Energy-Efficient Speed Planner for Connected and Automated Electric Vehicles on Sloped Roads

This paper proposes an energy-efficient speed planning strategy for a connected and automated vehicle (CAV) considering the upcoming traffic and road gradient information, which can be provided by the vehicle-to-everything communication systems. Unlike human drivers, CAV that receives long and short...

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Bibliographic Details
Published inIEEE access Vol. 10; pp. 34654 - 34664
Main Authors Wang, Xiangfei, Park, Suyong, Han, Kyoungseok
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Automation
Automobiles
Batteries
Communications systems
Connected and automated vehicle
Constraint modelling
Driving conditions
electric vehicle
Electric vehicles
Energy efficiency
energy-efficiency improvement
energy-efficient driving
model predictive control
Power generation
Power management
Powertrain
Predictive control
Roads
Roads & highways
State of charge
Traffic information
Traffic speed
Trajectory
Vehicles
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Summary:This paper proposes an energy-efficient speed planning strategy for a connected and automated vehicle (CAV) considering the upcoming traffic and road gradient information, which can be provided by the vehicle-to-everything communication systems. Unlike human drivers, CAV that receives long and short sighted traffic and road geometry information can optimize their speed profile to increase energy efficiency, depending on the powertrain types. In particular, the developed speed planner reducing the battery output power through the energy-efficiency improvement systems in electrified vehicles. Consequently, the CAV that is aware of the existence of the upcoming road gradient increases the speed on the uphill, and decreases the speed on the downhill to minimize the battery output power, which is different from the natural behaviors of human-driven vehicles on sloped roads. To consider the constraints, the model predictive control-based speed planner is developed, and its effectiveness is verified under various driving conditions. Simulation results show that our approach significantly outperforms the alternative speed profiles in terms of battery energy-saving, achieving about 27.21% of the energy efficiency improvement.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3162871