Traction battery controller employing fine state of charge uncertainty boundaries

Abstract

The present disclosure provides a traction battery controller employing fine state of charge uncertainty boundaries. A system includes a battery and a controller. The battery has a state of charge (SOC) with SOC uncertainty. The controller is configured to charge and discharge the battery based on a capacity of the battery according to the SOC and a limit of the SOC uncertainty. The boundaries of the SOC uncertainty are based on a consideration of a plurality of SOC uncertainty factors, thereby reduced relative to based on a consideration of less than all of the SOC uncertainty factors.

Description

Technical Field

[0001] The present disclosure relates to detecting the capacity of a traction battery of an electrified vehicle for use in controlling operation of the traction battery and / or the vehicle. Background Art

[0002] Electrified vehicles include a traction battery for providing power to the vehicle's motor to propel the vehicle.Operating characteristics of the traction battery, such as its charge capacity, may be monitored for use in controlling operation of the traction battery and / or the vehicle. Summary of the Invention

[0003] A system includes a battery and a controller. The battery has a state of charge (SOC) with an SOC uncertainty. The controller is configured to charge and discharge the battery based on the capacity of the battery according to the SOC and a bound on the SOC uncertainty. The bound on the SOC uncertainty (i.e., an SOC uncertainty bound) is based on consideration of multiple SOC uncertainty factors, thereby reducing the charge relative to a system based on consideration of fewer than all SOC uncertainty factors.

[0004] In an embodiment, each SOC uncertainty factor is associated with its own SOC uncertainty bound. The SOC uncertainty bound is the minimum bound of the SOC uncertainty factors.

[0005] In an embodiment, each SOC uncertainty factor is associated with its own SOC uncertainty bound, and the bound for each SOC uncertainty factor is a range between negative and positive values. The SOC uncertainty bound is a range between (i) the negative value with the lowest magnitude among the negative values ​​and (ii) the positive value with the lowest magnitude among the positive values.

[0006] A first of the SOC uncertainty factors may indicate SOC uncertainty based on uncertainty in a voltage measurement of the battery and an SOC-OCV (open circuit voltage) table used together when estimating the SOC. The bounds on the SOC uncertainty are reduced relative to considering only the first of the SOC uncertainty factors. Another of the SOC uncertainty factors may indicate SOC uncertainty based on a range of physically possible amounts of SOC uncertainty. Another of the SOC uncertainty factors may indicate SOC uncertainty based on uncertainty in an integrated ampere-hour measurement used when estimating the SOC. Another of the SOC uncertainty factors may indicate SOC uncertainty based on a distributed voltage measurement of the battery used when estimating the SOC. Another of the SOC uncertainty factors may indicate SOC uncertainty based on uncertainty regarding differences in an SOC-OCV (open circuit voltage) table at different voltage measurements, where the SOC-OCV table with different voltage measurements is used to estimate the SOC.

[0007] A method includes detecting a state of charge (SOC) of a battery; estimating an uncertainty in the SOC based on consideration of a plurality of SOC uncertainty factors; and detecting a capacity of the battery based on the SOC and the estimated SOC uncertainty. The estimated SOC uncertainty is reduced relative to an estimated SOC uncertainty based on consideration of fewer than all of the SOC uncertainty factors, resulting in a capacity with reduced uncertainty. The method also includes charging and discharging the battery based on the capacity.

[0008] An electrified vehicle includes a traction battery and a controller. The controller is configured to detect a state of charge (SOC) of the traction battery and estimate an uncertainty in the SOC based on consideration of a plurality of SOC uncertainty factors. The estimated SOC uncertainty is reduced relative to an estimated SOC uncertainty based on consideration of fewer than all of the SOC uncertainty factors. The controller is configured to charge and discharge the traction battery based on a capacity of the traction battery according to the SOC and the estimated SOC uncertainty. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A block diagram of a battery electric vehicle (BEV) is shown;

[0010] Figure 2 A block diagram showing an arrangement for a traction battery controller of a BEV to monitor a traction battery of the BEV;

[0011] Figure 3 shows a block diagram of a traction battery controller including a capacity estimator for estimating the capacity of the traction battery based on the state of charge (SOC) of the traction battery;

[0012] Figure 4 shows a block diagram depicting SOC uncertainty factors considered by a traction battery controller in estimating an SOC uncertainty bound for the SOC of a traction battery;

[0013] Figure 5 a graph illustrating exemplary values ​​of SOC uncertainty factors considered by a traction battery controller when estimating an SOC uncertainty bound; and

[0014] Figure 6 A flow chart depicting operation of a traction battery controller to control a traction battery and / or a vehicle according to the SOC of the traction battery with an estimated SOC uncertainty bound based on the capacity of the traction battery is shown. DETAILED DESCRIPTION

[0015] Detailed embodiments of the present disclosure are disclosed herein; however, it should be understood that the disclosed embodiments are merely examples of the present disclosure that may be embodied in various and alternative forms. The drawings are not necessarily drawn to scale; some features may be exaggerated or minimized to illustrate details of particular components. Therefore, the specific structural and functional details disclosed herein should not be construed as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present disclosure.

[0016] Now refer to Figure 1 , a block diagram of an electrified vehicle (EV) 12 in the form of a battery electric vehicle (BEV) is shown. The BEV 12 includes a powertrain having one or more traction motors ("motors") 14, a traction battery ("battery" or "battery pack") 24, and a power electronics module 26 (e.g., an inverter). In the BEV configuration, the traction battery 24 provides all propulsion power, and the vehicle does not have an engine. In other variations, the EV can be a plug-in or conventional hybrid electric vehicle (PHEV, HEV) that also has an engine.

