Battery capacity attenuation model parameter identification method and electronic device

Abstract

The application provides a battery capacity attenuation model parameter identification method and electronic equipment, and belongs to the technical field of power batteries. The method filters the data uploaded to the cloud by the vehicle to obtain an effective charging segment. The first battery capacity of the effective charging segment is calculated by using a capacity increment method, and the second battery capacity of the effective charging segment is calculated by using a battery capacity attenuation model. Then, a target function for representing the difference between the first battery capacity and the second battery capacity is constructed, and the ampere-hour attenuation coefficient and the standing attenuation coefficient that make the value of the target function minimum are solved. The application uses the actual running data of the vehicle uploaded by the vehicle end as the data basis, and can reflect the real attenuation characteristics of the battery capacity of the vehicle end. At the same time, the ampere-hour attenuation coefficient and the standing attenuation coefficient of the battery capacity attenuation model are identified by calculating the first battery capacity of the effective charging segment by using the capacity increment method, so that the accuracy of the battery capacity calculated by the battery capacity attenuation model can be improved.

Description

Technical Field

[0001] This application relates to the field of power battery technology, and in particular to a method and electronic device for identifying battery capacity decay model parameters. Background Technology

[0002] The capacity of lithium-ion batteries decreases with use. Establishing an accurate model to describe the capacity decay of lithium-ion batteries is of great significance for the entire life cycle of lithium-ion batteries, including their use, after-sales service, and recycling.

[0003] Currently, related technologies rely on battery or battery cycle charge-discharge capacity test and storage capacity test data to fit a battery capacity decay model. This method is based on experimental test data; however, in practical applications, the experimental environment cannot fully simulate the real vehicle environment, and the types of test conditions are limited. This leads to deviations in the battery capacity calculations performed using the model fitted by this method in real-world applications. Summary of the Invention

[0004] The main objective of this application is to propose a method and electronic device for identifying parameters of a battery capacity degradation model. The aim is to determine the degradation parameters of the battery capacity degradation model by minimizing the difference between the first battery capacity calculated based on an effective charging segment and the second battery capacity calculated using the battery capacity degradation model, thereby improving the accuracy of the calculated battery capacity.

[0005] To achieve the above objectives, a first aspect of this application proposes a method for identifying battery capacity decay model parameters, the method comprising:

[0006] The data uploaded by vehicles to the cloud is filtered to obtain effective charging segments;

[0007] Based on the effective charging segment, calculate the state of charge increment and charging capacity during the battery charging phase, and calculate the first battery capacity based on the state of charge increment and the charging capacity.

[0008] Calculate the cumulative power throughput and equivalent rest duration of the battery corresponding to the effective charging segment, and input the cumulative power throughput and equivalent rest duration into the battery capacity degradation model to calculate the second battery capacity. The equivalent rest duration is the battery's rest duration. The battery capacity degradation model has degradation parameters to be identified. The degradation parameters include an ampere-hour degradation coefficient and a rest duration degradation coefficient. The ampere-hour degradation coefficient is the influence coefficient of the cumulative power throughput on battery life degradation, and the rest duration degradation coefficient is the influence coefficient of the equivalent rest duration on battery life degradation.

[0009] Construct an objective function to characterize the difference between the capacity of the first battery and the capacity of the second battery;

[0010] The ampere-hour attenuation coefficient and the shelving attenuation coefficient that minimize the value of the objective function are obtained by solving.

[0011] In this embodiment, valid charging segments are obtained by filtering data uploaded from the vehicle to the cloud. The first battery capacity of the valid charging segment is then calculated using the capacity increment method, and the second battery capacity is calculated using a battery capacity degradation model. An objective function is then constructed to characterize the difference between the first and second battery capacities. By solving for the objective function, the ampere-hour degradation coefficient and the shelving degradation coefficient of the battery capacity degradation model can be obtained. This application uses actual vehicle operating data uploaded from the vehicle as the data foundation, which can reflect the true degradation characteristics of the vehicle's battery capacity. Simultaneously, using the capacity increment method to calculate the first battery capacity of the valid charging segment to identify the ampere-hour degradation coefficient and the shelving degradation coefficient of the battery capacity degradation model can improve the accuracy of the calculated battery capacity.

[0012] In one embodiment of this application, the step of filtering the data uploaded by the vehicle to the cloud to obtain effective charging segments includes:

[0013] The data uploaded by the vehicle to the cloud is filtered to obtain the charging segments in which the state of charge of the battery is greater than the first preset threshold.

[0014] Select a number of first charging segments from the charging segments in which the battery management system does not shut down during charging;

[0015] Based on the battery current and polarization voltage drop at the start of charging for each of the first charging segments, and the battery state during the first duration after charging ends, a valid charging segment is selected from each of the first charging segments.

[0016] In this embodiment, considering the possibility of data interruption during vehicle data upload via the vehicle network, if the battery management system shuts down and then restarts during the interruption, two charging segments may be merged into one. This can lead to inaccurate battery capacity calculation. Therefore, to ensure the accuracy of battery capacity calculation, the data uploaded by the vehicle to the cloud needs to be filtered to obtain valid charging segments. This allows for battery capacity calculation based on valid charging segments, improving the reliability of capacity calculation.

[0017] In one embodiment of this application, selecting a valid charging segment from the first charging segments based on the battery current and polarization voltage drop at the start of charging and the battery state during a first duration after charging ends includes:

[0018] Detect whether the current of the battery at the start of the first charging segment is less than a second preset threshold.

[0019] If the current of the battery at the start of the first charging segment is less than the second preset threshold, then determine whether the polarization voltage drop of the battery at the start of the first charging segment is less than or equal to the third preset threshold.

[0020] If the polarization voltage drop of the battery at the start of the first charging segment is less than or equal to the third preset threshold, then it is determined whether the battery is in a non-power supply state during the first time period after the end of the first charging segment.

[0021] If the battery is in a non-power supply state during the first time period after the first charging segment ends, then the first charging segment is determined to be a valid charging segment.

[0022] In this embodiment of the application, by presetting a second preset threshold, a third preset threshold, and a first duration, the current of the battery and the polarization voltage drop of the battery at the start of charging of the first charging segment, as well as the state of the battery during the first duration after charging ends, can be accurately determined, thereby effectively selecting the effective charging segment from each of the first charging segments.

[0023] In one embodiment of this application, calculating the state of charge increment during the battery charging phase based on the effective charging segment includes:

[0024] Based on the effective charging segment, calculate the battery open-circuit voltage at the start of charging and the battery open-circuit voltage after charging ends;

[0025] The battery open-circuit voltage at the start of charging is transformed by the characteristic curve of battery open-circuit voltage-state of charge to obtain the battery state of charge at the start of charging.

