Nonlinear health evaluation for lithium-ion battery within full-lifespan

You, Heze; Zhu, Jiangong; Wang, Xueyuan; Jiang, Bo; Sun, Hao; Liu, Xinhua; Wei, Xuezhe; Han, Guangshuai; Ding, Shicong; Yu, Hanqing; Li, Weihan; Sauer, Dirk Uwe; Dai, Haifeng

doi:10.1016/j.jechem.2022.04.013

Cited by 96 publications

(15 citation statements)

References 39 publications

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“…Dai and co-workers proposed a brand-new and more promising health evaluation indicator-"state of nonlinear aging (SoNA)" to fully describe the nonlinear aging phenomenon during battery degradation. [239] Two SoNA quantitative methods were developed to quickly and accurately quantitatively calculate the degree of nonlinearity and a knee-point identification method was developed which could provide early cycle life prediction.…”

Section: Cell and Pack Levelmentioning

confidence: 99%

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

Liu

Zhang

et al. 2022

Advanced Energy Materials

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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

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Section: Cell and Pack Levelmentioning

confidence: 99%

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

Liu

Zhang

et al. 2022

Advanced Energy Materials

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“…This is very favorable for the local overcharge and the formation of Lidendrites (Yin et al, 2020). Regarding the quasi-solid electrolyte, suitable matrices are highly required, which should meet the following requirements: first, high porosity and good wettability so that it can quickly impregnate enough liquid electrolytes to obtain high ionic conductivity; then, high Li transfer number to achieve homogenous Li-ion migration and reduce ion concentration gradient; additionally, influential functional groups to limit the shuttle of polysulfide and obtain excellent long-term cycling stability (Chen S. et al, 2021;You et al, 2022); moreover, good mechanical properties to inhibit dendrite growth; finally, high thermal stability (Han et al, 2019).…”

Section: General Strategies For Designing Electrolyte Materialsmentioning

confidence: 99%

Recent Progress in Quasi/All-Solid-State Electrolytes for Lithium–Sulfur Batteries

et al. 2022

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Lithium–sulfur batteries have received increasing research interest due to their superior theoretical capacity, cost-effectiveness, and eco-friendliness. However, the commercial realization of lithium–sulfur batteries faces critical obstacles, such as the significant volume change of sulfur cathodes over the de/lithiation processes, uncontrollable shuttle effects of polysulfides, and the lithium dendrite issue. On this basis, the lithium–sulfur battery based on solid-state electrolytes was developed to alleviate the previously mentioned problems. This article aims to provide an overview of the recent progress of solid-state lithium–sulfur batteries related to various kinds of solid-state electrolytes, which mainly include three aspects: the fundamentals and current status of lithium–sulfur solid-state batteries and several adopted solid-state electrolytes involving polymer electrolyte, inorganic solid electrolyte, and hybrid electrolyte. Furthermore, the future perspective for lithium–sulfur solid-state batteries is presented. Finally, this article proposed an initiation for new and practical research activities and paved the way for the design of usable lithium–sulfur solid-state batteries.

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“…[1] Maximizing the operating life is necessary for the economic DOI: 10.1002/aenm.202301452 feasibility of batteries in infrastructures such as EVs and smart grids. [2] As an unpredictable and time-dependent electrochemical power source, the performance of LIBs deteriorates progressively during cyclic aging and calendar aging, with capacity fade and power decay over the battery's lifetime, which becomes a limiting factor for the battery's long life. Batteries have a broad range of designs for each component (electrodes, electrolyte, conductor, separator, etc.…”

Section: Introductionmentioning

confidence: 99%

Accurate Model Parameter Identification to Boost Precise Aging Prediction of Lithium‐Ion Batteries: A Review

Ding,

Li,

Dai

et al. 2023

Advanced Energy Materials

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Precise prediction of lithium‐ion cell level aging under various operating conditions is an imperative but challenging part of ensuring the quality performance of emerging applications such as electric vehicles and stationary energy storage systems. Accurate and real‐time battery‐aging prediction models, which require an exact understanding of the degradation mechanisms of battery components and materials, could in turn provide new insights for materials and battery basic research. Furthermore, the primary barrier to meaningful artificial intelligence/machine learning for accelerating the prediction period is the exploitation of accurate aging mechanistic descriptors. This review comprehensively summarizes the evolution of deterioration mechanisms at the material and cell level in different environments and usage scenarios, including the intricate relationships between aging mechanisms, degradation modes, and external influences, which are the cornerstones of modeling simulation and machine learning techniques. Recent advances in electrochemical models coupled with internal battery degradation mechanisms as well as identification and tracking of aging parameters are shown, with particular emphasis on electrode balance and the anticipated trend of machine learning‐assisted reliable remaining useful life prediction. Precise simulation prediction of cell level aging will continue to play an essential role in advanced smart battery research and management, enhancing its performance while shortening experimental sequences.

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Nonlinear health evaluation for lithium-ion battery within full-lifespan

Cited by 96 publications

References 39 publications

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

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

Recent Progress in Quasi/All-Solid-State Electrolytes for Lithium–Sulfur Batteries

Accurate Model Parameter Identification to Boost Precise Aging Prediction of Lithium‐Ion Batteries: A Review

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