Battery capacity estimation uncertainty

Abstract

The present disclosure provides "battery capacity estimation uncertainty". An automotive power system adjusts a maximum discharge power of a traction battery as a function of an estimated capacity. The estimated capacity depends on data from the traction battery at a moment selected based on incremental state of charge uncertainty associated with the moment. The automotive power system may also store the data.

Description

Technical Field

[0001] This disclosure relates to estimating the battery capacity of an electric vehicle (EV). More specifically, this disclosure relates to minimizing the uncertainty in battery capacity estimation. Background Technology

[0002] Electric vehicles rely on traction batteries that supply power to the motors for propulsion. Over time, the capacity of these traction batteries may decrease. Onboard computers can be configured to update the battery capacity. Summary of the Invention

[0003] An automotive electric system includes a traction battery and a controller that adjusts the maximum discharge power of the traction battery based on an estimated capacity, the estimated capacity depending on data from the traction battery at a time selected based on time-related incremental state-of-charge uncertainties.

[0004] One method involves adjusting the maximum discharge power of a traction battery based on an estimated capacity, the estimated capacity depending on data from the traction battery at a time corresponding to an incremental state-of-charge uncertainty value less than a predefined threshold.

[0005] A vehicle includes a motor, a traction battery, and a controller that commands the discharge of the traction battery from the motor based on data from the traction battery at a time selected based on a time-related uncertainty in net ampere-hour throughput. Attached Figure Description

[0006] Figure 1 An exemplary box topology of an electrified vehicle is shown, illustrating the powertrain and energy storage components.

[0007] Figure 2 An exemplary flowchart is shown for the process of determining battery capacity and operating the vehicle.

[0008] Figure 3 An exemplary graph of a lookup table for battery state of charge uncertainty is shown. Detailed Implementation

[0009] This document describes embodiments. However, it should be understood that the disclosed embodiments are merely examples and other embodiments may take various and alternative forms. The drawings are not necessarily drawn to scale. Some features may be enlarged or minimized to show details of specific components. Therefore, the specific structural and functional details disclosed herein should not be construed as limiting, but merely as a representative basis for teaching those skilled in the art.

[0010] The various features shown and described with reference to any of the accompanying drawings can be combined with features shown in one or more other drawings to produce embodiments not explicitly shown or described. The combinations of features shown provide representative embodiments for typical applications. However, for a particular application or implementation, various combinations and modifications of features consistent with the teachings of this disclosure may be desired.

[0011] This disclosure specifically proposes a method and system for estimating the battery capacity of an EV. More specifically, this disclosure proposes a method and system for minimizing the uncertainty in battery capacity estimation.

[0012] Figure 1 A plug-in hybrid electric vehicle (PHEV) is illustrated. The PHEV 112 may include one or more electric motors (electric motors) 114 mechanically coupled to a hybrid transmission 116. The motors 114 may operate as either motors or generators. Additionally, the hybrid transmission 116 is mechanically coupled to an engine 118. The hybrid transmission 116 is also mechanically coupled to a drive shaft 120, which is mechanically coupled to wheels 122. When the engine 118 is on or off, the motors 114 can provide propulsion and deceleration capabilities. The motors 114 can also act as generators and can provide fuel economy benefits by recovering energy lost as heat in the friction braking system. The motors 114 can also reduce vehicle emissions by allowing the engine 118 to operate at more efficient speeds and by allowing the PHEV 112 to operate in electric mode under certain conditions when the engine 118 is off.

[0013] The traction battery or battery pack 124 stores energy that can be used by the motor 114. The vehicle battery pack 124 can provide a high-voltage DC output. The traction battery 124 can be electrically connected to one or more battery electronic control modules (BECMs) 125. The BECM 125 can be equipped with one or more processors and software applications configured to monitor and control various operations of the traction battery 124. The traction battery 124 can also be electrically connected to one or more power electronic modules 126. The power electronic modules 126 can also be referred to as power inverters. One or more contactors 127 can isolate the traction battery 124 and BECM 125 from other components when open, and connect the traction battery 124 and BECM 125 to other components when closed. The power electronic modules 126 can also be electrically connected to the motor 114 and provide the ability to transfer energy bidirectionally between the traction battery 124 and the motor 114. For example, the traction battery 124 can provide DC voltage, while the motor 114 can operate using three-phase AC current. The power electronics module 126 can convert DC voltage into three-phase AC current for use by the motor 114. In regenerative mode, the power electronics module 126 can convert the three-phase AC current from the motor 114, which acts as a generator, into DC voltage compatible with the traction battery 124. The description herein also applies to pure electric vehicles. For pure electric vehicles, the hybrid transmission 116 can be a gearbox connected to the motor 114 and the engine 118 may not be present.

