WS23: CPS-Based Modeling and Optimization Control of Renewable Energy Vehicles
( CFP )

Workshop Code: x6225


  • Teng Liu
    Affiliation: Ph.D., Research Fellow, University of Waterloo, Canada

  • Yuan Zou
    Affiliation: Ph.D., Professor, Beijing Institute of Technology, China

  • Xudong Zhang
    Affiliation: Ph.D., Assistant Professor, Beijing Institute of Technology, China

  • Huilong Yu
    Affiliation: Ph.D., Research Fellow, University of Waterloo, Canada

  • Yechen Qin
    Affiliation: Ph.D., Postdoctoral Fellow, Beijing Institute of Technology, Canada

  • Weichao Zhuang
    Affiliation: Ph.D., Assistant Professor, Southeast University, China

Scope and Goals

Cyber physical systems (CPSs) are defined as the system where physical and software components are deeply intertwined to exhibit multiple and distinct behavioral modalities and interact with each other in a myriad of ways that change with context. Recent increased demands of performance and complex usage pattern accelerate advancements in the research field of CPSs.

Being a typical application of CPS in green transportation, hybrid electric vehicles (HEVs) show great potential to reduce energy consumption and air pollution. In such a system, hybrid electric powertrain and driving environments constitute the physical resources, communication and control data compose the cyber part of this system. Strong nonlinearities and uncertainties of the interactions between the cyber and physical resources increase difficulties in control, management and optimization of HEVs. Specially, energy management of HEV is critical and several challenges remain to be resolved, such as optimization, calculation time and adaptability.

This workshop aims to provide up-to-date research and development advances in modeling and optimization control of renewable energy vehicles.

Topics of Interest

  • Dynamic modelling of vehicle powertrain
  • CPS-based optimization control for vehicles
  • Parallel reinforcement learning-based energy management
  • Testing, verification and assessment of hybrid tracked vehicles
  • Energy management of multimode hybrid electric vehicles
  • Advanced battery management systems
  • Parallel driving-based connected hybrid electric vehicles
  • Battery electric vehicle dynamics and control