a moving average (MA) part. Anel, denoted as ARMA (p, q) is expressed as follows:

ixti +q

i=1iti (2.3)

ime series or index-based data; a is constant; is whiteR order; q is MA order; and are model coefcients.to infer the SOC from impedance measurement,

4] developed a two-electrode electro-chemical modeleasured impedance data for validation. The inputs of

dependdata uthat thing andone-st

2.3. Ar

Anple imin modto varifrom, ahas betemman inpuspecinodesof neceas weito ano

Inresistato estiimpedsive exIn [12]a preddictedthe curdischaassignesate fopatternacquirinitialinstantthis mtoring.low fo

2.4. El

EIScal reainacceEIS, ancircuitat certeled cowill foeasy todue tostate timportSOC esship becapacifrequefunctiominingcompleteness and representativeness of the historicaln training. In real world applications, it is more likelytorical data is incomplete and recursive model train-ating is required to make a reasonable estimation andead prediction.

al neural network (ANN)

, which consists of various nodes and layers, is a sim-n of human brain. It requires little expert knowledgecomplex systems and adopts a black box approach

sources of data. Due to its simplicity in handling datatructure of, complex or even unknown systems, ANNone of the most widely used methods in complex sys-ng. A typical simple neural network consists of 3 layers:er, a hidden layer and anoutput layer. Dependingon theds, such as number of inputs andoutputs, thenumber ofin different layers can be dened, for convenience or out. The lines connecting each pair of nodes are denotedwhich are literally mapping functions from one spacespace.attery internal parameters, such as charge transfernd electrolyte resistance, were used as inputs to ANNthe SOC. Other approaches avoided the need for

measurement and utilized much less resource inten-al measurements as the inputs to the neural network.neural networks were used to adaptively predict whenined voltage level would be reached. The rst NN pre-

remaining useful energy and remaining usage time indischarge cycle. The inputs of this NN included a presetoltage and the temperature measured. The second NNeights on the rst neural network in order to compen-nufacturing variation, aging effects and specic usageder testing conditions. The inputs of this NN were allom external measurements, including initial voltage,ber of cycles, the beginning of discharge period, and thehich the initial measurements are available. However,was proposed specically for lead-acid battery moni-assumption made was that the discharge current wasmost of the time.

chemical impedance spectroscopy (EIS)

een widely used to provide insight into electrochemi-s happenedwithin chemical batterieswhich are usuallyto common sensory technology. Before implementing

ropriate electrochemical model (e.g. imaginary batteryuld be proposed rst. EIS can then trigger AC signalsequencies and calculate the numerical values of mod-nents such as resistors, capacitors and inductors, whiche foundation for further analysis. However, EIS is not, and results from EIS are hard to reproduce mainlyact that systems being measured must maintain steadyghout testing. In [13], Blanke et al. emphasized theof adopting appropriate methods in using EIS. For

tion, it was determined that there is a close relation-en battery SOC and a specic frequency range at whichmpedanceequaled inductive impedance.Moreover, thisange (f) varied monotonically and reproducibly as abattery SOC. Therefore, SOC could be found by deter-desired frequency range f.


nded

2.5. Extend

Extende(KF) for a nTaylor seriefunctions foprocess rescomputatiocannot deasince rst oaccuracy in

Plett [15ing Coulommolecule-stroscopy. Tsuch as impSOC estimamodeling.accuracy, acorrectingtreatment oeffect. EKFwstate vectoandupdateformulated

yk = OCV(zk

where yk ischemical hoperand; Rof this appand laborioused in realtionship waamong battemployed, awas discussmation equthe equatiowas genericcapacities. Iunder highlwas reformerrors indu

ons bcuracectioin sige va

CV(S

ppor

portd re

sucheth-dimrminm incondof trused18],tioned cultagehe SOnt cbothporteFig. 1. A complete picture of operation of exte

ed Kalman lter (EKF)

