Title of Invention

METHOD AND APPARATUS FOR PREDICTING CHANGE IN AN OPERATING STATE OF AN ELECTRIC ENERGY STORAGE DEVICE

Abstract A method for predicting change in an operating state, e.g. state of life, for an electrical energy storage device (74) includes establishing a plurality of values for an operating parameter, e.g. current, of the electrical energy storage device and, for each respective value, determining a corresponding change in the operating state for the energy storage device based upon the respective value. Preferably, change in the state of life is determined based upon an integration of electrical current, a depth of discharge of the energy storage device (74), and an operating temperature factor of the electrical energy storage device (74).
Full Text


METHOD AND APPARATUS FOR PREDICTING CHANGE IN AN OPERATING STATE
OF AN ELECTRIC ENERGY STORAGE DEVICE
TECHNICAL FIELD
[0001] This invention pertains generally to an electrical energy storage
device. More particularly, the invention is concerned with predicting effects
upon an electrical energy storage device.
BACKGROUND OF THE INVENTION
[0002] Various hybrid propulsion systems for vehicles use electrical energy
storage devices to supply electrical energy to electrical machines, which are
operable to provide motive torque to the vehicle, often in conjunction with an
internal combustion engine. An exemplary hybrid powertrain architecture
comprises a two-mode, compound-split, electro-mechanical transmission
which utilizes an input member for receiving power from a prime mover
power source and an output member for delivering power from the
transmission to a vehicle driveline. First and second electric machines, i.e.
motor/generators, are operatively connected to an energy storage device for
interchanging electrical power therebetween. A control unit is provided for
regulating the electrical power interchange between the energy storage device
and the electric machines. The control unit also regulates electrical power
interchange between the first and second electric machines.
[0003] One of the design considerations in vehicle powertrain systems is an
ability to provide consistent vehicle performance and component/system
service life. Hybrid vehicles, and more specifically the battery pack systems
utilized therewith, provide vehicle system designers with new challenges and
tradeoffs. It has been observed that service life of an electrical energy storage
device, e.g. a battery pack system, increases as resting temperature of the
battery pack decreases. However, cold operating temperature introduces
limits in battery charge/discharge performance until temperature of the pack is


increased. A warm battery pack is more able to supply required power to the
vehicle propulsion system, but continued warm temperature operation may
result in diminished service life.
[0004] Modern hybrid vehicle systems manage various aspects of operation
of the hybrid system to effect improved service life of the battery. For
example, depth of battery discharge is managed, amp-hour (A-h) throughput is
limited, and convection fans are used to cool the battery pack. Ambient
environmental conditions in which the vehicle is operated has largely been
ignored. However, the ambient environmental conditions may have
significant effect upon battery service life. Specifically, same models of
hybrid vehicles released into various geographic areas throughout North
America would likely not result in the same battery pack life, even if all the
vehicles were driven on the same cycle. The vehicle's environment must be
considered if a useful estimation of battery life is to be derived. Additionally,
customer expectations, competition and government regulations impose
standards of performance, including for service life of battery packs, which
must be met.
[0005] It would be useful to include in a hybrid control system an ability to
estimate or otherwise determine a potential effect that an operating parameter,
e.g. electrical current level, has on life of a battery pack, in order to use such
information to proactively control operation of the hybrid powertrain system
to optimize battery life.
SUMMARY OF THE INVENTION
[0006] A method for predicting change in an operating state for an electrical
energy storage device includes establishing a plurality of values for an
operating parameter of the electrical energy storage device and, for each
respective value, determining a corresponding change in the operating state for
the energy storage device based upon the respective value.


