翟苏巍, 李文云, 周成, 汪成, 侯世玺. 基于改进概率神经网络的储能电池荷电状态估计[J]. 智慧电力, 2024, 52(2): 94-100.
引用本文: 翟苏巍, 李文云, 周成, 汪成, 侯世玺. 基于改进概率神经网络的储能电池荷电状态估计[J]. 智慧电力, 2024, 52(2): 94-100.
ZHAI Su-wei, LI Wen-yun, ZHOU Cheng, WANG Cheng, HOU Shi-xi. State-of-Charge Estimation of Energy Storage Batteries Based on Modified Probabilistic Neural Networks[J]. Smart Power, 2024, 52(2): 94-100.
Citation: ZHAI Su-wei, LI Wen-yun, ZHOU Cheng, WANG Cheng, HOU Shi-xi. State-of-Charge Estimation of Energy Storage Batteries Based on Modified Probabilistic Neural Networks[J]. Smart Power, 2024, 52(2): 94-100.

基于改进概率神经网络的储能电池荷电状态估计

State-of-Charge Estimation of Energy Storage Batteries Based on Modified Probabilistic Neural Networks

  • 摘要: 锂离子电池荷电状态(SOC)估计技术是储能电站电池管理系统重要组成部分。为了实现对SOC的准确估算,提出一种改进概率神经网络(MPNN)用于储能电池荷电状态估计。相较于传统神经网络,结合概率函数和补偿机制的MPNN,不仅可避免陷入局部最优,而且具有更优秀的拟合能力,可进一步提高SOC估计精度。仿真实验表明,所提MPNN方法的SOC估计值平均绝对误差和均方误差均低于1%,获得了满意的性能。

     

    Abstract: State of charge(SOC)estimation technology for Li-ion battery is an important part of battery management system in energy storage power station. In order to achieve accurate SOC estimation,the paper proposes a modified probabilistic neural network(MPNN)to make an estimation of SOC. Compared with traditional neural networks,MPNN combined with probability function and compensation mechanism can not only avoid falling into local optimization,but also has better fitting ability,further improving SOC estimation accuracy. Simulation results show that the mean absolute error and mean square error of SOC estimation using the proposed MPNN method are both lower than 1%,and satisfactory performance is obtained.

     

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