U.S. patent application number 17/463980 was filed with the patent office on 2022-05-05 for battery model estimation based on battery terminal voltage and current transient due to load powered from the battery.
This patent application is currently assigned to Cirrus Logic International Semiconductor Ltd.. The applicant listed for this patent is Cirrus Logic International Semiconductor Ltd.. Invention is credited to Eric J. KING, Emmanuel MARCHAIS, John L. MELANSON, James T. NOHRDEN.
Application Number | 20220137143 17/463980 |
Document ID | / |
Family ID | 1000005856021 |
Filed Date | 2022-05-05 |
United States Patent
Application |
20220137143 |
Kind Code |
A1 |
MELANSON; John L. ; et
al. |
May 5, 2022 |
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.
Inventors: |
MELANSON; John L.; (Austin,
TX) ; MARCHAIS; Emmanuel; (Dripping Springs, TX)
; KING; Eric J.; (Austin, TX) ; NOHRDEN; James
T.; (Austin, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Cirrus Logic International Semiconductor Ltd. |
Edinburgh |
|
GB |
|
|
Assignee: |
; Cirrus Logic International
Semiconductor Ltd.
Edinburgh
GB
|
Family ID: |
1000005856021 |
Appl. No.: |
17/463980 |
Filed: |
September 1, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
63107727 |
Oct 30, 2020 |
|
|
|
63109573 |
Nov 4, 2020 |
|
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Current U.S.
Class: |
702/63 |
Current CPC
Class: |
G01R 31/392 20190101;
G01R 31/367 20190101; G01R 31/382 20190101; H02J 7/0048 20200101;
H02J 7/005 20200101; G01R 31/389 20190101; G01R 31/3648
20130101 |
International
Class: |
G01R 31/382 20060101
G01R031/382; G01R 31/36 20060101 G01R031/36; G01R 31/367 20060101
G01R031/367; G01R 31/392 20060101 G01R031/392; H02J 7/00 20060101
H02J007/00; G01R 31/389 20060101 G01R031/389 |
Claims
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.
Description
RELATED APPLICATION
[0001] The present disclosure claims priority to U.S. Provisional
Patent Application Ser. No. 63/107,727, filed Oct. 30, 2020, and to
U.S. Provisional Patent Application Ser. No. 63/109,573, filed Nov.
4, 2020, both of which are incorporated by reference herein in
their entireties.
FIELD OF DISCLOSURE
[0002] The present disclosure relates in general to circuits for
electronic devices, including without limitation personal portable
devices such as wireless telephones and media players, and more
specifically, to battery model estimation for a battery which may
be used to power components of an electronic device.
BACKGROUND
[0003] Portable electronic devices, including wireless telephones,
such as mobile/cellular telephones, tablets, cordless telephones,
mp3 players, and other consumer devices, are in widespread use.
Such a portable electronic device may include a battery (e.g., a
lithium-ion battery) for powering components of the portable
electronic device.
[0004] In operation, the terminal voltage of a battery may droop
under a load current due to internal output impedance of the
battery. Such output impedance may be modeled in a number of
suitable manners, including with an equivalent circuit model of a
series of parallel-coupled resistors and capacitors. Knowledge of
the detailed impedance of a battery may be useful for fuel-gauging
algorithms (e.g., for determining a battery open-circuit voltage
and state of charge, predicting power limits, and/or deriving
safety limits or safe operation limits of the battery (e.g., a
maximum voltage and maximum current of the battery terminal)).
[0005] Using traditional approaches, an impedance model is
typically estimated using a low-level sinusoidal test current or
short-term impulse. However, such approaches may impose loading
conditions on the battery which are different from real-use-case
conditions of a mobile device, which may lead to an inadequate
model.
[0006] In addition, using traditional approaches, estimation of a
battery impedance model having a multi-order complex impedance is
computationally expensive.
SUMMARY
[0007] In accordance with the teachings of the present disclosure,
one or more disadvantages and problems associated with existing
approaches to modeling battery impedance may be reduced or
eliminated.
[0008] In accordance with embodiments of the present disclosure, 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.
[0009] In accordance with these and other embodiments of the
present disclosure, a system for management of a battery that
powers a component of a device may include 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. 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.
[0010] In accordance with these and other embodiments of the
present disclosure, a method for estimating parameters of a battery
impedance model that models an output impedance of a battery may
include dividing the battery impedance model into a plurality of
separate impedance stages, wherein each separate impedance stage
approximates the battery impedance model within a particular
frequency range, the respective impedance in each separate
impedance stage comprises a primary impedance model with a primary
set of defining impedance parameters and a secondary impedance
model with a secondary set of impedance parameters, and the battery
impedance model is defined by a series connection of the respective
primary impedance models of the plurality of impedance stages. The
method may also include monitoring operation of the battery to
determine the primary set of defining impedance parameters and the
secondary set of impedance parameters.
