Cooperative eco-driving algorithms for light electric vehicles and their experimental validation on a reduced scale "Downscaling
The intelligent vehicle is already a reality, but it is necessary to know what will be its evolution in the following years. In addition, its global deployment not only passes through the vehicle itself but also extends to the infrastructure, so the public and private sectors should plan for the arrival of autonomous and cooperative vehicles, in their most developed state in a time horizon not excessively far away. In this connected mobility, the deliberate exchange of intentions between vehicles and infrastructure reduces the need to "guess" about surrounding traffic and allows for better coordination. Vehicles can thus cooperate rather than compete in urban and highway areas, contributing harmoniously to improved mobility and overall efficiency. This co-operation is about exchanging information and co-ordinating movements for a "common" goal. Even with the best intentions of human drivers, cooperation between conventional vehicles remains rather problematic due to missing information and the difficulty of coordinating while driving. It is clear, therefore, that the key roles in this program are played by efficient connectivity, by centralized or distributed algorithms capable of calculating the best movement instructions for all coordinated vehicles, and by delegated or autonomous driving vehicles that are programmed to follow these instructions. As for the common objective to be achieved, in addition to traffic fluidity and road safety, an important role must be assigned to energy efficiency and environmental quality. This requires that the energy consumption and pollutant emission characteristics of the cooperating vehicles be considered by the centralized control algorithms, and that the said algorithms be capable of calculating the optimal laws of motion with respect to these criteria, all within the constraints imposed by the real-time nature of this control. This is known as cooperative eco-driving. Automated eco-driving of a single vehicle has already shown convincing results in the past, both in terms of reducing CO2 emissions and pollutant emissions, not to mention the benefits for safety and driving comfort. Cooperative eco-driving of vehicles has been studied mainly for specific scenarios such as platooning, lane changing and intersection crossing. Proofs of concept mainly through simulation have shown interesting potential gains. While further research is needed to refine and improve these algorithms, The main research question addressed in this thesis is the validation of these concepts on a reduced scale “Downscaling” and to what extent the potential energy gains can be translated into reality.
Keywords: Connected and automated vehicles, eco-driving, cooperative control, optimal control
- Academic supervisors Dr SCIARRETTA Antonio, Ingénieur de Recherche IFPEN & Dr GRUYER Dominique, Directeur de Recherche 1er classe de l’Université Gustave Eiffel
- Doctoral School Sciences et Technologies de l'Information et de la Communication (STIC), https://www.universite-paris-saclay.fr/ecoles-thematiques-sciences-et-technologies-de-linformation-et-de-la-communication-stic
- IFPEN supervisor Dr. EL GANAOUI MOURLAN Ouafae, firstname.lastname@example.org
- PhD location Université Gustave EIFFEL and IFP Energies nouvelles, Rueil-Malmaison, France
- Duration and start date 3 years, starting as soon as possible
- Employer Université Gustave EIFFEL
- Academic requirements University Master degree in Mechanical or Electrical, Electronics, Information Engineering, Automotive Engineering
- Language requirements Fluency in English, fluency in French or willingness to learn French is a plus
- Other requirements Very good computer or automation skills
To apply, please send your cover letter and CV to the IFPEN supervisor indicated here above.
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