Operational data-driven SOH estimation and prediction for maritime battery system

01.04.2024 - 28.02.2025
Research funding project

Development of a data-driven machine learning model to estimate the SOH value of the ship's battery. Correlations with equivalent circuit parameters are analysed with the aim of improving the prediction model. Correlations between operational data, laboratory test results and equivalent circuit parameters will be identified to provide insights into the degradation and ageing process of batteries.

People

Project leader

Institute

Donation

  • AVL List GmbH

Research focus

  • Information Systems Engineering: 100%

Publications