Battery capacity increment curve function determination method and device and computer equipment

Abstract

The invention relates to a battery capacity increment curve function determination method and device and computer equipment. The method comprises the steps of collecting charging data of a battery according to a first frequency, wherein the first frequency is smaller than or equal to a preset frequency; determining a parameter value of the first voltage capacity curve function according to the charging data to obtain a second voltage capacity curve function; and determining a capacity increment curvilinear function according to the second voltage capacity curvilinear function. By adopting the method, the battery capacity increment curve function can be accurately determined under the condition of low sampling frequency.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular, to a method, device, and computer device for determining a battery capacity increment curve function. Background Art

[0002] With the rapid development of battery technology, lithium-ion batteries have been widely used in many fields such as electric vehicles and energy storage systems. However, as the battery usage time increases, the battery performance will gradually degrade and battery failures may even occur. There are various types of battery failures, among which, battery aging failure and internal short circuit failure in the battery are two relatively common and difficult-to-distinguish failure types.

[0003] The capacity increment curve function can effectively reflect the electrochemical reaction characteristics inside the battery by analyzing the change rate of the battery capacity with respect to the voltage during the charging or discharging process. Research shows that there is a close correlation between the peak value and peak area in the capacity increment curve function and the degree of battery aging and internal short circuit failure. Therefore, compared with traditional voltage and current signals, the IC curve has become a powerful tool for accurately distinguishing battery aging and internal short circuit failure.

[0004] However, traditional methods for extracting the capacity increment curve function usually rely on voltage and current data with a high sampling frequency, which has certain limitations in practical applications. The high sampling frequency not only increases the burden of data storage and processing but may also be restricted by hardware conditions, making it difficult to smoothly extract the capacity increment curve function. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a method, device, computer device, computer-readable storage medium, and computer program product for accurately determining the battery capacity increment curve function under low sampling frequency conditions.

[0006] In a first aspect, the present application provides a method for determining a battery capacity increment curve function, including:

[0007] Collecting charging data of the battery at a first frequency, where the first frequency is less than or equal to a preset frequency;

[0008] Determining parameter values of a first voltage-capacity curve function based on the charging data to obtain a second voltage-capacity curve function;

[0009] Determining a capacity increment curve function based on the second voltage-capacity curve function.

[0010] In a second aspect, the present application further provides a device for determining a battery capacity increment curve function, including:

[0011] A data acquisition module, configured to acquire charging data of a battery at a first frequency, where the first frequency is less than or equal to a preset frequency;

[0012] A first determination module, configured to determine parameter values of a first voltage-capacity curve function according to the charging data, so as to obtain a second voltage-capacity curve function;

[0013] A second determination module, configured to determine a capacity increment curve function according to the second voltage-capacity curve function.

[0014] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements some or all of the steps described in any method of the first aspect of the embodiments of the present application.

[0015] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, it implements some or all of the steps described in any method of the first aspect of the embodiments of the present application.

[0016] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, it implements some or all of the steps described in any method of the first aspect of the embodiments of the present application.

[0017] For the above method, device, computer device, computer-readable storage medium, and computer program product for determining the battery capacity increment curve function, the charging data of the battery is acquired at a first frequency, where the first frequency is less than or equal to a preset frequency; parameter values of a first voltage-capacity curve function are determined according to the charging data to obtain a second voltage-capacity curve function; and a capacity increment curve function is determined according to the second voltage-capacity curve function. By using the method for determining the battery capacity increment curve function provided in this embodiment, the battery capacity increment curve function can be accurately determined under the condition of a low sampling frequency. By reducing the dependence of the charging data on the sampling frequency, the flexibility and applicability of determining the capacity increment curve function are improved. Therefore, in practical applications, the capacity increment curve function can be determined more conveniently and efficiently to monitor the health status of the battery, and further, reliable technical support can be provided for battery management and maintenance. Description of the Drawings

[0018] To more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or the related art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0019] Figure 1 It is an application environment diagram of the method for determining the battery capacity increment curve function in an embodiment;

[0020] Figure 2 It is a schematic flowchart of the method for determining the battery capacity increment curve function in an embodiment;

[0021] Figure 3 It is a comparative schematic diagram of the change of the estimated error value of the capacity increment curve function obtained at different first frequencies with the charging cycle in an embodiment;

[0022] Figure 4 It is a schematic diagram of the change of the estimated error value of the capacity increment curve function at different iteration times in an embodiment;

[0023] Figure 5 It is a comparative schematic diagram of the fitting effect of the capacity increment curve at different iteration times in an embodiment;

[0024] Figure 6 It is a structural block diagram of the device for determining the battery capacity increment curve function in an embodiment;

