Multi-scale Fusion Model Based on Gated Recurrent Unit for Enhancing Prediction Accuracy of State-of-charge in Battery Energy Storage Systems
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Accurate prediction of the state-of-charge (SOC) of battery energy storage system (BESS) is critical for its safety and lifespan in electric vehicles. To overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction, this paper introduces a novel multi-scale fusion (MSF) model based on gated recurrent unit (GRU), which is specifically designed for complex multi-step SOC prediction in practical BESSs. Pearson correlation analysis is first employed to identify SOC-related parameters. These parameters are then input into a multi-layer GRU for point-wise feature extraction. Concurrently, the parameters undergo patching before entering a dual-stage multi-layer GRU, thus enabling the model to capture nuanced information across varying time intervals. Ultimately, by means of adaptive weight fusion and a fully connected network, multi-step SOC predictions are rendered. Following extensive validation over multiple days, it is illustrated that the proposed model achieves an absolute error of less than 1.5% in real-time SOC prediction.
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[91] identified six key variables strongly correlated with SOC through Pearson correlation analysis on real-world driving data, then adaptively combined these features using dynamic weights before inputting them into the GRU network.
“identified six key variables strongly correlated with SOC through Pearson correlation analysis on real-world driving data, then adaptively combined these features using dynamic weights before inputting them into the GRU network[91].”
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 [91]
Section C (Pearson Correlation Analysis) identifies six SOC-related input variables from driving and parked-charging data. Section 4 (Adaptive fusion method) shows that GRUs process those inputs first; learnable weights then fuse the GRU-generated feature representations before a fully connected prediction head. The claimed order—weighted fusion before GRU input—is not supported. (LLM, conf=0.89)
Experimental — accuracy not yet measuredThis approach effectively predicts the SOC of EV batteries within 1-5 min, achieving an RMSE error lower than that of the Transformer and the PatchTST network.
“This approach effectively predicts the SOC of EV batteries within 1-5 min, achieving an RMSE error lower than that of the Transformer and the PatchTST network - the top performers in standard time-series tasks.”
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 [91]
ML methods for feature extraction from raw input data are highly effective, with some methods even surpassing Transformer models and top implementations such as PatchTST in time series prediction.
“First, ML methods for feature extraction from raw input data are highly effective[90,91,103], with some methods even surpassing[91] Transformer models and top implementations such as PatchTST in time series prediction.”
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 [91]
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- Authors
- Liu, H., Liang, F., Hu, T., Hong, J., & Ma, H.
- Affiliations
- University of Science and Technology Beijing (CN)
- Venue
- Journal of Modern Power Systems and Clean Energy
- Published
- 2024
- Volume
- 12
- Issue
- 2
- Pages
- 405-414
- ISSN
- 2196-5420 (ISSN-L)
- 2196-5625
- OpenAlex
- W4393205853
- Cited by
- 17
- References
- 40
- Citations by year
- 2026: 7 · 2025: 6 · 2024: 4
- FWCI
- 1.83
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Journal of Modern Power Systems and Clean Energy (publishedVersion, cc-by)
- 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, battery energy storage, gated recurrent unit, multi-scale feature fusion, multi-step forecasting, electric vehicles, adaptive weight fusion, feature extraction
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