中国电机工程学报Issue(3)：445-452,8.DOI:10.13334/j.0258-8013.pcsee.2014.03.016
基于自适应无迹卡尔曼滤波算法的锂离子动力电池状态估计
States Estimation of Li-ion Power Batteries Based on Adaptive Unscented Kalman Filters
摘要
Abstract
When using the traditional unscented Kalman filter (UKF) to estimate the electric vehicle li-ion power battery state of charge (SOC), the inaccurate battery model often causes estimation error to increase. Adaptive unscented Kalman filter (AUKF) was used to solve this problem in this paper. AUKF is a kind of cyclic iterative algorithm, and using it can estimate the inner ohmic resistance of the battery model in real time. Therefore, it improves the accuracy of the battery model, and thus further improves the accuracy of battery SOC estimation. In addition, the battery state of health (SOH) also can be estimated because the inner ohmic resistance of the battery can characterize the battery SOH. The battery charged and discharged experiments were done under setting conditions and the experimental analysis showes that AUKF improves the estimation accuracy of battery SOC compared with UKF, and AUKF can accurately estimate the inner ohmic resistance of the battery.关键词
荷电状态/健康状态/自适应无迹卡尔曼滤波器/电动汽车/锂离子动力电池Key words
state of charge/state of health/adaptive unscented Kalman filter/electric vehicle/li-ion power battery分类
交通工程引用本文复制引用
魏克新,陈峭岩..基于自适应无迹卡尔曼滤波算法的锂离子动力电池状态估计[J].中国电机工程学报,2014,(3):445-452,8.基金项目
国家高技术研究发展计划项目(863计划)(2011AA11A279)。The National High Technology Research and Development of China 863 Program (2011AA11A279) （863计划）
