The GREYDIENT innovative training network aims at training a next generation of Early Stage Researchers (ESR) to fully sustain the ongoing transition of European personal mobility towards safe and reliable intelligent mobility systems via the recently introduced framework of grey-box modelling approaches. One of the main challenges that we currently face in this context is the integration of the data captured from the plenitude of sensors that are involved in a particular road-traffic scenario, ranging from monitoring car-component loading situations to power network-reliability estimations. The aim is to fully exploit the potential of merging these data with advanced computational models of components and systems that are widely available in industry in order to fully assess the momentarily safety. Grey box models are an answer to this pressing issue, as they are aimed at optimally integrating (black-box) data driven machine learning tools with (white-box) simulation models to greatly surpass the performance of either framework separately.
Therefore, GREYDIENT will train its ESR’s in a wide spectrum of fields, including the modelling, propagation and quantification of the relevant variabilities, the application of big data and machine learning methods, as well as the optimal combination of data-driven approaches with numerical models. All our ESR’s will obtain a PhD from an internationally respected University, build experience in communicating and disseminating their work, applying their research skills in a non-academic context and receive in-depth training in transferable skills such as commercialization, collaboration and entrepreneurship.
ESR 15 – Phimeca
Machine learning and physical modelling interactions for vehicle batteries optimisation
Objectives: (1) To develop a multi-physics numerical model to represent battery degradation and performance, (2) to implement this model in combination with real monitoring data in a functional mock-up interface (FMI) framework, (3) to develop a method to iteratively update the numerical model via Bayesian model updating and (4) to validate this method on a real electric vehicle battery pack case.
Expected results: A method for efficiently and effectively monitoring battery degradation and performance using an adaptive grey-box virtual twin approach, based on a FMI integration of a multi-physical model with online monitoring data
Applicant profile: Master of science in Engineering, Applied Mathematics or Computer Science or a related field, ideally with background in numerical analysis of systems or processes, machine learning and/or uncertainty quantification. Team player. Excellent communication skills (written and oral) in English.
Keywords: electric vehicle technology, functional mock-up interface, Bayesian model updating
PhD location: The recruited ESR will perform their research at Phimeca for a period of 3-4 years. They will also be able to conduct parts of their research at Sigma-Clermont, Politecnico di Milano and Leibniz University Hannover.
L’équipe PHIMECA est composée d’ingénieurs et de docteurs en mécanique, simulation numérique, fiabilité des structures, statistiques et probabilités, développement informatique. Si vous aussi vous avez une expérience plus ou moins grande dans l’un de ces métiers et si vous souhaitez rejoindre l’équipe de PHIMECA Engineering pour un CDI, un stage, une thèse ou un post-doc, n’hésitez pas à nous contacter !