A deep long-short-term-memory neural network for lithium-ion battery prognostics

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1 A deep long-shor-erm-memory neural nework for lhum-on baery prognoscs Ahmed Zakarae Hnch and Mohamed Tkoua Laboraory for Appled Mahemacs (LERMA) Mohammada School of Engneerng, Mohamed V Unversy, Raba, Morocco. ahm.zak.hn@gmal.com, koua@em.ac.ma Absrac Wh he ncreasng challenges n energy sorage, he mporance of lhum-on baeres relably canno be undersaed. The predcon of he baery exac me of falure can provde a cos-effcen manenance plan. In hs paper, we propose a novel daa-drven approach based on deep long-shor-erm-memory neural neworks LSTM for baery's remanng useful lfe (RUL) esmaon. The suggesed mehod uses he pas baery capacy, he me o dscharge and he operang emperaure o drecly predc he RUL. To valdae he proposed model, we conduc expermens usng he ASA lhum-on baery daase. The resuls show ha our mehod produces exceponal performances for RUL predcon under dfferen loadng and operang condons. Keywords Prognoscs; Deep learnng; Long-shor-erm-memory nework; lhum-on baery. 1. Inroducon Lhum-on baeres are a core componen n elecrc cars, unmanned aeral vehcles, power ools and personal devces (Walker, Rayman, and Whe 2015). Ther ncreasngly wdespread use s creded o her hgh energy densy, her long cycle lfe, he absence of he memory effec, along wh her lgher wegh compared o oher rechargeable baeres (Lu e al. 2013). Recen sudes sugges ha lhum-on baeres are prone o dfferen falure modes from shelf dscharge and hermal runaway o power and capacy fade (Arachchge, Pernpanayagam, and Jaras 2017). These ssues are arbued o hgh operaonal emperaure durng usage or sorage, overchargng, over-dschargng, or an ncreasng number of charges/dscharge cycles. Moreover, baery falure could lead o loss of operaons, reduced performances, and even dsasers (Wdodo e al. 2011) (D. Zhou e al. 2017). Consequenly, he relably, avalably, and safey of lhum-on baery s of promnen mporance. A curren approach o cope wh he menoned challenges s baery's prognoscs and healh managemen (PHM). Recen works nend o monor he baery degradaon process, assess s condon and predc he remanng useful lfe (RUL).Ths predcon can lead o an opmal msson or replacemen nerval plannng. There are manly wo approaches for lhum-on baery PHM: physcs of falure models and daa-drven models. Complee knowledge of he non-lnear dynamc elecrochemcal process governng he degradaon s necessary for he physcs of falure approach. Ths process s nracable, and he model parameers esmaon may need complcaed expermens and cosly devces whch reduce he use n pracce (Lu e al. 2017). On he oher hand, by usng run-o-falure sensors nformaon combned wh he correspondng operaonal and envronmenal condons, arfcal nellgence and sascal mehods can capure he nheren relaonshp and rends beween sensors values and he degradaon sae. Ths smplcy combned wh he rsng avalably of sensors daa sparked recen neress from he research communy for daa-drven lhum-on baeres RUL esmaon:(wu, Fu, and Guan 2016) revewed he daa-drven approaches for vehcle lhum-on baeres prognoscs up unl 2016,hese works use mehods rangng from relevance vecor machne (RVM)(J. Zhou e al. 2013),and suppor vecor regresson (SVR)(Wang e al. 2014), o arfcal neural neworks(a)(dong e al. 2012). Laer works propose a gray model GM (1,1) (D. Zhou e al. 2017), a mul-kernel suppor vecor machne (SVM) (Gao and Huang 2017), and a long-shor-erm-memory neural nework (LSTM)(Zhang e al. 2017). 2162

