Elektrikli araç uygulamalarında kullanılan lityum bataryalar için göreceli kapasite tahmin yöntemi

Yıl 2018, Cilt: 24 Sayı: 5, 809 - 816, 12.10.2018

Türev Sarıkurt , Abdülkadir Balıkçı

Öz

Günümüzde
fosil yakıt kullanımının çevresel zararları hakkındaki bilincin artması ve bu
yakıtların rezervlerindeki azalmadan dolayı temiz ulaşım konusu ilgi
çekmektedir. Elektrikli Araçlar (EA) bu alandaki önemli alternatiflerdendir.
Diğer batarya türlerine kıyasla kendiliğinden boşalma oranlarının düşük olması
ve yüksek enerji yoğunluğuna, yüksek güç yoğunluğuna ve yüksek açık devre
gerilimlerine sahip olmaları nedeniyle lityum tabanlı bataryalar EA
uygulamalarında yoğunlukla tercih edilir. Bataryaların performansı zaman ve
kullanım ile azalır. Bu nedenle EA uygulamalarında bataryanın sağlık ve ömür
bilgisi önemlidir. Bu çalışmada batarya sağlığı Göreceli Kapasite (GK) cinsinden
ifade edilmiş ve basit bir GK tahmin metodu önerilmiştir. GK bataryanın güncel
ve nominal kapasite değerlerinin karşılaştırılmasıdır. Önerilen metotta GK
bataryanın Referans Çevrim Sayısı (RÇS) kullanılarak elde edilmektedir. Bu
amaçla bataryanın terminal geriliminin belirli sinyaller altında değişimine
bağlı olarak bir RÇS modeli geliştirilmiştir. Daha önceki çalışmalarda
önerilmiş bir batarya modeli yaşlanma etkilerini de içerecek şekilde
geliştirilmiş, bataryanın farklı RÇS’deki davranışının benzetimi yapılmıştır.
Farklı RÇS’deki bataryaların aynı test sinyaline verdikleri tepkiler terminal
gerilimindeki değişimler üzerinden incelenmiştir. Bu değişimler sayısal
büyüklüklere dönüştürülerek RÇS modeli oluşturulmuş, RÇS-GK ilişkisinden
faydalanılarak GK elde edilmiştir. Metodun geçerliliği deneysel olarak da teyit
edilmiştir.

Anahtar Kelimeler

Lityum-İyon batarya, Elektrikli araçlar, Batarya sağlık durumu, Göreceli kapasite, Referans çevrim sayısı

A relative capacity estimation method for lithium batteries used in electric vehicle applications

Öz

Depending
on the consciousness about environmental harms of fossil fuel usage and
depletion in their reserves, the interest on clean transportation is rising
today. Electric vehicles (EV) are important alternatives on clean
transportation. In EV applications, lithium based batteries are commonly
preferred due to their relatively high energy and power densities, higher open
circuit voltages and lower self-discharge rates, when compared to other
secondary battery types. Performance of a battery decreases with age. Therefore
battery health and life information is important for reliable operation in EV
applications. In this study battery health is represented in terms of relative
capacity (RC) which is the comparison between actual and nominal capacity
values of a battery and a simple RC estimation method is proposed. In the
method, RC is estimated by using relative cycle number by using reference cycle
number (RCN). For this purpose a RCN model, which is based on the change of
terminal voltage under a significant load signal, is developed. A battery
model, which was proposed in an earlier study is improved in order to reflect
aging effects. Behaviors of batteries in different reference cycles are
simulated. Different responses of batteries to the same load signal, by means
of differences in terminal voltages are investigated. These differences are
transformed to numerical quantities to develop RCN model and thereafter RC is
estimated by using the relationship between RCN and RC. The method is validated
with experiments.

Anahtar Kelimeler

Lithium-Ion batteries, Electric vehicles, Battery state of health, Relative capacity, Reference cycle number