[0014] In addition to providing energy for propulsion, the traction battery 124 can also power other vehicle electrical systems. The vehicle includes a DC / DC converter module 128 that converts the high-voltage DC output of the traction battery 124 into a low-voltage DC supply compatible with other low-voltage vehicle loads. The output of the DC / DC converter module 128 can be electrically connected to an auxiliary battery 130 (e.g., a 12V battery).

[0015] Vehicle 112 may be a battery electric vehicle (BEV) or a plug-in hybrid electric vehicle (PHEV), wherein the traction battery 124 can be recharged via an external power source 136. The external power source 136 may be a connection to an electrical outlet. The external power source 136 may be a distribution network or grid provided by an electric utility company. The external power source 136 may be electrically connected to an electric vehicle power supply unit (EVSE) 138. The EVSE 138 may provide circuitry and controls to manage energy transfer between the power source 136 and vehicle 112. The external power source 136 may provide DC or AC power to the EVSE 138. The EVSE 138 may have a charging connector 140 for insertion into a charging port 134 in vehicle 112. The charging port 134 may be any type of port configured to transfer power from the EVSE 138 to vehicle 112. The charging port 134 may be electrically connected to a charger or an on-board power conversion module 132. The power conversion module 132 regulates the power supplied from the EVSE 138 to provide appropriate voltage and current levels to the traction battery 124. The power conversion module 132 can interact with the EVSE 138 to coordinate power delivery to the vehicle 112. The EVSE connector 140 may have pins that mate with corresponding recesses in the charging port 134. Alternatively, various components described as electrical connections may use wireless inductive connections to transfer power.

[0016] One or more electrical loads 146 may be connected to a high-voltage bus. Each electrical load 146 may have an associated controller for operating and controlling the electrical load 146 as appropriate. Examples of electrical loads 146 may be heating modules, air conditioning modules, etc.

[0017] The various components discussed may have one or more associated controllers to control and monitor the operation of the components. The controllers may communicate via a serial bus (e.g., a Controller Area Network (CAN)) or via discrete conductors. A system controller 150 may be present to coordinate the operation of the various components. It should be noted that system controller 150 is used as a general term and may include one or more controller devices configured to perform the various operations described in this disclosure. For example, system controller 150 may be programmed to enable powertrain control functions to operate the powertrain of vehicle 112. System controller 150 may also be programmed to enable telecommunications functions with various entities (e.g., servers) via a wireless network (e.g., a cellular network).

[0018] BECM 125 can be configured to perform various operations. For example, BECM 125 can be configured to periodically estimate the capacity of traction battery 124. The capacity of traction battery 124 may decrease over time. After a period of time, the capacity of traction battery 124 may be less than the design capacity when traction battery 124 was manufactured. Accurate estimation of the actual capacity can facilitate the operation and control of vehicle 112. For example, accurate estimation of the actual capacity can provide vehicle users with a better range estimate and influence charging and discharging operations.

[0019] Capacity estimation can be performed based on measurements of voltage and current during charging and / or discharging of the traction battery 124. In the example, the BECM 125 can measure the initial voltage of the traction battery at a first time point and the final voltage at a second time point via one or more voltage sensors. The voltage can be used to estimate the state of charge (SOC) of the traction battery at each corresponding time point via a lookup table (LUT). The SOC difference between the first and second time points can be recorded as ΔSOC. The BECM 125 can also measure the current input / output of the traction battery between the first and second time points and integrate the measured current to account for the net ampere-hour (Ah) throughput corresponding to ΔSOC. The total capacity of the traction battery can then be estimated based on ΔSOC and the net ampere-hour throughput. The above capacity estimation is associated with various uncertainties. For example, the LUT used to determine the battery SOC using the battery terminal voltage can be associated with the inherent SOC uncertainty U(SOC) at each different voltage point. The inherent SOC uncertainty U(SOC) can lead to uncertainties associated with the SOC difference U(ΔSOC). Furthermore, the net ampere-hour throughput measurement calculated via current integration may also be associated with uncertainty.

