Abdulkadir BALIKÇI, Turev SARIKURT, Murat CEYLAN

A parametric battery state of health estimation method for electric vehicle applications

Lithium-ion batteries are commonly preferred in electric vehicle applications. The relative capacity and state of health of a battery decrease with age. Therefore, accurate estimation of these parameters is essential. In this study a parametrical approach for estimation of battery state of health is proposed. A hybrid battery model that has a maximum error less than 3% is used. The relative capacity of the battery is estimated by using performance decrement with age. The method is validated by two different set of experiments. The first set is conducted with batteries that were aged by a controlled process and the second set is conducted with randomly aged batteries. The proposed method works successfully in both conditions with maximum error less than 5%.

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[1] Jongerden M, Haverkort B. Battery modeling. Enschede, Netherlands: University of Twente, 2008.
[2] K¨ohler U, K¨umpers J, Ullrich M. High performance nickel-metal hydride and lithium-ion batteries. J Power Sources 2002; 105:139-144.
[3] Scrosati B, Garche J. Lithium batteries: status, prospects and future. J Power Sources 2010; 195: 2419-2430.
[4] Hamidi SA, Ionel DM, Nasiri A. Modeling and management of batteries and ultracapacitors for renewable energy support in electric power systemsan overview. Electr Power Components Syst 2015; 43: 1434-1452.
[5] Choi D, Wang W, Yang Z. Material Challenges and Perspectives. In: Yuan X, Liu H, Zhang J, editors. Lithium-ion Batteries, Advanced Materials and Technologies. Boca Raton, FL, USA. CRC Press, 2012. pp. 1-9.
[6] Rahn CD, Wang CY. Battery Systems Engineering. Chichester, UK: Wiley, 2013.
[7] Hatzell KB, Sharma A, Fathy HK. A survey of long-term health modeling, estimation, and control of lithium-ion batteries: challenges and opportunities. In: 2012 American Control Conference; 2729 June 2012; Montreal, QC, Canada. pp. 584-591.
[8] Vutetakis DG, Viwanathan VV. Determining the state-of-health of maintenance-free aircraft batteries. In: Tenth Annual Battery Conference on Applications and Advances; 1316 January 1995; Long Beach, CA, USA. pp. 13-18.
[9] Goebel K, Saha B, Saxena A, Celaya JR, Christophersen JP. Prognostics in battery health management. IEEE Instrum Meas Mag 2008; 11: 33-40.
[10] Jaworski RK. Statistical parameters model for predicting time to failure of telecommunications batteries. In: International Telecommunication Energy Conference, INTELEC99; 69 June 1999; Copenhagen, Denmark: IEEE. pp. 479-484.
[11] Blanke H, Bohlen O, Buller S, De Doncker RW, Fricke B, Hammouche A, Linzen D, Thele M, Uwe D. Impedance measurements on leadacid batteries for state-of-charge, state-of-health and cranking capability prognosis in electric and hybrid electric vehicles. J Power Sources 2005; 144: 418-425.
[12] Meissner E, Richter G. Battery monitoring and electrical energy management. J Power Sources 2003; 116: 79-98.
[13] Bhangu BS, Bentley P, Stone DA, Bingham CM. Nonlinear observers for predicting state-of-charge and state-ofhealth of lead-acid batteries for hybrid-electric vehicles. IEEE T Veh Technol 2005; 54: 783-794.
[14] Kozlowski JD. Electrochemical cell prognostics using online impedance measurements and model-based data fusion techniques. In: IEEE Aerospace Conference; 815 March 2003; Big Sky, MT, USA: IEEE. pp. 3257-3270.
[15] Gao L, Liu S, Dougal RA. Dynamic lithium-ion battery model for system simulation. IEEE T Components Packag Technol 2002; 25: 495-505.
[16] Chiang Y, Sean W. Dynamical estimation of state-of-health of batteries by using adaptive observer. In: 2nd International Conference on Power Electronics and Intelligent Transportation System; 1920 December 2009; Shenzhen, China. pp. 110-115.
[17] Ragsdale M, Brunet J, Fahimi B. A novel battery identification method based on pattern recognition. In: IEEE Vehicle Power and Propulsion Conference (VPPC); 35 September 2008; Harbin, Hei Longjiang, China. pp. 1-6.
[18] Klass V, Behm M, Lindbergh G. A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation. J Power Sources 2014; 270: 262-272.
[19] Saha B, Goebel K, Poll S, Christophersen J. An integrated approach to battery health monitoring using Bayesian regression and state estimation. In: IEEE Autotestcon; 1720 September 2007; Baltimore, MD, USA. pp. 646-653.
[20] Han X, Ouyang M, Lu L, Li J. A comparative study of commercial lithium ion battery cycle life in electric vehicle: capacity loss estimation. J Power Sources 2014; 268: 658-669.
[21] Kim J, Cho BH. An innovative approach for characteristic analysis and state-of-health diagnosis for a Li-ion cell based on the discrete wavelet transform. J Power Sources 2014; 260: 115-130.
[22] Waag W, Fleischer C, Sauer DU. Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles. J Power Sources 2014; 258: 321-339.
[23] Ceylan M, Sarikurt T, Balikci A. A novel lithium-ion-polymer battery model for hybrid/electric vehicles. In: IEEE International Symposium on Industrial Electronics; 14 June 2014; Harbiye, ˙Istanbul, Turkey. pp. 366-369.
[24] Kim KH. Kokam. SLPB (superior lithium polymer battery) technical specification. Pajang Dong, Korea: Kokam, 2009.