Determination of characteristics of electrochemical systems using acoustic signals

Patent metadata

Field	Value
Publication number	US-11193979-B2
Application number	US-201816117421-A
Country	US
Kind code	B2
Filing date	Aug 30, 2018
Priority date	Sep 1, 2017
Publication date	Dec 7, 2021
Grant date	Dec 7, 2021

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.

First claim

What is claimed is:

1. A method of non-invasive analysis of electrochemical systems, the method comprising:
subjecting at least a first battery to at least a portion of a charge-discharge cycle;
at two or more time instances during at least the portion of the charge-discharge cycle, the two or more time instances corresponding to two or more states of charge of the first battery, transmitting acoustic signals through at least a portion of the first battery and receiving corresponding response signals;
determining a first acoustic dataset based at least in part on the transmitted acoustic signals and the response signals at the two or more states of charge;
generating a second acoustic dataset based on the first acoustic dataset, the second acoustic dataset being a smaller dataset of response signals compared to the first acoustic dataset; and
determining one or more physical characteristics of a first battery using at least the second acoustic dataset.

2. The method of claim 1 , wherein the second acoustic dataset comprises one or more data points related to the acoustic signals, each one of the one or more data points corresponding to one of a total signal amplitude, frequency content, first break time, centroid frequency, full width at half of a maximum of a main response peak in time domain, full width at half of a maximum of a main peak in frequency domain, standard deviation, skewness, or kurtosis of frequency distribution, decay rate of the response signal in time domain, area under positive amplitude, or area under negative amplitude of the acoustic signals.

3. The method of claim 1 , wherein the one or more physical characteristics of the first battery are determined at the two or more states of charge, the one or more physical characteristics comprising one or more of density, elastic modulus, bulk modulus, shear modulus, porosity, or thickness of the first battery.

4. The method of claim 1 , further comprising:
creating a first database with at least the second acoustic dataset for the first battery.

5. The method of claim 4 , further comprising:
including, in the first database, non-acoustic data related to the first battery, the non-acoustic data comprising one or more voltage, current, or temperature of the first battery at the two or more states of charge.

6. The method of claim 5 , further comprising:
including, in the first database, one or more waveforms of the transmitted acoustic signals or the response signals at the two or more states of charge.

7. The method of claim 6 , further comprising:
determining one or more characteristics of a second battery using at least the first database and one or more of acoustic data, or non-acoustic data of the second battery.

8. The method of claim 7 , wherein the one or more characteristics of the second battery comprise one or more of a state of charge (SOC), state of health (SOH), construction quality, remaining useful lifetime, state of power, or state of safety.

9. The method of claim 7 , further comprising:
training a machine learning model with at least the first database used as a training dataset; and
determining the one or more characteristics of the first battery or the second battery using the machine learning model.

10. The method of claim 1 , wherein
the acoustic signals comprise ultrasonic signals or elastic waves,
the acoustic signals are transmitted by one or more transducers, and
the response signals are received by one or more transducers.

11. A method of non-invasive analysis of electrochemical systems, the method comprising:
predicting physical characteristics of one or more batteries using at least a first database and one or more of acoustic data or non-acoustic data of a first battery,
wherein the first database comprises at least a first acoustic dataset formed by transforming a second acoustic dataset of the first battery, the second acoustic dataset comprising one or more data points representative of one or more of acoustic signals transmitted through at least a portion of the first battery and response signals to the transmitted signals at two or more states of charge of the first battery using one or more transducers, the first acoustic dataset being a smaller dataset of response signals compared to the second acoustic dataset.

12. The method of claim 11 , wherein the first acoustic dataset comprises at least a total signal amplitude of the response signals, first break time, centroid frequency, full width at half of a maximum of a main response peak in time domain, full width at half of a maximum of a main peak in frequency domain, standard deviation, skewness, or kurtosis of frequency distribution, decay rate of the response signal in time domain, area under positive amplitude, and an area under negative amplitude of the acoustic signals.

13. An apparatus comprising:
at least a first battery;
a battery management system configured to subject the first battery to at least a portion of a charge-discharge cycle;
one or more transducers configured to transmit acoustic signals through at least a portion of the first battery and receive corresponding response signals at two or more time instances during at least the portion of the charge-discharge cycle, the two or more time instances corresponding to two or more states of charge of the first battery; and
one or more processors configured to:
determine a first acoustic dataset based at least in part on the transmitted acoustic signals and the response signals at the two or more states of charge; and
transform the first acoustic dataset to generate a second acoustic dataset, the second acoustic dataset being a smaller dataset of response signals compared to the first acoustic dataset; and
determine one or more physical characteristics of a first battery using at least the second acoustic dataset.

14. The apparatus of claim 13 , wherein the second acoustic dataset comprises one or more data points related to the acoustic signals, each one of the one or more data points corresponding to one of a shift in time of flight, a total signal amplitude, first break time, centroid frequency, full width at half of a maximum of a main response peak in time domain, full width at half of a maximum of a main peak in frequency domain, standard deviation, skewness, or kurtosis of frequency distribution, decay rate of the response signal in time domain, area under positive amplitude, or area under negative amplitude of the acoustic signals.

15. The apparatus of claim 13 , wherein the one or more processors are further configured to generate a first database with at least the second acoustic dataset for the first battery.

16. The apparatus of claim 15 , further comprising one or more sensors configured to determine a temperature of the first battery, wherein
the battery management system is further configured to determine a voltage of the first battery, and
the first database further comprises non-acoustic data related to the first battery, non-acoustic data comprising one or more of the voltage or a temperature of the first battery at the two or more states of charge.

17. The apparatus of claim 16 , wherein the first database further comprises an acoustic dataset comprising one or more waveforms of the transmitted acoustic signals or the response signals at the two or more states of charge.

18. The apparatus of claim 17 , wherein the one or more processors are further configured to predict one or more characteristics of a second battery based on at least the first database and one or more of acoustic data, reduced a

Assignees

• Feasible Inc
• Univ Princeton

Inventors

• Steingart Daniel A
• Davies Greg
• Biswas Shaurjo
• Hsieh Andrew G
• Van Tassell Barry
• Hodson Thomas
• Dou shan

Classifications

• H01M10/486

  for measuring temperature · CPC title

• H01M10/48

  Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte (constructional details of current conducting connections for detecting conditions inside cells or batteries, e.g. details of voltage sensing terminals, H01M50/569) · CPC title

• G01N2291/02441

  Liquids in porous solids · CPC title

• G01N29/4481

  Neural networks · CPC title

• G01N29/12

  by measuring frequency or resonance of acoustic waves {(measuring frequency or resonant frequency of mechanical vibrations or acoustic waves in general G01H1/06, G01H3/04, G01H13/00; acoustic resonators G10K11/04; vibration or shock testing of structures G01M7/00)} · CPC title