2 everheless, here are several ssues n mos of hese sudes: frs, he relance on he consrucon of a healh ndcaor (HI) can nduce anoher source of generalsaon error. Then, he forecasng of HI unl s reach a falure hreshold usng one sep or mul-sep predcon can nduce compoundng errors. Fnally, here s a lack of works ha generalse he problem o dfferen envronmenal and operaonal condons. Snce 2012, deep learnng models acheved sgnfcan breakhroughs n machne vson, voce recognon and games (LeCun, Bengo, and Hnon 2015). These achevemens are arbued o he ably of deep neural neworks o auomacally learn proper represenaon drecly from he raw daa by sackng neural nework layers. As par of a generc prognosc framework (Hnch and Tkoua 2018), we propose a deep neural nework model based on deep long-shor-erm-memory neural nework (LSTM). The model uses he baery capacy, he dscharge me and he operang emperaure unl he predcon me, and combned wh he predcon me and he condon for falure o auomacally predc he remanng useful lfe RUL. The res of hs paper s organsed as follows: he deep neural nework archecure s presened n deals n secon2. In secon 3 we presen he expermenal valdaon. Then, conclusons are drawn n secon The mehodology We defne rul he remanng useful lfe of he baery (b) a predcon me, as he me unl he baery s capacy had reduced o a predefned hreshold fn-cap.in hs work, we model he funcon f ha esmaes he RUL of a baery gven he pas capacy seres cap, he pas dscharge me seres dsct 1:, he pas 1: operaonal emperaure seres Temp, he predcon me and he hreshold fn-cap : rul f cap,dsct, Temp,, fn-cap 1: 1: 1: 1: We model he funcon f usng a deep neural nework. By sackng an LSTM layer and several dense layers, he model can predc he RUL drecly from he sensors and operaonal daa. The followng subsecons descrbe he archecure and he overall ranng process. 2.1 The model archecure Fgure 1 shows he dealed archecure of our model. Fgure 1. The archecure of he deep neural nework We ake he combned capacy, dscharge me and emperaure vecor sequence as an npu layer; hen he emporal degradaon s represened by an LSTM layer. Recurren neural neworks (Rs) are a class of neural neworks desgned o model sequenal and me seres daa. They are based on a recurren connecon where he hdden sae a me h s a funcon of he hdden sae a me -1 h 1and he npu daa a me x : 2163

3 h σ W h,x b 1 In pracce, hs smple form of R s rarely used as dsplays an nably o learn long-erm dependency. The LSTM layer proposed by (Hochreer and Schmdhuber 1997) solves hs problem by offerng a more complex nernal sae represenaon. The LSTM layer adds a cell sae C o presens s long-erm memory n addon o he hdden sae h. The compuaon s hen dsrbued no several gaes execued sequenally: frs, he forge gae deermnes he peces of he long-erm memory o connue rememberng and he peces o gnore usng he new npu: f σ W h,x b f 1 f ex, he npu gae deermnes he nformaon ha should be exraced from he npu: ξ Tanh W h, x b ξ 1 ξ σ W h,x b 1 Fnally he oupu gae updae he cell sae and he hdden sae usng he pas and presen nformaon: C f C ξ 1 o σ W h,x b o 1 o h o.tanh C Where (bf, bξ, b, bo, Wf, Wξ, W, Wo) are he bas and he wegh marces, and σ s he sgmod funcon. Then, we concaenae he oupu of he hdden sae wh he predcon me and he hreshold capacy. The nex sep s o sack he fnal hree dense neural nework layer and he correspondng bach normalsaon (B) layers (Ioffe and Szegedy 2015).The B layer conrols he npu dsrbuon across layers, consequenly speedng up he ranng. Each dense layer s preceded by a B layer and use a Relu(x) =max (0, x) acvaon funcon. For smplcy, we keep he same number of neurons across he hdden sae of he LSTM layer, as well as he frs and he second dense layer. The fnal layer s a neuron ha predcs he RUL of he baery. 2.2 The ranng process To ran he model, we use he mean arcangen absolue percenage error (MAAPE) loss sfuncon (Km and Km 2016). Ths funcon holds several advanages over he mean absolue percenage error () funcon n measurng forecas accuracy; MAAPE s scale-ndependen, does no produce nfne values near zero, and s range of values s lmed whch make he neural nework ranng easer. 1 L 1 RUL RUL Arc an( ) RUL We ran he model o mnmse he specfed loss funcon usng he backpropagaon hrough me (BPTT) algorhm o compue he gradens of each mn-bach. Each mn-bach sample an npu vecor from every baery used n he ranng of our model. Ths ranng scheme avods he bas nduced by baeres wh a long lfeme. We use he Adadela algorhm for opmsaon (Zeler 2012). 2164

