The field of energy storage and renewable energy systems is moving towards more efficient and optimized solutions. Researchers are exploring new methods for real-time process control of electrode properties in lithium-ion battery manufacturing, which has the potential to reduce costs, CO2 emissions, and resource usage. Model predictive control approaches are being investigated for optimizing battery energy storage systems, continuous crystallization processes, and other applications. Additionally, there is a growing interest in developing more accurate models for electrochemical parameter identification of Li-ion batteries and optimizing energy collection in solar fields. Noteworthy papers include: Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning, which presents a novel approach for identifying key electrochemical parameters using deep reinforcement learning. Modeling energy collection with shortest paths in rectangular grids: an efficient algorithm for energy harvesting, which develops a general framework for minimizing rotational movements of solar trackers while maintaining energy production.