Modeling and state of charge estimation of lithium-ion battery

doi:10.1007/s40436-015-0116-3

Advances in Manufacturing ›› 2015, Vol. 3 ›› Issue (3): 202-211.doi: 10.1007/s40436-015-0116-3

Modeling and state of charge estimation of lithium-ion battery

Xi-Kun Chen, Dong Sun

School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, People's Republic of China

收稿日期:2014-06-23 修回日期:2015-07-01 出版日期:2015-09-25 发布日期:2015-07-29
通讯作者: Dong Sun E-mail:sundyfirst@126.com,sundyfirst@shu.edu.cn
基金资助:
Project supported by the National High Technology Research and Development of China 863 Program (Grant No. 2011AA11A247).

Modeling and state of charge estimation of lithium-ion battery

Xi-Kun Chen, Dong Sun

School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, People's Republic of China

Received:2014-06-23 Revised:2015-07-01 Online:2015-09-25 Published:2015-07-29
Contact: Dong Sun E-mail:sundyfirst@126.com,sundyfirst@shu.edu.cn
Supported by:
Project supported by the National High Technology Research and Development of China 863 Program (Grant No. 2011AA11A247).

摘要/Abstract

摘要： Modeling and state of charge (SOC) estimation of lithium-ion (Li-ion) battery are the key techniques of battery pack management system (BMS) and critical to its reliability and safety operation. An auto-regressive with exogenous input (ARX) model is derived from RC equivalent circuit model (ECM) due to the discrete-time characteristics of BMS. For the time-varying environmental factors and the actual battery operating conditions, a variable forgetting factor recursive least square (VFFRLS) algorithm is adopted as an adaptive parameter identification method. Based on the designed model, a SOC estimator using cubature Kalman filter (CKF) algorithm is then employed to improve estimation performance and guarantee numerical stability in the computational procedure. In the battery tests, experimental results show that CKF SOC estimator has a more accuracy estimation than extended Kalman filter (EKF) algorithm, which is widely used for Li-ion battery SOC estimation, and the maximum estimation error is about 2.3%.

关键词: Lithium-ion (Li-ion) battery, Variable forgetting factor recursive least square (VFFRLS), Cubature Kalman filter (CKF), Extended Kalman filter (EKF)

Abstract: Modeling and state of charge (SOC) estimation of lithium-ion (Li-ion) battery are the key techniques of battery pack management system (BMS) and critical to its reliability and safety operation. An auto-regressive with exogenous input (ARX) model is derived from RC equivalent circuit model (ECM) due to the discrete-time characteristics of BMS. For the time-varying environmental factors and the actual battery operating conditions, a variable forgetting factor recursive least square (VFFRLS) algorithm is adopted as an adaptive parameter identification method. Based on the designed model, a SOC estimator using cubature Kalman filter (CKF) algorithm is then employed to improve estimation performance and guarantee numerical stability in the computational procedure. In the battery tests, experimental results show that CKF SOC estimator has a more accuracy estimation than extended Kalman filter (EKF) algorithm, which is widely used for Li-ion battery SOC estimation, and the maximum estimation error is about 2.3%.

Key words: Lithium-ion (Li-ion) battery, Variable forgetting factor recursive least square (VFFRLS), Cubature Kalman filter (CKF), Extended Kalman filter (EKF)

Xi-Kun Chen, Dong Sun. Modeling and state of charge estimation of lithium-ion battery[J]. Advances in Manufacturing, 2015, 3(3): 202-211.

