Battery health state estimation method and device, equipment and storage medium

Abstract

The invention provides a battery health state estimation method and device, equipment and a storage medium, and relates to an energy storage battery technology. The method comprises the following steps: acquiring charging and discharging data of at least one charging process and / or at least one discharging process of a target battery within a preset duration; for each charging process or discharging process, calculating the local capacity of each partition based on the target charging and discharging data corresponding to each partition of the charging process or the discharging process; wherein each of the charging process and the discharging process comprises a plurality of partitions, and different partitions in the same charging process / discharging process correspond to different electrochemical reaction stages of the target battery; and calculating the SOH of the target battery according to each local capacity. According to the method and the device, a more accurate SOH can be obtained.

Description

Technical Field

[0001] This application relates to energy storage battery technology, and more particularly to a method, apparatus, device, and storage medium for estimating battery health status. Background Technology

[0002] State of Health (SOH) is a key indicator for measuring the current health of energy storage batteries. It reflects the ratio of battery capacity to its initial capacity and is a direct manifestation of battery performance degradation. By estimating the SOH, signs of battery aging, such as capacity loss and increased internal resistance, can be detected in a timely manner, facilitating appropriate maintenance measures to extend battery life.

[0003] Currently, one known technology provides a method for estimating SOH based on the relationship between battery voltage and battery state of charge (SOC). Specifically, this method relies on the voltage change characteristics of the battery under different SOCs and infers the battery's SOH by comparing the characteristic curves of aged batteries and new batteries.

[0004] The above process suffers from the drawback of low accuracy in obtaining SOH. Summary of the Invention

[0005] This application provides a method, apparatus, device, and storage medium for estimating the state of battery health (SOH) to obtain a more accurate SOH.

[0006] In a first aspect, this application provides a method for estimating the state of health of a battery, the method comprising:

[0007] Acquire charge and discharge data of the target battery for at least one charging process and / or at least one discharging process within a preset time period;

[0008] For each charging or discharging process, the local capacity of the partition is calculated based on the target charge / discharge data corresponding to each partition of the charging or discharging process; wherein, both the charging and discharging processes include multiple partitions, and different partitions of the same charging / discharging process correspond to different electrochemical reaction stages of the target battery;

[0009] The state of health (SOH) of the target battery is calculated based on the local capacity of each of the aforementioned cells.

[0010] In one possible implementation, the method further includes:

[0011] For each charging or discharging process, probability density function (PDF) analysis is performed based on the corresponding target voltage information to obtain the peak value of the PDF; the target voltage information is used to indicate voltage changes.

[0012] Based on the peak value, the charging process or the discharging process is divided to obtain a preset number of partitions corresponding to the charging process or the discharging process.

[0013] In one possible implementation, the target battery is a lithium iron phosphate battery; the step of dividing the charging process or the discharging process according to the peak value to obtain a preset number of partitions corresponding to the charging process or the discharging process includes:

[0014] Based on the voltages corresponding to two target peaks among the peak values, two plateau voltages of the target battery are determined during the charging or discharging process; each target peak value is greater than the other peak values ​​among the peak values.

[0015] Based on the two plateau voltages, the charging or discharging process of the target battery is divided into three regions: a voltage steep change region, a first plateau region, and a second plateau region.

[0016] In one possible implementation, the method further includes:

[0017] The charging and discharging data are segmented to obtain charging data corresponding to each charging process, and / or discharging data corresponding to each discharging process;

[0018] For each charging data point and each discharging data point, a preset algorithm is used for noise reduction to obtain the target voltage information for the corresponding charging or discharging process.

[0019] In one possible implementation, calculating the local capacity of a partition based on target charge / discharge data corresponding to each partition of the charging or discharging process includes:

[0020] Based on the ampere-hour integral algorithm, the first local capacity of the first plateau region and the second local capacity of the second plateau region are calculated.

[0021] Based on the first local capacity, the third local capacity partition of the voltage steep change region is calculated.

[0022] In one possible implementation, the method further includes:

[0023] When the target voltage information indicates that the target data of the first platform area or the second platform area is incomplete, the corresponding historical local capacity is obtained; the historical local capacity is the local capacity of the first platform area or the second platform area calculated based on the ampere-hour integration algorithm in the last time.

[0024] The local capacity of the first platform area or the second platform area is determined as the corresponding historical local capacity.

[0025] In one possible implementation, calculating the third local capacity of the voltage steep change region based on the first local capacity includes:

[0026] Based on the first local capacity, obtain the ratio coefficient between the voltage steep change region and the first plateau region;

[0027] The third local capacity of the voltage steep change region is calculated based on the proportionality coefficient; the proportionality coefficient is determined based on the electrochemical reaction ratios corresponding to the first plateau region, the second plateau region, and the voltage steep change region.

[0028] In one possible implementation, the method further includes:

[0029] Based on either laboratory data or a preset feature function, a target feature function relating the first local capacity to the scaling factor is established; the laboratory data includes experimental charge-discharge data of the target battery; the preset feature function includes: a sixth-order Fourier function, a polynomial function, and an exponential function.

[0030] The step of obtaining the ratio coefficient between the voltage steep change region and the first plateau region based on the first local capacity includes:

[0031] Substitute the first local capacity into the target feature function to obtain the scaling factor.

[0032] In one possible implementation, calculating the State of Health (SOH) of the target battery based on each of the local capacities includes:

[0033] Based on the local capacity of the same partition in each of the charging and / or discharging processes, calculate the local charging capacity and / or local discharging capacity of each target partition within the preset time period;

[0034] Calculate the current retention capacity of the target battery based on the partial charge capacity and / or the partial discharge capacity;

[0035] The state of energy (SOH) of the target battery is obtained by quotienting the retained capacity and the initial retained capacity.

[0036] In one possible implementation, calculating the local charging capacity and / or partial discharging capacity corresponding to each target partition within the preset time period includes:

[0037] When the preset duration is less than the preset value, for each target partition, the average local capacity corresponding to each charging process in the target partition is calculated to obtain the local charging capacity corresponding to the target partition;

[0038] And / or, calculate the average local capacity corresponding to each of the discharge processes when it is in the target partition, so as to obtain the partial discharge capacity corresponding to the target partition.

[0039] In one possible implementation, calculating the local charging capacity and / or partial discharging capacity corresponding to each target partition within the preset time period includes:

[0040] When the preset duration is not less than the preset value, for each target partition, the local charging capacity change pattern is determined based on the local capacity corresponding to each charging process when it is in the target partition.

[0041] Based on the local charging capacity change pattern, predict the local charging capacity of the target partition at the current moment;

[0042] and / or;

[0043] For each target partition, the variation law of partial discharge capacity is determined based on the local capacity corresponding to each discharge process when it is in the target partition;

[0044] Based on the variation pattern of the partial discharge capacity, the partial discharge capacity corresponding to the target partition at the current moment is predicted.

[0045] In one possible implementation, calculating the current retention capacity of the target battery based on the partial charge capacity and / or the partial discharge capacity includes:

[0046] The local charging capacity of each target partition within the preset time period is summed to obtain the total charging capacity within the preset time period.

[0047] The partial discharge capacity of each target partition within the preset time period is summed to obtain the total discharge capacity within the preset time period;

[0048] The retention capacity is obtained by averaging the total charging capacity and the total discharging capacity.

