Motion Planning for Energy Management in Autonomous Vehicles.

1997 ◽  
Author(s):  
John Baillieul
Keyword(s):  
Motion Planning ◽  
Energy Management ◽  
Autonomous Vehicles

Dynamic motion planning of autonomous vehicles

1991 ◽  
Vol 7 (2) ◽  
pp. 241-249 ◽  
Author(s):  
Z. Shiller ◽  
Y.-R. Gwo
Keyword(s):  
Motion Planning ◽  
Autonomous Vehicles ◽  
Dynamic Motion ◽  
Dynamic Motion Planning

scholarly journals Neuro-Fuzzy System for Energy Management of Conventional Autonomous Vehicles

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1745 ◽  
Author(s):  
Duong Phan ◽  
Alireza Bab-Hadiashar ◽  
Reza Hoseinnezhad ◽  
Reza N. Jazar ◽  
Abhijit Date ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Autonomous Vehicles ◽  
Pid Controller ◽  
Autonomous Vehicle ◽  
Subtractive Clustering ◽  
Energy Management System ◽  
Power Demand ◽  
Neuro Fuzzy ◽  
Simulation Results

This paper investigates the energy management system (EMS) of a conventional autonomous vehicle, with a view to enhance its powertrain efficiency. The designed EMS includes two neuro-fuzzy (NF) systems to produce the optimal torque of the engine. This control system uses the dynamic road power demand of the autonomous vehicle as an input, and a PID controller to regulate the air mass flow rate into the cylinder by changing the throttle angle. Two NF systems were trained by the Grid Partition (GP) and the Subtractive Clustering (SC) methods. The simulation results show that the proposed EMS can reduce the fuel consumption of the vehicle by 6.69 and 6.35 l/100 km using the SC and the GP, respectively. In addition, the EMS based on NF trained by GP and NF trained by SC can reduce the fuel consumption of the vehicle by 11.8% and 7.08% compared with the case without the controller, respectively.

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