卡尔曼滤波器
无味变换
控制理论(社会学)
扩展卡尔曼滤波器
荷电状态
健康状况
颗粒过滤器
集合卡尔曼滤波器
电池(电)
计算机科学
工程类
人工智能
功率(物理)
物理
量子力学
控制(管理)
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2021-08-11
卷期号:21 (22): 25449-25456
被引量:41
标识
DOI:10.1109/jsen.2021.3102990
摘要
This paper aims to improve the rapidity and accuracy of the State of Health (SOH) assessment for lithium-ion battery. By integrating the Unscented Kalman Filter (UKF) and Improved Unscented Particle Filter (IUPF) algorithm, SOH can be effectively evaluated. The UKF algorithm is used to estimate the state of charge (SOC), the IUPF algorithm is employed to identify the ohmic internal resistance. The novelty of the proposed strategy relies on the 4-dimensional IUPF filter that is split into a 3-dimensional UKF filter and a 1-dimensional IUPF filter. Experimental results demonstrate that more accuracy and a faster rate of SOH estimation can be achieved via the UKFIUPF algorithm compared to the IUPF approach.
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