Damoon Soudbakhsh: Data-Driven Modeling and Online Co-Estimation of State of Charge and Voltage Dynamics of Li-ion Batteries

We are entering a new era of electrification, marked by the widespread adoption of lithium-ion batteries across critical sectors such as transportation, renewable energy, and grid infrastructure. Despite their advantages, lithium-ion batteries still grapple with notable performance and safety issues, casting doubt on their long-term role in future electrification efforts. This raises an urgent question: how can we improve batteries to meet the diverse demands of real-world applications? Systems theory, control strategies, and learning-based approaches have emerged as powerful tools in the search for effective solutions. We aim to make the batteries more efficient by creating more accurate models that are battery specific and adjustable. Specifically, we introduce an interpretable, physics-inspired, data-driven approach for discovering governing equations and estimating the state-of-charge (SOC) and voltage dynamics of Li-ion batteries. SOC estimation is a key challenge in battery management systems, particularly for high-demand applications like electric vehicles, where errors in low and high SOC regions can limit performance. The proposed method leverages sparse identification, using a physics-based library of electrochemical functions to uncover governing equations that accurately capture battery dynamics. This approach ensures interpretability and physical consistency, addressing common issues in purely data-driven models, such as overfitting and lack of generalizability.