SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output

Chen, Junxiong; Zhang, Yu; Wu, Ji; Cheng, Weisong; Zhu, Qiao

doi:10.1016/j.energy.2022.125375

Cited by 362Advanced Battery Technologies ResearchAdvancements in Battery MaterialsControl Systems and Identification

SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output

Chen, Junxiong, Zhang, Yu, Wu, Ji, Cheng, Weisong, Zhu, Qiao·2023·Energy

DOI 10.1016/j.energy.2022.125375

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Chen et al. introduced the voltage mean of window data as an additional EI-LSTM input, significantly improving estimation accuracy during high-temperature dynamic discharge.
“Chen et al. introduced the voltage mean of window data as an additional EI-LSTM (extended input LSTM) input, significantly improving the estimation accuracy during high-temperature dynamic discharge[101].”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [101]
CNNs and RNNs have been used for SOC estimation, especially where time series data needs to be incorporated such as in electric vehicles.
“CNNs and RNNs [46], [47] have been used for SOC estimation, especially where time series data needs to be incorporated such as in electric vehicles.”
From Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement (2024) · cites this paper as [46]

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Authors: Chen, J., Zhang, Y., Wu, J., Cheng, W., & Zhu, Q.
Affiliations: Southwest Jiaotong University (CN)
Corresponding author: Zhu, Qiao
ORCID: Chen, Junxiong
Zhang, Yu
Wu, Ji
Zhu, Qiao
Venue: Energy
Publisher: Elsevier BV
Published: 2023-01
Volume: 262
Pages: 125375
ISSN: 0360-5442 (ISSN-L)
DOI: 10.1016/j.energy.2022.125375
OpenAlex: W4295125731
Cited by: 362
References: 46
Citations by year: 2026: 97 · 2025: 125 · 2024: 87 · 2023: 50 · 2022: 3
FWCI: 17.63
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Energy (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Funders: National Natural Science Foundation of China (51775450)
Sichuan Province Science and Technology Support Program (2019JDRC0025, 2022YFG0094)
International Science and Technology Cooperation Program of Sichuan Province
Topics: Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
Keywords: state of charge estimation, lithium-ion batteries, long short-term memory, recurrent neural networks, ampere-hour integration, output constraints, lithium iron phosphate batteries, temperature effects

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SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output

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