Accelerating transport electrification by machine learning

The technological blossom in artificial intelligence (AI) makes possible numerous advancements in various engineering disciplines. The applicant has been in the forefront of this AI innovation, and received seven world prizes in AI competitions.

For this fellowship, he intends to use his AI expertise to examine the role that AI technologies can play in accelerating transport electrification, and subsequently contributing to climate action.

In view of the strong vehicle industry in Gothenburg, Sweden, Chalmers and Scania AB collectively provide an outstanding research environment for this important topic.

More specifically, this project will consolidate data-driven electrochemical battery model, deep reinforcement learning based automated electric vehicle (AEV) speed control and AEV personalized routing recommendation model, which are three biggest challenges in the transition process towards electrified transport systems.

The fellowship will be co-supported by AI Innovation of Sweden, and Drive Sweden, with a hope to implement the expected findings in not only the vehicle industry but also transport management sectors.

This research is interdisciplinary in nature, and requires expertise from computer science, artificial intelligence, electrochemical engineering, electrical engineering, and transport engineering.

In this regard, the host institute, Chalmers University of Technology, has developed a well-designed career training and development plan for the applicant to work with renowned research leaders in relevant disciplines (e.g. Prof. Xiaobo Qu, Member of Academia Europaea; Prof.

Karl Johansson, Fellow of Royal Swedish Academy of Engineering Sciences) to enhance his readiness to become an assistant professor after this fellowship.

The applicant will also take advantage of the fertile research environment in Chalmers, and synchronize the workshops and reference groups with the host’s other EU and Swedish projects to disseminate the new findings.