Bayesian Parameter Estimation Applied to the Li-ion Battery Single Particle Model with Electrolyte Dynamics

Aitio, Antti; Marquis, Scott G.; Ascencio, Pedro; Howey, David A.

doi:10.1016/j.ifacol.2020.12.1770

Cited by 12 publications

(27 citation statements)

References 27 publications

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“…1D + 1D models are still too costly to evaluate for parameter identification schemes that require hundreds of thousands of simulations. For example, Aitio et al [17] instead use an asymptotically simplified model, the Single-Particle Model with electrolyte (SPMe). [40] The SPMe consists of ordinary differential equations (ODE) only.…”

Section: Physics-based Battery Modelsmentioning

confidence: 99%

“…[13,14] All of these have a stochastical element for efficiency but do not give error bars for their parameterizations. Bayesian algorithms such as Kalman Filters [15,16] and Markov-Chain Monte Carlo [17] simultaneously provide the optimal parameters and their uncertainty. Another method of adding error bars is to repeat the parameterization with artificial noise in the data that mimics the original noise.…”

Section: Introductionmentioning

confidence: 99%

“…Stability is thus enhanced at the cost of runtime, so only simplified battery models that simulate in milliseconds are currently used. [11,17] Uncertainty Quantification (UQ), i. e., estimation of the precision of parameter fits, is a significant advantage of Bayesian algorithms. Predicting battery performance and failure modes relies heavily on the accuracy of the model parameters.…”

Section: Introductionmentioning

confidence: 99%

“…A simple approximation for the Likelihood is a Gaussian distribution with the simulation-experiment agreement as its expectation value and some variance. [17] Finally, multiply the Likelihood with the Prior and normalize the result into a probability distribution…”

Section: Introduction To the Bayesian Principle (For Likelihood-free ...mentioning

confidence: 99%

See 3 more Smart Citations

Bayesian Parameterization of Continuum Battery Models from Featurized Electrochemical Measurements Considering Noise**

Yannick

Wolf

Latz

et al. 2022

Batteries & Supercaps

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Physico-chemical continuum battery models are typically parameterized by manual fits, relying on the individual expertise of researchers. In this article, we introduce a computer algorithm that directly utilizes the experience of battery researchers to extract information from experimental data reproducibly. We extend Bayesian Optimization (BOLFI) with Expectation Propagation (EP) to create a black-box optimizer suited for modular continuum battery models. Standard approaches compare the experimental data in its raw entirety to the model simulations.By dividing the data into physics-based features, our datadriven approach uses orders of magnitude less simulations. For validation, we process full-cell GITT measurements to characterize the diffusivities of both electrodes non-destructively. Our algorithm enables experimentators and theoreticians to investigate, verify, and record their insights. We intend this algorithm to be a tool for the accessible evaluation of experimental databases.

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Section: Physics-based Battery Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introduction To the Bayesian Principle (For Likelihood-free ...mentioning

confidence: 99%

See 2 more Smart Citations

Bayesian Parameterization of Continuum Battery Models from Featurized Electrochemical Measurements Considering Noise**

Yannick

Wolf

Latz

et al. 2022

Batteries & Supercaps

View full text Add to dashboard Cite

show abstract

“…Once published, we suggest that the filled-out checklist be a part of the Supporting Information and provide the necessary background to the readers. To give examples of filled-out checklists for different theoretical studies, each author populated a checklist for at least one of their published articles. ,,,− We aimed to cover many different types of battery modeling paradigms in the author-populated checklists. It also serves as a good starting reference for battery modeling best practices and shortcomings.…”

mentioning

confidence: 99%

A Minimal Information Set To Enable Verifiable Theoretical Battery Research

et al. 2021

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Estimation of Li‐Ion Degradation Test Sample Sizes Required to Understand Cell‐to‐Cell Variability**

Dechent

Greenbank

Hildenbrand

et al. 2021

Batteries & Supercaps

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Ageing of lithium-ion batteries results in irreversible reduction in performance. Intrinsic variability between cells, caused by manufacturing differences, occurs throughout life and increases with age. Researchers need to know the minimum number of cells they should test to give an accurate representation of population variability, since testing many cells is expensive. In this paper, empirical capacity versus time ageing models were fitted to various degradation datasets for commercially available cells assuming the model parameters could be drawn from a larger population distribution. Using a hierarchical Bayesian approach, we estimated the number of cells required to be tested. Depending on the complexity, ageing models with 1, 2 or 3 parameters respectively required data from at least 9, 11 or 13 cells for a consistent fit. This implies researchers will need to test at least these numbers of cells at each test point in their experiment to capture manufacturing variability.

show abstract

Bayesian Parameter Estimation Applied to the Li-ion Battery Single Particle Model with Electrolyte Dynamics

Cited by 12 publications

References 27 publications

Bayesian Parameterization of Continuum Battery Models from Featurized Electrochemical Measurements Considering Noise**

Bayesian Parameterization of Continuum Battery Models from Featurized Electrochemical Measurements Considering Noise**

A Minimal Information Set To Enable Verifiable Theoretical Battery Research

Estimation of Li‐Ion Degradation Test Sample Sizes Required to Understand Cell‐to‐Cell Variability**

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