Work

BatCAT deliverable 5.1: Cellular neural networks and multicriteria optimization

Year: 2025

Type: article

Abstract: This deliverable reports on the development and results of advanced battery health optimization and performance within T5.1 of the project. The work focuses on creating a self-supervised cellular neural network predictor to provide real-time and adaptable battery state of health by leveraging a student-teacher learning framework. Additionally, the deliverable covers T5.2, i.e., the implementation of multicriteria optimization for dynamic, staged constant-current charging protocols, which enables optimized balance charging speed, safety, and battery longevity.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.18063372

Authors: Klein, Peter, Baldan, Marco, Dey, Aditya, Al Machot, Fadi

Institutions: Fraunhofer Institute for Industrial Mathematics, Norwegian University of Life Sciences

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: green

Funder: European Commission