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Intelligent energy management system for conventional autonomous vehicles

Author

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  • Phan, Duong
  • Bab-Hadiashar, Alireza
  • Lai, Chow Yin
  • Crawford, Bryn
  • Hoseinnezhad, Reza
  • Jazar, Reza N.
  • Khayyam, Hamid

Abstract

Autonomous vehicles have been envisioned to increase vehicle safety, primarily via the reduction of accidents. However, their design could also affect the vehicle travel demand and energy consumption. Although battery-powered electric and hybrid-electric autonomous vehicles assume more widespread use than conventional autonomous vehicles, energy management is harder and more significant for conventional autonomous vehicles. As such, it is necessary to investigate how to manage energy consumption in conventional autonomous vehicles. In this paper, an energy management system is constructed and analyzed by using a road-power-demand model and an intelligent system to reduce fuel consumption for a conventional autonomous vehicle. The road-power-demand model utilizes three impact factors (i) environment-conditions (ii) driver-behavior, and (iii) vehicle-specifications. The proposed intelligent energy management system includes a fuzzy-logic-system with the aim of generating the desired engine torque, based on the vehicle road power demand and a PID controller to control the air/fuel ratio, by changing the throttle angle. Results show that the intelligent energy management system reduces the vehicle energy consumption from 7.2 to 6.71 L/100 km. Next, the parameters of the fuzzy-logic-system are intelligently optimized by the particle-swarm-optimization method and new results indicate that the vehicle energy consumption is reduced by around 9.58%.

Suggested Citation

  • Phan, Duong & Bab-Hadiashar, Alireza & Lai, Chow Yin & Crawford, Bryn & Hoseinnezhad, Reza & Jazar, Reza N. & Khayyam, Hamid, 2020. "Intelligent energy management system for conventional autonomous vehicles," Energy, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:energy:v:191:y:2020:i:c:s0360544219321711
    DOI: 10.1016/j.energy.2019.116476
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    References listed on IDEAS

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    Cited by:

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    3. Cheng, Shen & Zhao, Gaiju & Gao, Ming & Shi, Yuetao & Huang, Mingming & Yousefi, Nasser, 2021. "Optimal hybrid energy system for locomotive utilizing improved Locust Swarm optimizer," Energy, Elsevier, vol. 218(C).
    4. Duong Phan & Alireza Bab-Hadiashar & Reza Hoseinnezhad & Reza N. Jazar & Abhijit Date & Ali Jamali & Dinh Ba Pham & Hamid Khayyam, 2020. "Neuro-Fuzzy System for Energy Management of Conventional Autonomous Vehicles," Energies, MDPI, vol. 13(7), pages 1-16, April.
    5. Ilyes Tegani & Okba Kraa & Haitham S. Ramadan & Mohamed Yacine Ayad, 2023. "Practical Energy Management Control of Fuel Cell Hybrid Electric Vehicles Using Artificial-Intelligence-Based Flatness Theory," Energies, MDPI, vol. 16(13), pages 1-23, June.
    6. Duong Phan & Ali Moradi Amani & Mirhamed Mola & Ahmad Asgharian Rezaei & Mojgan Fayyazi & Mahdi Jalili & Dinh Ba Pham & Reza Langari & Hamid Khayyam, 2021. "Cascade Adaptive MPC with Type 2 Fuzzy System for Safety and Energy Management in Autonomous Vehicles: A Sustainable Approach for Future of Transportation," Sustainability, MDPI, vol. 13(18), pages 1-17, September.
    7. Ziad Al-Saadi & Duong Phan Van & Ali Moradi Amani & Mojgan Fayyazi & Samaneh Sadat Sajjadi & Dinh Ba Pham & Reza Jazar & Hamid Khayyam, 2022. "Intelligent Driver Assistance and Energy Management Systems of Hybrid Electric Autonomous Vehicles," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    8. Lu, Zhiming & Gao, Yan & Xu, Chuanbo, 2021. "Evaluation of energy management system for regional integrated energy system under interval type-2 hesitant fuzzy environment," Energy, Elsevier, vol. 222(C).

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