[0025] Figure 7 It is an internal structure diagram of a computer device in an embodiment;

[0026] Figure 8 It is an internal structure diagram of a computer device in another embodiment. Detailed implementation manners

[0027] In order to make the purpose, technical solutions and advantages of the present application more clear and understandable, the following further details the present application in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0028] The present application is made by the inventor based on the understanding and research of the following problems:

[0029] Traditional methods for extracting the capacity increment curve function usually rely on voltage and current data with a high sampling frequency, which have certain limitations in practical applications. Specifically, with the continuous popularization of battery technology, the need to monitor the health status of batteries has gradually emerged in more and more battery usage scenarios. Exemplarily, with the rapid development of electric vehicle technology, there is an increasing demand to monitor the battery status of electric vehicles.

[0030] However, on the one hand, in the scenario of monitoring the health status of electric vehicle batteries, a large number of sensors are required to monitor the voltage data and current data of each battery in a large-scale battery pack. In this case, high-precision sensors are needed to achieve high-sampling-frequency acquisition of charging data. Obviously, using a large number of high-precision sensors in a large-scale battery pack will undoubtedly lead to huge monitoring costs. On the other hand, in the scenario of monitoring the health status of electric vehicle batteries, high-frequency sampling will increase the energy consumption of the battery. In this case, long-term monitoring of the battery will undoubtedly seriously consume the battery energy and lead to a decline in battery durability. Therefore, the traditional method for extracting the capacity increment curve function based on high-sampling-frequency conditions not only increases the burden of data acquisition, data storage, and data processing, but may also be limited by hardware conditions in various battery monitoring scenarios, making it difficult to successfully extract the capacity increment curve function.

[0031] Based on this, this embodiment provides a method for determining the battery capacity increment curve function that can accurately determine the battery capacity increment curve function under low-sampling-frequency conditions. The method for determining the battery capacity increment curve function provided by the embodiments of the present application can be applied to an Figure 1 application environment as shown. Among them, the terminal 102 communicates with the battery management system 104 through a network, where the network can be an Ethernet or a controller area network. The battery management system 104 is connected to the battery, and the battery management system 104 is used to collect the charging data of the battery and send the charging data to the terminal 102. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, intelligent vehicle-mounted devices, projection devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. In an exemplary embodiment, as Figure 2 shown, a method for determining the battery capacity increment curve function is provided, and this method is described by taking the terminal in Figure 1 as an example, including the following steps 202 to step 206. Among them:

[0032] Step 202, collect the charging data of the battery at a first frequency, where the first frequency is less than or equal to a preset frequency.

[0033] The preset frequency is a non-high frequency. Thus, the first frequency less than or equal to the preset frequency is also a non-high frequency. In other words, the first frequency is a low frequency. Optionally, the preset frequency can be 0.1Hz, 0.05Hz, 0.01Hz or other non-high frequencies. Collecting the charging data of the battery at the first frequency can also be understood as collecting the charging data of the battery at a relatively long time interval.

[0034] Exemplarily, when the preset frequency is 0.1Hz, since the first frequency is less than or equal to the preset frequency, the collection time interval is at least 10 seconds. That is to say, the charging data of the battery is collected at least every 10 seconds. Similarly, when the preset frequency is 0.05Hz, the collection time interval is at least 20 seconds. That is to say, the charging data of the battery is collected at least every 20 seconds.

[0035] The charging data of the battery refers to various data related to the health state of the battery. Optionally, the charging data of the battery can include the charging current data, charging voltage data, and charging time data of the battery. Optionally, the charging data of the battery can be obtained by the battery management system from the battery in the charging state.

[0036] Optionally, the charging data of the battery is the charging data of the battery in the charging state within a preset power range. Optionally, the preset power range can be determined according to the battery type. Further optionally, the preset power range can be determined according to the general charging behavior corresponding to the battery type.

[0037] Exemplarily, when the battery type of the battery is an electric vehicle battery, due to the general charging habits of users and range anxiety, the charging behavior of the electric vehicle battery usually covers a power range of 50% - 85%. Therefore, in this case, the preset power range can be 50% - 85%. Thus, a more accurate capacity increment curve function can be determined based on the charging data that has a higher correlation with the actual charging behavior.

[0038] Specifically, the power range refers to the percentage of the current remaining capacity of the battery in the total capacity. Exemplarily, the preset power range of 50% - 85% means that the percentage of the current remaining capacity of the battery in the total capacity is 50% - 85%.

[0039] Optionally, the charging data of the battery in this embodiment can be collected based on cyclic charging and discharging of the battery under a preset charging mode.

