The field of electric motors and battery management is experiencing significant advancements, driven by innovative solutions to long-standing challenges. Researchers are exploring new approaches to cooling electric motors, such as the use of ducted liquids and optimized shaft geometries, which have shown promising results in improving heat transfer efficiency and reducing pressure drop. Additionally, there is a growing focus on developing more accurate and reliable methods for estimating the state of charge (SOC) of batteries, particularly in lithium iron phosphate batteries, where the flat open-circuit voltage characteristic poses significant challenges. Computational models and algorithms, such as dual extended Kalman filters and trust-based optimization techniques, are being developed to improve the accuracy and robustness of SOC estimation and optimization. These advancements have the potential to significantly improve the performance, efficiency, and durability of electric motors and batteries, and are expected to have a major impact on the development of electric vehicles and other applications. Noteworthy papers include: Residual Bias Compensation Filter for Physics-Based SOC Estimation in Lithium Iron Phosphate Batteries, which presents a novel approach to SOC estimation that achieves high accuracy and robustness. High-Performance Rotor Cooling with Ducted Liquid in Completely Cold-Formed Modular Motor Shaft, which demonstrates a significant improvement in cooling efficiency and pressure management. Socio-cognitive agent-oriented evolutionary algorithm with trust-based optimization, which introduces a novel trust-based optimization technique that outperforms conventional methods in many cases.