[0049] In one possible implementation, acquiring charge / discharge data of the target battery during at least one charging process and / or at least one discharging process within a preset time period includes:

[0050] Acquire raw data of the target battery within the preset time period; the raw data includes: total charging and discharging current of the actual vehicle, and voltage of individual battery cells during charging and discharging of the actual vehicle.

[0051] The charge / discharge data is obtained by preprocessing the raw data; the preprocessing includes at least one of the following: detection and correction processing, and cleaning processing.

[0052] In one possible implementation, the charge / discharge data is charge / discharge data that meets preset requirements, and the preset requirements are met when the operating condition of the charge / discharge data is any one of the preset operating conditions; the preset operating conditions include: AC charging operating condition.

[0053] Secondly, this application provides a battery health status estimation device, the device comprising:

[0054] The acquisition module is used to acquire charge and discharge data of the target battery during at least one charging process and / or at least one discharging process within a preset time period;

[0055] The first calculation module is used to calculate the local capacity of each partition of the charging or discharging process based on the target charge-discharge data corresponding to each partition of the charging or discharging process for each charging or discharging process; wherein, both the charging and discharging processes include multiple partitions, and different partitions of the same charging / discharging process correspond to different electrochemical reaction stages of the target battery;

[0056] The second calculation module is used to calculate the state of health (SOH) of the target battery based on the local capacity of each of the aforementioned local batteries.

[0057] Thirdly, this application provides an electronic device, including a processor and a memory communicatively connected to the processor;

[0058] The memory stores computer-executed instructions;

[0059] The processor executes computer execution instructions stored in the memory to implement the method as described in any of the first aspects.

[0060] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any of the first aspects.

[0061] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method as described in any of the first aspects.

[0062] This application provides a method, apparatus, device, and storage medium for estimating the state of health (SOH) of a battery. In the method, for any target battery, charge / discharge data corresponding to at least one charging or discharging process within a preset time period is first acquired. Each charging and discharging process includes multiple partitions, and different partitions of the same charging or discharging process correspond to different electrochemical reaction stages of the target battery. Based on this, for each charging or discharging process, the electronic device calculates the local capacity of that partition based on the target charge / discharge data corresponding to each partition. Finally, the electronic device calculates the SOH of the target battery based on the local capacities. The method of this application can analyze the performance of the target battery at different electrochemical reaction stages in greater detail, thereby effectively reducing errors caused by the nonlinear characteristics and aging of the target battery, and thus helping to ensure the accuracy of the SOH. Attached Figure Description

[0063] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0064] Figure 1 This is an application scenario diagram of a battery health state estimation method provided in an embodiment of this application;

[0065] Figure 2 A flowchart illustrating a battery health state estimation method provided in this application embodiment. Figure 1 ;

[0066] Figure 3 A flowchart illustrating a battery health state estimation method provided in this application embodiment. Figure 2 ;

[0067] Figure 4 A schematic diagram of a segmentation scheme provided for an embodiment of this application;

[0068] Figure 5 This application provides a schematic diagram of a process for determining the platform voltage.

[0069] Figure 6 A partitioning diagram provided for an embodiment of this application;

[0070] Figure 7 A flowchart illustrating a battery health state estimation method provided in this application embodiment. Figure 3 ;

[0071] Figure 8 A schematic diagram illustrating a battery health state estimation method provided in this application embodiment;

[0072] Figure 9This is a schematic diagram of the structure of a battery health state estimation system provided in an embodiment of this application;

[0073] Figure 10 An algorithm verification diagram for a battery health state estimation method provided in an embodiment of this application;

[0074] Figure 11 This is a schematic diagram of the structure of a battery health state estimation device provided in an embodiment of this application;

[0075] Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0076] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0077] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0078] With the increasing popularity of electric vehicles, battery state of health (SOH) assessment has become particularly important. SOH is a key indicator for measuring the current health of energy storage batteries. It reflects the ratio of battery capacity to its initial capacity and is a direct manifestation of battery performance degradation. By estimating the battery's SOH, signs of battery aging, such as capacity loss and increased internal resistance, can be detected in a timely manner, facilitating appropriate maintenance measures to extend the battery's lifespan.

[0079] Currently, known technologies estimate a battery's SOH (State of Charge) based on the relationship between battery voltage and SOC. Specifically, this method relies on the battery's voltage variation characteristics at different SOCs, and infers the battery's SOH by comparing the characteristic curves of aged and new batteries.

[0080] For example, a method and apparatus for estimating battery health status are provided in the known art, wherein the method includes:

[0081] Step 1: Based on the preset battery capacity of the mth digit and the partial charge or discharge capacity of each SOC within its SOC range, calculate the first dV / dSOC data for each SOC under the preset battery capacity of the mth digit; where m is a positive integer less than or equal to M.

[0082] Step 2: Calculate the second dV / dSOC data corresponding to each SOC based on the pre-stored dV-dSOC characteristic function. The dV-dSOC characteristic function is obtained by charging or discharging the target battery in the new battery state with a preset current. The preset current is no greater than 1 / 20QBOL, where QBOL represents the retention capacity of the target battery in the new battery state.

[0083] Step 3: Based on the first dV / dSOC data and the second dV / dSOC data corresponding to each SOC at the preset battery capacity m, calculate the overall dV / dSOC data deviation of multiple SOCs; determine the smallest overall dV / dSOC data deviation from all overall dV / dSOC data deviations; determine the retention capacity after aging the preset battery capacity corresponding to the smallest overall dV / dSOC data deviation; divide the retention capacity of the target battery after aging by the retention capacity of the target battery in the new battery state to obtain the SOH of the target battery.

[0084] In the above process, for step one, since the current SOH of the battery is unknown, the initial SOC is incorrect. Therefore, the first dV / dSOC data for each SOC at the preset battery capacity of the m-th cell will inevitably be misaligned. Furthermore, to obtain stable dV / dSOC data, the battery needs to be left to stand at each SOC for an extended period, a condition that cannot be met in practical applications, resulting in a large dV / dSOC error.

[0085] Furthermore, if step one is performed at the full charge and discharge time, i.e., SOC=0 or SOC=100, then for batteries like lithium iron phosphate that have a long voltage plateau period, there will be a long-term situation where dV / dSOC=0. Therefore, step one is difficult to perform in a short charge or discharge segment, while in practical applications there are many scenarios involving short charge or discharge segments.

[0086] For step two, the use of the pre-stored dV-dSOC feature function is based on laboratory test data. In actual application scenarios, the battery operates under complex conditions and has a variety of aging paths. The laboratory test environment cannot reproduce the actual application scenarios. Furthermore, the model trained based on laboratory test data is prone to significant errors in predicting SOH compared to the SOH in actual application scenarios.

[0087] Furthermore, for step three, since the dV / dSOC data obtained in step one and the dV / dSOC data obtained in step two are unreliable, the accuracy of the SOH obtained in this step cannot be guaranteed.

[0088] To address the aforementioned problems, this application provides a method, apparatus, device, and storage medium for estimating the state of health (SOH) of a battery. Specifically, the method of this application is executed by any electronic device. The electronic device first acquires charge-discharge data corresponding to at least one charging and / or discharging process of the target battery within a preset time period. For any charging or discharging process, based on the target charge-discharge data corresponding to each zone, the local capacity of the corresponding zone is calculated. Finally, the electronic device calculates the current retention capacity of the target battery based on each local capacity, and determines the SOH of the target battery based on the retention capacity. The zones of the same charging and discharging process are divided based on different electrochemical reaction stages of the target battery during the process.

