This application is divided out of Indian patent application no.
1238/K0LNP/2006
Technical Field
The present invention relates to the implementation of a
method and apparatus for estimating battery charge power
and discharge power.
Background Art
A number of high-performance battery applications
require precise real-time estimates of the power available
to be sourced by the battery pack. For example, in Hybrid
Electric Vehicles (HEVs) and Battery Electric Vehicles
(BEVs), the vehicle controller requires continuous up-to-
date information from the Battery Management System (BMS)
regarding the power that may be supplied to the electric
motor from the battery pack, and power that may be supplied
to the pack via regenerative braking or by active
recharging via the motor. One current technique in the art,
called the HPPC (Hybrid Pulse Power Characterization)
method, performs this task of estimation by using the
voltage limits to calculate the maximum charge and
discharge limits. As described in the PNGV (Partnership for
New Generation Vehicles) Battery Test Manual, Revision 3,
February 2001, published by the Idaho National Engineering
and Environment Laboratory of the U.S. Department of Energy,
the HPPC method estimates maximum cell power by considering
only operational design limits on voltage. It does not
consider design limits on current, power, or the battery
state-of-charge (SOC). Also the method produces a crude
prediction for horizon At. Each cell in the battery pack is
modeled by the approximate relationship

where OCV(zk(t)) is the open-circuit-voltage of cell k
at its present state-of-charge (zk(t)) and E is a constant
representing a cell's internal resistance. Different values
of R may be used for charge and discharge currents, if
desired, and are denoted as Rchg and Rdis, respectively.
Since the design limits Vmin < Vk(t) < Vmax must be enforced,
the maximum discharge current may be calculated as
constrained by voltage, as shown below

The maximum magnitude charge current may be similarly
calculated based on voltage. Note, however, that charge
current is assumed negative in sign by convention employed
in the present invention (although the opposite convention
may be used with minor modifications to the method) , so
that maximum-magnitude current is a minimum in the signed
sense. It is

