Advertisement

A Novel Method for Estimating State-of-Charge in Power Batteries for Electric Vehicles

  • Nan Zhang
  • Yunshan Zhou
  • Qiang Tian
  • Xiaoying Liao
  • Feitie ZhangEmail author
Regular Paper
  • 4 Downloads

Abstract

Estimation of the state-of- charge (SOC) of power batteries has always been the focus of electric vehicle users’ criticism. Accurate SOC is beneficial for extending the mileage of electric vehicles and the life of the battery pack. The key to improving SOC accuracy is to establish its accurate model and combine it with an appropriate estimation algorithm. Based on characterization experiments related to SOC, this paper describes a second-order charge–discharge resistor–capacitor model that can accurately simulate external characteristics of the battery and identify them online. An improved adaptive unscented Kalman filter algorithm based on Sage–Husa is introduced to estimate SOC. The reliability of the algorithm is verified by building a MATLAB/Simulink simulation model. The results show that the improved algorithm displays increased robustness and can quickly converge to the true value; the steady-state error is also within a small range.

Keywords

Adaptive unscented Kalman filter Equivalent circuit model Power battery State-of-charge 

List of Symbols

U

Voltage

I

Current

R

Internal ohmic resistance

Q

The battery capacity

y

Output

x

Input

e

Error

λ

Forgetting factor

wk

The process noise of the system

vk

The observation noise of the system

Qk

The covariance of the process noise

Rk

The covariance of the observed noise

qk

The mean of wk

rk

The mean of vk

Notes

Acknowledgements

First of all, I would like to thank my supervisor, Professor Zhou Yunshan, for his great support and encouragement during my study and work. The topic selection, research, and writing of the paper were all completed under the strict requirements and patient guidance of Prof. Zhou. Prof. Zhou has been committed to the research and development of key technologies for automotive power transmission and electronic control for many years; his rich experience in engineering projects, selfless research spirit, and strategic vision of cutting-edge technology have had a profound impact on me. Secondly, thanks to Tian Qiang, Xiong Huanjian, Li Hangyang and other brothers for pointing me in the right direction when I encountered difficulties in my studies. Thanks to all the members of the lab; the laboratory’s good learning atmosphere and research environment which have enabled me to complete this paper successfully. Thanks to my girlfriend Liao Xiaoying for her concern and care in my life. Finally, I would like to express my heartfelt thanks to the experts and teachers who reviewed this paper despite their busy schedule. This work was supported by the National Natural Science Foundation of China (Grant No. 51475151).

Supplementary material

12541_2019_44_MOESM1_ESM.opj (1.5 mb)
Supplementary material 1 (OPJ 1582 kb)
12541_2019_44_MOESM2_ESM.opj (1004 kb)
Supplementary material 2 (OPJ 1005 kb)
12541_2019_44_MOESM3_ESM.opj (211 kb)
Supplementary material 3 (OPJ 212 kb)
12541_2019_44_MOESM4_ESM.opj (171 kb)
Supplementary material 4 (OPJ 171 kb)

References

  1. 1.
    Liu, G. M., Ouyang, M., Lu, L., & Li, J. (2012). Online monitoring of lithium-ion battery aging effects by internal resistance estimation in electric vehicles. In Proceedings of the 31st Chinese control conference (CCC) (pp. 6851–6855).Google Scholar
  2. 2.
    Snihir, I., Rey, W., & Verbitskiy, E. (2006). Battery open-circuit voltage estimation by a method of statistical analysis. Journal of Power Sources, 159(2), 1484–1487.CrossRefGoogle Scholar
  3. 3.
    Jwo, D. J., & Wang, S. H. (2007). Adaptive fuzzy strong tracking extended Kalman filtering for GPS navigation. IEEE Sensors Journal, 7(5), 778–789.CrossRefGoogle Scholar
  4. 4.
    Wu, J., Zhang, C., & Chen, Z. (2016). An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks. Applied Energy, 173, 134–140.CrossRefGoogle Scholar
  5. 5.
    Urbain, M., Rael, S., & Davat, B. (2007). State estimation of a lithium-ion battery through Kalman filter. Power Electronics Specialists Conference, 17(21), 2804–2810.Google Scholar
  6. 6.
    Tan, X. J. (2011). Design of electric vehicle battery management system. Zhongshan: Zhongshan University Press. (in Chinese).Google Scholar
  7. 7.
    Wang, Q., Jiao, W., & Zhao, P. (2017). Correlation between the model accuracy and model-based SOC estimation. Electrochimica Acta, 228, 146–159.CrossRefGoogle Scholar
  8. 8.
    Yu, Q., Rui, X., & Cheng, L. (2017). Lithium-ion battery parameters and state-of-charge joint estimation based on H-infinity and unscented Kalman filters. IEEE Transactions on Vehicular Technology, 66(10), 8693–8701.CrossRefGoogle Scholar
  9. 9.
    Plett, G. L. (2004). Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation. Journal of Power Sources, 134, 277–292.CrossRefGoogle Scholar
  10. 10.
    Verbrugge, M. (2007). Adaptive multi-parameter battery state estimator with optimized time-weighting factors. Journal of Applied Electrochemistry, 37(5), 605–616.CrossRefGoogle Scholar
  11. 11.
    Chiang, Y. H., Sean, W. Y., & Ke, J. C. (2011). Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electric vehicles. Journal of Power Sources, 196, 3921–3932.CrossRefGoogle Scholar
  12. 12.
    Wang, J. P., Guo, J. G., & Ding, L. (2009). An adaptive Kalman filtering based state of charge combined estimator for electric vehicle battery pack. Energy Conversion and Management, 50, 3182–3186.CrossRefGoogle Scholar
  13. 13.
    Chen, N., Hu, X., & Gui, W. (2014). Estimation of li-ion battery state of charging and state of healthy based on unsented Kalman filtering. In Control & decision conference.Google Scholar
  14. 14.
    Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M., & Whitaker, J. S. (2003). Ensemble square root filters. Monthly Weather Review, 131(7), 1485.CrossRefGoogle Scholar
  15. 15.
    Sage, A. P., & Husa, G. W. (1969). Adaptive filtering with unknown prior statistics. In Proceedings of joint automatic control conference (pp. 760–769).Google Scholar
  16. 16.
    Peng, S., Chen, C., Shi, H., & Yao, Z. (2017). State of charge estimation of battery energy storage systems based on adaptive unscented Kalman filter with a noise statistics estimator. IEEE Access, 5, 13202–13212.CrossRefGoogle Scholar

Copyright information

© Korean Society for Precision Engineering 2019

Authors and Affiliations

  • Nan Zhang
    • 1
  • Yunshan Zhou
    • 1
  • Qiang Tian
    • 1
  • Xiaoying Liao
    • 1
  • Feitie Zhang
    • 1
    Email author
  1. 1.College of Mechanical and Vehicle EngineeringHunan UniversityChangshaChina

Personalised recommendations