AIMESEV

Research areas:

• Robotics and eSafety

Status: In progress

Type Project: Public_Local

Project leaders: Ángel Llamazares; Pedro Revenga

Collaborators: Elena López; Luis M. Bergasa; Rafael Barea; Manuel Ocaña; Miguel Ángel G. Garrido; María Soledad Escudero

Proposed start date: 2022-12-01

Proposed end date: 2024-11-30

Description:

Artificial Intelligence based Management Energy System for Electric Vehicles

Develop a comprehensive energy management, taking into account the behaviour and habits of use of the EV, integrating the EV as an energy storage element in the power network (V2G) and creating an optimized route planner that estimates the consumption of the vehicle in a realistic way. Achieving in this way, a more sustainable and efficient energy management.

Urban mobility is undergoing a process of transformation thanks to new technologies for electric vehicles (EVs). Road transport electrification has a great potential to reduce greenhouse gas emissions and oil consumptions. However, the massive EVs use may cause a trouble on power grid due to the generation capacity needed to supply a huge recharging requirement. An optimal charging management leads to minimum effects on power grid, reducing peak power via introducing vehicle to grid (V2G) technology and lower charging costs. In addition, the use of intelligent route planning tools based on vehicle location information, degree of automation, traffic, available charging points, and state of the power grid represent an opportunity to produce changes in the mobility patterns of citizens, contributing to the implementation of a smarter mobility system and the sustainability of the transportation system. This project proposes a comprehensive power management system for EVs, in simulation and in a real proof-of-concept, which considers all the agents implied in the power grid at the same time: Smart Grid technologies, including V2G and optimal Electric Vehicle Route Planning with Recharging (EVRC) problem, with a realistic consumption estimator for EVs that takes into account users’ habits and routines, as we depict in Figure 1. With all these agents and using artificial intelligent (AI) techniques, the proposal will minimize the energy consumption and will optimize the power network management and electrical power resources use. The main breakthroughs of our proposal are: 1) Implement a realistic and accurate consumption estimator for the EVs. 2) Address a complete planning route system for EVs considering the factors from the vehicle, the user preferences and behaviours and the environment to advise to the driver of the efficiency of each possible route using metrics combining power use, cost and travel time. 3) Integral power management system, where the power demand, EV charge management and the vehicle as energy storage device is consider, using V2G technologies. 4) Development of a EVs charger prototype with V2G using the standard IEC 62196 connector. 5) Development a simulation and a real proof of concept in the Campus of the UAH where different agents involved in the SG are present. 6) All the software developed would be open-source and the data open-access.

Institutions

Robesafe Group. Department of Electronics. University of Alcalá. Polytechnic School. Campus Universitario Ctra. de Madrid-Barcelona, Km. 33,600 28871 Alcalá de Henares (Madrid) – Spain. (www.robesafe.es/)

     

Funding

Project funding by Ministerio de Ciencia, Innovación y Universidades. Ref: TED2021-130131A-I00

Members and Collaborators

• Ángel Llamazares Llamazares (Assistant Professor) Co-coordinator
• Pedro Revenga de Toro (Associate Professor) Co-coordinator
• Luis M. Bergasa (Full Professor).
• Rafael Barea (Full Professor).
• Elena López (Associate Professor)
• Manuel Ocaña Miguel (Associate Professor)
• Marisol Escudero (Lecturer)
• Carlos Gómez-Huélamo (PhD Researcher)
• Juan Felipe Arango (PhD Researcher)
• Rodrigo Gutiérrez (Postdoctoral Researcher)
• M.B. Rasheed (Postdoctoral Researcher)

 

Results

Journals Papers

• M.B. Rasheed; Angel Llamazares; Manuel Ocaña; Pedro Revenga. A Game-Theoretic Approach to Mitigate Charging Anxiety for Electric Vehicle Users Through Multi-Parameter Dynamic Pricing and Real-Time Traffic Flow. Energy. Elsevier, 15/06/2024. ISSN 1873-6785 DOI: 10.1016/j.energy.2024.132103
• Santiago Montiel-Marín; Angel Llamazares Llamazares; Miguel Antunes; Pedro A. Revenga; Luis M. Bergasa. Point Cloud Painting for 3D Object Detection with Camera and Automotive 3+1D RADAR Fusion. Sensors. 24 - 4,
  MDPI, 15/02/2024. ISSN 1424-8220 DOI: 10.3390/s24041244
• Javier Araluce; Luis M. Bergasa; Ángel Llamazares; Elena López-Guillén. Leveraging Driver Attention for an end-to-end Explainable Decision-making from frontal images. IEEE Transactions on Intelligent Transportation Systems. IEEE, 17/01/2024. ISSN 1558-0016 DOI: 10.1109/TITS.2024.3350337

Conferences

• Pablo Pardo-Decimavilla; Luis M. Bergasa; Santiago Montiel-Marín; Miguel Antunes Garcia; Angel Llamazares. Do You Act Like You Talk? Exploring Pose-based Driver Action Classification with Speech Recognition Networks. Proceedings of the 2024 International IEEE Intelligent Vehicles Symposium Workshops. IV24. June 2024
  
• Pardo-Decimavilla, P., Bergasa, L.M., López-Guillén, E., Llamazares, Á., Abdeselam, N., Ocaña, M. (2024). Driver Activity Recognition by Fusing Multi-object and Key Points Detection. In: Marques, L., Santos, C., Lima, J.L., Tardioli, D., Ferre, M. (eds) Robot 2023: Sixth Iberian Robotics Conference. ROBOT 2023. Lecture Notes in Networks and Systems, vol 976. Springer, Cham. https://doi.org/10.1007/978-3-031-58676-7_12. November 2023.
• Gómez-Huélamo, Carlos & Conde, Marcos & Gutierrez Moreno, Rodrigo & Barea, Rafael & Llamazares, Angel & Antunes García, Miguel & Bergasa, Luis. (2023). "Efficient Context-Aware Graph Transformer for Vehicle Motion Prediction," 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, 2023, pp. 4133-4140, doi: 10.1109/ITSC57777.2023.10422690. September 2023

 

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