[0089] Through the above process, when calculating SOH, electronic devices can analyze the performance of the target battery under different operating conditions in more detail and accurately reflect the actual capacity of the target battery in different operating ranges. This allows them to capture the characteristic changes of the target battery under different SOCs, effectively reduce errors caused by battery nonlinear characteristics and aging, and thus help ensure the accuracy of SOH.

[0090] It is understood that the method of this application is applicable to any use case of batteries that requires SOH estimation, such as in operating electric vehicles, electric ships, drones, energy storage systems, backup power supplies, etc. For example, Figure 1 This is an application scenario diagram of a battery health state estimation method provided in the embodiments of this application, such as... Figure 1 As shown, the method of this application can be used in a running electric vehicle. Specifically, the method of this application is executed by the battery management system (BMS) of the battery, or by any controller with computing power, either on or outside the electric vehicle; this embodiment does not limit this. In this embodiment, as... Figure 1 As shown, the method for estimating battery health status is specifically implemented through the battery's BMS.

[0091] In this application scenario, the BMS acquires charge and discharge data corresponding to at least one charging and / or discharging process within a preset time period prior to the current moment. For each charging or discharging process, based on the target charge and discharge data within its corresponding partition, the BMS calculates the local capacity of that partition. Finally, the BMS calculates the retention capacity based on each local capacity and further calculates the state of equilibrium (SOH) based on the retention capacity.

[0092] Based on the above settings, when estimating SOH, the BMS calculates the local capacity corresponding to each partition of each charging and / or discharging process within a preset time period, fully considering the different states of the battery at different electrochemical reaction stages, reducing the error caused by the different states at different electrochemical reaction stages, and thus obtaining a more accurate SOH when further estimating SOH based on these local capacities.

[0093] Understandable, Figure 1 The BMS shown is independent of the battery setup. In practical applications, the BMS can also be integrated inside the battery, but this embodiment does not limit this.

[0094] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Where the embodiments do not conflict, the following embodiments and features can be combined with each other. It is worth noting that any electronic device is used as the execution subject in the following detailed description.

[0095] This application provides a method for estimating battery health status. Figure 2 A flowchart illustrating a battery health state estimation method provided in this application embodiment. Figure 1 ,like Figure 2 As shown, the method provided in this application embodiment includes:

[0096] S201, acquire charge and discharge data corresponding to at least one charging process and / or at least one discharging process of the target battery within a preset time period.

[0097] It is understood that the target battery is any energy storage battery in use; for example, the target battery is specifically a battery in an electric vehicle.

[0098] In this embodiment, the electronic device is configured with a preset duration, specifically set based on user needs. Within the preset duration, the target battery in use includes at least one charging process and / or at least one discharging process. For example, for a battery used in an electric vehicle, the preset duration may involve scenarios such as charging and discharging while driving, thus corresponding to at least one charging process and / or discharging process.

[0099] Therefore, in this embodiment, the electronic device acquires charge / discharge data corresponding to at least one charging process and / or at least one discharging process within a preset time period. Specifically, for the charging process, the charge / discharge data is charging data, and for the discharging process, it is discharging data.

[0100] It is understood that the electronic device can respond to user requests by triggering the acquisition of charge / discharge data corresponding to at least one charging process and / or at least one discharging process within a preset duration, for subsequent estimation of the current State of Health (SOH). Alternatively, it can trigger the acquisition of charge / discharge data corresponding to at least one charging process and / or at least one discharging process within a preset duration at the end of each detection cycle. The specific detection cycle can be set by the user or determined by the electronic device based on the application scenario. In this embodiment, the timing of acquiring charge / discharge data within the preset duration for subsequent SOH calculation is not limited, as long as the preset duration is the same as the preset duration prior to the current moment.

[0101] As one possible implementation, electronic devices acquire charge and discharge data in the following ways:

[0102] Acquire raw data of the target battery within a preset time period; the raw data includes: total charging and discharging current of the actual vehicle, and the voltage of individual battery cells during charging and discharging of the actual vehicle.

[0103] After preprocessing the raw data, charge and discharge data are obtained; the preprocessing includes at least one of the following: detection and correction processing, and cleaning processing.

[0104] Specifically, in the case of batteries used in electric vehicles, electronic devices acquire and store raw data such as the total charging and discharging current and the voltage of individual battery cells during real-time operation of the vehicle within a preset time period through communication media such as vehicle hardware storage and transmission protocols.

[0105] Furthermore, the electronic device performs preprocessing on the raw data, such as detection and correction processing and cleaning processing, to obtain charging and discharging data. Specifically, the cleaning processing is used to clean up damaged, inaccurate, or unsuitable data for modeling, including cleaning the headers, time series, missing values, erroneous data, units, duplicate values, and data types of the raw data.

[0106] The above settings make the charge and discharge data cleaner and more standardized. Using the charge and discharge data obtained in this process for subsequent SOH calculations will help to obtain more accurate SOH.

[0107] Furthermore, as another possible implementation, the charge / discharge data is charge / discharge data that meets preset requirements, and the operating conditions of the charge / discharge data meet the preset requirements when any one of the preset operating conditions is met; the preset operating conditions include: AC charging condition, urban road discharging condition, highway constant speed cruise condition, etc. It can be understood that the charging and discharging processes of the target battery under the preset operating conditions can be clearly divided into zones.

[0108] In this embodiment, to ensure the accuracy of the solution, the charge and discharge data are further limited to charge and discharge data that meet preset requirements, that is, the charge and discharge data are limited to data obtained based on the charging process and / or the discharging process under preset operating conditions.

[0109] Specifically, when electronic devices acquire raw data, they determine whether the raw data meets preset requirements based on the operating conditions to which the raw data belongs. For raw data that meets the preset requirements, further preprocessing is performed, thereby saving the processing of unnecessary data and improving overall computing efficiency.

[0110] It is understandable that in practical applications, electronic devices can also preprocess the raw data to obtain charge and discharge data, and then further filter the charge and discharge data that meet the requirements based on the operating conditions to which each charge and discharge data belongs. This embodiment does not limit this.

[0111] S202, for each charging or discharging process, calculate the local capacity of the partition based on the target charging and discharging data corresponding to each partition of the charging or discharging process.

[0112] Both the charging and discharging processes include multiple zones, and different zones of the same charging / discharging process correspond to different electrochemical reaction stages of the target battery.

[0113] S203, calculate the State of Health (SOH) of the target battery based on the capacity of each local area.

[0114] It is understandable that each charging or discharging process of the target battery can be divided into a certain number of zones according to different electrochemical reaction stages. For example, for lithium iron phosphate batteries, both the charging and discharging processes include three electrochemical reaction stages: the first stage: LiC24, the second stage: LiC12, and the third stage: LiC6. Accordingly, the charging and discharging processes of lithium iron phosphate batteries can be divided into three zones.

[0115] It is understandable that, for each charging or discharging process, once the partition is determined, the target charging and discharging data corresponding to each partition can be further determined, and the target charging and discharging data is a part of the charging and discharging data.

[0116] In this embodiment, for each partition of each charging or discharging process, the electronic device calculates the local capacity corresponding to that partition based on the target charge / discharge data for that partition. Furthermore, the electronic device determines the current state of equilibrium (SOH) of the target battery based on the local capacity of each partition.

