Battery capacity confidence, capacity, SOH determination method, device, medium and equipment

Abstract

The present disclosure relates to a method, device, medium and equipment for determining battery capacity confidence, capacity and SOH, and relates to the field of vehicle battery technology. The method includes: determining corresponding target operating parameters based on a triggered target algorithm, and then determining a candidate confidence set based on the target algorithm and its corresponding target operating parameters. The candidate confidence set includes the candidate capacity confidence corresponding to the candidate battery capacity calculated by each preset algorithm in the target algorithm, and then determining the target capacity confidence of the target battery based on the original capacity confidence corresponding to the original battery capacity and the candidate capacity confidence corresponding to each candidate battery capacity. The target battery capacity can be determined based on the target capacity confidence, thereby determining a target battery capacity that better matches the current situation based on different battery conditions, so that the original battery capacity can be accurately updated by the target battery capacity to obtain a more accurate battery capacity health status.

Description

Technical Field

[0001] The present disclosure relates to the technical field of vehicle batteries, and in particular, to a method, device, storage medium, and vehicle equipment for determining battery capacity confidence, capacity, and SOH. Background Art

[0002] Battery capacity is an important parameter for evaluating the battery health status. In related technologies, using a single method to determine the battery capacity will lead to inaccurate determined battery capacity, which in turn leads to inaccurate calculated battery health status. Summary of the Invention

[0003] The purpose of the present disclosure is to provide a method, device, storage medium and vehicle equipment for determining battery capacity confidence, capacity and SOH (State of Health), which determines the corresponding target operating parameters based on the triggered target algorithm, and then determines a candidate confidence set based on the target algorithm and its corresponding target operating parameters. The candidate confidence set includes the candidate capacity confidence corresponding to the candidate battery capacity calculated by each preset algorithm in the target algorithm, and then determines the target capacity confidence of the target battery based on the original capacity confidence corresponding to the original battery capacity and the candidate capacity confidence corresponding to each candidate battery capacity. In order to be able to determine the target battery capacity based on the target capacity confidence, it is possible to determine the target battery capacity that better matches the current situation based on different battery conditions, so that the original battery capacity can be accurately updated through the target battery capacity, thereby obtaining a more accurate battery capacity health status.

[0004] To achieve the above objectives, according to a first aspect of an embodiment of the present disclosure, a method for determining battery capacity confidence is provided, comprising:

[0005] determining a target algorithm according to a trigger parameter of a target battery, wherein the target algorithm is at least one of a plurality of preset algorithms;

[0006] Obtaining a candidate confidence level set according to the target algorithm and the target operating parameters of the target battery corresponding to the target algorithm, wherein the candidate confidence level set includes candidate capacity confidence levels corresponding to candidate battery capacities calculated by each preset algorithm in the target algorithm;

[0007] The target capacity confidence level of the target battery is determined according to the original capacity confidence level corresponding to the original battery capacity and the candidate capacity confidence level corresponding to each candidate battery capacity.

[0008] Optionally, the plurality of preset algorithms include at least two of an ampere-hour integration method, an inflection point positioning method, and a life calendar method;

[0009] The trigger parameter includes at least one of a first state of charge, a second state of charge, and a charge rate of the target battery during charging, wherein the first state of charge is less than the second state of charge.

[0010] Optionally, determining the target algorithm according to the trigger parameter of the target battery includes:

[0011] When the first state of charge is less than a first preset threshold and the second state of charge is a fully charged state, determining the target algorithm includes the ampere-hour integration method and the life calendar method.

[0012] Optionally, determining the target algorithm according to the trigger parameter of the target battery includes:

[0013] When the first state of charge is less than a second preset threshold, the second state of charge is a fully charged state, and the charging rate is less than a preset rate, determining the target algorithm includes the inflection point positioning method and the life calendar method.

[0014] Optionally, the target operating parameter includes a confidence parameter;

[0015] The step of obtaining a candidate confidence set according to the target algorithm and the target operating parameters of the target battery corresponding to the target algorithm includes:

[0016] For any preset algorithm in the target algorithm, the candidate capacity confidence of the candidate battery capacity corresponding to the preset algorithm is determined according to the confidence parameter corresponding to the preset algorithm.

[0017] Optionally, when any one of the preset algorithms is the ampere-hour integration method, the confidence parameter includes a static voltage corresponding to the first state of charge and a stable time corresponding to the static voltage;

[0018] The determining, based on the confidence parameter corresponding to the preset algorithm, the candidate capacity confidence of the candidate battery capacity corresponding to the preset algorithm includes:

[0019] Based on the stabilization time of the static voltage and the target interval in which the static voltage is located, the first candidate capacity confidence level of the candidate battery capacity corresponding to the ampere-hour integration method is determined, the target interval is one of the platform area and the slope area of ​​the low charge interval in the voltage characteristic curve of the target battery, and the first candidate capacity confidence level is positively correlated with the stabilization time level.

[0020] Optionally, in the case where any one of the preset algorithms is the inflection point location method, the confidence parameter includes a candidate health state of the target battery;

[0021] The determining, based on the confidence parameter corresponding to the preset algorithm, the candidate capacity confidence of the candidate battery capacity corresponding to the preset algorithm includes:

[0022] According to the candidate health state of the target battery, a second candidate capacity confidence level of the candidate battery capacity corresponding to the inflection point positioning method is determined, where the second candidate capacity confidence level is positively correlated with the candidate health state.

[0023] Optionally, when any one of the preset algorithms is the life calendar method, the confidence parameter includes the driving mileage and driving time of the target battery;

[0024] The determining, based on the confidence parameter corresponding to the preset algorithm, the candidate capacity confidence of the candidate battery capacity corresponding to the preset algorithm includes:

[0025] A third candidate capacity confidence level of the candidate battery capacity corresponding to the life calendar method is determined according to the driving mileage and the driving time, where the third candidate capacity confidence level is negatively correlated with the driving mileage and the driving time.