[0040] Optionally, in this embodiment, when the charging data of the battery is collected based on a series battery pack, the multiple batteries in the series battery pack need to be batteries with the same rated capacity and the same model. Further, the multiple batteries in the series battery pack need to be cyclically charged and discharged based on the same preset charging mode to ensure that the charging data has high reliability.

[0041] Exemplarily, the charging data of the battery collected in this example is obtained by connecting 18650 ternary lithium-ion batteries with a rated capacity of 2.850 Ah in series in the same batch and cyclically charging and discharging them in a 0.5 CC-CV charging mode.

[0042] In an exemplary embodiment, the above-mentioned collecting the charging data of the battery according to the first frequency includes: collecting the initial charging data of the battery according to the first frequency; selecting a part of the initial charging data within a preset power range for data cleaning to obtain the charging data of the battery.

[0043] Among them, it can be that after selecting a part of the initial charging data within a preset power range, data cleaning is performed on the initial charging data based on the 3σ principle to obtain the charging data of the battery.

[0044] In an exemplary embodiment, the above-mentioned selecting a part of the initial charging data within a preset power range for data cleaning to obtain the charging data of the battery includes: after selecting a part of the initial charging data within a preset power range, determining the average value μ and the standard deviation σ of the part of the initial charging data; removing the abnormal data outside [μ - 3σ, μ + 3σ] in the part of the initial charging data to obtain the charging data of the battery.

[0045] In this embodiment, by selecting a part of the initial charging data within a preset power range, it can be ensured that there is a high practical correlation between the charging data finally used to determine the capacity increment curve function and the charging behavior of the battery. And, by removing noise data through data cleaning, the data quality of the charging data can be further ensured to ensure the accuracy of the determined battery capacity increment curve function.

[0046] Step 204, determine the parameter values of the first voltage-capacity curve function according to the charging data to obtain the second voltage-capacity curve function.

[0047] Among them, the voltage-capacity curve, that is, the Capacity-Voltage curve, is a curve used to characterize the relationship between the capacity and the voltage of the battery during the charging process or the discharging process. Based on this, the voltage-capacity curve function can reflect the capacity characteristics of the battery at different voltages.

[0048] The first voltage-capacity curve function is a voltage-capacity curve function with undetermined parameter values. The second voltage-capacity curve function is a voltage-capacity curve function obtained by determining the parameter values that match the actual battery characteristics based on the first voltage-capacity curve function.

[0049] Specifically, by differentiating the second voltage-capacity curve function, the incremental capacity curve function can be obtained. Conversely, by integrating the incremental capacity curve function, the second voltage-capacity curve can be obtained.

[0050] Specifically, the purpose of obtaining the second voltage-capacity curve function in this embodiment is to determine the incremental capacity curve function. The incremental capacity curve function usually has multiple peaks, and each peak corresponds to different electrochemical reactions or physical processes inside the battery. Based on this, optionally, a Lorentz function with a single-peak characteristic can be used to describe a single peak in the incremental capacity curve function. Therefore, the incremental capacity curve function can be matched by superimposing multiple single-peak Lorentz functions with undetermined parameter values to capture its multiple-peak characteristics. The number of single-peak Lorentz functions used for superimposition is the same as the number of peaks in the incremental capacity curve, that is, the number of peaks in the incremental capacity curve corresponds to the number of single-peak Lorentz functions for superimposition. Further, by integrating the superimposed result of multiple single-peak Lorentz functions with undetermined parameter values, the first voltage-capacity curve function can be obtained, that is, the fitting of the first voltage-capacity curve function is achieved.

[0051] Specifically, based on mathematical theorems, it can be known that the parameter values in the single-peak Lorentz function used to match the incremental capacity curve function will not change after integration. Therefore, the parameter values in the incremental capacity curve function are the same as those in the first voltage-capacity curve function. The parameter values in the incremental capacity curve function are used to describe each peak. Thus, optionally, the parameter values of the first voltage-capacity curve function can be the parameter values of each incremental capacity peak in the incremental capacity curve function. In other words, they are also the parameter values of each single-peak Lorentz function.

[0052] Step 206: Determine the incremental capacity curve function according to the second voltage-capacity curve function.

[0053] Among them, the incremental capacity curve function, that is, the Incremental Capacity curve, is a curve used to characterize the rate of change of the capacity with respect to the voltage (dQ / dV) of the battery during the charging or discharging process. The incremental capacity curve function can reflect the electrochemical reaction characteristics inside the battery, especially the characteristic changes during battery aging and internal short-circuit fault phenomena.

