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

US11226374B2 · kind B2 · utility

Assignees

• The Board of Trustees of the Leland Stanford Junior University · US
• Massachusetts Institute of Technology · US

Inventors

• Kristen Severson · Somerville, US
• Richard Dean Braatz · Arlington, US
• William C. Chueh · Menlo Park, US
• Peter M. Attia · Stanford, US
• Norman Luwei Jin · Palo Alto, US
• Stephen J. Harris · Bloomfield, US
• Nicholas Reed PERKINS · Somerville, US

Key dates

• Filing date: Oct 16, 2018
• Grant date: Jan 18, 2022
• Expiry date: Mar 30, 2040

Classification

• Technology area (CPC Y): Emerging Cross-Sectional Technologies
• CPC primary: Y02E60/10
• WIPO field: Electrical machinery, apparatus, energy
• WIPO sector: Electrical engineering

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 ΔQ(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.