[0017] The traction motor 14 is part of the BEV 12's powertrain, used to power the BEV's movement. In this regard, the traction motor 14 is mechanically connected to a transmission 16 of the BEV 12. The transmission 16 is mechanically connected to a drive shaft 20, which is mechanically connected to wheels 22 of the BEV 12. The traction motor 14 can provide propulsion to the BEV 12 and can also operate as a generator. Acting as a generator, the traction motor 14 can recover energy that would normally be lost as heat in the BEV 12's friction braking system.

[0018] The traction battery 24 stores electrical energy that can be used by the traction motor 14 to propel the BEV 12. The traction battery 24 typically provides a high-voltage (HV) direct current (DC) output. The traction battery 24 is electrically connected to a power electronics module 26. The traction motor 14 is also electrically connected to the power electronics module 26. The power electronics module 26 (such as an inverter) provides the ability to transfer energy bidirectionally between the traction battery 24 and the traction motor 14. For example, the traction battery 24 may provide a DC voltage, while the traction motor 14 may require three-phase alternating current (AC) current to function. The inverter 26 may convert the DC voltage into a three-phase AC current to operate the traction motor 14. In regenerative mode, the inverter 26 may convert the three-phase AC current from the traction motor 14, which acts as a generator, into a DC voltage compatible with the traction battery 24.

[0019] In addition to providing electrical energy for propulsion of the BEV 12 , the traction battery 24 may also provide electrical energy for use by other electrical systems of the BEV, including HV loads such as an electric heater and air conditioning system, and low voltage (LV) loads such as an auxiliary battery.

[0020] The traction battery 24 can be recharged by an external power source 36 (e.g., the electrical grid). The external power source 36 can be electrically connected to electric vehicle supply equipment (EVSE) 38. The EVSE 38 provides circuitry and controls for controlling and managing the transfer of electrical energy between the external power source 36 and the BEV 12. The external power source 36 can provide DC or AC power to the EVSE 38. The EVSE 38 can have a charging connector 40 for plugging into a charging port 34 of the BEV 12. The power conversion module 32 of the EV 12 (such as an onboard charger having a DC / DC converter) can condition the power supplied from the EVSE 38 to provide the appropriate voltage and current levels to the traction battery 24. The power conversion module 32 can interface with the EVSE 38 to coordinate the delivery of power to the traction battery 24.

[0021] The various components described above may have one or more associated controllers to control and monitor the operation of the components. The controllers may be microprocessor-based devices. The controllers may communicate via a serial bus (e.g., a controller area network (CAN)) or via discrete conductors.

[0022] For example, a system controller 48 ("vehicle controller") is present to coordinate the operation of the various components. The controller 48 includes electronics, software, or both to perform the necessary control functions for operating the BEV 12. The controller 48 may be a combined vehicle system controller and powertrain control module (VSC / PCM). Although the controller 48 is shown as a single device, the controller 48 may include multiple controllers in the form of multiple hardware devices, or multiple software controllers with one or more hardware devices. In this regard, references to a "controller" herein may refer to one or more controllers.

[0023] The controller 48 implements a battery energy control module (BECM) 50. The BECM 50 communicates with the traction battery 24. The BECM 50 is a traction battery controller operable to manage the charging and discharging of the traction battery 24 and to monitor the operating characteristics of the traction battery. The BECM 50 is operable to implement an algorithm for detecting (e.g., estimating) the operating characteristics of the traction battery 24. The BECM 50 controls the operation and performance of the traction battery 24 based on the operating characteristics of the traction battery. The operation and performance of other systems and components of the BEV 12 can be controlled by the BECM 50 and / or other controllers of the BEV based on the operating characteristics of the traction battery 24.

[0024] The operating characteristics of the traction battery 24 include its charge capacity ("capacity") and its state of charge (SOC). The capacity of the traction battery 24 indicates the maximum amount of electrical energy that the traction battery can store. The SOC of the traction battery 24 indicates the current amount of charge stored in the traction battery. The SOC of the traction battery 24 can be expressed as a percentage of the maximum amount of charge that can be stored in the traction battery (i.e., a percentage of capacity). The BECM 50 can output the SOC of the traction battery 24 to inform the driver of the BEV 12 how much charge remains in the traction battery, similar to a fuel gauge.

[0025] Another operational characteristic of the traction battery 24 is its power capability. The power capability of the traction battery 24 is a measure of the maximum amount of power the traction battery can provide (i.e., discharge) or receive (i.e., charge) within a specified time period. To this end, the power capability of the traction battery 24 corresponds to discharge power limits and charge power limits, which limit the amount of electrical power that can be supplied or received by the traction battery at a given time. These limits can be provided to other vehicle controls, for example, via a vehicle system controller (VSC), so that the information can be used by systems that can draw power from or provide power to the traction battery 24. The vehicle controls will know how much power the traction battery 24 can provide (discharge) or absorb (charge) in order to meet driver demand and optimize energy usage. Therefore, knowing the power capability of the traction battery 24 allows for the management of electrical loads and sources so that the requested power is within the limits that the traction battery can handle.

[0026] Now refer to Figure 2 , continue to refer to Figure 1 , a block diagram of an arrangement for the BECM 50 to monitor the traction battery 24 is shown. Figure 2 As indicated in FIG, the traction battery 24 is composed of a plurality of battery cells 52. The battery cells 52 are physically connected together (e.g., as shown) between a positive terminal (i.e., a positive power bus) and a negative terminal (i.e., a negative power bus). Figure 2 ). More generally, traction battery 24 includes one or more battery cell modules electrically connected together, and each battery cell module includes one or more battery cells 52 electrically connected together. To simplify the discussion, assume that the battery cell modules are connected in series and that the battery cells 52 are connected in series.