[0054] Optionally, based on the characteristics of the capacity increment curve function, after the capacity increment curve function is determined, the health status of the battery under test can be monitored based on the capacity increment curve function; further, when the battery under test is in a fault state, it can be determined whether the battery under test has a battery aging phenomenon or an internal short circuit fault phenomenon based on the capacity increment curve function.

[0055] Specifically, since the parameter values of the second voltage-capacity curve function have been determined, and based on mathematical theorems, it is known that the parameter values in the second voltage-capacity curve function are the same as the parameter values in the capacity increment curve function. Therefore, in an exemplary embodiment, the above method for determining the capacity increment curve function based on the second voltage-capacity curve function: taking the derivative of the second voltage-capacity curve function to obtain the capacity increment curve function.

[0056] In the above method for determining the battery capacity increment curve function, the charging data of the battery is collected at the first frequency, and the first frequency is less than or equal to the preset frequency; the parameter values of the first voltage-capacity curve function are determined according to the charging data to obtain the second voltage-capacity curve function; the capacity increment curve function is determined according to the second voltage-capacity curve function. By using the method for determining the battery capacity increment curve function provided in this embodiment, the battery capacity increment curve function can be accurately determined under the condition of low sampling frequency. By reducing the dependence of the charging data on the sampling frequency, the flexibility and applicability of determining the capacity increment curve function are improved. Therefore, this embodiment can more conveniently and efficiently determine the capacity increment curve function in practical applications to realize the monitoring of the health status of the battery, and further provide reliable technical support for battery management and maintenance.

[0057] In an exemplary embodiment, the above determining the parameter values of the first voltage-capacity curve function according to the charging data to obtain the second voltage-capacity curve function includes:

[0058] Determining the capacity data based on the charging current data and the charging time data in the charging data.

[0059] Based on the charging voltage data and the capacity data in the charging data, determining the voltage-capacity data including multiple data points.

[0060] Based on multiple data points, determining the parameter values of the first voltage-capacity curve function to obtain the second voltage-capacity curve function.

[0061] Among them, the charging current data refers to a series of data reflecting the change of the charging current magnitude with the charging time, which can reflect the dynamic change of the current during the charging process. The charging voltage data refers to a series of data reflecting the change of the voltage across the battery with the charging time. The charging time data corresponds to the charging current data and the charging voltage data respectively.

[0062] Capacity data refers to an indicator that reflects the amount of electric charge actually stored or released by a battery during the charging or discharging process, and can reflect the amount of electric power that the battery can provide under specific conditions.

[0063] In an exemplary embodiment, determining the capacity data based on the charging current data and the charging time data in the charging data includes: determining the charging current data and the charging time data based on the charging data; integrating the change of the charging current data with respect to the charging time data to determine the capacity data.

[0064] Exemplarily, represent the charging current data as I c , represent the maximum time data of the charging time data as T, and represent the capacity data as Q. Then the calculation formula for the capacity data Q is:

[0065] (1)

[0066] Exemplarily, since the capacity increment curve function is a curve used to characterize the rate of change of the capacity with respect to the voltage (dQ / dV) of the battery during the charging or discharging process, therefore, represent the change in capacity within the charging time interval as △Q, represent the change in voltage within the charging time interval as △V, and represent the capacity increment curve function as IC. Then the calculation formula for IC is:

[0067] (2)

[0068] Specifically, the multiple data points included in the voltage capacity data respectively characterize the capacity magnitudes at different charging voltages.

[0069] In an exemplary embodiment, determining the parameter values of the first voltage capacity curve function based on multiple data points to obtain the second voltage capacity curve function includes: determining the parameter values of the first voltage capacity curve function based on the Markov Chain Monte Carlo (MCMC) model and multiple data points.

[0070] In this embodiment, based on the charging data, the capacity data can be accurately determined. Thus, multiple data points that characterize the capacity magnitudes at different charging voltages can be determined, and the parameter values of the first voltage capacity curve function can be determined based on the multiple data points to obtain the second voltage capacity curve function. By ensuring the accuracy of the obtained voltage capacity data, the accuracy of the parameter values of the first voltage capacity curve function is ensured, the fitting ability of the finally obtained second voltage capacity curve function is improved, so that the second voltage capacity curve function can more accurately describe the voltage capacity relationship of the battery. Therefore, the capacity increment curve obtained based on the second voltage capacity curve function can determine the health state of the battery to be tested with a relatively high prediction accuracy.

[0071] In an exemplary embodiment, the derivative result of the first voltage-capacity curve function includes a first capacity increment peak and a second capacity increment peak; the first voltage-capacity curve function includes an integrated first Lorentz function and an integrated second Lorentz function, where the first Lorentz function corresponds to the first capacity increment peak and the second Lorentz function corresponds to the second capacity increment peak.

