CTU FEE Moodle
Battery testing, modeling, and state estimation
B232 - Summer 23/24
This is a grouped Moodle course. It consists of several separate courses that share learning materials, assignments, tests etc. Below you can see information about the individual courses that make up this Moodle course.
Battery testing, modeling, and state estimation - BVM13TMO
Main course
| Credits | 4 |
| Semesters | Summer |
| Completion | Assessment + Examination |
| Language of teaching | Czech |
| Extent of teaching | 2P+2C+2D |
Annotation
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.
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
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 outlines
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
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/BVM13TMO
• 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
• 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.
Battery testing, modeling, and state estimation - BEVM13TMO
| Credits | 4 |
| Semesters | Summer |
| Completion | Assessment + Examination |
| Language of teaching | English |
| Extent of teaching | 2P+2C+2D |
Annotation
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.
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
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 outlines
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
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
• 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.