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Efficient heat dissipation in batteries is important for thermal management against thermal runaway and chemical instability at elevated temperatures. Nevertheless, thermal transport processes in battery materials have not been well understood especially considering their complicated microstructures. In this study, lattice thermal transport in lithium cobalt oxide (LiCoO 2 ), a popular cathode material for lithium ion batteries, is investigated via molecular dynamics-based approaches and thermal resistance models. A LiCoO 2 single-crystal is shown to have thermal conductivities in the order of 100 W m −1 K −1 with strong anisotropy, temperature dependence, and size effects. By comparison, polycrystalline LiCoO 2 is more isotropic with much lower thermal conductivities. This difference is caused by random grain orientations, the thermal resistance of grain boundaries, and size-dependent intra-grain thermal conductivities that are unique to polycrystals. The grain boundary thermal conductance is calculated to be in the range of 7.16–25.21 GW m −2 K −1 . The size effects of the intra-grain thermal conductivities are described by two empirical equations. Considering all of these effects, two thermal resistance models are developed to predict the thermal conductivity of polycrystalline LiCoO 2 . The two models predict a consistent thermal conductivity–grain size relationship that agrees well with molecular dynamics simulation results. The insights revealed by this study may facilitate future efforts on battery materials design for improved thermal management.
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Battery Charge Curve Prediction via Feature Extraction and Supervised Machine Learning
Abstract Real‐time onboard state monitoring and estimation of a battery over its lifetime is indispensable for the safe and durable operation of battery‐powered devices. In this study, a methodology to predict the entire constant‐current cycling curve with limited input information that can be collected in a short period of time is developed. A total of 10 066 charge curves of LiNiO2‐based batteries at a constant C‐rate are collected. With the combination of a feature extraction step and a multiple linear regression step, the method can accurately predict an entire battery charge curve with an error of < 2% using only 10% of the charge curve as the input information. The method is further validated across other battery chemistries (LiCoO2‐based) using open‐access datasets. The prediction error of the charge curves for the LiCoO2‐based battery is around 2% with only 5% of the charge curve as the input information, indicating the generalization of the developed methodology for predicting battery cycling curves. The developed method paves the way for fast onboard health status monitoring and estimation for batteries during practical applications.
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- Award ID(s):
- 1751605
- PAR ID:
- 10428727
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Science
- Volume:
- 10
- Issue:
- 26
- ISSN:
- 2198-3844
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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