[0020] This disclosure proposes a method for estimating the capacity of a traction battery 124 by selecting those data points associated with minimum uncertainty. More specifically, the uncertainty U(ΔSOC) associated with various ΔSOCs can be estimated. This disclosure records the battery data associated with the minimum uncertainty U(ΔSOC) of the trip and ignores those battery data associated with higher minimum uncertainties U(ΔSOC) of the trip.

[0021] Estimated capacity Q of traction battery 124 Est The uncertainty can be expressed by the following equation:

[0022]

[0023] in This represents the integral of the current (e.g., net ampere-hour throughput) between the first moment when the main contactor 127 closes and the subsequent second moment when the main contactor 127 closes. Both the first and second moments can be points in time prior to the current time. This represents the uncertainty associated with the net ampere-hour throughput measurement calculated via current integration.

[0024] In this example, It is very small and can be assumed to be approximately equal to zero. Therefore, equation (1) above can be simplified to:

[0025]

[0026] SOC LUT (V(CC1),T(CC1)) represents the SOC of the traction battery 124 estimated via the SOC-OCV lookup table at the first moment. LUT (V(CC2),T(CC2)) represents the SOC of the traction battery 124 estimated via the SOC-OCV lookup table at the second time point. The SOC-OCV lookup table can be stored non-volatilely in a storage device of the BECM 125 and / or in a storage device associated with other components of the vehicle 112. Like most lookup tables, the SOC-OCV lookup table may not be 100% accurate. Therefore, the SOC-OCV lookup process may inherently be associated with uncertainty. Equation (2) above introduces U(SOC) which reflects the uncertainty associated with the SOC-OCV lookup process at the first time point. LUT (V(CC1),T(CC1))) and U(SOC) reflecting the uncertainty associated with the SOC-OCV lookup process at the second time point. LUT (V(CC2),T(CC2))) is used to account for uncertainty. In the alternative example, the ampere-hour integral throughput component It is likely a dominant factor and cannot be ignored. For simplicity, the following example will be presented with the ampere-hour integral throughput component ignored.

[0027] Estimated capacity Q of traction battery 124 Est The following equation can be used to estimate it.

[0028]

[0029] Similar to equation (1), This represents the integral of the current between the first and second moments (e.g., net ampere-hour throughput). Here, the time interval between the first and second moments may only include the amount of time when the main contactor 127 is closed. As an example, the first moment could occur at 8:00 AM when vehicle 112 is driven from a customer's home to work for one hour. The vehicle might be parked for 8 hours and then driven home at 5:00 PM, which takes another hour. The second moment might occur when vehicle 112 is plugged in at home at 6:00 PM. In the example above, the total time is 2 hours (excluding the 8-hour parking time). The SOC difference between the first and second moments can be expressed as ΔSOC. The uncertainty associated with the SOC difference ΔSOC can be expressed as:

[0030]

[0031] From equation (4) above, it can be noted that the uncertainty U(ΔSOC) associated with the SOC difference ΔSOC can vary depending on the battery data collected at the first time point (e.g., CC1) and the subsequent second time point (e.g., CC2). Therefore, battery data corresponding to different time periods may affect the uncertainty U(ΔSOC) associated with the SOC difference ΔSOC. This disclosure proposes a method for selectively estimating the capacity of the traction battery 124 based on battery data associated with the minimum uncertainty U(ΔSOC) between the beginning and end of a time period, thereby improving the estimation accuracy.

[0032] refer to Figure 2 An exemplary flowchart illustrating a process for estimating battery capacity and operating a vehicle, according to an embodiment of this disclosure, is shown. Continuing to refer to... Figure 1 Process 200 can be implemented independently via EVSE 125. Alternatively, process 200 can be implemented jointly via BECM 125, system controller 150, and / or other components of vehicle 112 under substantially the same concept. For simplicity, the following description will refer to BECM 125. In this example, battery data at three time points are used. The first, second, and third time points occur sequentially. Before operation 202 begins, it is assumed that BECM 125 has already recorded the battery data associated with the first and second time points.

