A Compression Force Prediction Model for Mechanically Constrained Lithium-Ion Batteries
- 1Vehicle Safety Institute, Graz University of Technology, Inffeldgasse 13, 8010 Graz, Austria
- 2Battery4Life GmbH, Sandgasse 38c/2, 8010 Graz, Austria
Abstract
Lithium-ion batteries (LIBs) in electric vehicle battery packs are assembled under mechanical constraints that exert compression force during operation. During operation, battery cells undergo volumetric changes that cause thickness variations, known as battery swelling. The resulting swelling behavior under mechanical constraints generates force fluctuation. The prediction for force magnitude is critical, as insufficient force compromises electrical contact and structural stability, while excessive force accelerates structural degradation and increases safety risks. However, force sensors have not been widely implemented due to high costs, integration complexity, and reliability concerns, which limits the practical application of force prediction. In this study, we present a coupled electrochemical–mechanical model for predicting compression forces aligning with battery swelling behavior without requiring real-time force sensors during operation. The model predicts the evolution of compression force at an initial external pressure of 0.201 MPa, achieving a mean absolute percentage error (MAPE) of 4.48% during 1C discharge and 7.02% under multi-phase driving cycle conditions, compared with experimental results. The developed framework addresses the existing gap in predicting compression force for mechanically constrained LIBs. The proposed modeling approach enables real-time force monitoring in battery modules, thereby enhancing safety assessment capabilities through force predictions and optimizations.
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BibTeX
@article{yin2026compression,
title = {{A Compression Force Prediction Model for Mechanically Constrained Lithium-Ion Batteries}},
author = {Jun Yin and Christoph Drießen and Maximilian Schinagl and Markus Fasching and Patrick Höschele and Christian Ellersdorfer},
journal = {Batteries},
year = {2026},
month = oct,
doi = {10.3390/batteries12100417},
url = {https://www.mdpi.com/2313-0105/12/10/417},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A Compression Force Prediction Model for Mechanically Constrained Lithium-Ion Batteries
AU - Jun Yin
AU - Christoph Drießen
AU - Maximilian Schinagl
AU - Markus Fasching
AU - Patrick Höschele
AU - Christian Ellersdorfer
PY - 2026
DA - 2026/10/11
JO - Batteries
DO - 10.3390/batteries12100417
UR - https://www.mdpi.com/2313-0105/12/10/417
AB - Lithium-ion batteries (LIBs) in electric vehicle battery packs are assembled under mechanical constraints that exert compression force during operation. During operation, battery cells undergo volumetric changes that cause thickness variations, known as battery swelling. The resulting swelling behavior under mechanical constraints generates force fluctuation. The prediction for force magnitude is critical, as insufficient force compromises electrical contact and structural stability, while excessive force accelerates structural degradation and increases safety risks. However, force sensors have not been widely implemented due to high costs, integration complexity, and reliability concerns, which limits the practical application of force prediction. In this study, we present a coupled electrochemical–mechanical model for predicting compression forces aligning with battery swelling behavior without requiring real-time force sensors during operation. The model predicts the evolution of compression force at an initial external pressure of 0.201 MPa, achieving a mean absolute percentage error (MAPE) of 4.48% during 1C discharge and 7.02% under multi-phase driving cycle conditions, compared with experimental results. The developed framework addresses the existing gap in predicting compression force for mechanically constrained LIBs. The proposed modeling approach enables real-time force monitoring in battery modules, thereby enhancing safety assessment capabilities through force predictions and optimizations.
SN - 2313-0105
ER -