首页|Multi-scale Fusion Model Based on Gated Recurrent Unit for Enhancing Prediction Accuracy of State-of-charge in Battery Energy Storage Systems
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 fu-sion(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 em-ployed to identify SOC-related parameters.These parameters are then input into a multi-layer GRU for point-wise feature ex-traction.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 con-nected network,multi-step SOC predictions are rendered.Fol-lowing 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.
Electric vehiclebattery energy storage system(BESS)state-of-charge(SOC)predictiongated recurrent unit(GRU)multi-scale fusion(MSF)
Hao Liu、Fengwei Liang、Tianyu Hu、Jichao Hong、Huimin Ma
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School of Com-puter and Communication Engineering,University of Science and Technology Beijing,Beijing,China
School of Mechanical Engineering,Univer-sity of Science and Technology Beijing,Beijing,China
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