This prior art charge calculation method is limited in
several respects. First, as noted above, the method does
not use operational design limits in SOC, maximum current,
or maximum power in the computation. More importantly, the
cell model used is too primitive to give precise results.
Overly optimistic or pessimistic values could be generated,
either posing a safety of battery-health hazard or causing
inefficient battery use.
What is desired is a new method and apparatus for
battery charge estimation based on a battery cell model.
Such a cell model would be combined with a maximum-power
algorithm that uses the cell model to give better power
prediction. The new method would also take in operational
design limits such as SOC, current, and power.
BRIEF DESCRIPTION OF THE DRAWINGS
These and other features, aspects and advantages of the
present invention will become better understood with regard
to the following description, appended claims and
accompanying drawings where:
Fig. 1A is a flow chart that outlines the maximum
discharge estimation according to an embodiment of the
present invention;
Fig. 1B is a flow chart that outlines the minimum charge
estimation according to an embodiment of the present
invention;
Fig. 2 is a schematic block diagram showing the sensor
components of a power estimating embodiment of the present
invention;
Fig. 3 is an example plot of open-circuit-voltage (OCV)
as a function of state-of-charge for a particular cell
electrochemistry;
Fig. 4 is an example plot showing the derivative of OCV
as a function of state-of-charge for a particular cell
electrochemistry;
Fig. 5 is a plot showing the voltage prediction using
the cell model of the present invention;
Fig 6 is a zoom-in of the plot of voltage prediction for
one UDDS cycle at around 50% state-of-charge;
Fig. 7 is a state-of-charge trace for cell test;
Fig. 8 is a plot comparing static maximum power
calculations as functions of SOC for the PNGV HPPC method
and Method I of the present invention;
Fig. 9 is a plot showing that discharge power capability
estimates for cell cycle test comprising sixteen UDDS
cycles over an SOC range of 90% down to 10%;
Fig. 10 is zoomed-in plot of Fig. 9, showing about one
UDDS cycle;
Fig. 11 is a plot showing charging power capability
estimates for cell cycle test comprising sixteen UDDS
cycles over an SOC range of 90% down to 10%; and
Fig. 12 is zoomed-in plot of Fig. 11, showing about one
UDDS cycle.
Disclosure of the Invention
The present invention relates to a method and an
apparatus for estimating discharge and charge power of
battery applications, including battery packs used in
Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs).
One embodiment is a charge prediction method that
incorporates voltage, state-of-charge, power, and current
design constraints, works for a user-specified prediction
horizon At, and is more robust and precise than the state
of the art. The embodiment has the option of allowing
different modeling parameters during battery operation to
accommodate highly dynamic batteries used in Hybrid
Electric Vehicles (HEVs) and Electric Vehicles (EVs) where
such previous implementations were difficult.
An embodiment of the present invention calculates
maximum charge/discharge power by calculating the maximum
charge/discharge current using any combination of four
primary limits:
1. state-of-charge (SOC) limits
2. voltage limits
3 . current limits
4 . power limits
In one embodiment, the minimum absolute charge/discharge
current value from the calculations using state-of-charge
(SOC), voltage, and current limits is then chosen to obtain
the maximum absolute charge/discharge power. In one
embodiment, the maximum absolute charge/discharge power is
checked to ensure it is within the power limits. In one
embodiment, the maximum absolute charge/discharge power is
calculated in a way as to not violate any combination of
:he limits that may be used.
Prior methods do not use SOC limits in their estimation
f maximum charge/discharge power. The present invention
ncorporates the SOC of the battery cell or battery pack to
estimate the maximum charge/discharge current. The
estimation explicitly includes a user-defined time horizon
In one embodiment, the SOC is obtained by using a
Caiman filter. The SOC that is produced by Kalman filtering
also yields an estimate of the uncertainty value, which can
be used in the maximum charge/discharge calculation to
yield a confidence level of the maximum charge/discharge
current estimate.
Methods of the present inventions improve upon prior art
estimation of power based on voltage limits. In the present
invention, voltage limits are used to calculate the maximum
charge/discharge current in a way that includes a user-
defined time horizon At. Two primary cell model embodiments
are in the present invention for the calculation of maximum
charge/discharge power based on voltage limits. The first
is a simple cell model that uses a Taylor-series expansion
to linearize the equation involved. The second is a more
complex and accurate cell model that models cell dynamics
in discrete-time state-space form. The cell model can
incorporate a variety of inputs such as temperature,
resistance, capacity, etc. One advantage of using model-
based approach is that the same cell model may be used in
both Kalman filtering to produce the SOC and the estimation
of maximum charge/discharge current based of voltage limits.
Embodiments of the present invention also include
methods of charge estimation based on any combination of
the voltage, current, power, or SOC limits described above.
For example, charge estimation can be based on voltage
limits only, or combined with current limits, SOC limits
and/or power limits.
Embodiments of the present invention are directed to a
power apparatus that takes in data measurements from the
battery such as current, voltage, temperature, and feeding
such measurements to an arithmetic circuit, which includes
calculation means that performs the calculation methods
disclosed in the present invention to estimate the absolute
maximum charge or discharge power.
Best Mode for Carrying Out the Invention
Embodiments of the present invention relates to battery
charge estimation for any battery-powered application. In
one embodiment, the estimator method and apparatus find the
maximum absolute battery charge and/or discharge power
(based on present battery pack conditions) that may be
maintained for At seconds without violating pre-set limits
on cell voltage, state-of-charge, power, or current.
Figs. 1A and 1B illustrates an overview of the
embodiments of the present invention. Fig. 1A shows a
method for finding the maximum discharge power for a user-
defined time horizon At, i.e. how much power may be drawn
from the battery continuously for use for the next At time
period. In vehicle applications, accurate estimation of
maximum discharge power can help prevent the hazardous
occurrence of over-drawing the battery.
In step 10, the maximum discharge current is calculated
based on pre-set limits on state-of-charge. The estimation
explicitly includes a user-defined time horizon At. In one
embodiment, the SOC is obtained by using a kalman filtering
method. The SOC that is produced by Kalman filtering also
yields an estimate of the uncertainty value, which can be
used in the maximum charge/discharge calculation to yield a
confidence level of the maximum charge/discharge current
estimation. In another embodiment, a simple state-of-charge
is used. Step 10 is further described in the section titled
"Calculation Based on State-of-Charge (SOC) Limits."
The maximum discharge current is calculated based on
pre-set limits on voltage in step 12. The present invention
has two main model embodiments for calculation of maximum
charge/discharge power based on voltage limits, although it
is understood that other cell models could be used. Both
overcome the limitation of prior art discharge estimation
methods of giving a crude prediction of time horizon At.
The first is a simple cell model that uses a Taylor-series
expansion to linearize the equation involved. The second is
a more complex and accurate cell model that models cell
dynamics in discrete-time state-space form. The cell model
can incorporate a variety of inputs such as temperature,
resistance, capacity, etc. The two cell models are further
described in the section titled "Calculation Based on
Voltage Limits."
Then In step 14, the maximum discharge current is
calculated based on pre-set limits on current. In step 16,
the minimum of the three calculated current values from
steps 10, 12, and 14 is chosen. It is understood that the
execution order of steps 10, 12, 14 is interchangeable. It
is further understood that any combination of steps 10, 12,
and 14 may be omitted, if desired, in an implementation.
Using the chosen discharge current value, step 18
calculates the maximum discharge power. The calculated pack
power may be further refined in order to not violate
individual cell or battery pack power design limits.
Fig. 1B shows a method for finding the maximum absolute
charge power for a user-defined time horizon At, i.e. how
much power can be put back into the battery continuously
for the next At. time period. The details and progression of
the method mirror that of Fig. 1A. Since charge current is
considered to have a negative sign, the maximum absolute
current is the minimum current in a signed sense. In step
20, the minimum charge current is calculated based on pre-
set limits on state-of-charge. Again the SOC can be a
simple one or one obtained using the Kalman filtering
method. Then the minimum charge current is calculated based
on pre-set limits on voltage in step 22 in accordance with
a cell model, such as one of the two cell models described
in the present disclosure. Then in step 24, the minimum
charge current is calculated based on pre-set limits on
current. Then, in step 26, the maximum of the three
calculated current values from steps 20, 22, 24 is chosen.
Note again that the execution order of steps 20, 22, 24 is
interchangeable. It is further understood that any
combination of steps 20, 22, 24 may be used and any of the
steps 20, 22, 24 may be omitted, if desires, in an
implementation. Using the chosen charge current value, step
28 calculates the minimum charge power. The calculated pack
power may be further refined in order to not violate
individual cell or battery pack power design limits.
It is noted that modifications may be made to the method
embodiments as shown in Figs. 1A and 1B. For example, any
or all of the current calculation steps based on state-of-
charge and voltage limits may be removed. Also, the present
invention discloses several methods of calculating maximum
absolute charge and discharge current based on state-of-
charge, voltage limits, and current limits.
One embodiment of the present invention estimates the
maximum absolute charge and/or discharge power of a battery
pack. The battery pack, may be, for example, a battery pack
used in a hybrid electric vehicle or an electric vehicle.
The embodiment makes a number of denotations and limits,
including:
- using n to denote the number of cells in the target
battery pack where an estimation of charge and/or discharge
power is desired;
- using vk(t) to denote the cell voltage for cell number
k in the pack, which has operational design limits so that
must be enforced for all k :1 < k < n;
- using zk{t) to denote the state-of-charge for cell
number k in the pack, which has operational design limits
that must be enforced for all k :1 < k < n;
- using pk(t) to denote the cell power, which has a
operational design limits so that must be
enforced for all and
- using ik(t) to denote the cell current, which has a
operational design limits so that must be
enforced for all
Modifications can be made in alternate embodiments. For
example, any particular limit may be removed if desired by
replacing its value by ±°°, as appropriate. As an another
example, limits such as vmax, vmint, zmax, zmin, imax, imiUi pmax
Pmin may furthermore be functions of temperature and other
factors pertaining to the present battery pack operating
condition. In one embodiment, it is assumed that the
discharge current and power have positive sign and the
charge current and power have negative sign. Those skilled
in the art will recognize that other sign conventions may
be used, and that the description of the present invention
can be adapted to these conventions in a forthright manner.
In one embodiment, the model used for predicting charge
assumes that the battery pack comprises ns cell modules
connected in series, where each cell module comprises np
individual cells connected in parallel and ns >1, np > 1
Other configurations are possible and are accommodated by
slight modifications to the method as described.
Fig. 2 is a schematic block diagram showing the sensor
components of an embodiment of the present invention.
Battery 40 is connected to load circuit 48. For example,
load circuit 48 could be a motor in an Electric Vehicle
(EV) or Hybrid Electric Vehicle (HEV). In some embodiments
circuit 48 is a circuit that provides power and/or draws
power. Measurements of battery and individual cell voltage
are made with voltmeter(s) 44. Measurements of battery
current are made with ammeter 42. Battery and individual
cell temperatures are measured by temperature sensor(s) 46.
Voltage, current and temperature measurements are processed
with arithmetic circuit 50. Arithmetic circuit (estimator
means) 50, takes in the measurements from the sensor
components and perform the calculation methods of the
present invention for power estimation. In some embodiment,
temperature is not needed in the calculation methods.
1. Calculation Based in State-of-Charge (SQC) Limits
As shown in steps 10 and 20 of Figs 1A and 1B,
embodiments of the present invention calculate the maximum
charge/discharge current values using SOC limits. Various
embodiments also have the explicit inclusion of a time
horizon At in the calculation. The SOC limits are included
as follows. First, for a constant current ik, the SOC
recurrent relationship is described as:

