The SOC Estimation of Power Li-Ion Battery Based on ANFIS Model *

Transcription

1 Smar Grid and Renewable Energy, 202, 3, 5-55 hp://dx.doi.org/0.4236/sgre Published Online February 202 (hp:// 5 The SOC Esimaion of Power Li-Ion Baery Based on ANFIS Model * Tiezhou Wu, Mingyue Wang, Qing Xiao, Xieyang Wang School of Elecrical & Elecronic Engineering, Hubei Universiy of Technology, Wuhan, China. wz35@63.com, yueyueming@yahoo.cn Received Sepember 5 h, 20; revised Ocober h, 20; acceped Ocober 8 h, 20 ABSTRACT On basis of radiional baery performance model, paper analyzed he advanage and disadvanage of SOC esimaion mehods, inroduced Adapive Neuro-Fuzzy Inference Sysems which inegraed arificial neural nework and fuzzy logic have prediced SOC of baery. I s a baery residual capaciy model wih more generalizaion abiliy, adapabiliy and high precision. By analyzing he baery charge and discharge process, he key parameers of SOC are deermined and he experimenal model is modified in MATLAB plaform.experimenal resuls show ha he difference of SOC predicion and acual SOC is below 3%. The model can reflec he characerisics curve of he baery. SOC esimaion algorihm can mee he requiremens for precision. The resuls have a high pracical value. Keywords: Sae of Charge; ANFIS; Esimaion Mehod; Li-Ion Baery. Inroducion Baery is an energy source for elecric vehicles. In order o make sure he good performance of he baery pack and exend i s life, i is necessary o make good conrol and managemen for he baery pack. Li-ion baery has he advanages of high volage, high energy, long cycle life, low self-discharge, no memory effec, no polluion ec []. So i gradually replaces he ohers in elecric vehicles and hybrid elecric vehicle. In he ligh of he baery volage, curren, emperaure and oher real-ime online measuremen, he online and real-ime esimaion abou he SOC is possible. The value of SOC direcly reflecs he sae of he baery, i is appropriae o ell he differen performances beween he various cells in he baery pack by he differen SOC value of each cell, and he balanced charge can be operaed according he differen performances. The final aim is o exend he baery life, limi he maximum discharge curren and predic he driving range of elecric vehicles ec [2]. There is much improved room in he applicaion of baery SOC online and real-ime esimaion mehod. I is impossible o mee he acual requiremens as he error of esimaion more han 8% [3]. To improve he accuracy of SOC online and real-ime esi- * This work was suppored by naural science foundaion of Hubei province (research on soc esimaion mehod and equalizing charging of lihium-ion baery for HEV. No. ZRY530). Auhor: Tiezhou Wu (966-), male, associae professor, maer uor, Research direcion: signal analysis and sysem inegraion. maion, inensive sudy is necessary in measuremens mehod, he baery model and esimaion mehod. 2. Tradiional SOC Esimaion Mehod SOC show ha he percenage of remaining capaciy in marker capaciy [4], and ha is defined as he rae of remaining and he raio capaciy. The range is from 0% o 00%: Q SOC 00% () Q Here, Q n is he oal baery power; Q is he remaining baery capaciy a he ime of, and formula can be expressed as following: n 0 0 a Q Q i d (2) Here, Q 0 is he iniial capaciy of he baery, i d 0 a define he change of he baery capaciy from he ime of 0 o. Originally, some relaively simple algorihm was proposed, such as: inegrae he curren in amperes-ime mehod [5], open circui volage mehod by means of OCV and SOC correspondence relaionship and ec [6]. Then, he modified algorihm of hese mehods appeared, which added some or all amendmens of he emperaure, charge and discharge rae, charge and discharge efficiency. There are some advanced mehods o be pu for-

2 52 The SOC Esimaion of Power Li-Ion Baery Based on ANFIS Model ward as well, such as: fuzzy conrol algorihm, neural nework algorihm [7], Kalman filer algorihm [8], and he newly emerged impedance specroscopy mehod and C. Ehre s linear models mehod. Alhough people pay a lo of effor o research his problem, he resul is unsaisfacory. The mainly reason is ha he baery s highly nonlinear. Various mehods exis heir shorcomings, such as: amperes-ime mehod have error accumulaion; open circui volage mehod is no suiable for curren frequen flucuaion during driving; fuzzy conrol depend on engineering experience; neural neworks rely on he choice of sample [9]; Kalman filering depend on accurae baery and calculaion complexiy; impedance specroscopy mehod requires addiional funcion generaor, increase coss; linear model mehod is only suiable for low curren siuaion ec [0]. A presen, generally combine he several algorihms o esimae SOC []. 