[0007] In accordance with one embodiment, the operating state of the
electrical energy storage device is its state of life. Further, the operating
parameter of the electrical energy storage device is electrical current.
Preferably, change in the state of life is determined based upon an integration
of electrical current, a depth of discharge of the energy storage device, and, an
operating temperature factor of the electrical energy storage device. Depth of
discharge of the electrical energy storage device is preferably determined
based upon the electrical current. And, the operating temperature factor of the
electrical energy storage device is determined based upon the electrical current
and temperature of the electrical energy storage device.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] The invention may take physical form in certain parts and
arrangement of parts, an embodiment of which is described in detail herein
and illustrated in the accompanying drawings which form a part hereof, and
wherein:
[0009] Fig. 1 is a schematic diagram of an exemplary architecture for a
control system and powertrain, in accordance with the present invention;
[0010] Figs. 2 and 3 are algorithmic block diagrams, in accordance with the
present invention; and,
[0011] Fig. 4 is an exemplary data graph, in accordance with the present
invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
[0012] Referring now to the drawings, wherein the showings are for the
purpose of illustrating the invention only and not for the purpose of limiting
the same, Fig. 1 shows a control system and an exemplary hybrid powertrain
system which has been constructed in accordance with an embodiment of the
invention. The exemplary hybrid powertrain system comprises a plurality of
torque-generative devices operable to supply motive torque to a transmission
device, which supplies motive torque to a driveline. The torque-generative
devices preferably comprise an internal combustion engine 14 and first and


second electric machines 56, 72 operable to convert electrical energy supplied
from an electrical storage device 74 to motive torque. The exemplary
transmission device 10 comprises a two-mode, compound-split electro-
mechanical transmission having four fixed gear ratios, and includes a plurality
of gears operable to transmit the motive torque to an output shaft 64 and
driveline through a plurality of torque-transfer devices contained therein.
Mechanical aspects of exemplary transmission 10 are disclosed in detail in
U.S. Patent No. 6,953,409, entitled "Two-Mode, Compound-Split, Hybrid
Electro-Mechanical Transmission having Four Fixed Ratios", which is
incorporated herein by reference.
[0013] The control system comprises a distributed control module
architecture interacting via a local area communications network to provide
ongoing control to the powertrain system, including the engine 14, the
electrical machines 56, 72, and the transmission 10.
[0014] The exemplary powertrain system been constructed in accordance
with an embodiment of the present invention. The hybrid transmission 10
receives input torque from torque-generative devices, including the engine 14
and the electrical machines 56, 72, as a result of energy conversion from fuel
or electrical potential stored in electrical energy storage device (ESD) 74. The
ESD 74 typically comprises one or more batteries. Other electrical energy
storage devices that have the ability to store electric power and dispense
electric power may be used in place of the batteries without altering the
concepts of the present invention. The ESD 74 is preferably sized based upon
factors including regenerative requirements, application issues related to
typical road grade and temperature, and, propulsion requirements such as
emissions, power assist and electric range. The ESD 74 is high voltage DC-
coupled to transmission power inverter module (TPIM) 19 via DC lines
referred to as transfer conductor 27. The TPIM 19 transfers electrical energy
to the first electrical machine 56 by transfer conductors 29, and the TPIM 19
similarly transfer electrical energy to the second electrical machine 72 by
transfer conductors 31. Electrical current is transferable between the electrical
machines 56, 72 and the ESD 74 in accordance with whether the ESD 74 is


being charged or discharged. TPIM 19 includes the pair of power inverters and
respective motor control modules configured to receive motor control
commands and control inverter states therefrom to provide motor drive or
regeneration functionality.
[0015] The electrical machines 56, 72 preferably comprise known
motor/generator devices. In motoring control, the respective inverter
receives current from the ESD and provides AC current to the respective
motor over transfer conductors 29 and 31. In regeneration control, the
respective inverter receives AC current from the motor over the respective
transfer conductor and provides current to the DC lines 27. The net DC
current provided to or from the inverters determines the charge or discharge
operating mode of the electrical energy storage device 74. Preferably,
machine A 56 and machine B 72 are three-phase AC electrical machines and
the inverters comprise complementary three-phase power electronic devices.
[0016] The elements shown in Fig. 1, and described hereinafter, comprise a
subset of an overall vehicle control architecture, and are operable to provide
coordinated system control of the powertrain system described herein. The
control system is operable to gather and synthesize pertinent information and
inputs, and execute algorithms to control various actuators to achieve control
targets, including such parameters as fuel economy, emissions, performance,
driveability, and protection of hardware, including batteries of ESD 74 and
machines A and B 56, 72. The distributed control module architecture of the
control system comprises an engine control module ('ECM') 23, transmission
control module ('TCM') 17, battery pack control module ('BPCM') 21, and
the Transmission Power Inverter Module ('TPIM') 19. A hybrid control
module ('HCP') 5 provides overarching control and coordination of the
aforementioned control modules. There is a User Interface ('UI') 13 operably
connected to a plurality of devices through which a vehicle operator typically
controls or directs operation of the powertrain, including the transmission 10.
Exemplary vehicle operator inputs to the UI 13 include an accelerator pedal, a
brake pedal, transmission gear selector, and, vehicle speed cruise control.


