A generalized physics-based calendar life model for Li-ion cells

Rahimian, Saeed Khaleghi; Forouzan, Mehdi M.; Han, Sangwoo; Tang, Yifan

doi:10.1016/j.electacta.2020.136343

Cited by 19 publications

(15 citation statements)

References 38 publications

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“…A good example of justifying the structure of an empirically identified parameter sub-model is shown in work by Mathieu et al, 47 who used p-values to determine that all but one of several possible descriptors were statistically significant. Similarly, Rahimian et al 53 used a t test to evaluate the significance of parameters in their identified model. However, without thorough statistical validation, it is not possible to reach a detailed understanding of model behavior or predictive accuracy compared to other potential model structures.…”

Section: T U Tmentioning

confidence: 99%

Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning

Gasper

Gering

Dufek

et al. 2021

J. Electrochem. Soc.

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Various modeling techniques are used to predict the capacity fade of Li-ion batteries. Algebraic reduced-order models, which are inherently interpretable and computationally fast, are ideal for use in battery controllers, technoeconomic models, and multiobjective optimizations. For Li-ion batteries with graphite anodes, solid-electrolyte-interphase (SEI) growth on the graphite surface dominates fade. This fade is often modeled using physically informed equations, such as square-root of time for predicting solventdiffusion limited SEI growth, and Arrhenius and Tafel-like equations predicting the temperature and state-of-charge rate dependencies. In some cases, completely empirical relationships are proposed. However, statistical validation is rarely conducted to evaluate model optimality, and only a handful of possible models are usually investigated. This article demonstrates a novel procedure for automatically identifying reduced-order degradation models from millions of algorithmically generated equations via bi-level optimization and symbolic regression. Identified models are statistically validated using cross-validation, sensitivity analysis, and uncertainty quantification via bootstrapping. On a LiFePO 4 /Graphite cell calendar aging data set, automatically identified models utilizing square-root, power law, stretched exponential, and sigmoidal functions result in greater accuracy and lower uncertainty than models identified by human experts, and demonstrate that previously known physical relationships can be empirically "rediscovered" using machine learning.

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Section: T U Tmentioning

confidence: 99%

Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning

Gasper

Gering

Dufek

et al. 2021

J. Electrochem. Soc.

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“…In this study, the SEI growth is considered as a result of the two‐electron reaction, 19 following Equation (). The solvent diffusion and redox reaction kinetics are the rate‐determining factors.

2 {italicLi}^{+} + 2 e^{-} + Solvent \to SEI .

According to Safari's and Kamyab's work, 19,21 solvent diffusion through the initial SEI is coupled with the reaction kinetics on anode particles, and thus the side reaction current density is expressed by the cathodic Tafel formula, following Equation () 4 . k SEI is the kinetic rate constant of the SEI growth reaction, ε SEI is the porosity of the SEI, and D sol is the solvent diffusivity through SEI.…”

Section: Model Descriptionmentioning

confidence: 99%

“…By substituting the ∆ C sol in Equation () based on Equation (), we can get the reaction rate of SEI growth, as in Equation (), from which we can observe the contribution of the reaction kinetics and solvent diffusion to the side reaction rate, respectively. It should be noted that during the calendar aging process, the electrodes are assumed to be in equilibrium, 4 which means that j in Equation () and Equation () equals 0.

j_{SEI} goodbreak= goodbreak- {Fk}_{SEI} (ε_{SEI} C_{sol}^{0} goodbreak- normalΔ C_{sol}) normal\times normalexp ([], goodbreak- \frac{α_{SEI} F (ϕ_{s} goodbreak- ϕ_{e} goodbreak- U_{SEI} goodbreak- {jR}_{SEI})}{RT}),

j_{SEI} = - {italicFD}_{sol} \frac{Δ C_{sol}}{δ_{SEI}},

j_{SEI} = - \frac{ε_{SEI} C_{sol}^{0} F}{\frac{1}{k_{SEI} \exp [- \frac{α_{SEI} F (ϕ_{normals} - ϕ_{normale} - U_{SEI} - {italicjR}_{SEI})}{italicRT}]} + \frac{δ_{SEI}}{D_{sol}}} .

The material balance equation for SEI growth is shown in Equation (). C SEI is the substance concentration of the generated SEI, and the definition of a s is described in Equation (), where ε anode is the volume fraction of the solid phase of the anode, and r anode is the radius of the anode particles.…”

Section: Model Descriptionmentioning

confidence: 99%

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A mechanistic calendar aging model of lithium‐ion battery considering solid electrolyte interface growth

Zhu

Zhou

Ren

et al. 2022

Intl J of Energy Research

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Summary Calendar life accounts for most of the lifetime of the power lithium‐ion battery applied in electric vehicles, and thus should be investigated in detail. In this paper, a mechanistic calendar aging model of lithium‐ion battery is developed by adding the solid electrolyte interface (SEI) growth side reaction to the pseudo‐two‐dimensional electrochemical model. The model can accurately simulate the capacity degradation and evolution of the charging voltage profiles of a Lix(NiCoMn)1/3O2 ‐ graphite battery during high‐temperature (55°C) storage under various states of charge. The model is further validated by comparing the electrode degradations inside the batteries after calendar aging. The model predicted electrode degradations are consistent with the results identified using a dual‐tank model and post‐mortem analysis, confirming that SEI growth is the dominant degradation mechanism. Based on the validated model, internal changes in electrodes' structure induced by SEI growth during calendaring aging are discussed. The anodes of the calendar‐aged batteries suffer from severe pore clogging and film resistance increase, resulting in apparent power performance degradation of lithium‐ion battery. Finally, modeling analysis is conducted to find possible solutions to mitigate the battery calendar aging process. Rational designs of SEI by reducing the porosity and solvent diffusivity inside the SEI and the kinetic rate constant turn out to be effective in improving the calendar lifetime of lithium‐ion batteries. Novelty Statement Calendar life accounts for most of the lifetime of the lithium‐ion battery in electric vehicles. This study establishes a mechanistic calendar aging model of lithium‐ion battery considering solid electrolyte interface growth. The model can not only simulate the battery capacity degradation and the evolution of battery charging voltage profiles but also capture the internal degradation of the electrodes during the aging process. Modeling analysis is further conducted to find possible solutions to mitigate the calendar aging process of lithium‐ion battery.

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“…The author stated that the discharge behavior of the cell had a strong dependence on discharge C-rate and loss of cyclable lithium. Khaleghi Rahiman et al [19] developed a mathematical model to study cell life with various parameters. The author studied capacity loss and SEI formation in Li-ion batteries at different temperatures at different SOCs.…”

Section: Introductionmentioning

confidence: 99%

Modelling the effect of anode particle radius and anode reaction rate constant on capacity fading of Li-ion batteries

Jha

Krishnamurthy

2021

J. Electrochem. Sci. Eng.

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This paper investigates the effect of anode particle radius and anode reaction rate constant on the capacity fading of lithium-ion batteries. It is observed through simulation results that capacity fade will be lower when the anode particle size is smaller. Simulation results also show that the reaction rate constant for the anode reaction has a good impact on the capacity loss of a lithium-ion battery. The potential drop across the SEI layer (solid electrolyte interphase) is studied as a function of the anode particle radius and anode reaction rate constant. Modelling results are compared with experimental data and found to compare well.

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A generalized physics-based calendar life model for Li-ion cells

Cited by 19 publications

References 38 publications

Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning

Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning

A mechanistic calendar aging model of lithium‐ion battery considering solid electrolyte interface growth

Modelling the effect of anode particle radius and anode reaction rate constant on capacity fading of Li-ion batteries

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