Neuro-Fuzzy System for Energy Management of Conventional Autonomous Vehicles

Duong Phan, Alireza Bab-Hadiashar, Reza Hoseinnezhad, Reza N. Jazar, Abhijit Date, Ali Jamali, Dinh Ba Pham and Hamid Khayyam
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Duong Phan: School of Engineering, RMIT University, Melbourne, VIC 3083, Australia
Alireza Bab-Hadiashar: School of Engineering, RMIT University, Melbourne, VIC 3083, Australia
Reza Hoseinnezhad: School of Engineering, RMIT University, Melbourne, VIC 3083, Australia
Reza N. Jazar: School of Engineering, RMIT University, Melbourne, VIC 3083, Australia
Abhijit Date: School of Engineering, RMIT University, Melbourne, VIC 3083, Australia
Ali Jamali: Faculty of Mechanical Engineering, University of Guilan, Gilan Province 4199613776, Iran
Dinh Ba Pham: Division of Mechatronics, Mechanical Engineering Institute, Vietnam Maritime University, Haiphong 180000, Vietnam
Hamid Khayyam: School of Engineering, RMIT University, Melbourne, VIC 3083, Australia

Energies, 2020, vol. 13, issue 7, 1-16

Abstract: 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.

Keywords: autonomous vehicles; intelligent energy management system; neuro-fuzzy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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