扩展卡尔曼滤波器
荷电状态
控制理论(社会学)
估计员
卡尔曼滤波器
稳健性(进化)
电压
等效电路
协方差
计算机科学
电池(电)
工程类
算法
功率(物理)
数学
电气工程
物理
化学
统计
人工智能
基因
量子力学
生物化学
控制(管理)
作者
Yizhao Gao,Ngoc‐Trung Nguyen,Simona Onori
标识
DOI:10.4271/14-14-01-0003
摘要
<div>This article introduces an advanced state-of-charge (SOC) estimation method customized for 28 V LiFePO<sub>4</sub> (LFP) helicopter batteries. The battery usage profile is characterized by four consecutive current pulses, each corresponding to distinct operational phases on the helicopter: instrument check, key-on, recharge, and emergency power output stages. To establish a precise battery model for LFP cells, the parameters of a second-order equivalent-circuit model are identified as a function of C-rate, SOC, and temperature. Furthermore, the observability of the battery model is assessed using extended Lie derivatives. The signal-to-noise ratio (SNR) of the open-circuit voltage (OCV)–SOC relation is analyzed and employed to evaluate the estimator’s resilience against OCV flatness. The extended Kalman filter (EKF) and the unscented Kalman filter (UKF) are utilized for SOC estimation. The results emphasize the significance of meticulously choosing process and sensor noise covariance matrices to achieve a resilient SOC estimator for LFP cells. Furthermore, the UKF demonstrates superior robustness against OCV–SOC relationships compared to the EKF. Lastly, the UKF is selected for testing across various aircraft usage scenarios at 10°C, 25°C, and 45°C. The resultant root mean square errors for SOC estimation at these different temperatures are consistently below 2%, thereby validating the effectiveness of the UKF SOC estimation approach.</div>
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