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-fueled engine, power converters, and a battery energy buffer. Key tasks will include developing system-level models, optimising energy management, and coordinating power flow between subsystems to maximise
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, ensuring stable operation even as system dynamics evolve. Recent advances in Modular Multilevel Converter (MMC) topologies, along with developments in battery and supercapacitor technologies, create new
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research will contribute to the accelerated discovery and optimisation of next-generation materials, with the flexibility to focus on applications such as advanced battery cathodes, nuclear waste
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, thermal, electromagnetic or kinetic), are critical for the sustainable operation of wireless IoT devices and remote sensors. The world can reduce reliance on batteries and fossil-fuel-derived power if more
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triboelectric nanogenerator (TENG) to sustainably power ingestible medical devices. The aim is to harness mechanical energy from natural intestinal peristalsis to enable battery-free operation of autonomous
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of real-time digital twin (physical or Artificial Intelligent based) of electric propulsion system including propulsion motors, power converters, fuel cell and batteries etc within the real-time simulation
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SpeedGoat. Development of real-time digital twin (physical or Artificial Intelligent based) of electric propulsion system including propulsion motors, power converters, fuel cell and batteries etc within
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science, e.g, by leading to more effective batteries. The Research Assistant/Associate will join the Machine Learning Group at the Department of Engineering, working with Prof. José Miguel Hernández Lobato
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– both CO2 and non-CO2 (including contrails) – of various technologies, including electric aircraft (battery- and fuel cell-powered), hybrid-electric aircraft, next-generation propulsion systems
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the operational life of battery-powered devices and reducing the environmental impact of large-scale deployments. Advancements in this area support the development of sustainable technologies across various