Materials Map

Discover the materials research landscape. Find experts, partners, networks.

  • About
  • Privacy Policy
  • Legal Notice
  • Contact

The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

×

Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

To Graph

1.080 Topics available

To Map

977 Locations available

693.932 PEOPLE
693.932 People People

693.932 People

Show results for 693.932 people that are selected by your search filters.

←

Page 1 of 27758

→
←

Page 1 of 0

→
PeopleLocationsStatistics
Naji, M.
  • 2
  • 13
  • 3
  • 2025
Motta, Antonella
  • 8
  • 52
  • 159
  • 2025
Aletan, Dirar
  • 1
  • 1
  • 0
  • 2025
Mohamed, Tarek
  • 1
  • 7
  • 2
  • 2025
Ertürk, Emre
  • 2
  • 3
  • 0
  • 2025
Taccardi, Nicola
  • 9
  • 81
  • 75
  • 2025
Kononenko, Denys
  • 1
  • 8
  • 2
  • 2025
Petrov, R. H.Madrid
  • 46
  • 125
  • 1k
  • 2025
Alshaaer, MazenBrussels
  • 17
  • 31
  • 172
  • 2025
Bih, L.
  • 15
  • 44
  • 145
  • 2025
Casati, R.
  • 31
  • 86
  • 661
  • 2025
Muller, Hermance
  • 1
  • 11
  • 0
  • 2025
Kočí, JanPrague
  • 28
  • 34
  • 209
  • 2025
Šuljagić, Marija
  • 10
  • 33
  • 43
  • 2025
Kalteremidou, Kalliopi-ArtemiBrussels
  • 14
  • 22
  • 158
  • 2025
Azam, Siraj
  • 1
  • 3
  • 2
  • 2025
Ospanova, Alyiya
  • 1
  • 6
  • 0
  • 2025
Blanpain, Bart
  • 568
  • 653
  • 13k
  • 2025
Ali, M. A.
  • 7
  • 75
  • 187
  • 2025
Popa, V.
  • 5
  • 12
  • 45
  • 2025
Rančić, M.
  • 2
  • 13
  • 0
  • 2025
Ollier, Nadège
  • 28
  • 75
  • 239
  • 2025
Azevedo, Nuno Monteiro
  • 4
  • 8
  • 25
  • 2025
Landes, Michael
  • 1
  • 9
  • 2
  • 2025
Rignanese, Gian-Marco
  • 15
  • 98
  • 805
  • 2025

Thiede, Sebastian

  • Google
  • 12
  • 28
  • 179

University of Twente

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (12/12 displayed)

  • 2022Integration of Energy Oriented Manufacturing Simulation into the Life Cycle Evaluation of Lightweight Body Parts21citations
  • 2021Machine learning and simulation-based surrogate modeling for improved process chain operation18citations
  • 2021Modeling energy and resource use in additive manufacturing of automotive series parts with multi-jet fusion and selective laser sintering25citations
  • 2020Modeling the Impact of Manufacturing Uncertainties on Lithium-Ion Batteries65citations
  • 2020Industrie 4.0 in der Galvanotechnikcitations
  • 2020Root Cause Analysis in Lithium-Ion Battery Production with FMEA-Based Large-Scale Bayesian Networkcitations
  • 2020Integrated computational product and production engineering for multi-material lightweight structures13citations
  • 2020Agent-Based Simulation Approach for Occupational Safety and Health Planning1citations
  • 2020Model-based analysis, control and dosing of electroplating electrolytes19citations
  • 2019Modelling the Impact of Manufacturing Uncertainties on Lithium-Ion Batteries1citations
  • 2012A hierarchical evaluation scheme for industrial process chains9citations
  • 2011Synergies from process and energy oriented process chain simulation - A case study from the aluminium die casting industry7citations

Places of action

Chart of shared publication
Kaluza, Alexander
1 / 1 shared
Reimer, Lars
1 / 1 shared
Herrmann, Christoph
12 / 31 shared
Dér, Antal
3 / 3 shared
Gellrich, Sebastian
2 / 2 shared
Hürkamp, André
2 / 10 shared
Dröder, Klaus
2 / 24 shared
Rogall, Christopher
1 / 1 shared
Wiese, Mathias
1 / 2 shared
Leiden, Alexander
4 / 5 shared
Schmidt, Oke
2 / 3 shared
Krewer, Ulrike
2 / 13 shared
Röder, Fridolin
2 / 3 shared
Thomitzek, Matthias
2 / 3 shared
Kölle, Stefan
2 / 5 shared
Metzner, Martin
2 / 10 shared
Schwanzer, Peter
1 / 4 shared
Hirz, Mario
1 / 2 shared
Kirchhof, Michael
1 / 1 shared
Kornas, Thomas
1 / 1 shared
Haas, Klaus
1 / 1 shared
Behrens, Bernd-Arno
1 / 119 shared
Ossowski, Tim
1 / 4 shared
Lorenz, Ralf
1 / 6 shared
Schmid, Klaus
1 / 2 shared
Heinemann, Tim
2 / 2 shared
Machida, Wataru
1 / 1 shared
Kara, Sami
1 / 1 shared
Chart of publication period
2022
2021
2020
2019
2012
2011