[0026] The battery open-circuit voltage after charging is transformed by the characteristic curve of battery open-circuit voltage-state of charge to obtain the battery state of charge after charging.

[0027] The state of charge increment during the charging phase is obtained by subtracting the state of charge at the start of charging from the state of charge at the end of charging.

[0028] In this embodiment, the battery open-circuit voltage at the start of charging is converted into the battery state of charge at the start of charging by using the characteristic curve of battery open-circuit voltage-state of charge, and the battery open-circuit voltage at the end of charging is converted into the battery state of charge at the end of charging. Then, the battery state of charge at the end of charging is subtracted from the battery state of charge at the start of charging to obtain the increment of battery state of charge during the charging stage. This can improve the accuracy of battery state of charge and thus improve the accuracy of battery capacity calculation.

[0029] In one embodiment of this application, calculating the battery open-circuit voltage after charging is completed based on the effective charging segment includes:

[0030] Based on the effective charging segment, determine whether the vehicle enters a sleep state after charging is completed;

[0031] If the vehicle enters a sleep state and the sleep duration is longer than the first preset duration, the battery voltage uploaded for the first time after the vehicle wakes up from sleep will be extracted as the battery open circuit voltage after charging is completed.

[0032] If the vehicle does not enter a dormant state, the battery voltage at which the battery has been in a non-power supply state for a duration of a second preset duration is extracted as the battery open-circuit voltage after charging is completed.

[0033] In this embodiment, considering that the battery polarization voltage remains relatively high for a short period after charging ends, directly using the battery terminal voltage and polarization voltage during this short period would lead to a large error in the calculated battery open-circuit voltage. Therefore, this embodiment extracts the battery voltage uploaded for the first time after the vehicle wakes up from hibernation as the battery open-circuit voltage after charging ends when it is determined that the vehicle has entered a hibernation state and the hibernation duration is longer than a first preset duration. If the vehicle does not enter a hibernation state, the battery voltage when the battery has been in a non-power supply state for a duration reaching a second preset duration is extracted as the battery open-circuit voltage after charging ends. This ensures that an accurate battery open-circuit voltage is obtained after charging ends.

[0034] In one embodiment of this application, calculating the equivalent resting time of the battery corresponding to the effective charging segment includes:

[0035] Extract the actual storage time of the battery corresponding to the effective charging segment;

[0036] The equivalent storage time is obtained by multiplying the actual storage time by a conversion factor, wherein the conversion factor is related to temperature.

[0037] In this embodiment, the conversion coefficient is temperature-dependent, thus taking into account the influence of temperature on the lifespan of the stored battery, which can improve the accuracy of the obtained equivalent storage time.

[0038] In one embodiment of this application, the objective function for characterizing the difference between the first battery capacity and the second battery capacity is constructed as follows:

[0039]

[0040] In the formula, E opt Let n represent the objective function, i represent the number of effective charging segments, and C represent the effective charging segments. cap,m,i C represents the first battery capacity corresponding to the i-th effective charging segment. cap,cal,i W represents the second battery capacity corresponding to the i-th valid segment. i , where is the first weight, representing the weight of the capacity difference value corresponding to the i-th effective charging segment in the objective function.

[0041] In this embodiment of the application, by constructing an objective function to characterize the difference between the capacity of the first battery and the capacity of the second battery, the attenuation parameter that minimizes the value of the objective function can be obtained by selecting an optimization algorithm, such as a genetic algorithm or a bird flocking algorithm.

[0042] In one embodiment of this application, the method further includes:

[0043] Based on the effective charging segment, calculate the center value and standard deviation of the state of charge increment during the battery charging phase, and calculate the center value and standard deviation of the charging amount during the battery charging phase, wherein the state of charge increment during the battery charging phase conforms to a normal distribution, and the charging amount during the battery charging phase conforms to a normal distribution.

[0044] The standard deviation corresponding to the first battery capacity is calculated based on the center value and standard deviation of the state of charge increment and the center value and standard deviation of the charging capacity.

[0045] The first weight is adjusted based on the standard deviation corresponding to the first battery capacity.

[0046] In this embodiment of the application, the first weight is adjusted by the standard deviation corresponding to the first battery capacity, which can yield a more accurate attenuation parameter.

[0047] In one embodiment of this application, adjusting the first weight based on the standard deviation corresponding to the first battery capacity includes:

[0048] The smaller the standard deviation corresponding to the first battery capacity, the greater the first weight adjustment of the corresponding effective charging segment.

[0049] In this embodiment, the first weight is adjusted by the standard deviation corresponding to the first battery capacity, so that the first battery capacity with a smaller standard deviation is given a higher weight, thereby increasing the influence of the first battery capacity with a smaller error on the identification result and obtaining a more accurate attenuation parameter.

[0050] To achieve the above objectives, a second aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method provided in any embodiment of this application. Attached Figure Description

[0051] Figure 1 This is a flowchart of the method for identifying battery capacity decay model parameters provided in the embodiments of this application;

[0052] Figure 2 This is a flowchart of the steps for filtering data uploaded by a vehicle to the cloud to obtain effective charging segments, provided in an embodiment of this application.

[0053] Figure 3 This is a flowchart of the steps for selecting an effective charging segment from each first charging segment based on the battery current and polarization voltage drop at the start of charging and the battery state during the first duration after charging ends, provided in an embodiment of this application.

[0054] Figure 4 This is a flowchart of the steps for calculating the state of charge increment during the battery charging phase based on the effective charging segment provided in the embodiments of this application;

[0055] Figure 5 This is an equivalent circuit diagram of a lithium-ion battery provided in the embodiments of this application;

[0056] Figure 6 This is a flowchart of the steps for calculating the battery open-circuit voltage after charging is completed based on the effective charging segment provided in the embodiments of this application;

[0057] Figure 7 This is a schematic diagram of the characteristic curve of battery open-circuit voltage-state of charge provided in the embodiments of this application;

[0058] Figure 8 This is a flowchart of the steps for calculating the equivalent resting time of the battery corresponding to the effective charging segment, provided in an embodiment of this application.

