超声波传感器
荷电状态
电压
控制理论(社会学)
电池(电)
均方误差
灵敏度(控制系统)
等效电路
声学
电子工程
计算机科学
工程类
电气工程
功率(物理)
数学
物理
统计
控制(管理)
量子力学
人工智能
作者
Maoshu Xu,E Zhang,Sheng Wang,Yi Shen,Binchen Zou,Haomiao Li,Yimin Wan,Kangli Wang,Kai Jiang
出处
期刊:Applied Energy
[Elsevier]
日期:2024-02-01
卷期号:355: 122210-122210
被引量:1
标识
DOI:10.1016/j.apenergy.2023.122210
摘要
Accurate estimation of the State of Charge (SoC) plays a vital role in ensuring the efficient and safe operation of lithium iron phosphate (LFP) batteries. However, the flat open circuit voltage (OCV) curve of the LFP battery implies a low sensitivity to SoC, which results in large SoC estimation errors in the presence of noisy terminal voltage measurements. To address this challenge, an SoC estimation methodology utilizing an ultrasonic reflection response model is proposed, which is the first methodology regarding highly accurate and robust ultrasonic model-based SoC estimation under dynamic load profiles. Since ultrasound waves enable non-destructive acquisition of battery internal physical property changes directly associated with SoC, the ultrasonic battery near-surface reflection feature is extracted and demonstrated to exhibit a highly linear correlation with and higher sensitivity to SoC. We pioneeringly construct an empirical differential ultrasonic model to describe how the ultrasonic feature depends on the SoC and dynamic current. The advantage of such an ultrasonic model is demonstrated by theoretical and experimental results of an Adaptive Extend Kalman Filter (AEKF) and an Adaptive H-infinity Filter (AHIF) under various dynamic load profiles and temperatures. The Root Mean Square Error (RMSE) for ultrasonic model-based SoC estimation remains at approximately 1% across all tests, reducing by 36.7% compared to the voltage model, which shows its great potential in accurate SoC estimation.
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