Within the control system, each of the aforementioned control modules
communicates with other control modules, sensors, and actuators via a local
area network ('LAN') communications bus 6. The LAN bus 6 allows for
structured communication of control parameters and commands between the
various control modules. The specific communication protocol utilized is
application-specific. By way of example, one communications protocol is the
Society of Automotive Engineers standard J1939. The LAN bus and
appropriate protocols provide for robust messaging and multi-control module
interfacing between the aforementioned control modules, and other control
modules providing functionality such as antilock brakes, traction control, and
vehicle stability.
[0017] The HCP 5 provides overarching control of the hybrid powertrain
system, serving to coordinate operation of the ECM 23, TCM 17, TPIM 19,
and BPCM 21. Based upon various input signals from the UI 13 and the
powertrain, the HCP 5 generates various commands, including: an engine
torque command, clutch torque commands, for various clutches of the hybrid
transmission 10; and motor torque commands, for the electrical machines A
and B, respectively.
[0018] The ECM 23 is operably connected to the engine 14, and functions to
acquire data from a variety of sensors and control a variety of actuators,
respectively, of the engine 14 over a plurality of discrete lines collectively
shown as aggregate line 35. The ECM 23 receives the engine torque
command from the HCP 5, and generates an axle torque request. For
simplicity, ECM 23 is shown generally having bi-directional interface with
engine 14 via aggregate line 35. Various parameters that are sensed by ECM
23 include engine coolant temperature, engine input speed to the transmission,
manifold pressure, ambient air temperature, and ambient pressure. Various
actuators that may be controlled by the ECM 23 include fuel injectors, ignition
modules, and throttle control modules.
[0019] The TCM 17 is operably connected to the transmission 10 and
functions to acquire data from a variety of sensors and provide command


control signals, i.e. clutch torque commands to the clutches of the
transmission.
[0020] The BPCM 21 interacts with various sensors associated with the ESD
74 to derive information about the state of the ESD 74 to the HCP 5. Such
sensors comprise voltage and electrical current sensors, as well as ambient
sensors operable to measure operating conditions of the ESD 74 including,
e.g., temperature and internal resistance of the ESD 74. Sensed parameters
include ESD voltage, VBAT, ESD current, IBAT, and ESD temperature, TBAT.
Derived parameters preferably include, ESD internal resistance, RBAT, ESD
state of charge, SOC, and other states of the ESD, including available
electrical power, PBAT_MIN and PBAT_MAX.
[0021] The Transmission Power Inverter Module (TPIM) 19 includes the
aforementioned power inverters and motor control modules configured to
receive motor control commands and control inverter states therefrom to
provide motor drive or regeneration functionality. The TPIM 19 is operable
to generate torque commands for machines A and B based upon input from
the HCP 5, which is driven by operator input through UI 13 and system
operating parameters. Motor torques are implemented by the control
system, including the TPIM 19, to control the machines A and B. Individual
motor speed signals are derived by the TPIM 19 from the motor phase
information or conventional rotation sensors. The TPIM 19 determines and
communicates motor speeds to the HCP 5.
[0022] Each of the aforementioned control modules of the control system is
preferably a general-purpose digital computer generally comprising a
microprocessor or central processing unit, read only memory (ROM), random
access memory (RAM), electrically programmable read only memory
(EPROM), high speed clock, analog to digital (A/D) and digital to analog
(D/A) circuitry, and input/output circuitry and devices (I/O) and appropriate
signal conditioning and buffer circuitry. Each control module has a set of
control algorithms, comprising resident program instructions and calibrations
stored in ROM and executed to provide the respective functions of each


