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Development of a Machine Learning Model for ECM Parameter Estimation in an Automotive Module Using Imped-ance Data

Institut
Lehrstuhl für Fahrzeugtechnik
Typ
Semesterarbeit / Masterarbeit /
Inhalt
experimentell / theoretisch /  
Beschreibung

Equivalent Circuit Models (ECMs) are widely used in Battery Management Systems (BMS) for estimating key battery states such as State of Charge (SoC) and State of Health (SoH). These models rely on predefined parameters, typically stored in lookup tables and calibrated at the beginning of the battery's life. However, as the battery ages, these static parameters become inaccurate, reducing the effectiveness and accuracy of state estimation algorithms.

With the advent of onboard Electrochemical Impedance Spectroscopy (EIS), there is now a promising opportunity to update ECM parameters dynamically using impedance data. This thesis aims to develop a machine learning approach to estimate and update ECM parameters for individual cells within a series-connected automotive battery module at different stages of aging.

Tasks:

  • Comprehensive literature review on the fundamentals of EIS and lithium-ion batteries, ECM parameter estimation methods, and applicable machine learning approaches
  • Design of experiments to collect EIS data from an automotive battery module
  • Development and comparison of machine learning models for ECM parameter estimation and update
  • Analysis and evaluation of results using suitable metrics and validation techniques
  • Structured documentation and critical reflection on methodology and findings
Voraussetzungen

Required Profile:

  • Strong interest in electromobility and lithium-ion battery technology
  • Solid programming skills in Python and/or MATLAB
  • Independent, structured, and solution-oriented working style
  • Very good English or German language skills

If you are interested, please attach your CV and transcripts (including your current grade point average) from your Bachelor's and, if applicable, Master's degree to your application.

Verwendete Technologien
Lithium ion battery, Machine learning, ECM, EIS
Tags
FTM Studienarbeit, FTM EV, FTM EV Powertrain, FTM Lin
Möglicher Beginn
sofort
Kontakt
Yilei Lin, M.Sc.
Raum: MW 3511
Tel.: +49.89.289.10338
yilei.lintum.de
Ausschreibung