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Open AccessArticle

Neuro-Fuzzy System for Energy Management of Conventional Autonomous Vehicles

1
School of Engineering, RMIT University, Melbourne, VIC 3083, Australia
2
Division of Mechatronics, Mechanical Engineering Institute, Vietnam Maritime University, Haiphong 180000, Vietnam
3
Faculty of Mechanical Engineering, University of Guilan, Gilan Province 4199613776, Iran
*
Author to whom correspondence should be addressed.
Energies 2020, 13(7), 1745; https://doi.org/10.3390/en13071745
Received: 5 February 2020 / Revised: 26 March 2020 / Accepted: 31 March 2020 / Published: 5 April 2020
(This article belongs to the Special Issue Modelling, Control and Optimisation of Complex Energy Systems)
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. View Full-Text
Keywords: autonomous vehicles; intelligent energy management system; neuro-fuzzy autonomous vehicles; intelligent energy management system; neuro-fuzzy
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Phan, D.; Bab-Hadiashar, A.; Hoseinnezhad, R.; N. Jazar, R.; Date, A.; Jamali, A.; Pham, D.B.; Khayyam, H. Neuro-Fuzzy System for Energy Management of Conventional Autonomous Vehicles. Energies 2020, 13, 1745.

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