| Proj No. | A1121-251 |
| Title | Physics-informed Battery Aging and Degradation Modelling |
| Summary | Accurate battery state estimation is essential for improving the safety, efficiency, and longevity of modern energy storage systems, particularly in applications such as electric vehicles and renewable energy storage. Traditional data-driven approaches often provide high predictive accuracy but lack interpretability, while physics-based models can be computationally expensive and may not generalize well under real-world conditions. This project aims to develop an advanced Physics-Informed Machine Learning (PIMM) framework that integrates battery degradation physics with neural networks to enhance long-term predictive capabilities. The framework will be trained and validated using electrochemical impedance spectroscopy (EIS) data, experimental aging datasets, and high-fidelity simulations (e.g., PyBaMM, COMSOL) to ensure robustness across different battery chemistries and operational environments. The selected student will collaborate with research staff and postgraduate researchers to refine model architectures, optimize feature selection, and ensure robust real-world applicability. |
| Supervisor | Ast/P Yang Yun (Loc:S2 > S2 B2C > S2 B2C 105, Ext: +65 67905406) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Water & Energy Research Laboratory (Loc: S2.1-B3-03) |
| Single/Group: | Single |
| Area: | Electrical Power and Energy |
| ISP/RI/SMP/SCP?: |