[0059] Figure 9 This is a flowchart illustrating the steps for adjusting the first weight provided in an embodiment of this application;

[0060] Figure 10 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0062] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0064] Currently, with the development of battery technology, batteries have gradually become the power source for electrical devices such as vehicles and drones. However, battery lifespan degradation occurs throughout the entire battery lifecycle, thus requiring lifespan assessment. The capacity of lithium-ion batteries decreases with use. Establishing an accurate model describing the capacity degradation law of lithium-ion batteries is of great significance for the use, after-sales service, and recycling of lithium-ion batteries throughout their entire lifecycle.

[0065] Currently, related technologies rely on battery or battery cycle charge-discharge capacity test and storage capacity test data to fit a battery capacity degradation model. This method is time-consuming and cannot fully reflect the capacity degradation of batteries under real vehicle operating conditions, making it prone to significant deviations.

[0066] Based on this, embodiments of this application propose a method and electronic device for identifying battery capacity degradation model parameters. The aim is to determine the degradation parameters of the battery capacity degradation model by minimizing the difference between the first battery capacity calculated based on the effective charging segment and the second battery capacity calculated by the battery capacity degradation model, thereby improving the accuracy of the battery capacity calculation by the battery capacity degradation model.

[0067] Reference Figure 1 , Figure 1 This is a flowchart of the method for identifying battery capacity decay model parameters provided in the embodiments of this application, including but not limited to steps S110 to S150.

[0068] Step S110: Filter the data uploaded by the vehicle to the cloud to obtain valid charging segments.

[0069] In this embodiment, the vehicle uploads signals such as the state of charge (SOC) of the onboard battery, state of charge, battery voltage, battery current, battery temperature, battery high-voltage state, cumulative battery throughput, equivalent battery idle time, number of times the battery management system is powered on, net battery charge after this startup, and signal duration to a cloud database via the vehicle network at a certain sampling frequency. This facilitates subsequent calculation of battery capacity based on the data uploaded by the vehicle. The state of charge (SOC) reflects the remaining battery capacity and is defined as the ratio of remaining capacity to battery capacity.

[0070] This application embodiment calculates the net battery capacity on the vehicle side and uploads it to the cloud. This allows for subsequent calculation of the effective charging capacity of charging segments using the directly uploaded net battery capacity from the vehicle side. Since the current sampling time interval on the vehicle side is much shorter than that on the cloud side, the accuracy of the vehicle-side calculation of cumulative capacity throughput is significantly higher than that on the cloud side. Furthermore, even if cloud data transmission is interrupted, it will not affect the calculated value of the vehicle-side cumulative capacity throughput. This can improve the accuracy of battery capacity calculation to a certain extent.

[0071] In this embodiment, after receiving the data uploaded by the vehicle, the cloud stores it in a cloud database. By filtering the data in the cloud database, multiple valid charging segments can be obtained. Filtering valid charging segments from the vehicle's actual operating data allows for the identification of the true degradation characteristics of the vehicle's battery capacity. Therefore, calculating the battery capacity based on these valid charging segments improves the accuracy of the calculation.

[0072] Reference Figure 2 , Figure 2 This is a flowchart of the steps for filtering data uploaded by a vehicle to the cloud to obtain a valid charging segment, as provided in the embodiments of this application, including but not limited to steps S210 to S230.

[0073] Step S210: Filter the data uploaded by the vehicle to the cloud and obtain the charging segments where the state of charge of the battery is greater than the first preset threshold.

[0074] Step S220: Select multiple first charging segments from the charging segments in which the battery management system does not shut down during charging.

[0075] Step S230: Select an effective charging segment from each of the first charging segments based on the battery current and polarization voltage drop at the start of charging and the battery state during the first duration after charging ends.

[0076] In this embodiment, the increment of the state of charge (SOC) of a charging segment being greater than a first preset threshold is one of the conditions for selecting a valid charging segment. This first preset threshold can be determined based on the battery type and charging characteristics. In this embodiment, by filtering the data uploaded by the vehicle to the cloud, charging segments whose SOC increment is greater than the first preset threshold can be obtained. Next, considering the possibility of data interruption during data upload via the vehicle network, if the battery management system shuts down and then restarts during the data interruption, two charging segments might be merged into one. This would lead to inaccurate charging calculations. To avoid this anomaly, this embodiment further determines whether the battery management system is shut down during charging. If the battery management system is shut down during charging, the charging segment cannot be considered a valid charging segment. However, if the battery management system is not shut down during charging, the charging segment can be considered as a candidate set of valid charging segments. The candidate set includes multiple first charging segments where the battery management system is not shut down during charging. Furthermore, by considering the battery current and polarization voltage drop at the start of each first charging segment, as well as the battery state during the first period after charging ends, it is possible to effectively select the effective charging segments from each first charging segment.

[0077] Reference Figure 3 , Figure 3 This is a flowchart of the steps for selecting an effective charging segment from each first charging segment based on the battery current and polarization voltage drop at the start of charging and the battery state during the first duration after charging ends, provided in the embodiments of this application. This includes, but is not limited to, steps S310 to S340.

[0078] Step S310: Detect whether the current of the battery at the start of the first charging segment is less than the second preset threshold.

[0079] Step S320: If the current of the battery at the start of the first charging segment is less than the second preset threshold, then determine whether the polarization voltage drop of the battery at the start of the first charging segment is less than or equal to the third preset threshold.

[0080] Step S330: If the polarization voltage drop of the battery at the start of the first charging segment is less than or equal to the third preset threshold, then determine whether the state of the battery during the first time period after the end of the first charging segment is a state of not supplying power to the outside.

[0081] Step S340: If the battery is in a non-power supply state during the first time period after the first charging segment ends, then the first charging segment is determined to be a valid charging segment.

[0082] In this embodiment, for each first charging segment in the candidate set, it is detected whether the battery current at the start of charging is less than a second preset threshold. If the battery current at the start of charging is not less than the second preset threshold, the charging segment is determined not to be a valid charging segment. If the battery current at the start of charging is less than the second preset threshold, it is further determined whether the polarization voltage drop of the battery at the start of charging is less than or equal to a third preset threshold. If the polarization voltage drop of the battery at the start of charging is greater than the third preset threshold, the charging segment is determined not to be a valid charging segment. If the polarization voltage drop of the battery at the start of charging is less than or equal to the third preset threshold, it is further determined whether the battery is in a non-power-supply state during the first time period after charging ends. If the battery is not in a non-power-supply state during the first time period after charging ends, the charging segment is determined not to be a valid charging segment. If the battery is in a non-power-supply state during the first time period after charging ends, the charging segment can be determined to be a valid charging segment. In other words, the first charging segment in the candidate set must meet the following conditions: the battery current at the start of charging is less than the second preset threshold, the polarization voltage drop of the battery at the start of charging is less than or equal to the third preset threshold, and the battery is in a non-power supply state for the first time after charging ends. Then the first charging segment can be determined as a valid charging segment.

