Battery pack state of charge estimation

Abstract

A battery monitoring system for a vehicle includes a processor and a memory. The memory stores instructions for causing the processor to estimate a state of charge of the battery based on a current cell pressure of the battery when the state of charge of the battery is below a threshold state of charge and to estimate the state of charge of the battery based on a voltage output by the battery when the state of charge of the battery is greater than or equal to the threshold state of charge.

Description

Technical Field

[0001] The present disclosure relates to a method and system for actively managing pressure in a lithium metal battery or similar battery. Background Art

[0002] Vehicles, including gasoline and diesel powered vehicles, as well as electric and hybrid vehicles, have battery powered energy storage for purposes such as powering motors, electronics, and other vehicle subsystems. The vehicle battery system may be charged using a power source such as a charging station, other electric vehicle battery systems, and / or the electrical grid. When operating vehicle systems utilizing battery power, a controller uses the battery's current state of charge (SoC) to ensure that sufficient battery power is maintained for maintaining optimal performance of the vehicle systems that rely on the battery. It is not feasible to directly measure the state of charge within an operating vehicle, and most vehicles rely on an extrapolated or estimated SoC.

[0003] Current SoC estimation techniques rely on voltage measurements of the battery as the only measurement to estimate SoC. During most operating states, this estimate is close enough to be accurately utilized. However, the estimate fails at extremely low charge levels (e.g., when the battery is close to fully discharged) because the voltage-based estimate has difficulty taking into account the age of the battery pack.

[0004] Therefore, it is desirable to provide a battery monitoring system that accurately estimates the SoC throughout a complete charge / discharge cycle taking into account the life of the battery pack. Summary of the invention

[0005] In an exemplary embodiment, a battery monitoring system for a vehicle includes a processor and a memory, wherein the memory stores instructions for causing the processor to estimate the state of charge of the battery based on a current cell pressure of the battery when the state of charge of the battery is below a threshold state of charge, and to estimate the state of charge of the battery based on a voltage output by the battery when the battery is greater than or equal to the threshold state of charge.

[0006] In addition to one or more of the features described herein, estimating the state of charge of the battery based on the current cell pressure includes estimating the state of charge based on a rate of change of the current cell pressure.

[0007] In addition to one or more features described herein, the memory stores instructions configured to cause the processor to learn a battery cell pressure bias across multiple iterations.

[0008] In addition to one or more features described herein, current battery cell pressure is directly monitored.

[0009] In addition to one or more features described herein, the current battery cell pressure is derived from at least one associated sensor reading.

[0010] In addition to one or more features described herein, the battery is a lithium metal battery.

[0011] In addition to one or more of the features described herein, estimating the state of charge of the battery based on the current battery cell pressure of the battery includes: estimating via a first process when the battery is charging, and estimating via a second process when the battery is discharging.

[0012] In addition to one or more features described herein, the first process includes estimating the state of charge using a voltage-based state of charge estimator and estimating the state of charge using a battery cell pressure-based state of charge estimator, comparing the voltage-based state of charge estimate and the battery cell pressure-based state of charge estimate, and determining that the voltage-based state of charge is accurate when the difference between the voltage-based state of charge estimate and the battery cell pressure-based state of charge estimate is lower than a threshold, and determining that the voltage-based state of charge is inaccurate when the difference between the voltage-based state of charge estimate and the battery cell pressure-based state of charge estimate is greater than or equal to a threshold.

[0013] In addition to one or more of the features described herein, the first process also includes: learning a bias and slope of the battery pressure in response to determining that the voltage-based state of charge estimation is accurate, and applying the learned bias and slope of the battery cell pressure when estimating the state of charge using a battery pressure-based state of charge estimator.

[0014] In addition to one or more features described herein, the second process includes precalculating a slope a and a bias b of a current battery cell pressure of the battery, determining a current battery cell pressure Pr of the battery cell pressure. crt , calculate the pressure increase due to irreversible expansion (Δ cp ), according to Pr adj =Pr crt -Δ cp Adjust the measured pressure and adjust the measured pressure according to SOC = (Pr adj –b) / a Calculate the state of charge (SOC).

