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Cited by 80Advanced Battery Technologies ResearchControl Systems and IdentificationAdvancements in Battery Materials

State-of-Charge Estimation Method for Lithium-Ion Batteries Using Extended Kalman Filter With Adaptive Battery Parameters

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Yun, Jaejung, Choi, Yeonho, Lee, Jaehyung, Choi, Seonggon, Shin, Changseop·2023·IEEE Access
DOI 10.1109/access.2023.3305950
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Abstract

Accurate battery state-of-charge (SOC) estimation is important for the efficient and reliable operation of battery application systems. The extended Kalman filter (EKF), which is based on the battery model, is widely used as a real-time SOC estimation algorithm; furthermore, its accuracy depends on the model accuracy. However, the conventional EKF uses one value for each battery parameter (Ri,RdandCd) regardless of the SOC, even though their values change according to the SOC. To address this problem, this study proposes an improved EKF that applies battery parameters that change according to the SOC of a battery model. In the proposed method, the entire SOC was divided into several sections considering the deviation of the parameter values according to the SOC. Subsequently, the average values for each SOC section were calculated, and the values of the battery parameters were updated with the average values according to the SOC. To verify the performance of the proposed EKF, the parameters of commercial Li-ion batteries were extracted with dis-charge currents of 1C- and 2C-rates at ambient temperatures of 0 °C, 25 °C, and 45 °C, and MATLAB simulations were performed. Compared to the conventional EKF, the proposed EKF estimated the SOC more accurately under all the simulation conditions. Compared to the conventional EKF, the maximum reduced root-mean-square error and maximum error of the proposed method were 49.37% and 56.41%, respectively.

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  • Adaptive filtering techniques address variance in real-world battery systems, especially EVs, where battery characteristics evolve with aging, temperature change, and different loading patterns.

    “These techniques are developed to address the variance and variation inherent in real-world battery systems and more so in EV applications, where characteristics of batteries can evolve with time through aging, temperature change and different loading patterns [74].”

    From Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement (2024) · cites this paper as [74]

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Related

Authors
Yun, J., Choi, Y., Lee, J., Choi, S., & Shin, C.
Affiliations
  • School of Electrical Engineering, Chungbuk National University, Cheongju, South Korea
  • Chungbuk National University (KR)
  • School of Information and Communication Engineering, Chungbuk National University, Cheongju, South Korea
  • Department of Biosystems Engineering, Chungbuk National University, Cheongju, South Korea
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2023
Volume
11
Pages
90901-90915
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2023.3305950
OpenAlex
W4385938111
Cited by
79
References
29
Citations by year
2026: 25 · 2025: 32 · 2024: 19 · 2023: 3
FWCI
5.74
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Korean Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry
  • Ministry of Education (2020R1A6A1A12047945)
  • Ministry of Science and ICT, South Korea (NRF-2022R1F1A1074667)
  • Ministry of Agriculture, Food and Rural Affairs (32204304144031HD020)
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
state of charge estimation, lithium-ion batteries, extended Kalman filter, battery model, parameter extraction, discharge current, ambient temperature effects

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