Determination of characteristics of electrochemical systems using acoustic signals

Patent metadata

Field	Value
Publication number	US-2019072614-A1
Application number	US-201816117421-A
Country	US
Kind code	A1
Filing date	Aug 30, 2018
Priority date	Sep 1, 2017
Publication date	Mar 7, 2019
Grant date	—

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; and determining at least a reduced acoustic dataset comprising one or more data points representative of one or more of the transmitted acoustic signals or response signals at the two or more states of charge.

2 . The method of claim 1 , wherein the reduced acoustic dataset comprises one or more data points related to the acoustic signals, the data points comprising a shift in time of flight, 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 , further comprising determining one or more physical properties of the first battery at the two or more states of charge from the reduced acoustic dataset, the one or more physical properties 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 reduced acoustic dataset for the first battery.

5 . The method of claim 4 , further comprising including non-acoustic data related to the first battery to the first database, 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 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 in the first database.

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

8 . The method of claim 7 , wherein the predicted 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 performed in a machine learning model with at least the first database used as a training dataset.

10 . The method of claim 1 , wherein the acoustic signals comprise ultrasonic signals or elastic waves, wherein 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 characteristics of a second battery using at least a first database and one or more of acoustic data or non-acoustic data of the second battery, wherein the first database comprises at least a reduced 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, the transmitted signals and the response signals at two or more states of charge of the first battery.

12 . The method of claim 11 , wherein the reduced acoustic dataset comprises one or more of a shift in time of flight of the acoustic signals or a total signal amplitude of the response 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 a computer configured to determine at least a reduced acoustic dataset comprising one or more data points representative of one or more of the transmitted acoustic signals or response signals at the two or more states of charge.

14 . The apparatus of claim 13 , wherein the reduced acoustic dataset comprises one or more data points related to the acoustic signals, the data points comprising a shift in time of flight, 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.

15 . The apparatus of claim 13 , wherein the computer further comprises a first database with at least the reduced acoustic dataset for the first battery.

16 . The apparatus of claim 15 , further comprising one or more sensors configured to determine temperature of the first battery, wherein the battery management system is further configured to determine voltage of the first battery, and wherein the first database further comprises non-acoustic data related to the first battery, the non-acoustic data comprising one or more the voltage or 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 computer is further configured to predict characteristics of a second battery based on at least the first database and one or more of acoustic data, reduced acoustic data, or non-acoustic data of the second battery.

19 . The apparatus of claim 18 , wherein the predicted 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.

20 . The apparatus of claim 12 , wherein the acoustic signals comprise ultrasonic signals or elastic waves.

Assignees

• Feasible Inc
• Univ Princeton

Inventors

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

Classifications

• G06N20/10

  using kernel methods, e.g. support vector machines [SVM] · CPC title

• 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

• G01N29/50

  using auto-correlation techniques or cross-correlation techniques · 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