The field of energy systems is moving towards increased sustainability and efficiency, with a focus on reducing carbon emissions and improving the performance of various energy-related processes. Recent developments have highlighted the importance of advanced modeling and control techniques in achieving these goals. The use of data-driven approaches, such as Koopman Operator Theory and Extended Kalman Filters, has shown promise in predicting and controlling complex nonlinear systems, including those found in electric vehicles and shipboard carbon capture systems. Additionally, the development of more accurate and efficient algorithms for estimating the state of charge and health of lithium-ion batteries is critical for effective battery management. Noteworthy papers include:

• The paper on Adaptive Extended Kalman Filtering for battery state of charge estimation, which proposes a novel covariance adaptation technique and demonstrates superior estimation accuracy.
• The paper on Deep Neural Koopman Operator-based Economic Model Predictive Control of shipboard carbon capture systems, which presents a data-driven approach for achieving safe and energy-efficient process operation.