Where zk(t) is the present SOC for cell k, zk(t+?t) is
the predicted SOC At seconds into the future, C is the cell
capacity in ampere-seconds, and ?i is the Coulombic
efficiency factor at current level ik. Here, for simplicity
of presentation, it is assumed that ?i =1 for discharge
currents and for charge currents.
If there are design limits on SOC such that
for all cells in the pack, then current ik
can be computed such that these limits are not exceeded.
Simple algebra gives limits based on the SOC of each cell:

The pack maximum absolute currents-based only on cell
SOC-are then

This method assumes that there is a valid SOC estimate
available for every cell in the pack. If there is not the
case, then an approximate remedy would be to calculate

where z(t) is the pack SOC.
In one embodiment of the present invention, the power
predictive method can take into account more information
than simply the cell SOC. For example, a Kalman filter can
be used as a method to estimate all the cell SOCs in a pack.
Besides giving the SOC, Kalman filtering yields estimates
of the uncertainty of the SOC estimate itself. A method of
using Kalman filter to estimate SOC is described in
commonly assigned U.S. Patent No. 6,534,954, hereby
incorporated by reference.
Let the uncertainty have Gaussian distribution with
standard deviation, as estimated by the Kalman filter, be
denoted a sz. Then, the method yields a95.5% confidence
that the true SOC is within the estimate ±2 sz and a 99.7%
confidence that the true SOC is within the estimate ±3 sz.
This information can be incorporated into the estimate
of maximum current based on SOC to have very high
confidence that SOC design limits will not be violated.
This is done as (assuming a 3 sz confidence interval):

