State-of-Charge Estimation of Lithium-Ion Batteries via Long Short-Term Memory Network
Open accessAbstract
Accurate state-of-charge (SOC) estimation is critical for driving range prediction of electric vehicles and optimal charge control of batteries. In this paper, a stacked long short-term memory network is proposed to model the complex dynamics of lithium iron phosphate batteries and infer battery SOC from current, voltage, and temperature measurements. The proposed network is trained and tested using data collected from the dynamic stress test, US06 test, and federal urban driving schedule. The performance on SOC estimation is evaluated regarding tracking accuracy, computation time, robustness against unknown initial states, and compared with results from the model-based filtering approach (unscented Kalman filter). Moreover, different training and testing data sets are constructed to test its robustness against varying loading profiles. The experimental results show that the proposed network well captures the nonlinear correlation between SOC and measurable signals and provides better tracking performance than the unscented Kalman filter. In case of inaccurate initial SOCs, the proposed network presents quick convergence to the true SOC, with root mean square errors within 2% and mean average errors within 1%. Moreover, the estimation time at each time step is sub-millisecond, making it appropriate for real-time applications.
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Yang et al. first applied Stack-LSTM to SOC estimation, developing a single-step continuous estimator that outperformed UKF in accuracy.
“Yang et al. first applied Stack-LSTM to SOC estimation, developing a single-step continuous estimator that outperformed UKF in accuracy[83].”
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 [83]
Reference [76] proposed using a Long Short-Term Memory (LSTM) network to estimate battery State of Charge.
“In their study, [76] proposed the use of a Long Short-Term Memory (LSTM) network to estimate the State of Charge of batteries.”
From Recent developments and challenges in state-of-charge estimation techniques for electric vehicle batteries: A review (2024) · cites this paper as [76]
Under incorrect starting SOC values, the LSTM network of [76] demonstrated minimal execution time and great tracking accuracy, with maximum absolute error and root mean square error between 1% and 2%.
“Under incorrect starting SOC values, LSTM demonstrated minimal execution time and great tracking accuracy, reaching maximum absolute error and root mean square error between 1 % and 2 %, respectively.”
From Recent developments and challenges in state-of-charge estimation techniques for electric vehicle batteries: A review (2024) · cites this paper as [76]
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- Authors
- Yang, F., Song, X., Xu, F., & Tsui, K.-L.
- Affiliations
- City University of Hong Kong (HK)
- Googol Technology (China) (CN)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 53792-53799
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2941419825
- Cited by
- 234
- References
- 23
- Citations by year
- 2026: 27 · 2025: 35 · 2024: 32 · 2023: 43 · 2022: 34 · 2021: 34 · 2020: 23 · 2019: 6
- FWCI
- 11.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- CityU Scholars (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Research Grants Council, University Grants Committee (T32-101/15-R)
- General Research Fund (CityU 11206417)
- 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, lithium iron phosphate batteries, long short-term memory networks, stacked LSTM, unscented Kalman filter, electric vehicles, driving range prediction, battery management systems, real-time estimation, dynamic stress test, driving cycles
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