[0083] Step S120: Calculate the state of charge increment and charging capacity during the battery charging phase based on the effective charging segment, and calculate the first battery capacity based on the state of charge increment and charging capacity.

[0084] In this embodiment, after selecting effective charging segments, the state-of-charge increment and its standard deviation for the battery charging phase of the effective charging segment can be calculated. The charging capacity and its standard deviation for the effective charging segment can also be calculated. Therefore, the first battery capacity and its standard deviation can be further calculated based on the state-of-charge increment and its standard deviation, and the charging capacity and its standard deviation.

[0085] Reference Figure 4 , Figure 4 This is a flowchart of the steps for calculating the state of charge increment of a battery during the charging phase based on the effective charging segment, provided in the embodiments of this application, including but not limited to steps S410 to S440.

[0086] Step S410: Calculate the battery open-circuit voltage at the start of charging and the battery open-circuit voltage at the end of charging, based on the effective charging segments.

[0087] In the embodiments of this application, reference is made to Figure 5 , Figure 5 This is an equivalent circuit diagram of a lithium-ion battery provided in an embodiment of this application. Figure 5 As shown, the formula for calculating the battery open-circuit voltage (OCV) is given in equation (1). Where, U ocv It is the open-circuit voltage of the battery, V. t It is the voltage (terminal voltage) measured directly between the positive and negative terminals of the battery, V. G It is the total voltage drop of the battery.

[0088] U ocv =V t -V G (1).

[0089] As shown in equation (1), the open-circuit voltage U of the lithium-ion battery ocv The battery's terminal voltage V t Subtract the total voltage drop of the battery V G Total voltage drop V G The voltage drop V0 and polarization voltage drop of the battery are determined by its ohmic internal resistance. RC Composition, V G The calculation formula is shown in equation (2):

[0090] V G =V0+V RC (2).

[0091] The formula for calculating the voltage drop V0 under ohmic internal resistance is shown in equation (3):

[0092] V0 = R0I t (3);

[0093] Where R0 represents the ohmic internal resistance, I t This indicates the current flowing through the internal resistance.

[0094] Polarization voltage drop V of lithium-ion battery RC Can be used as Figure 4 The two RC circuits shown are equivalent. The polarization voltage drop V of the lithium-ion battery... RC The calculation formula is shown in equation (4):

[0095] V RC =V1+V2(4).

[0096] The recursive calculation formula for V1 is shown in equation (5):

[0097]

[0098] Among them, V 1,k V1, V at time k 1,k-1 It is V1, I at time k-1. t,k-1It is the battery current at time k-1, t s It is the time interval between time k and time k-1.

[0099] The recursive calculation formula for V2 is shown in equation (6):

[0100]

[0101] Among them, V 2,k V2 at time k, V 2,k-1 V2,t at time k-1 s It is the time interval between time k and time k-1, I t,k-1 It is the current of the battery at time k-1.

[0102] In this embodiment of the application, V is defined at the moment before charging begins. t V0, V1, and V2 are respectively denoted as V t,s V 0,s V 1,s V 2,s The actual V t,s V 0,s V 1,s V 2,s Both have a certain degree of error. Assume V t,s V 0,s V 1,s V 2,s All conform to a normal distribution, i.e., equations (7), (8), (9), and (10) are as follows:

[0103]

[0104]

[0105]

[0106]

[0107] Where, μ vts μ v0s μ v1s μ v2s They are V t,s V 0,s V 1,s V 2,s The central value of . They are V t,s V 0,s V 1,s V 2,s The variance. μ vs It is the measurement value at the moment before charging begins, μ v0s μv1s μ v2s The results are obtained by equations (8), (9), and (10) respectively.

[0108] Among them, variance The value is determined by the accuracy of the battery voltage detection sensor. It can be calculated using equation (11):

[0109]

[0110] in, When V0 = 0 The basic variance, k σv0 The variance varies with V 0,s The coefficient increases as the absolute value of the coefficient increases.

[0111] Similarly, It can be calculated using equations (12) and (13) respectively:

[0112]

[0113]

[0114] in, When V1 = 0 The basic variance, k σv1 The variance varies with V 1,s The coefficient increases as the absolute value of the coefficient increases. When V2 = 0 The basic variance, k σv2 The variance varies with V 2,s The coefficient increases as the absolute value of the coefficient increases.

[0115] The polarization voltage drop V of the battery was calculated. RC Afterwards, the open-circuit voltage U of the battery at the start of charging. ocv,s It can be calculated using formula (14):

[0116] U ocv,s =V t,s -V 0,s -V 1,s -V 2,s (14). Among them, the open-circuit voltage U of the battery at the start of charging. ocv,s It also conforms to a normal distribution, as shown in equation (15):

[0117] Where, μ ocv,s It is the open-circuit voltage U of the battery at the start of charging. ocv,s The central value of .

[0118] μ ocv,s =μvts +μ v0s +μ c1s +μ v2s (16);

[0119] Wherein is the open-circuit voltage U of the battery at the start of charging. ocv,s variance The calculation formula is shown in equation (17):

[0120]

[0121] Therefore, the open-circuit voltage U of the battery at the start of charging is calculated. ocv,s Following a normal distribution, the open-circuit voltage U of the battery at the start of charging can be calculated separately. ocv,s and its central value U ocv,s ,variance and standard deviation σ ocv,s .

[0122] Reference Figure 6 , Figure 6 This is a flowchart of the steps for calculating the battery open-circuit voltage after charging is completed based on the effective charging segment provided in the embodiments of this application, including but not limited to steps S610 to S630.

[0123] Step S610: Based on the effective charging segment, determine whether the vehicle enters a sleep state after charging is completed;

[0124] Step S620: If the vehicle enters a hibernation state and the hibernation duration is longer than the first preset duration, the battery voltage uploaded for the first time after the vehicle wakes up from hibernation is extracted as the battery open circuit voltage after charging is completed.

[0125] Step S630: If the vehicle does not enter a hibernation state, the battery voltage when the battery is in a state of not supplying power to the outside for a duration of a second preset duration is extracted as the battery open circuit voltage after charging is completed.

