Battery testing, modeling, and state estimation

Course guarantor:

Václav Knap

Lecturer:

Tomáš Haniš, Pavel Hrzina, Václav Knap, David Vošahlík

Tutor:

Tomáš Haniš, Pavel Hrzina, Václav Knap, David Vošahlík

Supervisor:

Department of Electrotechnology

Synopsis:

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.

Requirements:

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

Syllabus of lectures:

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

Syllabus of tutorials:

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

Study Objective:

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.

Study materials:

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

Note:

Time-table for winter semester 2024/2025:

Time-table is not available yet

Time-table for summer semester 2024/2025:

	06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue
Wed	roomT2:C4-264 09:15–10:45 (lecture parallel1) Dejvice roomT2:C4-264 11:00–12:30 (lecture parallel1 parallel nr.101) Dejvice roomT2:C4-264 09:15–10:45 (lecture parallel1) Dejvice roomT2:C4-264 11:00–12:30 (lecture parallel1 parallel nr.101) Dejvice roomT2:C4-264 09:15–10:45 (lecture parallel1) Dejvice roomT2:C4-264 11:00–12:30 (lecture parallel1 parallel nr.101) Dejvice roomT2:C4-264 11:00–12:30 (lecture parallel1 parallel nr.101) Dejvice roomT2:C4-264 11:00–12:30 (lecture parallel1 parallel nr.101) Dejvice
Thu
Fri

The course is a part of the following study plans: