电器与能效管理技术Issue(4)：64-70,78,8.DOI:10.16628/j.cnki.2095-8188.2019.04.012
基于改进模型和无迹卡尔曼滤波的锂离子电池荷电状态估计
State of Charge Estimation of Lithium-ion Battery Based on Improved Model and Unscented Kalman Filter
摘要
Abstract
The estimation of the state of charge (SOC) of power lithium-ion battery is the core and key technology of battery management system.The traditional ampere-hour integration method will produce the cumulative error which causes the result non-convergent.The Kalman filter method with first-order Thevenin equivalent circuit model can not obtain a better estimation result because of the limited model precision.Based on the two-order Thevenin equivalent circuit model, this paper uses the unscented Kalman filter (UKF) algorithm to modify the results of the improved ampere-hour integration method and improves the accuracy of SOC estimation.The SOC estimation experiments of the battery were carried out under 3 different working conditions.The experimental results show that the UKF algorithm with the two-order Thevenin equivalent circuit model can quickly converge in SOC estimation and obtain a high accuracy.关键词
锂离子电池/SOC估算/二阶Thevenin模型/无迹卡尔曼滤波Key words
lithium-ion battery/SOC estimation/two-order Thevenin model/unscented Kalman filter分类
信息技术与安全科学引用本文复制引用
任军,王凯,任宝森..基于改进模型和无迹卡尔曼滤波的锂离子电池荷电状态估计[J].电器与能效管理技术,2019,(4):64-70,78,8.基金项目
青岛市博士后基金 (2015118) （2015118）
山东省科技发展计划 (2017GGX50114) （2017GGX50114）