[0126] In this embodiment, the vehicle typically enters a sleep state within a few minutes after the battery is fully charged. In the sleep state, the vehicle battery data transmission to the cloud is suspended, and the battery management system is also powered off. This takes into account the significant voltage drop V of the battery's polarization voltage within a few minutes after charging ends. RC The voltage is still relatively high. If the battery terminal voltage V is used directly within a few minutes after charging is complete... t and polarization voltage drop V RCCalculating the battery open-circuit voltage after charging can lead to a large error in the calculated battery open-circuit voltage after charging. Therefore, in order to obtain an accurate battery open-circuit voltage after charging, this embodiment of the application needs to extract the battery terminal voltage after the battery has been fully rested after charging as the battery open-circuit voltage after charging. The battery being fully rested refers to the battery maintaining a first preset time without current input or output, which can be more than one hour. Specifically, if the vehicle enters a dormant state after the battery is fully charged and the dormant time is longer than the first preset time, the battery voltage uploaded for the first time after the vehicle wakes up from dormant state is extracted as the battery open-circuit voltage after charging. If the battery does not enter a dormant state after charging, the battery voltage when the battery is in a state of not supplying power to the outside for a duration that reaches a second preset time can be extracted as the battery open-circuit voltage after charging. The battery open-circuit voltage after charging also conforms to a normal distribution, as shown in equation (18):

[0127] Where, μ ocv,e It's U ocv,e The central value, It's U ocv,e The variance, σ ocv,e It's U ocv,e The standard deviation.

[0128] Among them, the battery open-circuit voltage U after charging is completed ocv,e The error is the variance The main components are the measurement error of the battery sensor and the residual polarization voltage group, which can be calculated using equation (19):

[0129]

[0130] in, It is the variance of the terminal voltage detection values. When V1 = 0 The basic variance When V2 = 0 The basic variance.

[0131] Therefore, the open-circuit voltage U of the battery after charging is calculated. ocv,e Following a normal distribution, the open-circuit voltage U of the battery after charging can be calculated separately. ocv,e and its central value μ ocv,e ,variance and standard deviation σ ocv,e .

[0132] Step S420: The battery open-circuit voltage at the start of charging is transformed by the characteristic curve of battery open-circuit voltage-state of charge to obtain the battery state of charge at the start of charging.

[0133] In this embodiment of the application, after calculating the battery open-circuit voltage at the start of charging of the effective charging segment, the battery open-circuit voltage at the start of charging can be transformed by the characteristic curve of battery open-circuit voltage-state of charge to obtain the battery state of charge and its standard deviation at the start of charging. Specifically, refer to Figure 7 , Figure 7 This is a schematic diagram of the characteristic curve of battery open-circuit voltage-state of charge provided in the embodiments of this application. The battery state of charge (SOC) at the start of charging is denoted as Z. s Then we have: Z s =f(U ocv,s (20), where U ocv,s This is the open-circuit voltage of the battery at the start of charging.

[0134] In this embodiment of the application, by Figure 7 As shown, since the characteristic curve of battery open-circuit voltage-state of charge is a non-linear curve, in order to ensure the battery state of charge Z at the start of charging... s To improve the accuracy of the calculation, this application uses the sigma point transformation to obtain a more accurate probability distribution. The sigma point transformation is used to calculate Z. s The process is as follows:

[0135] Step 1: According to Generate sigma points.

[0136] x0=μ ocv,s (twenty one);

[0137]

[0138]

[0139] Where κ is used to adjust the distance between the sigma point and the center value, κ can be set to 2, and x0 is the open-circuit voltage U of the battery at the start of charging. ocv,s The sigma point corresponding to the center value, x1 is the open-circuit voltage U of the battery at the start of charging. ocv,s The sigma point corresponding to the value to the right of the center value, x2 is the open-circuit voltage U of the battery at the start of charging. ocv,s The sigma point corresponding to the value to the left of the center value.

[0140] Step 2: Substitute the sigma point into equation (20) to calculate the battery state of charge (SOC) value corresponding to the sigma point.

[0141] Z s,0 =f(x0)=f(μ) ocv,s ) (twenty four);

[0142]

[0143]

[0144] Among them, Z s,0 Z is the battery state of charge value corresponding to the sigma point x0 obtained by performing a function calculation on the sigma point x0. s,1 Z is the battery state of charge value corresponding to sigma point x1 obtained by performing a function calculation on sigma point x1. s,2 It is the battery state of charge value corresponding to sigma point x2 obtained by performing a function calculation on sigma point x2.

[0145] Step 3: Calculate Z s The central value and variance.

[0146] Define Z s The center value is μ zs The variance is

[0147]

[0148]

[0149] Z can be considered s It still conforms to a normal distribution.

[0150] Step S430: The battery open-circuit voltage after charging is transformed by the characteristic curve of battery open-circuit voltage-state of charge to obtain the battery state of charge after charging.

[0151] In this embodiment, similarly, after calculating the battery open-circuit voltage after the effective charging segment ends, the battery open-circuit voltage after charging can be transformed using the characteristic curve of battery open-circuit voltage-state of charge to obtain the battery state of charge after charging. Specifically, the battery state of charge (SOC) after charging is denoted as Z. e Then we have: Z e =f(U ocv,e (29), where U ocv,e This is the open-circuit voltage of the battery after charging is complete.

[0152] Similarly, in the embodiments of this application, by Figure 7 As shown, since the characteristic curve of battery open-circuit voltage-state of charge is a non-linear curve, in order to ensure the battery state of charge Z after charging is completed... e To improve the accuracy of the calculation, this application uses the sigma point transformation to obtain a more accurate probability distribution. The sigma point transformation is used to calculate Z. e The process is as follows:

[0153] Step 1: According to Generate sigma points.

[0154] x0=μ ocv,e (30);

[0155]

[0156]

[0157] Wherein, κ is used to adjust the distance between the sigma point and the center value, κ can be set to 2, and x0 is the battery open-circuit voltage U after charging is completed. ocv,e The sigma point corresponding to the center value, x1 is the battery open-circuit voltage U after charging is completed. ocv,e The sigma point corresponding to the value to the right of the center value, x2 is the battery open-circuit voltage U after charging is completed. ocv,e The sigma point corresponding to the value to the left of the center value.

[0158] Step 2: Substitute the sigma point into equation (29) to calculate the battery state of charge (SOC) value corresponding to the sigma point.

[0159] Z e,0 =f(x0)=f(μ) ocv,e (30);

[0160]

[0161]

[0162] Among them, Z e,0 Z is the battery state of charge value corresponding to the sigma point x0 obtained by performing a function calculation on the sigma point x0. e,1 Z is the battery state of charge value corresponding to sigma point x1 obtained by performing a function calculation on sigma point x1. e,2 It is the battery state of charge value corresponding to sigma point x2 obtained by performing a function calculation on sigma point x2.

