This research explores Time-Sensitive Networking (TSN) and improved scheduling of TT and AVB traffic using the ITWA-SP approach. Visit: networkscience.researchw.com For Enquiries: contactus@researchw.com Award Nomination: networkscience-conferences.researchw.com/award-nomination/?ecategory=Awards&rcategory=Awardee #networking,#cybersecurity,#LAN,#WAN,#firewall,#IPaddressing,#VPN,#cloudnetwork,#TCPIP,#DNS,#datasecurity,#bandwidth,#routers,#switches,#subnetting,#networkengineer,#networkinfrastructure,#packetflow,#topology,#loadbalancing
Optimizing the effect of battery relaxation on electrochemical impedance spectroscopy measurement for real-time SOC estimation using transfer learning Conventional Electrochemical Impedance Spectroscopy (EIS) measurements require extended battery rest periods, restricting real-time use in battery management systems (BMS) and limiting generalization across chemistries. This work introduces transfer learning to significantly shorten rest time and improve model adaptability, enabling more practical and scalable EIS integration for real-world state of charge (SOC) estimations. EIS experiments were performed on 52 Ah Lithium Iron Phosphate (LFP) and 3.6 Ah Nickel Cobalt Manganese (NCM) cells at varying SOCs, with and without rest periods. The transfer learning-based Deep Neural Network (DNN-TL) model achieved high SOC estimation accuracy for LFP cells with mean squared error (MSE) of 0.0063 and mean absolute error (MAE) of 0.0664, improving MSE by 77.58% and MAE by 50.92% compared to stan...
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