[0117] Specifically, based on the local capacity of each partition, the electronic device can determine the current holding capacity of the target battery, and further, the SOH can be calculated based on the holding capacity.

[0118] In the method provided in this embodiment, the electronic device acquires charge and discharge data corresponding to at least one charging and / or discharging process of the target battery within a preset time period. For each charging or discharging process, the local capacity of that region is calculated based on the target charge and discharge data within its corresponding region. Finally, the retention capacity is calculated based on each local capacity, and the state of equilibrium (SOH) is further calculated based on the retention capacity.

[0119] The method in this embodiment ensures that the final SOH is obtained under the premise of fully considering the different states of the target battery at different electrochemical reaction stages and conforming to the battery aging path as much as possible, thereby reducing the error caused by the different states at different electrochemical reaction stages and thus obtaining a more accurate SOH.

[0120] Furthermore, Figure 3 A flowchart illustrating a battery health state estimation method provided in this application embodiment. Figure 2 This application provides a detailed description of the partitioning process based on the foregoing embodiments. For example... Figure 3 As shown, the method in this embodiment includes:

[0121] S301, perform segmentation processing on the charge and discharge data to obtain the charging data corresponding to each charging process, and / or the discharge data corresponding to each discharging process.

[0122] In this embodiment, the electronic device acquires a charge / discharge flag bit, and performs segmentation processing on the charge / discharge data based on the charge / discharge flag bit to obtain at least one piece of charging data and / or at least one piece of discharging data, corresponding to at least one charging process and / or at least one discharging process.

[0123] As is understandable, the charge / discharge flag is a flag in the BMS or other power management system used to indicate the current state of the battery. This flag determines whether the battery is charging or discharging, and is specifically set and updated by the BMS or other power management system based on information such as current direction and charger connection status. Electronic devices obtain this charge / discharge flag by interacting with the target battery's BMS or other power management system.

[0124] Specifically, Figure 4 A schematic diagram of segmentation provided for an embodiment of this application, such as... Figure 4 As shown, the charging and discharging data within a preset time period is segmented according to the charging and discharging flag bits to obtain 3 pieces of charging data and 3 pieces of discharging data in the figure, which correspond to 3 charging processes and 3 discharging processes respectively. Figure 4 In the middle, the long dashed line is used to identify the charge / discharge flag.

[0125] S302 uses a preset algorithm to perform noise reduction processing for each charging data and each discharging data to obtain the target voltage information for the corresponding charging or discharging process.

[0126] The target voltage information is used to indicate voltage changes.

[0127] In this embodiment, the target voltage information corresponding to the charging or discharging process is specifically a target voltage curve, obtained based on the charging or discharging data of that process. Specifically, the electronic device performs noise reduction processing on each charging and discharging data point to obtain the corresponding target voltage curve. In this embodiment, the preset algorithm can be any of the following: Savitzky-golay convolution (SG) smoothing algorithm, Kalman filter algorithm, moving average algorithm, wavelet transform algorithm, etc., as long as it can perform noise reduction processing on the voltage curve obtained from the charging and discharging data. This embodiment does not limit this specific algorithm.

[0128] Specifically, this embodiment uses the SG smoothing algorithm as an example to explain in detail the process of noise reduction processing for charging and discharging data:

[0129] For each charging or discharging data point, based on the SG smoothing algorithm, the width of the filtering window is first set to x, and the measurement points are x = (-m, -m+1, ..., 0, 1, ..., m-1, m). A K-1 degree polynomial is used to fit the data within the window to obtain a K-1 degree equation. Based on the K-1 degree equation, a system of N equations consisting of K linear equations is formed. Finally, the least squares method is used to obtain the matrix operator, and the smoothed output value is solved to obtain the corresponding target voltage curve.

[0130] In the above process, the electronic device acquires the charge / discharge flag of the target battery and slices the charge / discharge data within a preset time period to obtain the charging data corresponding to each charging process and / or the discharging data corresponding to each discharging process. Furthermore, the electronic device performs noise reduction processing on the charging and / or discharging data to obtain the corresponding target voltage curve, thereby reducing the influence of various noise sources received during data acquisition. This makes the obtained target voltage curve more accurate, which in turn helps ensure the accuracy of subsequent SOH calculations.

[0131] S303 performs probability density function (PDF) analysis based on the corresponding target voltage information for each charging or discharging process to obtain the peak value of the PDF.

[0132] S304, based on the peak value, divide the charging or discharging process into a preset number of partitions corresponding to the charging or discharging process.

[0133] In this embodiment, the electronic device uses PDF analysis of target voltage information to determine the plateau voltage during the charging or discharging process for partitioning. In practical applications, the electronic device can also use differential analysis, cluster analysis, machine learning methods, etc., to determine the plateau voltage; this embodiment does not limit this approach.

[0134] Understandably, when determining the plateau voltage of a target battery using any of the methods described above, the specific number of plateau voltages is related to the chemical composition and design of the target battery. For example, a lithium iron phosphate battery typically has two plateau voltages to divide the charging or discharging process into three zones, corresponding to the three electrochemical reaction stages of the lithium iron phosphate battery during that charging or discharging process. A lead-acid battery, on the other hand, usually has only one plateau voltage, while a sodium-sulfur battery typically has multiple plateau voltages.

[0135] Specifically, as an example, when the target battery is a lithium iron phosphate battery, the process of dividing the charging and discharging processes based on PDF is explained in detail as follows:

[0136] Step 1: Based on the voltages corresponding to the two target peaks within the peak values, determine the two plateau voltages of the target battery during the charging or discharging process. The target peak values ​​are both greater than the other peak values ​​within the peak values.

[0137] Step 2: Based on the two plateau voltages, the charging or discharging process of the target battery is divided into three regions: the voltage steep change region, the first plateau region, and the second plateau region.

[0138] It is understood that lithium iron phosphate batteries contain two plateau voltages. Therefore, in this embodiment, the electronic device uses the voltages corresponding to the two largest target peaks among the peak values ​​as the two plateau voltages. Furthermore, the electronic device divides the corresponding charging or discharging process into three zones based on these two plateau voltages.

[0139] Specifically, for each charging or discharging process, a PDF analysis is performed on the corresponding target voltage curve to obtain the probability that the voltage random variable falls within each value interval. The voltage is then divided into intervals using dv, resulting in the entire voltage interval being divided into n parts from a preset range. The preset range indicates the minimum and maximum voltage limits and can be set based on requirements, for example, 2.0-3.7V. Based on this, the probability of each part within the entire voltage interval is:

[0140] P(v≤x≤v+dv)=count(v, v+dv) / sum(2.0, 3.7)*100

[0141] Where count(v, v+dv) is the number of data points contained in the dv sampling space, and sum(2.0, 3.7) is the number of data points in the entire sampling space.

[0142] Furthermore, the voltages corresponding to the two largest peaks of the obtained probability distribution function are determined as the plateau voltages of the target battery, and the curve is divided into three regions based on these two plateau voltages: the voltage steep change region, the first plateau region, and the second plateau region.

[0143] In this embodiment, the electronic device specifically uses probability density function analysis to achieve partitioning, which facilitates the identification of multiple platform voltages and is therefore applicable to partitioning different batteries, while also offering advantages in flexibility and robustness. Furthermore, based on the characteristics of lithium iron phosphate batteries, the electronic device can intuitively distinguish the three partitions through probability density function analysis.

