Abstract: 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.

Abstract: 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.

Abstract: Methods, systems, and media for modifying user interface colors are provided. In some embodiments, the method comprises: receiving a video and color palette information, wherein each color of the color palette information indicates a color of an element of a user interface in which the video is to be presented; identifying a first color for the element, wherein the first color corresponds to a first portion of the video; causing the first portion of the video to be presented, wherein the element of the user interface having the first color is presented; identifying a second color for the element, wherein the second color corresponds to a second portion of the video; and modifying an appearance of the element by changing the color of the element from the first color to the second color while presenting the second portion of the video.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Microscopically ordered solid electrolyte architectures for solid-state and hybrid Li ion batteries are disclosed. The architecture comprises at least one porous scaffold comprising a lithium conducting ceramic that is porous enough to be infiltrated with cathode or anode active material in an amount sufficient to enable energy densities greater than 300 Wh/kg. Methods of making these microscopically ordered solid electrolyte architecture by fabricating at least one green ceramic scaffold and applying at least one heat treatment step are also disclosed.

Abstract: Solid state or bulk hybrid batteries comprising a plurality of composite electrodes with high loading of electrochemically-active materials, a dendrite-blocking separator placed between the anode and the cathode, a secondary phase between the electrochemically-active materials and the solid-state or hybrid electrolyte and methods thereof are disclosed. Methods of making and using the same are also disclosed.