Subject description - BEVM13TMO

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BEVM13TMO Battery testing, modeling, and state estimation
Roles:  Extent of teaching:2P+2C+2D
Department:13113 Language of teaching:EN
Guarantors:Knap V. Completion:Z,ZK
Lecturers:Haniš T., Hrzina P., Knap V. Credits:4
Tutors:Haniš T., Hrzina P., Knap V., Vošahlík D. Semester:L

Anotation:

The course provides an introduction to batteries and battery systems management. Students will learn how to test, model, parameterize battery models or build algorithms for estimating battery states (e.g. state of charge and lifetime). The course combines theoretical knowledge with practical experience to give students the skills needed to solve real-world problems in the rapidly developing field of battery technology.

Study targets:

Students will receive points (grades) for the exercise report or homework submitted, which will then form the basis for the exam, where the grade can be further influenced. Assessment is given for the semester project, which is based on the exercises and assignments. The exam is in the form of a debate over the semester project.

Content:

The course provides an introduction to batteries and battery systems management. Students will learn how to test, model, parameterize battery models or build algorithms for estimating battery states (e.g. state of charge and lifetime). The course combines theoretical knowledge with practical experience to give students the skills needed to solve real-world problems in the rapidly developing field of battery technology.

Course outlines:

1) Introduction to Batteries
2) Battery Management Systems for Batteries
3) Electrical Circuit Models and Their Discretization
4) Characterization, Parametrization, and Validation of Battery Models
5) State Estimation using Kalman Filters and Least Square Methods
6) Non-linear Kalman Filters and Parameter Estimation Techniques
7) State-of-Charge Estimation
8) Online Parameter Identification and Other Functionalities
9) Online State-of-Health Estimation
10) Offline State-of-Health Estimation and Diagnostics
11) Data-Driven Methods, Machine Learning, and Artificial Intelligence
12) Integration of Algorithms, Battery Pack Management, and System Management
13) Control Systems and Optimization in Applications
14) Reserve

Exercises outline:

1) Introduction and Safety in the Laboratory
2) Battery Management Systems
3) MATLAB
4) Battery Testing
5) Implementation of Mathematical Models
6) Parametrization and Validation of Battery Models
7) State-of-Charge Estimation
8) State-of-Charge Estimation
9) Online State-of-Health Estimation
10) Online Parameter Identification and Other Functionalities
11) Data-Driven Methods, Machine Learning, and Artificial Intelligence
12) Integration of Algorithms, Battery Pack Management, and System Management
13) Offline State-of-Health Estimation and Diagnostics
14) Reserve

Literature:

https://moodle.fel.cvut.cz/courses/BEVM13TMO •Plett, G.L., Battery Management Systems: Battery Modeling, vol. 1, Artech House, 2015, ISBN: 978-1-63081-023-8. •Plett, G.L., Battery Management Systems: Equivalent-Circuit Methods, vol. 2, Artech House, 2016, ISBN: 978-1-63081-027-6. •Simon, D.: Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches. Wiley, 2006, ISBN: 978-0-471-70858-2 •Lewis, F. L., L. Xie, D. Popa: Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory, CRC Press, 2005. ISBN 978-1-4200-0829-6

Requirements:

Knowledge of basic circuit theory, linear algebra, statistics, dynamical systems models, and MATLAB is recommended.

Subject is included into these academic programs:

Program Branch Role Recommended semester


Page updated 20.4.2024 17:51:11, semester: L/2023-4, Z/2024-5, Z/2023-4, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)