Patents by Inventor Norman Jin

Norman Jin has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Data-driven model for lithium-ion battery capacity fade and lifetime prediction
    Patent number: 11226374
    Abstract: A method of using data-driven predictive modeling to predict and classify battery cells by lifetime is provided that includes collecting a training dataset by cycling battery cells between a voltage V1 and a voltage V2, continuously measuring battery cell voltage, current, can temperature, and internal resistance during cycling, generating a discharge voltage curve for each cell that is dependent on a discharge capacity for a given cycle, calculating, using data from the discharge voltage curve, a cycle-to-cycle evolution of cell charge to output a cell voltage versus charge curve Q(V), generating transformations of ?Q(V), generating transformations of data streams that include capacity, temperature and internal resistance, applying a machine learning model to determine a combination of a subset of the transformations to predict cell operation characteristics, and applying the machine learning model to output the predicted battery operation characteristics.
    Type: Grant
    Filed: October 16, 2018
    Date of Patent: January 18, 2022
    Assignees: The Board of Trustees of the Leland Stanford Junior University, Massachusetts Institute of Technology
    Inventors: Kristen Ann Severson, Richard Dean Braatz, William C. Chueh, Peter M. Attia, Norman Jin, Stephen J. Harris, Nicholas Perkins
  • Data-driven Model for Lithium-ion Battery Capacity Fade and Lifetime Prediction
    Publication number: 20190113577
    Abstract: A method of using data-driven predictive modeling to predict and classify battery cells by lifetime is provided that includes collecting a training dataset by cycling battery cells between a voltage V1 and a voltage V2, continuously measuring battery cell voltage, current, can temperature, and internal resistance during cycling, generating a discharge voltage curve for each cell that is dependent on a discharge capacity for a given cycle, calculating, using data from the discharge voltage curve, a cycle-to-cycle evolution of cell charge to output a cell voltage versus charge curve Q(V), generating transformations of ?Q(V), generating transformations of data streams that include capacity, temperature and internal resistance, applying a machine learning model to determine a combination of a subset of the transformations to predict cell operation characteristics, and applying the machine learning model to output the predicted battery operation characteristics.
    Type: Application
    Filed: October 16, 2018
    Publication date: April 18, 2019
    Inventors: Kristen Ann Severson, Richard Dean Braatz, William C. Chueh, Peter M. Attia, Norman Jin, Stephen J. Harris, Nicholas Perkins