[0015] In another exemplary embodiment, a method for estimating a battery state of charge includes estimating the battery state of charge based on a current battery cell pressure of the battery when the battery state of charge is below a threshold state of charge, and estimating the battery state of charge based on a voltage output by the battery when the battery state of charge is greater than or equal to a threshold state of charge.

[0016] In addition to one or more of the features described herein, estimating the state of charge of the battery based on the current cell pressure includes estimating the state of charge based on a rate of change of the cell pressure.

[0017] In addition to one or more of the features described herein, including learning a cell pressure offset across multiple iterations of the process and storing the learned cell pressure offset in a memory.

[0018] In addition to one or more features described herein, the battery is a lithium metal battery.

[0019] In addition to one or more of the features described herein, estimating the state of charge of a battery based on a current battery cell pressure of the battery when the state of charge of the battery is below a threshold state of charge includes: estimating the state of charge of the battery via a first process when the battery is charging, and estimating the state of charge of the battery via a second process when the battery is discharging.

[0020] In addition to one or more features described herein, the first process includes estimating the state of charge using a voltage-based state of charge estimator and estimating the state of charge using a battery cell pressure-based state of charge estimator, comparing the voltage-based state of charge estimate and the battery cell pressure-based state of charge estimate, and determining that the voltage-based state of charge is accurate when the difference between the voltage-based state of charge estimate and the battery cell pressure-based state of charge estimate is lower than a threshold, and determining that the voltage-based state of charge is inaccurate when the difference between the voltage-based state of charge estimate and the battery cell pressure-based state of charge estimate is greater than or equal to a threshold.

[0021] In addition to one or more features described herein, the first process also includes learning a bias and slope of the battery cell pressure in response to determining that the voltage-based estimate is accurate, and applying the learned bias and slope of the battery cell pressure when estimating the charge state using the battery cell pressure-based estimator.

[0022] In addition to one or more features described herein, the second process includes precalculating the slope a and the bias b of the battery cell pressure, determining the current battery cell pressure Pr crt , calculate the pressure increase due to irreversible expansion (Δ cp ), according to Pr adj =Pr crt -Δ cp Adjust the measured pressure and adjust the measured pressure according to SOC = (Pr adj –b) / a Calculate the state of charge (SOC).

[0023] In yet another exemplary embodiment, a vehicle includes a propulsion system, a battery system, and a battery monitoring system, wherein the propulsion system includes at least one motor, the battery system includes a battery based on battery cells, wherein the battery monitoring system includes a memory and a processor, and the battery monitoring system is configured to perform the following processes: when the state of charge of the battery is lower than a threshold state of charge, estimating the state of charge of the battery based on a current battery cell pressure of the battery, and when the battery is greater than or equal to the threshold state of charge, estimating the state of charge of the battery based on a voltage output by the battery.

[0024] In addition to one or more of the features described herein, estimating the state of charge of the battery based on the current battery cell pressure of the battery includes: estimating via a first process when the battery is charging, and estimating via a second process when the battery is discharging.

[0025] The above features and advantages and other features and advantages of the present disclosure are apparent from the following detailed description when taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Other features, advantages and details appear, by way of example only, from the following detailed description, which refers to the accompanying drawings, in which:

[0027] Figure 1 is a schematic diagram of the vehicle;

[0028] Figure 2 is a graph illustrating a reversible pressure window of an exemplary battery cell;

[0029] Figure 3 is a graph showing battery cell pressure in relation to charge capacity during a charging operation;

[0030] Figure 4 is a graph showing battery cell pressure in relation to charge capacity during a discharge operation;

[0031] Figure 5 is a graph showing voltage versus charge capacity at a very low charge state;

[0032] Figure 6 is a control process for estimating the state of charge based on battery cell pressure during a discharge operation; and

[0033] Figure 7 is a control process for estimating the state of charge based on battery cell pressure during a charging operation. DETAILED DESCRIPTION

[0034] The following description is merely exemplary in nature and is not intended to limit the present disclosure, its application or use. It should be understood that throughout the drawings, corresponding reference numerals represent identical or corresponding components and features. As used herein, the term module refers to a processing circuit, which may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, and / or other suitable components that provide the described functionality.

[0035] According to an exemplary embodiment, a battery monitoring system actively monitors battery parameters including both battery cell pressure and age of a lithium metal battery or similar battery structure, and estimates the battery's state of charge based on changes in battery pressure over time.

[0036] Figure 1 An embodiment of a motor vehicle 10 is shown that includes a body 12 that at least partially defines an occupant compartment 14. The body 12 also supports various vehicle subsystems, including a propulsion system 16, and other subsystems for supporting the functions of the propulsion system 16 and other vehicle components, such as a braking subsystem, a suspension system, a steering subsystem, a fuel injection subsystem, an exhaust subsystem, etc.

[0037] The vehicle 10 may be an internal combustion engine vehicle, an electric vehicle (EV), or a hybrid vehicle. In one embodiment, the vehicle 10 is a hybrid vehicle that includes an internal combustion engine system 18 and at least one motor system. For example, the propulsion system 16 includes a first motor 20 and a second motor 21. The motors 20 and 21 may be configured to drive wheels on opposite sides of the vehicle 10. Any number of motors positioned at various locations may be used.

[0038] The vehicle 10 includes a battery system 22 that may be electrically connected to the electric machines 20 and 21 and / or other components, such as vehicle electronics. The battery system 22 may be configured as a rechargeable energy storage system (RESS).

[0039] In an embodiment, the battery system 22 includes a battery assembly, such as a high voltage battery pack 24 having a plurality of battery modules 26. Each battery module 26 includes a plurality of individual battery cells arranged in a battery pack. The battery system 22 may also include a monitoring system 28 (e.g., a RESS controller) configured to receive measurements from sensors 30. Each sensor 30 may be an assembly or system having one or more sensors for measuring various battery and environmental parameters such as temperature, battery pack gap distance, battery pack pressure, current, and voltage. The monitoring system 28 includes components such as a processor, memory, interface, bus, and / or other suitable components.

[0040] The battery system 22 is electrically connected to a direct current (DC)-DC converter module 32 and an inverter module 34. The inverter module 34 (e.g., a traction power inverter unit or TPIM) converts the direct current (DC) power from the battery assembly into three-phase alternating current (AC) power to drive the motor. In an embodiment, the inverter module 34 includes a first inverter 36 connected to the motor 20 and a second inverter 38 connected to the motor 21.

[0041] In some embodiments, the battery module 26 is composed of lithium metal cells or similar types of cells. During operation of the vehicle, as the cells are charged and discharged (referred to as a charge / discharge cycle), the pressure in each cell changes. During each charge / discharge cycle, most of the pressure increase is reversible, however, a portion is irreversible. As the battery pack ages, the irreversible pressure changes cause an upward creep in the battery pack pressure.

[0042] Continue to refer Figure 1 , Figure 2 A graph 200 is shown illustrating the expansion of a given battery cell over multiple cycles, with the vertical axis representing the battery cell pressure and the horizontal axis representing the number of charge / discharge cycles. A new battery pack (e.g., with zero charge / discharge cycle count) starts with a pressure at a minimum pressure value 202. As the pressure increases during the charge / discharge cycles, the pressure approaches a maximum reversible pressure value 204. The distance between the minimum pressure 202 and the maximum reversible pressure 204 is referred to as the reversible pressure window. Any increase in pressure beyond the maximum reversible value is irreversible and moves the minimum pressure 202 and the maximum pressure 204 upward by an irreversible amount to new positions 202', 204'. Over multiple cycles, the reversible pressure window shifts upward, as can be seen at 202" and 204".