4 3. Expermenal valdaon We evaluae he proposed deep neural nework usng he baery daase from ASA Prognoscs Cener of Excellence (PCoE) (Saha and Goebel 2007). In hs daase, The lhum-on rechargeable baeres were run n baches of 4 hrough successve cycles of charge, dscharge, and mpedance a emperaures rangng from o 4. Chargng was conduced n a consan curren of 1.5A unl he baery volage reached 4.2V and hen mananed n a consan volage unl he charge curren dropped o 20mA.The dscharge was carred a dfferen curren profles unl he baery reaches he end volage.the expermens were ermnaed when he cells reached he hreshold capacy.table 1 shows he varous expermenal condons of he dfferen baeres. In he cases where he hreshold capacy was no se, we ook he las measured capacy as he hreshold capacy. Table 1. Ls of baeres and he correspondng loadng and operaonal condons. Baery Idenfer Dscharge curren(a) End volage (V) Threshold capacy(ahr) Baery #5 Baery #6 Baery #7 Baery #25 Baery #26 Baery #27 Baery #28 Baery #29 Baery #30 Baery #31 Baery #32 Baery #33 Baery #34 Baery #36 Baery #38 Baery #39 Baery #40 Baery #42 Baery #43 Baery #44 Baery #45 Baery #46 Baery #47 Baery #48 Baery #49 Baery #50 Baery #51 Baery #52 Baery #54 Baery #55 Baery #56 A 0.05Hz square wave loadng profle of amplude and 50% duy cycle Mulple fxed load curren levels ( and ) 2.0V 2.0V 2.0V 2V 2V 1.hr 1.hr 1.hr 1.hr 1.hr 1.hr 1.hr 1.hr 1.hr 1.hr 1.hr 1.hr 1.hr 1.hr 1.hr 1.hr 1.hr Operang emperaure ( C) and 4 and 4 and 4 To benchmark our mehod, we use he same expermenal framework employed by (Mosallam, Medjaher, and Zerhoun 2016) (.e., he same daase paron scheme, he same cos merc). The baeres are dvded no a ranng and a esng se as shown n able

5 Table 2.The ranng and esng daase Tranng daase Baery #5 Baery #7 Baery #25 Baery #26 Baery #27 Baery #29 Baery #31 Baery #32 Baery #33 Baery #36 Baery #38 Baery #40 Baery #42 Baery #44 Baery #45 Baery #46 Baery #48 Baery #49 Baery #50 Baery #51 Baery #54 Baery #56 Tesng daase Baery #6 Baery #28 Baery #30 Baery #34 Baery #39 Baery #43 Baery #47 Baery #52 Baery #55 The chosen merc s he mean absolue percenage error : 1 1 RUL RUL RUL The overall cos s he average : f 1 1 The model s mplemened usng he Keras lbrary (Cholle 2017).The ranng and RUL predcon are run on an Ubunu Lnux machne wh a vda GTX 1070 GPU. We se he parameers of he Adadela opmser o her defaul values. We ran he model for epochs. The number of neuron n he oupu of he LSTM layer and he wo dense layers s 18. Table 3 presens he predcon resuls of he esng se. The predcon performance on all he esed baeres s sasfacory (mn() < 5). Moreover, Fgure 2 dsplays a plo of he predced and real RUL for all cycles of he baery wh he wors predcon performance (.e., Baery #34). We can observe ha he predcon s correc when he baery s close o s end of lfe. Table 3.The baeres Tesng baery Baery #6 Baery #28 Baery #30 Baery #34 Baery #

6 Baery #43 Baery #47 Baery #52 Baery # Fgure 2. The Predced RUL of Baery #34 Table 4 aggregaes he resuls no he overall cos and compares wh he work of (Mosallam, Medjaher, and Zerhoun 2016). The sgnfcan dscrepancy demonsraes he superory of our approach. f of our mehod Table 4.The model performance % % f of (Mosallam, Medjaher, and Zerhoun 2016) 3. Concluson In hs work, we nroduce an orgnal deep neural nework for lhum-on baery prognoscs based on LSTM layers. The proposed archecure demonsraes a clear performance advanage compared o he benchmark. Furhermore, he end-o-end naure eases he modellng process as no exper knowledge s nvolved. However, n onlne applcaons, he capacy s dffcul o measure.moreover, n rsk-sensve applcaons uncerany esmaon s essenal. Fuure works mus exrac feaures from he dscharge volage and he curren drecly whou usng he capacy and mus provde calbraed uncerany esmaons. Acknowledgemens We lke o express our sncere graude o he ASA Ames Prognoscs Cener of Excellence for provdng he baery daa se. References Arachchge, Buddh, Suresh Pernpanayagam, and Raul Jaras. Enhanced Prognosc Model for Lhum Ion Baeres Based on Parcle Fler Sae Transon Model Modfcaon. Appled Scences 7 (11): 1172,

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