Novel estimation solution on lithium-ion battery state of charge with current-free detection algorithm

Novel estimation solution on lithium-ion battery state of charge with current-free detection algorithm

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Circuits, Devices & Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Lithium-ion battery as an efficient, sustainable, and clean energy for electric vehicles (EVs) and smart devices becomes more popular with the worldwide demand for reduction of greenhouse gas emission. In all kinds of applications, an accurate real-time estimation for state of charge (SOC) of battery is necessary. Some conventional methods usually need to sample both battery currents and voltages. This article presents a novel SOC estimation algorithm without current detection. This algorithm just acquires the port voltages of cell to calculate the open-circuit voltage (OCV) which is related to SOC. By extracting a large number of battery voltages based on a step response, some important parameters that can track battery working process are determined. In order to verify the algorithm feasibility and accuracy, it has been tested on a commercial common field-programmable gate array (FPGA) in different application conditions. The algorithm accuracy is mainly limited by model accuracy and sampling sensor accuracy. The maximum error between ideal SOC and calculated SOC by this algorithm is within 4%, and the mean error is about 0.99%. So, this high-feasibility, accredited accuracy, easy integration, and low-cost solution has bright potential in smarter future.


    1. 1)
      • 1. Osaka, T., Mukoyama, D., Nara, H.: ‘Review development of diagnostic process for commercially available batteries, especially lithium ion battery by electrochemical impedance spectroscopy’, J. Electrochem., 2015, 162, (14), pp. A2529A2537.
    2. 2)
      • 2. National Aeronautics and Space Administration, Houston: ‘Crewed space vehicle battery safety requirements: engineering directorate propulsion and power division revision C’, January 2014, Section 4.4.4. Cell Matching, p. 19.
    3. 3)
      • 3. Cheng Ka-Wai, E.: ‘Review of battery management systems for electric vehicles’, Energy Syst. Electr. Hybrid Vehicles, 2016, 12, pp. 349371.
    4. 4)
      • 4. Lam, K.H.: ‘Solar energy harvesting for electric vehicles’, Energy Syst. Electr. Hybrid Vehicles, 2016, 5, pp. 129153.
    5. 5)
      • 5. Andrews, C.: ‘UK needs better charging [electric vehicles]’, Eng. Technol., 2017, 12, (2), pp. 6263.
    6. 6)
      • 6. Waag, W., Fleischer, C., Sauer, D.U.: ‘Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles’, J. Power Sources, 2014, 258, (64), pp. 321339.
    7. 7)
      • 7. Amar, A.B., Kouki, A.B., Cao, H.: ‘Power approaches for implantable medical devices’, Sensors, 2015, 15, pp. 2888928914.
    8. 8)
      • 8. Kezunovic, M., McCalley, J., Overbye, T.: ‘Smart grids and beyond: achieving the full potential of electricity systems’, Proc. IEEE, Special Centennial Issue, May 2012, vol. 100, pp. 13291341.
    9. 9)
      • 9. Dey, S., Mohon, S., Pisu, P., et al: ‘Sensor fault detection, isolation and estimation in lithium-ion batteries’, IEEE Trans. Control Syst. Technol., 2016, 24, (6), pp. 21412149.
    10. 10)
      • 10. Juang, L.W., Kollmeyer, P.J., Zhao, R., et al: ‘The impact of DC bias current on the modeling of lithium iron phosphate and lead-acid batteries observed using electrochemical impedance spectroscopy’, IEEE Energy Conversion Congress and Exposition (ECCE), September 2104.
    11. 11)