[0011] In accordance with these and other embodiments of the
present disclosure, a system for estimating parameters of a battery
impedance model that models an output impedance of a battery may
include one or more inputs configured to receive information
regarding operation of the battery and battery monitoring
circuitry. The battery monitoring circuitry may be configured to
divide the battery impedance model into a plurality of separate
impedance stages, wherein each separate impedance stage
approximates the battery impedance model within a particular
frequency range, the respective impedance in each separate
impedance stage comprises a primary impedance model with a primary
set of defining impedance parameters and a secondary impedance
model with a secondary set of impedance parameters, and the battery
impedance model is defined by a series connection of the respective
primary impedance models of the plurality of impedance stages, The
battery monitoring circuitry may also be configured to monitor
operation of the battery to determine the primary set of defining
impedance parameters and the secondary set of impedance
parameters.
[0012] Technical advantages of the present disclosure may be
readily apparent to one skilled in the art from the figures,
description and claims included herein. The objects and advantages
of the embodiments will be realized and achieved at least by the
elements, features, and combinations particularly pointed out in
the claims.
[0013] It is to be understood that both the foregoing general
description and the following detailed description are examples and
explanatory and are not restrictive of the claims set forth in this
disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] A more complete understanding of the present embodiments and
advantages thereof may be acquired by referring to the following
description taken in conjunction with the accompanying drawings, in
which like reference numbers indicate like features, and
wherein:
[0015] FIG. 1 illustrates a block diagram of selected components of
an example power delivery network, in accordance with embodiments
of the present disclosure;
[0016] FIG. 2 illustrates an example graph of an open circuit
voltage of a battery versus the battery's state of charge, in
accordance with embodiments of the present disclosure;
[0017] FIG. 3 illustrates a circuit diagram of selected components
of an equivalent circuit model for a battery, in accordance with
embodiments of the present disclosure;
[0018] FIG. 4 illustrates a circuit diagram of selected components
of an equivalent circuit model for a battery depicting estimation
of a battery impedance model in stages, in accordance with
embodiments of the present disclosure;
[0019] FIG. 5 illustrates a circuit diagram of selected components
of an equivalent impedance model for a first estimation stage, in
accordance with embodiments of the present disclosure;
[0020] FIG. 6 illustrates a circuit diagram of selected components
of an equivalent impedance model for a second estimation stage, in
accordance with embodiments of the present disclosure;
[0021] FIG. 7 illustrates a circuit diagram of selected components
of an equivalent impedance model for a third estimation stage, in
accordance with embodiments of the present disclosure;
[0022] FIG. 8 illustrates a circuit diagram of selected components
of an equivalent impedance model for a fourth estimation stage, in
accordance with embodiments of the present disclosure;
[0023] FIG. 9 illustrates a block diagram of selected components of
a battery model estimator, in accordance with embodiments of the
present disclosure;
[0024] FIG. 10A illustrates a block diagram of a least-squares fit
method for estimating primary and second battery model parameters,
in accordance with embodiments of the present disclosure; and
[0025] FIG. 10B illustrates a block diagram of a least-squares fit
method for estimating primary battery model parameters, in
accordance with embodiments of the present disclosure.
DETAILED DESCRIPTION
[0026] FIG. 1 illustrates a block diagram of selected components of
an example power delivery network 10, in accordance with
embodiments of the present disclosure. In some embodiments, power
delivery network 10 may be implemented within a portable electronic
device, such as a smart phone, tablet, game controller, and/or
other suitable device.
[0027] As shown in FIG. 1, power delivery network 10 may include a
battery 12 and a load 18. As shown in FIG. 1, when loaded by load
18, battery 12 may generate a battery voltage V.sub.CELL across its
terminals and deliver a battery current I.sub.CELL to load 18. In
some embodiments, battery 12 may comprise a lithium-ion battery.
Load 18 may represent any electric component, electronic component,
and/or combination thereof. For example, load 18 may include any
suitable functional circuits or devices of power delivery network
10, including without limitation power converters, processors,
audio coder/decoders, amplifiers, display devices, etc. Further,
although not explicitly shown in FIG. 1, power delivery network 10
may also include control circuitry for controlling operation of
battery 12 and/or load 18.
[0028] As also shown in FIG. 1, power delivery network 10 may
include battery monitoring circuitry 20. Battery monitoring
circuitry 20 may include any suitable system, device, or apparatus
configured to monitor battery voltage V.sub.CELL and battery
current I.sub.CELL. Further, battery monitoring circuitry 20 may
include a battery model estimator 24 configured to receive monitor
battery voltage V.sub.CELL and a sense voltage V.sub.SNS across a
sense resistor 22 indicative of battery current I.sub.CELL, and
based thereon, estimate a battery impedance model for battery 12,
as described in greater detail below. Battery model estimator 24
may be implemented with a processing device, including without
limitation a microprocessor, digital signal processor,
application-specific integrated circuit, field-programmable gate
array, electrically-erasable programmable read only memory, complex
programmable logic device, and/or other suitable processing device.
In some embodiments, battery monitoring circuitry 20 may monitor a
temperature associated with battery 12, and battery model estimator
24 may estimate the impedance model based on battery voltage
V.sub.CELL, a sense voltage V.sub.SNS, and the sensed
temperature.
