PyBOP: A Python package for battery model optimisation and parameterisation
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866908767995559936 |
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| author | Planden, Brady Courtier, Nicola E. Robinson, Martin Khetarpal, Agriya Planella, Ferran Brosa Howey, David A. |
| author_facet | Planden, Brady Courtier, Nicola E. Robinson, Martin Khetarpal, Agriya Planella, Ferran Brosa Howey, David A. |
| contents | The Python Battery Optimisation and Parameterisation (PyBOP) package provides methods for estimating and optimising battery model parameters, offering both deterministic and stochastic approaches with example workflows to assist users. PyBOP enables parameter identification from data for various battery models, including the electrochemical and equivalent circuit models provided by the popular open-source PyBaMM package. Using the same approaches, PyBOP can also be used for design optimisation under user-defined operating conditions across a variety of model structures and design goals. PyBOP facilitates optimisation with a range of methods, with diagnostics for examining optimiser performance and convergence of the cost and corresponding parameters. Identified parameters can be used for prediction, on-line estimation and control, and design optimisation, accelerating battery research and development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15859 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PyBOP: A Python package for battery model optimisation and parameterisation Planden, Brady Courtier, Nicola E. Robinson, Martin Khetarpal, Agriya Planella, Ferran Brosa Howey, David A. Systems and Control The Python Battery Optimisation and Parameterisation (PyBOP) package provides methods for estimating and optimising battery model parameters, offering both deterministic and stochastic approaches with example workflows to assist users. PyBOP enables parameter identification from data for various battery models, including the electrochemical and equivalent circuit models provided by the popular open-source PyBaMM package. Using the same approaches, PyBOP can also be used for design optimisation under user-defined operating conditions across a variety of model structures and design goals. PyBOP facilitates optimisation with a range of methods, with diagnostics for examining optimiser performance and convergence of the cost and corresponding parameters. Identified parameters can be used for prediction, on-line estimation and control, and design optimisation, accelerating battery research and development. |
| title | PyBOP: A Python package for battery model optimisation and parameterisation |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2412.15859 |