d Kalman lter (EKF) is an extension of Kalman lteron-linear application. By using partial derivatives ands expansion, EKF linearizes the Predict and Updater current estimates. After linearization, the remainingembles that when using a traditional Kalman lter. An diagram is illustrated in Fig. 1 [14]. EKF, however,l with systems with highly non-linear characteristicsrder Taylor series approximation cannot give enougha highly non-linear case.] reviewed various SOC estimation methods includ-b counting, chemistry-dependent, OCV measurement,cale electro-chemical model and impedance spec-he disadvantages of these methods were pointed outracticality for HEV applications, inaccuracy in dynamiction and extensive expertise required in micro-scaleTo enable dynamic SOC estimation with enhancedphysics-based circuit model named enhanced self-(ESC) model was proposed with emphasis on thef hysteresis effect, temperature effect and relaxationas implemented based the proposedmodel andmodel

r including SOC (as discussed in [16]) was predictedd recursively. The enhancedself-correctingmodelwasas follows:

) + hk + fil(ik) = R ik (2.4)

variatithe acthe selresultand tru

Vk = O

2.6. Su

Suption antasksbasedmto hightransfoproblestraintsubsetand be

In [estimaincludand vostep. T(constaout inThe rethe estimated voltage; k is index; z is SOC; h is electro-ysteresis; l() is some dynamic operation ltering itsis battery resistance; i is current. The main drawbackroach is the use of OCVSOC tables, which are costlyus to obtain, and are inaccurate to some extent when-world applications. In [17], a modied OCVSOC rela-s proposed with enhanced accuracy and consistencyeries of the same type. A simple battery circuit wasnd the use of dual EKFs for SOC and capacity estimationed. The circuit model is shown in Fig. 2 the voltage esti-ation referenced in (2.5). The calculation of the OCV inn relied on the modied OCVSOC relationship, whichenough for cells with comparable variations in initial

n order to improve the robustness of theEKF frameworky non-linear conditions, the measurement noise modelulated to function as a countermeasure for large voltageced by the extreme operating conditions and inherent

However, inThere is a p

Fig. 2. Simplelibrium potencapacitance.Kalman lter [14].

etween a simple model and the true system. However,y of capacity estimation seems highly dependent onn of initial values, and inappropriate initialization couldnicant delay in the convergence for estimated valueslues.

OCk, Cn,k) Vd,k Ri ik (2.5)

t vector machine (SVM)

vector machine (SVM) is a state-of-the-art classica-gression algorithm widely used in signal processingas text recognition and bioinformatics. As a kernelod, SVMprojects the original low-dimension data spaceension feature space. This projection is equivalent tog non-linear problems in lower dimension to linearhigher dimension. Regulated by the well-dened con-itions (KarushKuhnTucker conditions), only a smallaining data referred to as support vectors will remainin formulating classication and regression equations.

Hansen and Wang used SVM to build an empirical SOCmodel. No battery circuit was needed. The input vectorrrent, voltage, SOC calculated from the previous stepchange in the last 1 s. The output was SOC in currentC estimationmodelwas trained using steady state dataurrent pulse) only. The evaluation of results was carriedthe steady state SOC test and dynamic state SOC test.d RMS errors were around 5% and 5.76% respectively.

formation about the asserted true SOCwas not given.ossibility that the true SOC was de facto calculated

battery circuit. OCV represents open circuit voltage, also called equi-tial. Rd and Ri are internal resistances. Cd is equivalent double layer


using Coulomb counter, as is the case for the true SOC in a similardynamic test (5th urban dynamometer driving schedule) discussedin [16]. Since Coulomb countingwas dismissed by the author in therst place, it could be questionable to take results from Coulombcounter asrequires nCanderror tto be time-c

3. Voltage

The effomodel, whierties of asimulate babattery enepower suppzero downtas the instatery, and thsource, duein voltage ecircuit voltativeness offailures sucfew estimaindustries amainly becafor purposeracies wereadvanced mlenges.

In [24], Gis capable odischarge rcircuit is siadaptive cocess. A referrate and amwas then ator and pounder varyithis modelparametersthe model rdeterioratioand simuladischarge scdifferent frotemperatur

To comprates, Hirsccharge voltanormalizedvoltage curdischarge lcapacity asization metand time beized time scaccuracy ofnormalizativations undusability tovalidation mcase.

