CAJ | 학술논문

通过对电池容量估计问题的研究，分析了径向基函数神经网络方法和电动汽车动力电池SOC估计的原理。介绍了将径向基函数神经网络方法运用于电动汽车动力电池SOC估计的模型，对实验结果进行了分析和总结。实验结果表明：电池SOC估计模型可以通过蓄电池的工作电压、工作电流和表面温度参数来估计蓄电池的荷电状态实时值，采用径向基函数神经网络方法可以大大提高SOC值的精度。
통과대전지용량고계문제적연구，분석료경향기함수신경망락방법화전동기차동력전지SOC고계적원리。개소료장경향기함수신경망락방법운용우전동기차동력전지SOC고계적모형，대실험결과진행료분석화총결。실험결과표명：전지SOC고계모형가이통과축전지적공작전압、공작전류화표면온도삼수래고계축전지적하전상태실시치，채용경향기함수신경망락방법가이대대제고SOC치적정도。
Combining with the estimation of the batteries＇ capacity, this paper analyzed the method of radial basis function neural network and the principle of estimating the state of charge （SOC） of electric vehicles power batteries. In this work we estimated the state of charge （SOC） of electric vehicles power batteries by using radial basis function neural network based on a simplified model, and analyzed the experimental results. The results showed that we could estimate the real duration of the state of charge （SOC） by using the parameters of batteries＇ operating voltage, current and surface temperature in the model. We could greatly improve the accuracy of the SOC by using the radial basis function neural network