[0033] At operation 202, BECM 125 determines that battery data associated with the third time point has become available. Battery data may include various parameters. For example, battery data may include battery voltage, temperature, and ampere-hours measured at the third time point. BECM 125 can be configured to periodically measure battery data in response to the satisfaction of one or more predefined measurement conditions. For example, measurement conditions may include time elapsed conditions that allow BECM 125 to perform new measurements after a predefined time period has elapsed. Measurement conditions may also include charge / discharge conditions that allow BECM 125 to perform new measurements after the vehicle has been charged and / or discharged.

[0034] Having collected battery data associated with the first, second, and third time periods, at operation 204, the BECM 125 estimates three separate uncertainties associated with the three time periods based on equation (4) above. More specifically, the BECM 125 estimates the first uncertainty U(ΔSOC) associated with the first time period, which begins at the first time period and ends at the second time period. CC1,CC2 The second uncertainty U(ΔSOC) associated with the second time period that begins at the second time point and ends at the third time point. CC2,CC3 ), and the third uncertainty U(ΔSOC) associated with the third time period that begins at the first time point and ends at the third time point. CC1,CC3 In this example, the third time period is equal to the sum of the first and second time periods. Although the uncertainty of ΔSOC between different times is used in the calculations of this embodiment, this disclosure is not limited thereto. In alternative examples, capacity uncertainty associated with different times (e.g., U(Q)) can be used under a similar concept. Est_CC1,CC2 In this example, it is assumed that the ampere-hour integral component is close to zero and is therefore ignored.

[0035] Having estimated the uncertainty U(ΔSOC) associated with the three time periods, at operation 206, BECM125 compares the uncertainties and determines the lowest uncertainty among the three for recording. For example, at operation 208, if BECM125 determines the first uncertainty U(ΔSOC) associated with the first time period... CC1,CC2 If the value is the lowest among the three, then process 200 proceeds to operation 210, and BECM 125 updates the estimated battery capacity based on data from the first and second time points, ignoring battery data associated with the third time point.

[0036] After updating the estimated battery capacity Q Est In the case of operation 212, BECM 125 is based on the updated battery capacity Q. EstTo operate vehicle 112 and / or traction battery 124. Operations performed by BECM 125 can include various examples. When vehicle 112 is driven, BECM 125 can use a newer battery capacity Q. Est To adjust the discharge of the traction battery 124. For example, in response to determining the battery capacity Q since the last estimate. Est The BECM 125 has been reduced, allowing for a shorter estimated driving range and a reduction in the maximum discharge power of the traction battery 124 to conserve energy. Alternatively, the BECM 125 can be based on a newer battery capacity Q. Est To adjust the charging operation. As an example, in response to determining the battery capacity Q... Est The BECM 125 has been reduced, which can reduce the power and / or total amount of battery charging via EVSE 138 and / or regenerative charging.

[0037] At operation 214, BECM 125 stores vehicle data from the second and third time points and deletes vehicle data from the first time point. This is because the vehicle data from the first time point is the oldest and has already been used to update the estimated battery capacity Q. Est Therefore, the data can be deleted from the vehicle's memory to save storage space. The stored data from the second and third time points can include various entries. For example, the BECM 125 can store data entries associated with the corresponding time point (such as SOC, uncertainty, and throughput) in the onboard storage device for future use.

[0038] If the first uncertainty is U(ΔSOC) CC1,CC2 If the value is not the lowest, then process 200 proceeds from operation 208 to operation 216, such that BECM is based on the previously estimated battery capacity Q that has not been updated. Est To operate vehicle 112 and / or traction battery 124.

[0039] Process 200 proceeds from operation 216 to operation 218 to further determine the second uncertainty U(ΔSOC) associated with the second time period. CC2,CC3 Is it the lowest of the three? If the answer is yes, then process 200 proceeds to operation 214 as described above to store vehicle data from the second and third time points and delete vehicle data from the first time point.

[0040] If the answer to operation 218 is no, it indicates the third uncertainty U(ΔSOC) associated with the third time period. CC1,CC3If the value of the vehicle data is the lowest among the three, then process 200 proceeds to operation 220 to store vehicle data from the first and third time points and delete vehicle data from the second time point. For similar reasons, vehicle data from the second time point that is not the latest and is associated with relatively high uncertainty can be deleted to save storage space. Conversely, vehicle data from the first time point can be stored in the storage device for future reference.

