荷电状态
残余物
稳健性(进化)
开路电压
扩展卡尔曼滤波器
锂离子电池
电池(电)
卡尔曼滤波器
等效电路
电压
工程类
计算机科学
控制理论(社会学)
算法
化学
电气工程
功率(物理)
物理
人工智能
基因
量子力学
生物化学
控制(管理)
作者
Dongliang Gong,Ying Gao,Yalin Kou
标识
DOI:10.1002/ente.202100235
摘要
Herein, the improved open‐circuit voltage (OCV) estimation method is developed and applied to estimate the model parameters and state of charge (SOC) for lithium‐ion batteries simultaneously. First, the OCV and SOC mapping relationship with temperature dependence is explored with the help of the low‐current OCV test and the improved OCV estimation method is proposed for all test temperatures. Afterward, the dual adaptive extended Kalman filter (DAEKF) based on the residual sequence is utilized to identify the model parameters and estimate the SOC simultaneously. Finally, the proposed approach is verified with 50% initial SOC error and compared with the residual sequence‐based DEKF method at different temperatures under the Federal Urban Driving Schedule (FUDS) test. The results of this study indicate that the proposed DAEKF based on the proposed improving OCV estimation method and residual sequence could achieve higher SOC estimation accuracy with good robustness at all test temperatures.
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