[0144] Specifically, Figure 5 This is a schematic diagram illustrating a process for determining the platform voltage, provided in an embodiment of this application. Figure 6 This is a schematic diagram of a partition provided for an embodiment of this application. Figure 5 The target voltage curve of a discharge process shown in (A) is analyzed using PDF, and the results are as follows: Figure 5 The result shown in (B) yields two plateau voltages. The electronic device is based on... Figure 5 The two obtained plateau voltages are used to partition the discharge process, resulting in the following: Figure 6 The three partitions are shown.

[0145] This application further provides a method for calculating the local capacity corresponding to each partition, and a method for calculating the State of Health (SOH) based on each local capacity. Figure 7 A flowchart illustrating a battery health state estimation method provided in this application embodiment. Figure 3 ,like Figure 7 As shown in the embodiments of this application, the method for calculating the local capacity corresponding to each partition in each charging or discharging process includes:

[0146] S701 calculates the first local capacity of the first plateau region and the second local capacity of the second plateau region based on the ampere-hour integration algorithm.

[0147] Specifically, in this embodiment, for each charging or discharging process, when the electronic device calculates the first local capacity of the first platform region and the second local capacity of the second platform region, it first determines whether the target voltage information corresponding to the first platform region or the second platform region indicates whether the target charging and discharging data of the first platform region or the second platform region is complete.

[0148] Furthermore, if the target voltage information indicates that the target charge and discharge data of the first or second plateau region is complete, then the first local capacity of the first plateau region and the second local capacity of the second plateau region are directly calculated based on the ampere-hour integration algorithm.

[0149] If the target voltage information indicates that the target charge / discharge data for the first or second platform region is incomplete, the corresponding historical local capacity is obtained; the historical local capacity is the local capacity of the first or second platform region calculated based on the ampere-hour integration algorithm in the last time; the local capacity of the first or second platform region is determined as the corresponding historical local capacity.

[0150] More specifically, in this embodiment, for each charging or discharging process, when calculating the local capacity of the first and second plateau regions, the electronic device first determines, based on the target voltage curve, whether the target voltage curve covers the entire range of the current partition, and then determines whether the target charge / discharge data of the first or second plateau region is complete. It can be understood that if the target voltage curve covers the entire range of the first plateau region, the target charge / discharge data of the first plateau region is determined to be complete; otherwise, the target charge / discharge data of the first plateau region is determined to be incomplete.

[0151] Furthermore, when the electronic device determines that the target charge / discharge data for the first or second platform region is complete, it calculates the first or second local capacity based on the ampere-hour integration algorithm. The ampere-hour integration algorithm is expressed as: Among them, t 起始 and t 终止 These are used to indicate the start and end times of the current charging or discharging process, respectively, and are determined based on the charge / discharge flag bits. q represents the first or second local capacity, calculated using the charge / discharge current of the integrated current.

[0152] When the electronic device determines that the target charge / discharge data for the first or second platform region is incomplete, it obtains the historical local capacity of the first or second platform region calculated based on the ampere-hour integration algorithm and uses this historical local capacity as the local capacity of the corresponding platform region in the current operation.

[0153] Through the above process, for partitions containing incomplete target charge and discharge data, considering the slow change in battery capacity, the historical local capacity of the previous partition can be used as the local capacity for the current time to ensure the integrity of the data used to calculate the local capacity. This solves the problem that it is difficult to extract the partition capacity completely in some charge and discharge conditions, thereby improving the accuracy of the final calculation results.

[0154] S702, based on the first local capacity, calculate the third local capacity of the voltage steep change region.

[0155] It is understandable that in practical applications, there are some operating conditions that make it difficult to obtain the local capacity of the voltage steep change region, or the obtained local capacity has a large error. Therefore, based on the electrochemical reaction principle of lithium iron phosphate batteries, this embodiment proposes to calculate the third local capacity of the voltage steep change region by using the first local capacity, making the method of this embodiment more in line with actual application scenarios and ensuring the accuracy of SOH calculation in actual use.

[0156] Specifically, as one possible implementation, the electronic device calculates the third local capacity through the following process:

[0157] Based on the first local capacity, obtain the ratio coefficient between the voltage steep change region and the first plateau region;

[0158] The third local capacity of the voltage steep change region is calculated based on the proportionality coefficient; the proportionality coefficient is determined based on the electrochemical reaction ratios corresponding to the first plateau region, the second plateau region, and the voltage steep change region.

[0159] More specifically, the electronic device substitutes the first local capacity into the target characteristic function to obtain the scaling factor.

[0160] The target characteristic function is determined based on the following process: a target characteristic function for the first local capacity and the scaling factor is established based on laboratory data and any one of the preset characteristic functions; the laboratory data includes experimental charge and discharge data of the target battery; the preset characteristic functions include: sixth-order Fourier function, polynomial function, and exponential function.

[0161] It is understandable that for lithium iron phosphate batteries, the first local capacity of the first plateau region and the third local capacity of the voltage steep change region have the following proportional relationship: W = q1 / (q1 + q2), where W represents the proportionality coefficient, q1 represents the third local capacity of the voltage steep change region, and q2 represents the first local capacity of the first plateau region.

[0162] Furthermore, in this embodiment of the application, when fitting the target feature function using a fitting tool, the sixth-order Fourier function is used as the basis, combined with laboratory data for fitting. Specifically:

[0163] The sixth-order Fourier function is expressed as: Where q2 is the independent variable of the target feature function, specifically the first local capacity of the first plateau region indicated in the laboratory data; j represents the order, a0, a j and b jThese are the coefficients of each term; sin() represents the sine function; cos() represents the cosine function; ω represents the frequency. It can be understood that g0(q2) is W, expressed as q1 / (q1+q2). Substituting the first and third local capacities indicated by the laboratory data into the aforementioned sixth-order Fourier function, we obtain the values ​​of the other parameters besides the variables q2 and g0(q2), thus obtaining the target characteristic function used to indicate the g0(q2)-q2 mapping relationship.

[0164] Based on this, when calculating the third local capacity of the voltage steep change region, the electronic device substitutes the first local capacity of the first plateau region in actual application into q2 of the above-mentioned target characteristic function to obtain g0(q2), that is, W. Further, the electronic device substitutes W and q2 into W = q1 / (q1+q2) to obtain the third local capacity q1.

[0165] It is understood that in practical applications, the target feature function can also be determined based on other preset feature functions, and this embodiment does not limit this.

[0166] In the above process, the electronic device calculates the local capacity of the voltage steep change region by combining the relationship between the first plateau region and the voltage steep change region. This can compensate for the difficulty in accurately obtaining the voltage steep change region in practical applications, thus obtaining a more accurate SOH estimation result, and the calculation process is simple. Furthermore, determining the relationship between the proportional coefficient and the first local capacity through laboratory data and the sixth-order Fourier function is beneficial to further improve the accuracy of the SOH estimation value.

[0167] S703, based on the local capacity of the same partition in each charging process and / or each discharging process, calculates the local charging capacity and / or partial discharging capacity corresponding to each target partition within a preset time period.

[0168] Understandably, when calculating the current State of Harmony (SOH) of the target battery based on the local capacity of each partition, it is necessary to first calculate the current retention capacity of the target battery based on the local capacity of each partition. Retention capacity specifically refers to the effective capacity that the battery can still retain after a certain period of storage or cycling, and is obtained based on the battery capacity after a certain period or cycle.