2. Calculation Based on Voltage Limits
Besides taking SOC limits into account, embodiments of
the present invention correct a limitation in the prior art
HPPC method for applying voltage limits (steps 12 and 22 of
Figs. 1A and 1B). In the HPPC method, if the cell model of
equation (1) is assumed, and that Rchg and Rdis are the
cell's Ohmic resistances, then equation (2) and equation
(3) predict the instantaneously available current, not the
constant value of current that is available for the next At
seconds. If cases where At is large, the result of the
calculation poses a safety or battery-health issue, as the
cells may become over/under charged.
To overcome this problem, an embodiment of the present
invention uses the following cell model:

This modifies the previous cell model in equation (1) .
Note that this model cannot be directly solved in closed
form for the maximum current ik since Zk(t+?t) is itself a
function of current (cf.(4)) and OCV (-) is a nonlinear
relationship. Note that other cell models can be used as
well.
Two method embodiments are directed to solving (7) for
the maximum absolute value of ik(t).
2.1Method I: Taylor-series expansion
The first method uses a Taylor-series expansion to
linearize the equation, so that at approximate value of 1
can be solved. It is assumed that OCV(-) is differentiable
at point Zk(t), which gives the result

where the first-order residual
as in Q. Note that the change in SOC over At
seconds is generally small, so the following approximation
may be applied

In one embodiment, both the function OCV (2) and its
derivative ?OCV(z)/ ?z might be computed from some known
mathematical relationship for OCV(z), (e.g., Nernst's
equation) using either analytic or numeric methods, or by a
table lookup of empirical data. This quantity is positive
for most battery electrochemistries over the entire SOC
range, so the values computed by (8) and (9) are smaller in
magnitude than those from (2) and (3) for the same values
of tfdis and Rchg.
The HPPC procedure compensates for its inaccuracy by
using modified values of Rdis and Rchg, determined
experimentally, that approximate the denominator terms in
(8) and (9) . This can not be accurate over the entire SOC
range, however, as is not constant,
particularly near extreme values of z.
Discharge and charge currents with all limits enforced
are computed as (steps 16 and 26 of Figs. 1A and 1B)

and power may be calculated using the sum of all cell
powers. These are equal to the product of the maximum
allowed current and the predicted future voltage.

Maximum and minimum cell and pack power limits may also
be imposed in this calculation. Note that in all equations,
OCV(z), C, vmax, vmin, zmax, zmin, imax, imin, Kchg, and Rdis may be
functions of temperature and other factors pertaining to
the present battery pack operating conditions.
2.2 Method II: Using a Comprehensive Cell Model
The method of solving (7) presented in the previous
section requires less computational intensity. A second
method embodiment of the present invention may be used when
more computational power is available. This second method
assumes a more precise mathematical model of cell dynamics,
which might be in a discrete-time state-space form such as
the coupled pair of equations

where m is the discrete time sample index, the vector
function of time xk[m] is called the "state" of the system,
uk[m] is the input to the system, which includes cell
current ik[m] as a component, and might also include
temperature, resistance, capacity and so forth, and f(-)
and g(.) are functions chosen to model cell dynamics.
alternative model forms, including continuous-time state-
space forms, differential and difference equations might
also be used. It is assumed that there is a method to
compute SOC given the model that is implemented.
For convenience of presentation, it is assumed that the
cell model is in a discrete-time state-space form. Also
assume that At seconds may be represented in discrete time
as T sample intervals. Then, this model can be used to
predict cell voltage At seconds into the future by

where xk[m+T] may be found by simulating (14) for T time
samples. It is assumed that the input remains constant from
time index m to m+T, so if temperature change (for example)
over this interval is significant, it must be included as
part of the dyn amies modeled by (14) and not as a part of
the measured input uk[m].
The method then uses a bisection search algorithm to
find and by looking for the ik (as a member of
the uk vector) that causes equality in

to find and by looking for the ik that causes
equality in

to find . A special case is when the state
equation (14) is linear-that is, when

where A and B are constant matrices. The model presented
in Section 3, entitled "An Example Cell Model," is an
example where this is the case. Then, for input uk constant
time m to m+T, leading to

Most of these terms may be pre-computed without
knowledge of uk in order to speed calculation using the
bisection algorithm.
Once the SOC-based current limits and are
computed using (5) and (6), and the voltage-based current
limits and are computed using (16) and (17),
overall current limits may be computed using (10) and (11)
(steps 16 and 26 of Figs. 1A and 1B). Power is then
computed as

with uk containing as its value for current, and
with uk containing as its value for current.
1.2.1 Bisection search
To solve (16) and (17), a method to solve for a root of
a nonlinear equation is required. In one embodiment the
bisection search algorithm is used for this requirement.
The bisection search algorithm looks for a root of f(x)
(i.e., a value of x such that f(x)=0) where it is known a
priori that the root lies between values x1 < root < x2.
One way of knowing that a root lies in this interval is
that the sign of f (x1) is different from the sign of f (x2),
Each iteration of the bisection algorithm evaluates the
function at the midpoint xmid = (x1 + x2)/2. Based on the
sign of the evaluation, either x1 or x2 is replaced by xmid
to retain different signs on of f(x1) and f(x2). It is
evident that the uncertainty in the location of the root is
halved by this algorithmic step. The bisection algorithm
repeats this iteration until the interval between x1 and x2,
and hence the resolution of the root of f (x) is as small as
desired. If £ is the desired root resolution, then the
algorithm will require at most
iterations. The bisection method is listed in Listing 1.
1.2.2 Finding maximum/minimum current
To determine maximum discharge and charge current for
any particular cell, bisection is performed on (16) and
(17). Bisection is incorporated in the overall algorithm ad
follows. First, three stimulations are performed to
determine cell voltages At seconds into the future for cell
current ik = 0, ik = inin, and ik = imax. If cell voltages are
predicted to be between vmin and vmax for the maximum
dis/charge rates, then these maximum rates may be used. If
the cell voltages, even during rest, are outside of bounds,
then set the maximum rates to zero. Otherwise, the true
maximum rate may be found by bisecting between rate equal
to zero and its maximum value. Bisection is performed
between current limits (Imin, 0) or (0, imax) .

