State of Charge Estimation of Lithium-Ion Batteries Using Stacked Encoder–Decoder Bi-Directional LSTM for EV and HEV Applications
Open accessAbstract
Energy storage technologies are being used excessively in industrial applications and in automobiles. Battery state of charge (SOC) is an important metric to be monitored in these applications to ensure proper and safe functionality. Since SOC cannot be measured directly, this paper puts forth a novel machine learning architecture to improve on the existing methods of SOC estimation. This method consists of using combined stacked bi-directional LSTM and encoder–decoder bi-directional long short-term memory architecture. This architecture henceforth represented as SED is implemented to overcome the nonparallel functionality observed in traditional RNN algorithms. Estimations were made utilizing different open-source datasets such as urban dynamometer driving schedule (UDDS), highway fuel efficiency test (HWFET), LA92 and US06. The least Mean Absolute Error observed was 0.62% at 25 °C for the HWFET condition, which confirms the good functionality of the proposed architecture.
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Research shows that the ideal input sequence length improves the model's learning capacity and prediction precision, while the model's depth, determined by the number of hidden layers, also influences the final output quality.
“Research shows that the ideal input sequence length improves the model’s learning capacity and prediction precision, while the model’s depth, determined by the number of hidden layers, also influences the final output quality.”
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 [115]
Section 3.2 says sequence length is set to optimize estimation accuracy, and Section 4 reports that changing the number of Bi-LSTM layers affects accuracy. The paper does not establish the additional claim that choosing an ideal sequence length improves the model’s learning capacity. Page numbers are unavailable in our copy. (LLM, conf=0.84)
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References 40 · Cited by 26
Research works printed but not linked: 1
- [17]Cui Z. Ke R. Wang Y. Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction arXiv 2018 1801.02143
- Authors
- Terala, P. K., Ogundana, A. S., Foo, S. Y., Amarasinghe, M. Y., & Zang, H.
- Affiliations
- Florida State University (US)
- Corresponding author
- Foo, Simon Y.
- Venue
- Micromachines
- Publisher
- MDPI AG
- Published
- 2022-08-26
- Volume
- 13
- Issue
- 9
- Pages
- 1397
- ISSN
- 2072-666X (ISSN-L)
- PMC
- PMC9504128
- OpenAlex
- W4293761086
- Cited by
- 26
- References
- 40
- Citations by year
- 2026: 8 · 2025: 6 · 2024: 6 · 2023: 6
- FWCI
- 1.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Micromachines (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Keywords
- state of charge estimation, lithium-ion batteries, bidirectional LSTM, recurrent neural networks, electric vehicles, hybrid electric vehicles, battery monitoring
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