Bayesian Analysis of Interpretable Aging across Thousands of Lithium-ion Battery Cycles
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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_ | 1866912326767083520 |
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| author | Berliner, Marc D. Kim, Minsu Cui, Xiao Lam, Vivek N. Asinger, Patrick A. Bazant, Martin Z. Chueh, William C. Braatz, Richard D. |
| author_facet | Berliner, Marc D. Kim, Minsu Cui, Xiao Lam, Vivek N. Asinger, Patrick A. Bazant, Martin Z. Chueh, William C. Braatz, Richard D. |
| contents | The Doyle-Fuller-Newman (DFN) model is a common mechanistic model for lithium-ion batteries. The reaction rate constant and diffusivity within the DFN model are key parameters that directly affect the movement of lithium ions, thereby offering explanations for cell aging. This work investigates the ability to uniquely estimate each electrode's diffusion coefficients and reaction rate constants of 95 Tesla Model 3 cells with a nickel cobalt aluminum oxide (NCA) cathode and silicon oxide--graphite (LiC$_\text{6}$--SiO$_{\text{x}}$) anode. The parameters are estimated at intermittent diagnostic cycles over the lifetime of each cell. The four parameters are estimated using Markov chain Monte Carlo (MCMC) for uncertainty quantification (UQ) for a total of 7776 cycles at discharge C-rates of C/5, 1C, and 2C. While one or more anode parameters are uniquely identifiable over every cell's lifetime, cathode parameters become identifiable at mid- to end-of-life, indicating measurable resistive growth in the cathode. The contribution of key parameters to the state of health (SOH) is expressed as a power law. This model for SOH shows a high consistency with the MCMC results performed over the overall lifespan of each cell. Our approach suggests that effective diagnosis of aging can be achieved by predicting the trajectories of the parameters contributing to cell aging. As such, extending our analysis with more physically accurate models building on DFN may lead to more identifiable parameters and further improved aging predictions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10439 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Analysis of Interpretable Aging across Thousands of Lithium-ion Battery Cycles Berliner, Marc D. Kim, Minsu Cui, Xiao Lam, Vivek N. Asinger, Patrick A. Bazant, Martin Z. Chueh, William C. Braatz, Richard D. Systems and Control The Doyle-Fuller-Newman (DFN) model is a common mechanistic model for lithium-ion batteries. The reaction rate constant and diffusivity within the DFN model are key parameters that directly affect the movement of lithium ions, thereby offering explanations for cell aging. This work investigates the ability to uniquely estimate each electrode's diffusion coefficients and reaction rate constants of 95 Tesla Model 3 cells with a nickel cobalt aluminum oxide (NCA) cathode and silicon oxide--graphite (LiC$_\text{6}$--SiO$_{\text{x}}$) anode. The parameters are estimated at intermittent diagnostic cycles over the lifetime of each cell. The four parameters are estimated using Markov chain Monte Carlo (MCMC) for uncertainty quantification (UQ) for a total of 7776 cycles at discharge C-rates of C/5, 1C, and 2C. While one or more anode parameters are uniquely identifiable over every cell's lifetime, cathode parameters become identifiable at mid- to end-of-life, indicating measurable resistive growth in the cathode. The contribution of key parameters to the state of health (SOH) is expressed as a power law. This model for SOH shows a high consistency with the MCMC results performed over the overall lifespan of each cell. Our approach suggests that effective diagnosis of aging can be achieved by predicting the trajectories of the parameters contributing to cell aging. As such, extending our analysis with more physically accurate models building on DFN may lead to more identifiable parameters and further improved aging predictions. |
| title | Bayesian Analysis of Interpretable Aging across Thousands of Lithium-ion Battery Cycles |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2504.10439 |