[0027] The BECM 50 is operable to monitor pack level (i.e., traction battery level) characteristics of the traction battery 24, such as battery current 56, battery voltage 58, and battery temperature 60. The battery current 56 is the current output from (i.e., discharging) or input (i.e., charging) to the traction battery 24. The battery voltage 58 is the terminal voltage of the traction battery 24.

[0028] The BECM 50 is also operable to measure and monitor cell-level characteristics of the battery cells 52 of the traction battery 24. For example, the terminal voltage, current, and temperature of one or more of the battery cells 52 may be measured. The BECM 50 may use battery sensors 54 to measure the cell-level characteristics. The battery sensors 54 may measure characteristics of one or more battery cells 52. The BECM 50 may utilize Nc battery sensors 54 to measure characteristics of all battery cells 52. Each battery sensor 54 may transmit its measurements to the BECM 50 for further processing and coordination. The functionality of the battery sensors 54 may be internally incorporated into the BECM 50.

[0029] The traction battery 24 may have one or more temperature sensors (such as thermistors) in communication with the BECM 50 to provide data indicative of the temperature of the battery cells 52 of the traction battery for the BECM to monitor the temperature of the traction battery and / or the battery cells. The BEV 12 may also include a temperature sensor to provide data indicative of the ambient temperature for the BECM 50 to monitor the ambient temperature.

[0030] The BECM 50 controls the operation and performance of the traction battery 24 based on the monitored traction battery-level characteristics and battery cell-level characteristics. For example, the BECM 50 may use the monitored characteristics to detect operating characteristics of the traction battery 24 (e.g., capacity, SOC, power capability, etc. of the traction battery), such as for use in controlling the traction battery and / or the BEV 12.

[0031] like Figure 2 As shown in FIG, one or more contactors 61 are provided to inhibit or allow current to flow to / from the traction battery 24 through the power bus. Specifically, the contactors 61 are operable to electrically disconnect the traction battery 24 from / to the charging / discharging system of the BEV 12. The charging / discharging system includes components that charge the traction battery 24 or act as a load to draw power from the traction battery. Thus, the charging / discharging system may include, among other components, the inverter 26 and the power conversion module 32. The contactors 61 may be placed in various suitable locations within the BEV 12, such as between the positive power bus and the inverter 26.

[0032] The BECM 50 is configured to open or close the contactor 61 based on a message / request from the controller 48. The controller 48 is configured to detect when the BEV 12 is turned on (i.e., switched on) or off (i.e., switched off) based on an activation input (e.g., a user pressing a button associated with activating / deactivating the BEV). When the BEV 12 is to be activated, the controller 48 provides an activation request to the BECM 50 to close the contactor 61, thereby coupling the traction battery 24 to the charging / discharging system. When the BEV 12 is to be deactivated, the controller 48 provides a deactivation request to the BECM 50 to open the contactor 61, thereby deactivating the traction battery 24 from the charging / discharging system. Furthermore, the controller 48 is configured to cause the BECM 50 to close the contactor 61 by issuing an activation request when the traction battery 24 is to be charged or discharged. Similarly, the controller 48 is configured to cause the BECM 50 to open the contactor 61 by issuing a deactivation request when the traction battery 24 is not to be charged or discharged.

[0033] Now refer to Figure 3 , shows a block diagram of the BECM 50. The BECM 50 includes an actuator 72 for operating the contactor 61 in a closed / open position. The BECM 50 also includes a battery characteristic estimator (BCE) 74. The BCE 74 is configured to estimate the operating characteristics of the traction battery 24, including the capacity, SOC, and power capacity of the traction battery. The BCE 74 includes a capacity estimator 76 to estimate the capacity of the traction battery 24. The operations performed by the BCE 74 (and more generally, the BECM 50) in estimating the capacity of the traction battery 24 will now be described.

[0034] The capacity Q of the traction battery 24 can be estimated as follows:

[0035]

[0036] I is the current of the traction battery 24, Δt is the sampling time, SOC i is the initial SOC of the traction battery, and SOC f is the final SOC of the traction battery.

[0037] According to equation (1), the estimated value of the capacity of the traction battery 24 depends on the SOC of the traction battery. Therefore, if the SOC of the traction battery 24 is fully known (specifically, if each of the initial SOC and the final SOC is fully known), the estimated capacity will be accurate. Conversely, if the SOC of the traction battery 24 is not fully known, the estimated capacity will be less accurate. In other words, the accuracy of the capacity estimation depends on the accuracy of the SOC.

[0038] The accuracy of the SOC depends on the amount of uncertainty (i.e., error) in the SOC. An SOC with less uncertainty has a higher accuracy than an SOC with higher uncertainty. For this reason, SOC uncertainty is an important component in estimating battery capacity. The charging and discharging of the traction battery 24 is controlled based on the operating characteristics of the traction battery (including its capacity) and / or other operating characteristics that depend on the capacity. Therefore, accurate capacity estimation, and therefore, estimating the SOC with less uncertainty, is highly desirable. Generally, the SOC uncertainty estimate influences the overall capacity estimate, resulting in tighter bounds for better capacity estimation accuracy, faster update rates, better DTE (Distance to Energy) and electric charge / discharge limit calculations, etc.