[0026] Optionally, before determining the target capacity confidence level of the target battery based on the original capacity confidence level corresponding to the original battery capacity and the candidate capacity confidence level corresponding to each candidate battery capacity, the method further includes:

[0027] Obtaining a fourth candidate capacity confidence level and an original update duration corresponding to the original battery capacity, where the original update duration is the duration from the moment when the battery capacity of the target battery is updated to the original battery capacity to the current moment;

[0028] An original capacity confidence level corresponding to the original battery capacity is determined according to the fourth candidate capacity confidence level and the original update duration, where the original capacity confidence level is negatively correlated with the original update duration.

[0029] Optionally, before determining the target capacity confidence level of the target battery based on the original capacity confidence level corresponding to the original battery capacity and the candidate capacity confidence level corresponding to each candidate battery capacity, the method further includes:

[0030] Obtaining a fourth candidate capacity confidence and an original throughput corresponding to the original battery capacity, where the original throughput is the total charge and discharge volume of the target battery within the time period corresponding to the original update duration;

[0031] An original capacity confidence level corresponding to the original battery capacity is determined according to the fourth candidate capacity confidence level and the original throughput, where the original capacity confidence level is negatively correlated with the original throughput.

[0032] According to a second aspect of an embodiment of the present disclosure, a method for determining battery capacity is provided, including:

[0033] determining a target algorithm according to a trigger parameter of a target battery, wherein the target algorithm is at least one of a plurality of preset algorithms;

[0034] Obtaining at least one set of candidate battery capacity parameters according to the target algorithm and the target operating parameters of the target battery corresponding to the target algorithm, wherein a preset algorithm in the target algorithm corresponds one-to-one to the candidate battery capacity parameters, and each set of candidate battery capacity parameters includes a candidate battery capacity and a candidate capacity confidence level corresponding to the candidate battery capacity;

[0035] According to the original capacity confidence level corresponding to the original battery capacity and the candidate capacity confidence level corresponding to each candidate battery capacity, a target battery capacity is determined from the original battery capacity and the candidate battery capacity. The target battery capacity is used to update the current original battery capacity.

[0036] Optionally, the plurality of preset algorithms include at least two of an ampere-hour integration method, an inflection point positioning method, and a life calendar method;

[0037] The trigger parameter includes at least one of a first state of charge, a second state of charge, and a charge rate of the target battery during charging, wherein the first state of charge is less than the second state of charge.

[0038] Optionally, determining the target algorithm according to the trigger parameter of the target battery includes:

[0039] When the first state of charge is less than a first preset threshold and the second state of charge is a fully charged state, determining the target algorithm includes the ampere-hour integration method and the life calendar method.

[0040] Optionally, determining the target algorithm according to the trigger parameter of the target battery includes:

[0041] When the first state of charge is less than a second preset threshold, the second state of charge is a fully charged state, and the charging rate is less than a preset rate, determining the target algorithm includes the inflection point positioning method and the life calendar method.

[0042] Optionally, the target operating parameters include a capacity parameter and a confidence parameter;

[0043] The step of obtaining at least one set of candidate battery capacity parameters according to the target algorithm and the target operating parameters of the target battery corresponding to the target algorithm includes:

[0044] For any preset algorithm in the target algorithm, determine the candidate battery capacity corresponding to the preset algorithm according to the capacity parameter corresponding to the preset algorithm;

[0045] According to the confidence parameter corresponding to the preset algorithm, the candidate capacity confidence of the candidate battery capacity corresponding to the preset algorithm is determined.

[0046] Optionally, when any one of the preset algorithms is the ampere-hour integration method, the confidence parameter includes a static voltage corresponding to the first state of charge and a stable time corresponding to the static voltage;

[0047] The determining, based on the confidence parameter corresponding to the preset algorithm, the candidate capacity confidence of the candidate battery capacity corresponding to the preset algorithm includes:

[0048] Based on the stabilization time of the static voltage and the target interval in which the static voltage is located, the first candidate capacity confidence level of the candidate battery capacity corresponding to the ampere-hour integration method is determined, the target interval is one of the platform area and the slope area of ​​the low charge interval in the voltage characteristic curve of the target battery, and the first candidate capacity confidence level is positively correlated with the stabilization time level.

[0049] Optionally, when any one of the preset algorithms is the inflection point positioning method, the confidence parameter includes the health status of the target battery;

[0050] The determining, based on the confidence parameter corresponding to the preset algorithm, the candidate capacity confidence of the candidate battery capacity corresponding to the preset algorithm includes:

[0051] A second candidate capacity confidence level of the candidate battery capacity corresponding to the inflection point positioning method is determined according to the health state of the target battery, where the second candidate capacity confidence level is positively correlated with the health state.

[0052] Optionally, when any one of the preset algorithms is the life calendar method, the confidence parameter includes the driving mileage and driving time of the target battery;

[0053] The determining, based on the confidence parameter corresponding to the preset algorithm, the candidate capacity confidence of the candidate battery capacity corresponding to the preset algorithm includes:

[0054] A third candidate capacity confidence level of the candidate battery capacity corresponding to the life calendar method is determined according to the driving mileage and the driving time, where the third candidate capacity confidence level is negatively correlated with the driving mileage and the driving time.

[0055] Optionally, determining the target battery capacity from the original battery capacity and the candidate battery capacity according to the original capacity confidence level corresponding to the original battery capacity and the candidate capacity confidence level corresponding to each candidate battery capacity includes:

[0056] A battery capacity corresponding to a maximum value of the original capacity confidence factor and the candidate capacity confidence factor corresponding to each candidate battery capacity is determined as the target battery capacity.

[0057] Optionally, before determining the target battery capacity from the original battery capacity and the candidate battery capacity according to the original capacity confidence level corresponding to the original battery capacity and the candidate capacity confidence level corresponding to each candidate battery capacity, the method further includes:

[0058] Obtaining a fourth candidate capacity confidence, an original update duration, and an original throughput corresponding to the original battery capacity, where the original update duration is the duration from the moment the battery capacity of the target battery is updated to the original battery capacity to the current moment, and the original throughput is the total charge and discharge volume of the target battery during the time period corresponding to the original update duration;

[0059] An original capacity confidence level corresponding to the original battery capacity is determined according to the fourth candidate capacity confidence level, the original update duration, and the original throughput, where the original capacity confidence level is negatively correlated with the original update duration and the original throughput.

