Researchers propose a hybrid data driven framework combining VMD, ISSA, and MKSVR to enhance battery SOH estimation and RUL prediction. The approach achieves accurate predictions with high stability.
Researchers develop novel feature extraction methods for multigraphs to predict shortest path costs, improving airport operations and sustainability. The proposed statistics-based and learning-based approaches show promising results, outperforming traditional exact search algorithms in computational efficiency.
The article reviews energy management strategies for hybrid electric vehicles and aircraft, highlighting challenges and solutions. It also discusses the application of energy management in flying cars driven by hybrid electric propulsion systems.
The Internet-of-Batteries (IoB) system utilizes IoT principles to gather data from EV batteries, analyzing health and performance, identifying faults, and optimizing usage. Machine learning approaches enhance decision-making for improved battery performance, increased range, and reduced costs.