Massachusetts Institute of Technology

Leland Stanford Junior University

Toyota Research Institute, Inc.

Basf Se Corporation

William E Gent

Patrick Kenichi Herring

William C Chueh

Richard Dean Braatz

Benben Jiang

Stephen Joel Harris

Heino Sommer

Chirranjeevi Balaji Gopal

Tobias Maximilian Teufl

Vivek Nath Lam

Xiao Cui

Michael Forsuelo

Patrick A Asinger

Bruis Van Vlijmen

Fabian Mohr

Gabriel B Crane

Peter M Csernica

Grace M Busse

Jordan K Lampert

Kipil Lim

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.

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

System, methods, and other embodiments described herein relate to improving the cycling of batteries by using data and a hierarchical Bayesian model (HBM) for predicting the cycle life of a cycling protocol. In one embodiment, a method includes classifying cycle life of a battery into a class using battery data from cycling with a protocol, wherein the class represents cycle life distributions of cycling protocols. The method also includes quantifying, using the class in a HBM, variability for the battery induced by the protocol. The method also includes predicting, using the HBM, an adjusted cycle life for the protocol according to the variability. The method also includes communicating the adjusted cycle life to operate the battery.

Systems and methods for predicting the cycle life of cycling protocols

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Redwood City, CA, United States of America

William E Gent