In [26], Puglia et al. designed a high power Li-ion battery device(300Vand1.2MWh) for theUSnavysAdvancedSEALDeliverySys-tems. To ensure the reliability and safety of this large battery pack,failure prevention measures were employed with an emphasis on

motheondratores fatermationcuitsucen if

acity

acityle eneasedd pectorythedepeumad pes, thme.OC in7]. Tpacirmorof inrcuitsmatirge (le cineductntrorefertionngs aattercomere arciteent

s aref simed tgeds. Inn imcan core, imosttedcoun33],cor

id bworkd onr setthe true SOC. Besides, a good SVM regression modele tuning of someempirical parameters, such as constantube, amongothers. This trial anderrorprocess is likelyonsuming.

estimation

rt of voltage estimation is to establish a sound batterych addresses chemical, electrical and physical prop-battery to some extent and is adequate enough tottery performances under different conditions. A soundrgy estimation method not only guarantees consistently to any electrical devices, but also supports near-ime performance for safety critical applications suchllation of cardiac pacemaker powered by Li-ion bat-e adoption of a Li-ion battery as emergency energyto its low self-discharge rate. There are 2 areas of focusstimation. One area is to propose efcient OCV (openge) estimation. The other one is to improve the effec-voltage monitoring in protection circuit so that severeh as overcharging and overheating can be prevented. Ation methods [1923] that have long been adopted byre receiving more and more criticisms and suspicionsuse these methods required numerous expensive testsof extensive table-makings and unsatisfactory accu-reported frequently under specic conditions. Moreethods and technologies are needed to tackle the chal-

ao et al. built an empirical Li-ion battery model whichf estimating OCV (open circuit voltage) under a range ofates and ambient temperatures. The proposed batterymilar to that in Fig. 2, some ideas of model referencentrol (MRAC) [25] were embodied in themodeling pro-ence dischargemodelwith arbitrarily chosen dischargebient temperatures was set rst. This reference modeldapted by introducing a rate factor, temperature fac-tential correction terms to t in discharge scenariosng discharge rates and temperatures. The accuracy ofrelies greatly on the ne-tuning of various empirical, such as the rate factor, the temperature factor, andesistances. Other concerns include the effect of batteryn against the accordance between experimental datation data, as well as the applicability of this model toenarios in which discharge rates and temperatures arem those used in modeling (in this case, rate factor ande factor are unknown).ensate for the effects of aging and varying dischargeh et al. [19] established a universal normalized dis-ge curve for lead-acid batteries. The comparison of themeasurement to the universal normalized dischargeve would lead to the determination of percent of theevel and reserve time (an equivalent entity to totalclaimed). One benecial consequence of this normal-hod is that the relationship between discharge voltagecomes almost linear for the most parts of the normal-ope (close to 90%). This feature denitely benets thereserve time estimation. However, since this empiricalon method was proposed purely based on the obser-er certain specic conditions, it is hard to assess itsmore dynamic operating proles. In addition, carefulust be made to apply this approach to Li-ion battery

voltageloadonThe sectheopemeasucess ispropagand cirrate. Instop ev

4. Cap

Capavailabof incrreducesatisfasupplyhighlymaximrent, lobatteriover ti100% Syear [2able caFurthedesigntion ci

Estiof-chaavailabdetermby conin a coas theapplicameanitrast, bhas be

Thewidelydependmodelance oreviewchallenproleplays averterTherefis theadvocainto ac

In [build alead-acral netinvolveanothenitoring. The rst of these measures involves addingcellwhencell voltage risesbeyondapreset voltage level.measure, to be employed if the rstmeasure fails, alertsr or automatically reduces charging current. If these twoil, the nal measure is employed and the charging pro-inated. However, the challenge lies in the fact that therate of failures under extreme cases, such as crashes

shorts, is likely to be much faster than the monitoringh scenarios, the generation of excessive heat will notthe circuit is switched off.

