Bridging Multiscale Characterization Technologies and Digital Modeling to Evaluate Lithium Battery Full Lifecycle

Abstract

The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/aenm.202200889. realize carbon-neutral growth have been put forward by countries all over the world, e.g., China aims to cut CO 2 emissions net-zero by 2060. [1] A promising means to reduce the dependence of our societies on fossil fuels is the development of advanced energy materials. [2] Lithium-ion batteries (LIBs) show great potential as high performance energy storage devices for consumer electronics, electrified transportation, and grid-level energy storage systems because of their inherent advantages, such as high energy density, high working voltage, low self-discharge rate, and long cycle stability, over other traditional battery systems (e.g., lead-acid battery, nickel-metal hydride battery). [3] Despite impressive progress in LIB technologies since the first invention of commercial LIBs in 1992, [4] further expansion and large-scale application of LIBs are currently hindered by limited fast charging ability, relatively low energy density, safety concerns under thermal/mechanical/electrical abuse conditions, capacity decay, and cycle stability. [4,5] Government around the world has set ambitious targets to promote more practical and higher performance batteries technology, such as the American "Battery 500 project" (500 Wh kg −1 in 2021), Chinese "Made in China 2025" (400 Wh kg −1 in 2025), Japanese "RISING II" (500 Wh kg −1 in 2030). [6] Achieving higher energy density has been a priority in recent LIB research to realize longer

Thermal Behavior of Lithium- and Sodium-Ion Batteries: A Review on Heat Generation, Battery Degradation, Thermal Runway – Perspective and Future Directions

“…Machine learning-based algorithms are being employed in tandem with reduced-order electrochemical models or equivalent circuit models to capture the local dynamics of highly nonlinear systems such as batteries. Liu et al 71 provides a detailed discussion on machine learning-based frameworks for digital modeling. Digital twins, which act as a virtual model for a physical system, are gaining popularity.…”

Interlayer‐Engineering and Surface‐Substituting Manganese‐Based Self‐Evolution for High‐Performance Potassium Cathode

“…[1][2][3] Lithium-ion batteries (LIBs) are electrochemical energy storage systems, which have been widely adopted in portable electronic device and electric vehicle industries. [4,5] However, the supply of lithium resources is limited and cannot meet the market demand; thus, potassium (K)-ion batteries (PIBs), which use the similar electrochemical principle as LIBs, have gained attention due to their abun-There have been a number of well-established proposals to tackle this issue (e.g., pillar structures or pinning effects). [25][26][27][28][29][30][31][32][33][34][35] Larger inert cations as interlayer pillars to stabilize the structure in alkali metal layers have been proposed.…”

Digital Twin Enables Rational Design of Ultrahigh‐Power Lithium‐Ion Batteries

“…[30,31] In the field of battery research, digital twin technique has widely been applied in the estimation of battery state of charge, capacity, and resistance based on the interaction with real-time data. [31][32][33][34] However, very few publications have adopted digital twin technique for design and optimization of LIBs, [35,36] much less in high power LIBs.…”