Multi-scale modelling of Lithium-ion batteries: From transport phenomena to the outbreak of thermal runaway

Katrašnik, Tomaž; Mele, Igor; Zelič, Klemen

doi:10.1016/j.enconman.2021.114036

Cited by 45 publications

(12 citation statements)

References 64 publications

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“…As such, multi-scale modeling has often been applied to study the degradation of battery materials from the atomic scale all the way to full cell simulations. 113,114 Density functional theory (DFT) and molecular dynamics (MD) simulations have often been used to determine the material degradation properties at the atomic level. Key properties such as lithium dendrite growth and SEI formations can be studied.…”

Section: Outlook and Conclusionmentioning

confidence: 99%

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Machine learning-inspired battery material innovation

Sun

Seh

2023

Energy Adv.

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Section: Outlook and Conclusionmentioning

confidence: 99%

“…As such, multi-scale modeling has often been applied to study the degradation of battery materials from the atomic scale all the way to full cell simulations. 113,114…”

Section: Outlook and Conclusionmentioning

confidence: 99%

Machine learning-inspired battery material innovation

Sun

Seh

2023

Energy Adv.

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“…Aging models based on SEI formation and Li‐plating have been integrated into P2D models to simulate the battery capacity losses. [ 181,208,211,239–248 ] The additionally required equations are summarized in Table 4 . The SEI formation (Equation (43d)) and Li‐plating (Equation (44)) are frequently described by cathodic Tafel expressions in order to reduce the computational complexity of the simulations.…”

Section: Applications To Libsmentioning

confidence: 99%

“…[232,233] Aging models based on SEI formation and Li-plating have been integrated into P2D models to simulate the battery capacity losses. [181,208,211,[239][240][241][242][243][244][245][246][247][248] The additionally required Table 4. Governing equations of losses in Li inventory models.…”

Section: Wwwadvancedsciencenewscommentioning

confidence: 99%

“…Both the SEI growth and Li plating are added to the P2D model to investigate the influences on the battery performance. [181,208,[243][244][245][246][247][248][249] Yang et al [208] introduced the SEI growth and Li plating into a P2D model. They simulated the capacity behavior of a graphite/LiNi 0.6 Co 0.2 Mn 0.2 O 2 pouch cell and found that it changed from a linear to a nonlinear capacity loss dependency (Figure 16a).…”

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Porous Electrode Modeling and its Applications to Li‐Ion Batteries

Chen

Danilov

Eichel

et al. 2022

Advanced Energy Materials

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Figure 12. a-c) Schematic representation of the electrode potential at electrode/electrolyte interfaces. [145] Red areas indicate the electrode region and blue the electrolyte region. a) The Butler-Volmer approach does not consider charges at the interface. b) More detailed representation of the electrode/ electrolyte interface, considering specifically adsorbed species (green area) and screening counter charges at the surface of the electrode (dark red) and in the electrolyte (dark blue). c) Reduced electrode/electrolyte interface considering the surface charge in the electrode and screening charge in the electrolyte. d) Typical examples of impedance spectra for a porous electrode with insertion-type reactions. [151] Impedance simulations of porous graphite electrodes in contact with electrolyte with e) various particle sizes and f) electrode porosities. [152] a-c) Reproduced with permission. [145]

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Multiscale modeling for enhanced battery health analysis: Pathways to longevity

Yang,

Zhang,

Wang

et al. 2024

Carbon Neutralization

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The issues of health assessment and lifespan prediction have always been prominent challenges in the large‐scale application of lithium‐ion batteries (LIBs). This paper reviews the multiscale modeling techniques and their applications in battery health analysis, including atomic scale computational chemistry, particle scale reaction simulations, electrode scale structural models, macroscale electrochemical models, and data‐driven models at the system level. Multiscale modeling offers a profound insight into material behavior and the aging process of batteries, thereby providing a valuable reference for both estimation and management strategies of battery state of health. To extend the battery lifespan, the utilization of artificial intelligence for material discovery and manufacturing process optimization, the implementation of end‐cloud collaborative battery management systems, and the design of a multiscale simulation integration platform are considered. A management framework aimed at extending battery life is further proposed. This framework offers a promising roadmap for addressing health analysis challenges in LIBs, ultimately leading to more reliable, efficient, and durable solutions for next‐generation batteries.

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Multi-scale modelling of Lithium-ion batteries: From transport phenomena to the outbreak of thermal runaway

Cited by 45 publications

References 64 publications

Machine learning-inspired battery material innovation

Machine learning-inspired battery material innovation

Porous Electrode Modeling and its Applications to Li‐Ion Batteries

Multiscale modeling for enhanced battery health analysis: Pathways to longevity

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