estimation

is rated in ampere-hours (Ah), which quanties theergy stored in a battery. The loss of capacity, as a resultimpedance, mainly on a batterys cathode, will cause

rformance when electrical devices cannot operate at alevel and functional failure when the battery fails to

required energy and power. The rate of capacity loss isndent on the charging/discharging conditions such ascharge voltage, depth of discharge, magnitude of cur-roles, and temperature. Specic to traditional Li-ione Li-ion battery also suffers permanent capacity lossFor example, a typical laptop Li-ion battery stored at25 C will irreversibly lose 20% of total capacity everyhus, there is a need to accurately estimate the avail-ty for reliability and better management of energy use.e, accurate battery capacity estimation will benet thenovative materials for battery fabrication and protec-that balance battery longevity and energy needs.

on of battery capacity is closely related to that of state-SOC), as SOC is usually dened as the ratio betweenapacity and rated capacity. Manufacturers, however,the rated capacity, or nominal capacity, for a batterying measurements under a constant discharge rate andlled environment. Therefore, the use of rated capacityence point often becomes inappropriate for real-worlds. As mentioned previously, SOC may have differentnddenitions for specic applications and goals. In con-y capacity ismorewell-dened, theestimationofwhicha standalone research eld.e a few battery capacity estimation models that ared in the literature, including linear model [28], rate-model [2831] and relaxation model [2830,32]. Thesebased on different assumptions and have a unique bal-plicity, accuracy, computational cost etc. Park et al. [28]he pros and cons of the aforementioned models, andtheir effectiveness and accuracy in varying operatingthis review it was determined that the DC/DC converterportant role in capacity estimation since a DC/DC con-hange a load prole, based on its own characteristics.t was decided that experimentation and measurementreliable way to estimate true capacity. They further

that the improved battery capacity model should taket the effect of the DC/DC converter.Chan et al applied articial neural network (ANN) torelation between discharge current and capacity foratteries. This is a single input and single output neu-with 4 nodes in the hidden layer. The training processe set of currentcapacity pairs within one cycle andof data from the same cycle in the testing process. The


results were compared with capacity measurements with an aver-age error rate below 1%. However, since too little data was used totrain the neural network, the model could be subjected to over-tting of training data. As well, the modeling approach assumedthat aging arole in capabatteries, b

Extendetion, usuallyKalman ltchanging vachanging vusing two seof tackling land interesfade and SOdesired, wiresult in redstate/paramEKFs may e

In [34],the relationing internalVup and Iupnormal chameasured amagnitudesresistancemwere appliethis approacess is veryignored inapplicablemodeling, oaddition, ifcations, carthe relationas highly no

Externaltemperaturof inuentiambient teinherent prcauses of dloss, changeanode, cathbe studied.

In [5], Lcycled overto study thbattery capchemical imRaman spec(CSAFM) wchemical pcathode. Thity loss at thfade. Seconaccumulatiface) layervarious opetions in SOCfocus of thchemical an(LiNi0.8Co0.LiPF6) and c

battery degradation mechanisms were identied: dissolution andnon-uniformity of the SEI layer on the anode;morphology changes,phase separation and capacity loss on the cathode; current collec-tor corrosion on the cathode; and chemical contamination of the

lyte.

ain

ainisiduedegr baore cinist pes do.theo exservobilies mdata

thoun thearebve bch as

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is acatiot vecatedconc ouain

sionsionlity tmsulate

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wasainapp

was-life

rticle

ticlethe sticlesthatcertnd degradation of the battery would not play a majorcity estimation, which could be the case in lead-acidut unfortunately not for Li-ion batteries.d Kalman lter has also been used in capacity estima-together with SOC estimation. In [16], a dual extended

