Post-Doctoral Research Visit F / M Strategies for Optimal EV Charging Infrastructure and Power [...]

INRIA; Montbonnot-Saint-Martin; Sur place; EUR 35 000 - 50 000; Plein temps

Résumé du poste

INRIA recherche un chercheur pour rejoindre l'équipe DANCE, spécialisée dans l'optimisation et la modélisation des réseaux de mobilité électrique. Le candidat travaillera sur le développement d'algorithmes d'optimisation pour les stations de charge et intégrera ces solutions dans des outils de simulation avancés. Ce poste offre des avantages tels que des congés annuels, des horaires flexibles et un environnement de travail collaboratif.

Prestations

Remboursement partiel des frais de transport public

7 semaines de congés annuels + 10 jours RTT

Horaires flexibles et options de télétravail

Équipement professionnel fourni

Activités sociales, culturelles et sportives

Accès à la formation professionnelle

Couverture sociale

Qualifications

• Compétences en optimisation et en modélisation physique.
• Expérience avec des algorithmes d'optimisation et des stratégies d'IA.
• Capacité à travailler sur des projets de recherche appliquée.

Responsabilités

• Développer des modèles d'optimisation pour les stations de charge.
• Intégrer des algorithmes d'optimisation dans eMob-Twin V2.
• Analyser les résultats par rapport aux politiques d'urbanisme.

Connaissances

Optimisation

Mathématiques appliquées

Modélisation physique

Intelligence Artificielle

Description du poste

Working Context. This research will be conducted by the DANCE research team (webpage): DANCE (“Dynamics and Control of Networks”) is a joint team of GIPSA-lab and Inria Grenoble–Rhône-Alpes. Our team has strong expertise in modeling, estimation, and control of large-scale networks with applications to Electromobility. The research will be part of the PEPR Digitalisation et Décarbonation des Mobilités.

Scientific Context

Our group has developed eMob-TwinV1 based on the findings of the ERC-AdG Scale-FreeBack (emob-twin.inrialpes.fr), resulting in an e-mobility simulation tool driven by digital twin technology. eMob-Twin serves various purposes including forecasting, analysis, and unlocking EV flexibility, catering to companies, stakeholders, and electricity markets. Initially designed for the Grenoble metropolitan area, a new version, eMob-TwinV2, is under development to encompass other metropolitan cities in France, with auto-calibration functionalities. It focuses on electric vehicle (EV) mobility and their state of charge, integrating multi-power charging stations. In the context of the PEPR-FORBAC initiative, we aim to optimize charging station placement for efficient coverage and power delivery density per unit area, using the steady-state solution of the dynamic electromobility model and energy constraints. Results will be integrated into eMob-Twin V2 as a toolbox.

Work Program

The work program includes development in model extensions, large-scale optimization challenges, and software implementation:

1. Expanding the existing model to include Charge Stations with capacities, driver-user pricing, and grid integration.
2. Developing efficient optimization algorithms (including machine learning and AI strategies) for station placement and power density, scalable to the entire network.
3. Integrating these algorithms into eMob-Twin V2 and analyzing outcomes compared to policies and urban planning tools like SDRIVE.

Software integration will be supported by our local Research Engineers team.

References

• Rodriguez-Vega, M., Canudas-de Wit, C., Nunzio, G.D., and Othman, B. (2023). "A graph-based mobility model for electric vehicles in urban traffic networks: Application to the Grenoble metropolitan area." European Control Conference (ECC), Bucharest.
• Mourgues, R., and Canudas-De-Wit, C., Rodriguez-Vega, M. (2023). "Optimal location of EV public charging stations based on a macroscopic urban electromobility model." IEEE Conference on Decision and Control (CDC), 3122–3129.

Background Requirements

Optimization, Applied Mathematics, Physical Modeling, AI

Advantages

• Partial reimbursement of public transport costs
• 7 weeks of annual leave + 10 RTT days + possible exceptional leave (e.g., for sick children, moving)
• Flexible working hours and teleworking options
• Professional equipment (computers, videoconferencing)
• Social, cultural, and sports activities
• Vocational training access
• Social security coverage