[0060] According to a third aspect of an embodiment of the present disclosure, a method for determining a battery capacity health state is provided, comprising:

[0061] The battery capacity health state of the target battery is determined according to the target battery capacity and factory capacity of the target battery, wherein the target battery capacity is obtained according to the battery capacity confidence determination method provided in the first aspect of the present disclosure.

[0062] According to a fourth aspect of an embodiment of the present disclosure, there is provided an apparatus, including:

[0063] a memory having a computer program stored thereon;

[0064] A processor is used to execute the computer program in the memory to enable the device to perform the steps of the battery capacity confidence determination method provided by the first aspect of the present disclosure, or to enable the device to perform the steps of the battery capacity determination method provided by the second aspect of the present disclosure, or to enable the device to perform the steps of the battery capacity health status determination method provided by the third aspect of the present disclosure.

[0065] According to a fifth aspect of an embodiment of the present disclosure, a non-volatile storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the steps of the battery capacity confidence determination method provided by the first aspect of the present disclosure are implemented, or, when the program is executed by a processor, the steps of the battery capacity determination method provided by the second aspect of the present disclosure are implemented, or, when the program is executed by a processor, the steps of the battery capacity health status determination method provided by the third aspect of the present disclosure are implemented.

[0066] According to a sixth aspect of an embodiment of the present disclosure, an electric energy device is provided, comprising the apparatus provided in the fifth aspect of the present disclosure.

[0067] Through the above technical solution, the corresponding target operating parameters are determined based on the triggered target algorithm, and then a candidate confidence set is determined based on the target algorithm and its corresponding target operating parameters. The candidate confidence set includes the candidate capacity confidence corresponding to the candidate battery capacity calculated by each preset algorithm in the target algorithm. Then, the target capacity confidence of the target battery is determined based on the original capacity confidence corresponding to the original battery capacity and the candidate capacity confidence corresponding to each candidate battery capacity. In this way, the target battery capacity can be determined based on the target capacity confidence, and the target battery capacity that better matches the current situation can be determined based on different battery conditions, so that the original battery capacity can be accurately updated through the target battery capacity, thereby obtaining a more accurate battery capacity health status.

[0068] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following detailed description, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0070] Figure 1 The figure is a flow chart showing a method for determining battery capacity according to an exemplary embodiment.

[0071] Figure 2 The figure is a flow chart showing a method for updating battery capacity according to an exemplary embodiment.

[0072] Figure 3 FIG. 1 is a schematic diagram showing a voltage characteristic curve of a battery according to an exemplary embodiment.

[0073] Figure 4 The figure is a block diagram showing a device for determining battery capacity according to an exemplary embodiment. DETAILED DESCRIPTION

[0074] The following describes the specific embodiments of the present disclosure in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present disclosure and are not intended to limit the present disclosure.

[0075] Battery capacity is an important parameter for evaluating battery health. Methods for calculating battery capacity include the ampere-hour integration method, the life calendar method, and the inflection point location method. The ampere-hour integration method requires a greater depth of discharge and calculates the battery's actual available capacity based on changes in charge capacity and state of charge. The life calendar method utilizes a life calendar model to determine the battery's capacity trend over aging based on parameters such as time, battery throughput, and self-discharge rate. The inflection point location method displays a voltage characteristic curve at low current conditions and determines the actual available capacity by locating the inflection point.

[0076] In the related art, using a single method to determine the battery capacity will result in inaccurate determined battery capacity, which in turn will result in inaccurate calculated battery health status.

[0077] In response to the above technical problems, the present disclosure provides a battery capacity confidence, capacity, SOH determination method, device, storage medium and vehicle equipment, which determines the corresponding target operating parameters based on the triggered target algorithm, and then determines the candidate confidence set according to the target algorithm and its corresponding target operating parameters. The candidate confidence set includes the candidate capacity confidence corresponding to the candidate battery capacity calculated by each preset algorithm in the target algorithm, and then determines the target capacity confidence of the target battery according to the original capacity confidence corresponding to the original battery capacity and the candidate capacity confidence corresponding to each candidate battery capacity. In order to be able to determine the target battery capacity according to the target capacity confidence, it is possible to determine the target battery capacity that better matches the current situation according to different battery conditions, so that the original battery capacity can be accurately updated by the target battery capacity, thereby obtaining a more accurate battery capacity health status.

[0078] Figure 1 is a flow chart showing a method for determining battery capacity confidence according to an exemplary embodiment. Figure 2 FIG. 1 is a flow chart showing a method for updating battery capacity according to an exemplary embodiment. Figure 1 and Figure 2 As shown, the method may include the following steps:

[0079] In step S101 , a target algorithm is determined according to a trigger parameter of a target battery. The target algorithm is at least one of a plurality of preset algorithms.

[0080] In this embodiment, the target battery is the battery whose capacity is to be determined. Multiple preset algorithms may be pre-set. For example, the multiple preset algorithms may include at least two of the ampere-hour integration method, the life calendar method, and the inflection point location method. Each preset algorithm is assigned a corresponding trigger condition. Based on the trigger parameters of the target battery, it is determined which preset algorithm's trigger conditions the trigger parameters meet, and at least one preset algorithm is determined from the multiple preset algorithms to obtain a target algorithm. If the trigger parameters of any preset algorithm meet the trigger conditions of the preset algorithm, the preset algorithm is triggered and determined as one of the target algorithms. The trigger parameters may include at least one of a first state of charge (SOC), a second SOC, and a charge rate of the target battery during charging, with the first SOC being less than the second SOC. The first SOC may be the starting SOC of the target battery during this charging period, and the second SOC may be the ending SOC of the target battery during this charging period.

