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This study introduces a novel Sequence-to-Sequence (Seq2Seq) deep learning model for predicting lithium-ion batteries' remaining useful life. We address the challenge of extrapolating battery performance from high-rate to low-rate charging conditions, a significant limitation in previous studies. Experiments were also conducted on commercial cells using charge rates from 1C to 3C. Comparative analysis of fully connected neural networks, convolutional neural networks, and long short-term memory networks revealed their limitations in extrapolating to untrained conditions. Our Seq2Seq model overcomes these limitations, predicting charging profiles and discharge capacity for untrained, low-rate conditions using only high-rate charging data. The Seq2Seq model demonstrated superior performance with low error and high curve-fitting accuracy for 1C and 1.2C untrained data. Unlike traditional models, it predicts complete charging profiles (voltage, current, temperature) for subsequent cycles, offering a comprehensive view of battery degradation. This method significantly reduces battery life testing time while maintaining high prediction accuracy. The findings have important implications for lithium-ion battery development, potentially accelerating advancements in electric vehicle technology and energy storage. [ABSTRACT FROM AUTHOR]

The electrochemical performance of Li-ion batteries strongly depends on the interaction between atomic scale and micro scale phenomena in high capacity electrode materials such as silicon and sulfur. Local thermodynamic interactions between host and guest species on atomic-scale significantly influence transfer kinetics and deformation kinematics on the micro-scale. We propose a multi-scale model to characterize the electrochemical and mechanical response of an amorphous silicon thin film during discharge/charge cycling. In the atomic-scale simulation, the stress-dependent energy barrier for the migration of lithium and the molar excess Gibbs free energy were calculated using density functional theory. These atomic-scale effects account for the nonlinear lithium diffusion behavior in the continuum simulation. In the continuum simulation, we considered the coupled diffusion and large deformation model on the cell-scale to determine the non-equilibrium cell potential as a function of the surface lithium concentration using Butler-Volmer kinetics. We clearly show that Li macroscopic kinetics is significantly affected by the stress induced by the volumetric strain associated with diffusion and the mixing formation energy of Li x Si. Our simulation results demonstrate that the multi-scale model is consistent with experimental observations at different C-rates. [ABSTRACT FROM AUTHOR]

The mechanical properties of cathode materials are among the critical factors that directly affect the lifespan of lithium-ion batteries (LIBs). However, a significant challenge remains in understanding the exact mechanism of mechanical degradation of present cathode materials that results in significant loss of performance during long-term operation. Herein, a comparative study on the correlation between mechanical strength (e.g. particle hardness) and cycle performance of the cathode materials (particularly those with layered structures) is presented. By improving the particle hardness of cathode materials via Mg doping, the formation of undesirable microcracks within the particles during cycling can be effectively suppressed, thus improving cycle performance. Structural and electrochemical analyses are performed in order to further elucidate the mechanical degradation mechanism of LIBs during repeat cycles, and identify a possible approach to the enhancement of electrochemical performance and long-term durability in present cathode materials. Image 1 • A strategy for improving the cycle performance of NCM cathode is presented. • The particle hardness of NCM can be effectively enhanced by Mg doping. • The enhanced particle hardness of Mg-NCM positively affects the cycle performance. • The formation of microcracks within Mg-NCM can be suppressed during cycles. [ABSTRACT FROM AUTHOR]

[Display omitted] • Defect in graphene-silicon(d-Gr/Si) composites enhances electrochemical performance. • Li adsorption energies in the d-Gr/Si were less than -0.6 eV and 0.25 eV at pristine Gr/Si. • Defected Gr/Si is more thermionically stable than pristine Gr/Si. • Charge transfer on defects improves electrical conduction and mechanical strength at interfaces. • Our analyses elucidate that defect engineering enhances Li capacity and mechanical strength. Carbon-coated silicon materials are considered as promising anode materials in high-capacity lithium-ion batteries (LIBs). Theoretically, using graphene as the anode material in the LIB would afford high electrical conductivity, mechanical stability of Si, and suppression of the unstable solid–electrolyte interface. However, its usage is hindered by its electrochemical characteristic, which is not electrochemically active when combined with lithium. Therefore, research on graphene as the anode and coated material in LIBs has been conducted using defect engineering to enhance the storage capacity of graphene. Although the electrochemical characteristics of various defects in graphene have been studied experimentally and theoretically, graphene-based composite anode materials such as graphene–silicon composite electrodes have rarely been studied from the electrochemical and mechanical perspectives. In this study, lithium adsorptions are conducted on various defected graphene and graphene–silicon composites using density functional theory calculation. The formation energies of Li on the various defected graphene are assessed, and the mechanical strengths of the graphene–silicon composites are analyzed. Our calculations validate that the defects in graphene enhance the electrochemical adsorptions and interfacial mechanical strengths of the graphene and graphene–silicon composites. During lithiation, the defects mediate greater interfacial adhesion of the silicon–graphene composite. Hence, we elucidate that defected graphene increases the electro-chemo mechanical stabilities of silicon composites in high-capacity LIBs. [ABSTRACT FROM AUTHOR]