ers framework was proposed for state estimation (fastriables such as SOC) and parameter estimation (slowlyariables such as capacity). The main justications forparate EKFs insteadof onewere to circumvent theneedarge matrix and to provide exibility for specic needsts (e.g. one may be only interested in tracking capacityC; in this case, other variable estimations, though notll inevitably utilize valuable computational power anduced overall computation speed). Based on the speciceter models involved, some interactions between twoxist.a multivariate linear model was disclosed to establishship between capacity and amultitude of inputs includ-DC resistance, open circuit voltage (OCV), temperature,(voltage and current measured at the transition fromrge to over-charge), Vdn and Idn (voltage and currentt the transition from overcharge to normal charge). Theof Vup, Iup, Vdn and Idn were derived from an internaleasurementprocess inwhichcontrolled currentpulsesd to the battery. There are several concerns regardingch. Firstly, OCV can be measured directly but the pro-time-consuming. Secondly, capacitive resistance was

resistance measurement (though this practice may befor lead-acid batteries). Thirdly, for such data-drivenver-tting or under-tting is always a big challenge. Inthis approachwere to be applied to Li-ion battery appli-emust be taken in calculating resistive impedance sinceship between the current and the voltage is regardedn-linear [35].measurements such as voltage, current and surfacee, though easy to access, are subject to a multitudeal factors, such as charging rate, discharging rate, andmperature, and therefore do not well represent theoperties of Li-ion batteries. In investigating the rootegradation mechanisms and their effect on capacitys in the electrical, chemical and physical properties ofode and electrolyte during controlled cycling test must

i-ion pouch cells (LiNi0.8Co0.15Al0.05O2/graphite) were100% DOD (depth of discharge) at 25 C and 60 C

e adverse affects of temperature extremes on Li-ionacity. Advanced inspection methods such as electro-pedance spectroscopy (EIS), X-ray diffraction (XRD),troscopy and current-sensing atomic force microscopyere used to investigate the changes in the electro-roperties and the physical properties of anode andere are twoconclusions of this research. First, the capac-e cathode accounted for themajority of overall capacityd, the root cause of capacity loss at the cathode is theon of a low-conductivity SEI (solid electrolyte inter-on the cathode. Zhang et al. [6] studied the effects ofrating conditions such as ambient temperatures, varia-and cycling proles, on a baseline cell chemistry. The

is study was on characterizing the shifting electrical,d physical properties of the anode (carbon), cathode

2O2), electrolyte (diethyl carbonateethylene carbonateurrent collectors. Four factors that contribute to Li-ion

electro

5. Rem

Remand resystemtion foin a mbe admwithoucircuitenablecated tsuch atery Mbatterihistoryetc. Alfocus ieffortsthat hacies su

5.1. Re

RVMclassisupporformulRVM isabilistiwhilemdiscusregrescapabiOckhareform

p(tn|xnwheredistribregresFurthe

Ininternaunderlas chachangemodeldict remchooselationend-of

5.2. Pa

Parmatesof parPDF istion uning-useful-life (RUL) prediction

ng-useful-life (RUL), also called remaining service lifelife, refers to the available service time left before a

rades to an unacceptable level. Successful RUL predic-tteries is highly desired; it enables failure preventionontrollable manner in that effective maintenance cantered in appropriate time to correct impending faultsrmanently damaging battery as traditional protectionIn addition, accurate RUL prediction for batteries candevelopment of new innovative service models dedi-ploring new opportunities and markets. An example ofice model will be introduced in Section 6: Smart Bat-ty Service System. Successful RUL prediction for Li-ionust take into account current health status of battery,/information, failure mechanisms, failure propagation,gh RUL prediction for Li-ion batteries has not been theliterature up to now, an increasing number of researcheingdeveloped, as canbe seen in thenumerousprojectseen recently sponsored, or led directly, by federal agen-DOE and NASA.

nce vector machine (RVM)

machine learning technique for solving regression andn problems. RVM has nearly identical function form totor machines (SVM), however, the ways that they areand used are dramatically different. Compared to SVM,structed under Bayesian framework and thus has prob-tputs. Also, RVM models are sparser than SVM models,taining comparable accuracy in results. Amore detailedof RVM and SVM can be found in [36]. When used forproblems, the most distinguished merit of RVM is itso control under-tting and over-ttingEssentiallyRazor at work [37]. A regression problem in RVM isd as follows:

