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, 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 strengths 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.
Day 1: Introduction to AI and battery state estimation – Xin Sui, Remus Teodorescu, & Nicolai André Weinreich
- Lecture 1: Introduction to Smart Battery: how AI makes battery smart - Remus Teodorescu
- Lecture 2: AI in general part 1 - Nicolai André Weinreich
- Lecture 3: AI in general part 2 - Nicolai André Weinreich
- Exercise 1: Applying AI in an end-to-end project - Nicolai André Weinreich
- Lecture 4: Lithium-ion batteries basis - Xin Sui
- Lecture 5: Introduction to SOX (SOC, SOT, SOH, RUL) - Xin Sui
- Lecture 6: SOH estimation using machine learning - Xin Sui
- Exercise 2: Training an NN for SOC estimation - Xin Sui and Nicolai André Weinreich
Day 2: Applied AI in battery modeling and balancing – Xin Sui, Roberta Di Fonso, & Yicun Huang
- Lecture 7: AI for battery modeling and optimization Part 1 - Roberta Di Fonso
- Lecture 8: AI for battery modeling and optimization Part 2 - Roberta Di Fonso
- Exercise 3: Clustering and classification of data - Roberta Di Fonso
- Lecture 9: State-of-the-art AI algorithms in battery lifetime prediction - Xin Sui
- Lecture 10: Physics-based machine learning - Yicun Huang
- Lecture 11: Physics-informed Neural Networks - Yicun Huang
- Exercise 4: Physics-informed Neural Networks - Yicun Huang
- Exercise 5: Training a NN for SOH estimation- Xin Sui
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
Organizer: Prof. Remus Teodorescu ret@energy.aau.dk & Postdoc Xin Sui xin@energy.aau.dk
Lecturers: Dr. Xin Sui, Aalborg University, Denmark
Prof. Remus Teodorescu, Aalborg University, Denmark
PhD student Nicolai André Weinreich, Aalborg University, Denmark
Dr. Roberta Di Fonso, Aalborg University, Denmark
Dr. Yicun Huang, Chalmers University of Technology, Sweden
ECTS: 2.0
Date/Time: 23-24 November 2023
Deadline 2 November 2023
Price: 6000 DKK for PhD students outside of Denmark and 8000 DKK for the Industry excl. VAT
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. Guests at AAU Energy can free attend the course.
Payment: An online link will be announced after the deadline for registration
Place: AAU Energy, Aalborg
Max no. of participants: 30
- Teacher: Xin Sui
- Teacher: Remus Teodorescu
- Teacher: Nicolai André Weinreich