computer. Information transfer between the various computers is preferably
accomplished using the aforementioned LAN 6.
[0023] Algorithms for control and state estimation in each of the control
modules are typically executed during preset loop cycles such that each
algorithm is executed at least once each loop cycle. Algorithms stored in the
non-volatile memory devices are executed by one of the central processing
units and are operable to monitor inputs from the sensing devices and execute
control and diagnostic routines to control operation of the respective device,
using preset calibrations. Loop cycles are typically executed at regular
intervals, for example each 3.125, 6.25, 12.5, 25 and 100 milliseconds during
ongoing engine and vehicle operation. Alternatively, algorithms may be
executed in response to occurrence of an event.
[0024] The action described hereinafter occurs during active operation of the
vehicle, i.e. that period of time when operation of the engine and electrical
machines are enabled by the vehicle operator, typically through a 'key-on'
action. Quiescent periods include periods of time when operation of the
engine and electrical machines are disabled by the vehicle operator, typically
through a 'key-off action. In response to an operator's action, as captured by
the UI 13, the supervisory HCP control module 5 and one or more of the other
control modules determine required transmission output torque, To.
Selectively operated components of the hybrid transmission 10 are
appropriately controlled and manipulated to respond to the operator demand.
For example, in the exemplary embodiment shown in Fig. 1, when the
operator has selected a forward drive range and manipulates either the
accelerator pedal or the brake pedal, the HCP 5 determines how and when the
vehicle is to accelerate or decelerate. The HCP 5 also monitors the parametric
states of the torque-generative devices, and determines the output of the
transmission required to effect a desired rate of acceleration or deceleration.
Under the direction of the HCP 5, the transmission 10 operates over a range of
output speeds from slow to fast in order to meet the operator demand.
[0025] Referring now to Fig. 2, a method and apparatus to estimate a state-
of-life ('SOL') of an energy storage device useable in a hybrid control system


in real-time is described. The exemplary method and apparatus to estimate
state-of-life ('SOL') of the energy storage device in the hybrid control system
in real-time is disclosed in detail in U.S. Patent Application No.
/ , Attorney Docket No. GP-307586, entitled "Method and
Apparatus for Real-time Life Estimation of an Electric Energy Storage
Device", which is incorporated herein by reference. The exemplary method
and apparatus to estimate state-of-life comprises an algorithm that monitors an
electrical current and a state-of-charge and temperature of the electrical energy
storage device 74 during operation. Temperature of the electrical energy
storage device 74 is further monitored during quiescent periods of ESD
operation. Quiescent periods of ESD operation are characterized by ESD
power flow that is de minimus whereas active periods of ESD operation are
characterized by ESD power flow that is not de minimus. That is to say,
quiescent periods of ESD operation are generally characterized by no or
minimal current flow into or out of the ESD. With respect to an ESD
associated with a hybrid vehicle propulsion system for example, quiescent
periods of ESD operation may be associated with periods of vehicle inactivity
(e.g. powertrain, including electric machines, is inoperative such as during
periods when the vehicle is not being driven and accessory loads are off but
may include such periods characterized by parasitic current draws as are
required for continuing certain controller operations including, for example,
the operations associated with the present invention). Active periods of ESD
operation in contrast may be associated with periods of vehicle activity (e.g.
accessory loads are on and/or the powertrain, including electric machines, is
operative such as during periods when the vehicle is being driven wherein
current flows may be into or out of the ESD). The state of life ('SOL') of the
electrical energy storage device 74 is determined based upon the ESD current,
the state of charge of the ESD, and the temperature of the ESD during
quiescent and active periods of operation. The inputs to calculation of SOL,
include ESD internal resistance RBAT, ESD temperature TBAT, ESD state of
charge SOC, and ESD current IBAT. These are known operating parameters
measured or derived within the distributed control system. From these