2) = N(y(xn;w), 2) (5.1)the regression target (output) which follows normalwith mean y and variance 2; x is the input; y is theodelwithout noise; andw is the regression coefcient.

lysis and derivation can be found in [3638].an RVM regression model was built using batteryameters inferred from an electro-chemical model. Theassumption was that the internal parameters, suchtransfer resistance and electrolyte resistance, woulddually as battery degradation proceeded. And RVMused to accurately track the degradation trend. To pre-ing-useful-life, particlelter (PF)wasused to adaptivelyropriate coefcients for the RVM model and extrapo-applied from the latest degradation model to nd thepoint, which determined the length of the RUL.

lter (PF)

lter is a sequential Monte Carlo method, which esti-tate PDF (probability distribution function) from a set and their associatedweights. Thebenetofusing stateit enables appropriate management of inherent estima-ainty. The use of weight, on the other hand, adjusts the


state PDF to its most likely form. The particles are inferred recur-sively by two alternate phases: a prediction phase and a updatephase. In thepredictionphase, the valueof eachparticle for thenextstep is estimated by previous step information. Nomeasurement orobservationof each partmeasureme

Saha etcients of anand chargeresistance, cto infer futand chargeinterval betpreset capameasuremeworld applimeasuremeEIS equipmeity model weffect into abatterymodlated to giveinternal batment. Insterepresent eand concen

6. Conclus

The Li-ioing energysector and rability of Liconcern sinbility, and thigh currento evolve anter how gooover time, atal impactsable to detethe Li-ion bsatisfactorydevelopmeies. VariousSOC estimaprediction.sions will gichallenges o

Thoughpoint out thels and desmarket penmore goverproviders anoration andlatest advanZ.E, an elecited mileagCoulomb Tebased Novucontrol, datElectriciteing large sc

installing charging stations at private homes and various publicsites [43].

wled

s resatives (IMl totfult and

nces

ridca.S. Jard(2006. Depaearchozlowemenferenhim, R230hang,pp, Aeissn

kipediGuiheStateing th3.ee, J. Kew OC7. PESSalki300Patill/Dischmarthargelankeelch

olina. Plett. Plettee, J. Kansen. Hirs

iversatery DPredic. Levine to aerve ti. Biagedisch. Brilmmeth

. Nguyerve Tao, Shnoloonopuglia,igns,. Depaark, Ang lithium o, 200Fulle94) 1.indenPedratemshiassceedinputinhan,204is involved in this step. In the update phase, the valueicle estimated in the prediction phase is comparedwithnts and updated accordingly.al. [39] applied particle lter to estimate the coef-exponential growth model for electrolyte resistance

transfer resistance. The relationship among electrolyteharge transfer resistance and capacity was establishedure capacity from the predicted electrolyte resistancetransfer resistance. Finally, RUL was calculated as theween the current cycle and the end-of-life (denoted bycity value) cycle. However, the reliance on impedancent inhibits this prediction methodologys use in real-cations, mainly due to the cost of equipment, stringentnt requirements and space constraints. Without usingnt, Saha andGoebel [40] againbuilt an empirical capac-hich took coulombic efciency factor and relaxationccount. Particle lterwas used to estimate the values ofel components; the future capacity valuewas extrapo-RUL estimation. Compared to the previous paper [39],tery parameters were not inferred from EIS measure-ad, a combination of empirical models was explored tonergy losses incurred by IR drop, activation polarizationtration polarization.

ion

nbattery has beenwidely regarded as themost promis-storage solution to electrify the U.S. transportationeduce the consumption of gasoline. However, the reli--ion battery in high current application has long been ace large Li-ion batteries tend to have lower thermal sta-he phenomenon of capacity loss becomes very severe int application. Li-ion technology will of course continued the reliability of such batteries will improve. Nomat-d a Li-ion battery is, however, the system will degradend the rate of degradation is affected by environmen-and operation proles. Therefore, it is desirable to bect the underlying degradation and to predict how soonattery will fail or reach a level that cannot guaranteeperformance. This paper reviews recent research and