3. ANFIS s Theorem and Srucure 3.. Combinaion he Fuzzy Technology and Neural Nework Single neural nework, jus a black box sysem, is lacking of performance ha could no provide he heurisic knowledge for he SOC s predicaion of baery. Therefore, I is no appropriae o express he knowledge based on he rules and no good o make use of he exising experience and knowledge. These drawbacks will increase he nework s raining ime. A single fuzzy predicive can simply realize he learning heurisic knowledge bu can ge he accurae resul. Because he performance of self-learning and adapive capaciy is weak, i is difficul o form auomaically and adjus he fuzzy rules of membership funcion local exremism. Combinaion of boh can ge he exac value a any condiions, and meanwhile can also undersand he esimaion process, can ge he advanage of neural nework and fuzzy sysems. In he fuzzy conrol sysem, fuzzy reasoning is a map o he relaionship of inpu-oupu. Inpu as he premise (he error E, he change rae of E and oher fuzzy volume), makes he non-fuzziness conrol oupu. Since neurons can map any funcion relaionships, i also can be used o make he fuzzy inference come rue. In addiion, he neural nework can realize fuzziness and nonfuzziness. So ha he neural nework can represen all fuzzy conrol.we call his fuzzy conrol which is based on neural nework. I has many advanages, such as compuing and he number of knowledge experience are independen; allowed o conain a small amoun of error experience (because i can be auomaically excluded in he sudy)and can be parallel, disribued compuing ec. Thus his fuzzy neural nework, neural nework-based fuzzy conrol, was used in hybrid elecric vehicle energy managemen sysem [2] ANFIS s Consrucion Arificial Neuro-Fuzzy Inference Sysems [3] is exensively used in he field of modeling, decision-making, signal processing and conrol. Here, he srucure of ANFIS and is learning rule would be inroduced. Assuming ha he fuzzy neural nework has wo inpus x and y, one oupu z. For he firs-order Sugeno fuzzy model [4], he following rules: Rule : If x is A and y is B, hen f px qy r Rule 2: If x is A 2 and y is B 2, hen f2 px 2 qy 2 r2 The corresponding equivalen ANFIS model srucure shown in Figure, he same floor node here has he same funcion. Layer : A and B are inpu variables fuzzy ses, This layer node acivaion funcion on behalf of he membership funcion of fuzzy variables, he oupu represens fuzzy resul called membership, one of he node ransfer funcion can be expressed as: O f x i, 2 (3),i xi, j y j 2 O f y j 3, 4 (4) Commonly he Gaussian funcion is used as he acivaion funcion. Layer 2: muliply any wo memberships which ge by he fuzzy, so he oupu represens fuzzy rules or applicable degree of inensiy. 2,i i xi yi O w f x f y i, 2 (5) Layer 3: normalize each rule s apply degree: O 3, i wi wi w w 2 i, 2 Layer 4: calculae each rule s conclusion: (6) zi pix qiy r i i, 2 (7) Layer 5: Calculae he oupu of all rules and ha he Figure. Equivalen ANFIS srucure.

3 The SOC Esimaion of Power Li-Ion Baery Based on ANFIS Model 53 oupu of he sysem oupu: z wz w2z2 (8) p,, i qi ri are unknown, hrough he algorihm raining, ANFIS can ge hem a a specified arge o achieve he purpose of fuzzy modeling. 4. SOC Esimaion Based on ANFIS 4.. SOC Esimaion Model Based on ANFIS Hybrid operaion process is very complicaed. During he working process, he baery s SOC would be affeced by many facors, such as: environmenal emperaure, iniial volage, baery resisance, working hours, and ec. The car will mee all kinds of problems in running, such as acceleraion, climbing, cold, hea, rain, ec, as he baery power source is also influence by hese condiions. Of course, he ideal neural nework model is he more comprehensive inpu he beer he oupu of he mapping, he more close o he acual condiions. However, many inpu daa rely on a variey of insrumens and sensors o ge. More inpu daa requires more coss, in ligh of his poin and wih he prerequisie of geing saisfied resul, less inpu is good for resul, which does no only reduce he difficuly of dealing wih he problem, bu also reduce coss. In his paper, hree inpus variables are available he volage V, curren I, and cells surface emperaure T, SOC s remaining baery charge percenage of capaciy as only one oupu value is prediced, as shown in Figure 2. I is very difficul o realize he mapping from he hree-dimensional space o one-dimensional by means of radiional mehod. For overcoming his problem, he appearance of developmen of fuzzy logic mehod is.o come wih he ide of fashion. Through a large number of ypical es daa, he curve o exrac some of he rules wih regulariy, ha is human work experience, and hen use he fuzzy logic of he reasoning o achieve