parameters, an A-h integration factor 110, a depth of discharge ('DOD') factor
112, a driving temperature factor 114, and a resting temperature factor, TREST,
116 are determined, and provided as input to determine a parameter for SOL.
The operating parameters used to calculate SOL include: ESD current, IBAT,
which is monitored in real-time, measured in amperes, and integrated as a
function of time; magnitude of electrical current flowing through the ESD 74
during each active charging and discharging event; ESD state-of-charge
('SOC'), including depth-of-discharge ('DOD'); and, ESD temperature factor
during active periods of operation, referred to as TDRIVE. The inputs of RBAT,
TBAT, SOC, and IBAT, are known operating parameters within the distributed
control system. The input TRHST iS a derived parametric value.
[0026] Referring now to Fig. 3, a schematic diagram of an algorithm,
preferably executed in one of the aforementioned control modules, is
described which is executed in the control system to pre-calculate an array of
possible changes in ESD state of life, SOLdelta, for a subsequent time-step,
k+1, for each control degree of freedom. In this embodiment, the selected
control degree of freedom comprises ESD current, IBAT. The algorithm is
executed to determine an effect upon ESD state of life at a subsequent time-
step for the array of ESD electrical current levels, to optimize vehicle
operation and control based upon SOL of the ESD 74. This comprises
estimating values for a change in SOL, referred to as SOLdelta, over a range of
current levels, as follows.
[0027] The estimated SOL factor is represented by Eq. 1:
[0028] SOLk+1 = SOLdelta(x,y) + SOLk [1]
[0029] wherein:
[0030] SOLk+1 is a State of Life parameter calculated for a subsequent
iteration, k+1, typically a time step equal to elapsed time until the subsequent
loop cycle in the control system;
[0031] SOLk is the most recently calculated State of Life parameter;


[0032] SOLdelta(x,y) comprises parameter, SOLdelta, calculated for given x,
y values; and,
[0033] SOLdelta(x,y) comprises a vector containing a range of the
parameters, SOLdelta, wherein values for x are held constant, while values for y
are incremented over a range. The SOLdelta parameter determined is preferably
used by the aforementioned hybrid vehicle control system for optimization in
conjunction with other system constraints. This is shown graphically with
reference to Fig. 4, and specifically items 170, 172, 174, and 176.
[0034] Referring again to Fig. 3, the algorithm operates by monitoring input
parameter TBAT_K, comprising temperature of the ESD 74 at point in time, k.
The ESD current for the subsequent time step, k+1, referred to as IBAT_K+1,
comprises the aforementioned array of ESD electrical current values as shown
at 138, in this instance from -200 amperes to +200 amperes in incremental
values of 100 amperes, wherein the positive and negative symbols refer to
direction of current flow, for charging and discharging of the ESD 74,
respectively. All other parameters (y) in Eq. 1 to calculate SOLdelta are held
constant. The input parameters used in calculation of SOLdelta, including
current-integration 110, depth of discharge factor 112, driving temperature
factor 114, are determined for each value of ESD current for the subsequent
time step, IBAT_K+1. A second array 144, comprising a table of SOLdeita values
determined based upon ESD current, IBAT_K+1, is calculated and useable by the
control system to make decisions regarding subsequent operation of the
vehicle.
[0035] Estimation of total cumulated effect on current A-h integration
component 110 can be directly calculated for of the array of current values
138, in this instance from -200 amperes to +200 amperes in incremental values
of 100 amperes, for time step k+1. The A-h integration component 110 to
SOLdelta is used to calculate a final value for SOLdeita for each cell in the
SOLdelta(x, y) vector. A cumulative value of A-h/mile driven is generally
known for each vehicle, and typically comprises a direct linear relationship
between values for ESD current and SOLdelta.


[0036] Estimation of effect upon depth of discharge (DOD) is knowable, as
follows. A parametric value for SOC is known at time, k. The value for
electrical current, shown with reference to vector 138, is used in calculation of
a parametric value for SOC, as shown in 136, wherein the resulting SOCk+1 is
calculated. This value is then compared to the SOCDOD-LOCK, which comprises a
ESD state-of-charge achieved at the subsequent calculation cycle were the
proposed current commanded. The contribution of the DOD effect on SOC
increases as the system exceeds the SOC-DOD LOCK IN threshold, which has
a parametric value of 75% in this embodiment. This means the system
penalizes departures from a SOC target area. The system greatly penalizes
SOC when, for example, an action by the controller causes ESD discharge
below a set value, e.g. 40%, as a result of an action such as an extended
vehicle acceleration. A resulting parametric value for Depth-of-Discharge 112
is passed into the DOD - SOL impact table. This SOL delta component is
then submitted to the SOLdeita calculation.
[0037] Estimating an effect based upon ESD operating temperature
comprises calculating estimates of heat transfer to the ESD caused by the
upcoming change of the control parameter, IBAT. This provides an indication
of an amount the ESD is warmed up during the elapsed time. The ESD
heating value is determined by inputting each value for the current, IBAT_K+1, to
a mathematical model of the ESD 74, which includes one or more vectors or
matrices of resistance as a function of SOC and temperature. The matrix may
be based upon predetermined calibration based upon laboratory data or a
calculated resistance from a control module. The calculation is further based
upon thermal mass of the ESD 74, and any ESD cooling system capability.
An estimate of thermal change 134 is determined, based on a control
operation, as shown in block 134, and referred to as a difference between ESD
temperatures at times k and k+1, i.e. (TBAT_K+1 - TBAT_K), which is determined
based upon the control parameter, IBAT at time, k+1. Driving temperature
factor is determined at block 114, which is passed to the SOLdelta calculation of
block 142 for the timestep, k+1. This result occurs because operating
temperatures and resting temperatures affect ESD total life. The time


