Multi-Scale Fusion Model Based on Gated Recurrent Unit for Enhancing Prediction Accuracy of State-of-Charge in Battery Energy Storage Systems  

作　　者：Hao Liu Fengwei Liang Tianyu Hu Jichao Hong Huimin Ma 

机构地区：[1]School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing,China [2]School of Mechanical Engineering,University of Science and Technology Beijing,Beijing,China [3]IEEE

出　　处：《Journal of Modern Power Systems and Clean Energy》2024年第2期405-414,共10页现代电力系统与清洁能源学报（英文）

基　　金：supported in part by the National Natural Science Foundation of China(No.62172036).

摘　　要：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.

关 键 词：Electric vehicle battery energy storage system(BESS) state-of-charge(SOC)prediction gated recurrent unit(GRU) multi-scale fusion(MSF). 

分 类 号：TM91[电气工程—电力电子与电力传动]