高俊岭, 张义哲.基于PSO RBF神经网络的锂电池SOC估算[J].重庆工商大学学报(自然科学版),2020,37(2):37-41
GAO Jun-ling,ZHANG Yi-zhe.SOC Estimation of Lithium Battery Based on PSO RBF Neural Network[J].Journal of Chongqing Technology and Business University(Natural Science Edition),2020,37(2):37-41
基于PSO RBF神经网络的锂电池SOC估算
SOC Estimation of Lithium Battery Based on PSO RBF Neural Network
  
DOI:
中文关键词:  锂电池  SOC  RBF  PSO
英文关键词:lithium battery  SOC  RBF  PSO
基金项目:
作者单位
高俊岭, 张义哲 安徽理工大学 电气与信息工程学院安徽 淮南 232000 
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中文摘要:
      针对电动汽车锂电池荷电状态(State Of Charge,SOC)的精准估算,提出一种优化的径向基(Radial Basis Function,RBF)神经网络算法;通过粒子群(Particle Swarm Optimization,PSO)算法优化RBF神经网络的参数及结构,确定RBF神经网络中的基函数的宽度以及中心;根据锂电池的充、放电机理,将SOC的影响因子电压(U)、电流(I)、内阻(R)、温度(T)作为输入向量,在 Matlab中进行仿真实验;实验表明方法能够实现准确、快速、便捷的锂电池的SOC估算,其预测结果和实际测量结果的误差在4%以下,符合SOC预测误差5%的技术指标要求,对于电动汽车锂电池SOC的估算有着一定的实际应用意义。
英文摘要:
      An optimized Radial Basis Function (RBF) neural network algorithm was presented to accurately estimate the State Of Charge (SOC) of lithium batteries in electric vehicles. The parameters and structure of RBF neural network are optimized by Particle Swarm Optimization (PSO) algorithm, and the width and center of basis function in RBF neural network are determined. According to the charging and discharging mechanism of lithium batteries, voltage (U), current (I), internal resistance (R) and temperature (T) of the influence factors of SOC are taken as input vectors to conduct simulation experiments in Matlab. Experiments show that this method can achieve accurate, fast and convenient SOC estimation of lithium batteries. The error between prediction results and actual measurement results is less than 4%, which meets the technical index requirement of SOC prediction error of 5%.It has certain practical application significance for the estimation of lithium batteries SOC of electric vehicles.
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