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 featu...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.展开更多
传统的电池荷电状态(State of Charge,SOC)估计方法是基于精确的数学模型,它依赖于大量的建模假设和经验参数,故模型预测SOC精度是有限的;为了提高动力电池SOC预测的精度,提出利用人工鱼群算法(Artificial Fish Swarm Algorithm,AFSA)...传统的电池荷电状态(State of Charge,SOC)估计方法是基于精确的数学模型,它依赖于大量的建模假设和经验参数,故模型预测SOC精度是有限的;为了提高动力电池SOC预测的精度,提出利用人工鱼群算法(Artificial Fish Swarm Algorithm,AFSA)优化径向基神经网络(RBF)对SOC进行预测,解决了RBF网络参数选择的不确定性;仿真实验结果表明:方法能方便、快速、准确地实现对SOC的预测,且具有实际使用价值。展开更多
基金supported in part by the National Natural Science Foundation of China(No.62172036).
文摘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.
文摘传统的电池荷电状态(State of Charge,SOC)估计方法是基于精确的数学模型,它依赖于大量的建模假设和经验参数,故模型预测SOC精度是有限的;为了提高动力电池SOC预测的精度,提出利用人工鱼群算法(Artificial Fish Swarm Algorithm,AFSA)优化径向基神经网络(RBF)对SOC进行预测,解决了RBF网络参数选择的不确定性;仿真实验结果表明:方法能方便、快速、准确地实现对SOC的预测,且具有实际使用价值。
