Massachusetts Institute of Technology

Leland Stanford Junior University

Norman Jin

Stephen Joel Harris

William C Chueh

Kristen Ann Severson

Richard Dean Braatz

Peter M Attia

Nicholas Perkins

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 &#x394;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.

Data-driven model for lithium-ion battery capacity fade and lifetime prediction

Growing community of inventors

Palo Alto, CA, United States of America

Norman Jin