State of charge estimation for a group of lithium-ion batteries using long short-term memory neural network
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The present paper estimates for the first time the State of Charge (SoC) of a high capacity grid-scale lithium-ion battery storage system used to improve the power profile in a distribution network. The proposed long short-term memory (LSTM) neural network model can overcome the problems associated with the nonlinear battery model and adapt to the complexity and uncertainty of the estimation process. The accuracy of the developed model was compared with results obtained from Feed-Forward Neural Network (FFNN) topology and Deep-Feed-Forward Neural Network (DFFNN) topology under three different time series. The system was trained using real data from the Al-Manara PV power plant. The LSTM with learn-and-adapt-to-train-date properties, as well as the idea of “forget gate,” shows exceptional ability to determine the SoC under various ID data. The LSTM properly calculated the SoC for all three-time models with a maximum standard error (MSE) of less than 0.62%, while the FFNN and DFFNN provided a fair estimate for the SoC with MSEs of 5.37 to 9.22% and 4.03 to 7.37%, respectively. The promising results can lead to excellent monitoring and control of battery management systems.
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Almaita et al. demonstrated that the inherent noise resistance of LSTM remains effective even when transitioning from single cells to complex battery packs, validating its reliability as a SOC estimator.
“Almaita et al. demonstrated that the inherent noise resistance of LSTM remains effective even when transitioning from single cells to complex battery packs, validating its reliability as a SOC estimator[107].”
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 [107]
Section 4.2.2 reports accurate LSTM state-of-charge estimates for a group of batteries under different operating conditions, supporting its reliability for battery packs. The Introduction contrasts this pack-level study with earlier work focused largely on single cells. But the paper does not report a test of noise resistance or show that such resistance persists in a transition from single cells to packs. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDespite fewer epochs, the LSTM outperformed an MLP model, reducing the MSE by a factor of 11 and exhibiting superior capability to capture battery dynamics.
“Despite fewer epochs, the LSTM outperformed an MLP model, reducing the MSE by a factor of 11 and exhibiting superior capability to capture battery dynamics.”
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 [107]
Tables 2 and 4 (page unavailable) report model 3 MSEs of 0.0537 for the FFNN and 0.0048 for the LSTM—approximately an 11-fold reduction. Section 4.2.2 also says the LSTM learned battery dynamics better than the feed-forward networks. But both models were tested for 150 epochs, so the claim that this comparison used fewer epochs is not supported. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAlmaita et al. utilized LSTM to estimate the SOC of utility-scale LIB storage systems, achieving an MSE below 0.62%.
“Almaita et al. utilized LSTM to estimate the SOC of utility-scale LIB storage systems, achieving an MSE below 0.62%[107].”
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 [107]
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- Authors
- Almaita, E., Alshkoor, S., Abdelsalam, E., & Almomani, F.
- Affiliations
- Tafila Technical University (JO)
- Al Hussein Technical University (JO)
- Qatar University (QA)
- Corresponding author
- Almomani, Fares
- Venue
- Journal of Energy Storage
- Publisher
- Elsevier BV
- Published
- 2022-08
- Volume
- 52
- Pages
- 104761
- ISSN
- 2352-152X (ISSN-L)
- OpenAlex
- W4280604784
- Cited by
- 84
- References
- 47
- Citations by year
- 2026: 23 · 2025: 20 · 2024: 16 · 2023: 22 · 2022: 3
- FWCI
- 4.16
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Energy Storage (publishedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Funders
- Qatar National Library
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Energy › Energy Engineering and Power Technology › Power Systems and Renewable Energy
- Keywords
- state of charge estimation, lithium-ion batteries, long short-term memory neural network, battery management systems, photovoltaic power plants
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