Hao Wang received his B.Eng. and M.S. from the Beijing Institute of Technology (BIT) in 2019 and 2022, respectively. During his studies, he received several top honours, including the National Scholarship for Undergraduate Students and the National Scholarship for Graduate Students. He joined the McMaster Automotive Resource Centre (MARC) in September 2022 and is currently pursuing a Ph.D. in mechanical engineering under the supervision of Dr. Ali Emadi. His research focuses on the modelling and control of electrified powertrains, with particular emphasis on deep reinforcement learning and machine learning for energy management systems. He also leads the Electrified Powertrain Research Thrust Area in an industrial collaboration with Stellantis North America.
Full Profile
Hao Wang began his studies at the Beijing Institute of Technology (BIT) in 2015, where he developed a strong foundation in mechanical engineering, automotive engineering, electric vehicle systems, and control theory. He ranked near the top of his class and further strengthened his technical problem-solving and teamwork skills through engineering competitions. In 2019, he received the National Scholarship for Undergraduate Students and the Beijing Outstanding Undergraduate Student Award.
After completing his bachelor’s degree, Hao was admitted directly to BIT’s master’s program and joined the National Engineering Laboratory for Electric Vehicles under the supervision of Prof. Hongwen He. During his master’s studies, he contributed to national R&D projects and developed expertise in vehicle modelling, simulation, hardware-in-the-loop testing, and AI-based vehicle control. His work applied model predictive control and deep reinforcement learning to energy-efficient control strategies for electric vehicles. He also received the National Scholarship for Graduate Students and the Beijing Outstanding Graduate Student Award.
In September 2022, Hao joined Dr. Ali Emadi’s research group at MARC, where he is currently pursuing a Ph.D. in mechanical engineering. His research focuses on the modelling and control of electrified powertrains, including the safe application of deep reinforcement learning-based control to vehicle systems and real-time validation of supervisory control strategies on embedded systems. He also leads the Electrified Powertrain Research Thrust Area in an industrial collaboration project on advanced energy management systems for electric vehicles.
