| Proj No. | A1075-251 |
| Title | AI-Driven Optimization of Wireless Charging Systems for Electric Vehicles |
| Summary | This project seeks to revolutionize the design of wireless charging systems for electric vehicles (EVs) by integrating Artificial Intelligence (AI) methodologies. Focusing on critical parameters such as coil shape, number of turns, coil spacing, magnetic core material, and geometric design, the objective is to leverage AI algorithms to systematically analyze and optimize these elements. The AI system will learn from vast datasets and simulations, exploring diverse configurations to identify the most efficient and power-dense wireless charging setups. The outcome is expected to enhance the overall efficiency of EV charging, reduce charging times, and improve the power density of wireless charging systems. This research aligns with the growing demand for innovative and sustainable solutions in the electric transportation sector, showcasing the potential of AI in advancing the optimization of wireless charging infrastructure for EVs. |
| Supervisor | A/P Tang Yi (Loc:S2 > S2 B2A > S2 B2A 07, Ext: +65 67905416) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Clean Energy Research (Loc: S2-B7c-05) |
| Single/Group: | Single |
| Area: | Electrical Power and Energy |
| ISP/RI/SMP/SCP?: |