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Machine Learning, Predictive Modeling, and Validation – for Battery State-of-Health Estimation 2023

AAU Energy

By Associate Professor, Daniel-Ioan Stroe

PhD Course: Machine Learning, Predictive Modeling, and Validation – for Battery State-of-Health Estimation

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.

AAU Energy

Pontoppidanstræde 101, Room 1.015, 9220 Aalborg East

  • 22.05.2023 08:30 - 23.05.2023 16:30
    : 01.05.2023

  • English

  • On location

AAU Energy

Pontoppidanstræde 101, Room 1.015, 9220 Aalborg East

22.05.2023 08:30 - 23.05.2023 16:30
: 01.05.2023

English

On location

By Associate Professor, Daniel-Ioan Stroe

PhD Course: Machine Learning, Predictive Modeling, and Validation – for Battery State-of-Health Estimation

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.

AAU Energy

Pontoppidanstræde 101, Room 1.015, 9220 Aalborg East

  • 22.05.2023 08:30 - 23.05.2023 16:30
    : 01.05.2023

  • English

  • On location

AAU Energy

Pontoppidanstræde 101, Room 1.015, 9220 Aalborg East

22.05.2023 08:30 - 23.05.2023 16:30
: 01.05.2023

English

On location

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 course will present an end-to-end framework from when data is gathered to a model has been created and put to use for state-of-health estimation. The models will include linear models, support vector regression, Gaussian process regression, and various neural network structures. The general aim of these methods is to predict capacity degradation 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.

Find the detailed course program on PhD Moodle: phd.moodle.aau.dk/course/view.php?id=2189 

Programme

Topic and lecturer
Day 1: Lithium-ion batteries and ML-based feature extraction and reduction - Daniel-Ioan Stroe and Søren B. Vilsen
  • 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 - Daniel-Ioan Stroe and Søren B. Vilsen
  • Linear models, and shrinkage methods
  • Kernel methods such as support vector regression and Gaussian process regression
  • Neural networks with a short introduction to 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.

Price

8000 DKK for the Industry and 6000 DKK for PhD students outside of Denmark (VAT-FREE Education)

The Danish universities have entered into an agreement that allows PhD students at a Danish university (except Copenhagen Business School) the opportunity to free of charge take a subject-specific course at another Danish university.
Read more here: https://phdcourses.dk/  

Questions

hr@energy.aau.dk   

More information

https://www.energy.aau.dk/research/phd   

Course literature       

  1. X. Sui, S. He, S. B. Vilsen, J. Meng, R. Teodorescu, D.-I. Stroe, “A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery,” Applied Energy, Volume 300, 2021, 117346, https://doi.org/10.1016/j.apenergy.2021.117346.
  2. S. B. Vilsen and D. -I. Stroe, "Transfer Learning for Adapting Battery State-of-Health Estimation From Laboratory to Field Operation," in IEEE Access, vol. 10, pp. 26514-26528, 2022, doi: 10.1109/ACCESS.2022.3156657.
  3. Kevin P. Murphy, “Probabilistic Machine Learning: An Introduction,”The MIT Press, 2022
  4. T. Hastie, R. Tibshirani, J. Friedman, ”The Elements of Statistical Learning,” Springer Series in Statistics, 2nd edition, 2017