[0039] Existing methods that simply use the slope of the SOC-OCV (open-circuit voltage) curve and voltage measurement errors to estimate SOC are often not the best representation of the true SOC uncertainty. Therefore, in this case, the capacity estimate may not be the most accurate. For example, capacity estimation methods such as those using adaptive filters may not effectively utilize individual capacity estimates because the filtering weights depend on the SOC uncertainty estimate and other factors.

[0040] The capacity with a bound on the overall estimation uncertainty (or error) can be expressed as:

[0041]

[0042] Equation (2) is the sum of the capacity estimate from Equation (1) and the overall capacity uncertainty bound U(Q) expressed by:

[0043]

[0044] The overall capacity uncertainty bound U(Q) is the amount of uncertainty (eg, tolerance) in the capacity estimate.

[0045] Equation (2) relates to both the method for estimating the capacity Q and the uncertainty of the capacity estimate U(Q).

[0046] The overall capacity uncertainty bound U(Q) provides an overall estimation error bound for the capacity estimate. As stated in equation (3), the overall capacity uncertainty bound U(Q) includes the previously introduced factors: ∑IΔt, which is the integral of the current (net ampere-hour throughput) of the traction battery 24 between contactor closure event #1 and contactor closure event #2; SOC i , which is the SOC at contactor closing event #2; and SOC f , which is the SOC at contactor closing event #1. ΣIΔt is measured by sensing the current between the two closing events. SOC iand SOC f (which depends on the voltage and temperature of the traction battery 24 ) is obtained via a SOC-OCV lookup table.

[0047] As further elaborated in equation (3), the overall capacity uncertainty bound U(Q) also includes the following factors: U(∑IΔt), which is the uncertainty in the net ampere-hour throughput measurement; U(SOC i ), which is the uncertainty of the SOC-OCV lookup at contactor closing event #2; and U(SOC f ), which is the uncertainty of SOC-OCV lookup at contactor closing event #1.

[0048] According to the above equation, the ratio percentage of the overall capacity uncertainty bound U(Q) to the capacity Q is expressed by the following equation:

[0049]

[0050] As explained, the amount of the overall capacity uncertainty bound U(Q) depends on the uncertainty components U(ΣIΔt), U(SOC i ) and U(SOC f ) is the amount of uncertainty.

[0051] According to the present disclosure, the BECM 50 is operable to reduce the uncertainty component U(SOC i ) and U(SOC f ) is the amount of uncertainty in the SOC error. That is, the BECM 50 is operable to estimate tighter bounds on the SOC error and thereby reduce the SOC uncertainty. The SOC uncertainty "U(SOC)" or SOC uncertainty bound is the sum of the U(SOC) values ​​obtained individually and / or in combination. i ) and U(SOC f The BECM 50 estimates tighter (ie, refined) SOC uncertainty bounds to more accurately estimate the capacity of the traction battery 24 .

[0052] As mentioned above, existing methods for SOC uncertainty estimation involve simply using the slope of the SOC-OCV curve and the voltage measurement error to estimate SOC. In this regard, taking into account the uncertainties of OCV and SOC, the SOC uncertainty U(SOC) is calculated using the slope of the SOC-OCV curve at a given OCV estimate. This is expressed by the following equation:

[0053]

[0054] ΔSOC is the SOC uncertainty, is the slope of the SOC-OCV curve, and ΔOCV is the OCV uncertainty.

[0055] Existing methods assume that SOC uncertainty is limited by OCV error and the slope of the OCV-SOC curve. However, compared with the true bounds on SOC uncertainty, existing methods may produce larger SOC estimation uncertainty bounds because they do not further consider the following SOC uncertainty factors.

[0056] One SOC uncertainty factor not considered is that the SOC uncertainty is between (-100%, 100%), since SOC is defined as a percentage falling within the range of 0% to 100%. Another SOC uncertainty factor not considered is that the SOC uncertainty is bounded by the known capacity error (either from BOL (Beginning of Life) or after some operation), the current integration error, and the initial SOC error. Another SOC uncertainty factor not considered is that the SOC uncertainty should be assumed to be uniformly distributed within its own error bounds, and correspondingly, the SOC uncertainty should be derived from the average SOC error based on the OCV-SOC lookup table and averaged over the distribution of potential OCV values. Another SOC uncertainty factor not considered is that the SOC uncertainty can never be higher than the accumulated error of the previous SOC uncertainty and the total ampere-hour integration based on the SOC change uncertainty.

[0057] The BECM 50 according to the present disclosure considers at least two or more of these SOC uncertainty factors when estimating SOC uncertainty. Each SOC uncertainty factor is associated with its own SOC error bound. The BECM 50 uses the minimum of all SOC error bounds to estimate SOC uncertainty.

[0058] In one embodiment, the BECM 50 considers at least one of the SOC uncertainty factors considered by existing methods and the aforementioned SOC uncertainty factors not considered by existing methods. In another embodiment, the BECM 50 considers at least all of the SOC uncertainty factors considered by existing methods and the aforementioned SOC uncertainty factors not considered by existing methods.

[0059] Now refer to Figure 4, a block diagram 80 is shown that depicts the SOC uncertainty factors considered by the BECM 50 when estimating the SOC uncertainty bounds for the SOC of the traction battery 24. As described, in summary, there are different ways to establish SOC error bounds at any given time. The first SOC uncertainty factor is that due to the definition of SOC, the SOC error cannot exceed + / -100% at any given moment, as indicated by block 82. The second SOC uncertainty factor is that the SOC error cannot be greater than the combination (in a statistical manner) of the initial SOC error, capacity error, and ampere-hour integration error, as indicated by block 84. The third SOC uncertainty factor is that the SOC error from the SOC-OCV curve can be established using uniformly distributed OCV errors at a given OCV value and averaged, as indicated by block 86. The fourth SOC uncertainty factor is that the SOC error can be established using the SOC-OCV curve at a given OCV value, OCV error, and SOC-OCV slope value, as indicated by block 88. The overall SOC error cannot exceed any of these SOC error bounds, as indicated by block 90.

