This studentship will develop physics-informed Edge AI methods for predictive health management of batteries and power electronics in electrified vehicles under real-world driving conditions.

Their findings are detailed in the study “Efficient Energy Consumption: Leveraging AI Models for Appliance Detection,” published in the Journal of Low Power Electronics and Applications, where the ...

Abstract: Effective fault identification and diagnosis are critical in modern power systems to ensure operational reliability and reduce economic losses. This research describes a novel approach that ...

Abstract: Electrical faults in transmission lines are a critical concern in power systems, as they impact the stability and reliability of electricity distribution. Rapid fault identification and ...

The power industry is experiencing unprecedented demand growth, driven largely by data centers and artificial intelligence (AI) applications. This surge is creating both opportunities and challenges ...

In response to escalating environmental challenges and the global energy crisis, Europe has established ambitious targets to reduce greenhouse gas emissions and increase the production of renewable ...

Stroke remains one of the leading causes of global mortality and long-term disability, driving the urgent need for accurate and early risk prediction tools. Traditional models such as the Framingham ...

Wireless power transfer (WPT) systems transmit electrical energy from a power source to a load without physical connectors or wires, using electromagnetic fields. This idea goes as far back as the ...