[0081] In step S102, a candidate confidence set is obtained based on the target algorithm and the target operating parameters of the target battery corresponding to the target algorithm, wherein the candidate confidence set includes the candidate capacity confidence corresponding to the candidate battery capacity calculated by each preset algorithm in the target algorithm.

[0082] In this embodiment, based on each preset algorithm in the target algorithm, the target operating parameters corresponding to each preset algorithm can be obtained. The target operating parameters can be determined from the operating parameters of the target battery. And according to the target operating parameters corresponding to each preset algorithm, based on the corresponding preset algorithm, a candidate confidence set is calculated, wherein the candidate confidence set includes the candidate capacity confidence corresponding to the candidate battery capacity calculated by each preset algorithm in the target algorithm. That is, each preset algorithm in the target algorithm can calculate a corresponding candidate battery capacity and a candidate capacity confidence corresponding to the candidate battery capacity, and the candidate capacity confidence represents the accuracy of the corresponding candidate battery capacity.

[0083] In step S103 , the target capacity confidence level of the target battery is determined according to the original capacity confidence level corresponding to the original battery capacity and the candidate capacity confidence level corresponding to each candidate battery capacity.

[0084] In this embodiment, the target battery capacity can be selected based on the confidence levels corresponding to multiple battery capacities. Specifically, the candidate capacity confidence level corresponding to each candidate battery capacity and the original capacity confidence level corresponding to the original battery capacity can be obtained. The multiple confidence levels are compared, and the maximum confidence level can be determined as the target capacity confidence level of the target battery. The original battery capacity is the current battery capacity, which is the battery capacity after the last update. If this is the first time triggering the determination of the target battery capacity, the original battery capacity value can be 0, and the original capacity confidence level can also be 0.

[0085] After the target capacity confidence level is obtained, the corresponding candidate battery capacity may be determined as the target battery capacity, so as to update the current original battery capacity and obtain the latest and more accurate target battery capacity.

[0086] In one possible implementation, for the triggering conditions of various preset algorithms, the life calendar method can be triggered in real time, that is, the corresponding candidate battery capacity and the candidate capacity confidence corresponding to the candidate battery capacity can be obtained in real time based on the life calendar method.

[0087] In a possible implementation, determining a target algorithm according to trigger parameters of a target battery may include the following steps:

[0088] When the first state of charge is less than a first preset threshold and the second state of charge is a fully charged state, the target determination algorithm includes an ampere-hour integration method and a life calendar method.

[0089] In this embodiment, the lifespan calendar method is triggered in real time, meaning the target algorithm includes the lifespan calendar method. When the first state of charge is less than a first preset threshold and the second state of charge is fully charged, the trigger parameter is determined to meet the trigger conditions corresponding to the ampere-hour integration method, and the target algorithm can be determined to include the ampere-hour integration method. The first preset threshold can be set based on actual conditions and can be adjusted based on different battery types. For example, the value range of the first preset threshold can be 1%-35%. A fully charged state means that the battery's state of charge is at least greater than 97%.

[0090] In a possible implementation, determining a target algorithm according to trigger parameters of a target battery may include the following steps:

[0091] When the first state of charge is less than a second preset threshold, the second state of charge is a fully charged state, and the charging rate is less than a preset rate, the target determination algorithm includes an inflection point positioning method and a life calendar method.

[0092] In this embodiment, the lifespan calendar method is triggered in real time, meaning the target algorithm includes the lifespan calendar method. When the first state of charge is less than the second preset threshold and the second state of charge is fully charged, the trigger parameters are determined to meet the trigger conditions corresponding to the inflection point positioning method, and the target algorithm can be determined to include the inflection point positioning method. The second preset threshold is greater than the first preset threshold. The second preset threshold can be set based on actual conditions and can be adjusted based on different battery types. For example, the second preset threshold can range from 55% to 65%. A fully charged state refers to a battery state of charge of at least 97%.

[0093] In one possible implementation, the target operating parameter includes a confidence parameter;

[0094] Obtaining a candidate confidence set according to a target algorithm and target operating parameters of a target battery corresponding to the target algorithm may include the following steps:

[0095] For any preset algorithm in the target algorithm, the candidate capacity confidence of the candidate battery capacity corresponding to the preset algorithm is determined according to the confidence parameter corresponding to the preset algorithm.

[0096] In this embodiment, the target operating parameter includes a confidence parameter, wherein the confidence parameter is used to determine the confidence level corresponding to the battery capacity. The confidence level of the candidate battery capacity corresponding to the preset algorithm can then be determined based on the confidence parameter corresponding to the preset algorithm.

[0097] In one possible implementation, the target operating parameters may further include capacity parameters, and the battery capacity is calculated for each preset algorithm in the target algorithm to obtain a candidate battery capacity parameter corresponding to each preset algorithm. Specifically, for any preset algorithm in the target algorithm, a candidate battery capacity corresponding to the preset algorithm may be calculated based on the preset algorithm according to the capacity parameter corresponding to the preset algorithm.

[0098] The ampere-hour integration method uses the capacity ΔQ / ΔSOC calculated from charging to a fully charged state at a known SOC (battery state of charge), where ΔQ is the charged capacity and ΔSOC is the change in SOC after charging. The accuracy of this method is based on the magnitude of ΔSOC and the accuracy of SOC. That is, the capacity parameters corresponding to the ampere-hour integration method are the charged capacity and the change in SOC after charging. The inflection point positioning method uses the voltage characteristic curve that appears during low-current charging to locate the high inflection point. Based on the principle that the capacity Q1 at the high inflection point remains unchanged after aging, the final capacity is calculated by adding the capacity Q2+Q1 charged after the high inflection point. Q1 is the inflection point capacity and Q2 is the capacity charged after the inflection point. That is, the capacity parameters corresponding to the inflection point positioning method are the inflection point capacity and the capacity charged after the inflection point. The calculation accuracy of this method depends on the accuracy of the capacity at the high inflection point. The life calendar method calculates the battery's health status in real time by inputting mileage and time into the model, thereby outputting the corresponding capacity value. When the ampere-hour integration method and the inflection point positioning method are not triggered for a long time, the capacity value obtained by this method can be updated with a certain step size. That is, the capacity parameters corresponding to the life calendar method are the driving mileage and driving time of the target battery, and the driving time is the factory usage time of the target battery.

