Cited by 423Battery Modeling and ManagementControl Systems and IdentificationGaussian Processes and Bayesian Inference

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Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression

Deng, Zhongwei, Hu, Xiaosong, Lin, Xianke, Che, Yunhong, Xu, Le, Guo, Wenchao·2020·Energy

DOI 10.1016/j.energy.2020.118000

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Abstract

Abstract Since a battery pack consists of hundreds of cells in series and parallel, inconsistencies between cells make it difficult to create an explicit model to simulate its behaviors effectively. Therefore, the widely used and sophisticated model-based methods (such as Kalman filters) are difficult to apply to SOC (state of charge) estimation of battery packs. In this paper, a data-driven method based on Gaussian process regression (GPR) is proposed to provide a feasible solution. Its superiority includes the ability to approximate nonlinearity accurately, nonparametric modeling, and probabilistic predictions. First, a feature extraction strategy, including data preprocessing, correlation analysis, and principal component analysis, is employed to obtain a compacted input set with a high correlation with SOC. Second, the squared exponential kernel function is used, and the automatic relevance determination is applied to optimize the weights of features. Third, besides the regular GPR model, an autoregressive GPR model is also constructed to further improve estimation accuracy and confidence. The experimental results verify that the autoregressive model has better SOC estimation performance than the regular model, and its estimation error under different dynamic cycles, temperatures, aging conditions, and even extreme conditions is lower than 3.9%, and the confidence interval is also much narrower.

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Authors: Deng, Z., Hu, X., Lin, X., Che, Y., Xu, L., & Guo, W.
Affiliations: Chongqing University (CN)
Ontario Tech University (CA)
Shanghai Jiao Tong University (CN)
Corresponding author: Hu, Xiaosong
Lin, Xianke
ORCID: Deng, Zhongwei
Lin, Xianke
Che, Yunhong
Xu, Le
Guo, Wenchao
Venue: Energy
Publisher: Elsevier BV
Published: 2020-08
Volume: 205
Pages: 118000
ISSN: 0360-5442 (ISSN-L)
DOI: 10.1016/j.energy.2020.118000
OpenAlex: W3033613562
Semantic Scholar: a491ddd78e3d1e5acaa49684b5aa2f2270b68d97
Cited by: 423
References: 40
Citations by year: 2026: 42 · 2025: 79 · 2024: 70 · 2023: 70 · 2022: 77 · 2021: 73 · 2020: 12
FWCI: 21.46
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Energy (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Funders: Natural Sciences and Engineering Research Council of Canada
Natural Science Foundation Project of Chongqing, Chongqing Science and Technology Commission
National Natural Science Foundation of China
Topics: Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
Keywords: state of charge estimation, lithium-ion battery packs, Gaussian process regression, data-driven estimation, cell inconsistency, feature extraction, principal component analysis, automatic relevance determination, Gaussian kernel, autoregressive models, probabilistic forecasting, battery degradation, temperature effects

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Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression

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