The DOI matches its Crossref record; 26 of the 27 references it cites were found in a registry or an index.
A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM
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“The resulting PSO-LSTM network outperformed others, achieving a 0.43% reduction in MSE for isothermal estimation.”
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 [80]
Citations
Cites
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IEEE Transactions on Neural Networks and Learning Systems
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Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles
Xiong, R., Cao, J., Yu, Q., He, H., & Sun, F. (2017) · IEEE Access · doi:10.1109/access.2017.2780258 · Cited by 1,046
State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network
Yang, F., Li, W., Li, C., & Miao, Q. (2019) · Energy · doi:10.1016/j.energy.2019.03.059 · Cited by 543
State-of-charge estimation of lithium-ion batteries using LSTM and UKF
Yang, F., Zhang, S., Li, W., & Miao, Q. (2020) · Energy · doi:10.1016/j.energy.2020.117664 · Cited by 461
A deep learning method for online capacity estimation of lithium-ion batteries
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Data Augmentation for Deep Neural Network Acoustic Modeling
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Online capacity estimation of lithium-ion batteries with deep long short-term memory networks
Li, W., Sengupta, N., Dechent, P., Howey, D. A., Annaswamy, A. M., & Sauer, D. U. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228863 · Cited by 372
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- Jiang, 2021, A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter, Energy, 219
- Sun, 2020, State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator, Energy, 214
Cited by
Sorted by citations
Overview of batteries and battery management for electric vehicles
Liu, W., Placke, T., & Chau, K. T. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.03.016 · Cited by 776
A review on second-life of Li-ion batteries: prospects, challenges, and issues
Shahjalal, M., Roy, P. K., Shams, T., Fly, A., Chowdhury, J. I., Ahmed, M. R., & Liu, K. (2021) · Energy · doi:10.1016/j.energy.2021.122881 · Cited by 520
Cui, Z., Wang, L., Li, Q., & Wang, K. (2021) · International Journal of Energy Research · doi:10.1002/er.7545 · Cited by 392
Gu, X., See, K. W., Li, P., Shan, K., Wang, Y., Zhao, L., Lim, K. C., & Zhang, N. (2022) · Energy · doi:10.1016/j.energy.2022.125501 · Cited by 385
SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output
Chen, J., Zhang, Y., Wu, J., Cheng, W., & Zhu, Q. (2023) · Energy · doi:10.1016/j.energy.2022.125375 · Cited by 362
Metadata
- Authors
- Ren, X., Liu, S., Yu, X., & Dong, X.
- Affiliations
- Qilu University of Technology (CN)
- Corresponding author
- Liu, Shulin
- Venue
- Energy
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 234
- Pages
- 121236
- ISSN
- 0360-5442 (ISSN-L)
- OpenAlex
- W3172280330
- Cited by
- 375
- References
- 27
- Citations by year
- 2026: 41 · 2025: 78 · 2024: 108 · 2023: 85 · 2022: 53 · 2021: 9
- FWCI
- 20.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Natural Science Foundation of Shandong Province (ZR2020ME206, ZR2020QF064)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- 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, battery management systems, long short-term memory neural network, particle swarm optimization, LSTM hyperparameter optimization, noise injection, interference suppression
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