Insights Into Lithium‐Ion Battery Cell Temperature and State of Charge Using Dynamic Electrochemical Impedance Spectroscopy

介电谱 电化学 荷电状态 电池(电) 锂(药物) 离子 锂离子电池 电阻抗 材料科学 分析化学(期刊) 光谱学 化学 电气工程 电极 热力学 物理化学 物理 工程类 有机化学 医学 功率(物理) 量子力学 内分泌学
作者
L. M. Knott,E. J. Long,Colin P. Garner,Ashley Fly,Benjamin Reid,Annette R. Atkins
出处
期刊:International Journal of Energy Research [Wiley]
卷期号:2024 (1)
标识
DOI:10.1155/2024/9657360
摘要

Understanding and accurately determining battery cell properties is crucial for assessing battery capabilities. Electrochemical impedance spectroscopy (EIS) is commonly employed to evaluate these properties, typically under controlled laboratory conditions with steady‐state measurements. Traditional steady‐state EIS (SSEIS) requires the battery to be at rest to ensure a linear response. However, real‐world applications, such as electric vehicles (EVs), expose batteries to varying states of charge (SOC) and temperature fluctuations, often occurring simultaneously. This study investigates the impact of SOC and temperature on EIS in terms of battery properties and impedance. Initially, SSEIS results were compared with dynamic EIS (DEIS) outcomes after a full charge under changing temperatures. Subsequently, DEIS was analysed using combined SOC and temperature variations during active charging. The study employed a commercial 450 mAh lithium‐ion (Li‐ion) cobalt oxide (LCO) graphite pouch cell, subject to a 1C constant current (CC)–constant voltage (CCCV) charge for SSEIS and CC charge for DEIS, with SOC ranging from 50% to 100% and cell temperatures from 10 to 35°C. The research developed models to interpolate battery impedance data, demonstrating accurate impedance predictions across operating conditions. Findings revealed significant differences between dynamic data and steady‐state results, with DEIS more accurately reflecting real‐use scenarios where the battery is not at equilibrium and exhibits concentration gradients. These models have potential applications in battery management systems (BMSs) for EVs, enabling health assessments by predicting resistance and capacitance changes, thereby ensuring battery cells’ longevity and optimal performance.
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