[0029] Lithium-ion batteries are typically known to operate from
4.5 V down to 3.0 V, known as an open circuit voltage V.sub.OC of
the battery (e.g., battery 12). As a battery discharges due to a
current drawn from the battery, the state of charge of the battery
may also decrease, and open circuit voltage V.sub.OC (which may be
a function of state of charge) may also decrease as a result of
electrochemical reactions taking place within the battery, as shown
in FIG. 2. Outside the range of 3.0 V and 4.5 V for open circuit
voltage V.sub.OC, the capacity, life, and safety of a lithium-ion
battery may degrade. For example, at approximately 3.0 V,
approximately 95% of the energy in a lithium-ion cell may be spent
(i.e., state of charge is 5%), and open circuit voltage V.sub.OC
would be liable to drop rapidly if further discharge were to
continue. Below approximately 2.4 V, metal plates of a lithium-ion
battery may erode, which may cause higher internal impedance for
the battery, lower capacity, and potential short circuit. Thus, to
protect a battery (e.g., battery 12) from over-discharging, many
portable electronic devices may prevent operation below a
predetermined end-of-discharge voltage. Knowledge of the output
impedance may be useful in determining open circuit voltage
V.sub.OC and other parameters of battery 12.
[0030] FIG. 3 illustrates a block diagram of selected components of
an equivalent circuit model for battery 12, in accordance with
embodiments of the present disclosure. As shown in FIG. 3, battery
12 may be modeled as having a battery cell 32 having an open
circuit voltage V.sub.OC in series with a plurality of parallel
resistive-capacitive sections 34 (e.g., parallel
resistive-capacitive sections 34-1, 34-2, . . . , 34-N) and further
in series with an equivalent series resistance 36 of battery 12,
such equivalent series resistance 36 having a resistance of
R.sub.0. Resistances R.sub.1, R.sub.2, . . . R.sub.N, and
respective capacitances C.sub.1, C.sub.2, . . . , C.sub.N may model
battery chemistry-dependent time constants .tau..sub.1,
.tau..sub.2, . . . , .tau..sub.N, that may be lumped with open
circuit voltage V.sub.OC and equivalent series resistance 36. The
series of impedance sections represented by resistive-capacitive
sections 34 and equivalent series resistance 36 may represent
diffusion processes that occur at different rates inside battery
12. Cutoff frequencies of parallel resistive-capacitive sections 34
may respectively be given by:
f c .times. 1 = 1 .tau. 1 = 1 2 .times. .pi. .times. R 1 .times. C
1 ; ##EQU00001## f c .times. 2 = 1 .tau. 2 = 1 2 .times. .pi.
.times. R 2 .times. C 2 ; ##EQU00001.2## ##EQU00001.3## f c .times.
N = 1 .tau. 3 = 1 2 .times. .pi. .times. R N .times. C N ;
##EQU00001.4##
wherein .pi. represents the well-known mathematical constant
defined as the ratio of a circle's circumference to its diameter,
and wherein parallel resistive-capacitive sections 34 are ordered
such that f.sub.cN< . . . <f.sub.c2<f.sub.c1.
[0031] Notably, an electrical node depicted with voltage
V.sub.CELL-EFF in FIG. 3 may capture the time varying discharge
behavior of battery 12, and battery voltage V.sub.CELL may be an
actual voltage seen across the output terminals of battery 12.
Voltage V.sub.CELL-EFF may not be directly measurable, and thus
battery voltage V.sub.CELL may be the only voltage associated with
battery 12 that may be measured to evaluate battery state of
health. Also of note, at a current draw of zero (e.g.,
I.sub.CELL=0), battery voltage V.sub.CELL may be equal to voltage
V.sub.CELL-EFF which may in turn be equal to an open circuit
voltage V.sub.OC at a given state of charge.
[0032] To estimate an impedance model of battery 12, battery model
estimator 24 may divide the output impedance of battery 12 into a
number of stages, thus breaking down the estimation problem into
several identification problems of lower order. Such estimation in
stages may be possible due to the fact that cutoff frequencies (or
time constants) of the diffusion processes represented by the
various parallel resistive-capacitive sections 34 may be separated
by an order of magnitude or more.
[0033] Although an impedance model of battery 12 may include any
number N of stages as shown in FIG. 3, battery model estimator 24
may estimate an impedance model using any suitable number of stages
and performing an estimation for each individual stage. For
example, in some embodiments, battery model estimator 24 may
estimate the impedance model of battery 12 in four estimation
stages 40-1, 40-2, 40-3, and 40-4, as shown in FIG. 4. Estimation
stage 40-1 may estimate resistance R.sub.0, resistance R.sub.1, and
capacitance C.sub.1, estimation stage 40-2 may estimate resistance
R.sub.2 and capacitance C.sub.2, estimation stage 40-3 may estimate
resistance R.sub.3 and capacitance C.sub.3, and estimation stage
40-4 may estimate resistance R.sub.4 and capacitance C.sub.4.