integrated current factor from block 110, the DOD factor from block 112, and
the driving temperature factor from block 114 comprise the inputs to Block
142, which determines a parametric value for SOLdelta for each current value of
the array of current values input to the algorithm from block 138. An array of
values as previously described, are created in the SOLdelta(x,y) vector 144.
[0038] As an example, operating a hybrid vehicle to maximize ESD current
and charging typically leads to large amounts of current passing through the
ESD. The parameter for A-h/mile is likely higher than for an average
operator, and the A-h component for calculation of SOLdelta(x,y) likely
reflects fairly high values for SOLdelta at all positive and negative current
values. However, because the control system has enough time to adapt to
driving style of various operators (usually more astute drivers attempt to
maximize operation in a recharging mode, e.g. regenerative braking, and
identify areas of ESD boost), the State of Charge in this example remains at an
optimal level around 75% +/- 2%. At the given moment of the calculation,
with SOC at 74.5, the SOCDOD-LOCK value of block 136 was at 74.9. The
instantaneous DOD at this point is only 0.4 % DOD. This translates to a
relatively small effect upon SOLdelta for all Ibat/k+i current values. Stated
differently, in the next timestep, there is limited risk to SOL related directly to
a large depth of discharge.
[0039] Lastly, when the operator most likely started with a high ESD
temperature while passing large quantities of current through the ESD, which
most likely warms it beyond the capability of its cooling system, the effect due
to low current levels are likely reasonable. However at higher positive
charging currents there would be larger effect upon SOLdeita, due to a future
potential for ESD heating. There would be lesser increases in the SOLdelta for
larger discharging currents as well, but not as large as the charging currents
because discharge currents have less resistance than charging currents.
[0040] Referring again to Fig. 4, a datagraph showing a state-of-life factor as
a function of vehicle operating time and mileage is shown. Included is a target
profile 160, comprising an idealized, linear change in SOL over time and
distance driven. A second line 170 comprises a system wherein initial SOL v.


time is above the idealized profile, potentially leading to a shorter service life
for the ESD 74. Therefore there is a need to have a less aggressive use of the
ESD in subsequent usage to optimize ESD life. A third line 180 comprises a
system wherein the initial SOL v. time is below the idealized profile, leading
to an extended service life for the ESD 74. In this instance, the operating
system may be able to more aggressively use the electric machines 56, 72 to
propel the vehicle. Furthermore, such a system facilitates more effective
utilization of the hybrid propulsion system in a vehicle used in a climate
having lower ambient temperatures. Referring now to items 172, 174, 176,
there is shown three values for SOLdelta that have been calculated in
accordance with Eq. 1 above, and the invention as described herein. This
information is useable by the hybrid control system to decide on an
appropriate level of operation of the electrical machines, in terms of electrical
current flow, for the subsequent step, while taking into account effect on life
of the ESD using the SOL factor. Therefore, electric machine currents can be
controlled in accordance with the general objective of maintaining SOL in
accordance with the target profile 160. Varying degrees of control techniques
can effect this objective including, for example, establishing (e.g. setting or
dictating) machine currents where aggressive control is warranted, e.g. where
actual SOL requires gross adjustments to comply with the target profile or
SOL is on a track to premature end of life relative to the target profile.
Alternatively, merely establishing machine current limits may be more
appropriate where less aggressive control is warranted, e.g. where actual SOL
requires minor adjustments to comply with the target profile or SOL is on a
track to an extended end of life relative to the target profile. In general, it is
desirable to converge the actual SOL to the target profile quickly while
minimizing overshoot.
[0041] This embodiment describes a method and system to predetermine an
effect of a change in an operating parameter, e.g. ESD current, upon an
operating state of the device, e.g. ESD state of life. For other applications,
there may be more degrees of control, so effects on SOLdelta may be calculated
across other control parameters, and passed to the control system. It is


