The field of energy storage and conversion is witnessing significant developments, driven by the increasing need for efficient and reliable systems. Researchers are focusing on improving the accuracy and efficiency of computational models, particularly in the areas of battery health monitoring and thermal management. The integration of machine learning and physics-informed methods is becoming a key trend, enabling the development of more accurate and data-efficient models. Notably, the use of neural networks and parameterized models is allowing for real-time monitoring and estimation of internal battery variables, as well as improved temperature estimation in electric motors. These advancements have the potential to significantly impact the performance and lifespan of energy storage and conversion systems. Noteworthy papers include: Toward Multi-Fidelity Machine Learning Force Field for Cathode Materials, which develops a framework for enhancing the data efficiency of computational results. Real-Time Physics-Aware Battery Health Monitoring from Partial Charging Profiles via Physics-Informed Neural Networks, which demonstrates the strong potential of physics-informed machine learning to advance real-time battery management systems.