[0034] In operation, battery model estimator 24 may estimate the
full battery impedance model as the sum of respective primary
impedance models 42 (e.g., primary impedance models 42-1, 42-2,
42-3, and 42-4) for each of estimation stages 40. A primary
impedance model 42 for a particular estimation stage 40 may define
a main feature of the full battery impedance model within a
frequency band FB.sub.M centered around the cutoff frequency (e.g.,
f.sub.c1, f.sub.c3, f.sub.c3, f.sub.c4) associated with the primary
impedance of such estimation stage 40. For instance, if a primary
impedance model 42-M of an estimation stage 40-M has a single
cutoff frequency defined by resistance R.sub.M in parallel with
capacitance C.sub.M, then a frequency f.sub.cM may be the cutoff
frequency of such primary impedance model 42-M.
[0035] However, a primary impedance of an estimation stage 40-M may
not be directly identified from characteristics of battery voltage
V.sub.CELL and battery current I.sub.CELL in frequency band
FB.sub.M, because the primary impedances of other estimation stages
40 may contribute to the frequency response of the full battery
impedance model within frequency band FB.sub.M. Accordingly,
battery model estimator 24 may estimate the impedance model for
each estimation stage 40-M using a secondary impedance model for
such estimation stage 40-M, such secondary impedance model
modelling the residual impedance of the neighboring preceding
estimation stages (e.g., estimation stages 40-1 to 40[M-1]) and
subsequent estimation stages (e.g., estimation stages 40[M+1] to
40-N) over the frequency band FB.sub.M for such estimation stage
40-M. For example, for estimation stage 40-1, the subsequent
estimation stages 40-2, 40-3, and 40-4 with primary impedance
models R.sub.2.parallel.C.sub.2, R.sub.3.parallel.C.sub.3,
R.sub.4.parallel.C.sub.4, have a residual impedance within
frequency band FB.sub.1 associated with estimation stage 40-1,
which may be modeled with a lumped secondary impedance model 44-1
R.sub.2.parallel.C.sub.2, as shown in FIG. 5.
[0036] As depicted in FIG. 5, an impedance model for estimate stage
40-1 may include a primary impedance model 42-1 with impedance
R.sub.0+R.sub.1.parallel.C.sub.1 having primary parameters R.sub.0,
R.sub.1, and C.sub.1 modeling the effects of impedance near and
above cutoff frequency f.sub.c1. Primary impedance model 42-1 may
be in series with secondary impedance model 44-1 with impedance
R.sub.2.parallel.C.sub.2 and having secondary parameters R.sub.2
and C.sub.2 modeling the effects of residual impedance of
neighboring estimation stages 40 near cutoff frequency
f.sub.c1.
[0037] In operation, battery model estimator 24 may filter
measurements of battery voltage V.sub.CELL and battery current
I.sub.CELL, with a bandpass filter centered around cutoff frequency
f.sub.c1, or alternatively highpass filter such measurements with a
high-pass filter having a cutoff frequency above cutoff frequency
f.sub.c2 but below cutoff frequency f.sub.c1. Further, battery
model estimator 24 may use a least-squares method or other
appropriate fit approach in order to fit primary parameters
R.sub.0, R.sub.1, and C.sub.1 and secondary parameters R.sub.2 and
C.sub.2 to the filtered battery voltage V.sub.CELL and battery
current I.sub.CELL sequence.
[0038] Battery model estimator 24 may perform two estimation steps
to estimate such parameters. In a first estimation step (e.g., as
described with reference to FIG. 10A, below), a least-squares
method may estimate both primary parameters R.sub.0, R.sub.1, and
C.sub.1 and secondary parameters R.sub.2 and C.sub.2
simultaneously. In a second estimation step (e.g., as described
with reference to FIG. 10B, below), battery model estimator 24 may
set secondary parameters R.sub.2 and C.sub.2 of the residual
impedance by subsequent estimates of the primary impedance in the
neighboring estimation stages 40, and may only least-squares fit
primary impedance parameters R.sub.0, R.sub.1, and C.sub.1 of
estimation stage 40-1 during the second estimation step. Such
second estimation step may iteratively improve accuracy of the
fitted parameters. Thus, in this example, battery model estimator
24 may set secondary parameters R.sub.2 and C.sub.2 of estimation
stage 40-1 from an estimation of the primary parameters R.sub.2 and
C.sub.2 of estimation stage 40-2.
[0039] As depicted in FIG. 6, an impedance model for estimate stage
40-2 may include a primary impedance model 42-2 with impedance
R.sub.2.parallel.C.sub.2 having primary parameters R.sub.2 and
C.sub.2 modeling the effects of impedance near cutoff frequency
f.sub.c2. Primary impedance model 42-2 may be in series with
secondary impedance model 44-2a with impedance
R.sub.3.parallel.C.sub.3 having secondary parameters R.sub.3 and
C.sub.3 and with another secondary impedance model 44-2b with
impedance R.sub.L1=R.sub.0+R.sub.1, wherein secondary parameters
R.sub.L1, R.sub.3, and C.sub.3 model the effects of residual
impedance of neighboring estimation stages 40 near cutoff frequency
f.sub.c2.
