Machine Learning, Predictive Modeling, and Validation – for Battery State-of-Health Estimation (2024)
May 21 - May 22
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 room 1.015, 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.
Description:
Day 1: Lithium-ion batteries and ML-based feature extraction and reduction by Daniel-Ioan Stroe and Søren B. Vilsen; 7.5 hours
– Introduction to lithium-ion batteries and battery performance parameters for SOH
– Overview of machine learning methods, the bias-variance trade-off, and cross-validation.
– Feature extraction (manual extraction).
– Feature reduction through principal components analysis and multi-dimensional scaling.
Day 2: Machine Learning for battery SOH estimation by Daniel-Ioan Stroe and Søren B. Vilsen; 7.5 hours
– Linear models, selection, and shrinkage methods.
– Kernel methods: support vector regression and Gaussian process regression.
– Neural networks: DNN and RNN.
– Automatic feature extraction and reduction by using neural networks.
Prerequisites: Fundamental understanding of probability and statistics is recommended. Furthermore, basic knowledge of either R, Matlab, or python is strongly recommended.
Form of evaluation: Students are expected to solve several exercises and deliver an individual report with solutions and comments.
Disclaimer:
DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