[0163] Step 3: Calculate Z e The central value and variance.

[0164] Define Z e The center value is μ ze The variance is

[0165]

[0166]

[0167] Z can be considered e It still conforms to a normal distribution.

[0168] In this embodiment, a sigma-point transformation is used to convert the probability distribution of the battery open-circuit voltage (i.e., normal distribution) into the probability distribution of the state of charge (i.e., normal distribution). Since the battery open-circuit voltage (OCV)-state of charge (SOC) curve is a non-linear curve, using a sigma-point transformation can improve the accuracy of the transformed state of charge.

[0169] Step S440: Subtract the battery state of charge at the start of charging from the battery state of charge at the end of charging to obtain the battery state of charge increment during the charging phase.

[0170] In this embodiment, after calculating the battery state of charge (SOC) and its standard deviation at the start of charging for the effective charging segment, and the battery SOC and its standard deviation at the end of charging, the SOC increment during the charging phase is obtained by subtracting the SOC at the start of charging from the SOC at the end of charging. Specifically, the SOC increment during the charging phase of the effective charging segment is denoted as Z. ch Then Z ch It can be calculated using formula (35):

[0171] Z ch =Z e -Z s (35);

[0172] Therefore, Z ch The probability distribution is

[0173] Where, μ zch =μ ze -μ zs μ zch The increment of the state of charge during the charging phase, Z ch The central value, The increment of the state of charge during the charging phase, Z ch The variance, σ ze State of charge increment Z during the charging phase ch The standard deviation.

[0174] In this embodiment of the application, the state-of-charge increment Z of the battery charging phase of the effective charging segment is calculated. ch and its central value μ zch and variance Then, the charging capacity of the effective charging segment, its center value, and variance can be further calculated.

[0175] Specifically, the amount of charge generated during the battery charging phase of an effective charging segment is denoted as A. ch Charging capacity A ch Follows a normal distribution μ Ach Is it the charging capacity A? ch The central value, Is it the charging capacity A? ch The variance. The net battery charge at the start of charging for this effective charging segment is extracted from the cloud database as μ. net,s The net battery charge at the end of charging is μ net,e μ Ach It equals the difference between the two, i.e., μ. Ach =μ net,e -μ net,s .in, It is mainly determined by the accuracy of the battery's current sensor and the amount of charge.

[0176]

[0177] Where, k σAch It is a coefficient that reflects the accuracy of the current sensor, and abs is an absolute value function.

[0178] Therefore, the charging capacity A of the effective charging segment can be calculated. ch and its central value μ Ach ,variance and standard deviation σ Ach .

[0179] In this embodiment of the application, the state-of-charge increment Z of the battery charging phase of the effective charging segment is calculated. ch and its central value μ zch and variance And the charging capacity A of the effective charging segment ch and its central value μ Ach and variance The capacity of the first battery and its standard deviation can be further calculated.

[0180] Specifically, the charging capacity A of the effective charging segment ch and state of charge increment Z ch The calculated first battery capacity is denoted as C. cap,m C cap,m It follows a normal distribution. C cap,m The increment of the state of charge Z of the effective charging segment ch and charging capacity A ch The calculation yields the result shown in equation (37):

[0181]

[0182] According to the error propagation formula for division, we can obtain:

[0183]

[0184]

[0185]

[0186] Where, μ cap,m The first battery capacity C cap,m The central value, The first battery capacity C cap,m The variance, σ cap,m The first battery capacity C cap,m The standard deviation.

[0187] Step S130: Calculate the cumulative power throughput and equivalent rest time of the battery corresponding to the effective charging segment, and input the cumulative power throughput and equivalent rest time into the battery capacity degradation model to calculate the second battery capacity. The equivalent rest time is the rest time of the battery. The battery capacity degradation model has degradation parameters to be identified. The degradation parameters include the ampere-hour degradation coefficient and the rest time degradation coefficient. The ampere-hour degradation coefficient is the influence coefficient of the cumulative power throughput on the battery life degradation, and the rest time degradation coefficient is the influence coefficient of the equivalent rest time on the battery life degradation.

[0188] In this embodiment, the first battery capacity C of the effective charging segment is calculated using the capacity increment method. cap,m Subsequently, the cumulative power throughput and equivalent resting time of the battery corresponding to the effective charging segment can be further calculated, and the cumulative power throughput and equivalent resting time can be input into the battery capacity decay model to calculate the second battery capacity. Here, the equivalent resting time is the battery's resting time. To ensure that the obtained equivalent resting time is more accurate, in one embodiment, the equivalent resting time is the battery's equivalent resting time at 25°C.

[0189] Specifically, the process of calculating the cumulative battery capacity throughput corresponding to an effective charging segment is as follows:

[0190] Let A be the cumulative power throughput of the battery management system corresponding to the i-th effective charging segment during the p-th power-on process. l,p The cumulative battery throughput is calculated on the vehicle side, and the result is uploaded to the cloud database via the vehicle-to-everything (V2X) network. The formula for calculating the cumulative battery throughput is as follows:

[0191]

[0192] Among them, A l,p[t] is the cumulative power throughput at the end of the p-th power-on, A l,e,p-1 I is the cumulative power throughput at the end of the (p-1)th power-on, t is the time of the pth power-on, and I t It is the battery current during the p-th power-on.

[0193] Let A represent the cumulative power throughput corresponding to the i-th effective charging segment. l,i This application embodiment can extract the cumulative power throughput A at the start time of the charging of the i-th valid charging segment from the cloud database. l,p [0], representing the cumulative power throughput A corresponding to the i-th effective charging segment. l,i .

[0194] Reference Figure 8 , Figure 8 This is a flowchart of the steps for calculating the equivalent resting time of the battery corresponding to the effective charging segment provided in the embodiments of this application, including but not limited to steps S810 to S820.

[0195] Step S810: Extract the actual storage time of the battery corresponding to the effective charging segment;

[0196] Step S820: Multiply the actual storage time by the conversion factor to obtain the equivalent storage time, where the conversion factor is related to temperature.

[0197] In this embodiment, the actual storage time of the battery corresponding to the effective charging segment is first extracted, and then the actual storage time is multiplied by a conversion factor to obtain the equivalent storage time. The conversion factor is temperature-dependent. Specifically, the process of calculating the equivalent storage time corresponding to the effective charging segment is as follows:

[0198] Let t be the equivalent resting time corresponding to the p-th power-on of the battery management system for the i-th effective charging segment. d,p In this embodiment, the equivalent resting time can represent the battery's equivalent resting time at 25°C. The equivalent resting time is obtained by multiplying the battery's actual resting time by a conversion factor, which is temperature-dependent. The battery's equivalent resting time is calculated on the vehicle side, and the calculation result is uploaded to the cloud database via the vehicle network. The start time of the p-th battery management system startup is denoted as t. s,p The end time is denoted as t. e,p Then t d,p The calculation formula is as follows.

