锂电池荷电状态(state of charge,SOC)的准确估计对于新型储能系统的高效运行至关重要,为提升锂电池SOC估计的精度,提出了一种基于分数阶无迹卡尔曼滤波(fractional order unscented Kalman filter,FOUKF)算法和带自适应遗忘因子的递推最小二乘法(recursive least square method with adaptive forgetting factor,AFFRLS)来估计锂电池的SOC.首先,提出了基于分数阶微积分理论的二阶RC模型来对锂电池特性进行建模.然后进行脉冲表征测试,获得电池的端电压,并基于AFFRLS的方法完成参数辨识.此外,所提出的基于FOUKF的算法应用于电池放电实验中进行SOC估计.最后,从最大绝对误差(MAE)、平均绝对误差(AAE)和均方根误差(RMSE)3项预测指标与对比方法进行比较.实验结果表明,FOUKF算法对SOC的估计 MAE小于2%,AAE以及RMSE均小于0.8%,实验结果表明所提算法具有较高的精度和抗干扰能力.
State of charge estimation for lithium-ion batteries for energy storage based on the fractional unscented Kalman filter
Efficient operation of new energy storage systems relies heavily on accurately estimating the state of charge(SOC)of lithiumion batteries.In order to improve the accuracy of estimating SOC of lithium batteries,a method based on fractional order unscented Kalman filter(FOUKF)algorithm and recursive least square method with adaptive forgetting factor(AFFRLS)is proposed to estimate SOC of lithium battery.Firstly,a second-order RC model based on fractional-order calculus theory was developed to model the lithium battery characteristics.Then perform a pulse characterization test to obtain the battery terminal voltage,and complete parameter identification based on AFFRLS.In addition,the proposed algorithm based on FOUKF is applied to estimate SOC in battery discharge experiments.Finally,compared the three prediction indicators of maximum absolute error(MAE),average absolute error(AAE)and root mean square error(RMSE)with the comparison method.The experimental results show that the estimated MAE of SOC by FOUKF algorithm is less than 2%,and the AAE and RMSE are both less than 0.8%.The experimental results show that the proposed algorithm has high accuracy and anti-interference ability.
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