Interoperable Characterization Data for Battery Models and Gigafactories

Ferry Kienberger

Keysight Technologies, Austria


Dr. Ferry Kienberger is Keysight Austria Country Manager and Keysight Labs Linz Group leader on battery science. Prior to this he was Scientist at Agilent Technologies working on nanotechnology.

His University education includes a PhD in 2002 on Technical Physics and the Habilitation in Nanotechnology at JKU Linz in 2019.

The scientific track record includes 130+ scientific peer reviewed publications (including Nature Publishing Group, AAAS Science, PNAS USA, and IEEE Transactions) with an H-factor 41 and 5500 citations; he supervised 10 PhD theses.

He was coordinator and partner in 15+ EU projects for Keysight and Agilent, 7 national projects, 2 international projects (Economic Development Board EDB Singapore), and 3 EMPIR metrology EU projects.  He serves as a vice-chair for the Horizon Europe program and is a former member of the OECD Business and industry advisory council.


Interoperable Characterization Data for Battery Models and Gigafactories

Materials characterization and battery test methods are developed including advanced calibrations and error correction methods. Standard operating procedures are provided for electrochemical impedance spectroscopy, including metrological evaluation of accuracy and error sources. Interoperable data formats are developed, and the test methods are evaluated in a round-robin interlaboratory comparison together with automotive manufacturers and national metrology institutes. We show how the test data is used as input data to modeling algorithms, including physics based FEM (finite element method) models, gray box models (e.g. equivalent electrical circuit models), and black box machine learning models. Thereby, important parameters of the cell performance are provided, which are relevant for evaluating the SoH (State of Health) and second life applications of batteries. The interoperable data formats are relevant for the larger scientific and industrial ecosystem, providing robust industrial use cases for battery manufacturing Gigafactories and for the machine learning community.