[0199] t d,p =t d,p-1 +k T t z,p (42);

[0200] Among them, t d,p-1It is the equivalent rest duration during the p-1th battery management system startup, k T It is the conversion factor between equivalent shelving time and actual shelving time, t z,p t is the time difference between the end time of the (p-1)th battery management system startup and the start time of the pth battery management system startup. z,p =t s,p -t e,p-1 ; where t s,p It is the start time of the p-th battery management system power-on, t e,p-1 This is the end time of the (p-1)th battery management system startup. Since the capacity decay rate during battery storage is significantly affected by temperature, this embodiment multiplies the actual storage time at different temperatures by a conversion factor k. T Convert to reference temperature T ref The equivalent shelving time. T The calculation formula is shown in equation (43):

[0201]

[0202] Among them, E KT The conversion factor k is the effect of the resting temperature. T The degree of influence, T is the storage temperature. In this embodiment, the battery temperature at the start of the p-th battery management system power-on is used as the storage temperature T. ref This is a reference temperature.

[0203] Therefore, the equivalent resting time corresponding to the effective charging segment can be calculated, and the influence of temperature on capacity decay during battery resting is fully considered, which can improve the accuracy of the obtained equivalent resting time.

[0204] In this embodiment, after calculating the cumulative power throughput and equivalent rest time of the effective charging segment, the cumulative power throughput and equivalent rest time of the effective charging segment are input into the battery capacity decay model for calculation to obtain the second battery capacity. Specifically, the battery capacity decay model is shown in equation (44):

[0205]

[0206] In the formula, C cap,cal,i C represents the second battery capacity corresponding to the i-th effective charging segment, calculated using the battery capacity decay model. cap,init Indicates the initial capacity of the battery when it leaves the factory; A l,i t represents the cumulative power throughput corresponding to the i-th effective charging segment. d,i k represents the equivalent hold-up time corresponding to the i-th valid charging segment. Ah k represents the attenuation coefficient in ampere-hours. tThis represents the attenuation coefficient during shelving.

[0207] In this embodiment of the application, the ampere-hour attenuation coefficient k Ah The cumulative power throughput has an impact coefficient on battery life degradation, and the storage degradation coefficient k is the coefficient. t This represents the coefficient of influence of equivalent storage time on battery life degradation. The ampere-hour degradation coefficient k. Ah and shelving attenuation coefficient k t These are the degradation parameters to be identified in the battery capacity degradation model.

[0208] Step S140: Construct an objective function to characterize the difference between the capacity of the first battery and the capacity of the second battery.

[0209] In this embodiment, after calculating the first battery capacity of the effective charging segment using the capacity increment method and the second battery capacity of the effective charging segment using the battery capacity decay model, a target function can be further constructed to characterize the difference between the first and second battery capacities. The calculation formula for the target function is shown in equation (45):

[0210]

[0211] In the formula, E opt Let n represent the objective function, i represent the number of effective charging segments, and C represent the effective charging segments. cap,m,i C represents the first battery capacity corresponding to the i-th effective charging segment. cap,cal,i W represents the second battery capacity corresponding to the i-th valid segment. i , where is the first weight, representing the weight of the capacity difference value corresponding to the i-th effective charging segment in the objective function.

[0212] It should be noted that C cap,m,i It follows a normal distribution. In the calculation process, μ cap,m,i (C cap,m,i The center value) is used as C cap,m,i .

[0213] Reference Figure 9 , Figure 9 This is a flowchart of the steps for adjusting the first weight provided in the embodiments of this application, including but not limited to steps S910 to S930.

[0214] Step S910: Based on the effective charging segment, calculate the center value and standard deviation of the state of charge increment during the battery charging stage, and calculate the center value and standard deviation of the charging amount during the battery charging stage. The state of charge increment during the battery charging stage conforms to a normal distribution, and the charging amount during the battery charging stage conforms to a normal distribution.

[0215] Step S920: Calculate the standard deviation corresponding to the first battery capacity based on the center value and standard deviation of the state of charge increment and the center value and standard deviation of the charging capacity.

[0216] Step S930: Adjust the first weight according to the standard deviation corresponding to the first battery capacity.

[0217] In this embodiment, based on the effective charging segment, the center value and standard deviation of the state of charge increment during the battery charging stage, as well as the center value and standard deviation of the charging capacity during the battery charging stage, can be calculated. The state of charge increment during the battery charging stage conforms to a normal distribution, and the charging capacity during the battery charging stage also conforms to a normal distribution. Therefore, the standard deviation corresponding to the first battery capacity can be calculated based on the center value and standard deviation of the state of charge increment and the center value and standard deviation of the charging capacity. Specifically, refer to the aforementioned equations (37), (38), (39), and (40). That is, the first battery capacity C of the effective charging segment can be calculated using the capacity increment method. cap,m,i and its corresponding standard deviation σ cap,m,i , where the standard deviation σ cap,m,i This can be used to represent the first battery capacity C of the effective charging segment that can be calculated using the capacity increment method. cap,m,i The reliability of the battery. Therefore, the first battery capacity C can be used to determine its reliability. cap,m,i The reliability, i.e., through the first battery capacity C cap,m,i The corresponding standard deviation σ cap,m,i To adjust the first weight W in the objective function i Specifically, the smaller the standard deviation corresponding to the first battery capacity, the larger the first weight of the corresponding effective charging segment. This is because the standard deviation σ... cap,m,i The smaller the value, the higher the first battery capacity C. cap,m,i The higher the reliability, the better. Therefore, by giving the standard deviation σ... cap,m,i Smaller first battery capacity C cap,m,i By assigning higher weights, the influence of the first battery capacity, which has a smaller error, on the identification results can be increased, thus resulting in more accurate identification results.

[0218] Specifically, the first weight W i The calculation method is shown in equation (46):

[0219]

[0220] In the formula, n represents the number of effective charging segments, i represents the effective charging segment, and σ cap,m,i W represents the standard deviation corresponding to the first battery capacity. i This indicates the first weight.

[0221] Step S150: Solve for the ampere-hour attenuation coefficient and the shelving attenuation coefficient that minimize the value of the objective function.