[0060] In more detail, the first SOC uncertainty factor (i.e., potential error bound #1) is about adding upper and lower physically possible bounds to the SOC uncertainty estimate. This is the easiest SOC uncertainty factor to implement. For the SOC estimate at any time, the error in the SOC uncertainty cannot exceed + / -100%. Therefore, the BECM 50 considers this limit for SOC calculations. As indicated in block 82, the first SOC error bound is represented as [-d1, d1], where d1 = SOC_error_max = 100%, and -d1 = SOC_error_min = -100%.

[0061] The second SOC uncertainty factor (i.e., potential error bound #2) concerns the SOC error due to initial SOC error, Ah error, and capacity error. There are use cases where the traction battery 24 begins operation with an accurate initial SOC value. For example, (even for lithium iron phosphate (LFP) traction batteries), if discharging begins at a relatively high SOC or if charging begins at a relatively low SOC after a period, the initial SOC error may be within, for example, 2% of the true value, depending on the voltage measurement accuracy. For such use cases, another SOC error bound may be found. The Ah error has been established as, for example, 1%. The capacity error should be well within, for example, 30% of the true value. This can be reduced even further if the use of the traction battery 24 is considered. Because the traction battery 24 begins with BOL accuracy (BOL accuracy of capacity, which is within, for example, 2%), and assuming the algorithm has established a capacity estimate within the desired bounds, the BECM 50 sets the capacity error to 5%.

[0062] This SOC uncertainty factor percentage "d" can be quantified according to the following equation:

[0063]

[0064] “RU” stands for relative uncertainty.

[0065] Using the labeled values ​​for each RU component, calculate the SOC uncertainty factor percentage "d" according to the following equation:

[0066]

[0067] The value of this percentage factor can then be converted to an "absolute error": d2 = f(OCV) * d, for example, if the SOC looked up from the OCV is 70%. In this case, as indicated in block 84, the second SOC error bound is represented as [-d2, d2], where d2 = SOCError_Ah_max = 5.48% * 0.70 = 3.84%, and -d2 = SOCError_Ah_min = -3.84%.

[0068] The third SOC uncertainty factor (potential error bound #3) relates to SOC error based on the assumption of uniform distribution of OCV errors and the SOC-OCV curve. It should be understood that the SOC-OCV curve (typically exhibiting a one-to-one relationship across the 0% to 100% SOC range for certain types of traction batteries) can have different slopes at different SOC values ​​(or OCV values). The BECM 50 uses the SOC-OCV curve slope to find the worst-case slope, and this value is used as a "divider" to obtain uniformly distributed OCV values ​​when needed. This represents a worst-case scenario. Once the OCV and error bounds are established based on the distribution grid size, the BECM 50 calculates all SOC values ​​relative to a "reference" SOC (which is based on the OCV value itself). The BECM 50 averages all SOC errors on one side; the positive and negative SOC errors can be calculated as described above. As indicated in block 86 , the third SOC error bound is represented as [−d3b, d3a], where d3a=SOCError_SOCCOV_VError_Probability_Maximum and −d3b=SOCError_SOCOCV_VError_Probability_Minimum (the magnitudes of these two percentage values ​​d3a and −d3b may be different).

[0069] An example of calculating the third SOC error limit is as follows. This example assumes the following information is obtained using the relevant SOC-OCV curve: Estimated OCV = 3.32V (60.999% SOC); OCV estimation error = + / - 12mV; OCV range (with uncertainty) = [3.308, 3.332]; Minimum slope = 1mV / 1% SOC; and calculated SOC error from the OCV distribution: [-7.6667 - 5.0000 - 3.0000 - 2.3333 - 1.6667 - 1.0000 - 0.8571 - 0.7143 - 0.5714 - 0.4286 - 0.2857 - 0.1429 - 0.0000 0.1429 0.2857 0.4286 0.5714 0.7143 0.8571 1.0000 1.1538 1.30771.4615 1.6154 1.7692]. In this case, SOC uncertainty (positive, average) = 0.9423%; and SOC uncertainty (negative, average) = -1.9722%. Therefore, the SOC uncertainty bounds due to the SOC-OCV slope variation and OCV measurement uncertainty are [-1.9722%, 0.9423%].

[0070] The fourth SOC uncertainty factor (potential error bound #4) relates to existing methods involving the use of the SOC-OCV curve slope and voltage measurement error to estimate the SOC. To this end, the SOC error can be established using the SOC-OCV curve at a given OCV value, OCV error, and SOC-OCV slope value according to equation (5) above. As indicated in block 88, the fourth SOC error bound is represented as [-d4b, d4a], where d4a = SOCError_SOCOCV_VError_max, and -d4b = SOCError_SOCOCV_VError_min (the magnitudes of these two percentage values, d4a and -d4b, may differ).

