A method for state of charge and state of health estimation of lithium-ion battery based on adaptive unscented Kalman filter
Open accessAbstract
The state of lithium-ion battery is a key indicator for the battery management system (BMS) of electric vehicles (EVs). State of charge (SOC) and state of health (SOH) of the power cell are the main parameters of the BMS during operation. In this paper, an adaptive unscented Kalman filter algorithm (AUKF) is presented for the joint estimation of SOC and SOH of lithium-ion batteries. Firstly, this paper develops a 2-RC equivalent circuit model and identifies the model parameters using recursive least squares algorithm with forgetting factor. Then, the SOC and SOH of the battery are estimated simultaneously by AUKF. Finally, the accuracy of the proposed method is verified under different operating conditions. The experiment results show that the maximum SOC estimation error is under 0.08% by the proposed method. Compared with the unscented Kalman filtering (UKF), it is shown that the proposed method is more accurate and reliable. An effective method is provided for state estimation for battery management system.
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Kalman filter provides a means for modeling battery aging and temperature changes, enhancing SOC estimation accuracy.
“It also gives a means for modeling battery aging and temperature changes, thereby enhancing the accuracy with which the SOC of the battery is estimated [59].”
From Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement (2024) · cites this paper as [59]
Cites
Li, Y., Abdel-Monem, M., Gopalakrishnan, R., Berecibar, M., Nanini-Maury, E., Omar, N., van den Bossche, P., & Van Mierlo, J. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.10.092 · Cited by 531 · see citing works
Sun, D., Yu, X., Wang, C., Zhang, C., Huang, R., Zhou, Q., Amietszajew, T., & Bhagat, R. (2021) · Energy · doi:10.1016/j.energy.2020.119025 · Cited by 242 · see citing works
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Pohlmann, S., Mashayekh, A., Stroebl, F., Karnehm, D., Kuder, M., Neve, A., & Weyh, T. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111649 · Cited by 32 · see citing works
Advanced Intelligent approach for state of charge estimation of lithium-ion battery
Kumar, D., Rizwan, M., & Panwar, A. K. (2023) · Energy Sources, Part A: Recovery, Utilization, and Environmental Effects · doi:10.1080/15567036.2023.2249427 · Cited by 23 · 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
- Authors
- Liu, S., Dong, X., Yu, X., Ren, X., Zhang, J., & Zhu, R.
- Affiliations
- Linyi University (CN)
- Qilu University of Technology (CN)
- Shandong Normal University (CN)
- Corresponding author
- Liu, Shulin
- Venue
- Energy Reports
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 8
- Pages
- 426-436
- ISSN
- 2352-4847 (ISSN-L)
- OpenAlex
- W4304774456
- Cited by
- 56
- References
- 2
- Citations by year
- 2026: 5 · 2025: 18 · 2024: 22 · 2023: 11
- FWCI
- 3.48
- 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)
- Funders
- Natural Science Foundation of Shandong Province (ZR2020ME206, ZR2020QF064)
- 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, state of health estimation, adaptive unscented Kalman filter, battery management systems, electric vehicles, parameter estimation, recursive least squares with forgetting factor
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