[0040] In operation, battery model estimator 24 may filter
measurements of battery voltage V.sub.CELL and battery current
I.sub.CELL with a bandpass filter centered around cutoff frequency
f.sub.c2, or alternatively highpass filter such measurements with a
high-pass filter having a cutoff frequency above cutoff frequency
f.sub.c3 but below cutoff frequency f.sub.c2. Further, battery
model estimator 24 may use a least-squares method or other
appropriate fit approach in order to fit primary parameters R.sub.2
and C.sub.2 and secondary parameters R.sub.L1, R.sub.3, and C.sub.3
to the filtered battery voltage V.sub.CELL and battery current
I.sub.CELL sequence.
[0041] Battery model estimator 24 may perform two estimation steps
to estimate such parameters. In a first estimation step (e.g., as
described with reference to FIG. 10A, below), a least-squares
method may estimate both primary parameters R.sub.2 and C.sub.2 and
secondary parameters R.sub.L1, R.sub.3, and C.sub.3 simultaneously.
In the second estimation step (e.g., as described with reference to
FIG. 10B, below), battery model estimator 24 may set secondary
parameters R.sub.L1, R.sub.3, and C.sub.3 of the residual impedance
by subsequent estimates of the primary impedance in the neighboring
estimation stages 40, and may only least-squares fit primary
impedance parameters R.sub.2 and C.sub.2 of estimation stage 40-2
during the second estimation step. Such second estimation step may
iteratively improve accuracy of the fitted parameters. Thus, in
this example, battery model estimator 24 may set secondary
parameters R.sub.2 and C.sub.2 of estimation stage 40-2 from an
estimation of the primary parameters R.sub.3 and C.sub.3 of
estimation stage 40-3 and from an estimation of the primary
parameters R.sub.0 and R.sub.1 of estimation stage 40-1.
[0042] As depicted in FIG. 7, an impedance model for estimation
stage 40-3 may include a primary impedance model 42-3 with
impedance R.sub.3.parallel.C.sub.3 having primary parameters
R.sub.3 and C.sub.3 modeling the effects of impedance near cutoff
frequency f.sub.c3. Primary impedance model 42-3 may be in series
with secondary impedance model 44-3a with impedance
R.sub.4.parallel.C.sub.4 having secondary parameters R.sub.4 and
C.sub.4 and with another secondary impedance model 44-3b with
impedance R.sub.L2=R.sub.0+R.sub.1+R.sub.2, wherein secondary
parameters R.sub.L2, R.sub.4, and C.sub.4 model the effects of
residual impedance of neighboring estimation stages 40 near cutoff
frequency f.sub.c3.
[0043] In operation, battery model estimator 24 may filter
measurements of battery voltage V.sub.CELL and battery current
I.sub.CELL with a bandpass filter centered around cutoff frequency
f.sub.c3, or alternatively highpass filter such measurements with a
high-pass filter having a cutoff frequency above cutoff frequency
f.sub.c4 but below cutoff frequency f.sub.c3. Further, battery
model estimator 24 may use a least-squares method or other
appropriate fit approach in order to fit primary parameters R.sub.3
and C.sub.3 and secondary parameters R.sub.L2, R.sub.4, and C.sub.4
to the filtered battery voltage V.sub.CELL and battery current
I.sub.CELL sequence.
[0044] Battery model estimator 24 may perform two estimation steps
to estimate such parameters. In a first estimation step (e.g., as
described with reference to FIG. 10A, below), a least-squares
method may estimate both primary parameters R.sub.3 and C.sub.3 and
secondary parameters R.sub.L2, R.sub.4, and C.sub.4 simultaneously.
In the second estimation step (e.g., as described with reference to
FIG. 10B, below), battery model estimator 24 may set secondary
parameters R.sub.L2, R.sub.4, and C.sub.4 of the residual impedance
by subsequent estimates of the primary impedance in the neighboring
estimation stages 40, and may only least-squares fit primary
impedance parameters R.sub.3 and C.sub.3 of estimation stage 40-3
during the second estimation step. Such second estimation step may
iteratively improve accuracy of the fitted parameters. Thus, in
this example, battery model estimator 24 may set secondary
parameters R.sub.3 and C.sub.3 of estimation stage 40-3 from an
estimation of the primary parameters R.sub.4 and C.sub.4 of
estimation stage 40-4 and from an estimation of the primary
parameters R.sub.0, R.sub.1, and R.sub.2 of estimation stages 40-1
and 40-2.
[0045] As depicted in FIG. 8, an impedance model for estimate stage
40-4 may include a primary impedance model 42-4 with impedance
R.sub.4.parallel.C.sub.4 having primary parameters R.sub.4 and
C.sub.4 modeling the effects of impedance near cutoff frequency
f.sub.c4. Primary impedance model 42-4 may be in series with
secondary impedance model 44-4 with impedance
R.sub.L3=R.sub.0+R.sub.1+R.sub.2+R.sub.3, wherein secondary
parameter R.sub.L3 models the effects of residual impedance of
neighboring estimation stages 40 near cutoff frequency
f.sub.c4.
[0046] In operation, battery model estimator 24 may filter
measurements of battery voltage V.sub.CELL and battery current
I.sub.CELL with a bandpass filter centered around cutoff frequency
f.sub.c4. Further, battery model estimator 24 may use a
least-squares method or other appropriate fit approach in order to
fit primary parameters R.sub.4 and C.sub.4 and secondary parameter
R.sub.L3 to the filtered battery voltage V.sub.CELL and battery
current I.sub.CELL sequence.