[0071] When estimating the SOC uncertainty, the BECM 50 uses a minimum SOC error bound based on all of the first, second, third, and fourth SOC uncertainty factors, as indicated in block 90. ​​This minimum SOC error bound is the SOC uncertainty bound. In contrast to prior art methods in which the SOC uncertainty bound is a fourth SOC error bound based on the fourth SOC uncertainty factor, the SOC uncertainty bound is estimated based on all of the first, second, third, and fourth SOC uncertainty factors. In effect, these four SOC uncertainty factors, considered together, result in the BECM 50 estimating an SOC uncertainty bound that is less than the fourth SOC error bound. To this end, compared to prior art methods, the BECM 50 estimates a refined SOC uncertainty bound for the SOC of the traction battery 24 (i.e., a more limited or stricter SOC uncertainty bound that is less than the fourth SOC error bound). Subsequently, the BECM 50 estimates the capacity of the traction battery 24 based on the SOC of the traction battery with the estimated SOC uncertainty bound, and controls the traction battery and / or the BEV 12 based on the capacity. Of course, in relatively rare circumstances, the fourth SOC error bound is the lowest SOC uncertainty bound (i.e., the first uncertainty factor, the second uncertainty factor, the third uncertainty factor, and the fourth SOC uncertainty factor considered together result in the BECM 50 estimating the SOC uncertainty bound to be equal to the fourth SOC error bound).

[0072] Now refer to Figure 5 , and continue to refer to Figure 4, a graph 100 illustrating exemplary values ​​of SOC uncertainty factors considered by the BECM 50 when estimating the SOC uncertainty bound. Graph 100 further explains in detail how the BECM 50 estimates the SOC uncertainty bound using a minimum SOC error bound based on all first, second, third, and fourth SOC uncertainty factors. In operation, once all possible sources of SOC uncertainty (i.e., d1, -d1, d2, -d2, d3a, -d3b, d4a, and -d4b) have been enlisted and calculated, the minimum amount of SOC uncertainty (from each positive / negative percentage side) can be used to estimate the SOC uncertainty. On the positive percentage side, SOC uncertainty: USOC+ = minimum (d1, d2, d3a, d4a). On the negative percentage side, SOC uncertainty: USOC- = maximum (-d1, -d2, -d3b, -d4b). The final SOC uncertainty range: final USOC = [USOC-, USOC+]. Absolute SOC uncertainty: Absolute USOC = [-max(abs(USOC-), USOC+), max(abs(USOC-), USOC+)]. These values ​​are "absolute errors" because they are true percentages of SOC units, not relative to some reference value.

[0073] As shown in graph 100, for these exemplary values ​​of the SOC uncertainty factor, the minimum SOC uncertainty amount on the positive percentage side is d3a, and the minimum SOC uncertainty amount on the negative percentage side is -d2. Therefore, the final SOC uncertainty range is [-d2, d3a], indicated by range 102. It should be noted that the final SOC uncertainty range is lower than each of the SOC uncertainty ranges [d2, -d2], [d3a, -d3b], [d1, -d1], and [d4a, -d4b], represented by ranges 104, 106, 108, and 110, respectively.

[0074] Now refer to Figure 6 , and continue to refer to Figure 3 、 Figure 4 and Figure 5, a flowchart 110 is shown that depicts the BECM 50 controlling the operation of the traction battery 24 and / or the BEV 12 based on the capacity of the traction battery according to the SOC of the traction battery with an estimated SOC uncertainty bound. In operation, the BECM 50 determines whether a start request has been received, as indicated by decision block 112. If received, the BECM 50 closes the contactor 61 to electrically couple the traction battery 24 to the charging / discharging system of the BEV 12 (i.e., a key-on event), as indicated by block 114. The BECM 50 then sets operating characteristics, including a power limit, based on the estimated capacity of the traction battery 24 after the last deactivation of the BEV 12, as indicated by block 116. The BECM 50 then controls the charging / discharging of the traction battery 24 based on the operating characteristics of the traction battery, as indicated by block 118.

[0075] The BECM 50 continues to control the charging / discharging of the traction battery 24 until a disable request is received, as indicated by decision block 120. Upon receiving the disable request, the BECM 50 opens the contactor 61 to electrically decouple the traction battery 24 from the charge / discharge system (i.e., a shutdown event), as indicated by block 122. The BECM 50 then estimates the capacity of the traction battery 24 using the capacity estimator 76 described above, as indicated by block 124. Additionally, as indicated by flowchart 110, the BECM 50 can estimate the capacity of the traction battery 24 when no start request is received.

[0076] As described, according to the present disclosure, the BECM 50 considers various SOC uncertainty factors when estimating the uncertainty of the SOC of the traction battery 24 for use in estimating the capacity of the traction battery. By considering the various SOC uncertainty factors, the estimated SOC uncertainty is generally more stringent (i.e., more limited or reduced) than the SOC uncertainty estimated by considering only one of the SOC uncertainty factors. Specifically, the estimated SOC uncertainty is generally more stringent than the SOC uncertainty estimated by considering only the SOC uncertainty factors associated with existing methods involving SOC-OCV slope and voltage uncertainty. In addition to the SOC uncertainty factors associated with existing methods, other SOC uncertainty factors considered by the BECM 50 include factors associated with voltage measurement distribution, differences in actual SOC-OCV curves at different voltage values, physically possible values ​​of SOC uncertainty, and operational SOC uncertainty.

[0077] In summary, the BECM 50 provides the benefit of achieving tighter SOC uncertainty for capacity estimation. Compared to existing methods, the method implemented by the BECM 50 can significantly reduce SOC estimation uncertainty based on given battery cell properties, OCV measurements (estimates), and the estimation uncertainties associated therewith. Specifically, the reduced SOC estimation uncertainty may result in a smaller number of samples used to accurately estimate battery capacity and may result in a larger weight being used in the adaptive filter for battery capacity estimation. Separate from capacity estimation, the reduced SOC estimation uncertainty may result in better use of OCV estimates for SOC initialization purposes. This, in turn, will support more accurate SOC and DTE estimates for traction battery control.

