基于密度函数的模糊混合SOC估计方法

Fuzzy hybrid algorithm for SOC estimation based on density function

针对传统锂离子电池荷电状态估算中开路电压-荷电状态(OCV-SOC)曲线拟合及估算算法精度的问题,在二阶RC模型的基础上,引入高斯多项式来更好地拟合SOC和开路电压曲线,同时利用带自适应遗忘因子的最小二乘法进行模型参数的在线辨识,并将所得模型参数带入由扩展卡尔曼滤波器和安时积分法组成的模糊混合算法中,最终完成对SOC的估算。实验结果表明,该联合算法能够迅速收敛初始误差,静态恒流下SOC最大误差在1.1%以内,欧洲循环驾驶标准工况(NEDC)下SOC和端电压的均方根误差分别为0.12%和1.82%,具有很好的估算精度和鲁棒性,可以实现对SOC的准确估算。

Aiming at the accuracy of OCV-SOC curve fitting and estimation algorithm in traditional state of charge estimation of lithium-ion battery,based on the second-order RC model,Gaussian polynomial was introduced to better fit SOC and open circuit voltage curve.At the same time,the least square method with adaptive forgetting factor was used for on-line identification of model parameters,the obtained model parameters were introduced into the fuzzy hybrid algorithm composed of extended Kalman filter and ampere hour integral method,and the SOC estimation was completed.The experimental results show that the joint algorithm can quickly converge the initial error.The maximum error of SOC under static and constant current is less than 1.1%,and the root mean square errors of SOC and terminal voltage under NEDC are 0.12%and 1.82%,respectively.It has good estimation accuracy and robustness,and can realize the accurate estimation of SOC.