Battery model estimation based on battery terminal voltage and current transient due to load powered from the battery

Abstract

A method of management of a battery that powers a component of a device may include monitoring a terminal voltage and a terminal current of the battery under a load that is drawing a current on the battery to provide power to a component of the device and modeling the battery as a battery model that approximates a relationship between the monitored terminal voltage and terminal current over at least one of: a certain frequency range; a certain duration, a certain amplitude range, an applied load, a set of conditions of the battery, and a set of conditions of the load. The relationship between the terminal voltage and the terminal current may have a frequency-dependent characteristic including at least two time constants. The two time constants may represent a time-varying relationship between an input and output of the battery model.

What is claimed is: 
     
         1 . A method of management of a battery that powers a component of a device, comprising:
 monitoring a terminal voltage and a terminal current of the battery under a load that is drawing a current on the battery to provide power to a component of the device;   modeling the battery as a battery model that approximates a relationship between the monitored terminal voltage and terminal current over at least one of: a certain frequency range, a certain duration, a certain amplitude range, an applied load, a set of conditions of the battery, and a set of conditions of the load;   wherein:
 the relationship between the terminal voltage and the terminal current has a frequency-dependent characteristic including at least two time constants; and 
 the two time constants represent a time-varying relationship between an input and output of the battery model. 
   
     
     
         2 . The method of  claim 1 , wherein the battery model has parameters and the method further comprises determining the model parameters through an optimization function. 
     
     
         3 . The method of  claim 2 , wherein the optimization function is a least squares fit. 
     
     
         4 . The method of  claim 2 , wherein the optimization function is a frequency- or time-weighted variant of a least squares fit. 
     
     
         5 . The method of  claim 1 , wherein the battery model includes at least one of: a linear model of the battery, a non-linear model of the battery, a parameterized equivalent circuit model that models impedance of the battery, a physics-based model, a combination of an equivalent circuit model and a physics-based model, a Kalman filter, and an extended Kalman filter. 
     
     
         6 . The method of  claim 1 , further comprising isolating and filtering the terminal voltage and the terminal current over one or more frequency bands in order to model the battery. 
     
     
         7 . The method of  claim 1 , further comprising using the battery model to predict battery characteristics. 
     
     
         8 . The method of  claim 7 , wherein the battery characteristics include at least one of: a maximum available power of the battery, a state of charge of the battery, a state of health of the battery, and an internal state of the battery. 
     
     
         9 . The method of  claim 8 , wherein the internal state may include at least one of an open-circuit voltage of the battery, an internal overpotential state of the battery, a lithium-ion anode potential of the battery, and some other state representing a condition of the battery that may lead to degradation of its chemistry. 
     
     
         10 . The method of  claim 1 , wherein:
 the battery model includes a parameterized equivalent circuit model that models impedance of the battery; and   the battery model includes parameters for modeling an impedance of the battery including resistive, capacitive, and/or inductive circuit elements in parallel or in series.   
     
     
         11 . The method of  claim 10 , wherein impedances of the circuit elements are time varying. 
     
     
         12 . The method of  claim 10 , wherein impedances of the circuit elements have nonlinear characteristics. 
     
     
         13 . A system for management of a battery that powers a component of a device, the system comprising:
 battery monitoring circuitry configured to monitor a terminal voltage and a terminal current of the battery under a load that is drawing a current on the battery to provide power to a component of the device; and   a battery model estimator configured to model the battery as a battery model that approximates a relationship between the monitored terminal voltage and terminal current over at least one of: a certain frequency range, a certain duration, a certain amplitude range, an applied load, a set of conditions of the battery, and a set of conditions of the load;   wherein:
 the relationship between the terminal voltage and the terminal current has a frequency-dependent characteristic including at least two time constants; and 
 the two time constants represent a time-varying relationship between an input and output of the battery model. 
   
     
     
         14 . The system of  claim 13 , wherein the battery model has parameters and the battery model estimator is further configured to determine the model parameters through an optimization function. 
     
     
         15 . The system of  claim 14 , wherein the optimization function is a least squares fit. 
     
     
         16 . The system of  claim 14 , wherein the optimization function is a frequency- or time-weighted variant of a least squares fit. 
     
     
         17 . The system of  claim 13 , wherein the battery model includes at least one of: a linear model of the battery, a non-linear model of the battery, a parameterized equivalent circuit model that models impedance of the battery, a physics-based model, a combination of an equivalent circuit model and a physics-based model, a Kalman filter, and an extended Kalman filter. 
     
     
         18 . The system of  claim 13 , wherein the battery model estimator is further configured to isolate and filter the terminal voltage and the terminal current over one or more frequency bands in order to model the battery. 
     
     
         19 . The system of  claim 13 , wherein the battery model estimator is further configured to predict battery characteristics using the battery model. 
     
     
         20 . The system of  claim 19 , wherein the battery characteristics include at least one of: a maximum available power of the battery, a state of charge of the battery, a state of health of the battery, and an internal state of the battery. 
     
     
         21 . The system of  claim 20 , wherein the internal state may include at least one of an open-circuit voltage of the battery, an internal overpotential state of the battery, a lithium-ion anode potential of the battery, and some other state representing a condition of the battery that may lead to degradation of its chemistry. 
     
     
         22 . The system of  claim 13 , wherein:
 the battery model includes a parameterized equivalent circuit model that models impedance of the battery; and   the battery model includes parameters for modeling an impedance of the battery including resistive, capacitive, and/or inductive circuit elements in parallel or in series.   
     
     
         23 . The system of  claim 22 , wherein impedances of the circuit elements are time varying. 
     
     
         24 . The system of  claim 22 , wherein impedances of the circuit elements have nonlinear characteristics.