[0078] Although exemplary embodiments have been described above, these embodiments are not intended to describe all possible forms of the present disclosure. Indeed, the terms used in this specification are descriptive rather than restrictive, and it should be understood that various changes may be made without departing from the spirit and scope of the present disclosure. In addition, the features of various implemented embodiments may be combined to form other embodiments of the present disclosure.

[0079] According to the present invention, a system is provided having: a battery having a state of charge (SOC) with an SOC uncertainty; and a controller configured to charge and discharge the battery based on the capacity of the battery according to the SOC and a limit on the SOC uncertainty, the limit on the SOC uncertainty being based on consideration of multiple SOC uncertainty factors and thereby reduced relative to consideration of less than all of the SOC uncertainty factors.

[0080] According to an embodiment: each SOC uncertainty factor is associated with its own said SOC uncertainty limit; and said limit of said SOC uncertainty is the minimum limit of said SOC uncertainty factors.

[0081] According to an embodiment: each SOC uncertainty factor is associated with its own SOC uncertainty limit, and the limit of each SOC uncertainty factor is a range between negative values ​​and positive values; and the limit of the SOC uncertainty is a range between (i) the negative value with the lowest magnitude among the negative values ​​and (ii) the positive value with the lowest magnitude among the positive values.

[0082] According to an embodiment, a first one of the SOC uncertainty factors indicates the SOC uncertainty based on uncertainty in a voltage measurement of the battery and an SOC-OCV (open circuit voltage) table used together when estimating the SOC; and the bound on the SOC uncertainty is reduced relative to a limit based on considering only the first one of the SOC uncertainty factors.

[0083] According to an embodiment, another one of the SOC uncertainty factors indicates the SOC uncertainty based on a range of physically possible amounts of SOC uncertainty.

[0084] According to an embodiment: another of the SOC uncertainty factors is indicative of the SOC uncertainty based on uncertainty in an integrated ampere-hour measurement used in estimating the SOC.

[0085] According to an embodiment: another one of the SOC uncertainty factors is indicative of the SOC uncertainty based on distributed voltage measurements of the battery used in estimating the SOC.

[0086] According to an embodiment: another of the SOC uncertainty factors indicates the SOC uncertainty based on uncertainty about differences in an SOC-OCV (open circuit voltage) table at different voltage measurements, wherein the SOC-OCV table with the different voltage measurements is used to estimate the SOC.

[0087] According to an embodiment: the controller is further configured to detect and update the capacity when the battery is charged and discharged.

[0088] According to an embodiment: the battery is a traction battery of an electric vehicle.

[0089] According to the present invention, a method includes: detecting a state of charge (SOC) of a battery; estimating an uncertainty in the SOC based on consideration of multiple SOC uncertainty factors; detecting a capacity of the battery based on the SOC and the estimated SOC uncertainty, wherein the estimated SOC uncertainty is reduced relative to the uncertainty of the SOC estimated based on consideration of less than all of the SOC uncertainty factors, so that the capacity has a reduced uncertainty; and charging and discharging the battery based on the capacity.

[0090] In one aspect of the invention: each SOC uncertainty factor is associated with a bound on the uncertainty of the SOC; and the estimated SOC uncertainty is a minimum bound on the SOC uncertainty factors.

[0091] In one aspect of the invention: each SOC uncertainty factor is associated with a limit of the uncertainty of the SOC, and the limit of each SOC uncertainty factor is a range between a negative value and a positive value; and the estimated SOC uncertainty is a range between (i) the negative value with the lowest magnitude among the negative values ​​and (ii) the positive value with the lowest magnitude among the positive values.

[0092] In one aspect of the present invention, a first one of the SOC uncertainty factors indicates the uncertainty of the SOC based on the uncertainty of the voltage measurement value of the battery and the SOC-OCV (open circuit voltage) table used together when detecting the SOC; and the estimated SOC uncertainty is reduced relative to that based on considering only the first one of the SOC uncertainty factors.

[0093] In one aspect of the invention, another of the SOC uncertainty factors indicates the SOC uncertainty based on one of: a range of physically possible amounts of SOC uncertainty; an uncertainty in an integrated ampere-hour measurement used to detect the SOC; a distributed voltage measurement of the battery used to detect the SOC; or uncertainty regarding differences in an SOC-OCV (open circuit voltage) table at different voltage measurements, wherein the SOC-OCV table with the different voltage measurements is used to detect the SOC.

[0094] According to the present invention, an electrified vehicle is provided having: a traction battery; and a controller configured to detect a state of charge (SOC) of the traction battery and estimate an uncertainty in the SOC based on consideration of a plurality of SOC uncertainty factors, the estimated SOC uncertainty being reduced relative to an estimated SOC uncertainty based on consideration of fewer than all of the SOC uncertainty factors; and the controller further configured to charge and discharge the traction battery based on a capacity of the traction battery as a function of the SOC and the estimated SOC uncertainty.

[0095] According to an embodiment: each SOC uncertainty factor is associated with a limit on the uncertainty of the SOC; and the estimated SOC uncertainty estimated by the controller is a minimum limit of the SOC uncertainty factors.

[0096] According to an embodiment: each SOC uncertainty factor is associated with a limit of the uncertainty of the SOC, and the limit of each SOC uncertainty factor is a range between a negative value and a positive value; and the estimated SOC uncertainty estimated by the controller is a range between (i) a negative value with the lowest magnitude among the negative values ​​and (ii) a positive value with the lowest magnitude among the positive values.