his experience, i ofen can achieve beer resuls. The design makes full use of fuzzy logic reasoning is simple, srong robusness and accuracy of neural nework sysems, and because neural nework sysem for he hree- inpu single-oupu sysem, making he hidden nodes is grealy reduced, easy o implemen SOC Esimaes o Realize Based on ANFIS Collec Daa, Analyze and Creae Daa Ses and Tes Daa Ses A 22 C - 27 C ambien emperaure of he laboraory, we do consan flow duraion discharge es for a manufacurer 3.3 V Ah LiFePO 4 baeries SLFP-PT30, a he condiion of 0.48 C, 0.95 C,.43 C (i.e. 5 A, 0 A, 5 A). According o he relevan informaion of manufacurer o ake 0.48 C, 0.95 C,.43 C consan curren baery discharge coninued o discharge erminaion volage is 3.8 V, 3.24 V, 3.28 V. The following is he fiing curve in he condiion of differen discharge curren and baery volage discharge ime. I is shown in Figure 3. I can be seen from he curves ha he volage s downward rend under he condiion of large discharge curren is faser han hen he relaively small one Deerminaion he ANFIS Nework Srucure In order o make use of MATLAB fuzzy oolbox anfis simulaion of he daa colleced, we choose he funcion of genfisl inside. Funcion genfisl hrough he way of grid pariion o given daa se o generae a fuzzy inference sysem, which can be used in conjuncion wih he funcion anfis. By funcion genfisl generaed he fuzzy inference sysem inpu and oupu membership funcion curves are o ensure ha cover he enire inpu and oupu space evenly divided on he basis of is inpu and oupu membership funcions of he ype and number specified in he use, you can also use shor provincial value. Provide Training daa and es daa: Wih he differen discharge rae of he volage, curren, SOC and ime series. The odd iems were regarded as he raining daa, and he even iems as he auhenicaion daa Deerminaion he Type of Inpu and Oupu Membership Funcion Usually, ANFIS nework could provide 8 variable pa- Figure 2. Adapive fuzzy neural nework predicion SOC values model diagram. Figure 3. Differen discharge curren-volage measuremens.

4 54 The SOC Esimaion of Power Li-Ion Baery Based on ANFIS Model rameers of he funcion ype MF. In he curren paper, he Gaussian membership funcion (Gaussmf) is applicaion [5]. Since ANFIS is a Sugeno ype fuzzy sysem, so here are wo oupu variables membership funcions, namely: consan and linear funcions. In his paper, consan, ha he firs order Sugeno fuzzy sysem Divinaion Inpu Variable Space Firs deermine he maximum and minimum inpu variables: hen order he colleced daa o obain he minimum and maximum inpu variables; finally, esablish hree fuzzy ses for each inpu variable, he corresponding generaed resuls are high, low, medium membership funcions, he inpu space is he inpu variables corresponding o he produc of he membership. The oupu value from corresponding is beween 0 and. 5. Experimenal Validaion and Analysis The selecion of raining daa may give unreliable resuls bring some facors as before menioned, This addiion no only requires some pre-work on he availabiliy of daa, raining process and he final resul of model checking is also very imporan. In general, he resul model es procedure is used for raining hose who do no use as he inpu/oupu daa, o compare he model is or no rained o a very good mach and predic hese daa. The essence of BP algorihm is o solve he minimum problem of he error funcion, using seepes descen for nonlinear programming mehod, in accordance wih he error funcion of he negaive gradien direcion o modify he weighs, so i exisence he disadvanage of low learning efficiency, slow convergence, and vulnerable Local minimum sae, relaively poor nework generalizaion abiliy. The following is a BP nework, ANFIS model comparison in prediced remaining baery capaciy. Figure 4 shows he compared curve beween predicive value and measured one of he remaining capaciy of 8 A consan discharge. Figure 5 shows he remaining capaciy under he 20A consan discharge prediced and measured values of he conras curve. According o he Figures 4 and 5, under he experimenal condiion, compared he SOC s predicive value and acual one, he majoriy of relaive error could be conrolled wihin 5%. Wha s more, he predicive effec of ANFIS model is beer and he error could be conrolled wihin 3%, which would no only mee he requiremens of indusrial applicaions, bu is suiable o apply for he acual predicive research. And from he view of raining seps and raining ime, ANFIS model o predic SOC is more efficien, ideal for real-ime predicion. Figure 4. 8 A consan curren discharge of he predicive value of SOC compared wih he real. Figure A consan curren discharge of he predicive value of SOC compared wih he real.

