Welcome to Machine learning, predictive modelling, and validation – an example for battery state-of-health estimation (2021)


Description: Machine learning (ML) and advanced predictive statistical techniques are gaining widespread use in the field of electrical engineering as a whole, and for state-of-health modelling of Lithium-ion batteries in particular. The introduction of ML and statistics in electrical engineering is a consequence of the field slowly subsidising some of the more expensive laboratory testing by using data collected during real-life operating conditions. The upside of using ML and predictive statistics is that, in many instances, these methods can achieve an acceptable precision using a reduced amount of laboratory testing. However, it comes at the cost of added model complexity and the loss of some of the explanatory power when compared to the physics based state-of-health models.  

This two-day course introduces key aspects of machine learning, predictive modelling, and model validation. Focusing on quantitative predictive models for Lithium-ion battery state-of-health modelling. The models will include sequential and non-sequential approaches, univariate and multivariate outcomes, Bayesian and frequentist frameworks, as well as the reduction and selection of features for modelling. The general aim of these methods is to predict capacity degradation, resistance increase, and remaining useful life based on a combination of laboratory and field data. 

Exemplifications of some of the discussed topics will be made through exercises in R and Matlab

Read more about the course here: https://www.et.aau.dk/events/

Organizer: Associate Professor Daniel-Ioan Stroe - dis@et.aau.dk

Lecturers: Postdoc. Søren B. Vilsen, Associate Professor Daniel-Ioan Stroe 

ECTS: 2.0

Time: 19-20 May 2021

Place: Aalborg University - ONLINE by Zoom

Zip code: 
9220

City: Aalborg

Number of seats: 30

Deadline: 28 April 2021


Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.