z-logo
open-access-imgOpen Access
Modeling the Impact of Manufacturing Uncertainties on Lithium-Ion Batteries
Author(s) -
Oke Schmidt,
Matthias Thomitzek,
Fridolin Röder,
Sebastian Thiede,
Christoph Herrmann,
Ulrike Krewer
Publication year - 2020
Publication title -
journal of the electrochemical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.258
H-Index - 271
eISSN - 1945-7111
pISSN - 0013-4651
DOI - 10.1149/1945-7111/ab798a
Subject(s) - tortuosity , porosity , materials science , process (computing) , battery (electricity) , calendering , coating , lithium ion battery , manufacturing process , process engineering , composite material , computer science , engineering , thermodynamics , physics , power (physics) , operating system
This paper describes and analyzes the propagation of uncertainties from the lithium-ion battery electrode manufacturing process to the structural electrode parameters and the resulting varying electrochemical performance. It uses a multi-level model approach, consisting of a process chain simulation and a battery cell simulation. The approach enables to analyze the influence of tolerances in the manufacturing process on the process parameters and to study the process-structure-property relationship. The impact of uncertainties and their propagation and effect is illustrated by a case study with four plausible manufacturing scenarios. The results of the case study reveal that uncertainties in the coating process lead to high deviations in the thickness and mass loading from nominal values. In contrast, uncertainties in the calendering process lead to broad distributions of porosity. Deviations of the thickness and mass loading have the highest impact on the performance. The energy density is less sensitive against porosity and tortuosity as the performance is limited by theoretical capacity. The latter is impacted only by mass loading. Furthermore, it is shown that the shape of the distribution of the electrochemical performance due to parameter variation aids to identify, whether the mean manufacturing parameters are close to an overall performance optimum.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom