基于MEA-BP神经网络的电池储能系统SOC状态评估
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摘要
提出了一种基于MEA-BP神经网络的电池储能系统荷电状态(SOC)预测方法,可准确评估储能电池的SOC状态。选择较易获得的电池外特性电流、电压和温度作为BP神经网络的输入,SOC状态作为神经网络输出,并利用思维进化算法(MEA)优化网络结构,对电池储能系统SOC状态进行评估。将评估结果与传统BP神经网络评估结果进行对比,结果表明,该方法可实现对电池储能系统SOC的准确评估,且预测误差小,具有良好的工程应用前景。
A novel method of the SOC estimation for the battery energy storage system( BESS) based on MEABP neural network was proposed. The external characteristics of the battery is selected as the input of BP neural network,the operating status of the SOC is regarded as the output,the weights of the BP neural network are optimized by the mind evolutionary algorithm( MEA). The simulation results show that compared with the traditional BP neural network,MEA-BP network has higher accuracy and has a broad prospect of engineering application.
A novel method of the SOC estimation for the battery energy storage system( BESS) based on MEABP neural network was proposed. The external characteristics of the battery is selected as the input of BP neural network,the operating status of the SOC is regarded as the output,the weights of the BP neural network are optimized by the mind evolutionary algorithm( MEA). The simulation results show that compared with the traditional BP neural network,MEA-BP network has higher accuracy and has a broad prospect of engineering application.
引文
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[13]艾格林,孙永辉,卫志农,等.基于MEA-Elman神经网络的光伏发电功率短期预测[J].电网与清洁能源,2016,32(4):119-125.
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[2]刘征宇,杨俊斌,张庆,等.基于QPSO-BP神经网络的锂电池SOC预测[J].电子测量与仪器学报,2013,27(3):224-228.
[3]于海芳,逯仁贵,朱春波,等.基于安时法的镍氢电池SOC估计误差校正[J].电工技术学报,2012,27(6):12-18.
[4]时玮,姜久春,李索宇,等.磷酸铁锂电池SOC估算方法研究[J].电子测量与仪器学报,2010,24(8):769-774.
[5]汤秀芬,米晨.VRLA电池SOC估算的改进方法[J].电气应用,2014,33(20):78-81.
[6]汪奂伶,侯朝勇,贾学翠,等.电化学储能电池荷电状态(SOC)估算技术对比分析[J].电器与能效管理技术,2017(6):76-83.
[7]LEE J H,CHOI W.Novel state-of-charge estimation method for lithium polymer batteries using electrochemical impedance spectroscopy[J].Journal of Power Electronics,2011,11(2):237-243.
[8]陈宗海.Comparison study on the battery So C estimation with EKF and UKF algorithms[J].Energies,2013,6(10):5088-5100.
[9]罗伟林,张立强,吕超,等.锂离子电池寿命预测国外研究现状综述[J].电源学报,2013(1):140-144.
[10]王元章,李智华,吴春华,等.基于BP神经网络的光伏组件在线故障诊断[J].电网技术,2013,37(8):2094-2100.
[11]师洪涛,杨静玲,丁茂生,等.基于小波-BP神经网络的短期风电功率预测方法[J].电力系统自动化,2011,35(16):44-48.
[12]刘俊.思维进化算法在BP神经网络拟合非线性函数中的应用研究[J].绵阳师范学院学报,2015,34(2):79-83.
[13]艾格林,孙永辉,卫志农,等.基于MEA-Elman神经网络的光伏发电功率短期预测[J].电网与清洁能源,2016,32(4):119-125.
[14]LIU H,TIAN H,LIANG X,et al.New wind speed forecasting approaches using fast ensemble empirical model decomposition,genetic algorithm,mind evolutionary algorithm and artificial neural networks[J].Renewable Energy,2015,83:1066-1075.
[15]张以帅,赖惠鸽,李勇,等.基于MEA优化BP神经网络的天然气短期负荷预测[J].自动化与仪表,2016,31(5):15-19.
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