Smart Battery II: Artificial Intelligence in Battery State Estimation (2024)
November 27 - November 28
Organizer: Prof. Remus Teodorescu Aalborg University and Dr. Xin Sui, Aalborg University
Lecturers:
Postdoc. Xin Sui, Postdoc, Aalborg University
Roberta Di Fonso, Aalborg University
Prof. Remus Teodorescu, Aalborg University
Assoc. Prof. Changfu zou, Chalmers University of Technology
ECTS: 2.0
Date/Time: 27-28 November 2024
Deadline: 06 November 2024
Place: AAU Energy, Pontoppidanstraede 101 room 1.015, Aalborg, Denmark
Max no. of participants: 30
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.
Day 1: Introduction to Artificial Intelligence and battery state estimation – Remus Teodorescu, Nicolai André Weinreich & Xin Sui (8 hours)
- Introduction to Smart Battery: how AI makes battery smart
- AI basics
- Estimation and prediction in general
- Lithium-ion battery basics
- Introduction to State of charge, state of health, and lifetime prediction
- Battery characteristics and performance parameters
Day 2: Artificial Intelligence for battery State estimation – Xin Sui & Changfu Zou (8 hours)
- Aging description and tests
- Data preprocessing including data cleaning, data alignment, feature extraction
- SOX (SOC, SOT, SOH) estimation using AI
- Short-term and long-term SOH prediction using AI
- Support vector regression, Gaussian process regression, neural networks, Transfer learning, and multitask learning
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.
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