Systems and methods for predicting battery life using data from a diagnostic cycle

US11768249B2 · kind B2 · utility

Assignees

• TOYOTA RESEARCH INSTITUTE, INC. · US
• Massachusetts Institute of Technology · US
• The Board of Trustees of the Leland Stanford Junior University · US

Inventors

• William C. Chueh · Menlo Park, US
• Bruis van Vlijmen · San Francisco, US
• William E. Gent · Redwood City, US
• Vivek Nath Lam · Stanford, US
• Patrick K. Herring · Mountain View, US
• Chirranjeevi Balaji Gopal · San Jose, US
• Patrick A. Asinger · Boston, US
• Benben Jiang · Cambridge, US
• Richard Dean Braatz · Arlington, US
• Xiao Cui · Beijing, CN
• Gabriel B. Crane · Stanford, US

Key dates

• Filing date: Mar 31, 2021
• Grant date: Sep 26, 2023
• Expiry date: Jun 30, 2041

Classification

• Technology area (CPC Y): Emerging Cross-Sectional Technologies
• CPC primary: Y02T10/70
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

System, methods, and other embodiments described herein relate to improving the estimation of battery life. In one embodiment, a method includes measuring electrochemical data of a battery cell associated with an electrochemical reaction triggered by a test during a diagnostic cycle. The method also includes determining a feature associated with the degradation of the battery cell from the electrochemical data. The method also includes predicting an end-of-life (EOL) of the battery cell by using the feature in a machine learning (ML) model.