估计员
荷电状态
电池(电)
电压
卡尔曼滤波器
计算机科学
系列(地层学)
控制理论(社会学)
工作(物理)
数学优化
工程类
数学
机械工程
人工智能
物理
电气工程
功率(物理)
控制(管理)
统计
量子力学
古生物学
生物
作者
Peipei Xu,Junqiu Li,Qiao Xue,Fengchun Sun
标识
DOI:10.1016/j.est.2022.104559
摘要
State of Charge (SoC) estimation for LiFePO4 (LFP) batteries is particularly challenging due to the flat open circuit voltage (OCV) characteristics. This paper proposes a novel method of SoC estimation for LFP batteries based on expansion force which has not been investigated in the existing literature. After a series of experiments, it is found that the expansion force is more sensitive to SoC than voltage and independent of the dynamic current. However, the estimation work is still technically challenging because of the non-monotonic relationship between expansion force and SoC. To cope with it, an expansion force model based on the least-square support vector machine (LSSVM) method is firstly exploited, which is employed as the measurement equation of adaptive unscented Kalman filters (AUKF). Meanwhile, the moving window method is applied to enhance the adaptability of the established model and then an accurate SoC estimation result is attained. Finally, the proposed method is evaluated via sufficient experiment data covering different temperatures and constraint conditions. Experimental results show that the root means square errors of measure equation and SoC estimation can be bounded within 1% and 0.54%. In conclusion, the proposed method provides a new perspective for SoC estimation of LFP batteries.
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