Kaynakça

• Ajanovic A. “The future of electric vehicles: prospects and impediments”. Wiley Interdisciplinary Reviews: Energy and Environment, 4(6), 521-536, 2015.
• Deng D. “Li-Ion batteries: basics, progress, and challenges”. Energy Science & Engineering, 3(5), 385-418, 2015.
• Scrosati B, Garche, J. “Lithium batteries: Status, prospects and future”. Journal of Power Sources, 195(9), 2419-2430, 2010.
• Köhler U, Kümpers J, Ullrich M. “High performance nickel-metal hydride and lithium-ion batteries”. Journal of Power Sources, 105(1), 139-144, 2002.
• Nazri GA, Pistoia G. Lithium Batteries, Science and Technology. New York, USA, Springer Science & Business. 2008.
• Vetter J, Novak P, Wagner MR, Veit C, Möller KC, Besenhard JO, Winter M, Wohlfahrt-Mehrens M, Vogler C, Hammouche A. “Ageing mechanisms in lithium-ion batteries”. Journal of Power Sources, 147(1-2), 269-281, 2005.
• Hatzell KB, Sharma A, Fathy HK. “A survey of long-term health modeling, estimation, and control of Lithium-ion batteries: Challenges and opportunities”. American Control Conference, Montreal, Canada, 27-29 June 2012.
• Berecibar M, Gandiaga I, Villareal I, Omar N, van Mierlo J, van den Bossche P. “Critical review of state of health estimation methods of Li-ion batteries for real applications”. Renewable and Sustainable Energy Reviews, 56, 572-587, 2016
• Fei Z, Guangjun L, Lijin F. “Battery state estimation using Unscented Kalman Filter”. International Conference on Robotics and Automation, ICRA ’09, Kobe, Japan, 12-17 May 2009.
• Lee S, Kim J, Lee J, Cho BH. “State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge”. Journal of Power Sources, 185(2), 1367-1373, 2008.
• Plett, GL. “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation”. Journal of Power Sources, 134(2), 277-292, 2004..
• Remmlinger J, Bucholz M, Soczka-Guth T, Dietmayer K. “On-board state-of-health monitoring of lithium-ion batteries using linear parameter-varying models”. Journal of Power Sources, 239, 689-695, 2013.
• Kim IS. “A technique for estimating the state of health of lithium batteries through a dual-sliding-mode observer”. IEEE Transactions on Power Electronics, 25(4), 1013-1022, 2010.
• Singh P, Reisner D. “Fuzzy logic-based state-of-health determination of lead acid batteries”. 24th Annual International Telecommunications Energy Conference, Montreal, Quebec, Canada, 29 September-2 October 2002
• Zenati A, Desprez P, Razik H. “Estimation of the SOC and the SOH of Li-ion batteries, by combining impedance measurements with the fuzzy logic inference”. 36th Annual Conference of IEEE Industrial Electronics, IECON 2010, Glendale, Arizona, USA, 07-10 November 2010.
• Schweiger HG, Obeidi O, Komesker A, Raschke A, Schiemann M, Zehner C, Gehner M, Keller M, Birke P. “Comparison of several methods for determining the internal resistance of lithium ion cells”. Sensors, 10(6), 5604-5625, 2010.
• Waag W, Fleischer C, Sauer DU. “Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles”. Journal of Power Sources, 258, 321-339, 2014
• Wei X, Zhu B, Xu W. “Internal resistance identification in vehicle power lithium-ion battery and application in lifetime evaluation”. International Conference on In Measuring Technology and Mechatronics Automation, ICMTMA’09, Hunan, China, 11-12 April 2009.
• Andre D, Meiler M, Steiner K, Walz H, Soczka-Guth T, Sauer DU. “Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. II: Modelling”. Journal of Power Sources, 196(12), 5349-5356, 2011.
• Blanke H, Bohlen O, Buller S, de Doncker RW, Fricke B, Hammouche A, Linzen D, Thele M, Sauer DU. “Impedance measurements on lead-acid batteries for state-of-charge, state-of-health and cranking capability prognosis in electric and hybrid electric vehicles”. Journal of Power Sources, 144(2), 418-425, 2005.
• Galeotti M, Giammanco C, Cina L, Cordiner S, di Carlo A. “Synthetic methods for the evaluation of the State of Health (SOH) of nickel-metal hydride (NiMH) batteries”. Energy Conversion and Management, 92, 1-9, 2015.
• Kozlowski JD. “Electrochemical cell prognostics using online impedance measurements and model-based data fusion techniques”. IEEE Aerospace Conference, Montana, USA, 8-15 March 2003.
• Andre D, Meiler M, Steiner K, Walz H, Soczka-Guth T, Sauer DU. “Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. I: Experimental Investigation”. Journal of Power Sources, 196(12), 5334-5341, 2011.
• Goebel K, Saha B, Saxena A, Celaya JR, Christophersen JP. “Prognostics in Battery Health Management”. IEEE Instrumentation & Measurement Magazine, 11(4), 33-40, 2008.
• Onori S, Spagnol P, Marano V, Guezennec Y, Rizzoni G. “A new life estimation method for lithium-ion batteries in plug-in hybrid electric vehicles applications”. International Journal of Power Electronics, 4(3), 302-319, 2012.
