Project description:
This project develops a data-driven, household-centric framework to assess when vehicle-to-grid (V2G) participation is economically viable for private electric vehicle owners. Using large-scale travel surveys from the UK, US, and Australia together with historical electricity price signals, we construct representative daily mobility profiles and optimise overnight charging/discharging schedules under realistic constraints such as plug-in uncertainty and next-day travel requirements. Battery wear is internalised via a transparent throughput-based degradation cost, enabling robust scenario and sensitivity analysis. The resulting outputs quantify profitability boundaries across mobility contexts, markets, and service assumptions (including potential frequency-service revenue stacking).
Year: 2026
Project description
The project develops an optimization based framework to evaluate and improve the full life cycle economy of grid connected utility scale photovoltaic power plants. The framework considers the investment and replacement costs of key equipment (such as inverter and energy storage), time-varying operation and maintenance costs and efficiency losses (power constraints, SOC dynamics, etc.), as well as the market income under the restriction of actual electricity price and grid connection and transmission.
The core method is to establish a cross time discrete optimization model on an hourly scale to jointly optimize the output of the inverter and the charging and discharging of energy storage, so as to maximize the net income under the premise of meeting the requirements of grid connection and operation.
Year: 2026
Project description:
The Intelligent Infant Monitoring System using a Smart Crib was successfully designed, implemented, and validated to enhance infant safety through real-time, non-invasive monitoring. The system integrates ultrasonic sensors to measure an infant's distance from crib boundaries, a distributed pressure sensor array to monitor weight distribution and posture across the mattress surface, and an accelerometer to detect crib tilt or abnormal motion. All sensor data is processed in real time and presented on a PC-based monitoring interface, where the crib is logically divided into safe and unsafe zones, providing clear visual feedback on the infant's safety status. When unsafe conditions are detected, the system automatically triggers visual alerts, including a red LED warning, enabling caregivers to respond promptly. The completed prototype was extensively tested under multiple operating scenarios and demonstrated reliable detection of infant movement, posture changes, and safety risks. By combining multiple sensor modalities, the system delivers robust performance independent of lighting conditions and offers a practical, scalable, and cost-effective solution for intelligent infant safety monitoring in both domestic and healthcare environments.
Year: 2025
