Liminal Insights, Inc.

Princeton University

Feasible, Inc.

Shan Dou

Andrew Gaheem Hsieh

Shaurjo Biswas

Barry James Van Tassell

Andrew M Raphael

Daniel Artemis Steingart

Greg Davies

Marc Juzkow

Dennis Yu

Jason Y Yu

Thomas Hodson

Aleksandr Kiessling

Elizabeth M Lee

Systems, techniques, and computer-implemented processes are provided for acoustic signal based analysis of thin-films, electrode coatings, and other components of batteries. Data analytics on signals obtained by ultrasound excitation of materials is used to analyze electrode coating parameters, analyzing separators, and other battery components. Using the disclosed techniques in battery manufacturing and production can lead to reduction in wastage of damaged/scrapped battery cells and shorten production time.

Acoustic signal based analysis of films

Systems and methods for acoustic signal based analysis, include obtaining acoustic response signal data of at least a portion of a battery cell, the acoustic response signal data comprising waveforms generated by transmitting one or more acoustic excitation signals into at least the portion of the battery cell and recording response vibration signals to the one or more acoustic excitation signals. One or more metrics are determined from at least the acoustic response signal data, the one or more metrics being determined based on correlation of the one or more metrics to one or more characteristics of battery cells and a reference model is generated from the one or more metrics. A test battery can be evaluated using the reference model. Actionable insights or recommendations can be generated based on the evaluation. The reference model can also be updated based on the evaluation.

Acoustic signal based analysis of batteries

Systems and techniques for measuring process characteristics including electrolyte distribution in a battery cell. A non-destructive method for analyzing a battery cell includes determining acoustic features at two or more locations of the battery cell, the acoustic features based on one or more of acoustic signals travelling through at least one or more portions of the battery cell during one or more points in time or responses to the acoustic signals obtained during one or more points in time, wherein the one or more points in time correspond to one or more stages of electrolyte distribution in the battery cell. One or more characteristics of the battery cell are determined based on the acoustic features at the two or more locations of the battery cell.

Systems and methods for evaluating electrolyte wetting and distribution

Systems and methods for prediction of state of charge (SOH), state of health (SOC) and other characteristics of batteries using acoustic signals, includes determining acoustic data at two or more states of charge and determining a reduced acoustic data set representative of the acoustic data at the two or more states of charge. The reduced acoustic data set includes time of flight (TOF) shift, total signal amplitude, or other data points related to the states of charge. Machine learning models use at least the reduced acoustic dataset in conjunction with non-acoustic data such as voltage and temperature for predicting the characteristics of any other independent battery.

Determination of characteristics of electrochemical systems using acoustic signals

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Berkeley, CA, United States of America

Shan Dou