SESSION LIST

SESSION DETAILS

SS06: Parallel-Driving-Based Control Optimization of Intelligent Electrified Vehicles: from CPS to CPSS
( CFP)

Session Code: cwx1f

Organizers

  • Chen Lv
    Affiliation: Ph.D., Research Fellow, Cranfield University, UK
    E-mail: c.lyu@cranfield.ac.uk

  • Hongyan Guo
    Affiliation: Ph.D., Associate Professor, Jilin University, China
    E-mail: guohongyan220@163.com

  • Hong Wang
    Affiliation: Ph.D., Research Associate, University of Waterloo, Canada
    E-mail: hong.wang@uwaterloo.ca

  • Lei Zhang
    Affiliation: Ph.D., Assistant Professor, Beijing Institute of Technology, China
    E-mail: lei_zhang@bit.edu.cn

Scope and Goals

As a typical Cyber-Physical System, an intelligent electrified vehicle (iEV) represents a perfect combination of the two promising technologies of energy and intelligence. Beyond CPS, the Cyber-Physical-Social Systems (CPSS), which is more tightly conjoined, coordinated, and integrated with human and social characteristics, has been steadily growing to be an emerging research focus. In this context, the emerging parallel driving, which is a cloud-based CPSS framework aiming at synergizing connected automated driving, offers an ample solution for achieving smarter, safer and more efficient performances of intelligent electrified vehicles in future transportation. This special session aims to compile the latest research and development advances in parallel-driving-based iEV.

Topics of Interest

  • Dynamic modelling of vehicles and mechatronic systems
  • CPSS-based control and optimization for vehicles
  • Cloud-based monitoring and management of vehicle systems
  • Parallel driving and parallel electrified vehicle
  • Driver-vehicle interaction and collaboration in iEVs
  • Advanced control and estimation of electrified powertrains
  • Electrified vehicle dynamics and control
  • Testing, verification and assessment of intelligent vehicles
  • Fault diagnosis and fault tolerant control of automotive systems
  • Integration of electrified vehicles within ITS and/or smart grid
  • Advanced battery management systems