Online Semiparametric Identification of Lithium-Ion Batteries Using the Wavelet-Based Partially Linear Battery Model
Open accessAbstract
Battery model identification is very important for reliable battery management as well as for battery system design process. The common problem in identifying battery models is how to determine the most appropriate mathematical model structure and parameterized coefficients based on the measured terminal voltage and current. This paper proposes a novel semiparametric approach using the wavelet-based partially linear battery model (PLBM) and a recursive penalized wavelet estimator for online battery model identification. Three main contributions are presented. First, the semiparametric PLBM is proposed to simulate the battery dynamics. Compared with conventional electrical models of a battery, the proposed PLBM is equipped with a semiparametric partially linear structure, which includes a parametric part (involving the linear equivalent circuit parameters) and a nonparametric part [involving the open-circuit voltage (OCV)]. Thus, even with little prior knowledge about the OCV, the PLBM can be identified using a semiparametric identification framework. Second, we model the nonparametric part of the PLBM using the truncated wavelet multiresolution analysis (MRA) expansion, which leads to a parsimonious model structure that is highly desirable for model identification; using this model, the PLBM could be represented in a linear-in-parameter manner. Finally, to exploit the sparsity of the wavelet MRA representation and allow for online implementation, a penalized wavelet estimator that uses a modified online cyclic coordinate descent algorithm is proposed to identify the PLBM in a recursive fashion. The simulation and experimental results demonstrate that the proposed PLBM with the corresponding identification algorithm can accurately simulate the dynamic behavior of a lithium-ion battery in the Federal Urban Driving Schedule tests.
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ECM parameters may be time variant because of aging, temperature, and other factors, leading to deterioration of the model.
“These parameters may be time variant because of aging, temperature, and other factors leading to deterioration of the model [30].”
From Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement (2024) · cites this paper as [30]
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Self-citationMathew, M. (2018) · UWSpace (University of Waterloo)
- Authors
- Mu, D., Jiang, J., & Zhang, C.
- Affiliations
- Beijing Jiaotong University (CN)
- Corresponding author
- Mu, Dazhong
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2013-05-21
- Volume
- 6
- Issue
- 5
- Pages
- 2583-2604
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W1994766951
- Cited by
- 17
- References
- 37
- Citations by year
- 2025: 1 · 2024: 4 · 2023: 2 · 2022: 1 · 2021: 2 · 2020: 2 · 2019: 2 · 2018: 1 · 2014: 2
- FWCI
- 1.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energies (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (51277010)
- 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, semiparametric identification, wavelet multiresolution analysis, online parameter estimation, open-circuit voltage, equivalent circuit model, cyclic coordinate descent, battery management systems
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