2. An example Cell Model
An example cell model for the present invention power
estimation methods is presented herein, with illustrations
given to show the performance of the two methods compared
to the prior art PNGV HPPC method. The ceil model is a
discrete-time state-space model of the form of (14) and
(15) that applies to battery cells. The model, named
"Enhanced Self-Correcting Cell Model," is further described
in the article "Advances in EKFLiPB SOC Estimation," by the
inventor, published in CD-ROM and presented in Proc. 20th
Electric Vehicle Symposium (EVS20) in Long Beach CA,
(November 2003) and is hereby fully incorporated by
reference. It is understood this model is an example model
only and that a variety of suitable alternate models can be
used.
The "Enhanced Self-Correcting Cell Model" includes
effects due to open-circuit-voltage, internal resistance,
voltage time constants, and hysteresis. For the purpose of
example, the parameter values are fitted to this model
structure to model the dynamics of high-power Lithium-Ion
Polymer Battery (LiPB) cells, although the structure and
methods presented here are general.
State-of-charge is captured by one state of the model.
This equation is

where AT represents the inter-sample period (in seconds),
and C represents the cell capacity (in ampere-seconds).
The time-constants of the cell voltage response are
captured by several filter states. If there is let to be nt
time constants, then

The matrix may be a diagonal matrix with
real-valued entries. If so, the system is stable if all
entries have magnitude less than one. The vector
may simply be set to nf "1"s. The value of nt and the
entries in the Af matrix are chosen as part of the system
identification procedure to best fit the model parameters
to measured cell data.
The hysteresis level is captured by a single state

where y is the hysteresis rate constant, again found by
system identification.
The overall model state is

where the symbol(') is the matrix/vector transpose
operator. The state equation for the model is formed by
combining all of the individual equations, above. Note,
that at each time step, the state equation is linear in the
input

which speeds the prediction operation.
The output equation that combines the state values to
predict cell voltage is

where is a vector of constants that blend the
time-constant states together in the output, R is the cell
resistance (different values may be used for dis/charge),
and M is the maximum hysteresis level.
The open-circuit-voltage as a function of state-of-
charge for example Lithium Ion Polymer Battery (LiPB) cells
is plotted in Fig.3. This is an empirical relationship
found by cell testing. First, the cell was fully charged
(constant current to 4.2V, constant voltage to 200mA). Then,
the cell was discharged at the C/25 rate until fully
discharged (3.0V). The cell was then charged at the C/25
rate until the voltage was 4.2V. The low rates were used to
minimize the dynamics excited in the cells. The cell
voltage as a function of state of charge under discharge
and under charge were averaged to compute the OCV. This has
the effect of eliminating to the greatest extent possible
the presence of hysteresis and ohmic resistance in the
final function. For the purpose of computations involving
OCV, the final curve was digitized at 200 points and stored
in a table. Linear interpolation is used to look up values
in the table.
The partial derivative of OCV with respect to SOC for
these example cells is plotted in Fig. 4. This relationship
was computed by first taking finite differences between
points in the OCV plot in Fig. 3 and dividing by the
distance between points (i.e., Euler's approximation to a
derivative) . The resulting data is too noisy to be of
practical use, as shown in the gray line of Fig. 4. It was
filtered using a zero-phase low-pass filter, resulting in
the black line of Fig. 4, which may be used in the power
calculation. This relationship is also digitized at 200
points, and linear interpolation into the table of values
is used when computations requiring this function are
performed.
Other parameters are fit to the cell model. In
particular, the model employs four low-pass filter states
(nf =4), a nominal capacity of 7.5 Ah, and an inter-sample
interval of ?T = 1s. There is very close agreement between
the cell model voltage prediction and the cell true voltage.
This is illustrated in Fig. 5, which is a plot showing the
voltage prediction using the cell model of the present
invention. For this figure, the cell test was a sequence of
sixteen ODDS cycles, performed at room temperature,
separated by discharge pulses and five-minute rests, and
spread over the 90% to 10% SOC range. The difference
between true cell terminal voltage and estimated cell
terminal voltage is very small (a root-mean-squared (RMS)
voltage estimation error of less than 5mV) . To better
illustrate the model's fidelity, refer to the zoom on one
UDDS cycle in the 50% SOC region, shown in Fig. 6. The SOC
as a function of time is plotted in Fig. 7, which is a SOC
trace for cell test. The graph shows that SOC increases by
about 5% during each UDDS cycle, but is brought down about
10% during each discharge between cycles. The entire
operating range for these cells (10% SOC to 90% SOC,
delineated on the figure as the region between the thin
dashed lines) is excited during the cell test.
3. Comparing Maximum Power Calculations
The PNGV HPPC power estimation method gives a result
that is a function of only SOC. Therefore, it is possible
to graph available power versus SOC to summarize the
algorithm calculations. The first method proposed (Method
I: Taylor Series Expansion Methods) in this patent
disclosure is also possible to display in this way.
Estimated power is only a function of SOC, dOCV/dz (also a
function of SOC), and static limits on maximum current and
power. The second method (Method II: the Comprehensive Cell
Model Method), however, dynamically depends on all states
of the system. Two systems at the same state of charge, but
with different voltage time-constant state values or
hysteresis state levels will have different amounts of
power available. To compare power computed by the three
methods, dynamic tests must be conducted.
For the following result, a pack of LiPB cells is
assumed to be with ns = 40 and np = 1. The data to fit
models was collected from prototype hand-made cells jointly
developed by LG Chem (Daejeon, Korea) and Compact Power Inc.
(Monument, Colorado). Limits for the power calculations are
listed in Table 1. Each cell has a nominal capacity of 7.5
Ah, and At was ten seconds for both charge and discharge.
Table 1: Parameters for power calculation examples.

