Intelligent Driver Assistance and Energy Management Systems of Hybrid Electric Autonomous Vehicles

Ziad Al-Saadi, Duong Phan Van, Ali Moradi Amani, Mojgan Fayyazi, Samaneh Sadat Sajjadi, Dinh Ba Pham, Reza Jazar and Hamid Khayyam
Additional contact information
Ziad Al-Saadi: School of Engineering, RMIT University, Melbourne, VIC 3083, Australia
Duong Phan Van: Division of Mechatronics, Mechanical Engineering Institute, Vietnam Maritime University, Haiphong 180000, Vietnam
Ali Moradi Amani: School of Engineering, RMIT University, Melbourne, VIC 3083, Australia
Mojgan Fayyazi: School of Engineering, RMIT University, Melbourne, VIC 3083, Australia
Samaneh Sadat Sajjadi: School of Engineering, RMIT University, Melbourne, VIC 3083, Australia
Dinh Ba Pham: Division of Mechatronics, Mechanical Engineering Institute, Vietnam Maritime University, Haiphong 180000, Vietnam
Reza Jazar: School of Engineering, RMIT University, Melbourne, VIC 3083, Australia
Hamid Khayyam: School of Engineering, RMIT University, Melbourne, VIC 3083, Australia

Sustainability, 2022, vol. 14, issue 15, 1-21

Abstract: Automotive companies continue to develop integrated safety, sustainability, and reliability features that can help mitigate some of the most common driving risks associated with autonomous vehicles (AVs). Hybrid electric vehicles (HEVs) offer practical solutions to use control strategies to cut down fuel usage and emissions. AVs and HEVs are combined to take the advantages of each kind to solve the problem of wasting energy. This paper presents an intelligent driver assistance system, including adaptive cruise control (ACC) and an energy management system (EMS), for HEVs. Our proposed ACC determines the desired acceleration and safe distance with the lead car through a switched model predictive control (MPC) and a neuro-fuzzy (NF) system. The performance criteria of the switched MPC toggles between speed and distance control appropriately and its stability is mathematically proven. The EMS intelligently control the energy consumption based on ACC commands. The results show that the driving risk is extremely reduced by using ACC-MPC and ACC-NF, and the vehicle energy consumption by driver assistance system based on ACC-NF is improved by 2.6%.

Keywords: intelligent energy management; adaptive cruise control; autonomous vehicle; model predictive control; artificial intelligence; complex systems (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/15/9378/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/15/9378/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:15:p:9378-:d:877073

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().