遗传径向基函数神经网络估计纯电动汽车锂电池的荷电状态

Genetic RBF Neural Network for Estimating State-of-Charge of Lithium-Ion Batteries in a Pure Electric Vehicle

  • 摘要: 考虑到串联的锂电池是复杂的非线性系统,并且径向基函数神经网络(RBF NN)对于解决非线性问题有较好的特性,建立了锂电池SOC估计的RBF NN模型,并提出了一种基于遗传RBF NN的电动汽车锂电池SOC估计的方法. 利用在2010年上海世博园区中运营的纯电动汽车锂电池数据对遗传RBF NN进行训练和SOC估计的实验. 实验结果表明,SOC估计的均方根误差为0.0024,提高了估计的精度.

     

    Abstract: Considering the complexity of lithium-ion batteries in series and the advantages of radial basis function neural network (RBF NN) in solving nonlinear problems, RBF NN was used to set up a model of the SOC estimation and proposed a genetic RBF NN method for estimating the SOC of the lithium-ion batteries in a pure electric vehicle. The practical data obtained from the pure electric buses running during 2010 Shanghai World Expo was used to train the genetic RBF NN and to do the experiments of the SOC estimation. The experimental results show that the root mean square error of the SOC estimation is 0.002 4 and the estimation precision is improved.

     

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