Description

Lithium-ion batteries have a wide range of applications, and their safe and reliable operation is essential. However, due to the complex electrochemical reaction of the battery, the battery performance parameters show strong nonlinearity with aging. Therefore, as the main technologies in BMS, battery state estimation and lifetime prediction remain challenges. Artificial Intelligence (AI) technologies possess immense potential in inferring battery state, and can extract aging information (i.e., health indicators) from measurements and relate them to battery performance parameters, avoiding a complex battery modeling process. Therefore, this course aims to introduce the application of AI in Smart Battery state estimation. 

This two-day course introduces AI methods for estimating/predicting batteries’ state of charge (SOC), state of health (SOH), state of temperature (SOT), and remaining useful life (RUL). Key aspects include laboratory data preparation, data preprocessing, AI model training and selection.

In addition to the classic algorithms of AI, e.g., support vector regression, Gaussian process regression, neural networks, transfer learning, and multitask learning, the feature extraction and selection methods will be included in the discussion. 

In terms of training, two modes will be introduced (depending on the accuracy, robustness, and computation complexity of the selected AI algorithm), i.e., with feature extraction and without feature extraction. According to multiple case studies, the strength and drawbacks of different AI algorithms will be compared. 
Exemplifications of some of the discussed topics will be made through exercises in Python and MATLAB. 

Find the detailed course program on PhD Moodle: https://phd.moodle.aau.dk/course/view.php?id=2203 

Prerequisites

Fundamental understanding of characteristics of Li-ion batteries, and familiar with programming using MATLAB/Python.

Note: the course language is English. 

Form of evaluation

Students are expected to solve a few 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

www.energy.aau.dk/research/phd   

Course literature

1. Plett, Gregory. Battery Management Systems, Volume I: Battery Modeling, Artech House, 2015 (Chapters: 1, 2, 7 all pages, optionally Chapter 5)
2. Plett, Gregory. Battery Management Systems, Volume II: Equivalent-Circuit Methods, Artech House, 2015 (Chapters: 1, 3, 4, all pages)
3. Teodorescu, R.; Sui, X.; Vilsen, S.B.; Bharadwaj, P.; Kulkarni, A.; Stroe, D.-I. Smart Battery Technology for Lifetime Improvement. Batteries 2022, 8, 169. https://doi.org/10.3390/batteries8100169
4. Sui, X., He, S., Vilsen, S.B., Meng, J., Teodorescu, R. and Stroe, D.I., 2021. A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery. Applied Energy, 300, p.117346.
5. Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M. and Chen, Z., 2020. A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems. Renewable and Sustainable Energy Reviews, 131, p.110015.