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
Shen, S., Sadoughi, M., Chen, X., Hong, M., & Hu, C. K. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2019.100817 · Cited by 407
Data Augmentation for Deep Neural Network Acoustic Modeling
Cui, X., Goel, V., & Kingsbury, B. (2015) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2015.2438544 · Cited by 376
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
Research works printed but not linked: 3
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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
Items 101–105 of 105
Jayaraman, R., & Thottungal, R. (2024) · Electrical Engineering · doi:10.1007/s00202-023-02227-1 · Cited by 21
Ansari, S., Hannan, M. A., Ayob, A., Abdolrasol, M. G. M., & Dar, M. A. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2024.114711 · Cited by 20
Wu, Y., Bai, D., Zhang, K., Li, Y., & Yang, F. (2025) · Journal of Materials Informatics · doi:10.20517/jmi.2024.84 · Cited by 20
Cho, S., Han, D., Kim, J., & Kim, J. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111897 · Cited by 17
Liu, H., Liang, F., Hu, T., Hong, J., & Ma, H. (2024) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2023.000726 · Cited by 17
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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