Course syllabus

Course-PM

TRA445 TRA445 Advanced battery modelling and control lp3 VT25 (7.5 hp)

Course is offered by the department of Tracks

Contact details

Examiner

Assoc. Prof. Changfu Zou, changfu.zou@chalmers.se 

Teachers

Prof. Torsten Wik, tw@chalmers.se 

Dr.  Xiaolei Bian, xiaoleib@chalmers.se   

Dr.  Yicun Huang, yicun@chalmers.se    

Teaching assistant:

Lic. Qingbo Zhu, qingbo.zhu@chalmers.se

Student representatives

Sanjeev Santosh Deshpande, deshpandesanjeev48@gmail.com

Jiyang Fan, jiyangfan2024@163.com 

Ning Huang, 250723930gg@gmail.com 

Xinrong Mao, maoxinrong0127@163.com 

Michela Porcu, michelaporcu11@gmail.com 

 

Course purpose

Today, batteries play an extremely important role towards a sustainable society. Consequently, it is also crucial that batteries are used in a way that maximises their lifetime and performance. This course aims to equip students with knowledge and tools for that by a comprehensive understanding of advanced battery management systems (BMS), covering the critical aspects of battery modeling, control, and diagnostics. Through interdisciplinary collaboration and engagement with the latest research and industry practices, students will gain skills in analyzing battery dynamics, estimating battery states and model parameters, and knowing basic control of power and temperature. By the end of the course, participants will understand both physics-based and data-driven models to assess battery health, predict aging, manage power limits, and ensure efficient and safe operation of battery systems.

Schedule

TimeEdit

Course literature

Reference literature, further reading:

• Plett, G. L. (2015). Battery management systems, Volume I: Battery modeling. Artech House.
• Plett, G. L. (2015). Battery management systems, Volume II: Equivalent-circuit methods. Artech House.
• Plett, G. L., & Trimboli, M. S. (2024). Battery management systems volume III Physics-based methods (No. 313742). Artech House.

Course design

The course will consist of 10 lectures, 9 exercise sessions, and 1 lab visit.

Lecture Overview:

• Lecture 1: Introduction to BMS and its functionalities (Lecturer: Prof. Torsten Wik)
• Lecture 2: Equivalent circuit battery modelling and parameter identification (Lecturer: Dr. Xiaolei Bian)
• Lecture 3: Kalman filtering and battery state estimation (Lecturer: Dr. Xiaolei Bian)
• Lecture 4: State of power and voltage-based power limit estimation (Lecturer: Dr. Xiaolei Bian)
• Lecture 5: Constant power/voltage simulations and battery pack dynamics (Lecturer: Moritz Streb and Linus Hallberg, experts from Volvo AB)
• Lecture 6: Physics-based modelling of cell electrochemistry and aging (Lecturer: Dr. Yicun Huang)
• Lecture 7: Data-driven battery aging diagnostics and prognostics (Lecturer: Dr. Yizhou Zhang, ZEEKR)
• Lecture 8: Battery thermal models and thermal management (Lecturer: Dr. Yicun Huang)
• Lecture 9: Model-based optimal control (Lecturer: Björn Fridholm, expert from Volvo Cars)
• Lecture 10: Cell balancing techniques (Lecturer: Assoc. Prof. Changfu Zou)

Canvas is an important platform for enhancing the teaching and learning process for this course. It provides access to all relevant learning materials and facilitates communication between students and teachers. 

Changes made since the last occasion

A summary of changes made since the last occasion.

Learning objectives and syllabus

Learning objectives:

• Critically and creatively identify and/or formulate advanced architectural or engineering problems
• Master problems with open solutions spaces which includes to be able to handle uncertainties and limited information.
• Work in multidisciplinary teams and collaborate in teams with different compositions
• Orally and in writing explain and discuss information, problems, methods, design/development processes and solutions
• Identify and explain the functionalities of battery management systems (BMS) and their role in energy storage solutions.
• Apply equivalent circuit models to simulate battery cell behavior and performance under different operating conditions.
• Implement Kalman filters for battery state estimation, including state of charge (SOC) and state of health (SOH).
• Use thermal and equivalent circuit models to control the battery power and temperature.
• Explain physics-based and data-driven models to diagnose battery aging and predict future performance.

 

Link to the syllabus on Studieportalen.

https://www.chalmers.se/en/education/your-studies/find-course-and-programme-syllabi/course-syllabus/TRA445/?acYear=2025/2026

 

Examination form

The course is arranged with lectures before lunch and supervised computer exercises with demonstrations after lunch. Students are organized in groups and for each session there are realistic modelling, estimation and control tasks to be performed on real measurement data. Tasks not completed at those sessions are home-work until next exercise session. At the end of the course the results of all such tasks are summarized in a report and handed in.