First, the PNGV HPPC method and Method I of the present
invention are compared in Fig. 8, which is a plot comparing
static maximum power calculations as functions of SOC for
the PNGV HPPC method and Method I of the present invention.
The black curves correspond to charge power, and the gray
curves correspond to discharge power. Note that the
absolute value of power is plotted to avoid confusion due
to sign conventions. Considering first the calculations of
charge power, it is evident that the PNGV HPPC method
produces similar values to Method I in the mid-SOC range.
The slight differences are due to the fact that the 10-
second Rchg value used for the PNGV method and the
derivative-modified Rchg for Method I are not identical.
Outside the mid-SOC range, the graph shows that Method I
ramps power down in the neighborhood of zmax to avoid over-
charging the cell, whereas the PNGV method has no such
limits. At very low SOCs, the PNGV method over-predicts how
much power is available since there are no current limits
applied to the calculation. The Method I estimate is
automatically lower due to the large derivative in the
denominator of the calculation. This causes an anomaly near
zero SOC where the method under-predicts the available
charge power. However, since the cell will not be operated
in this range, this is not a concern.
Considering now the discharge power curves, the
comparison shows that Method I imposes limits on discharge
power to ensure that the cell is not under-charged, whereas
the PNGV method does not. In the SOC range from about 15%
to 35%, the two methods predict similar powers. For SOC
above about 35%, the power predicted by Method I saturates
because the maximum discharge current limit of 200A has
been reached. The PNGV method does not consider this limit.
At SOC around 99% the graph again shows an anomaly in the
Method I calculation where power is under-estimated due to
the large derivative term. This apparent glitch is not a
problem since the cell will not be operated in this range.
Figs. 9 through 13 show how the two main voltage-limit
based methods of power estimation of the present Invention
(Method I and Method II) compare to the prior art PNGV
method in the dynamic cell tests shown in Fig. 5. Fig. 9 is
a plot showing that discharge power capability estimates
for cell cycle test comprising sixteen UDDS cycles over an
SOC range of 90% down to 10%. Fig. 10 is zoomed-in plot of
Fig. 9, showing about one UDDS cycle. Fig. 11 is a plot
showing charging power capability estimates for cell cycle
test comprising sixteen UDDS cycles over an SOC range of
90% down to 10%. Fig. 12 is zoomed-in plot of Fig. 11,
showing about one UDDS cycle. Again, the absolute value of
power is plotted.
In the discussion that follows, the results of Method II
are considered to be the "true" capability of the cell.
This assumption is justified by the fidelity of the cell
model's voltage estimates, as supported by the data in Fig.
6. Fig. 9 shows that the three methods produce similar
estimates. In particular, Method I and Method II appear to
be nearly identical when viewed at this scale. At high SOCs,
the PNGV HPPC method predicts higher power than is actually
available (by as much as 9.8%), and at low SOCs, the PNGV
HPPC method under-predicts the available power. Only the
methods of the present invention include SOC bounds, which
explain why their predictions are so different from the
PNGV HPPC estimates at low SOC. If the vehicle controller
were to discharge at the rates predicted by the PNGV HPPC
method, the cell would be over-discharged in some cases
(lowering its lifetime), and under-utilized in other cases.
Fig. 10 zooms in on Fig. 9 (same region shown as in Fig. 6)
to show greater detail. In this region, the three methods
produce nearly identical predictions. A notable feature of
Method II, however, is that it takes into account the
entire dynamics of the cell when making a prediction.
Therefore, the strong discharges at around time 237 and 267
minutes draw the cell voltage down, and allows less
discharge power than the other two methods which only
consider SOC when making their estimate.
The three methods are also compared with respect to
charge power, shown in Fig. 11. At this scale, the
estimates appear nearly identical. Again, the PNGV HPPC
method does not consider SOC limits, so over-predicts
charge power at high SOCs. It also over-predicts power at
low SOCs as it ignores the increase to charge resistance at
low SOC. A zoom of this plot is shown in Fig. 12, which
accentuates the differences between the predictions. Here,
it can be seen that the strong discharges at around time
237 and 267 minutes allow for greater charging power, as
the voltage will not quickly change.
Industrial Applicability
While the methods described herein, and the apparatus
for carrying these methods into effect, constitute
preferred embodiments of the present invention, it should
be recognized that changes may be made therein without
departing from the spirit or scope of the present invention,
which is defined in the appended claims. For example, the
steps 10, 12, 14 disclosed in Fig. 1A can be executed in
different orders or used in different combinations and
steps 20, 22, 24 disclosed in Fig. 1B can be executed in
different orders or used in different combinations. Also,
various cell models can be substituted for the purpose of
estimating the maximum absolute charge/discharge power of a
battery/battery cell.
A method and apparatus for calculation of power
capability of battery packs using advanced cell model
predictive techniques has been described in conjunction
with one or more specific embodiments. This invention is
defined by the following claims and their full scope of
equivalents.
We Claim :
1, The method for estimating the minimum charge power
of a battery comprising:
calculating a minimum charge current of said battery
based on voltage limits of said battery;
calculating said minimum charge power based on said
maximum discharge current value.
2. The method of claim 1 further comprising:
calculating a minimum charge current of said battery
based on state-of-charge limits of said battery;
calculating a minimum charge current of said battery
based on current limits of said battery,
wherein said minimum charge power is calculated from a
maximum value of charge current chosen among said
calculated minimum charge current based on voltage limits,
said calculated minimum charge current based on state-of-
charge limits, and said calculated minimum charge current
based on current limits.
3. The method of claim 2. wherein said estimating of
minimum charge power takes into account a user-defined
horizon of ?t.
4. The method of claim 2. wherein said step of
calculating minimum charge current of said battery based on
state-of-charge limits obtains a state-of-charge by using a
Kalman filtering method.
5. The method of claim 4 wherein an estimate of the
uncertainty value yielded by said Kalman filtering method
is used to find a confidence level of said calculated
minimum charge current.
6. The method of claim 2 wherein said battery . is a
battery pack comprising n cells.
7. The method of claim 6 wherein said step of
calculating minimum charge current of said battery based on
state-of-charge limits calculates the current limits of
each cell k in said battery pack using the equation

