Researchers represented a cloud-based eco-driving solution for autonomous hybrid electric Bus Rapid Transit (BRT) buses in cooperative vehicle-infrastructure systems via dynamic programming and model predictive control. They validated the effectiveness of the the eco-driving solution in three distinct scenarios with intersections, stations, and ramps.
A paper proposing a cloud-based eco-driving solution was published in the journal Green Energy and Intelligent Transportation on November 29th, 2023.
In the last decade, the bus rapid transit (BRT) system that has emerged is a form of public transportation between rail transit and conventional buses. The BRT is usually configured with full-time, enclosed bus lanes, and offboard station service system, and thereby has a much purer driving environment due to segregated lanes and less dynamic traffic demand.
With the involvement of BRT buses, onboard units (OBUs), road-side units (RSUs), cloud-based computation servers, etc., the overall BRT eco-driving system is a typical example of a cyber-physical system. To realize eco-driving for autonomous hybrid electric BRT buses, researchers proposed two levels of eco-driving solutions: a global optimal scheduling solution and supervisory MPC-based energy management
The solution contains an upper-level cloud-based scheduling strategy and a lower-level onboard predictive energy management, which is conceived to function in a Cyber-physical system of the cooperative vehicle-infrastructure system.
In the upper level, a spatiotemporal scheduling model is presented for autonomous BRT buses. The cloud-based computation server collects relevant information about the traffic participants and road infrastructure via wireless communication. An optimal scheduling scheme is periodically calculated and sent to each running autonomous BRT bus to follow on the daily route.
In the lower level, MPC-based real-time energy management is developed to fulfill the solution of eco-driving of autonomous hybrid electric BRT buses. When the global scheduling scheme is planned in the computation server, a supervisory global SoC trajectory can be calculated based on the global scheduling scheme. Then, the global scheduling scheme and supervisory global state of charge (SoC) trajectory are sent from the computation server to the vehicle control unit (VCU) via the OBU. With the expected velocity profile and SoC trajectory, the autonomous bus can drive more efficiently and energy-saving by proper motion control and real-time energy management.
The further exploration of eco-driving solutions, including scheduling methods that incorporate dynamic velocity regulation and adaptive control of traffic lights in highly dynamic traffic environments, or lite onboard scheduling solutions that are capable of interacting with energy management, is an exciting avenue for future work.
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Reference
Author: Yuecheng Li a,c,d, Hongwen He b,c,*, Yong Chen a,c,d, Hao Wang e
Affiliations:
a School of Mechanical and Electrical, Beijing Information Science and Technology University, Beijing, China
b Department of Mechanical Engineering, Beijing Institute of Technology, Beijing, China
c Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing, China
d Beijing Laboratory for New Energy Vehicles, Beijing, China
e McMaster Automotive Resource Center, McMaster University, Hamilton, ON, Canada
Article link:
Journal: Green Energy and Intelligent Transportation
DOI: 10.1016/j.geits.2023.100122