nt in healthmonitoring andprognostics of Li-ion batter-algorithms, models and approaches are discussed fortion, voltage estimation, capacity estimation and RULIt is our hope that these summarizations and discus-ve readers a broad perspective on both progress in, andf Li-ion battery health monitoring and prognostics.not discussed in this review, it is also worthwhile toe importance of establishing innovative business mod-igning unique managerial strategies in promoting theetration of hybrid or battery electric vehicles. More andnments, automotive manufacturers, charging servicedenergy companies are reachingagreementsof collab-committed todeploy electric vehicles inmass scale. Theces include Better Place and Renault launching Fluencetric car featuring a xed-priced package of unlim-e and utilizing wind energy (with DONG Energy) [41];chnologies installing ChargePoint stations at Missouri-s International, Inc that deliver benets such as accessa collection and Smart Grid compatibility [42]; Toyota,de France (EDF) and the city of Strasbourg launch-ale PHEV demonstration in Strasbourg that highlights

Ackno

ThiCooperSystemgratefuthoughconten

Refere

[1] hyb[2] A.K

(7)[3] U.S

Res[4] J. K

surCon

[5] J. S222

[6] X. Z148

[7] E. M[8] Wi[9] J.V.

Themin200

[10] S. La n200

[11] A.J.293

[12] J.N.inga SRec

[13] H. B[14] G. W

Car[15] G.L[16] G.L[17] S. L[18] T. H[19] M.D

UnBatfor

[20] D.Etimres

[21] R.Vtery

[22] G.Hand

[23] T.VRes

[24] L. GTec

[25] R. M[26] F. P

Des[27] U.S[28] S. P

usiposUSA

[29] T.F.(19

[30] H. L[31] M.

Sys[32] C. C

ProCom

[33] C. C201gements

earch is supported by the NSF Industry/UniversityResearch Center (I/UCRC) for Intelligent MaintenanceS) at the University of Cincinnati. The authors are

Patrick Brown for reviewing this paper and providingcomments. His expertise is invaluable in improving thepresentation of this paper.

rs.com, 2009, December 2009 Dashboard: Year-End Tally.ine, D. Lin, D. Banjevic, Mechanical Systems and Signal Processing 20) 14831510.rtment of Energy, 2008 Annual Progress Report on Energy Storageand Development, 2008.ski, Electrochemical cell prognostics using online impedance mea-ts and model-based data fusion techniques, in: 2003 IEEE Aerospacece, Proceedings, Anonymous 7, 2003.. Kostecki, T. Richardson, Journal of Power Sources 112 (1) (2002).P. Ross Jr., R. Kostecki, Journal of the Electrochemical Society (2001),463.er, G. Richter, Journal of Power Sources 116 (12) (2003) 7998.a, State of Charge, 2010.en, H. Singh, T.G. Palanisamy,Method and Apparatus for Determiningof Charge of a Lithium-ion Battery, Method and Apparatus for Deter-e State of Charge of a Lithium-ion Battery, 10/349, 847 (6586130),

