Intelligent Battery Management Systems
Hochschule
FH Münster University of Applied Sciences
Fachgebiete
Electrical Engineering
Computer Science
Dauer
6 months
Zeitraum
flexible
Deadline
February 15 for fall internship
October 1 for spring internship
Supervisor
Prof. Dr.-Ing. Peter Glösekötter
Projektbeschreibung
The main focus of this project is to determine the State of Charge (SoC) at the cell level. For this, machine learning algorithms will be used to develop a robust model for the cell. The model can then be applied to all other cell types, allowing a more accurate determination of the SoC, leading to better monitoring of the batteries and thus extending their lifetime.
Also, an Electrochemical Impedance Spectroscopy (EIS for short) measurement for State of Health (SoH) and State of Charge (SoC) will be performed at the stack level. Here, one EIS unit per stack is used to measure all cells. This measurement is intended to determine the state of the batteries at the stack level and to detect and correct potential problems at an early stage.
Aufgaben
Ddepending on the project:
- Specification of the requirements for the battery storage system.
- Manufacturing of test cells and optimization of the assembly technique.
- Analysis of the current density distribution of the cell.
- Interpretation of the test results.
Anforderungen
Bachelor's degree in electrical engineering, physics or comparable engineering-related field of study.
Sprachen
English
Möglicher Beginn
No preferences
Credits
Students can receive credits for the lab project, and a Research Certificate is available.
Bezahlung
Negotiable