公开(公告)号：US20190219547A1

公开(公告)日：2019-07-18

申请号：US16296360

申请日：2019-03-08

Applicant: The Trustees of Princeton University

Inventor： Daniel Artemis Steingart ,  Shoham Bhadra ,  Andrew Gaheem Hsieh ,  Benjamin Hertzberg ,  Peter James Gjeltema ,  Clarence Worth Rowley, III ,  Alexandre S.R. Goy ,  Jason Wolf Fleischer

IPC: G01N29/44 ,  G01N29/07 ,  G01N29/46 ,  G01N29/04

CPC classification number: G01N29/4427 ,  G01N29/043 ,  G01N29/07 ,  G01N29/4436 ,  G01N29/46 ,  G01N2291/0231 ,  G01N2291/2698

Abstract: 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.

公开(公告)号：US20190064123A1

公开(公告)日：2019-02-28

申请号：US16150655

申请日：2018-10-03

Applicant: The Trustees of Princeton University

Inventor： Daniel Artemis Steingart ,  Shoham Bhadra ,  Andrew Gaheem Hsieh ,  Benjamin Hertzberg ,  Peter James Gjeltema ,  Clarence Worth Rowley, III ,  Alexandre S.R. Goy ,  Jason Wolf Fleischer

IPC: G01N29/44 ,  G01N29/46 ,  G01N29/07 ,  G01N29/04

CPC classification number: G01N29/4427 ,  G01N29/043 ,  G01N29/07 ,  G01N29/4436 ,  G01N29/46 ,  G01N2291/0231 ,  G01N2291/2698

Abstract: 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.