Enevate Corporation

Hong Zhao

Benjamin Yong Park

Liwen Ji

Ian Russell Browne

Giulia Canton

Sam Keene

Xianyang Li

Heidi Leighette Anderson

Younes Ansari

Mya Le Thai

Vincent Giordani

Samuel Keene

Qing Zhang

Sung Ju Cho

Myunghwan Jeong

Daniel Sylvinson Muthiah Ravinson

Electrodes, electrolytes and/or separators for energy storage devices comprising functional boron-containing chemicals are disclosed. The energy storage device comprises a first electrode and a second electrode, wherein at least one of the first electrode and the second electrode is a Si-based electrode, a separator between the first electrode and the second electrode, and an electrolyte composition. Functional boron-containing chemicals may serve as additives to one or more of the electrodes, electrolyte and/or separator.

Boron-containing chemicals as cathode additives, Si anode additives, electrolyte additives or separator modifiers for Li-ion batteries

Systems and methods are provided for managing operation of silicon-dominant electrode cells. Polarization in a silicon-dominant cell during charge/discharge cycles may be assessed, with the polarization being assessed, at least in part, based on one or both of charge rate and discharge rate. One or more adjustments may be determined based on the assessing of polarization, and the one or more adjustments may be applied to operation of the silicon-dominant cell. The one or more adjustments configured to compensate for at least some of the effects of polarization in the silicon-dominant cell.

Polarization compensation in silicon-dominant electrode cells

Methods and systems are provided for key predictors and machine learning for configuring cell performance. One or more parameters relating to the cell may be measured, via a measurement apparatus, with the cell including a cathode, a separator, and a silicon-dominant anode, and the cell may be managed, based on the one or more parameters, with the managing including predetermining cycle life of the cell based on the one or more parameters using a machine learning model. The cell may be within a battery pack that includes a plurality of cells. The battery pack may be in an electric vehicle. At least one parameter may be measured before a formation process of the cell. At least one parameter may be measured during the formation process. At least one parameter may be measured during cycling of the cell.

Method and system for key predictors and machine learning for configuring cell performance

Methods and systems are provided for key predictors and machine learning for configuring cell performance. One or more parameters relating to operation of a cell may be measured, via a measurement apparatus, with the cell including a cathode, a separator, and a silicon-dominant anode, and cell performance may be managed, based on the one or more parameters, with the managing including assessing the cell performance using a machine learning model. The cell may be within a battery pack that includes a plurality of cells, each of which including a cathode, a separator, and a silicon-dominant anode. One or more of the plurality of cells from the battery pack in response to a determination, based on the assessing, of a different performance of the one or more of the plurality of cells. The battery pack may be in an electric vehicle.

Electrolyte formulations for energy storage devices are disclosed. The energy storage device comprises a first electrode and a second electrode, where one or both of the first electrode and the second electrode is a Si-based electrode, a separator between the first electrode and the second electrode, and an electrolyte composition. Electrolyte formulations as described herein are electrolyte compositions comprising two or more components such as solvents, co-solvents, salts and/or additives. In some embodiments, three or more, four or more, five or more, six or more, seven or more, or eight or more components are included in the electrolyte composition.

Electrolyte formulations for optimal performance in Si-containing lithium ion batteries

Electrolytes and electrolyte additives for energy storage devices comprising cyanate based compounds are disclosed. The energy storage device comprises a first electrode and a second electrode, wherein at least one of the first electrode and the second electrode is a Si-based electrode, a separator between the first electrode and the second electrode, an electrolyte comprising at least two electrolyte co-solvents, wherein at least one electrolyte co-solvent comprises a cyanate based compound.

Silicon-based energy storage devices with electrolyte containing cyanate based compounds

Electrolytes and electrolyte additives for energy storage devices comprising silyl amine compounds or derivatives thereof are disclosed. The energy storage device comprises a first electrode and a second electrode, where one or both of the first electrode and the second electrode is a Si-based electrode, a separator between the first electrode and the second electrode, an electrolyte, and at least one electrolyte additive selected from silyl amine compounds or derivatives thereof.

Silicon-based energy storage devices with silyl amine compounds or derivatives of silyl amine containing electrolyte additives

Electrolytes and electrolyte additives for energy storage devices comprising dihydrofuranone based compounds are disclosed. The energy storage device comprises a first electrode and a second electrode, wherein at least one of the first electrode and the second electrode is a Si-based electrode, a separator between the first electrode and the second electrode, an electrolyte comprising at least two electrolyte co-solvents, wherein at least one electrolyte co-solvent comprises a dihydrofuranone based compound.

Silicon-based energy storage devices with electrolyte containing dihydrofuranone based compound

Silicon-dominate battery electrodes, battery cells utilizing the silicon-dominate battery electrodes, and methods of manufacturing are disclosed. Such a battery cell includes a cathode, a separator, an electrolyte, and an anode. The anode comprises a current collector and active material on the current collector. The active material layer includes at least 50% silicon. A ratio of the electrolyte to Ah is over 2 g/Ah.

Silicon based lithium ion battery and improved cycle life of same

A method for key predictors and machine learning for configuring battery cell performance may include providing a cell that includes a cathode, a separator, and a silicon-dominant anode; measuring a plurality of parameters of the cell; and using a machine learning model to determine cycle life based on the plurality of measured parameters, where one of the measured parameters includes second cycle coulombic efficiency. The plurality of parameters may include initial coulombic efficiency, cell impedance values, open-circuit voltage, cell thickness, and impedance after degassing. A first subset of the plurality of parameters may be measured before a formation process. A second subset of the plurality of parameters may be measured during a formation process, where the plurality of parameters may include a voltage reached during a first 10% of a first formation cycle. A third subset of the plurality of parameters may be measured during cycling of the cell.

A method for key predictors and machine learning for configuring battery cell performance may include providing a cell that may include a cathode, a separator, and a silicon-dominant anode; measuring a plurality of parameters of the cell; and using a machine learning model to determine cell performance based on the plurality of measured parameters. The plurality of parameters may include initial coulombic efficiency and/or second cycle coulombic efficiency. Cells may be classified based on the determined cell performance and similarly performing cells may be binned together. A battery pack may be provided with a plurality of cells. The plurality of cells may be assessed during cycling using the machine learning model. One or more of the plurality of cells may be replaced when the assessing determines a different performance of the one or more of the plurality of cells. The battery pack may be in an electric vehicle.

Systems and methods are provided for polarization compensation in silicon-dominant electrode cells. One or more adjustments to operation of a silicon-dominant cell maybe applied based on effects of polarization in the silicon-dominant cell during charge/discharge cycles, with the one or more adjustments configured to compensate for at least some of the effects of polarization in the silicon-dominant cell.

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Aliso Viejo, CA, United States of America

Hong Zhao