[0169] In this embodiment, when calculating the retention capacity, the electronic device combines the local capacity of each partition during each charging and / or discharging process within a preset time period to obtain an accurate retention capacity. Specifically, in this embodiment, for each target partition, the local charging capacity corresponding to the target partition is calculated based on the local capacity of each charging process in the target partition, and the local discharging capacity corresponding to the target partition is calculated based on the local capacity of each discharging process in the target partition.

[0170] It is understandable that the number of local capacities within the same zone during each charging and / or discharging process is consistent with the number of charging and / or discharging processes within the preset duration. The local charging capacity or partial discharging capacity is consistent with the number of zones, meaning that each zone corresponds to a total local charging and / or discharging capacity.

[0171] As a design, when the preset duration is less than the preset value, for each target partition, the average local capacity corresponding to each charging process when it is in the target partition is calculated to obtain the local charging capacity corresponding to the target partition.

[0172] And / or, calculate the average local capacity corresponding to each discharge process when it is in the target zone, so as to obtain the partial discharge capacity corresponding to the target zone.

[0173] As another design, when the preset duration is not less than the preset value, for each target partition, the local charging capacity change pattern is determined based on the local capacity corresponding to each charging process when it is in the target partition; based on the local charging capacity change pattern, the local charging capacity corresponding to the target partition at the current moment is predicted.

[0174] And / or; for each target partition, based on the local capacity corresponding to each discharge process when it is in the target partition, determine the law of change of partial discharge capacity; based on the law of change of partial discharge capacity, predict the partial discharge capacity corresponding to the target partition at the current time.

[0175] In this embodiment, the preset value is set based on experience, for example, it can be five days. Accordingly, the preset duration includes 5 charging processes and 3 discharging processes.

[0176] Based on this, if the preset duration is less than five days, for any target zone among the three zones of the first plateau zone, the second plateau zone, and the voltage steep change zone, the electronic device performs average processing on the local capacity of each charging process in that target zone to obtain the local charging capacity corresponding to the target zone. Similarly, the corresponding partial discharge capacity can be obtained.

[0177] If the preset duration is not less than five days, then for any target zone among the three zones of the first plateau zone, the second plateau zone, and the voltage steep change zone, the electronic device determines the local charging capacity change pattern based on the local capacity of each charged zone in that target zone, and predicts the local charging capacity at the current moment based on the local charging capacity change pattern. Similarly, the corresponding partial discharge capacity can be obtained.

[0178] The method in this embodiment allows for the direct averaging of partial charging and / or partial discharging capacities when the user-set preset duration is short, avoiding complex calculations and thus ensuring computational efficiency. When the user-set preset duration is long, predicting the current partial charging or partial discharging capacity based on the local capacity within the preset duration helps obtain a more accurate retention capacity.

[0179] S704 calculates the current retention capacity of the target battery based on the partial charge capacity and / or partial discharge capacity.

[0180] In this embodiment, after obtaining the partial charge capacity and / or partial discharge capacity, the electronic device further calculates the current retention capacity of the target battery based on it.

[0181] Specifically, as a design, the electronic device sums up the partial charging capacity of each target partition within a preset time period to obtain the total charging capacity within the preset time period; and sums up the partial discharge capacity of each target partition within a preset time period to obtain the total discharge capacity within the preset time period.

[0182] Furthermore, the electronic device averages the total charging capacity and the total discharging capacity to obtain the retention capacity.

[0183] It is understandable that the total charging capacity is the sum of the local charging capacities of each target zone, and its quantity is one. Similarly, the total discharging capacity is also one.

[0184] In practical applications, electronic devices may use only the total charging capacity or the total discharging capacity as the retention capacity, and this embodiment does not limit this.

[0185] S705, the retention capacity and the initial retention capacity are quotiented to obtain the SOH of the target battery.

[0186] It is understandable that the initial retention capacity is the nominal capacity of the target battery in its brand-new state. The electronic device can obtain the current state of equilibrium (SOH) of the target battery by quotienting the current retention capacity and the initial retention capacity.

[0187] In this embodiment, the retention capacity is determined based on the total charging capacity and the total discharging capacity, which allows for a more comprehensive evaluation of the overall performance of the target battery. Furthermore, by taking the average value, some measurement errors or fluctuations that may occur during the charging or discharging process can be smoothed out, resulting in a more stable and reliable retention capacity value, and thus a more accurate State of Harmony (SOH).

[0188] As an example, Figure 8 This is a schematic diagram illustrating a battery health state estimation method provided in an embodiment of this application, such as... Figure 8As shown, the electronic device first acquires raw data of the target battery within a preset time period. Next, it preprocesses the raw data and filters out charge / discharge data that meets the requirements. Further, the electronic device divides each charging and / or discharging process of the target battery within the preset time period into three zones: a voltage steep change zone, a first plateau zone, and a second plateau zone. Finally, the electronic device calculates the local capacity of each zone, calculates the retention capacity based on each local capacity, and calculates the current state of equilibrium (SOH) of the target battery based on the retention capacity.

[0189] It is understood that electronic devices are equipped with a battery health status estimation system, and the electronic devices can implement the methods involved in the aforementioned method embodiments through this battery health status estimation system. Figure 9 This is a schematic diagram of the structure of a battery health state estimation system provided in an embodiment of this application, as shown below. Figure 9 As shown, the battery health estimation system may include a battery detection unit, a control unit, a charge / discharge execution unit, and a memory chip.

[0190] Specifically, the battery detection unit is used to detect and collect the status data of each battery cell in the battery pack in real time, and upload the acquired status data to the control unit. More specifically, the battery detection unit includes a current acquisition module, a voltage acquisition module, and a temperature acquisition module. The current acquisition module and the voltage acquisition module both include sampling chips and connecting harnesses. The current acquisition module is used to acquire the bus voltage, and the voltage acquisition module is used to acquire the voltage of each battery cell. The temperature acquisition module includes a temperature sampling chip and a temperature sensor, used to acquire the temperature of each battery cell.

[0191] The control unit receives state data detected by the battery detection unit according to the sampling step size. This state data includes the aforementioned current, voltage, and temperature. More specifically, the control unit includes a target operating condition identification module, a coulomb counter module, an inflection point identification module, and a health state estimation module. The target operating condition identification module determines whether to calculate the state of equilibrium (SOH) for the current operating condition; the coulomb counter module calculates the charge / discharge capacity; the inflection point identification module identifies the charge / discharge inflection point; and the health state estimation module estimates the current SOH. The control unit then stores the results output by the target operating condition identification module, the coulomb counter module, the inflection point identification module, and the health state estimation module, along with the received raw data, into the memory chip.

[0192] In addition to storing the above-mentioned contents, the memory chip is also used to store the preset duration, target feature function, etc. involved in the aforementioned method embodiments.

[0193] Understandably, the charge / discharge actuator is used to perform the charging and discharging of the battery.

[0194] It is worth noting that in the diagram, solid arrows indicate data flow, while dashed arrows indicate signal flow. P+ and P- represent the positive and negative terminals of the battery pack, respectively, and L1, L2, and L3 represent the circuits used to collect current, temperature, and voltage data, respectively.

