Descripción del proyecto
Un nuevo modelo para lograr mejores baterías en los vehículos eléctricos
Los vehículos eléctricos (VE) son el futuro. Entonces, ¿qué depara el futuro a las baterías de los VE? Para que estas baterías sean fáciles de cargar y menos complicadas, es importante mejorar el sistema de gestión de la batería (BMS, por sus siglas en inglés), que monitoriza, controla y protege los bloques de baterías de iones de litio de los VE. Teniendo en cuenta que las baterías de los VE están sujetas a ciclos operativos rigurosos y a condiciones ambientales variables que dan lugar a interacciones muy complicadas, el equipo del proyecto DeepBMS, financiado con fondos europeos, desarrollará un nuevo modelo para mejorar la funcionalidad del BMS en los VE. Los algoritmos eficientes basados en el aprendizaje profundo por refuerzo registrarán el enrevesado comportamiento de la batería, que varía con el tiempo. En DeepBMS también se aumentará la fiabilidad y se extenderá la vida útil de la batería al mejorar la precisión de la estimación en una amplia gama de temperaturas y durante toda la vida útil de las baterías.
Objetivo
Battery Management System (BMS) plays a pivotal role in monitoring, control, and protecting the Electric Vehicle (EV) Lithium-ion battery packs. In vehicular applications, batteries are usually subjected to harsh operating cycles and varying environmental conditions leading to very complicated interactions of different aging factors and unforeseeable modeling uncertainties. Therefore, the classical model-based techniques cannot completely handle the foregoing factors, which always leave an unwanted state estimation error in the BMS. This project intends to apply a multidisciplinary approach by combining the advantages of deep reinforcement learning and classical model-based techniques to improve the BMS functionality in EVs. Specifically, DeepBMS aims to: 1-Develop efficient deep reinforcement learning-based algorithms which are able to capture the convoluted time-varying behavior of battery and can gradually improve themselves by learning in real-time 2- Combine the beneficial features of model-based and data-driven techniques to improve the state estimation accuracy in a wide temperature range and over the full life span of the batteries, thereby increasing the reliability and extending the battery lifetime. The interdisciplinary nature of DeepBMS is very strong, involving a combination of control and state estimation theory, power electronics, battery storage systems, and machine learning. The supervisor and candidate have excellent complemental research experiences in these fields providing the necessary competencies to bring the project to successful completion. The project ensures two-way transfer of knowledge including training of the candidate in cutting-edge advanced techniques in a state-of-the-art laboratory, which improves his future career prospects. Likewise, DeepBMS is in line with the EU strategic action plan on batteries and its results have a great potential to be further developed at the fundamental and applied levels through follow-up research.
Ámbito científico
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectrical engineeringelectric energy
- natural scienceschemical scienceselectrochemistryelectric batteries
- social sciencessocial geographytransportelectric vehicles
- natural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learning
Programa(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régimen de financiación
HORIZON-AG-UN - HORIZON Unit GrantCoordinador
9220 Aalborg
Dinamarca