%0 Journal Article
%T 一种基于优化粒子滤波的锂电池SOC估计算法<br>An improved particle filter algorithm for Li-ion batteries SOC estimation
%A 吴兰花
%A 杨秀芝
%A 郑明魁
%A 苏凯雄
%J 福州大学学报(自然科学版)
%D 2018
%R 10.7631/issn.1000-2243.17084
%X 在构建锂电池状态空间模型基础上，提出一种基于优化粒子滤波的锂电池SOC估计算法，将BP神经网络应用到粒子滤波的权值更新过程中，实现锂电池SOC估计. 利用某公司提供的磷酸铁锂电池测试数据，对所提出的算法进行验证，对比算法估计结果与SOC实测结果. 结果表明，相对于PF算法，提出的改进算法具有更好的SOC估计性能.<br>This paper puts forward a kind of optimized particle filter algorithm to realize Li-ion batteries’ state-of-charge estimation，by introducing the BP neural network into the weights update process in the particle filter，with a suitable state space model for lithium batteries. To verify the proposed method，the lithium battery SOC estimate experiments are performed with the LiFePO4 batteries discharging data，and the algorithm estimated value and the SOC measured are compared. The results show that，compared with PF algorithm，the improved algorithm proposed in the paper has better SOC estimation performance
%K 磷酸铁锂电池 荷电状态 BP神经网络 粒子滤波&lt
%K br&gt
%K LiFePO4 batteries charge state BP neural network partical filter
%U http://xbzrb.fzu.edu.cn/ch/reader/view_abstract.aspx?file_no=201802006&flag=1