Newcastle University, Singapore Campus, Singapore

Biography: Dr Cheng Siong Chin. He received his Ph.D. in Applied Control Engineering at Research Robotics Centre in Nanyang Technological University (NTU) in 2008 and M.Sc. in Advanced Control and Systems Engineering from The University of Manchester in 2001. He also graduated with a B.Eng in Mechanical and Aerospace Engineering from NTU in 2000.
He has published nearly 80 journal papers, book chapters, and book and conference papers involving modeling, simulation, remote monitoring, noise prediction, and control systems design of mechatronic systems. Since 2013, he obtained 2 research grants from Singapore Maritime Institute (SMI) and 2 Industrial Postgraduate Programme (EDB-IPP) on his research areas in high endurance and sustainable mechatronic systems design, acoustic modeling and prediction. He has 2 US patent awards on printed circuit cable assembly and electronic device torsion testing of the one-inch hard disc drive. He is an active reviewer for IEEE Transactions on Mechatronics, Industrial Electronics and Renewable Energy. He had authored a book entitled Computer-aided Control Systems Design: Practical Applications using MATLAB and Simulink published by CRC Press, USA, 2012. Fellow of the Higher-Education Academy (FHEA), Fellow of IMarEST (FIMarEST), Senior Member of IEEE (SMIEEE), Chartered Engineer, European Engineer and a member of IET.

Speech Title: Design of Temperature-Dependent LiFePO4 Battery for Actual Embedded Applications
Abstract: A computational efficient battery pack model with thermal consideration is essential for simulation prototyping before real-time embedded implementation. The proposed model provides a coupled equivalent circuit and convective thermal model to determine the state-of-charge (SOC) and temperature of the LiFePO4 battery working in a real environment. A cell balancing strategy applied to the proposed temperature-dependent battery model balanced the SOC of each cell to increase the lifespan of the battery. The simulation outputs are validated by a set of independent experimental data at a different temperature to ensure the model validity and reliability. In summary, a smart battery management system (BMS) with high efficiency active cell balancing technology and intelligent self-learning battery state of charge (SOC) estimation for the lithium-ion battery was developed.