[0047] Battery model estimator 24 may perform two estimation steps
to estimate such parameters. In a first estimation step (e.g., as
described with reference to FIG. 10A, below), a least-squares
method may estimate both primary parameters R.sub.4 and C.sub.4 and
secondary parameter R.sub.L3 simultaneously. In the second
estimation step (e.g., as described with reference to FIG. 10B,
below), battery model estimator 24 may set secondary parameter
R.sub.L3 of the residual impedance by subsequent estimates of the
primary impedance in the neighboring estimation stages 40, and may
only least-squares fit primary impedance parameters R.sub.4 and
C.sub.4 of estimation stage 40-3 during the second estimation step.
Such second estimation step may iteratively improve accuracy of the
fitted parameters. Thus, in this example, battery model estimator
24 may set secondary parameters R.sub.4 and C.sub.4 of estimation
stage 40-4 from an estimation of the primary parameters R.sub.0,
R.sub.1, R.sub.2, and R.sub.3 of estimation stages 40-1, 40-2, and
40-3.
[0048] The first estimation step for each estimation stage 40 may
only be needed once when no a priori estimate of secondary
parameters is defined. Once the primary parameters of every
estimation stage 40 have been estimated via this first estimation
step, battery model estimator 24 may pass on the primary parameters
as secondary parameters of neighboring estimation stages 40 to
simplify and constrain the estimation process, as well as
potentially leading to iteratively more accurate estimates of the
full battery impedance model.
[0049] Once the full model parameters are estimated, battery model
estimator 24 may estimate open circuit voltage V.sub.OC, voltage
V.sub.CELL-EFF, internal overpotential states of battery 12, a
lithium-ion anode potential, and/or other state representing a
condition of the battery that may lead to degradation of its
chemistry, by monitoring current I.sub.CELL (e.g., which may be
indicated by sense voltage V.sub.SNS). Battery model estimator 24
may estimate such overpotential states with a simple filter model
or with a Kalman filter, for example.
[0050] FIG. 9 illustrates a block diagram of selected components of
battery model estimator 24, in accordance with embodiments of the
present disclosure. As shown in FIG. 9, battery model estimator 24
may sample battery voltage V.sub.CELL and battery current
I.sub.CELL with analog-to-digital converters (ADCs), and battery
model estimator 24 may decimate such sampled battery voltage
V.sub.CELL and battery current I.sub.CELL for each estimation stage
40 to a lower rate sufficient to obtain a suitable estimation. Such
signal decimation may reduce an amount of processing required in
estimating the battery impedance model while still maintaining
desirable numerical precision. Such decimated signals are bandpass
filtered near the respective cutoff frequencies f.sub.c1, f.sub.c2,
f.sub.c3, and f.sub.c4 for estimation stages 40, and once filtered,
battery model estimator 24 may perform least squares fits to
determine the various primary and secondary parameters, as
described above. Further, once the battery impedance model has been
established (and which may be dynamically updated over time),
battery model estimator 24 may apply such battery impedance model
to the monitored battery current I.sub.CELL to estimate open
circuit voltage V.sub.OC, voltage V.sub.CELL-EFF, internal
overpotential states of battery 12, a lithium-ion anode potential,
and/or other state representing a condition of the battery that may
lead to degradation of its chemistry.
[0051] FIG. 10A illustrates a block diagram of a least-squares fit
method for estimating primary and second battery model parameters,
in accordance with embodiments of the present disclosure. The
least-squares fit method depicted in FIG. 10A may be used to
perform the "first estimation step" described for each of
estimation stages 40. As shown in FIG. 10A, linear combinations of
delayed samples of the decimated, band-pass-filtered measurements
of battery voltage V.sub.CELL and battery current I.sub.CELL may be
used to form a set of regressors. A cross-correlation matrix of
regressors may be low-pass filtered at the respective cutoff
frequency (e.g., f.sub.c1, f.sub.c2, f.sub.c3, f.sub.c4) for the
particular estimation stage 40. Such low-pass filter may comprise a
simple accumulate-and-dump filter or a first-order low-pass filter
with a bandwidth lower than the lowest cutoff frequency present in
the impedance model for such estimation stage 40. Battery model
estimator 24 may use the filtered correlations to determine a
least-squares fit solution.
[0052] FIG. 10B illustrates a block diagram of a least-squares fit
method for estimating primary battery model parameters, in
accordance with embodiments of the present disclosure. The
least-squares fit method depicted in FIG. 10B may be used to
perform the "second estimation step" described for each of
estimation stages 40. As shown in FIG. 10B, in the second
estimation step, the least-squares solution may be further
restrained by presetting the secondary parameters to lead to a more
accurate estimate of the primary parameters.
