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DTSTART;VALUE=DATE:20241127
DTEND;VALUE=DATE:20241129
DTSTAMP:20260403T230951
CREATED:20240424T102849Z
LAST-MODIFIED:20240424T102849Z
UID:10001125-1732665600-1732838399@ddsa.dk
SUMMARY:Smart Battery II: Artificial Intelligence in Battery State Estimation (2024)
DESCRIPTION:Organizer: Prof. Remus Teodorescu Aalborg University and Dr. Xin Sui\, Aalborg University \nLecturers:\n Postdoc. Xin Sui\, Postdoc\, Aalborg University\n Roberta Di Fonso\, Aalborg University\n Prof. Remus Teodorescu\, Aalborg University\n Assoc. Prof. Changfu zou\, Chalmers University of Technology \nECTS: 2.0 \nDate/Time: 27-28 November 2024 \nDeadline: 06 November 2024 \nPlace: AAU Energy\, Pontoppidanstraede 101 room 1.015\, Aalborg\, Denmark \nMax no. of participants: 30 \nDescription: 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. \nIn 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. \nDay 1: Introduction to Artificial Intelligence and battery state estimation – Remus Teodorescu\, Nicolai André Weinreich & Xin Sui (8 hours) \n\n       Introduction to Smart Battery: how AI makes battery smart\n       AI basics\n       Estimation and prediction in general\n       Lithium-ion battery basics\n       Introduction to State of charge\, state of health\, and lifetime prediction\n       Battery characteristics and performance parameters\n 	 \n\nDay 2: Artificial Intelligence for battery State estimation – Xin Sui & Changfu Zou (8 hours) \n\n       Aging description and tests\n       Data preprocessing including data cleaning\, data alignment\, feature extraction\n       SOX (SOC\, SOT\, SOH) estimation using AI\n       Short-term and long-term SOH prediction using AI\n       Support vector regression\, Gaussian process regression\, neural networks\, Transfer learning\, and multitask learning\n 	 \n\nPrerequisites: Fundamental understanding of characteristics of Li-ion batteries\, and familiar with programming using MATLAB/Python. Note: the course language is English. \nForm of evaluation: Students are expected to solve a few exercises and deliver an individual report with solutions and comments. \n  \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/smart-battery-ii-artificial-intelligence-in-battery-state-estimation-2024/
LOCATION:Aalborg University                Pontoppidanstraede 101 room 1.015\, Aalborg
CATEGORIES:PhD Course
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20240521
DTEND;VALUE=DATE:20240523
DTSTAMP:20260403T230951
CREATED:20240424T082725Z
LAST-MODIFIED:20240424T082725Z
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SUMMARY:Machine Learning\, Predictive Modeling\, and Validation – for Battery State-of-Health Estimation (2024)
DESCRIPTION:Organiser: Associate Professor Daniel-Ioan Stroe\, dis@energy.aau.dk \nLecturers:\n Assistant Prof. Søren B. Vilsen (AAU-MATH)\n Associate Professor Daniel-Ioan Stroe (AAU-Energy)  \nECTS: 2.0 \nDate: 21 – 22 May 2024 \nDeadline: 30 April 2024 \nPlace: AAU Energy\, Pontoppidanstraede 101 room 1.015\, Aalborg\, Denmark \nFormat: in person \nMax no. of participants: 30 \nThis 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. \nDescription: \nDay 1: Lithium-ion batteries and ML-based feature extraction and reduction by Daniel-Ioan Stroe and Søren B. Vilsen; 7.5 hours \n– Introduction to lithium-ion batteries and battery performance parameters for SOH\n – Overview of machine learning methods\, the bias-variance trade-off\, and cross-validation.\n – Feature extraction (manual extraction).\n – Feature reduction through principal components analysis and multi-dimensional scaling. \nDay 2: Machine Learning for battery SOH estimation by Daniel-Ioan Stroe and Søren B. Vilsen; 7.5 hours \n– Linear models\, selection\, and shrinkage methods.\n – Kernel methods: support vector regression and Gaussian process regression.\n – Neural networks: DNN and RNN.\n – Automatic feature extraction and reduction by using neural networks. \nPrerequisites: Fundamental understanding of probability and statistics is recommended. Furthermore\, basic knowledge of either R\, Matlab\, or python is strongly recommended. \nForm of evaluation: Students are expected to solve several exercises and deliver an individual report with solutions and comments. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/machine-learning-predictive-modeling-and-validation-for-battery-state-of-health-estimation-2024/
LOCATION:Aalborg University                Pontoppidanstraede 101 room 1.015\, Aalborg
CATEGORIES:PhD Course
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