Determination of characteristics of electrochemical systems using acoustic signals

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.

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.