[0043] Conventionally, the state of charge of a battery is estimated based solely on feedback from the voltage measurement of the battery. Voltage-based estimation methods are established in the art. Although this estimation provides sufficiently accurate estimates over most of the charging window, voltage-based estimation may be inaccurate at extremely low states of charge. Figure 5An exemplary graph 500 is shown, which shows the voltage on the vertical axis relative to the state of charge (SoC) on the horizontal axis. For most of the charging process, the output voltage 502 tracks the output voltage of the discharge process 504 in an almost one-to-one correspondence (see the first region 510). The one-to-one correspondence allows easy estimation based on voltage measurements during the charging and discharging processes. However, as the state of charge approaches zero (the second region 520), the voltage 502 deviates sharply from the voltage 504, and the estimation is no longer accurate. In order to provide accurate estimation across all states of charge, the battery monitoring system 28 utilizes a combination of a voltage-based estimation method and a battery cell pressure-based estimation method. Since the characteristics of the charging process and the discharging process are different at extremely low states of charge, the inclusion of different state of charge estimation methods for the charging process and for the discharging process ensures that the state of charge is accurately monitored during each process. Accurate monitoring in turn ensures that the state of charge of the battery pack can be properly controlled without overcharging or allowing the battery pack 28 to discharge to a level below the desired level.

[0044] Continue to refer Figure 1 and Figure 2 , Figure 3 A graph 300 is shown of battery pack cell pressure (vertical axis) versus battery pack charge capacity (horizontal axis) over time during a plurality of charging operations 301-305. Similarly, Figure 4 A graph 400 shows cell pressure (vertical axis) versus discharge capacity (horizontal axis) of a battery pack over time during a plurality of discharge operations 401-405. The vertical intervals between each operation 301-305, 401-405 are exaggerated to illustrate the effect, and actual operations may have a significantly greater number of charge / discharge operations.

[0045] Referring to both graphs 300, 400 together, each line represents a different charge / discharge operation. As the battery pack ages and undergoes more charge / discharge operations, the starting and ending cell pressures shift upward along the entire charge / discharge operation due to irreversible pressure increases. The upward shift is a bias caused by the irreversible pressure increase. However, the slope of the pressure change (i.e., the rate of change of the cell pressure) does not change from one operation to the next, and the slope of the pressure change is nearly constant during discharge. In addition, the cell pressure can be directly measured using appropriate sensors within the sensor package 30 and / or estimated with high accuracy using conventionally accessible sensors.

[0046] When the battery monitoring system 28 knows the bias and slope of the battery cell pressure during a discharge or charge operation, the pressure can be normalized by removing the bias, and the pressure can be used to estimate the state of charge during a discharge or charge operation.

[0047] A process flow for monitoring and controlling the state of charge during both charging and discharging operations within a battery monitoring system is included. Figure 6 and Figure 7 process), the battery monitoring system 28 is able to monitor and control the state of charge through both charging and discharging operations, and can accurately estimate the state of charge even at extremely low state of charge levels.

[0048] Continue to refer Figures 1 to 5 , Figure 6 An exemplary state of charge estimation flow chart 600 is shown, which the battery monitoring system 28 or any similar battery controller uses to monitor the state of charge of a lithium metal battery during discharge. The state of charge estimation is operated using both voltage-based estimation and battery cell pressure-based estimation. Initially, the battery monitoring system 28 obtains a voltage measurement result 602. The voltage measurement result 602 is provided to a first state of charge estimation module 604. The state of charge estimation module 604 uses an existing voltage-based estimation process to determine a first estimated state of charge of the battery pack. The voltage-based estimate 606 is output from the first state of charge estimator 604 and provided to a bias module 610.

