13.30 – 13.55
*This presentation is in English*
This presentation will explore the integration of Artificial Intelligence (AI) with Electrochemical Impedance Spectroscopy (EIS) to enhance battery technology. EIS is a powerful diagnostic tool used to analyze battery behavior, providing insights into key parameters such as state of health (SOH), internal resistance, and degradation mechanisms. Different AI-driven approaches, including machine learning (ML) and deep learning (DL) models, that can efficiently process and interpret EIS data to predict battery aging trends.
By leveraging these AI techniques, we can enhance battery lifetime estimation, optimize performance, and enable real-time health monitoring for various energy storage applications. In addition to aging estimation, EIS benefits on battery modeling by enabling the development of high-fidelity equivalent circuit models (ECMs) for advanced (Battery Management Systems). These BMS will be explored.
Speaker: Ning Zhangsheng – University of Twente
Nederlands