[0099] Figure 3 is a schematic diagram showing a voltage characteristic curve of a battery according to an exemplary embodiment. Figure 3 As shown, in one possible implementation, when any preset algorithm is the ampere-hour integration method, the confidence parameters corresponding to any preset algorithm include the static voltage corresponding to the first state of charge and the stable time corresponding to the static voltage. Determining the candidate capacity confidence of the candidate battery capacity corresponding to the preset algorithm based on the confidence parameters may include the following steps:

[0100] According to the stabilization time of the static voltage and the target interval of the static voltage, the first candidate capacity confidence level of the candidate battery capacity corresponding to the ampere-hour integration method is determined. The target interval is one of the platform area and the slope area of ​​the low-charge interval in the voltage characteristic curve of the target battery. The first candidate capacity confidence level is positively correlated with the stabilization time level.

[0101] In this embodiment, the target battery may be a lithium-ion battery, and its voltage characteristic curve is a curve showing how the OCV (Open Circuit Voltage) of the battery changes with the state of charge of the battery, such as Figure 3 As shown, the voltage characteristic curve includes a low charge interval L, a medium charge interval M and a high charge interval H, wherein the low charge interval L includes three regions L1, L2 and L3 in sequence, L1 and L3 are slope regions, and L2 is a platform region.

[0102] The first state of charge can be determined by the static voltage. The stabilization duration of the static voltage is the rest time when the target battery is charged or discharged to reach the static voltage, that is, the rest time of the target battery before reaching the static voltage, and the rest time is the duration of neither charging nor discharging. When the preset algorithm is the ampere-hour integration method, the confidence parameters corresponding to the ampere-hour integration method can be obtained: the static voltage corresponding to the first state of charge and the stabilization duration corresponding to the static voltage. The interval corresponding to the static voltage is first determined, and then the confidence level of the first candidate capacity of the candidate battery capacity obtained by the ampere-hour integration method is determined based on the stabilization duration.

[0103] For example, the SOC variation involves the starting SOC and the fully charged SOC, with the fully charged SOC being a fixed value of 100%. Therefore, for the ampere-hour integral, the factor affecting the candidate capacity confidence of the candidate battery capacity mainly depends on the accuracy of the SOC at the low point, that is, the accuracy of the first state of charge. The SOC at the low point can be determined by the static voltage. That is, after discharging or charging is completed, the voltage begins to return to a stable state. As time goes by, the voltage recovery becomes more stable. At this time, the current SOC value can be obtained by checking the charge corresponding to the voltage, thereby obtaining the first state of charge. The accuracy of the SOC is affected by the voltage location and the rest time. When the voltage falls in the slope region, such as L1 or L3, the SOC is discernible. Therefore, the SOC accuracy is higher under the same rest time, and the SOC can be given a higher quality factor. In the plateau region, the discernibility is low, resulting in low SOC accuracy. Its quality factor can be increased with the extension of the rest time. For example, when the voltage falls in the slope area and the standstill time is greater than or equal to the first preset time, the confidence level of the first state of charge may be a first confidence value, wherein the first preset time may range from 0.1 to 5 hours, and the first confidence value may range from 70% to 100%. When the standstill time is less than the first preset time, the confidence level may be 0, or increase with the increase of the standstill time; when the voltage falls in the plateau area, the standstill time is greater than or equal to the first preset time, the confidence level of the first state of charge may be a second confidence value, the first confidence value is greater than the second confidence value, and the second confidence value may range from 65% to 75%. As the standstill time increases, the confidence level of the first state of charge will increase linearly. When the standstill time reaches the second preset time, the confidence level of the first state of charge increases to the first confidence value. The second preset time may range from 5.5 to 20 hours. When the standstill time is less than the first preset time, the confidence level may be 0, or increase with the increase of the standstill time.

[0104] In a possible implementation, when any preset algorithm is an inflection point location method, the confidence parameter corresponding to any preset algorithm includes a candidate health state of the target battery;

[0105] Determining the candidate capacity confidence of the candidate battery capacity corresponding to the preset algorithm according to the confidence parameter corresponding to the preset algorithm may include the following steps:

[0106] According to the candidate health state of the target battery, a second candidate capacity confidence level of the candidate battery capacity corresponding to the inflection point positioning method is determined, and the second candidate capacity confidence level is positively correlated with the candidate health state.

[0107] In this embodiment, the candidate health state of the target battery, i.e., SOH, can be obtained to determine the second candidate capacity confidence of the candidate battery capacity corresponding to the inflection point positioning method. The candidate health state can be determined based on the candidate battery capacity calculated by the inflection point positioning method. The second candidate capacity confidence is positively correlated with the candidate health state, i.e., the larger the value of the candidate health state, the greater the second candidate capacity confidence.

[0108] For example, since the inflection point positioning method utilizes the inflection point, i.e. Figure 3 The slope area between the middle M and H areas, the capacity at full charge, is used to infer the final capacity of the battery, that is, the sum of the inflection point capacity Q1 and the charging capacity Q2 after the inflection point. The charging capacity Q2 from the inflection point to the full charge is an accurate value and is not affected by other factors. Therefore, the factor affecting the inflection point positioning method is mainly the accuracy of the inflection point capacity Q1, that is, the change in the inflection point position. The inflection point position is only affected by the degree of aging of the battery, that is, the health status of the battery. As the battery pack ages, the position of the inflection point will be offset, resulting in a decrease in the accuracy of the capacity at the inflection point. Therefore, for a new battery pack, its SOH = 100%, and the confidence level of the battery capacity calculated using the inflection point positioning method is the third confidence value. The value range of the third confidence value can be 85%-95%. As the battery pack ages, the confidence level of the calculated battery capacity will decrease linearly. When the battery pack SOH drops to the first preset health value, the confidence level of the battery capacity will drop to the fourth confidence level, where the first preset health value may range from 65% to 75%, and the fourth confidence level may range from 55% to 65%.