[0053] In accordance with the foregoing, methods and systems may be
provided to estimate parameters of a full battery impedance model
that models an output impedance of a battery that powers a load and
that is valid over a full range of frequencies of interest. The
full battery model may be separated into a number N of separate
impedance estimation stages, wherein N is an integer of 2 or
greater. Each impedance estimation stage may approximate a full
battery impedance model for a limited frequency range. For each of
the separate impedance estimation stages, the respective impedance
in each stage may include a primary impedance model with a primary
set of defining parameters and a secondary impedance model with a
secondary set of parameters. The full battery impedance model may
be defined by a series connection of all the primary impedance
models. Thus, the primary parameters may define the parameters of
the full battery model.
[0054] A full range of frequencies of interest for the battery
model may be from 1 mHz to 10 KHz. Methods and systems disclosed
herein may further use measured voltage and current associated with
a transient response of the battery under a switching load such as
naturally exists in a mobile device. In some embodiments, a
broadband test-excitation of the battery may be used to generate
such voltage and current.
[0055] The primary impedance model of an impedance estimation stage
M (wherein M is between 1 and N) may define a main feature (such as
an electrical component or the combination of electrical components
that define the dominant impedance over that frequency range) of
the impedance of the full battery model over a frequency range for
the impedance estimation stage M. The secondary impedance model of
an impedance estimation stage M (M is between 1 and N) may define a
residual feature of the primary impedance model of each impedance
estimation stage 1 to M-1 and M+1 to N over the frequency range of
the impedance estimation stage M. The secondary impedance model may
be a lumped model of impedance of the primary impedance estimation
stages 1 to M-1 and M+1 to N.
[0056] In accordance with the systems and methods disclosed herein,
battery monitoring circuitry may monitor a terminal voltage and
terminal current of a battery. For each impedance estimation stage
M, the battery monitoring circuitry may band-pass filter the
terminal voltage and terminal current in a frequency range
associated with such impedance estimation stage M. The battery
monitoring circuitry may apply a least-squares fit method to the
bandpass-filtered terminal voltage and terminal current
measurements to estimate parameters of the full battery impedance
model over the impedance estimation stage M.
[0057] The least-squares fit method may be performed in two steps.
The first estimation step may include estimating the primary and
secondary parameters of the impedance estimation stage M; as in the
first estimation step, sufficient a priori knowledge of the
secondary parameters may not be available in order to perform
estimation. The second estimation step may involve presetting the
secondary parameters of the impedance estimation stage M and only
estimating the primary parameters of the impedance estimation stage
M using a least-squares fit method. In some instances, the
secondary parameters may become stale and the battery monitoring
circuitry may execute the first estimation step and the second
estimation step again. In some embodiments, the first parameters
step may be performed only once, and the second parameters step may
be repeatedly performed (e.g., for tracking parameters).
[0058] In some embodiments, the battery monitoring circuitry may
constrain some of the estimated parameters based on a priori
knowledge of a valid range for the respective parameters. The
battery monitoring step may constrain one or more estimated
parameters by characterizing a population of samples of the
battery.
[0059] The battery monitoring circuitry may use a multi-rate
implementation wherein, for each impedance estimation stage M, the
battery monitoring circuitry uses a sample rate that is sufficient
to accurately fit the full battery model over the defined frequency
range of such impedance estimation stage M but is low enough to
reduce processing requirements of the least-squares fit. In some
embodiments, such sample rate may be a sample rate that is at least
two times a high cutoff frequency of the bandpass filter of the
impedance estimation stage M.
[0060] The battery model estimation described herein may be
disabled, or its adaptation rate reduced, when a signal-to-noise
ratio (SNR) over a frequency band of interest is below a
predetermined threshold. Such disabling of battery model estimation
or reduction of the adaption rate for estimating parameters may
occur if a load powered from a battery is idle or has strong
spectral content out of the frequency band of interest. A noise
figure required to estimate the SNR over the frequency band may be
estimated a priori when the load is absent or a minimal load is
detected in the frequency band (that may occur, for instance, when
a mobile device is idle). A noise figure may be equally estimated
from a voltage and current sensor noise specification and bandpass
filter parameters.
[0061] Estimation of parameters of an impedance estimation stage M
is disabled if content over a frequency band for the impedance
estimation stage M is not sufficiently spectrally diverse to
estimate the impedance estimation stage M parameters unambiguously
(overfit). The spectral condition may be determined by monitoring
some terms of a cross-correlation matrix of a least-squares
fit.
[0062] The full battery impedance model may be implemented with a
discrete time infinite impulse response filter, such as a Kalman
filter, for example. The full battery impedance model and inferred
states may be used for fuel-gauging algorithms or power limits
estimation of the battery.
[0063] Battery monitoring circuitry may apply the full battery
model to a monitored battery voltage and/or battery current to
estimate internal states of the full battery model such as
overpotentials and an open circuit voltage. In some embodiments, a
terminal voltage and terminal current of the battery under a load
that is drawing a current on the battery to provide power to the
load of the device may be monitored. The battery may be modeled as
a battery model that approximates the relationship between the
monitored terminal voltage and terminal current over one of the
following: (1) a certain frequency range, (2) a certain duration or
amplitude range or an applied load, or (3) a set of conditions of
the battery or the load, wherein battery monitoring circuitry may
be able to estimate a voltage associated with the battery (e.g.,
open circuit voltage) from the terminal current.