5 The SOC Esimaion of Power Li-Ion Baery Based on ANFIS Model Conclusion Based on he analysis of he radiional sae of charge (SOC) esimaion mehod, he curren paper proposed a baery residual capaciy model, feaured wih more generalizaion abiliy, adapabiliy and high precision. By analyzing he baery charge and discharge process, i is o deermine he key parameers of SOC and hen modify he experimenal model in MATLAB plaform. Through BP nework predicion wih comparison of experimenal simulaion of SOC values, indicaing ha he ANFIS has srong abiliy of adapaion and generalizaion, his mehod reduces he esimaion error of SOC o less han 3%, i can be used for inelligen monioring sysem in hybrid car. REFERENCES [] Z. Bin, Volage Characerisics of Li-Ion Power Baery for EVs, Chinese Baery Indusry, Vol. 4, No. 6, 2009, pp [2] S. Pang, J. Farrell and J. Du, Baery Sae-of-Charge Esimaion, Proceedings of he American Conrol Conference, Arlingon, June 200, pp [3] J. Chiasson and B. Vairamohan, Esimaing he Sae of Charge of a Baery, American Conrol Conference, 4-6 June 2003, pp doi:0.09/tcst [4] B. Zhang, C. T. Lin and Q. S. Chen, Performance of LiFePO 4 /C Li-Ion Baery for Elecric Vehicle, Chinese Journal of Power Sources, Vol. 32, No. 2, 2008, pp [5] L. C. Tao, W. J. Ping and C. Q. Shi, Mehods for Sae of Charge Esimaion of EV Baeries and Their Applicaion, Baery Bimonhly, Vol. 34, No. 5, 2004, pp [6] S. J. Lee, J. H. Kim, J. M. Lee and B. H. Cho, The Sae and Parameer Esimaion of an Li-Ion Baery Using a New OCV-SOC Concep, Power Elecronics Specialiss Conference, Orlando, 7-27 June 2007, pp doi:0.09/pesc [7] M. A. C. Valdez, J. A. O. Valera and M. J. O. Areaga, Esimaing Soc in Lead-Acid Baeries Using Neural Neworks in a Microconroller-Based Charge-Conroller, Inernaional Join Conference on Neural Nework, Vancouver, 30 Ocober 2006, pp [8] D. H. Feng, W. X. Zhe and S. Z. Chang, Sae and Parameer Esimaion of a HEV Li-ion Baery Pack Using Adapive Kalman Filer wih a New SOC-OCV Concep, Inernaional Conference on Measuring Technology and Mecharonics Auomaion, Zhangjiajie, -2 April 2009, pp [9] Q. Gang and C. Yong, Neural Nework Esimaion of Baery Pack SOC for Elecric Vehicles, Journal of Liaoning Technical Universiy, Vol. 25, No. 2, 2006, pp [0] A. R. P. Roba and F. R. Salmasi, Sae of Charge Esimaion for Baeries in HEV Using Locally Linear Model Tree (LOLIMOT), Proceeding of Inernaional Conference on Elecrical Machines and Sysems, Seoul, 8- Ocober 2007, pp [] T. X. Hui, D. H. Nan, F. Bo and Q. Y. Peng, Research on Esimaion of Lihium-Ion Baery SOC for Elecric Vehicle, Chinese Journal of Power Sources, Vol. 34, No., 200, pp [2] L. G. Hen, J. Hai and W. H. Ying, The SOC Compue Model of Baeries Based on Fuzzy Neural Nework, Journal of Tes and Measuremen Technology, Vol. 2, No. 5, 2007, pp [3] Z. H. Li, H, L. Ping and Z. Z. Hua, Sudy of Inelligen Predicion of he SOC of MH/Ni Baery for Elecric Vehicle, Machinery & Elecronics, No. 0, 2006, pp. 7-. [4] L. Y. Hong, Sub Lineariy of Generalized Sugeno Fuzzy Inegrals, Journal of Easern Liaoning Universiy (Naural Science), Vol. 7, No., 200, pp [5] W. Tao, Q. Hao and C. Yang, A New Mehod of Fuzzy Inerpolaive Reasoning Based on Gaussian-Type Membership Funcion, Fourh Inernaional Conference on Innovaive Compuing Informaion and Conrol, Kaohsiung, 7-9 December 2009, pp