[0109] In one possible implementation, when any of the preset algorithms is a life calendar method, the confidence parameters include the driving mileage and driving time of the target battery;

[0110] Determining the candidate capacity confidence of the candidate battery capacity corresponding to the preset algorithm according to the confidence parameter corresponding to the preset algorithm may include the following steps:

[0111] A third candidate capacity confidence level of the candidate battery capacity corresponding to the life calendar method is determined according to the driving mileage and the driving time. The third candidate capacity confidence level is negatively correlated with the driving mileage and the driving time.

[0112] In this embodiment, the confidence parameters corresponding to the life calendar method are obtained: the target battery's driving mileage and driving time. The driving mileage is the number of miles driven using the target battery, and the driving time is the target battery's factory usage. Based on the driving mileage and driving time, the confidence level of the third candidate capacity for the candidate battery capacity corresponding to the life calendar method is determined. The confidence level of the third candidate capacity is negatively correlated with the driving mileage and driving time. That is, the greater the driving mileage, the lower the confidence level of the third candidate capacity, and the longer the driving time, the lower the confidence level of the third candidate capacity.

[0113] In one possible implementation, before determining the target battery capacity from the original battery capacity and the candidate battery capacity based on the original capacity confidence level corresponding to the original battery capacity and the candidate capacity confidence level corresponding to each candidate battery capacity, the original capacity confidence level corresponding to the original battery capacity may be determined first. The determination method may include the following steps:

[0114] Obtain the fourth candidate capacity confidence and original update duration corresponding to the original battery capacity, where the original update duration is the duration from the moment the battery capacity of the target battery is updated to the original battery capacity to the current moment; determine the original capacity confidence corresponding to the original battery capacity based on the fourth candidate capacity confidence and the original update duration, where the original capacity confidence is negatively correlated with the original update duration.

[0115] In one possible implementation, before determining the target battery capacity from the original battery capacity and the candidate battery capacity based on the original capacity confidence level corresponding to the original battery capacity and the candidate capacity confidence level corresponding to each candidate battery capacity, the original capacity confidence level corresponding to the original battery capacity may be determined first. The determination method may include the following steps:

[0116] Obtain a fourth candidate capacity confidence and an original throughput corresponding to the original battery capacity, where the original throughput is the total charge and discharge volume of the target battery during the time period corresponding to the original update duration. Determine an original capacity confidence corresponding to the original battery capacity based on the fourth candidate capacity confidence and the original throughput, where the original capacity confidence is negatively correlated with the original throughput.

[0117] In this embodiment, the fourth candidate capacity confidence corresponding to the original battery capacity can be updated based on at least one of the original update duration and the original throughput to obtain the original capacity confidence. The fourth candidate capacity confidence corresponding to the original battery capacity is the confidence corresponding to the original battery capacity determined according to the corresponding confidence parameter when the original battery capacity is calculated. When any algorithm is triggered, the larger one is selected to update the current original battery capacity and original capacity confidence based on the confidence of the battery capacity. As the battery usage time and throughput increase, that is, the original update duration and the original throughput increase, the confidence corresponding to the original battery capacity will gradually decrease based on the fourth candidate capacity confidence until the confidence after triggering any algorithm is greater than the confidence corresponding to the original battery capacity, and it can be updated again. For example, when the confidence level is updated to the fourth candidate capacity confidence level corresponding to the original battery capacity, the confidence level corresponding to the original battery capacity will drop by a first preset value every time the cumulative capacity exceeds a nominal capacity. The value range of the first preset value may be 1%-2%. For every 5 days that the time increases, the confidence level corresponding to the original battery capacity will drop by a second preset value. The value range of the second preset value may be 1%-2%.

[0118] In another exemplary embodiment, a method for determining battery capacity is provided, including:

[0119] determining a target algorithm according to a trigger parameter of a target battery, wherein the target algorithm is at least one of a plurality of preset algorithms;

[0120] Obtaining a set of candidate battery capacities according to the target algorithm and the target operating parameters of the target battery corresponding to the target algorithm, wherein the set of candidate battery capacities includes candidate battery capacities calculated by each preset algorithm in the target algorithm;

[0121] The candidate battery capacity calculated by the preset algorithm corresponding to the target capacity confidence is determined as the target battery capacity of the target battery. The target battery capacity is used to update the current original battery capacity. The target capacity confidence is obtained according to the battery capacity confidence determination method described in the above embodiment.

[0122] In this embodiment, a set of candidate battery capacities can also be obtained based on the target algorithm and the target operating parameters of the target battery corresponding to the target algorithm, wherein the set of candidate battery capacities includes the candidate battery capacities calculated by each preset algorithm in the target algorithm, that is, each preset algorithm in the target algorithm can calculate a corresponding candidate battery capacity. Then, according to the battery capacity confidence determination method in the above embodiment, the target capacity confidence can be obtained, and based on the target capacity confidence, the target battery capacity can be determined from multiple candidate battery capacities and the original battery capacity. After obtaining the target battery capacity, the current original battery capacity can be updated to obtain the latest and more accurate battery capacity of the target battery. Among them, the method for obtaining the candidate battery capacity set based on the target algorithm and the target operating parameters of the target battery corresponding to the target algorithm can refer to the relevant explanation in the above-mentioned battery capacity confidence determination method, and will not be repeated here.

[0123] In one possible implementation, the battery capacity determination method may include the following steps:

[0124] According to the trigger parameters of the target battery, a target algorithm is determined, where the target algorithm is at least one of a plurality of preset algorithms; according to the target algorithm and the target operating parameters of the target battery corresponding to the target algorithm, at least one group of candidate battery capacity parameters is obtained, wherein the preset algorithms in the target algorithm correspond one-to-one to the candidate battery capacity parameters, and each group of candidate battery capacity parameters includes a candidate battery capacity and a candidate capacity confidence level corresponding to the candidate battery capacity; according to the original capacity confidence level corresponding to the original battery capacity and the candidate capacity confidence level corresponding to each candidate battery capacity, a target battery capacity is determined from the original battery capacity and the candidate battery capacity, and the target battery capacity is used to update the current original battery capacity.