[0041] Process 200 can be executed sequentially. At operation 222, when battery data associated with the new third time point becomes available, BECM 125 assigns the two stored time points as the new first and second time points to prepare for the next battery capacity Q. Est Therefore, if process 200 moves from operation 214 to operation 222, then the second and third time points become the new first and second time points. Otherwise, if process 200 moves from operation 220 to operation 222, then the first and third time points become the new first and second time points.

[0042] The operations in procedure 200 can be applied to various examples. (See reference) Figure 3 An exemplary graph 300 of a lookup table for SOC uncertainty according to an embodiment of this disclosure is shown. The horizontal axis of graph 300 represents the SOC of the traction battery 124 in percentage terms, and the vertical axis of graph 300 represents the SOC uncertainty U(SOC) in percentage terms. Figure 3 As shown, two SOC uncertainty U (SOC) lookup tables are illustrated. More specifically, graph 300 includes a measurement lookup table 302 corresponding to cases where OCV measurements are available, and an estimation lookup table 304 corresponding to cases where OCV measurements are unavailable and should be estimated. Typically, the estimation lookup table 304 can be associated with higher uncertainty compared to the measurement lookup table 302.

[0043] The availability of an OCV measurement for the traction battery 124 can depend on various factors. For example, the availability of the measurement can depend on vehicle usage factors, including the length of time the vehicle is parked between charging and driving use, customer usage scenarios, battery cell polarization conditions, etc. For instance, when the usage scenario allows the traction battery 124 to fully relax, a relatively accurate OCV measurement may be available, and the BECM 125 can use the measured voltage to calculate the SOC, and thus determine the SOC uncertainty U(SOC) accordingly. However, if the usage scenario does not allow the traction battery 124 to fully relax, an accurate OCV measurement may not be available, and the OCV may be estimated to determine the SOC and the associated SOC uncertainty U(SOC). In this example, it is assumed that the measured OCV (and therefore the measured uncertainty 302) is associated with an uncertainty of + / -3mV, and the estimated OCV (and therefore the estimated uncertainty 304) is associated with an uncertainty of + / -15mV.

[0044] Typically, after the battery voltage has reached the OCV, the traction battery 124 fully relaxes, making the SOC-OCV curve valid. The traction battery 124 is considered to be polarized immediately after charging or discharging activity. Therefore, the voltage of the traction battery 124 does not immediately reach the OCV after charging or discharging activity. In the absence of charging or discharging activity, the voltage of the traction battery 124 may reach the OCV after a period of time (e.g., 1 hour).

[0045] Referring to Table 1 below, exemplary usage scenarios for vehicle 112 are shown:

[0046]

[0047]

[0048] Table 1 Vehicle Usage Scenarios

[0049] In this example shown in Table 1, six moments are used to measure or estimate the OCV. At the first moment, BECM 125 determines that the traction battery 124 is sufficiently relaxed, and therefore the OCV can be measured as the voltage across the battery terminals. BECM 215 determines the SOC to be 95% based on the measured OCV value, and uses the measurement lookup table 302 to determine the SOC uncertainty U(SOC) to be 0.27%. At the second moment, BECM 125 determines that the traction battery 124 is not sufficiently relaxed, and therefore only the OCV can be estimated. BECM 215 determines the SOC to be 65% based on the estimated OCV value, and uses the estimation lookup table 304 to determine the SOC uncertainty U(SOC) to be 1.58%. At the third moment, BECM 125 determines that the traction battery 124 is sufficiently relaxed, and therefore the OCV can be measured as the voltage across the battery terminals. BECM 215 determines the SOC to be 70% based on the measured OCV value, and uses the measurement lookup table 302 to determine the SOC uncertainty U(SOC) to be 0.30%. At the fourth time point, BECM 125 determines that the traction battery 124 is sufficiently relaxed, and therefore the OCV can be measured as the voltage across the battery terminals. BECM 215 determines the SOC to be 50% based on the measured OCV value, and uses the measurement lookup table 302 to determine the SOC uncertainty U(SOC) to be 0.67%. At the fifth time point, BECM 125 determines that the traction battery 124 is not sufficiently relaxed, and therefore only the OCV can be estimated. BECM 215 determines the SOC to be 35% based on the estimated OCV value, and uses the estimation lookup table 304 to determine the SOC uncertainty U(SOC) to be 5.00%. At the sixth time point, BECM 125 determines that the traction battery 124 is sufficiently relaxed, and therefore the OCV can be measured as the voltage across the battery terminals. BECM 215 determines the SOC to be 90% based on the measured OCV value, and uses the measurement lookup table 302 to determine the SOC uncertainty U(SOC) to be 0.27%. Each of the time points from the first to the sixth is also marked at the corresponding position on the graph 300.

