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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Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
A review on the key issues for lithium-ion battery management in electric vehicles
Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.10.060 · Cited by 4,837
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.033 · Cited by 1,768
A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions
Schulz, E., Speekenbrink, M., & Krause, A. (2018) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2018.03.001 · Cited by 1,735
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
Li, Y., Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109254 · Cited by 1,193
Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles
Waag, W., Fleischer, C., & Sauer, D. U. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.02.064 · Cited by 1,059
State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures
Xing, Y., He, W., Pecht, M., & Tsui, K. L. (2014) · Applied Energy · doi:10.1016/j.apenergy.2013.07.008 · Cited by 977
State estimation for advanced battery management: Key challenges and future trends
Hu, X., Feng, F., Liu, K., Zhang, L., Xie, J., & Liu, B. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109334 · Cited by 857
Self-citation
A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve
Yang, D., Zhang, X., Pan, R., Wang, Y., & Chen, Z. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.03.015 · Cited by 765
Data Preprocessing For Supervised Leaning
Kotsiantis, S. B., Kanellopoulos, D. N., & Pintelas, P. (2007) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1082415 · Cited by 669

Show all 40 references

Research works printed but not linked: 2

Plett GL. Efficient battery pack state estimation using bar-delta filtering. Conference Efficient battery pack state estimation using bar-delta filtering.
Liu X, He Y, Chen Z. State-of-Charge estimation for power Li-ion battery pack using V min-EKF. Conference State-of-Charge estimation for power Li-ion battery pack using V min-EKF. IEEE, p. 27-31.

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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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