The expansion of lithium-ion batteries from consumer electronics to larger-scale transport and energy storage applications has made understanding the many mechanisms responsible for battery degradation increasingly important. The literature in...
The performance of lithium-ion battery packs are often extrapolated from single cell performance however uneven currents in parallel strings due to cell-to-cell variations, thermal gradients and/or cell interconnects can reduce the overall performance of a large scale lithium-ion battery pack. In this work, we investigate the performance implications caused by these factors by simulating six parallel connected batteries based on a thermally coupled single particle model with the solid electrolyte interphase growth degradation mechanism modelled. Experimentally validated simulations show that cells closest to the load points of a pack experience higher currents than cells further away due to uneven overpotentials caused by the interconnects. When a cell with a four times greater internal impedance was placed in the location with the higher currents this actually helped to equalise the cell-to-cell current distribution, however if this was placed at a location furthest from the load point this would cause a ~6% reduction in accessible energy at 1.5 C. The influence of thermal gradients can further affect this current heterogeneity leading to accelerated aging. Simulations show that in all cases, cells degrade at different rates in a pack due to the uneven currents, with this being amplified by thermal gradients. In the presented work a 5.2% increase in degradation rate, from-7.71 mWh/cycle (isothermal) to-8.11 mWh/cycle (non-isothermal) can be observed. Therefore, the insights from this paper highlight the highly coupled nature of battery pack performance and can inform designs for higher performance and longer lasting battery packs.
Whilst extensive research has been conducted on the effects of temperature in lithium-ion batteries, mechanical effects have not received as much attention despite their importance. In this work, the stress response in electrode particles is investigated through a pseudo-2D model with mechanically coupled diffusion physics. This model can predict the voltage, temperature and thickness change for a lithium cobalt oxide-graphite pouch cell agreeing well with experimental results. Simulations show that the stress level is overestimated by up to 50% using the standard pseudo-2D model (without stress enhanced diffusion), and stresses can accelerate the diffusion in solid phases and increase the discharge cell capacity by 5.4%. The evolution of stresses inside electrode particles and the stress inhomogeneity through the battery electrode have been illustrated. The stress level is determined by the gradients of lithium concentration, and large stresses are generated at the electrode-separator interface when high Crates are applied, e.g. fast charging. The results can explain the experimental results of particle fragmentation close to the separator and provide novel insights to understand the local aging behaviors of battery cells and to inform improved battery control algorithms for longer lifetimes.
Predicting lithium-ion battery degradation is worth billions to the global automotive, aviation and energy storage industries, to improve performance and safety and reduce warranty liabilities. However, very few published models...
Physics-based electrochemical battery models derived from porous electrode theory are a very powerful tool for understanding lithium-ion batteries, as well as for improving their design and management. Different model fidelity, and thus model complexity, is needed for different applications. For example, in battery design we can afford longer computational times and the use of powerful computers, while for real-time battery control (e.g. in electric vehicles) we need to perform very fast calculations using simple devices. For this reason, simplified models that retain most of the features at a lower computational cost are widely used. Even though in the literature we often find these simplified models posed independently, leading to inconsistencies between models, they can actually be derived from more complicated models using a unified and systematic framework. In this review, we showcase this reductive framework, starting from a high-fidelity microscale model and reducing it all the way down to the Single Particle Model (SPM), deriving in the process other common models, such as the Doyle-Fuller-Newman (DFN) model. We also provide a critical discussion on the advantages and shortcomings of each of the models, which can aid model selection for a particular application. Finally, we provide an overview of possible extensions to the models, with a special focus on thermal models. Any of these extensions could be incorporated into the microscale model and the reductive framework re-applied to lead to a new generation of simplified, multi-physics models.
has been published in nal form at http://dx.doi.org/10.1002/nme.5269. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. SUMMARY (TO BE REVISED ONCE TEXT IS COMPLETE) An adaptive Cracking ParticleMethod is developed to model the crack propagation process. A set of cracking particles are used to describe the crack patterns and can be easily updated when the crack propagates. The change of cracking angle is recorded in discrete segments of broken lines which makes this methodology suitable to model discontinuous cracks. The node arrangements can be set up automatically by the adaptivity approach throughout the whole propagation steps to keep the accuracy and efficiency of the analysis. The system stiffness is calculated and stored in local matrices, so only a small influenced domain should be recalculated for each step while the remainder can be read directly form the storage, which greatly reduces the computational expense. The methodology is applied to several 2D crack problems and good agreements are achieved
The failure behavior of geometrically asymmetric sandwich beams with a metal foam core is analytically and experimentally investigated. New initial failure modes of the asymmetric sandwich beams are observed under three-point bending, i.e., face yield, face wrinkling, core shear A, core shear AB, core shear A-AB, and indentation. It is shown that the initial failure modes of sandwich beams depend on the span of the beam, the thicknesses of top and bottom face sheets, core height and material properties. We derived the analytical formulae for the initial failure loads and then constructed the initial failure mechanism maps for the geometrically asymmetric sandwich beams. It is shown that the analytically predicted initial failure mechanism maps are in good agreement with the experimental results, which are clearly different from the symmetric sandwich beams. As a preliminary application, the minimum weight designs are presented for asymmetric metal sandwich beams.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.