[0064] The relationship between the monitored terminal voltage and
terminal current may have a frequency dependent characteristic
including at least two time constants. The at least two time
constants represent a time-varying relationship between an input
and an output of a linear or nonlinear model of the battery. As
described above, the battery model may have model parameters. The
model parameters may be determined through an optimization
function. The model parameters and the battery model may be used to
predict battery characteristics.
[0065] Although the foregoing contemplates battery monitoring
circuitry configured to estimate a battery model, in some
embodiments, a battery model may be determined in another manner.
For example, a battery model may comprise one or more of the
following: (1) a linear or non-linear model of the battery, (2) a
parameterized equivalent circuit model that models impedance of the
battery, (3) a physics-based model, (4) a combination of an
equivalent circuit model and a physics-based model, or (5) a Kalman
filter or extended Kalman filter. In the case of a parameterized
equivalent circuit model, the parameters for modeling an impedance
of the battery may include resistive, capacitive and/or inductive
circuit elements that may be in parallel or in series and which
element impedances may be time varying and/or may have nonlinear
characteristics.
[0066] A filtered monitored terminal voltage and current may be
used to derive an estimated signal-to-noise ratio (SNR) metric or
power spectral density (PSD) metric. Parameters of the battery
model relevant to the one or more frequency bands may be adapted if
sufficient SNR is detected and if the PSD is sufficient to prevent
overfitting the battery model. Adaptation of the parameters of the
battery model relevant to the one or more frequency bands may be
disabled during an idle period or under a constant direct current
(DC) load. The adaptation rate of a parameter may be a function of
the estimated SNR and of the expected parameter rate of change. A
noise figure required for deriving the SNR may be determined when
no load or very light load is present such as when the device
powered by the battery enters into a sleep mode.
[0067] The optimization function may be a least square fit or a
frequency or time weighted variant of a least square fit. The
battery model may be used in conjunction with a fuel-gauging
algorithm. The battery model may be used to estimate a limit of the
battery and limit the power drawn from the battery. The battery
model may be used to protect the battery from conditions that
degrade the battery lifespan. The battery model may be used to
design optimized charging or loading conditions of the battery.
[0068] A component drawing power from the battery may be a
component on a mobile device, such as a central processing unit
(CPU), a graphics processing unit (GPU), a power management unit
(PMU), an amplifier, an audio, haptic or other actuator, a flash
light-emitting-diode (LED), LED screen, a micro-electro-mechanical
systems (MEMS) sensor or other sensor, and/or a passive
component.
[0069] As used herein, when two or more elements are referred to as
"coupled" to one another, such term indicates that such two or more
elements are in electronic communication or mechanical
communication, as applicable, whether connected indirectly or
directly, with or without intervening elements.
[0070] This disclosure encompasses all changes, substitutions,
variations, alterations, and modifications to the example
embodiments herein that a person having ordinary skill in the art
would comprehend. Similarly, where appropriate, the appended claims
encompass all changes, substitutions, variations, alterations, and
modifications to the example embodiments herein that a person
having ordinary skill in the art would comprehend. Moreover,
reference in the appended claims to an apparatus or system or a
component of an apparatus or system being adapted to, arranged to,
capable of, configured to, enabled to, operable to, or operative to
perform a particular function encompasses that apparatus, system,
or component, whether or not it or that particular function is
activated, turned on, or unlocked, as long as that apparatus,
system, or component is so adapted, arranged, capable, configured,
enabled, operable, or operative. Accordingly, modifications,
additions, or omissions may be made to the systems, apparatuses,
and methods described herein without departing from the scope of
the disclosure. For example, the components of the systems and
apparatuses may be integrated or separated. Moreover, the
operations of the systems and apparatuses disclosed herein may be
performed by more, fewer, or other components and the methods
described may include more, fewer, or other steps. Additionally,
steps may be performed in any suitable order. As used in this
document, "each" refers to each member of a set or each member of a
subset of a set.
[0071] Although exemplary embodiments are illustrated in the
figures and described below, the principles of the present
disclosure may be implemented using any number of techniques,
whether currently known or not. The present disclosure should in no
way be limited to the exemplary implementations and techniques
illustrated in the drawings and described above.
[0072] Unless otherwise specifically noted, articles depicted in
the drawings are not necessarily drawn to scale.
[0073] All examples and conditional language recited herein are
intended for pedagogical objects to aid the reader in understanding
the disclosure and the concepts contributed by the inventor to
furthering the art, and are construed as being without limitation
to such specifically recited examples and conditions. Although
embodiments of the present disclosure have been described in
detail, it should be understood that various changes,
substitutions, and alterations could be made hereto without
departing from the spirit and scope of the disclosure.
[0074] Although specific advantages have been enumerated above,
various embodiments may include some, none, or all of the
enumerated advantages. Additionally, other technical advantages may
become readily apparent to one of ordinary skill in the art after
review of the foregoing figures and description.
[0075] To aid the Patent Office and any readers of any patent
issued on this application in interpreting the claims appended
hereto, applicants wish to note that they do not intend any of the
appended claims or claim elements to invoke 35 U.S.C. .sctn. 112(f)
unless the words "means for" or "step for" are explicitly used in
the particular claim.
* * * * *