[0195] As an example, Figure 10 This diagram illustrates the algorithm verification of a battery health state estimation method provided in this application. In the diagram, the darker dots represent the predicted capacity obtained based on the battery health state estimation method of this application, corresponding to the retained capacity in the aforementioned method embodiments; the darker dots represent the measured capacity during actual vehicle operation. Figure 10 It can be seen that the predicted capacity obtained by the method of this application is basically consistent with the actual measured capacity. Furthermore, it can be seen that the SOH calculated based on the predicted capacity is basically consistent with the actual SOH.

[0196] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0197] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0198] The above embodiments introduce a battery health state estimation method from the perspective of process flow. The following embodiments introduce a battery health state estimation device from the perspective of virtual module or virtual unit. For details, please refer to the following embodiments.

[0199] Figure 11 This is a schematic diagram of the structure of a battery health state estimation device provided in an embodiment of this application, as shown below. Figure 11 As shown, in this embodiment, the battery health status estimation device may include:

[0200] The acquisition module 111 is used to acquire charge and discharge data of the target battery during at least one charging process and / or at least one discharging process within a preset time period;

[0201] The first calculation module 112 is used to calculate the local capacity of each partition for each charging or discharging process based on the target charging and discharging data corresponding to each partition of the charging or discharging process; wherein, both the charging and discharging processes include multiple partitions, and different partitions of the same charging / discharging process correspond to different electrochemical reaction stages of the target battery;

[0202] The second calculation module 113 is used to calculate the state of health (SOH) of the target battery based on the local capacity.

[0203] In one possible implementation of this application embodiment, the apparatus further includes a partitioning module (not shown in the figure), which is specifically used for:

[0204] For each charging or discharging process, probability density function (PDF) analysis is performed based on the corresponding target voltage information to obtain the peak value of the PDF; the target voltage information is used to indicate voltage changes.

[0205] Based on the peak value, the charging or discharging process is divided into a preset number of partitions corresponding to the charging or discharging process.

[0206] In one possible implementation of this application embodiment, the partitioning module is specifically used for:

[0207] Based on the voltages corresponding to the two target peaks in the peak values, determine the two plateau voltages of the target battery during the charging or discharging process; the target peak values ​​are all greater than the other peak values ​​in the peak values.

[0208] Based on the two plateau voltages, the charging or discharging process of the target battery is divided into three regions: the voltage steep change region, the first plateau region, and the second plateau region.

[0209] In one possible implementation of this application embodiment, the apparatus further includes a processing module (not shown in the figure), which is specifically used for:

[0210] The charging and discharging data are segmented to obtain the charging data corresponding to each charging process, and / or the discharging data corresponding to each discharging process;

[0211] For each charging data point and each discharging data point, a preset algorithm is used for noise reduction to obtain the target voltage information for the corresponding charging or discharging process.

[0212] In one possible implementation of this application embodiment, the first calculation module 112 is specifically used for:

[0213] Based on the ampere-hour integral algorithm, the first local capacity of the first plateau region and the second local capacity of the second plateau region are calculated.

[0214] Based on the first local capacity, the third local capacity partition of the voltage steep change region is calculated.

[0215] In one possible implementation of this application embodiment, the first calculation module 112 is further configured to:

[0216] When the target voltage information indicates that the target charge and discharge data of the first or second plateau region is incomplete, the corresponding historical local capacity is obtained; the historical local capacity is the local capacity of the first or second plateau region calculated based on the ampere-hour integration algorithm in the last time.

[0217] The local capacity of the first platform area or the second platform area is determined as the corresponding historical local capacity.

[0218] In one possible implementation of this application embodiment, the first calculation module 112 is specifically used for:

[0219] Based on the first local capacity, obtain the ratio coefficient between the voltage steep change region and the first plateau region;

[0220] The third local capacity of the voltage steep change region is calculated based on the proportionality coefficient; the proportionality coefficient is determined based on the electrochemical reaction ratios corresponding to the first plateau region, the second plateau region, and the voltage steep change region.

[0221] In one possible implementation of this application embodiment, the first calculation module 112 is further configured to:

[0222] Based on laboratory data and any one of the preset characteristic functions, establish the target characteristic function of the first local capacity and the scaling factor; the laboratory data includes the experimental charge and discharge data of the target battery; the preset characteristic functions include: sixth-order Fourier function, polynomial function, and exponential function.

[0223] The first calculation module 112 is specifically used for:

[0224] Substitute the first local capacity into the target characteristic function to obtain the scaling factor.

[0225] In one possible implementation of this application embodiment, the second calculation module 113 is specifically used for:

[0226] Based on the local capacity of the same partition during each charging process and / or each discharging process, calculate the local charging capacity and / or partial discharging capacity of each target partition within a preset time period.

[0227] Calculate the current retention capacity of the target battery based on the partial charge capacity and / or partial discharge capacity;

[0228] The state of energy (SOH) of the target battery is obtained by quotienting the retained capacity and the initial retained capacity.

[0229] In one possible implementation of this application embodiment, the second calculation module 113 is specifically used for:

[0230] When the preset duration is less than the preset value, for each target partition, the average local capacity corresponding to each charging process when it is in the target partition is calculated to obtain the local charging capacity corresponding to the target partition.

[0231] And / or, calculate the average local capacity corresponding to each discharge process when it is in the target zone, so as to obtain the partial discharge capacity corresponding to the target zone.

[0232] In one possible implementation of this application embodiment, the second calculation module 113 is specifically used for:

[0233] When the preset duration is not less than the preset value, for each target partition, the local charging capacity change pattern is determined based on the local capacity corresponding to each charging process when it is in the target partition.

[0234] Based on the local charging capacity variation pattern, predict the local charging capacity of the target partition at the current moment.

[0235] and / or;

[0236] For each target zone, the variation law of partial discharge capacity is determined based on the local capacity corresponding to each discharge process when it is in the target zone;

[0237] Based on the variation law of partial discharge capacity, the partial discharge capacity corresponding to the target partition at the current time is predicted.

[0238] In one possible implementation of this application embodiment, the second calculation module 113 is specifically used for:

[0239] The local charging capacity of each target zone within a preset time period is summed to obtain the total charging capacity within the preset time period.

[0240] The partial discharge capacity of each target zone within a preset time period is summed to obtain the total discharge capacity within the preset time period.

[0241] The retention capacity is obtained by averaging the total charging capacity and the total discharging capacity.

[0242] In one possible implementation of this application embodiment, the acquisition module 111 is specifically used for:

[0243] Acquire raw data of the target battery within a preset time period; the raw data includes: total charging and discharging current of the actual vehicle, and the voltage of individual battery cells during charging and discharging of the actual vehicle.

[0244] After preprocessing the raw data, charge and discharge data are obtained; the preprocessing includes at least one of the following: detection and correction processing, and cleaning processing.

[0245] In one possible implementation of this application embodiment, the charge / discharge data is charge / discharge data that meets preset requirements, and the operating condition of the charge / discharge data meets the preset requirements when any of the preset operating conditions are met; the preset operating conditions include: AC charging operating condition.

[0246] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0247] This application provides an electronic device. Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 12 As shown, Figure 12 The illustrated electronic device includes a processor 121 and a memory 122. The processor 121 and the memory 122 are connected, for example, via a bus 123. Optionally, the electronic device may also include a transceiver 124. It should be noted that in practical applications, the transceiver 124 is not limited to one type, and the structure of this electronic device does not constitute a limitation on the embodiments of this application.