[0049] The bias module 610 also receives direct pressure measurements 612 from the battery pack. In some operations, direct pressure measurements are only needed during discharge operations and are therefore not received during charging operations. In other examples, direct pressure measurements 612 are always received and applied by the bias module 610 only when a learned bias is needed. In yet another example, pressure can be derived from relevant available sensor measurements without direct measurement.

[0050] The learned bias and slope are stored, and the estimated state of charge is provided to the state of charge check 630. The state of charge check 630 determines whether the state of charge is below a threshold indicating that the voltage is no longer a reliable estimator. When the estimated state of charge is not below the threshold ("No"), the estimated state of charge using the voltage flag 650 is provided to the state of charge estimation module 604, and a signal 608 is provided to the bias module 610 to continue learning the bias and slope. Using the voltage flag 650 to estimate the state of charge causes the state of charge estimation module 604 to estimate the state of charge based on the voltage for the next iteration.

[0051] When the estimated state of charge is equal to or lower than the threshold ("yes"), the process 600 stops learning the bias and slope, and the estimated state of charge using the battery cell pressure flag 640 is provided to the state of charge estimation module 604, so that the state of charge estimation module 604 uses the battery cell pressure to estimate the state of charge for the next iteration. The state of charge estimation module 604 stores the current estimated state of charge and the corresponding pressure and voltage, and determines the next estimated state of charge based on the measured pressure and taking into account the learned bias.

[0052] When the battery is operated at a medium to high state of charge (for example, Figure 5 In the first region 510 of FIG. 5 , process 600 typically uses a conventional voltage-based state of charge estimation method. In addition, the bias (upward shift due to irreversible expansion) and slope (pressure change over time) of the pressure of the lithium metal battery are learned and coordinated with the voltage estimate when the battery is operating at a medium to high state of charge. Figure 5 When operating in the second region 520 of the battery, the charge state is estimated based on the slope of the battery cell pressure adjusted by the learned bias.

[0053] Continue to refer Figure 1-6 , Figure 7 The adjusted process 700 is shown as applied during a charging or discharging operation.

[0054] Initially, the battery monitoring system 28 (see Figure 1 ) estimates the state of charge using voltage estimator 710. The battery monitoring system 28 then checks in low battery state check 720 to determine if the state of charge is low. When the state of charge is not low, the check selects to use a conventional voltage-based state of charge estimator in use voltage-based state of charge selector 730 and returns to voltage estimator 710.

[0055] When the state of charge check 720 indicates that the state of charge is low, the battery monitoring system initiates a process for monitoring the state of charge 704. Initially, the battery monitoring system 28 determines an initial slope in a determine initial slope step 740 by precalculating a slope (a) and an offset (b) in the low state of charge region using Pr=a*SOC+b, where the initial slope and offset are received from the battery specifications stored in the battery monitoring system, and then iterates using the updated slope and offset stored from the battery system memory.

[0056] Once the initial slope and offset are identified, the battery monitoring system measures or estimates the current pressure (Pr crtAs used herein, estimating the current pressure of a battery cell refers to deriving the pressure from one or more sensor measurements, where the one or more sensor measurements do not directly measure the battery cell pressure. The pressure increase due to irreversible expansion (Δ cp ), in the pressure adjustment step 770, according to Pr adj =Pr crt -Δ cp After adjusting the pressure, in the step 780 of calculating the low battery state, according to SOC=(Pr adj –b) / a Calculates the power status in the low power status area.

[0057] The term "one" does not indicate a limitation of quantity, but rather indicates the presence of at least one of the referenced items. Unless the context clearly indicates otherwise, the term "or" means "and / or". References to "aspects" throughout the specification mean that a particular element (e.g., a feature, structure, step, or characteristic) described in conjunction with that aspect is included in at least one aspect described herein, and may or may not be present in other aspects. In addition, it should be understood that the described elements may be combined in any suitable manner in the various aspects.

[0058] When an element such as a layer, film, region, or substrate is referred to as being "on" another element, it can be directly on the other element or intervening elements may also be present. In contrast, when an element is referred to as being "directly on" another element, there are no intervening elements present.

