Princeton University

Clarence Worth Rowley, Iii

Andrew Gaheem Hsieh

Benjamin Hertzberg

Jason Wolf Fleischer

Peter James Gjeltema

Shoham Bhadra

Alexandre S R Goy

Daniel Artemis Steingart

Daniel Artemus Steingart

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.

Apparatus and method for determining state of change (SOC) and state of health (SOH) of electrical cells

A method, an apparatus and a system for interrogating a battery in order to determine one or more of: (i) its state of charge (SOC), (ii) its state of health (SOH), (iii) physical state of one or more internal components or parts, which utilizes at least one sound source for transmitting a signal (e.g., a sound wave or sound pulse through or across the battery, and at least one sound receiver for receiving a signal from the battery, which received signal is representative of the physical state of the battery being interrogated. The interrogation method is noninvasive, namely does not require the depletion of a portion of the charge of the battery being tested or settlement or the destruction of the battery in order to evaluate one or more of (i), (ii) and (iii).

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Princeton, NJ, United States of America

Clarence Worth Rowley, Iii