An extended Kalman filter based SOC estimation method for Li-ion battery
Open accessAbstract
In recent years, the global environmental pollution and energy crisis are becoming more and more serious. The Li-ion battery is widely used in vehicles due to long cycle life and high energy density. The state of charge (SOC) of Li-ion battery is an important indicator. The accurate estimation of SOC can ensure the safe operation of Li-ion battery. However, the traditional estimation method, the ampere-hour integration method, has a cumulative error and cannot maintain good results for a long time in an operating environment with the Gaussian noise. To this end, this paper firstly applies Thevenin equivalent circuit model of a battery to establish estimation model, and it can reflect the working state of the battery. Then, the extended Kalman filtering algorithm is employed to solve the estimation error caused by Gaussian noise. Finally, the test system is built in MATALAB/Simulink to investigate the performance of the proposed method. Simulation results show that the proposed method achieves better performance, and it has higher estimation accuracy in comparison with traditional methods under different working conditions.
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If the Kalman filter model is not well calibrated, it may give wrong SOC estimates.
“if the model used here is not well calibrated, the filter might give wrong SOC estimates [60].”
From Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement (2024) · cites this paper as [60]
EKF operates on the linearization of the non-linear function concept, which makes use of first-order equations of Taylor series and partial derivatives.
“EKF operates on the linearization of the non-linear function concept [51], which makes use of first-order equations of Taylor series and partial derivatives.”
From Recent developments and challenges in state-of-charge estimation techniques for electric vehicle batteries: A review (2024) · cites this paper as [51]
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Yun, J., Choi, Y., Lee, J., Choi, S., & Shin, C. (2023) · IEEE Access · doi:10.1109/access.2023.3305950 · Cited by 79 · see citing works
Barik, S., & Saravanan, B. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.113623 · Cited by 63 · see citing works
Dineva, A. (2024) · Batteries · doi:10.3390/batteries10100356 · Cited by 52 · see citing works
Liu, H., Liang, F., Hu, T., Hong, J., & Ma, H. (2024) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2023.000726 · Cited by 17 · see citing works
Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement
Rehman, A. (2024) · Power System Technology · doi:10.52783/pst.906 · Cited by 12 · see citing works
Pei, L., Yu, C., Wang, T., Yang, J., & Wang, W. (2024) · Energies · doi:10.3390/en17081824 · Cited by 3 · see citing works
- Authors
- Cui, Z., Hu, W., Zhang, G., Zhang, Z., & Chen, Z.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Aalborg University (DK)
- Corresponding author
- Zhang, Guozhou
- Venue
- Energy Reports
- Publisher
- Elsevier BV
- Published
- 2022-08
- Volume
- 8
- Pages
- 81-87
- ISSN
- 2352-4847 (ISSN-L)
- OpenAlex
- W4213414089
- Cited by
- 108
- References
- 0
- Citations by year
- 2026: 19 · 2025: 31 · 2024: 30 · 2023: 21 · 2022: 7
- FWCI
- 6.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energy Reports (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- VBN Forskningsportal (Aalborg Universitet) (submittedVersion, cc-by-nc-nd)
- Funders
- Sichuan Province Science and Technology Support Program (19ZDYF1887)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Keywords
- lithium-ion batteries, state of charge estimation, extended Kalman filter, Thevenin equivalent circuit model, Gaussian noise, battery management systems, automotive batteries
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