• Feng X, Li J, Ouyang M, Lu L, Li J, He X. “Using probability density function to evaluate the state of health of lithium-ion batteries”. Journal of Power Sources, 232, 209-218, 2013.
• Ng KS, Moo CS, Chen YP, Hsieh YC, “Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries”. Applied Energy, 86(9), 1506-1511, 2009.
• Spotnitz R. “Simulation of capacity fade in lithium-ion batteries”. Journal of Power Sources, 113, 72-80, 2003.
• Kokam, “SLPB (Superior Lithium Polymer Battery) Technical Specification”. (12.5.2009).
• Sarikurt T, Ceylan M, Balikci A. “A hybrid battery model and state of health estimation method for lithium-ion batteries”. IEEE International Energy Conference, ENERGYCON, Dubrovnik, Croatia, 13-16 May 2014.
• Sarikurt Y, Ceylan M, Balikci A. “An analytical battery state of health estimation method”. IEEE International Symposium on Industrial Electronics, ISIE, Istanbul, Turkey, 1-4 June 2014.
• Sarikurt T, Ceylan M, Balikci A, “A parametric battery state of health estimation method for electric vehicle applications”. Turkish Journal of Electrical Engineering & Computer Sciences, 25(4), 2860-2870, 2017.
• Ceylan M, Sarikurt T, Balikci A. “Elektrikli araçlarda kullanılan lityum-iyon bataryalar için model geliştirilmesi”. 5. Enerji Verimliliği ve Kalitesi Kongresi, Kocaeli, Türkiye, 23-24 Mayıs 2013.
• Ceylan M, Sarikurt T, Balikci A. “A novel lithium-ıon-polymer battery model for hybrid/electric vehicles”. IEEE International Symposium on Industrial Electronics, ISIE, Istanbul, Turkey, 1-4 June 2014.
• Rao R, Vrudhula S, Rakhmatov DN. “Battery modeling for energy aware system design”. Computer, 36(12), 77-87, 2003.
• Rakhmatov D. “Battery voltage modeling for portable systems”. ACM Transactions on Design Automation of Electronic Systems, 14(2), 1-36, 2009.
• Newman J, Thomas KE, Hafezi H, Wheeler DR. “Modeling of lithium-ion batteries”. Journal of Power Sources, 119, 838-843, 2003.
• Fang W, Kwon OJ, Wang CY. “Electrochemical-thermal modeling of automotive Li-ion batteries and experimental validation using a three-electrode cell”. International Journal of Energy Research, 34(2), 107-115, 2010.
• Stetzel KD, Aldrich LL, Trimboli MS, Plett GL. “Electrochemical state and internal variables estimation using a reduced-order physics-based model of a lithium-ion cell and an extended Kalman filter”. Journal of Power Sources, 278, 490-505, 2015.
• Li Y, Liao C, Wang L, Wang L, Xu D. “Subspace-based modeling and parameter identification of lithium-ion batteries”. International Journal of Energy Research, 38(8), 1024-1038, 2014.
• Feinauer J, Brereton T, Spettl A, Weber M, Manke I, Schmidt V, “Stochastic 3D modeling of the microstructure of lithium-ion battery anodes via Gaussian random fields on the sphere”. Computer Material Science, 109, 137-146, 2015.
• Yang XG, Taenaka B, Miller T, Snyder K. “Modeling validation of key life test for hybrid electric vehicle batteries”. International Journal of Energy Research, 34(2), 171-181, 2010.
• Dubarry M, Vuillaume N, Liaw BY. “Origins and accommodation of cell variations in Li-ion battery pack modeling” International Journal of Energy Research, 34(2) 216-231, 2010.
• Xu L, Wang J, Chen Q. “Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model”. Energy Conversion and Management, 53(1), 33-39, 2012.
• Jongerden M, Haverkort B. “Which battery model to use?”. IET Software, 3(6), 445-457, 2009.
• Xun J, Liu R, Jiao K. “Numerical and analytical modeling of lithium ion battery thermal behaviors with different cooling designs”. Journal of Power Sources, 233, 47-61, 2013.
• Thirugnanam K, Ezhil RJTP, Singh M, Kumar P. “Mathematical modeling of li-ion battery using genetic algorithm approach for V2G applications”. IEEE Transactions on Energy Conversion, 29(2), 332-343, 2014.
• Chen M, Rincon-Mora GA. “Accurate electrical battery model capable of predicting runtime and I-V Performance”. IEEE Transactions on Energy Conversion, 21(2), 504-511, 2006.
• Dubarry M, Liaw BY. “Development of a universal modeling tool for rechargeable lithium batteries”. Journal of Power Sources, 174(2), 856-860, 2007.
• Smith K, Kim GH, Darcy E, Pesaran A. “Thermal/electrical modeling for abuse-tolerant design of lithium ion modules”. International Journal of Energy Research, 34(2), 204-215, 2010.
• He, H, Zhang X, Xiong R, Xu Y, Guo H. “Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles”. Energy, 39(1), 310-318, 2012.
• Samadani E, Mastali M, Farhad S, Fraser RA, Fowler M. “Li ion battery performance and degredation in electric vehicles under different usage scenarios”. International Journal of Energy Research, 31, 135-147, 2015.
• Gomez J, Nelson R, Kalu EE, Weatherspoon MH, Zheng JP, “Equivalent circuit model parameters of a high-power Li-ion battery: Thermal and state of charge effects”. Journal of Power Sources, 196(10), 4826-4831, 2011.