| Proj No. | A1123-251 |
| Title | Machine Learning-Powered Sensorless Battery Management for Hybrid Solid-State Battery Packs |
| Summary | In this project, the student will collaborate with research staff and postgraduate students to design a next-generation battery management system (BMS) for advanced hybrid-solid-state battery (HSSB) packs, eliminating the need for intrusive sensors. The BMS will utilize a machine learning (ML)-enhanced electrochemical-thermo-mechanical-aging (ETMA) model, optimized for efficient deployment on cost-effective microcontrollers. This innovative system will provide real-time monitoring of essential parameters for each HSSB cell, such as terminal voltage, charging/discharging current, temperature, internal pressure, state-of-charge (SoC), state-of-health (SoH), and remaining useful life (RUL). By enabling precise real-time tracking of these metrics, the proposed BMS significantly improves the safety and lifespan of HSSB packs while maintaining an economical design. This project aims to develop the world’s first high-performance BMS for HSSB packs without intrusive sensors, placing it at the cutting edge of innovation and approximately five years ahead of the anticipated widespread adoption of HSSBs. Its success will further solidify Singapore’s position as a global leader in advanced battery technologies. |
| 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?: |