GREYDIENT project

Objectives

Francesco Cancelliere will be supervised by Jean-Marc BOURINET and Sylvain GIRARD for a thesis funded by the European Union through the ITN program.

Develop

a multi-physics numerical model to represent the degradation and performances of batteries,

Implement

this model in combination with real monitoring data in a FMI frame,

Develop

a method to iteratively update a numerical model via a bayesian update of the model,

Validate

this method on a real case of electrical vehicle battery pack case.

Subject

ESR 15 will develop an active-learning grey-box virtual twin approach for the monitoring of battery degradation and performance in electric vehicles.

Specifically, ESR15 will combine multi-physical (physio-chemical and structural) differential algebraic equation representations of the underlying battery physics (e.g., via 0D/1D simulation in the Modelica language) with data coming from online battery monitoring systems via the Functional Mock-up Interface (FMI) framework.

In this way, a highly flexible grey-box virtual twin can be designed. First, a pen-and-paper design of the battery model and monitoring structure is made, based on a mathematical formalisation of the problem. This theoretic development is then implemented in the FMI framework, where the theoretical DAE models are integrated with data. Furthermore, ESR15 will apply advanced sensitivity analysis techniques to actively determine both how the sensor lay-out, as well as the model representation can iteratively be improved.

Finally, based on Bayesian data assimilation methods, the developed framework will ensure the accuracy of the DAE representation of the battery model automatically. The developments will be validated via a case study involving a real electric vehicle battery pack equipped with the necessary sensors.

More information on the ITN program here.

GREYDIENT project

Objectives

Expected results

Subject