福州大学学报（自然科学版）2018，Vol.46Issue(2)：186-191,6.DOI:10.7631/issn.1000-2243.17084
一种基于优化粒子滤波的锂电池SOC估计算法
An improved particle filter algorithm for Li-ion batteries SOC estimation
摘要
Abstract
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.关键词
磷酸铁锂电池/荷电状态/BP神经网络/粒子滤波Key words
LiFePO4 batteries/charge state/BP neural network/partical filter分类
信息技术与安全科学引用本文复制引用
吴兰花,杨秀芝,郑明魁,苏凯雄..一种基于优化粒子滤波的锂电池SOC估计算法[J].福州大学学报（自然科学版）,2018,46(2):186-191,6.基金项目
福建省发改委2014年产业技术联合创新专项资助项目(0101-1502) （0101-1502）
福州市科技计划资助项目(2015-G-61) （2015-G-61）