[0125] In this embodiment, the target battery is the battery whose capacity is to be determined. Multiple preset algorithms may be pre-set. For example, these multiple preset algorithms may include the ampere-hour integration method, the life calendar method, and the inflection point location method. Each preset algorithm is assigned a corresponding trigger condition. Based on the trigger parameters of the target battery, it is determined which preset algorithm's trigger conditions the trigger parameters meet, and then at least one preset algorithm is determined from the multiple preset algorithms to obtain the target algorithm. For any preset algorithm, if the trigger parameters meet the trigger conditions of the preset algorithm, the preset algorithm is triggered and determined as one of the target algorithms. The trigger parameters may include at least one of the first state of charge, the second state of charge, and the charge rate of the target battery during charging, with the first state of charge being less than the second state of charge. The first state of charge may be the starting charge state of the target battery during this charging period, and the second state of charge may be the ending charge state of the target battery during this charging period.

[0126] Based on each preset algorithm in the target algorithm, the target operating parameters corresponding to each preset algorithm can be obtained. The target operating parameters can be determined from the operating parameters of the target battery. And according to the target operating parameters corresponding to each preset algorithm, based on the corresponding preset algorithm, at least one set of candidate battery capacity parameters is calculated, wherein the preset algorithms in the target algorithm correspond one-to-one to the candidate battery capacity parameters, that is, each preset algorithm in the target algorithm can calculate a set of corresponding candidate battery capacity parameters. Each set of candidate battery capacity parameters includes a candidate battery capacity and a candidate capacity confidence level corresponding to the candidate battery capacity, that is, each preset algorithm in the target algorithm can calculate a corresponding candidate battery capacity and a candidate capacity confidence level corresponding to the candidate battery capacity, and the candidate capacity confidence level represents the accuracy of the corresponding candidate battery capacity.

[0127] The target battery capacity can be selected based on the confidence levels corresponding to multiple battery capacities. Specifically, the candidate capacity confidence level corresponding to each candidate battery capacity and the original capacity confidence level corresponding to the original battery capacity can be obtained. The multiple confidence levels can be compared, and the battery capacity corresponding to the maximum confidence level can be determined as the target battery capacity. The original battery capacity is the current battery capacity, which is the battery capacity after the last update. If this is the first time to trigger the determination of the target battery capacity of the target battery, the value of the original battery capacity may be 0, and the original capacity confidence level may also be 0. After obtaining the target battery capacity, the current original battery capacity may be updated to obtain the latest and more accurate battery capacity of the target battery. In another exemplary embodiment, a method for determining the health status of a battery capacity is also provided, including:

[0128] The battery capacity health state of the target battery is determined according to the target battery capacity and the factory capacity of the target battery. The target battery capacity is obtained according to the battery capacity determination method in the above embodiment.

[0129] In this embodiment, the target battery capacity may be directly divided by the factory capacity to obtain the battery capacity health status of the target battery.

[0130] Figure 4 FIG. 1 is a block diagram of a device according to an exemplary embodiment. Figure 4 The apparatus 400 includes one or more processors 422, and a memory 432 for storing a computer program executable by the processor 422. The computer program stored in the memory 432 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor 422 may be configured to execute the computer program so that the battery capacity determination apparatus performs the above-described battery capacity determination method.

[0131] In addition, the device 400 may further include a power supply component 426 and a communication component 490. The power supply component 426 may be configured to perform power management of the device 400, and the communication component 490 may be configured to implement communication, such as wired or wireless communication, of the device 400. In addition, the device 400 may further include an input / output interface 498. The device 400 may operate based on an operating system stored in the memory 432.

[0132] In another exemplary embodiment, a vehicle is provided, comprising the battery capacity determination device provided in the above embodiment.

[0133] In another exemplary embodiment, a non-volatile storage medium including program instructions is further provided. When executed by a processor, the program instructions implement the steps of the aforementioned method for determining battery capacity confidence, or the steps of the aforementioned method for determining battery capacity, or the steps of the aforementioned method for determining battery capacity health status. For example, the non-volatile storage medium may be the aforementioned memory 432 including the program instructions. The program instructions may be executed by the processor 422 of the battery capacity determination apparatus 400 to perform the aforementioned method for determining battery capacity.

[0134] In another exemplary embodiment, a computer program product is also provided, which includes a computer program that can be executed by a programmable device, and the computer program has a code portion for executing the above-mentioned battery capacity confidence determination method when executed by the programmable device, or, a code portion for executing the above-mentioned battery capacity determination method, or, a code portion for executing the above-mentioned battery capacity health status determination method.

[0135] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details of the above embodiments. Within the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the scope of protection of the present disclosure.

[0136] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0137] In addition, the various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A method for determining battery capacity confidence, characterized in that: include: determining a target algorithm according to a trigger parameter of a target battery, wherein the target algorithm is at least one of a plurality of preset algorithms; Obtaining a candidate confidence level set according to the target algorithm and the target operating parameters of the target battery corresponding to the target algorithm, wherein the candidate confidence level set includes candidate capacity confidence levels corresponding to candidate battery capacities calculated by each preset algorithm in the target algorithm; The target capacity confidence level of the target battery is determined according to the original capacity confidence level corresponding to the original battery capacity and the candidate capacity confidence level corresponding to each candidate battery capacity.

2. The method for determining battery capacity confidence according to claim 1, wherein: The plurality of preset algorithms include at least two of an ampere-hour integration method, an inflection point positioning method, and a life calendar method; The trigger parameter includes at least one of a first state of charge, a second state of charge, and a charge rate of the target battery during charging, wherein the first state of charge is less than the second state of charge.

3. The method for determining battery capacity confidence according to claim 2, wherein: The step of determining a target algorithm according to the trigger parameters of the target battery includes: When the first state of charge is less than a first preset threshold and the second state of charge is a fully charged state, determining the target algorithm includes the ampere-hour integration method and the life calendar method.

