Skip to main content
SpringerLink
Log in

Modeling and state of charge estimation of lithium-ion battery

  • Published:
Advances in Manufacturing Aims and scope Submit manuscript

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%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  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

    Google Scholar 

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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

Project supported by the National High Technology Research and Development of China 863 Program (Grant No. 2011AA11A247).

Author information

Authors and Affiliations

  1. School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 200072, People’s Republic of China

    Xi-Kun Chen & Dong Sun

Authors
  1. Xi-Kun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dong Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dong Sun.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, XK., Sun, D. Modeling and state of charge estimation of lithium-ion battery. Adv. Manuf. 3, 202–211 (2015). https://doi.org/10.1007/s40436-015-0116-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40436-015-0116-3

Keywords

Search

Navigation