参考文献

1. Habiballah RE, Unnati O (2013) Battery management system: an overview of its application in the smart grid and electric vehicles. IEEE Ind Electron Mag 6:4-16

2. Zhang JL, Lee J (2011) A review on prognostics and health monitoring of Li-ion battery. J Power Sources 196:6014-6077

3. Barre A, Deguilhem B, Grolleau S et al (2013) A review on lithium-ion battery aging mechanisms and estimations for automotive applications. J Power Sources 241:680-689

4. Lu LG, Han XB, Li JQ et al (2013) A review on the key issue for lithium-ion battery management in electric vehicle. J Power Sources 226:272-282

5. Waag W, Fleischer C, Sauer DU (2014) Critical review of the method of lithium-ion batteries in electric and hybrid vehicles. J Power Sources 258:321-339

6. Rezvanizaniani SM, Liu ZC, Chen Y et al (2014) Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility. J Power Sources 256:110-124

7. Seaman A, Dao TS, Mcphee J (2014) A survey of mathematicsbased equivalent-circuit and electrochemical battery models for hybrid and electric vehicle simulation. J Power Sources 256:410-423

8. Schmidt AP, Bitzer M, Imre AW et al (2010) Experiment-driven electrochemical modeling and systematic parameterization for a lithium-ion battery cell. J Power Sources 195:5071-5080

9. Moura SJ, Chaturvedi NA, Krstic M (2012) PDE estimation techniques for advanced battery management systems—part 1: SOC estimation. In: American control conference, Montréal, Canada, pp 559-565

10. Moura SJ, Chaturvedi NA, Krstic M (2012) PDE estimation techniques for advanced battery management systems—part 2: SOH identification. In: American control conference, Montréal, Canada, pp 566-571

11. Marcicki J, Canova M, Conlisk AT et al (2013) Design and parameterization analysis of a reduced-order electrochemical model of graphite/LiFePO₄ cells for SOC/SOH estimation. J Power Sources 237:310-324

12. Hu X, Li S, Peng H (2012) A comparative study of equivalent circuit models for Li-ion batteries. J Power Sources 198:359-367

13. Sitterly M, Wang LY, Yin GG et al (2011) Enhanced identification of battery models for real-time battery management. IEEE Trans Sustain Energy 2(3):300-308

14. Andre D, Meiler M, Steiner K et al (2011) Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. 1. Experimental investigation. J Power Sources 196:5334-5341

15. Andre D, Meiler M, Steiner K et al (2011) Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. 2. Modelling. J Power Sources 196:5349-5356

16. Hu Y, Yurkovich S, Guezennec Y et al (2009) A technique for dynamic battery model identification in automotive applications using linear parameter varying structures. Control Eng Pract 17(10):1190-1201

17. Li Y, Wang LF, Liao CL et al (2014) Rescursive modeling and online identification of lithium-ion batteries for electric vehicle application. Sci China Technol Sci 57(2):403-413

18. Constantin P, Jacob B, Silviu C (2008) A robust variable forgetting factor recursive least-squares algorithm for system identification. IEEE Signal Proc Lett 15:597-600

19. Yuan SF, Wu HJ, Yin CL (2013) State of charge estimation using the extended Kalman filter for battery management systems based on the ARX battery model. Energies 6:444-470

20. Weng CH, Sun J, Peng H (2014) A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge and state-ofhealth monitoring. J Power Sources 258:228-237

21. Plett GL (2004) Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs—Part 1: background. J Power Sources 134:252-261

22. Plett GL (2004) Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs—Part 2: modeling and identification. J Power Sources 134:262-276

23. Plett GL (2004) Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs—Part 3: parameter estimation. J Power Sources 134:277-292

24. Plett GL (2006) Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs. Part 1: introduction and state estimation. J Power Sources 161: 1356-1368

25. Plett GL (2006) Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs. Part 2: simultaneous state and parameter estimation. J Power Sources 161:1369-1384

26. Li JH, Barillas JK, Guenther C et al (2013) A comparative study of state of charge estimation algorithm for LiFePO₄ batteries used in electric vehicles. J Power Sources 230:244-250

27. Arasaratnam I, Haykin S (2009) Cubature Kalman filters. IEEE Trans Autom Control 54(6):1254-1269

Modeling and state of charge estimation of lithium-ion battery

Modeling and state of charge estimation of lithium-ion battery

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