介电谱
电池(电)
电阻抗
直流电
恒流
电流(流体)
材料科学
计算机科学
分析化学(期刊)
电气工程
电子工程
电化学
化学
工程类
电极
电压
物理
热力学
功率(物理)
物理化学
色谱法
作者
Jia Guo,Yaolin Xu,Pengwei Li,Kjeld Pedersen,Miran Gaberšček,Daniel‐Ioan Stroe
标识
DOI:10.1002/cphc.202400528
摘要
Abstract Electrochemical impedance spectroscopy (EIS), a conventional and alternating‐current‐(AC)‐based technique for impedance measurement, is commonly used in battery diagnosis. However, it requires expensive equipment and demanding operating conditions and is complex and model‐dependent in data analysis. Recently, novel direct current (DC) analytics have emerged as an alternative to EIS. They are simple yet powerful, being capable of revealing impedance information that traditionally could only be obtained through EIS and determining Li‐ion diffusion coefficient. Besides, a complete EIS spectrum can be predicted based on constant current charging curves in the support of machine learning methods. This work highlights the similarities and discrepancies between DC techniques and EIS in the electrochemical analysis of Li‐ion batteries. Looking ahead, DC techniques may be a promising substitute for EIS in future battery diagnosis, requiring simplified equipment while offering a deep understanding of battery impedance and its underlying electrochemical processes.
科研通智能强力驱动
Strongly Powered by AbleSci AI