[0097] According to an embodiment: a first of the SOC uncertainty factors indicates the uncertainty of the SOC based on the uncertainty of the voltage measurement value of the battery and the SOC-OCV (open circuit voltage) table used together when detecting the SOC; and the estimated SOC uncertainty estimated by the controller is reduced relative to that based on considering only the first of the SOC uncertainty factors.

[0098] According to an embodiment: a first of the SOC uncertainty factors indicates the uncertainty of the SOC based on uncertainties in a voltage measurement of the battery and an SOC-OCV (open circuit voltage) table used together when detecting the SOC; and a second of the SOC uncertainty factors indicates the SOC uncertainty based on a range of physically possible amounts of SOC uncertainty; and a third of the SOC uncertainty factors indicates the SOC uncertainty based on uncertainty in an ampere-hour integrated measurement used to detect the SOC.

Claims

1. A system comprising: a battery having a state of charge (SOC) with SOC uncertainty; as well as A controller is configured to charge and discharge the battery based on the capacity of the battery according to the SOC and a bound on the SOC uncertainty, the bound on the SOC uncertainty being based on consideration of multiple SOC uncertainty factors and thereby reduced relative to consideration of less than all of the SOC uncertainty factors.

2. The system of claim 1, wherein: Each SOC uncertainty factor is associated with its own said SOC uncertainty bound; and The bound on the SOC uncertainty is a minimum bound on the SOC uncertainty factor.

3. The system of claim 1 , wherein: Each SOC uncertainty factor is associated with its own said SOC uncertainty bound, and said bound for each SOC uncertainty factor is a range between negative and positive values; and The bound on the SOC uncertainty is a range between (i) the negative value having the lowest magnitude among the negative values ​​and (ii) the positive value having the lowest magnitude among the positive values.

4. The system of claim 1 , wherein: A first of the SOC uncertainty factors indicates the SOC uncertainty based on uncertainty in a voltage measurement of the battery and an SOC-OCV (open circuit voltage) table used together in estimating the SOC; and The bound on the SOC uncertainty is reduced relative to a calculation based on considering only the first of the SOC uncertainty factors.

5. The system of claim 4, wherein: Another of the SOC uncertainty factors indicates the SOC uncertainty based on a range of amounts of SOC uncertainty that are physically possible.

6. The system of claim 4, wherein: Another of the SOC uncertainty factors indicates the SOC uncertainty based on uncertainty in an integrated ampere-hour measurement used in estimating the SOC.

7. The system of claim 4, wherein: Another of the SOC uncertainty factors is indicative of the SOC uncertainty based on distributed voltage measurements of the battery used in estimating the SOC.

8. The system of claim 4, wherein: Another of the SOC uncertainty factors indicates the SOC uncertainty based on uncertainty regarding differences in an SOC-OCV (open circuit voltage) table at different voltage measurements, wherein the SOC-OCV table with the different voltage measurements is used to estimate the SOC.

9. A method comprising: Detect the battery's state of charge (SOC); estimating uncertainty of the SOC based on consideration of a plurality of SOC uncertainty factors; detecting a capacity of the battery based on the SOC and the estimated SOC uncertainty, the estimated SOC uncertainty being reduced relative to an uncertainty of the SOC estimated based on consideration of less than all of the SOC uncertainty factors such that the capacity has a reduced uncertainty; as well as The battery is charged and discharged based on the capacity.

10. The method of claim 9, wherein: Each SOC uncertainty factor is associated with a bound on the uncertainty of the SOC, and the bound on each SOC uncertainty factor is a range between a negative value and a positive value; and The estimated SOC uncertainty is a range between (i) the negative value having the lowest magnitude among the negative values ​​and (ii) the positive value having the lowest magnitude among the positive values.

11. The method of claim 9, wherein: A first of the SOC uncertainty factors indicates the uncertainty of the SOC based on uncertainty of a voltage measurement value of the battery and an SOC-OCV (open circuit voltage) table used together in detecting the SOC; and The estimated SOC uncertainty is reduced relative to a calculation based on considering only the first of the SOC uncertainty factors.

12. An electric vehicle comprising: traction batteries; as well as a controller configured to detect a state of charge (SOC) of the traction battery and estimate an uncertainty in the SOC based on consideration of a plurality of SOC uncertainty factors, the estimated SOC uncertainty being reduced relative to an estimated SOC uncertainty based on consideration of fewer than all of the SOC uncertainty factors; and The controller is further configured to charge and discharge the traction battery based on a capacity of the traction battery according to the SOC and the estimated SOC uncertainty.

13. The electrified vehicle of claim 12, wherein: Each SOC uncertainty factor is associated with a bound on the uncertainty of the SOC; and The estimated SOC uncertainty estimated by the controller is a minimum bound of the SOC uncertainty factor.

14. The electrified vehicle of claim 12, wherein: Each SOC uncertainty factor is associated with a bound on the uncertainty of the SOC, and the bound on each SOC uncertainty factor is a range between a negative value and a positive value; and The estimated SOC uncertainty estimated by the controller is a range between (i) the negative value having the lowest magnitude among the negative values ​​and (ii) the positive value having the lowest magnitude among the positive values.

15. The electrified vehicle of claim 12, wherein: A first of the SOC uncertainty factors indicates the uncertainty of the SOC based on uncertainty of a voltage measurement value of the battery and an SOC-OCV (open circuit voltage) table used together in detecting the SOC; and The estimated SOC uncertainty estimated by the controller is reduced relative to an estimate based on considering only the first of the SOC uncertainty factors.

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