[0059] Unless otherwise indicated herein, all test standards are the most current standards in effect as of the filing date of this application or, if priority is claimed, the filing date of the earliest priority application in which the test standards appear.

[0060] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0061] Although the above disclosure has been described with reference to exemplary embodiments, it will be appreciated by those skilled in the art that various changes may be made and equivalents may be substituted for its elements without departing from its scope. In addition, many modifications may be made to adapt specific circumstances or materials to the teachings of the disclosure without departing from the basic scope of the disclosure. Therefore, it is intended that the disclosure is not limited to the specific embodiments disclosed, but will include all embodiments falling within its scope.

Claims

1. A battery monitoring system for a vehicle, comprising: A processor and a memory, the memory storing instructions for causing the processor to estimate the state of charge of the battery based on a current battery cell pressure of the battery when the state of charge of the battery is lower than a threshold state of charge, and to estimate the state of charge of the battery based on a voltage output by the battery when the state of charge of the battery is greater than or equal to the threshold state of charge, and optionally, wherein the battery is a lithium metal battery.

2. The battery monitoring system according to claim 1, wherein: Estimating the state of charge of the battery based on the current cell pressure includes estimating the state of charge based on a rate of change of the current cell pressure, and wherein the memory stores instructions configured to cause the processor to learn a cell pressure bias across multiple iterations.

3. The battery monitoring system according to claim 1, wherein: The current battery cell pressure is monitored directly.

4. The battery monitoring system according to claim 1, wherein: The current battery cell pressure is derived from at least one associated sensor reading.

5. The battery monitoring system according to claim 1, wherein: Estimating the state of charge of the battery based on the current cell pressure of the battery includes estimating via a first process when the battery is charging and estimating via a second process when the battery is discharging.

6. The battery monitoring system according to claim 5, wherein: The first process includes: estimating the state of charge using a voltage-based state of charge estimator and estimating the state of charge using a cell pressure-based state of charge estimator; comparing the voltage-based state of charge estimate with the cell pressure-based state of charge estimate; Determining that the voltage-based state of charge is accurate when a difference between the voltage-based state of charge estimate and the cell pressure-based state of charge estimate is below a threshold, and determining that the voltage-based state of charge is inaccurate when the difference between the voltage-based state of charge estimate and the cell pressure-based state of charge estimate is greater than or equal to the threshold; and The second process includes precalculating the slope a and the bias b of the current battery cell pressure of the battery, determining the current battery cell pressure Pr crt , calculate the pressure increase due to irreversible expansion (Δ cp ), according to Pr adj =Pr crt -Δ cp Adjust the measured pressure and adjust the measured pressure according to SOC = (Pr adj – b) / a calculates the state of charge (SOC).

7. The battery monitoring system according to claim 6, wherein: The first process also includes: in response to determining that the voltage-based state of charge estimation is accurate, learning the bias and slope of the battery cell pressure, and when using the battery cell pressure-based state of charge estimator to estimate the state of charge, applying the learned bias and slope of the battery cell pressure.

8. A method for estimating a state of charge of a battery, comprising: The battery state of charge is estimated based on a current cell pressure of the battery when the battery state of charge is below a threshold state of charge, and the battery state of charge is estimated based on a voltage output by the battery when the battery state of charge is greater than or equal to the threshold state of charge.

9. The method according to claim 8, wherein: Estimating the state of charge of the battery based on the current cell pressure includes estimating the state of charge based on a rate of change of the cell pressure, and learning a cell pressure bias across multiple iterations and storing the learned cell pressure bias in a memory.

10. The method according to claim 8, wherein: When the battery state of charge is lower than a threshold state of charge, estimating the battery state of charge based on the current battery cell pressure of the battery includes: when the battery is charging, estimating the battery state of charge via a first process, and when the battery is discharging, estimating the battery state of charge via a second process.

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