[0248] Processor 121 may be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 121 may also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0249] Bus 123 may include a pathway for transmitting information between the aforementioned components. Bus 123 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Bus 123 may be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 12 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0250] The memory 122 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0251] The memory 122 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 121. The processor 121 is used to execute the application code stored in the memory 122 to implement the content shown in the foregoing method embodiments.

[0252] This application also provides a computer-readable storage medium, which may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. Specifically, the computer-readable storage medium stores program instructions, which are used to implement the service message processing methods in the above embodiments.

[0253] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the technical solution of the above method embodiments. Its implementation principle and technical effects are similar, and will not be repeated here.

[0254] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0255] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0256] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for estimating battery health status, characterized in that, The method includes: Acquire charge and discharge data of the target battery for at least one charging process and / or at least one discharging process within a preset time period; For each charging or discharging process, the local capacity of the partition is calculated based on the target charge / discharge data corresponding to each partition of the charging or discharging process; wherein, both the charging and discharging processes include multiple partitions, and different partitions of the same charging / discharging process correspond to different electrochemical reaction stages of the target battery; The state of health (SOH) of the target battery is calculated based on the local capacity of each of the aforementioned cells.

2. The method according to claim 1, characterized in that, The method further includes: For each charging or discharging process, probability density function (PDF) analysis is performed based on the corresponding target voltage information to obtain the peak value of the PDF; the target voltage information is used to indicate voltage changes. Based on the peak value, the charging process or the discharging process is divided to obtain a preset number of partitions corresponding to the charging process or the discharging process.

3. The method according to claim 2, characterized in that, The target battery is a lithium iron phosphate battery; the step of dividing the charging process or the discharging process according to the peak value to obtain a preset number of partitions corresponding to the charging process or the discharging process includes: Based on the voltages corresponding to two target peaks among the peak values, two plateau voltages of the target battery are determined during the charging or discharging process; each target peak value is greater than the other peak values ​​among the peak values. Based on the two plateau voltages, the charging or discharging process of the target battery is divided into three regions: a voltage steep change region, a first plateau region, and a second plateau region.

4. The method according to claim 2 or 3, characterized in that, The method further includes: The charging and discharging data are segmented to obtain charging data corresponding to each charging process, and / or discharging data corresponding to each discharging process; For each charging data point and each discharging data point, a preset algorithm is used for noise reduction to obtain the target voltage information for the corresponding charging or discharging process.

5. The method according to claim 2 or 3, characterized in that, The calculation of the local capacity of a partition based on the target charge / discharge data corresponding to each partition of the charging or discharging process includes: Based on the ampere-hour integral algorithm, the first local capacity of the first plateau region and the second local capacity of the second plateau region are calculated. Based on the first local capacity, the third local capacity partition of the voltage steep change region is calculated.

6. The method according to claim 5, characterized in that, The method further includes: When the target voltage information indicates that the target charge / discharge data of the first or second platform region is incomplete, the corresponding historical local capacity is obtained; the historical local capacity is the local capacity of the first or second platform region calculated based on the ampere-hour integration algorithm in the last time. The local capacity of the first platform area or the second platform area is determined as the corresponding historical local capacity.

7. The method according to claim 5, characterized in that, The calculation of the third local capacity of the voltage steep change region based on the first local capacity includes: Based on the first local capacity, obtain the ratio coefficient between the voltage steep change region and the first plateau region; The third local capacity of the voltage steep change region is calculated based on the proportionality coefficient; the proportionality coefficient is determined based on the electrochemical reaction ratios corresponding to the first plateau region, the second plateau region, and the voltage steep change region.

8. The method according to claim 7, characterized in that, The method further includes: Based on either laboratory data or a preset feature function, a target feature function relating the first local capacity to the scaling factor is established; the laboratory data includes experimental charge-discharge data of the target battery; the preset feature function includes: a sixth-order Fourier function, a polynomial function, and an exponential function. The step of obtaining the ratio coefficient between the voltage steep change region and the first plateau region based on the first local capacity includes: Substitute the first local capacity into the target feature function to obtain the scaling factor.

9. The method according to any one of claims 1-3, characterized in that, The calculation of the State of Health (SOH) of the target battery based on each of the local capacities includes: Based on the local capacity of the same partition in each of the charging and / or discharging processes, calculate the local charging capacity and / or local discharging capacity of each target partition within the preset time period; Calculate the current retention capacity of the target battery based on the partial charge capacity and / or the partial discharge capacity; The state of energy (SOH) of the target battery is obtained by quotienting the retained capacity and the initial retained capacity.

10. The method according to claim 9, characterized in that, The calculation of the local charging capacity and / or partial discharging capacity corresponding to each target partition within the preset time period includes: When the preset duration is less than a preset value, for each target partition, the average local capacity corresponding to each charging process in the target partition is calculated to obtain the local charging capacity corresponding to the target partition; And / or, calculate the average local capacity corresponding to each of the discharge processes when it is in the target partition, so as to obtain the partial discharge capacity corresponding to the target partition.

11. The method according to claim 9, characterized in that, The calculation of the local charging capacity and / or partial discharging capacity corresponding to each target partition within the preset time period includes: When the preset duration is not less than the preset value, for each target partition, the local charging capacity change pattern is determined based on the local capacity corresponding to each charging process when it is in the target partition. Based on the local charging capacity change pattern, predict the local charging capacity of the target partition at the current moment; and / or; For each target partition, the variation law of partial discharge capacity is determined based on the local capacity corresponding to each discharge process when it is in the target partition; Based on the variation pattern of the partial discharge capacity, the partial discharge capacity corresponding to the target partition at the current moment is predicted.

12. The method according to claim 9, characterized in that, The step of calculating the current retention capacity of the target battery based on the partial charge capacity and / or the partial discharge capacity includes: The local charging capacity of each target partition within the preset time period is summed to obtain the total charging capacity within the preset time period. The partial discharge capacity of each target partition within the preset time period is summed to obtain the total discharge capacity within the preset time period; The retention capacity is obtained by averaging the total charging capacity and the total discharging capacity.

13. The method according to any one of claims 1-3, characterized in that, The acquisition of charge and discharge data of the target battery during at least one charging process and / or at least one discharging process within a preset time period includes: Acquire raw data of the target battery within the preset time period; the raw data includes: total charging and discharging current of the actual vehicle, and voltage of individual battery cells during charging and discharging of the actual vehicle. The charge / discharge data is obtained by preprocessing the raw data; the preprocessing includes at least one of the following: detection and correction processing, and cleaning processing.

14. The method according to any one of claims 1-3, characterized in that, The charging and discharging data is the charging and discharging data that meets the preset requirements. The operating conditions of the charging and discharging data meet the preset requirements when any one of the preset operating conditions is met. The preset operating conditions include: AC charging operating conditions.

15. A battery health status estimation device, characterized in that, The device includes: The acquisition module is used to acquire charge and discharge data of the target battery during at least one charging process and / or at least one discharging process within a preset time period; The first calculation module is used to calculate the local capacity of each partition of the charging or discharging process based on the target charge-discharge data corresponding to each partition of the charging or discharging process for each charging or discharging process; wherein, both the charging and discharging processes include multiple partitions, and different partitions of the same charging / discharging process correspond to different electrochemical reaction stages of the target battery; The second calculation module is used to calculate the state of health (SOH) of the target battery based on the local capacity of each of the aforementioned local batteries.

16. An electronic device, characterized in that, The electronic device includes a processor and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-14.

17. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-14.

18. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1-14.

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