Publications

From cathode formulation to dried electrode: in this publication, a hybrid digital model integrates physics-based simulations with deep learning to predict Li-ion cathode electrode microstructure, offering a powerful tool to accelerate battery design and optimization.

To improve the design and accelerate the adoption of Sodium-Ion Batteries (SIBs), it is necessary to improve our understanding of the electrochemical behavior of Hard Carbon (HC) negative electrodes. We report here a novel electrochemical model that unravels the sodiation mechanism of HC electrodes.

The electrolyte wetting of a lithium ion battery cylindrical cell is explored in this study with a 3D resolved continuum model that considers the exact spiral geometry found in commercial 18650 cells. The jelly roll architecture and capillary pressure are shown to be key determinants of the wetting degree and electrolyte distribution within the cell.

The Microporous Layer (MPL) plays a crucial role in Proton Exchange Membrane Fuel Cells (PEMFCs), as it influences the overall transport properties within these devices. This study introduces a novel Machine Learning (ML) approach to optimize the MPL microstructure and properties.

In this study, we present a hybrid Physics-Assisted Machine Learning (PAML) model that integrates Deep Learning (DL) techniques with the classical Discrete Element Method (DEM) to simulate slurry drying during a lithium-ion battery electrode manufacturing process. This model predicts the microstructure evolution leading to the formation of the electrode as a time-series along the drying process.

This publication demonstrates the reconstruction of battery electrochemical impedance spectroscopy (EIS) curves from time-domain pulse testing and the distribution of relaxation times (DRT) analysis. In the proposed approach, the DRT directly utilizes measured current data instead of simulated current patterns, thereby enhancing robustness against current variations and data anomalies.

This publication examines how Machine Learning (ML) models can help to overcome the time-consuming and costly optimiziation of electrochemical cells for energy storage in manufacturing. While, typically, large amounts of data are required for ML models, this new approach proposes a simple application of Transfer Learning (TL) which only needs a small amount of data.

In this study, we investigate the electrochemical performance of carbon-based electrodes with ionic liquid electrolytes, employing calibrated impedance spectroscopy and FEM modelling. Our findings compare activated carbon electrodes with advanced graphene electrodes, revealing how electrode structure influences ion transport mechanisms.

In this publication, electrochemical scanning microwave microscopy (EC-SMM) is introduced as a means to address the demand for new, sustainable and lightweight material for the swift and efficient storage of high energy densities. EC-SMM enables local measurement of electrochemical properties with nanometer spatial resolution and sensitivity down to atto-Ampere electrochemical currents.

This publication describes a new Battery Testing Ontology (BTO) for battery testing and quality control which offers an assortment of battery cell tests specifying required test hardware and calibration procedures, mechanical figuring of batteries and electrical measurement data. It aligns with the Elementary Multiperspective Material Ontology (EMMO) and other ontologies for interoperability.

Quality control is highly relevant for the safety, sustainability, and efficiency of the battery manufacturing process. An early and reliable detection of failures in the production chain is important. The article presents a method for detecting micrometric imperfections and contaminations on the battery separator before filling the battery stack with the electrolyte.