介电谱
电阻抗
锂离子电池
材料科学
电池(电)
计算机科学
电子工程
电气工程
工程类
化学
电化学
电极
物理
物理化学
量子力学
功率(物理)
作者
Xutao Liu,Shengyu Tao,Shiyi Fu,Ruifei Ma,Tingwei Cao,Hongtao Fan,Junxiong Zuo,Xuan Zhang,Yu Wang,Yaojie Sun
出处
期刊:Applied Energy
[Elsevier BV]
日期:2024-04-15
卷期号:364: 123221-123221
被引量:9
标识
DOI:10.1016/j.apenergy.2024.123221
摘要
Electrochemical Impedance Spectroscopy (EIS) plays a crucial role in characterizing the internal electrochemical states of lithium-ion batteries and proves to be effective for estimating battery states. Traditional EIS measurement, however, requires expensive electrochemical workstations with time-consuming signal injection, especially in low-frequency regions, thus limiting its practical applications. Here we show that applying our proposed pulse-like Binary Multi-Frequency Signals (BMFS) as the excitation signal in the EIS measurement, which simultaneously possesses numerous frequency components and maintains high energy at each frequency component, will significantly improve test speed while retaining accuracy. The applicability of the BMFS under various cathode material types, including nickel cobalt manganese (NCM), lithium cobalt oxide (LCO), and lithium iron phosphate (LFP) is demonstrated. The robustness of the signal is experimentally verified through varying C-rates and measurement window lengths. The BMFS, requiring only 30 s per test, can achieve test results with an amplitude error of 1% and a phase error of 1° as compared with those obtained from traditional EIS tests. Moreover, BMFS can also be applied in online EIS measurement scenarios, favorable for real-world applications. This work enables accurate and rapid acquisition of EIS results, which is currently expensive and time-consuming to obtain, ensuring a faster and more nuanced characterization of the internal states of many battery systems in an affordable and accessible manner, especially in data-driven and machine-learning approaches.
科研通智能强力驱动
Strongly Powered by AbleSci AI