4. The method for determining battery capacity confidence according to claim 2, wherein: The step of determining a target algorithm according to the trigger parameters of the target battery includes: When the first state of charge is less than a second preset threshold, the second state of charge is a fully charged state, and the charging rate is less than a preset rate, determining the target algorithm includes the inflection point positioning method and the life calendar method.

5. The method for determining battery capacity confidence according to claim 2, wherein: The target operating parameters include confidence parameters; The step of obtaining a candidate confidence set according to the target algorithm and the target operating parameters of the target battery corresponding to the target algorithm includes: For any preset algorithm in the target algorithm, the candidate capacity confidence of the candidate battery capacity corresponding to the preset algorithm is determined according to the confidence parameter corresponding to the preset algorithm.

6. The method for determining battery capacity confidence according to claim 5, wherein: In a case where any one of the preset algorithms is the ampere-hour integration method, the confidence parameter includes a static voltage corresponding to the first state of charge and a stable time corresponding to the static voltage; The determining, based on the confidence parameter corresponding to the preset algorithm, the candidate capacity confidence of the candidate battery capacity corresponding to the preset algorithm includes: Based on the stabilization time of the static voltage and the target interval in which the static voltage is located, the first candidate capacity confidence level of the candidate battery capacity corresponding to the ampere-hour integration method is determined, the target interval is one of the platform area and the slope area of ​​the low charge interval in the voltage characteristic curve of the target battery, and the first candidate capacity confidence level is positively correlated with the stabilization time level.

7. The method for determining battery capacity confidence according to claim 5, wherein: In a case where any one of the preset algorithms is the inflection point location method, the confidence parameter includes a candidate health state of the target battery; The determining, based on the confidence parameter corresponding to the preset algorithm, the candidate capacity confidence of the candidate battery capacity corresponding to the preset algorithm includes: According to the candidate health state of the target battery, a second candidate capacity confidence level of the candidate battery capacity corresponding to the inflection point positioning method is determined, where the second candidate capacity confidence level is positively correlated with the candidate health state.

8. The method for determining battery capacity confidence level according to claim 5, wherein: In the case where any one of the preset algorithms is the life calendar method, the confidence parameters include the driving mileage and driving time of the target battery; The determining, based on the confidence parameter corresponding to the preset algorithm, the candidate capacity confidence of the candidate battery capacity corresponding to the preset algorithm includes: A third candidate capacity confidence level of the candidate battery capacity corresponding to the life calendar method is determined according to the driving mileage and the driving time, where the third candidate capacity confidence level is negatively correlated with the driving mileage and the driving time.

9. The method for determining battery capacity confidence according to any one of claims 1 to 8, wherein: Before determining the target capacity confidence level of the target battery based on the original capacity confidence level corresponding to the original battery capacity and the candidate capacity confidence level corresponding to each candidate battery capacity, the method further includes: Obtaining a fourth candidate capacity confidence level and an original update duration corresponding to the original battery capacity, where the original update duration is the duration from the moment when the battery capacity of the target battery is updated to the original battery capacity to the current moment; An original capacity confidence level corresponding to the original battery capacity is determined according to the fourth candidate capacity confidence level and the original update duration, where the original capacity confidence level is negatively correlated with the original update duration.

10. The method for determining battery capacity confidence according to any one of claims 1 to 8, characterized in that: Before determining the target capacity confidence level of the target battery based on the original capacity confidence level corresponding to the original battery capacity and the candidate capacity confidence level corresponding to each candidate battery capacity, the method further includes: Obtaining a fourth candidate capacity confidence and an original throughput corresponding to the original battery capacity, where the original throughput is the total charge and discharge volume of the target battery within the time period corresponding to the original update duration; An original capacity confidence level corresponding to the original battery capacity is determined according to the fourth candidate capacity confidence level and the original throughput, where the original capacity confidence level is negatively correlated with the original throughput.

11. A method for determining battery capacity, characterized in that: include: determining a target algorithm according to a trigger parameter of a target battery, wherein the target algorithm is at least one of a plurality of preset algorithms; Obtaining a set of candidate battery capacities according to the target algorithm and the target operating parameters of the target battery corresponding to the target algorithm, wherein the set of candidate battery capacities includes candidate battery capacities calculated by each preset algorithm in the target algorithm; The candidate battery capacity calculated by the preset algorithm corresponding to the target capacity confidence is determined as the target battery capacity of the target battery, and the target battery capacity is used to update the current original battery capacity. The target capacity confidence is obtained according to the battery capacity confidence determination method according to any one of claims 1-10.

12. The battery capacity determination method according to claim 11, characterized in that: The target operating parameters include capacity parameters; The step of obtaining a set of candidate battery capacities according to the target algorithm and the target operating parameters of the target battery corresponding to the target algorithm includes: For any preset algorithm in the target algorithm, the candidate battery capacity corresponding to the preset algorithm is determined according to the capacity parameter corresponding to the preset algorithm.

13. A method for determining the health status of battery capacity, characterized in that: include: The battery capacity health state of the target battery is determined according to the target battery capacity and the factory capacity of the target battery, wherein the target battery capacity is obtained according to the battery capacity determination method according to any one of claims 11-12.

14. A non-volatile storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, it implements the steps of the battery capacity confidence determination method described in any one of claims 1-10, or, when the program is executed by the processor, it implements the steps of the battery capacity determination method described in any one of claims 11-12, or, when the program is executed by the processor, it implements the steps of the battery capacity health status determination method described in claim 13.

15. A device, characterized in that include: a memory having a computer program stored thereon; A processor for executing the computer program in the memory so that the device performs the steps of the battery capacity confidence determination method described in any one of claims 1 to 10, or so that the device performs the steps of the battery capacity determination method described in any one of claims 11 to 12, or so that the device performs the steps of the battery capacity health status determination method described in claim 13.

16. An electric energy device, characterized in that: Comprising the device of claim 15.

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