Head: Antoine Picot
The CODIASE team focuses on optimal real–time management of energy conversion, as well as degradation monitoring and state of health modeling of electrical systems. Its field of application is wide-ranging, extending from electrical grids and insulators to fuel cells and converters. Through its research work, the team aims to achieve synergy between control and diagnosis approaches. These approaches position us at the interface between electrical engineering, control and signal processing.
The permanent team includes 8 university researchers, 1 CNRS researcher, 1 emeritus professor and 1 technician. In addition, there is an average of 10 PhD. students each year.
The group collaborates with several LAPLACE teams: GENESYS, CS, MDCE, LM. It also has numerous industrial partnerships, including Nidec, Schaeffler, EMotors, Schneider, Helion, Safran and Airbus.
On the control side, our research focuses on optimal real–time management of energy conversion. In line with the team’s project and identity, this work is more focused on methodological developments and systems approaches. In particular, this translates into work on the optimal control of power flows in electrical networks, the mutualization and cooperation of electrical actuators, but also the control of converters with a large number of degrees of freedom for better decision-making on the scale of the switching period.
The team proposes methods to cope with the growing complexity of electrical systems. From a local point of view, the diversity of different subsystems in terms of dynamics, operating modes and conditions, as well as accessible information, represents a first challenge for their control or monitoring. On a global level, the interaction of these ever-increasing numbers of different systems requires increasingly complex modeling and algorithms, with computation times that are prohibitive for real-time application. The group is working on simplifying models to enable the control of complex multi-systems.
Our research focuses on these “local/global” aspects, so as not to end up with distinct “offline/online” aspects. The aim is to integrate these two levels on control boards, probably requiring a study of the distributed control of these systems.
The team has developed its methodological research into the parametric identification of models for monitoring the state of health of objects (fuel cells, machine insulators, OLEDs, etc.), as well as data–based methods, using statistical and artificial intelligence–based approaches. The group’s research into monitoring and diagnosis goes beyond the object, as it is very often associated with a “system” environment that needs to be taken into account, and thus focuses on methodologies able of dealing with complex problems.
One of the major challenges is to model the dynamics of degradation and ageing, so as to be able to predict lifespan. We are working both on model-based methodologies, in particular for analyzing test campaigns, and on data-based methodologies, so as to learn the behavior of the system in a healthy and/or faulty state without any a priori knowledge. The group aims to move towards “hybrid” methodologies that take advantage of both models and learning techniques for unknown phenomena. The group also has recognized expertise in design of experiments (DOE) modeling. Through this method, the group focuses on the representativeness and choice of data to be used to build a reliable model, but also on the interaction between different constraints and the impact of dynamic constraints (cycling).
The group emphasizes its commitment to developing models that can be interpreted and explained, so as to maintain a link with the physics of the objects under study, which is a key differentiating factor and leads to the development of complementary methods.
The team has several experimental set-up at its N7 site
We also work in collaboration with other laboratory teams and may share their experimental resources:
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