[0050] When both the SOC and the corresponding SOC uncertainty U(SOC) are available, the uncertainty associated with the SOC difference ΔSOC can be determined for each time period using the above equation (4). For example, the uncertainty associated with the SOC difference ΔSOC within the first time period, which begins at the first time point and ends at the second time point, can be calculated using equation (4) as follows:

[0051]

[0052] Similarly, the uncertainty associated with the SOC difference ΔSOC over the second time period, which begins at the first time point and ends at the third time point, can be calculated using equation (4).

[0053]

[0054] BECM can perform calculations for each time period in a similar manner and generate the following:

[0055] The results are shown in Table 2:

[0056]

[0057] Table 2 shows the U(ΔSOC) results for different time periods.

[0058] For Table 2 above, U(ΔSOC) varies significantly depending on the start and end times. More specifically, the optimal time period, starting at the first time point and ending at the fourth time point, is associated with the lowest SOC difference uncertainty (e.g., 1.601%), while the worst time period, starting at the fourth time point and ending at the fifth time point, is associated with the highest SOC difference uncertainty (e.g., 33.628%).

[0059] BECM 125 can select one or more time periods associated with the lowest SOC difference uncertainty U (ΔSOC) for battery capacity estimation and remove battery data from other times / time periods. For example, if BECM 125 is configured to select a predetermined number of time periods as two, the optimal time period starting at the first time period and ending at the fourth time period (e.g., 1.601%) and the second optimal time period starting at the fourth time period and ending at the sixth time period (e.g., 1.801%) will be selected. Alternatively, BECM 125 can be configured to select a flexible number of time periods based on one or more predefined thresholds for the SOC difference uncertainty U (ΔSOC). For example, a 5% threshold can be applied to various time periods. In this case, the third optimal time period starting at the third time period and ending at the fourth time period (e.g., 3.655%) and the fourth optimal time period starting at the third time period and ending at the sixth time period (e.g., 2.027%) will also be selected. Alternatively, BECM 125 can use all capacity estimates with local minimum uncertainty. Additional filters can be applied to determine how to process individual estimates.

[0060] In an alternative example, BECM 125 can determine the uncertainty between different SOCs (and different estimated capacities) using the integral component of ampere-hour throughput presented in Equation (1) under essentially the same concept. In one example, the integral component of ampere-hour throughput can be used instead of ΔSOC to determine the uncertainty. In an alternative example, the integral component of ampere-hour throughput can be used to determine the uncertainty in addition to ΔSOC.

[0061] The algorithms, methods, or processes disclosed herein may be delivered to or implemented by a computer, controller, or processing device, which may include any dedicated electronic control unit or programmable electronic control unit. Similarly, the algorithms, methods, or processes may be stored in various forms as data and instructions executable by a computer or controller, including but not limited to information permanently stored on a non-writable storage medium such as a read-only memory device and information reproducibly stored on a writable storage medium such as an optical disc, random access memory device, or other magnetic and optical media. The algorithms, methods, or processes may also be implemented as software executable objects. Alternatively, the algorithms, methods, or processes may be implemented, in whole or in part, using suitable hardware components such as application-specific integrated circuits, field-programmable gate arrays, state machines, or other hardware components or devices, or firmware, a combination of hardware and software components.

[0062] While exemplary embodiments have been described above, these embodiments are not intended to describe all possible forms covered by the claims. The terms used in this specification are descriptive and not restrictive, and it should be understood that various changes may be made without departing from the spirit and scope of this disclosure. The terms "a processor" and "multiple processors" are used interchangeably herein, just as the terms "a controller" and "multiple controllers" are used interchangeably.

