Phase-Field Simulation and Machine Learning Study of the Effects of Elastic and Plastic Properties of Electrodes and Solid Polymer Electrolytes on the Suppression of Li Dendrite Growth

Advances in performance degradation mechanism and safety assessment of LiFePO4for energy storage

In the context of 'energy shortage', developing a novel energy-based power system is essential for advancing the current power system towards low-carbon solutions. As the usage duration of lithium-ion batteries for energy storage increases, the nonlinear changes in their aging process pose challenges to accurately assess their performance. This paper focuses on the study LiFeO4(LFP), used for energy storage, and explores their performance degradation mechanisms. Furthermore, it introduces common battery models and data structures and algorithms, which used for predicting the correlation between electrode materials and physical parameters, applying to state of health assessment and thermal warning. This paper also discusses the establishment of digital management system. Compared to conventional battery networks, dynamically reconfigurable battery networks can realize real-time monitoring of lithium-ion batteries, and reduce the probability of fault occurrence to an acceptably low level.

Computational approach inspired advancements of solid-state electrolytes for lithium secondary batteries: from first-principles to machine learning

The increasing demand for high-security, high-performance, and low-cost energy storage systems (EESs) driven by the adoption of renewable energy is gradually surpassing the capabilities of commercial lithium-ion batteries (LIBs). Solid-state electrolytes (SSEs), including inorganics, polymers, and composites, have emerged as promising candidates for next-generation all-solid-state batteries (ASSBs). ASSBs offer higher theoretical energy densities, improved safety, and extended cyclic stability, making them increasingly popular in academia and industry. However, the commercialization of ASSBs still faces significant challenges, such as unsatisfactory interfacial resistance and rapid dendrite growth. To overcome these problems, a thorough understanding of the complex chemical-electrochemical-mechanical interactions of SSE materials is essential. Recently, computational methods have played a vital role in revealing the fundamental mechanisms associated with SSEs and accelerating their development, ranging from atomistic first-principles calculations, molecular dynamic simulations, multiphysics modeling, to machine learning approaches. These methods enable the prediction of intrinsic properties and interfacial stability, investigation of material degradation, and exploration of topological design, among other factors. In this comprehensive review, we provide an overview of different numerical methods used in SSE research. We discuss the current state of knowledge in numerical auxiliary approaches, with a particular focus on machine learning-enabled methods, for the understanding of multiphysics-couplings of SSEs at various spatial and time scales. Additionally, we highlight insights and prospects for SSE advancements. This review serves as a valuable resource for researchers and industry professionals working with energy storage systems and computational modeling and offers perspectives on the future directions of SSE development.

Phase-Field Simulation of a Dynamic Protective Layer for the Inhibition of Dendrite Growth in Zinc Metal Batteries

Metallic zinc (Zn) has been considered one of the most promising anode materials for next-generation aqueous Zn batteries due to its low redox potential and high storage capacity. However, excessive dendrite formation in Zn metal, corrosion, the evolution of hydrogen gas during the cycling process, and the poor Zn-ion (Zn2+) transport from the electrolyte to the electrode limit its practical application. One of the most effective strategies to suppress Zn dendrite growth and promote Zn2+ transport is to introduce suitable protective layers between the Zn metal electrode and the electrolyte. Herein, we mathematically simulated the dynamic interactions between the Zn deposition on the anode and the resulting displacement of a protective layer that covers the anode, the latter of which can simultaneously inhibit Zn dendrite growth and enhance the Zn2+ transport through the interface between the Zn anode and the protective layer. Our simulation results indicate that a protective layer of high Zn2+ diffusivity not only improves the deposition rate of the Zn metal but also prevents dendrite growth by homogenizing the Zn2+ concentration at the anode surface. In addition, it is revealed that the anisotropic Zn2+ diffusivity in the protective layer influences the 2D diffusion of Zn2+. Higher Zn2+ diffusivity perpendicular to the Zn metal surface inhibits dendrite growth, while higher diffusivity parallel to the Zn metal surface promotes dendrite growth. Our work thus provides a fundamental understanding and a design principle for controlling anisotropic Zn2+ diffusion in the protective layer for better suppression of dendrite growth in Zn metal batteries.

Thin Solid Polymer Electrolyte with High-Strength and Thermal-Resistant via Incorporating Nanofibrous Polyimide Framework for Stable Lithium Batteries

Polyethylene oxide (PEO) based polymer electrolytes show promise in expanding the practical applications of lithium (Li) batteries. However, their applications in Li batteries are usually restricted owing to the lack of mechanical strength, poor oxidative stability, and relatively large thickness. Herein, a nanofibrous polyimide (PI) framework enhanced plasticized-PEO solid electrolyte is prepared to realize good mechanical and electrochemical performances. Following the configuration with the PI matrix, this "polymer in polymer" composite electrolyte with a thickness of 17.5 µm exhibits enhanced mechanical strength (13.9 MPa) and outstanding thermal stability. Additionally, it preserves the high ionic conductivity (2.25 × 10-4  S cm-1 , 25 °C). The Li||Li symmetrical battery with the modified electrolyte could achieve a steady Li plating/stripping of more than 500 h, and the critical current density reaches up to 0.6 mA cm-2 at ambient temperature. The LiFePO4 batteries delivery favorable capacity of 132.2 mAh g-1 with capacity retentions of 96.4% and 85.9% after 500 and 1000 cycles at 1 C, respectively. Acceptable cycling performance also could be achieved in LiNi0.5 Co0. 2 Mn0. 3 O2 solid batteries via an inorganic-rich artificial cathode electrolyte interphase.

Localized Recrystallization of a Lithium-Metal Anode during Fast Stripping in High-Activity Liquid Electrolytes

The lithium-metal anode is one of the most promising candidates for "beyond-lithium-ion" batteries thanks to its high specific capacity and low negative electrochemical potential. However, the electrode-electrolyte interface instability hinders its commercialization in rechargeable batteries. During cycles of charging and discharging, the lithium-metal anode is electrochemically plated and stripped along with the morphological evolution, which determines the cycling performance. In this work, with a phase-field model, we computationally characterize the morphological evolution dynamics during the plating and stripping steps at the lithium-metal-electrolyte interface. Our model is valid in a wide range of lithium concentrations in liquid electrolytes by incorporating nonidealities of electrolyte solutions into the interfacial reaction kinetics. Intriguingly, at fast stripping, i.e., high discharging overpotential, we observe an unexpected localized recrystallization phenomenon in high-lithium-ion-concentration valley regions. This recrystallization phenomenon mitigates the overall reaction rate heterogeneity and provides a potential approach to improving the morphological stability. Furthermore, we systematically investigate the correlation between the recrystallization phenomenon and lithium-ion activity and draw a simplified phase diagram for the overpotential-dependent recrystallization.