      • 11. Gholizade-Narm, H., Charkhgard, M.: ‘Lithium-ion battery state of charge estimation based on square-root unscented Kalman filter’, IET Power y Electron., 2013, 6, (9), pp. 18331841.
    12. 12)
      • 12. Xia, B.: ‘State of charge estimation of lithium-ion batteries using an adaptive cubature Kalman filter’, Energies, 2015, 8, pp. 59165936.
    13. 13)
      • 13. Aung, H., Low, K.S.: ‘Temperature dependent state-of-charge estimation of lithium ion battery using dual spherical unscented Kalman filter’, IET Power Electron., 2015, 8, (10), pp. 20262033.
    14. 14)
      • 14. Li, P.C., Chen, N., Chen, J.S., et al: ‘A state-of-charge estimation method based on an adaptive proportional-integral observer’, 2016 IEEE Vehicle Power and Propulsion Conf. (VPPC), Hangzhou, 2016, pp. 16.
    15. 15)
      • 15. Mohammad, C., Haddad, Z.M.: ‘Design of adaptive H∞ filter for implementing on state-of-charge estimation based on battery state-of-charge-varying modelling’, IET Power Electron., 2015, 8, (10), pp. 18251833.
    16. 16)
      • 16. Meng, J., Ricco, M., Luo, G., et al: ‘An overview and comparison of online implementable SOC estimation methods for lithium-ion battery’, IEEE Trans. Ind. Appl., 2018, 54, (2), pp. 15831591.
    17. 17)
      • 17. Houlian, W., Gongbo, Z.: ‘State of charge prediction of supercapacitors via combination of kalman filtering and backpropagation neural network’, IET Electr. Power Appl., 2018, 12, (4), p. pp. 588594.
    18. 18)
      • 18. Taimoor, Z., Kun, X., Weimin, L: ‘Machine learning an alternate technique to estimate the state of charge of energy storage devices’, Electron. Lett., 2017, 53, (25), pp. 16651666.
    19. 19)
      • 19. Hannan, M.A., Lipu, M.S.H., Hussain, A., et al: ‘Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm’, IEEE. Access., 2018, 6, pp. 1006910079.
    20. 20)
      • 20. Daniil, N., Drury, D., Mellor, P.H.: ‘Performance comparison of diffusion, circuit-based and kinetic battery models’, IEEE Energy Conversion Congress and Exposition (ECCE), 2015, pp. 13821389.
    21. 21)
      • 21. Jiang, J., Ruan, H., Sun, B., et al: ‘A reduced low-temperature electro-thermal coupled model for lithium-ion batteries’, Appl. Energy, 2016, 177, (1), pp. 804816.
    22. 22)
      • 22. Hu, X., Li, S., Peng, H.: ‘A comparative study of equivalent circuit models for Li-ion batteries’, J. Power Sources, 2012, 198, (198), pp. 359367.
    23. 23)
      • 23. Sangwan, V., Vakacharla, V.R., Kumar, V.R., et al: ‘Estimation of state of charge for Li-ion battery using model adaptive extended Kalman filter’, 2017, 7th Int. Conf. Power Systems (ICPS), Pune, May 2017, pp. 726731.
    24. 24)
      • 24. Nagata, Y.J., Kawasaki, K., Nakamoto, H.: ‘A battery management system adapted for an energy harvester with a low-power state of charge monitoring method and a 24 microwatt intermittently enabled Coulomb counter’, 2018, IEEE Applied Power Electronics Conf. Exposition (APEC), San Antonio, March 2018, pp. 35563562.
    25. 25)
      • 25. Huangfu, Y., Saeed, M., Xu, J., et al: ‘Nonlinear parameter optimization of PI observer for highly accurate SOC estimation of Li-ion batteries’, 2017, IECON 2017 – 43rd Annual Conf. the IEEE Industrial Electronics Society, Beijing, October 2017, pp. 52665273.
    26. 26)
      • 26. He, F., Shen, W. X., Kapoor, A., et al: ‘H infinity observer based state of charge estimation for battery packs in electric vehicles’, 2016 IEEE 11th Conf. Industrial Electronics and Applications (ICIEA), Hefei, June 2016, pp. 694699.

Related content

This is a required field
Please enter a valid email address