[0063] As previously described, features of the various embodiments can be combined to form other embodiments of the invention that may not be explicitly described or shown. While various embodiments may have been described as offering advantages or preferences over other embodiments or prior art implementations in terms of one or more desired characteristics, those skilled in the art will recognize that one or more features or characteristics can be compromised to achieve desired overall system properties, depending on the specific application and implementation. These properties may include, but are not limited to, strength, durability, marketability, appearance, packaging, size, serviceability, weight, manufacturability, ease of assembly, etc. Therefore, embodiments described as less desirable than other embodiments or prior art implementations in terms of one or more characteristics are not outside the scope of this disclosure and may be desirable for a particular application.

[0064] According to the present invention, an automotive electric system is provided, comprising: a traction battery; and a controller programmed to adjust the maximum discharge power of the traction battery according to an estimated capacity, the estimated capacity depending on data from the traction battery at a time selected based on an incremental state-of-charge uncertainty associated with that time.

[0065] According to an embodiment, the controller is also programmed to selectively store the data based on the incremental state of charge uncertainty.

[0066] According to an embodiment, the data includes the state of charge of the traction battery.

[0067] According to an embodiment, some of the states of charge are measured and other states of charge are estimated.

[0068] According to an embodiment, the estimated capacity also depends on the charging or discharging of the traction battery during the time period corresponding to the stated time.

[0069] According to an embodiment, the uncertainty of the incremental state of charge depends on the state of charge of the traction battery.

[0070] According to the present invention, a method includes: adjusting the maximum discharge power of a traction battery based on an estimated capacity, said estimated capacity depending on data from the traction battery at a time corresponding to an incremental state-of-charge uncertainty value less than a predefined threshold.

[0071] In one aspect of the invention, the method includes selectively storing the data based on the incremental state of charge uncertainty value.

[0072] In one aspect of the invention, the data includes the state of charge of the traction battery.

[0073] In one aspect of the invention, the method includes measuring some of the states of charge and estimating other states of charge.

[0074] In one aspect of the invention, the estimated capacity also depends on the charging or discharging of the traction battery during the time period corresponding to the said time.

[0075] In one aspect of the invention, the incremental state of charge uncertainty value depends on the state of charge of the traction battery.

[0076] According to the present invention, a vehicle is provided having: an electric motor; a traction battery; and a controller programmed to command discharge of the traction battery from the electric motor based on data from the traction battery at a time selected based on a time-related uncertainty in net ampere-hour throughput.

[0077] According to an embodiment, the controller is also programmed to selectively store the data based on the net ampere-hour throughput uncertainty.

[0078] According to an embodiment, the data includes the current of the traction battery.

Claims

1. An automotive electrical system, comprising: Traction battery; and A controller is programmed to adjust the maximum discharge power of the traction battery based on an estimated capacity, the estimated capacity depending on data from the traction battery at a time selected based on time-related incremental state-of-charge uncertainties.

2. The automotive electrical system of claim 1, wherein the controller is further programmed to selectively store the data based on the incremental state of charge uncertainty.

3. The automotive electrical system of claim 1, wherein the data includes the state of charge of the traction battery.

4. The automotive electrical system of claim 3, wherein some of the states of charge are measured and other states of charge are estimated.

5. The vehicle electrical system of claim 1, wherein the estimated capacity further depends on the charging experienced by the traction battery during the time period corresponding to the said time.

6. The automotive electrical system of claim 1, wherein the incremental state of charge uncertainty depends on the state of charge of the traction battery.

7. A method comprising: The maximum discharge power of the traction battery is adjusted based on an estimated capacity, which depends on data from the traction battery at a time corresponding to an incremental state-of-charge uncertainty value less than a predefined threshold.

8. The method of claim 7, further comprising selectively storing the data based on the incremental state of charge uncertainty value.

9. The method of claim 7, wherein the data includes the state of charge of the traction battery.

10. The method of claim 9, further comprising measuring some of the states of charge and estimating other states of charge.

11. The method of claim 7, wherein the estimated capacity further depends on the charging experienced by the traction battery during the time period corresponding to the time.

12. The method of claim 7, wherein the incremental state of charge uncertainty depends on the state of charge of the traction battery.

13. A vehicle comprising: Electric motor; Traction battery; and A controller programmed to command the discharge of the traction battery from the motor based on data from the traction battery at the time selected based on an incremental state-of-charge uncertainty associated with the time.

14. The vehicle of claim 13, wherein the controller is further programmed to selectively store the data based on the incremental state of charge uncertainty.

15. The vehicle of claim 13, wherein the data includes the state of charge of the traction battery.

Images