wherein denotes the minimum charge current based on
state-of-charge, Zk(t) denotes the cell state-of-charge at
time t, zmin denotes the state-of-charge design limits, ?
denotes the Coulombic efficiency factor, ?t denotes the
time horizon and C denotes the cell capacity.
8. The method of claim 7 wherein minimum charge
current of said battery pack based on state-of-charge
limits is

9. The method of claim 1 wherein said step of
calculating minimum charge current of said battery based on
voltage limits uses a cell model.
10. The method of claim 9 wherein cell model is solved
by a Taylor-series expansion.
11. The method of claim 9 wherein said cell model is
solved by using a discrete time-state space model.
12. The method of claim 9 wherein said battery is a
battery pack comprising n cells.
13. The method of claim 12 wherein said cell model is

wherein vk(t+?t) denotes the cell voltage for cell k for
the time period t units into the futute,
denotes the open cell voltage as a function of
the state of charge zk for cell k for the time period t
units into the future, R is a constant that denotes the
cell's internal resistance, and ik(t) denotes the cell
current.
14. The method of claim 13 wherein said minimum charge
current based on voltage limits is obtained by solving,
through Taylor-series expansion

wherein denotes the minimum charge current of cell
k, Rchg denotes the internal charge resistance of the cell,
? denotes the Coulombic efficiency factor,
denotes the derivative of the open cell voltage with
respect to the state-of-charge z, evaluated at the present
state-of-charge level zk(t).
15. The method of claim 14 wherein said
computed by a table lookup of empirical data.
16. The method of claim \3 wherein said cell model is
solved by using a discrete time-state space model.
17. The method of claim |6 wherein said discrete time-
state space model is

wherein m denotes the discrete time sample index, xk [m]
denotes the vector function of time and the state of the
system, uk[m] denotes the input to the system and includes
cell current iit[m] as a component, and f( ? ) and g(?) are
functions chosen to model the cell dynamics.
18 The method of claim 17 wherein said uk[m] inputs
includes temperature.
19. The method of claim 17 wherein said uk[m] inputs
includes resistance.
20. The method of claim 17 wherein said uk[m] inputs
includes capacity.
21. The method of claim )7 wherein is found looking
ik that causes equality in

wherein finds the cell voltage ?t
seconds into the future.
22. The method of claim 21 wherein said equality is
found by a using bisection search algorithm.
23. The method of claim 17 wherein the equation
is linear, wherein A and B
are constant matrices.
24. The method of claim ]Z wherein said maximum value of
charge current is chosen by using the equation
wherein
imin denotes said minimum charge current based on current
limits,
denotes maximum of minimum charge current based
on state-of-charge limits for each cell k,
denotes said maximum of minimum charge current on
voltage limits for each cell k.
25 The method of claim 24 wherein said minimum charge
power is calculated by solving

