Harnessing machine learning to develop sturdier batteries for fast-charging electric vehicles

Harnessing machine learning to develop sturdier batteries for fast-charging electric vehicles

Mar 05, 2021
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Abstract: Layered oxides widely used as lithium-ion battery electrodes are designed to be cycled under conditions that avoid phase transitions. Although the desired single-phase composition ranges are well established near equilibrium, operando diffraction studies on many-particle porous electrodes have suggested phase separation during delithiation. Notably, the separation is not always observed, and never during lithiation. These anomalies have been attributed to irreversible processes during the first delithiation or reversible concentration-dependent diffusion. However, these explanations are not consistent with all experimental observations such as rate and path dependencies and particle-by-particle lithium concentration changes. Here, we show that the apparent phase separation is a dynamical artefact occurring in a many-particle system driven by autocatalytic electrochemical reactions, that is, an interfacial exchange current that increases with the extent of delithiation. We experimentally validate this population-dynamics model using the single-phase material Lix(Ni1/3Mn1/3Co1/3)O2 (0.5 < x < 1) and demonstrate generality with other transition-metal compositions. Operando diffraction and nanoscale oxidation-state mapping unambiguously prove that this fictitious phase separation is a repeatable non-equilibrium effect. We quantitatively confirm the theory with multiple-datastream-driven model extraction. More generally, our study experimentally demonstrates the control of ensemble stability by electro-autocatalysis, highlighting the importance of population dynamics in battery electrodes (even non-phase-separating ones). In a leap for battery research, machine learning gets scientific smarts. The latest advance from a research collaboration with industry could dramatically accelerate the development of sturdier batteries for fast-charging electric vehicles. Scientists have taken a major step forward in harnessing machine learning to accelerate the design for better batteries: Instead of using it just to speed up scientific analysis by looking for patterns in data, as researchers generally do, they combined it with knowledge gained from experiments and equations guided by physics to discover and explain a process that shortens the lifetimes of fast-charging lithium-ion batteries. Principal Researchers: Jungjin Park, Hongbo Zhao, Stephen Dongmin Kang, David Shapiro, Jihyun Hong, Michael Toney, Martin Bazant, Will Chueh Institutions: SLAC National Accelerator Laboratory (SSRL Synchrotron) Stanford University Massachusetts Institute of Technology Lawrence Berkeley National Laboratory Funders: Toyota Research Institute DOE Advanced Battery Materials Research Program DOE Office of Science This research was funded by Toyota Research Institute. The Stanford Synchrotron Radiation Lightsource and Advanced Light Source are DOE Office of Science user facilities, and work there was supported by the DOE Office of Science [Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, Advanced Battery Materials Research Program].

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