Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries
Abstract
State-of-charge (SOC) estimation of lithium-ion batteries based on deep learning techniques, especially recurrent neural networks (RNNs), has recently garnered much attention. However, the potential of RNNs in SOC estimation has not been fully exploited in terms of the capture of temporal dependencies and the depth of model structure. In this paper, a stacked bidirectional long short-term memory (SBLSTM) neural network is proposed for SOC estimation. In contrast to unidirectional RNN-based methods, the proposed model employs bidirectional LSTM layers that enable it to capture battery temporal information in both forward and backward directions and summarize long-term dependencies from past and future contexts. Furthermore, the bidirectional LSTM layers are stacked to construct a deep structure that enables the model to characterize the non-linear and dynamic relationship between the input battery measurements and the output SOC on a layer-by-layer basis. By introducing the stacked multilayer and bidirectional recurrent structure, SBLSTM can completely utilize the battery temporal information to estimate SOC value. The experiments were conducted using two public battery datasets to evaluate the validity and applicability of the SBLSTM, and our findings indicate that it can achieve good SOC estimation accuracy for different battery types at various ambient temperature conditions.
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Bian et al. demonstrated that Stack-BiLSTM achieved estimation accuracies of 0.46% and 0.73% in isothermal and temperature-varying discharge tests, outperforming single-mechanism LSTM networks.
“Bian et al. demonstrated the efficacy of Stack-BRNNs, with Stack-BiLSTM achieving estimation accuracies of 0.46% and 0.73% in isothermal and temperature-varying discharge tests, outperforming single-mechanism LSTM networks[87].”
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 [87]
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- Authors
- Bian, C., He, H., & Yang, S.
- Affiliations
- Beihang University (CN)
- Corresponding author
- Yang, Shunkun
- Venue
- Energy
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 191
- Pages
- 116538
- ISSN
- 0360-5442 (ISSN-L)
- OpenAlex
- W2984331380
- Cited by
- 284
- References
- 34
- Citations by year
- 2026: 33 · 2025: 44 · 2024: 63 · 2023: 59 · 2022: 42 · 2021: 26 · 2020: 17
- FWCI
- 9.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Natural Science Foundation of China (61672080, 41402020502)
- 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, bidirectional LSTM, stacked recurrent neural networks, temporal dependencies, battery data, ambient temperature effects
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