Computer simulation for battery design and lifetime prediction

Salvadori, Alberto; Grazioli, Davide

doi:10.1016/b978-1-78242-377-5.00016-9

Cited by 4 publications

(4 citation statements)

References 89 publications

(114 reference statements)

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“…Research activities carried out worldwide over the last few years call attention to the multi-scale and multi-physics modeling of storage cells [4] to predict conditions to develop the next generation of batteries for higher capacity and longer cycling life. Computational simulations, based on rigorous theoretical modeling and coupled to validation and quantification of the uncertainties, have the potential to enhance batteries' performances, tailor architectural configurations toward optimal functioning of energy storage devices, and shape new materials for greater capacity and power release.…”

Section: Introductionmentioning

confidence: 99%

Computational modeling of Li-ion batteries

2016

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This review focuses on energy storage materials modeling, with particular emphasis on Li-ion batteries. Theoretical and computational analyses not only provide a better understanding of the intimate behavior of actual batteries under operational and extreme conditions, but they may tailor new materials and shape new architectures in a complementary way to experimental approaches. Modeling can therefore play a very valuable role in the design and lifetime prediction of energy storage materials and devices. Batteries are inherently multi-scale, in space and time. The macro-structural characteristic lengths (the thickness of a single cell, for instance) are order of magnitudes larger than the particles that form the microstructure of the porous electrodes, which in turn are scale-separated from interface layers at which atomistic intercalations occur. Multi-physics modeling concepts, methodologies, and simulations at different scales, as well as scale transition strategies proposed in the recent literature are here revised. Finally, computational challenges toward the next generation of Li-ion batteries are discussed.

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Section: Introductionmentioning

confidence: 99%

Computational modeling of Li-ion batteries

2016

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“…As pursued in [30], the electro-quasi-statics approximation of the Maxwell's equations is considered, by neglecting the time derivative of the magnetic field. For the sake of brevity we address the reader to [32,33,34] for details. The balance laws in the electrolyte domain V e include • Conservation of moving ions…”

Section: Electrolytementioning

confidence: 99%

“…The battery response will be evaluated upon constant current discharging at different C-rates. To this end, a uniform discharging current is prescribed on both sides of the battery as follows Figure 6: Log plots for the dimensionless ratio h/h 0 , ∆b/b 0 , and for the porosity for the evolution of the electrode geometry with n=0,1,... and α(n) as in (34).…”

Section: Boundary and Initial Conditionsmentioning

confidence: 99%

Quantitative investigation of the influence of electrode morphology in the electro-chemo-mechanical response of Li-ion batteries

Magri,

Boz,

Cabras

et al. 2021

Preprint

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In Li-ion batteries the electrochemical potential drives the redox reactions occurring at the interface between electrolyte and storage material, typically active particles for porous electrodes, allowing Li ions intercalation/extraction from electrodes. The limiting factor of the process is not solely related to the electrochemical affinity, but also to structural features as the morphology of the electrodes. In this note, the relevance of the electrodes architecture is investigated numerically, in terms of the electro-chemomechanical response of a battery cell with homogeneous electrodes and conventional liquid electrolyte. A suitable design of the electrodes induces major increments of battery efficiency.

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“…Wide range of computational modelling studies have been performed on electrochemical capacitators to evaluate the effect of various physical and chemical parameters such as average pore size/specific surface area and pseudocapacitive doping (heteroatoms or transition metal oxides) respectively on their electrochemical performance and cycle life. Most commonly used tools employed in modelling of energy storage systems include Monte Carlo (MC) simulations, density function theory (DFT), computational fluid dynamics simulation (CFD) and molecular dynamics (MD) [213][214][215][216][217][218][219][220][221]. With the turn of the century, modelling has seen increased used for the analysis of the performance of the energy storage systems.…”

Section: Computational Modelling Applications In Electrochemical Enermentioning

confidence: 99%

Current State and Future Prospects for Electrochemical Energy Storage and Conversion Systems

et al. 2020

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Electrochemical energy storage and conversion systems such as electrochemical capacitors, batteries and fuel cells are considered as the most important technologies proposing environmentally friendly and sustainable solutions to address rapidly growing global energy demands and environmental concerns. Their commercial applications individually or in combination of two or more devices are based on their distinguishing properties e.g., energy/power densities, cyclability and efficiencies. In this review article, we have discussed some of the major electrochemical energy storage and conversion systems and encapsulated their technological advancement in recent years. Fundamental working principles and material compositions of various components such as electrodes and electrolytes have also been discussed. Furthermore, future challenges and perspectives for the applications of these technologies are discussed.

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Computer simulation for battery design and lifetime prediction

Cited by 4 publications

References 89 publications

Computational modeling of Li-ion batteries

Computational modeling of Li-ion batteries

Quantitative investigation of the influence of electrode morphology in the electro-chemo-mechanical response of Li-ion batteries

Current State and Future Prospects for Electrochemical Energy Storage and Conversion Systems

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