understood that such modifications fall within the scope of the invention. It is
also understood that modifications in the transmission hardware are allowable
within the scope of the invention. The invention has been described with
specific reference to the preferred embodiments and modifications thereto.
Further modifications and alterations may occur to others upon reading and
understanding the specification. It is intended to include all such
modifications and alterations insofar as they come within the scope of the
invention.


WE CLAIM :
1. Method for predicting change in an operating state for an electrical energy
storage device, characterized in that said method comprises :
establishing a plurality of values for an operating parameter of the electrical
energy storage device; and
for each respective value, determining a corresponding change in the
operating state for the energy storage device based upon said respective
value.
2. The method as claimed in claim 1, wherein the operating state of the
electrical energy storage device comprises a state of life.
3. The method as claimed in claim 2, wherein the operating parameter of
the electrical energy storage device comprises electrical current.
4. The method as claimed in claim 3, wherein change in the state of life is
determined based upon an integration of electrical current, a depth of
discharge of the energy storage device, and, an operating temperature factor
of the electrical energy storage device.

5. The method as claimed in claim 4, wherein the depth of discharge of the
electrical energy storage device is determined based upon the electrical
current.
6. The method as claimed in claim 4, wherein the operating temperature
factor of the electrical energy storage device is determined based upon the
electrical current and temperature of the electrical energy storage device.


7. Method for predicting change in state of life of an electrical energy storage
device, comprising :
providing a plurality of potential currents for the electrical energy storage
device; and
for each potential current, predicting a respective effect upon electrical
energy storage device state of life.
8. The method as claimed in claim 7, wherein predicting effects upon
electrical energy storage device state of life comprises predicting changes in
the state of life based on at least one factor affected by electrical energy
storage device current.
9. The method as claimed in claim 8, wherein said at least one factor
affected by electrical energy storage device current comprises a factor
related to current integration over time.

10. The method as claimed in claim 8, wherein said at least one factor
affected by electrical energy storage device current comprises a factor
related to depth of discharge of the electrical energy storage device.
11. The method as claimed in claim 8, wherein said at least one factor
affected by electrical energy storage device current comprises a factor
related to temperature of the electrical energy storage device.


12. Apparatus for predicting change in state of life of an electrical energy
storage device, characterized in that said apparatus comprises :
a current sensor adapted for sensing current through the energy storage
device (ESD 74; and
a control system comprising a central processing unit (CPU) adapted to
receive a signal indicative of sensed energy storage device current ; wherein
said control system is provided with a battery pack control module (BPCM)
21 for interacting with the ESD current sensor to derive information about
the state of the ESD (74) to a hybrid control module (HCP5) for providing a
plurality of potential currents for the electrical energy storage device; and
for predicting a respective effect upon electrical energy storage device state
of life for each potential current.
13. The apparatus as claimed in claim 12, wherein predicting effects upon
electrical energy storage deice state of life comprises predicting changes in
the state of life based on at least one factor affected by electrical energy
storage device current.
14. The apparatus as claimed in claim 13, wherein said at least one factor
affected by electrical energy storage device current comprises a factor
related to current integration over time.
15. The apparatus as claimed in claim 13, wherein said at least one factor
affected by electrical energy storage device current comprises a factor
related to depth of discharge of the electrical energy storage device.




ABSTRACT


Method and apparatus for predicting change in an operating
state of an electric energy storage device


A method for predicting change in an operating state, e.g. state of life, for an
electrical energy storage device (74) includes establishing a plurality of
values for an operating parameter, e.g. current, of the electrical energy
storage device and, for each respective value, determining a corresponding
change in the operating state for the energy storage device based upon the
respective value. Preferably, change in the state of life is determined based
upon an integration of electrical current, a depth of discharge of the energy
storage device (74), and an operating temperature factor of the electrical
energy storage device (74).

