Dynamic Pro-Active Eco-Driving Control Framework for Energy-Efficient Autonomous Electric Mobility

Simin Hesami (), Majid Vafaeipour, Cedric De Cauwer, Evy Rombaut, Lieselot Vanhaverbeke and Thierry Coosemans
Additional contact information
Simin Hesami: The Mobility, Logistics, and Automotive Technology Research Center, Department of Electrical Engineering and Energy Technology, Vrije Universiteit Brussel, 1050 Brussels, Belgium
Majid Vafaeipour: The Mobility, Logistics, and Automotive Technology Research Center, Department of Electrical Engineering and Energy Technology, Vrije Universiteit Brussel, 1050 Brussels, Belgium
Cedric De Cauwer: The Mobility, Logistics, and Automotive Technology Research Center, Department of Electrical Engineering and Energy Technology, Vrije Universiteit Brussel, 1050 Brussels, Belgium
Evy Rombaut: The Mobility, Logistics, and Automotive Technology Research Center, Department of Business Technology and Operations, Vrije Universiteit Brussel, 1050 Brussels, Belgium
Lieselot Vanhaverbeke: The Mobility, Logistics, and Automotive Technology Research Center, Department of Business Technology and Operations, Vrije Universiteit Brussel, 1050 Brussels, Belgium
Thierry Coosemans: The Mobility, Logistics, and Automotive Technology Research Center, Department of Electrical Engineering and Energy Technology, Vrije Universiteit Brussel, 1050 Brussels, Belgium

Energies, 2023, vol. 16, issue 18, 1-19

Abstract: As autonomous vehicle technology advances, the development of energy-efficient control methodologies emerges as a critical area in the literature. This includes the behavior control of vehicles near signalized intersections, which still needs comprehensive exploration. Through connectivity, the adoption of promising eco-driving approaches can manage a vehicle’s speed profile to improve energy consumption. This study focuses on controlling the speed of an autonomous electric vehicle (AEV) both up and downstream of a signalized intersection in the presence of preceding vehicles. In order to achieve this, a dynamic pro-active predictive cruise control eco-driving (eco-PPCC) framework is developed that, instead of merely reacting to the preceding vehicle’s speed changes, uses the preceding vehicle’s upcoming data to actively adjust and optimize the speed profile of the AEV. The proposed algorithm is compared to the conventional Gipps and eco-PCC models for benchmarking and performance analysis through numerous scenarios. Additionally, real-world measurements are performed and taken to consider practical use cases. The results demonstrate that when compared to the two baseline methods, the proposed framework can add significant value to reducing energy consumption, preventing unnecessary stops at intersections, and improving travel time.

Keywords: eco-driving; energy management; energy efficiency; pro-active predictive cruise control; autonomous electric vehicles; self-driving vehicles; connected vehicles (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/18/6495/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/18/6495/ (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:jeners:v:16:y:2023:i:18:p:6495-:d:1235993

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

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