考虑环境温度影响的超级电容SOC加权融合估计方法

A Supercapacitor SOC Estimation Method Based on Weighted Fusion Considering Ambient Temperature Variation

超级电容荷电状态(SOC)的准确估计,直接决定了电动汽车的起动、爬升和加速性能,是电动汽车混合储能系统最重要的任务之一。为此,本文中提出了一种基于模糊熵加权融合的超级电容SOC估计方法。首先,利用粒子群算法辨识了-10、10、25和40℃下的戴维南模型参数,并且采用最近邻点法建立了其与温度之间的映射关系。然后,利用模糊熵设计了基于3种典型卡尔曼滤波的SOC加权融合估计方法。最后,选择自适应加权平均以及残差归一化加权融合的SOC估计方法用于进一步评估该方法的性能表征。结果表明,基于模糊熵加权融合的超级电容SOC估计方法能够提高超级电容SOC估计精度,尤其在高温环境(40℃)下提升效果更为显著。

Accurate estimation of the state of charge(SOC)of supercapacitors plays an important role in electric vehicle hybrid energy storage system,which directly determines the starting,climbing and accelerating performance of electric vehicles.Therefore,this paper proposes a supercapacitor SOC estimation method based on fuzzy entropy weighted fusion.Firstly,the Thevenin model parameters are identified by using the particle swarm algorithm under-10,10,25 and 40℃,and the nearest neighbor method is adopted to establish the mapping relation between the parameters and temperatures.Then,the fuzzy entropy is utilized to design a SOC weighted fusion estimation method based on three typical Kalman filters.Finally,the SOC estimation method of adaptive weighted averaging and residual normalized weighted fusion is selected to further evaluate the performance of the proposed method in this paper.The results show that supercapacitor SOC estimation method based on fuzzy entropy weighted fusion can improve the supercapacitor SOC estimation accuracy,especially in high ambient temperature environment(40℃).