Documents:

00714-kol-2007-abstract.pdf

00714-kol-2007-assignment.pdf

00714-kol-2007-claims.pdf

00714-kol-2007-correspondence others 1.1.pdf

00714-kol-2007-correspondence others 1.2.pdf

00714-kol-2007-correspondence others 1.3.pdf

00714-kol-2007-correspondence others.pdf

00714-kol-2007-description complete.pdf

00714-kol-2007-drawings.pdf

00714-kol-2007-form 1.pdf

00714-kol-2007-form 18.pdf

00714-kol-2007-form 2.pdf

00714-kol-2007-form 3.pdf

00714-kol-2007-form 5.pdf

00714-kol-2007-priority document.pdf

714-KOL-2007-(05-03-2012)-ABSTRACT.pdf

714-KOL-2007-(05-03-2012)-CLAIMS.pdf

714-KOL-2007-(05-03-2012)-DESCRIPTION (COMPLETE).pdf

714-KOL-2007-(05-03-2012)-DRAWINGS.pdf

714-KOL-2007-(05-03-2012)-EXAMINATION REPORT REPLY RECEIVED.pdf

714-KOL-2007-(05-03-2012)-FORM-1.pdf

714-KOL-2007-(05-03-2012)-FORM-2.pdf

714-KOL-2007-(05-03-2012)-FORM-3.pdf

714-KOL-2007-(05-03-2012)-FORM-5.pdf

714-KOL-2007-(05-03-2012)-OTHERS.pdf

714-KOL-2007-(05-03-2012)-PA-CERTIFIED COPIES.pdf

714-KOL-2007-(05-03-2012)-PETITION UNDER RULES 137.pdf

714-KOL-2007-(13-08-2012)-CORRESPONDENCE.pdf

714-KOL-2007-ASSIGNMENT.pdf

714-KOL-2007-CANCELLED PAGES.pdf

714-KOL-2007-CORRESPONDENCE OTHERS 1.4.pdf

714-KOL-2007-CORRESPONDENCE.pdf

714-KOL-2007-EXAMINATION REPORT.pdf

714-KOL-2007-FORM 18.pdf

714-KOL-2007-FORM 26.pdf

714-KOL-2007-GPA.pdf

714-KOL-2007-GRANTED-ABSTRACT.pdf

714-KOL-2007-GRANTED-CLAIMS.pdf

714-KOL-2007-GRANTED-DESCRIPTION (COMPLETE).pdf

714-KOL-2007-GRANTED-DRAWINGS.pdf

714-KOL-2007-GRANTED-FORM 1.pdf

714-KOL-2007-GRANTED-FORM 2.pdf

714-KOL-2007-GRANTED-FORM 3.pdf

714-KOL-2007-GRANTED-FORM 5.pdf

714-KOL-2007-GRANTED-SPECIFICATION-COMPLETE.pdf

714-KOL-2007-OTHERS.pdf

714-KOL-2007-PA.pdf

714-KOL-2007-PETITION UNDER RULE 137.pdf

714-KOL-2007-REPLY TO EXAMINATION REPORT.pdf

714-KOL-2007-TRANSLATED COPY OF PRIORITY DOCUMENT.pdf


Patent Number 256536
Indian Patent Application Number 714/KOL/2007
PG Journal Number 27/2013
Publication Date 05-Jul-2013
Grant Date 28-Jun-2013
Date of Filing 08-May-2007
Name of Patentee GM GLOBAL TECHNOLOGY OPERATIONS, INC
Applicant Address 300 GM RENAISSANCE CENTER DETROIT, MICHIGAN
Inventors:
# Inventor's Name Inventor's Address
1 ANDREW M. ZETTEL 1839 MICHELLE COURT ANN ARBOR, MICHIGAN 48105
2 ANTHONY H. HEAP 2969 LESLIE PARK CIRCLE ANN ARBOR, MICHIGAN 48105
PCT International Classification Number G08B13/14; G08B23/00
PCT International Application Number N/A
PCT International Filing date
PCT Conventions:
# PCT Application Number Date of Convention Priority Country
1 11/422,665 2006-06-07 U.S.A.