im, J. Lee, The stateandparameterestimationof anLi-ionbatteryusingVSOC concept, in: IEEE Power Electronics Specialists Conference,C 2007, Anonymous, 2007, pp. 27992803.nd, C. Fennie, P. Singh, Journal of Power Sources 80 (12) (1999).on, O. Gerard, SystemMonitoring the Discharging Period of the Charg-arging Cycles of a Rechargeable Battery, and Host Device IncludingBattery, System for Monitoring Charging/Discharging Cycles of Aable Battery, and Host Device Including a Smart Battery, 1998., O. Bohlen, S. Buller, Journal of Power Sources 144 (2) (2005) 418425., G. Bishop, An Introduction to the Kalman Filter, University of Northat Chapel Hill, Chapel Hill, NC, 1995., Journal of Power Sources 134 (2) (2004) 262276., Journal of Power Sources 134 (2) (2004) 277292.im, J. Lee, Journal of Power Sources 185 (2) (2008) 13671373., C.J. Wang, Journal of Power Sources 141 (2) (2005) 351358.ch, G.W. Mathiesen, P.K.Y. Ng, Battery Diagnostic Method Utilizingl Normalized Discharge Curve for Predicting Battery Reserve Time,iagnostic Method Utilizing a Universal Normalized Discharge Curveting Battery Reserve Time, 2002.e, A.K. Pasrija, Method and apparatus for predicting battery reservespecied end-voltage, method and apparatus for predicting batteryme to a specied end-voltage, 1994.tti, A.M. Pesco, Apparatus and method for adaptively predicting bat-arge reserve time, 1990.yer,W.H. Tiedemann,Dynamic state-of-charge indicator for abatteryod thereof, 1989.en, Method and Apparatus for Predicting the Remaining Capacity andime of a Battery on Discharge, 1997.. Liu, R.A. Dougal, IEEE Transactions on Components and Packaginggies 25 (3) (2002) 495.oli, IEEE Transactions on Automatic Control 19 (5) (1974) 474484.S. Cohen, J. Hall, V. Yevoli, Very Large Lithium Ion Cell and BatteryAnonymous.rtment of Energy, U.S. Retail Gasoline Historical Prices, 2010.. Savvides, M. Srivastava, Battery capacity measurement and analysisium coin cell battery, in: Proceedings of the 2001 International Sym-n Low Power Electronics and Design, Anonymous ACMNewYork, NY,1, pp. 382387.r, M. Doyle, J. Newman, Journal of the Electrochemical Society 141

, Handbook of Batteries, 2nd ed., McGrawHill, New York, 1995.m, Q. Wu, IEEE Transactions on very Large Scale Integration (VLSI)10 (5) (2002) 601607.erini, R. Rao, Pulsed battery discharge in communication devices, in:gs of the 5th Annual ACM/IEEE International Conference on Mobileg And Networking, Anonymous ACM, 1999, p. 95.E.W.C. Lo, S. Weixiang, Journal of Power Sources 87 (12) (2000).


[34] S. Hoenig, H. Singh, T.G. Palanisamy, Method and Apparatus for Predicting theAvailable Energy of a Battery, 2001.

[35] Gamry Instruments, Electrochemical Impedance Spectroscopy Theory: APrimer, 2010.

[36] C.M. Bishop, Pattern Recognition and Machine Learning, Springer, New York,2006, p. 433.

[37] M.E. Tipping, Bayesian Inference: An Introduction to Principles and Practice inMachine Learning, Advanced Lectures on Machine Learning, 2003, pp. 4162.

[38] Mike Tipping, Alex Smola, Sparse Bayesian Learning and the Relevance VectorMachine, 2007.

[39] B. Saha, S. Poll, K. Goebel, An Integrated Approach to Battery HealthMonitoringusing Bayesian Regression and State Estimation, Autotestcon, 2007 IEEE, 2007,pp. 646653.

[40] B. Saha, K. Goebel, Modeling Li-Ion Battery Capacity Depletion in a ParticleFiltering Framework, Anonymous, 2009.

[41] Anonymous, Better Place and Renault Launch Fluence Z.E., the First unlimitedMileage Electric Car Together with Innovative eMobility Packages, in EuropesFirst Better Place Center, 2011(03/29), 2011.

[42] Anonymous, Coulombs Charge Point Network Expands into Missouri NovusInternational Installs First Charge Point Stations for Electric Vehicles,2011(03/29), 2011.

[43] Anonymous, Toyota, EDF and Strasbourg Launch Large-Scale, 3-Year Plug-inHybrid Demonstration Project, 2011(03/29), 2010.

A review on prognostics and health monitoring of Li-ion batteryIntroductionState of charge (SOC) estimationFuzzy logicAutoregressive moving average (ARMA)Artificial neural network (ANN)Electrochemical impedance spectroscopy (EIS)Extended Kalman filter (EKF)Support vector machine (SVM)

Voltage estimationCapacity estimationRemaining-useful-life (RUL) predictionRelevance vector machine (RVM)Particle filter (PF)

ConclusionAcknowledgementsReferences

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