Massachusetts Institute of Technology

Leland Stanford Junior University

Toyota Research Institute, Inc.

Richard Dean Braatz

William C Chueh

Stephen Joel Harris

Patrick Kenichi Herring

Benben Jiang

William E Gent

J Christopher Love

Chirranjeevi Balaji Gopal

Kristen Ann Severson

Vivek Nath Lam

Nicholas Perkins

Peter M Attia

Amos Enshen Lu

Xiao Cui

Patrick A Asinger

Norman Jin

Bruis Van Vlijmen

Gabriel B Crane

Michael Forsuelo

Fabian Mohr

Craig A Mascarenhas

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

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

Aspects of the present disclosure relate to filtration systems and methods for production of biologically-produced products, which may include pharmaceutical and/or protein products. Certain biomanufacturing systems described herein comprise a bioreactor (e.g., a perfusion bioreactor, a chemostat) and a filter probe comprising a filter bundle comprising a plurality of hollow fibers (e.g., longitudinally aligned hollow fibers). According to some embodiments, a center-to-center distance between any two hollow fibers within the fiber bundle at one or more points along a length of the fiber bundle is relatively large (e.g., greater than or equal to an average outer diameter of the hollow fibers of the fiber bundle, greater than or equal to 1.1 times a minimum diameter of the two hollow fibers). In some embodiments, the hollow fibers within the fiber bundle are arranged in an array (e.g., a hexagonal, linear, annular, or square array).

Filtration systems and methods for manufacturing biologically-produced products

Growing community of inventors

Arlington, MA, United States of America

Richard Dean Braatz