[0222] In this embodiment, after constructing an objective function to characterize the difference between the capacity of the first battery and the capacity of the second battery, an optimization algorithm, such as a genetic algorithm or a bird flocking algorithm, is selected to solve for the ampere-hour attenuation coefficient k that minimizes the value of the objective function. Ah Shelving attenuation coefficient k t Then, the ampere-hour attenuation coefficient and the shelving attenuation coefficient k are calculated. t Substituting these formulas into the battery capacity decay model, i.e., equation (44), we can obtain an accurate battery capacity decay model.

[0223] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-mentioned method for identifying battery capacity degradation model parameters. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0224] Please see Figure 10 , Figure 10 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0225] The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0226] The memory 1002 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1002 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001 to execute the battery capacity degradation model parameter identification method of the embodiments of this application.

[0227] Input / output interface 1003 is used to implement information input and output;

[0228] The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0229] Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004);

[0230] The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0231] This application embodiment also provides a storage medium, which is a computer-readable storage medium, storing a computer program that, when executed by a processor, implements the above-described method for identifying battery capacity decay model parameters.

[0232] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0233] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0234] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0235] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0236] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0237] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0238] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0239] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0240] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0241] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0242] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0243] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for identifying parameters of a battery capacity decay model, characterized in that, The method includes: The data uploaded by vehicles to the cloud is filtered to obtain effective charging segments; Based on the effective charging segment, calculate the state of charge increment and charging capacity during the battery charging phase, and calculate the first battery capacity based on the state of charge increment and the charging capacity. Calculate the cumulative power throughput and equivalent rest duration of the battery corresponding to the effective charging segment, and input the cumulative power throughput and equivalent rest duration into the battery capacity degradation model to calculate the second battery capacity. The equivalent rest duration is the battery's rest duration. The battery capacity degradation model has degradation parameters to be identified. The degradation parameters include an ampere-hour degradation coefficient and a rest duration degradation coefficient. The ampere-hour degradation coefficient is the influence coefficient of the cumulative power throughput on battery life degradation, and the rest duration degradation coefficient is the influence coefficient of the equivalent rest duration on battery life degradation. Construct an objective function to characterize the difference between the capacity of the first battery and the capacity of the second battery; The ampere-hour attenuation coefficient and the shelving attenuation coefficient that minimize the value of the objective function are obtained by solving.

2. The method according to claim 1, characterized in that, The process of filtering the data uploaded by the vehicle to the cloud to obtain valid charging segments includes: The data uploaded by the vehicle to the cloud is filtered to obtain the charging segments in which the state of charge of the battery is greater than the first preset threshold. Select a number of first charging segments from the charging segments in which the battery management system does not shut down during charging; Based on the battery current and polarization voltage drop at the start of charging for each of the first charging segments, and the battery state during the first duration after charging ends, a valid charging segment is selected from each of the first charging segments.

3. The method according to claim 2, characterized in that, The step of selecting effective charging segments from each of the first charging segments based on the battery current and polarization voltage drop at the start of charging and the battery state during the first duration after charging ends includes: Detect whether the current of the battery at the start of the first charging segment is less than a second preset threshold. If the current of the battery at the start of the first charging segment is less than the second preset threshold, then determine whether the polarization voltage drop of the battery at the start of the first charging segment is less than or equal to the third preset threshold. If the polarization voltage drop of the battery at the start of the first charging segment is less than or equal to the third preset threshold, then it is determined whether the battery is in a non-power supply state during the first time period after the end of the first charging segment. If the battery is in a non-power supply state during the first time period after the first charging segment ends, then the first charging segment is determined to be a valid charging segment.

4. The method according to claim 1, characterized in that, Based on the effective charging segment, calculate the state of charge increment during the battery charging phase, including: Based on the effective charging segment, calculate the battery open-circuit voltage at the start of charging and the battery open-circuit voltage after charging ends; The battery open-circuit voltage at the start of charging is transformed by the characteristic curve of battery open-circuit voltage-state of charge to obtain the battery state of charge at the start of charging. The battery open-circuit voltage after charging is transformed by the characteristic curve of battery open-circuit voltage-state of charge to obtain the battery state of charge after charging. The state of charge increment during the charging phase is obtained by subtracting the state of charge at the start of charging from the state of charge at the end of charging.

5. The method according to claim 4, characterized in that, Based on the effective charging segment, calculate the battery open-circuit voltage after charging is completed, including: Based on the effective charging segment, determine whether the vehicle enters a sleep state after charging is completed; If the vehicle enters a sleep state and the sleep duration is longer than the first preset duration, the battery voltage uploaded for the first time after the vehicle wakes up from sleep will be extracted as the battery open circuit voltage after charging is completed. If the vehicle does not enter a dormant state, the battery voltage at which the battery has been in a non-power supply state for a duration of a second preset duration is extracted as the battery open-circuit voltage after charging is completed.

6. The method according to claim 1, characterized in that, Calculating the equivalent resting time of the battery corresponding to the effective charging segment includes: Extract the actual storage time of the battery corresponding to the effective charging segment; The equivalent storage time is obtained by multiplying the actual storage time by a conversion factor, wherein the conversion factor is related to temperature.

7. The method according to claim 1, characterized in that, The objective function to characterize the difference between the capacity of the first battery and the capacity of the second battery is as follows: In the formula, E opt Let n represent the objective function, i represent the number of effective charging segments, and C represent the effective charging segments. cap,m,i C represents the first battery capacity corresponding to the i-th effective charging segment. cap,cal,i W represents the second battery capacity corresponding to the i-th valid segment. i , where is the first weight, representing the weight of the capacity difference value corresponding to the i-th effective charging segment in the objective function.

8. The method according to claim 7, characterized in that, The method further includes: Based on the effective charging segment, calculate the center value and standard deviation of the state of charge increment during the battery charging phase, and calculate the center value and standard deviation of the charging amount during the battery charging phase, wherein the state of charge increment during the battery charging phase conforms to a normal distribution, and the charging amount during the battery charging phase conforms to a normal distribution. The standard deviation corresponding to the first battery capacity is calculated based on the center value and standard deviation of the state of charge increment and the center value and standard deviation of the charging capacity. The first weight is adjusted based on the standard deviation corresponding to the first battery capacity.

9. The method according to claim 8, characterized in that, The step of adjusting the first weight based on the standard deviation corresponding to the first battery capacity includes: The smaller the standard deviation corresponding to the first battery capacity, the greater the first weight adjustment of the corresponding effective charging segment.

10. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 9.

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