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

• Published in: International journal of energy research Vol. 2024; no. 1
• Main Authors: Knott, L. M., Long, E., Garner, C. P., Fly, A., Reid, B., Atkins, A.
• Format: Journal Article
• Language: English
• Published: Bognor Regis John Wiley & Sons, Inc 2024
• Subjects: Batteries; Capacitance; Cell culture; Cobalt; Cobalt oxides; Concentration gradient; Electric charge; Electric vehicles; Electrochemical impedance spectroscopy; Electrochemistry; Electrodes; Electrolytes; Graphite; Impedance; Lithium; Lithium-ion batteries; Management systems; Methods; Power management; Predictions; Spectroscopy; Spectrum analysis; State of charge; Steady state; Temperature; Temperature requirements
• ISSN: 0363-907X; 1099-114X
• DOI: 10.1155/2024/9657360

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.