State of Charge Estimation of Lithium-ion Battery Using Deep Convolutional Stacked Bidirectional LSTM
Abstract
State of Charge (SOC) estimation is critical for battery management systems (BMSs) to the safe and reliable operation, which nowadays is becoming more widely employed in Electric Vehicles (EV), smart grid systems, etc. In this study, a new method is introduced to conduct accurate SOC estimation for Li-ion batteries by applying a convolutional deep learning method. First, we convert battery signals into multi-channel images. By using images, a two-dimensional convolutional neural network (CNN) model is used to extract the features automatically without any need for expert knowledge and human interaction. Second, a stacked bidirectional long short-term memory (SBLSTM) is an estimator which catches the backward and forward temporal dependencies of battery sequential states. The proposed method works efficiently on estimating the SOC with online features extraction. On public battery dataset exposed to dynamic loading profiles, the SBLSTM network is assessed. Validation with an experimental dataset reveals that by taking into consideration the bidirectional dependencies of battery measurement data and sequential data, at various ambient temperatures, this approach can precisely estimate the SOC.
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Ardeshiri et al. converted battery signals into multi-channel images, applied 2D-CNN for feature extraction and then employed stacked bidirectional LSTM networks (SBiLSTM) to capture temporal sequence dependencies, achieving precise SOC estimation under varying ambient temperatures.
“For example, Ardeshiri et al. converted battery signals into multi-channel images, applied 2D-CNN for feature extraction and then employed stacked bidirectional LSTM networks (SBiLSTM) to capture temporal sequence dependencies, achieving precise SOC estimation under varying ambient temperatures[118].”
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 [118]
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- Authors
- Ardeshiri, R. R., & Ma, C.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Venue
- 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE)
- Publisher
- IEEE
- Published
- 2021-06-20
- Pages
- 01-06
- OpenAlex
- W3211237628
- Cited by
- 17
- References
- 22
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 4 · 2023: 4 · 2022: 4
- FWCI
- 2.97
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Symposium on Industrial Electronics (ISIE) (publishedVersion)
- 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, battery management systems, stacked bidirectional LSTM, convolutional neural networks, online feature extraction, ambient temperature effects, electric vehicles
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