wherein denotes the minimum charge power, np denotes
the number of cells in parallel, ns denotes the number of
cells in series, denotes said calculated minimum charge
current and denotes voltage of cell k for the time
period t units into the future.
26. The method of claim 25 wherein is approximated to •
be
wherein denotes the open cell voltage
as a function of the state of charge zk for cell k at time
t, ?t which denotes the time horizon, ?1 which denotes
the Coulombic efficiency factor, and C which denotes the
cell capacity; and Rchg which denotes the cell charge
internal resistance.
27. The method of claim 2 wherein any said state-of-
charge limits, voltage limits, and current limits can be
eliminated from said calculations by using positive
infinity or negative infinity to represent said eliminated
limits in said calculations.
28. The method of claim wherein any said state-of-
charge limits, voltage limits, and current limits is
dependent on temperature.
29. The method of claim Z wherein said calculated
minimum charge power is checked to ensure that it falls
within power limits of said battery.
30. A power estimating apparatus for estimating maximum
discharge power of a battery comprising:
a voltage measurement means for measuring the voltage of
said battery;
a temperature measurement means for measuring the
temperature of said battery;
a current measurement means for measuring the current of
said battery;
an estimator means for calculating maximum discharge
power of said battery,
wherein said estimator means uses measurements from said
voltage measurement means, said temperature measurement
means, and current measurement means and performs
calculations to estimate maximum discharge power of said
battery, said estimator means comprising:
means for calculating a maximum discharge current of
said battery based on voltage limits of said battery;
means for calculating a maximum discharge current of
said battery based on state-of-charge limits of said
battery;
means for calculating a maximum discharge current of
said battery based on current limits of said battery,
wherein said maximum discharge power is calculated from a
minimum value of discharge current chosen among said
calculated maximum discharge current based on voltage
limits, said calculated maximum discharge current based on
state-of-charge limits, and said calculated maximum
discharge current based on current limits.
31. The power estimating apparatus of claim 30 wherein
said estimator means takes into , account a user-defined
horizon of ?t.
32. The power estimating apparatus of claim 30 wherein
said means for calculating maximum discharge current of
said battery based on state-of-charge limits obtains a
state-of-charge by using a Kalman filtering method.
35. The power estimating apparatus of claim 32- wherein
an estimate of the uncertainty value yielded by said Kalman
filtering method is used to find a confidence level of said
calculated maximum discharge current.
34. The power estimating apparatus of claim 32 wherein
said battery is a battery pack comprising n cells.
35. The power estimating apparatus of claim 32 wherein
said means for calculating maximum, discharge current of
said battery based on voltage limits uses a cell model.
The power estimating apparatus of claim 32. wherein
cell model is solved by a Taylor-series expansion.
37. The power estimating apparatus of claim 32 wherein
said cell model is solved by using a discrete time-state
space model.
38 An power estimating apparatus for estimating
minimum charge power of a battery comprising:
a voltage measurement means for measuring the voltage of
said battery;
a temperature measurement means for measuring the
temperature of said battery;
a current measurement means for measuring the current of
said battery;
an estimator means for calculating minimum charge power
of said battery, wherein said estimator means uses
measurements from said voltage measurement means, said
temperature measurement means, and current measurement
means and performs calculations to estimate minimum charge
power of said battery, said estimator means comprising:
means for calculating a minimum charge current of said
battery based on voltage limits of said battery;
means for calculating a minimum charge current of said
battery based on state-of-charge limits of said battery;
means for calculating a minimum charge current of said
battery based on current limits of said battery, wherein
said minimum charge current power is calculated from a
maximum value of discharge current chosen among said
calculated minimum charge current based on voltage limits,
said calculated minimum charge current based on state-of-
charge limits, and said calculated minimum charge current
based on current limits.
39. The power estimating apparatus of claim 38 wherein
said estimator means takes into account a user-defined
horizon of At.
40. The power estimating apparatus of claim 38 wherein
said means for calculating minimum charge current of said
battery based on state-of-charge limits obtains a state-of-
charge by a Kalman filtering method.
41. The power estimating apparatus of claim 40 wherein
an estimate of the uncertainty value yielded by said Kalman
filtering method is used to find a confidence level of said
calculated maximum discharge current.
42. The power estimating apparatus of claim 40 wherein
said battery is a battery pack comprising n cells.
43. The power estimating apparatus of claim 44 wherein
said means for calculating minimum current of said battery
based on voltage limits uses a cell model.
44. The power estimating apparatus of claim 40 wherein
cell model is solved by a Taylor-series expansion.
45. The power estimating apparatus of claim 40 wherein
said cell model is solved by using a discrete time-state
space model.

The present invention relates to a method and an apparatus for estimating discharge and charge power
of battery applications, including battery packs used in Hybrid Electric Vehicles (HEV) and Electric
Vehicles (EV). One charge/discharge power estimating method incorporates voltage, state-of-charge
(SOC), power, and current designs constraints and works for a user-specified prediction time horizon of
& yen, Ät. At least two cell models are used in calculating maximum charge/discharge power based on
voltage limits. The first is a simple cell model that uses a Taylor-series expansion to linearize the
equation involved. The second is a more complex and accurate model that models cell dynamics in
discrete-time state-space form. The cell model can incorporate a inputs such as temperature, resistance,
capacity, etc. One advantage of using model-based approach is that the same model may be used in
both Kalman-filtering to produce the SOC and the estimation of maximum charge/discharge current
based on voltage limits.