STCKF Algorithm Based SOC Estimation of Li-Ion Battery by Dynamic Parameter Modeling

  • R. RamprasathEmail author
  • R. Shanmughasundaram
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 968)


State of Charge (SoC) is the important criterion which reflects the actual battery usage. So, the State of Charge (SoC) has to be precisely estimated for improving the life and the rate of utilization of the battery. During normal operation of the battery, parameters like charge and discharge efficiency, temperature, etc., tend to affect the accurate estimation of SoC. In this paper, for estimating battery SoC with higher accuracy, Strong Tracking Cubature Kalman Filtering (STCKF) is used and the battery model parameters are identified using the method of Recursive Least Square (RLS). Simulation results indicate, STCKF estimates the SoC values as that of Ampere-Hour (AH) method with very minimal error and the dynamically modeled battery parameter values follows the same discharge characteristics as that of real batteries.


Estimating SoC STCKF algorithm Lithium-ion battery Dynamic parameter modeling 


  1. 1.
    Zhou, X., Zhao, Y.: Study on the SOC estimation of power battery for electric vehicle. In: Mechanical Science and Technology for Aerospace Engineering, vol. 33, no. 2, pp. 263–266 (2014)Google Scholar
  2. 2.
    Li, Z., Lu, L., Ouyang, M.: Comparison of methods for improving SOC estimation accuracy through an ampere-hour integration approach. J. Tsinghua Univ. (Nat. Sci. Ed.) 50, 1293–1296 (2010)Google Scholar
  3. 3.
    Zhu, H., Liu, Y., Zhao, C.: Parameter identification and SOC estimation of lithium ion battery. J. Hunan Univ. 41, 37–42 (2014)Google Scholar
  4. 4.
    Muthumanikandan, S., Shanmughasundaram, R.: Estimation of state of charge of lithium ion battery using artificial neural networks. Int. J. Control. Theory Appl. 9, 4331–4338 (2016)Google Scholar
  5. 5.
    Krishnakumar, A., Shanmughasundaram, R.: Simplified SOC estimation by EKF in Li-ion cell. J. Adv. Res. Dyn. Control. Syst. 3, 616–622 (2018)Google Scholar
  6. 6.
    Wu, T., Hu, L.: Study of SOC estimation algorithm of power battery based on UKF. Power Electron. 48(4), 23–26 (2014)MathSciNetGoogle Scholar
  7. 7.
    Xu, Y., Shen, Y.: Improved battery state-of-charge estimation based on Kalman filter. J. Beijing Univ. Aeronaut. Astronaut. 40, 855–860 (2014)Google Scholar
  8. 8.
    Arasatranam, I., Haykin, S.: Cubature Kalman filters. IEEE Trans. Autom. Control 54(6), 1254–1269 (2009)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Ning, X., Ye, C., Yang, J.: Cubature Kalman filtering for orbit determination of space targets. Chin. J. Radio Sci. 29, 27–32 (2014)Google Scholar
  10. 10.
    Li, Z., Yang, W., Ding, D.: Strong tracking cubature Kalman filter for real-time orbit determination for impulse maneuver satellite. In: Proceedings of the 36th Chinese Control Conference, vol. 32, no. 31, pp. 5258–5263 (2017)Google Scholar
  11. 11.
    Shugang, J.: A parameter identification method for a battery equivalent circuit model. In: Proceedings of SAE World Congress, no. 2011–01–1367 (2011)Google Scholar
  12. 12.
    Zhang, X., Wang, Y., Chen, Z.: Model-based remaining discharge energy estimation of lithium-ion batteries. In: 3rd International Conference on Control, Automation and Robotics, pp. 510–513 (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Mechanical Engineering, Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatoreIndia
  2. 2.Department of Electrical and Electronics Engineering, Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatoreIndia

Personalised recommendations