公开(公告)号：US20220283243A1

公开(公告)日：2022-09-08

申请号：US17716495

申请日：2022-04-08

申请人： Enevate Corporation

发明人： Sam Keene ,  Giulia Canton ,  Ian Browne ,  Xianyang Li ,  Hong Zhao ,  Benjamin Park

IPC分类号： G01R31/396 ,  G01R31/367 ,  G01R31/392

摘要： 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.

公开(公告)号：US11283114B1

公开(公告)日：2022-03-22

申请号：US17215735

申请日：2021-03-29

申请人： Enevate Corporation

发明人： Sam Keene ,  Giulia Canton ,  Ian Browne ,  Xianyang Li ,  Hong Zhao ,  Benjamin Park

IPC分类号： H01M10/48 ,  H01M10/0525 ,  H01M4/38 ,  G06N5/04 ,  G06N20/00 ,  H01M4/02

摘要： 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.

公开(公告)号：US20220285749A1

公开(公告)日：2022-09-08

申请号：US17699674

申请日：2022-03-21

申请人： Enevate Corporation

发明人： Sam Keene ,  Giulia Canton ,  Ian Browne ,  Xianyang Li ,  Hong Zhao ,  Benjamin Park

IPC分类号： H01M10/48 ,  H01M10/0525 ,  H01M4/38 ,  G06N5/04 ,  G06N20/00

摘要： 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.

公开(公告)号：US11300631B1

公开(公告)日：2022-04-12

申请号：US17192877

申请日：2021-03-04

申请人： Enevate Corporation

发明人： Sam Keene ,  Giulia Canton ,  Ian Browne ,  Xianyang Li ,  Hong Zhao ,  Benjamin Park

IPC分类号： G01R31/00 ,  G01R31/396 ,  G01R31/367 ,  G01R31/392

摘要： 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.