Quan Zhou is Professor of Automotive Engineering with the School of Automotive Studies, Tongji University. He obtained his Ph.D. degree in Mechanical Engineering from the University of Birmingham, Birmingham, UK in 2019, which was distinguished as the sole awardee for the University’s Ratcliff Prize for the best postgraduate studies in the Science. Before joining Tongji University, Prof Zhou was Assistant Professor in Automotive Engineering at the University of Birmingham. His research interests include design and control methods for automotive powertrains and chassis systems. He has received major funds from China/UK government and the automotive industry including the NSFC Fund for Distinguished Overseas Young Scholars. He has published more than 90 peer-reviewed research papers with google scholar citations over 2500. He served as associate editors for many distinguished academic journals including Nature Communications Engineering, IEEE Transactions of Transportation Electrification, and IEEE Transactions of Vehicular Technology.

Balancing global performance (e.g., electric driving range and energy economy) and transient control (power and efficiency) performance is critical for electrified vehicles. Traditionally, control rules were designed by human experience based on the analysis of global optimization data (e.g., results obtained by dynamic programing). These cannot achieve the true global best performance in real-world driving. In this presentation, we will introduce the state-of-the-art in energy management methods and discuss AI methods for control rule representation and real-word adaption methods based on transfer learning and reinforcement learning. Some numerical and experimental studies will also be discussed as validation.