TR2024-114

Control Co-Design for Electric Vehicles with Driving Cycle Synthesis Encoding Road Traffic and Driver Characteristics


    •  Park, S., Wang, Y., Qiao, H., Sakamoto, Y., Wang, B., Liu, D., "Control Co-Design for Electric Vehicles with Driving Cycle Synthesis Encoding Road Traffic and Driver Characteristics", IEEE Conference on Control Technology and Applications (CCTA) 2024, August 2024.
      BibTeX TR2024-114 PDF
      • @inproceedings{Park2024aug,
      • author = {Park, Seho and Wang, Yebin and Qiao, Hongtao and Sakamoto, Yusuke and Wang, Bingnan and Liu, Dehong}},
      • title = {Control Co-Design for Electric Vehicles with Driving Cycle Synthesis Encoding Road Traffic and Driver Characteristics},
      • booktitle = {IEEE Conference on Control Technology and Applications (CCTA) 2024},
      • year = 2024,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2024-114}
      • }
  • MERL Contacts:
  • Research Areas:

    Control, Electric Systems, Multi-Physical Modeling, Optimization

Abstract:

This paper employs the control co-design paradigm to determine optimal motor size and optimal Heating Ventilation Air Conditioning (HVAC) control for system-level optimal performance of electric vehicle. Our work is motivated by the realization that whether an electric vehicle design and its control strategy best fit consumer depends on who operates it, where and how it is operated. To this end, we first propose a novel method to synthesize a customer-specific driving cycle which encodes traffic information and driver characteristics, effectively addressing where and by whom the electric vehicle is operated; then conduct physics-based model reduction and data-driven modeling for motor sizing and HVAC control design; and apply established control co-design approaches to jointly optimize the motor size and HVAC power control for a given driving cycle, addressing how it is operated. The proposed method is validated by performing electric motor design and open-loop HVAC control in numerical simulation, showcasing that the control co-design practice leads to an appropriate combination of motor size and HVAC control and more efficient operation for the given customer-specific driving cycle.