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

journal contribution
posted on 2024-11-02, 21:20 authored by Ziad Al-Saadi, Ali Moradi AmaniAli Moradi Amani, Mojgan Fayyazi, Samaneh Sadat SAJJADI, Gholamreza Nakhaie JazarGholamreza Nakhaie Jazar, Hamid KhayyamHamid Khayyam
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%.

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  1. 1.
    DOI - Is published in 10.3390/su14159378
  2. 2.
    ISSN - Is published in 20711050

Journal

Sustainability

Volume

14

Number

9378

Issue

15

Start page

1

End page

21

Total pages

21

Publisher

MDPI AG

Place published

Switzerland

Language

English

Copyright

Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Former Identifier

2006117751

Esploro creation date

2022-11-15

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