基于IACO-PF的锂电池SOC估算  

SOC of estimation of lithium battery based on IACO-PF

作　　者：单成鑫 李立伟 杨玉新 SHAN Chengxin;LI Liwei;YANG Yuxin(School of Electrical Engineering of Qingdao University;Weihai Innovation Institute of Qingdao University;Library of Qingdao University,Qingdao 266071,Shandong,China)

机构地区：[1]青岛大学电气工程学院 [2]青岛大学威海创新研究院 [3]青岛大学图书馆,山东青岛266071

出　　处：《储能科学与技术》2021年第3期1145-1152,共8页Energy Storage Science and Technology

基　　金：山东省自然科学基金项目(Y2008F23);山东省科技发展计划项目(2011GGB01123);山东省重点研发计划项目(2017GGX50114)。

摘　　要：本工作提出一种改进蚁群算法(IACO)优化粒子滤波(PF)来进行电池荷电状态(SOC)的估计,用来解决传统粒子滤波算法SOC估算时产生的粒子贫化问题。蚂蚁将替代粒子,在更新步骤前重新定位,通过提高粒子的多样性来解决粒子贫化问题;结合二阶Thevenin电池等效模型,得到算法所需的状态和观测方程,再根据脉冲放电试验进行参数辨识;采用IACO-PF算法和PF算法分别在脉冲放电和DST工况试验下进行SOC估算。试验结果表明,基于IACO-PF算法的锂电池SOC估算结果相比于传统PF算法更具有效性和准确性。An improved ant colony optimization algorithm(IACO)-optimized particle filter(PF)is proposed for battery state of charge(SOC)estimation,and it is used to solve the particle depletion problem caused by the traditional particle filter algorithm SOC estimation.The ants replace the particles and reposition them before the update step to solve the particle depletion problem by increasing the diversity of the particles.Combined with the second-order Thevenin battery equivalent model,the state and observation equations required by the algorithm are obtained,and parameter identification is then performed in accordance with the pulse discharge experiment.The IACO-PF and PF algorithms are used to estimate the SOC under pulse discharge and DST operating conditions.The experimental results show that the lithium battery SOC estimation result based on the IACO-PF algorithm is more effective and accurate than the traditional PF algorithm.

关 键 词：锂电池 荷电状态 蚁群算法 粒子滤波 

分 类 号：TM912[电气工程—电力电子与电力传动]