Machine Learning, Predictive Modeling, and Validation – for Battery State-of-Health Estimation (2024)

Organiser: Associate Professor Daniel-Ioan Stroe, dis@energy.aau.dk

Lecturers: Assistant Prof. Søren B. Vilsen (AAU-MATH), Associate Professor Daniel-Ioan Stroe (AAU-Energy)

ECTS: 2.0

Date: 21 – 22 May 2024

Deadline: 30 April 2024

Place: AAU Energy, Pontoppidanstraede 101, Aalborg, Denmark

Format: in person

Max no. of participants: 30

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 course will present an end-to-end framework from when data is gathered to a model has been created and used for state-of-health estimation.

• Day 1: Lithium-ion batteries and ML-based feature extraction and reduction 
• Day 2: Machine Learning for battery SOH estimation

 Prerequisites: Fundamental understanding of probability and statistics is recommended. Furthermore, basic knowledge of either R, Matlab, or python is strongly recommended.

Price:  6000 DKK for PhD students outside of Denmark and 8000 DKK for the Industry excl. VAT.
A link for online payment by credit card for externals participants will be annonced after deadline for registration

Find more information about the course (more details and course litterature)

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 3000 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.