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ISSN 2753-3239
CCC: 7
PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE
Edited by: J. Pombo
Paper 12.8

Failure Prediction of Railway Battery Cells Under Large Deformations

L. Pugi, D. Barbani, A. Kociu, M. Delogu, L. Berzi and N. Baldanzini

Department of Industrial Engineering of Florence (DIEF), University of Florence, Italy

Full Bibliographic Reference for this paper
L. Pugi, D. Barbani, A. Kociu, M. Delogu, L. Berzi, N. Baldanzini, "Failure Prediction of Railway Battery Cells Under Large Deformations", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 12.8, 2024, doi:10.4203/ccc.7.12.8
Keywords: lithium battery, large deformation, structural failure, finite element, thermal runaway, battery-operated trains.

Abstract
Batteries are a key component for developing innovative battery and hybrid multimodal trains that are currently candidates to substitute Diesel Powered Rolling Stock on non-electrified or partially electrified lines. Also, on conventional rolling stock, innovative high-performance batteries can contribute to weight lightening or ensure a higher and more reliable backup power for auxiliaries. Due to their extremely high energy content, innovative lithium batteries also introduce a potential risk of fire in case of thermal runaway or in case of crash-induced deformations able to cause internal short circuits. Failure phenomena associated with internal breakdown and short circuits involve modelling very small-scale phenomena associated with the non-linear strain of deformation of the internal thin layers of cells. In this work, authors focus their attention on finite element modelling techniques that should help to properly simulate and predict the strain-induced cells of batteries and how these results can be exploited appropriately to produce simplified or surrogate large-scale models that can be very useful to extend the study to large scale battery packs.

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