Co-Authors (by relevance)

  • Kaluza, Alexander
  • Reimer, Lars
  • Herrmann, Christoph
  • Dér, Antal
  • Gellrich, Sebastian
  • Hürkamp, André
  • Dröder, Klaus
  • Rogall, Christopher
  • Wiese, Mathias
  • Leiden, Alexander
  • Schmidt, Oke
  • Krewer, Ulrike
  • Röder, Fridolin
  • Thomitzek, Matthias
  • Kölle, Stefan
  • Metzner, Martin
  • Schwanzer, Peter
  • Hirz, Mario
  • Kirchhof, Michael
  • Kornas, Thomas
  • Haas, Klaus
  • Behrens, Bernd-Arno
  • Ossowski, Tim
  • Lorenz, Ralf
  • Schmid, Klaus
  • Heinemann, Tim
  • Machida, Wataru
  • Kara, Sami
OrganizationsLocationPeople

document

Modelling the Impact of Manufacturing Uncertainties on Lithium-Ion Batteries

  • Thiede, Sebastian
  • Schmidt, Oke
  • Herrmann, Christoph
  • Krewer, Ulrike
  • Röder, Fridolin
  • Thomitzek, Matthias
Abstract

The electrochemical properties of a lithium-ion battery are significantly influenced by the manufacturing process. Optimizing the manufacturing process requires to understand the relationship between the process parameters, the structural parameters and the resulting properties of the battery. Furthermore, tolerances in the single manufacturing steps lead to uncertainties which propagate along the process chain and affect the quality of cells. The uncertainties of the various structural parameters have differently strong influences on the properties. As such sensitive structural parameters need to be detected for a targeted optimization. Additionally the influence of cell-to-cell and lot-to-lot variations have a major influence on quality, and thus on the rejection rate when building battery packs. [1,2]<br/><br/>In our prior studies, we established a coupled multi-level model approach that is able to analyze the influence of tolerances in the manufacturing process and study the process-structure-property relationship. [3] In this approach, a process chain model is coupled with a physical battery model based on Doyle et. al. [4] The coupled model approach is visualized in Figure 1. The process chain simulation consists of single process models describing the relationship between the input process parameters and the structural parameters for each manufacturing step. Tolerances can be considered in each process step by integrating stochastically distributed process and structural parameters. The process chain simulation provides a characterized battery cell, which is the input for the battery cell simulation, where the electrochemical properties are determined.<br/><br/>In this work, the implementation of the coupled model approach and a first case study are presented. The case study is chosen in such way, that the effect of tolerances on the structural parameters, the properties, the propagation of uncertainties and interactions can be studied. Figure 1 shows the four investigated scenarios. In the first scenario, no uncertainties appear in all manufacturing steps. It is used to represent the reference point. Scenario two and three were chosen so that fluctuations occur in only one manufacturing process and propagate without interactions along the process chain. The tolerances either occur in the coating or the calendering process. The fourth scenario combines scenario two and three. This scenario is used to study interactions between the fluctuating processes.<br/><br/>The results of the process chain simulation for the structural parameters indicate that for each process step a mainly affected structural parameter could be identified: In the coating process, the electrode thickness is mainly affected and in the calendering process the electrode porosity. Additionally, it is shown that the interactions of uncertainties in different manufacturing steps show, that the deviations are not accumulated along the process chain. Furthermore, the results of the battery cell simulation reveal distinct impact dependent on the implemented structural parameters and the applied discharge rate. An optimum in the calculation area of the considered range of the deviations leads to a skewed distribution of the electrochemical properties. Finally, the sensitivity of the structural parameters on the electrochemical properties are studied and evaluated. The results provide guidance to focus on the most relevant manufacturing tolerances in order to effectively optimize the product quality and reduce costs.

Topics
  • impedance spectroscopy
  • simulation
  • Lithium
  • porosity