BattMo -- Battery Modelling Toolbox
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915688001568768 |
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| author | Raynaud, Xavier Nilsen, Halvor Møll Johansson, August Flores, Eibar Hendrix, Lorena Watson, Francesca Krishnamurthi, Sridevi Møyner, Olav Clark, Simon |
| author_facet | Raynaud, Xavier Nilsen, Halvor Møll Johansson, August Flores, Eibar Hendrix, Lorena Watson, Francesca Krishnamurthi, Sridevi Møyner, Olav Clark, Simon |
| contents | This paper presents the Battery Modelling Toolbox (BattMo), a flexible finite volume continuum modelling framework in MATLAB\textsuperscript{\textregistered} (\citeproc{ref-MATLAB}{The MathWorks Inc., 2025}) for simulating the performance of electro-chemical cells. BattMo can quickly setup and solve models for a variety of battery chemistries, even considering 3D designs such as cylindrical and prismatic cells. The simulation input parameters, including the material parameters and geometric descriptions, are specified through JSON schemas. In this respect, we follow the guidelines of the Battery Interface Ontology (BattINFO) to support semantic interoperability in accordance with the FAIR principles (\citeproc{ref-fair}{Wilkinson et al., 2016}). The Doyle-Fuller-Newman (DFN) (\citeproc{ref-Doyle1993ModelingCell}{Doyle et al., 1993}) approach is used as a base model. We include fully coupled thermal simulations. It is possible to include degradation mechanisms such as SEI layer growth, and the use of composite material, such as a mixture of Silicon and graphite. The models are setup in a hierarchical way, for clarity and modularity. Each model corresponds to a computational graph, which introduces a set of variables (the nodes) and functional relationship (the edges). This design enables the flexibility for changing and designing new models. The solver in BattMo uses automatic differentiation and support adjoint computation. We can therefore compute the derivative of objective functions with respect to all parameters efficiently. Gradient-based optimization routines can be used to calibrate parameters from experimental data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_17933 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BattMo -- Battery Modelling Toolbox Raynaud, Xavier Nilsen, Halvor Møll Johansson, August Flores, Eibar Hendrix, Lorena Watson, Francesca Krishnamurthi, Sridevi Møyner, Olav Clark, Simon Computational Physics 35Q60, 78A57, 92E20, 78M12, 80M12 I.6.5; J.2 This paper presents the Battery Modelling Toolbox (BattMo), a flexible finite volume continuum modelling framework in MATLAB\textsuperscript{\textregistered} (\citeproc{ref-MATLAB}{The MathWorks Inc., 2025}) for simulating the performance of electro-chemical cells. BattMo can quickly setup and solve models for a variety of battery chemistries, even considering 3D designs such as cylindrical and prismatic cells. The simulation input parameters, including the material parameters and geometric descriptions, are specified through JSON schemas. In this respect, we follow the guidelines of the Battery Interface Ontology (BattINFO) to support semantic interoperability in accordance with the FAIR principles (\citeproc{ref-fair}{Wilkinson et al., 2016}). The Doyle-Fuller-Newman (DFN) (\citeproc{ref-Doyle1993ModelingCell}{Doyle et al., 1993}) approach is used as a base model. We include fully coupled thermal simulations. It is possible to include degradation mechanisms such as SEI layer growth, and the use of composite material, such as a mixture of Silicon and graphite. The models are setup in a hierarchical way, for clarity and modularity. Each model corresponds to a computational graph, which introduces a set of variables (the nodes) and functional relationship (the edges). This design enables the flexibility for changing and designing new models. The solver in BattMo uses automatic differentiation and support adjoint computation. We can therefore compute the derivative of objective functions with respect to all parameters efficiently. Gradient-based optimization routines can be used to calibrate parameters from experimental data. |
| title | BattMo -- Battery Modelling Toolbox |
| topic | Computational Physics 35Q60, 78A57, 92E20, 78M12, 80M12 I.6.5; J.2 |
| url | https://arxiv.org/abs/2512.17933 |