Learning the P2D Model for Lithium-Ion Batteries with SOH Detection
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| Format: | Preprint |
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2025
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| _version_ | 1866929721108856832 |
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| author | McKay, Maricela Best Gopaluni, Bhushan Wetton, Brian |
| author_facet | McKay, Maricela Best Gopaluni, Bhushan Wetton, Brian |
| contents | Lithium ion batteries are widely used in many applications. Battery management systems control their optimal use and charging and predict when the battery will cease to deliver the required output on a planned duty or driving cycle. Such systems use a simulation of a mathematical model of battery performance. These models can be electrochemical or data-driven. Electrochemical models for batteries running at high currents are mathematically and computationally complex. In this work, we show that a well-regarded electrochemical model, the Pseudo Two Dimensional (P2D) model, can be replaced by a computationally efficient Convolutional Neural Network (CNN) surrogate model fit to accurately simulated data from a class of random driving cycles. We demonstrate that a CNN is an ideal choice for accurately capturing Lithium ion concentration profiles. Additionally, we show how the neural network model can be adjusted to correspond to battery changes in State of Health (SOH). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_14147 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning the P2D Model for Lithium-Ion Batteries with SOH Detection McKay, Maricela Best Gopaluni, Bhushan Wetton, Brian Machine Learning Chemical Physics 65M99 (Primary) 68T07, 78A57 (Secondary) I.2.6; J.2 Lithium ion batteries are widely used in many applications. Battery management systems control their optimal use and charging and predict when the battery will cease to deliver the required output on a planned duty or driving cycle. Such systems use a simulation of a mathematical model of battery performance. These models can be electrochemical or data-driven. Electrochemical models for batteries running at high currents are mathematically and computationally complex. In this work, we show that a well-regarded electrochemical model, the Pseudo Two Dimensional (P2D) model, can be replaced by a computationally efficient Convolutional Neural Network (CNN) surrogate model fit to accurately simulated data from a class of random driving cycles. We demonstrate that a CNN is an ideal choice for accurately capturing Lithium ion concentration profiles. Additionally, we show how the neural network model can be adjusted to correspond to battery changes in State of Health (SOH). |
| title | Learning the P2D Model for Lithium-Ion Batteries with SOH Detection |
| topic | Machine Learning Chemical Physics 65M99 (Primary) 68T07, 78A57 (Secondary) I.2.6; J.2 |
| url | https://arxiv.org/abs/2502.14147 |