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Cited by 62Advanced Battery Technologies ResearchControl Systems and IdentificationTarget Tracking and Data Fusion in Sensor Networks

Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach

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Zhang, Ming, Wang, Kai, Zhou, Yan-ting·2020·Complexity
DOI 10.1155/2020/8231243
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Abstract

Filtering based state of charge (SOC) estimation with an equivalent circuit model is commonly extended to Lithium-ion (Li-ion) batteries for electric vehicle (EV) or similar energy storage applications. During the last several decades, different implementations of online parameter identification such as Kalman filters have been presented in literature. However, if the system is a moving EV during rapid acceleration or regenerative braking or when using heating or air conditioning, most of the existing works suffer from poor prediction of state and state estimation error covariance, leading to the problem of accuracy degeneracy of the algorithm. On this account, this paper presents a particle filter-based hybrid filtering method particularly for SOC estimation of Li-ion cells in EVs. A sampling importance resampling particle filter is used in combination with a standard Kalman filter and an unscented Kalman filter as a proposal distribution for the particle filter to be made much faster and more accurate. Test results show that the error on the state estimate is less than 0.8% despite additive current measurement noise with 0.05 A deviation.

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  • Kalman filter is important in SOC estimation because it can accommodate noisy sensor information and model uncertainties.

    “Kalman filter is important in SOC estimation because it can accommodate noisy information from the sensors as well as uncertainties in the model [58].”

    From Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement (2024) · cites this paper as [58]

    The Introduction describes a coupled Kalman filter used for preliminary SOC estimation while accounting for linear and nonlinear model parameters. Section 2.3.2 also describes adding Gaussian noise to the measurements. The paper does not establish that a Kalman filter alone accommodates both sensor noise and model uncertainty; its proposed method uses a particle filter to correct the Kalman-filter SOC estimate. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

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References 54 · Cited by 62

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Authors
Zhang, M., Wang, K., & Zhou, Y.-T.
Affiliations
  • Qingdao University (CN)
  • College of Electrical Engineering, Qingdao University, Qingdao 266071, China
Corresponding author
Wang, Kai
ORCID
Venue
Complexity
Publisher
Wiley
Published
2020-01-10
Volume
2020
Pages
1-10
ISSN
  • 1076-2787 (ISSN-L)
  • 1099-0526
DOI
10.1155/2020/8231243
OpenAlex
W3000072960
Cited by
62
References
54
Citations by year
2026: 3 · 2025: 5 · 2024: 7 · 2023: 19 · 2022: 13 · 2021: 10 · 2020: 5
FWCI
4.35
Type
journal article
Language
en
Open access
Licence
Locations
  • Complexity (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Department of Science and Technology of Shandong Province (2019GGX103022)
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
  • Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
  • Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
Keywords
state of charge estimation, lithium-ion batteries, particle filter, hybrid filtering, sampling importance resampling, unscented Kalman filter, Kalman filter, equivalent circuit model, electric vehicles, online parameter estimation, error covariance

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