[0072] The parameter values of the first voltage-capacity curve function include the peak position, peak area, and peak width of the first capacity increment peak, as well as the peak position, peak area, and peak width of the second capacity increment peak.

[0073] Among them, the derivative result of the first voltage-capacity curve function includes a first capacity increment peak and a second capacity increment peak, that is to say, the capacity increment curve function obtained by differentiating the first voltage-capacity curve function includes a first capacity increment peak and a second capacity increment peak. Specifically, since the first voltage-capacity curve function includes an integrated first Lorentz function and an integrated second Lorentz function, thus, by differentiating the first voltage-capacity curve function with determined parameter values, a capacity increment curve function including the first Lorentz function and the second Lorentz function can be obtained.

[0074] Specifically, since the first Lorentz function corresponds to the first capacity increment peak and the second Lorentz function corresponds to the second capacity increment peak, therefore, the peak position, peak area, and peak width of the first capacity increment peak are actually the parameter values of the first Lorentz function, and the peak position, peak area, and peak width of the second capacity increment peak are actually the parameter values of the second Lorentz function.

[0075] The peak position refers to the specific coordinate on the voltage axis of the capacity increment peak value corresponding to the capacity increment peak in the capacity increment curve function, that is, the voltage value corresponding to the capacity increment peak value.

[0076] The peak area refers to the area formed between the capacity increment peak corresponding to the capacity increment curve function and the voltage axis. The peak area can be obtained by integrating the capacity increment curve function within the voltage range corresponding to the capacity increment peak.

[0077] The peak width refers to the distribution range of the capacity increment peak value corresponding to the capacity increment peak on the voltage axis in the capacity increment curve function.

[0078] Exemplarily, the capacity increment curve function includes a first capacity increment peak and a second capacity increment peak. The superposition form of the first Lorentz function and the second Lorentz function is used to match the capacity increment curve function. The capacity increment curve function that changes with the charging voltage is denoted as IC(x), the charging voltage data is denoted as x, the peak position, peak area, and peak width of the first capacity increment peak are respectively denoted as x1, A1, and ω1, and the peak position, peak area, and peak width of the second capacity increment peak are respectively denoted as x2, A2, and ω2. Then the capacity increment curve function IC(x) can be expressed by the following formula:

[0079] (3)

[0080] By integrating the capacity increment curve function, the first voltage-capacity curve function is obtained. The first voltage-capacity curve function that changes with the charging voltage is denoted as Q(x), and the constant term is denoted as C. Then the first voltage-capacity curve function Q(x) can be expressed by the following formula:

[0081] (4)

[0082] In this embodiment, by iterating the parameter values in the first voltage-capacity curve function, the parameter values that have strong correlations with the first capacity increment peak and the second capacity increment peak in the capacity increment curve function can be obtained, namely the peak position, peak area, and peak width. Thus, these iteratively obtained parameter values can be directly used as fault diagnosis features to diagnose faults for the battery. Therefore, this embodiment can provide a portable and efficient means for evaluating the health status of the battery; at the same time, since this embodiment can accurately obtain the peak position, peak area, and peak width of different capacity increment peaks even under low sampling frequency conditions, furthermore, this embodiment can significantly reduce the dependence on high sampling frequency conditions, and significantly reduce the hardware cost and data processing burden.

[0083] In an exemplary embodiment, the above method of determining the parameter values of the first voltage-capacity curve function based on multiple data points to obtain the second voltage-capacity curve function includes:

[0084] Generating second iteration parameter values based on the first iteration parameter values and the proposed distribution of the parameter values.

[0085] Based on the prior distribution of the parameter values, respectively determine the prior probability of the first iteration parameter values and the prior probability of the second iteration parameter values.

[0086] Substitute the multiple data points, the first iteration parameter values, and the second iteration parameter values into the likelihood function respectively to obtain the likelihood value of the first iteration parameter values and the likelihood value of the second iteration parameter values.

[0087] Determine the first acceptance rate of the second iteration parameter value based on the prior probability of the first iteration parameter value, the likelihood value of the first iteration parameter value, the prior probability of the second iteration parameter value, and the likelihood value of the second iteration parameter value.

[0088] Determine the parameter value of the first voltage capacity curve function based on the first iteration parameter value and the first acceptance rate, and obtain the second voltage capacity curve function.

[0089] The first iteration parameter value refers to the initialization parameter value set before starting the iteration of the parameter value of the first voltage capacity curve function. The first iteration parameter value is determined based on prior knowledge, expert experience, or preliminary estimation, and is an artificially selected initialization parameter value.

[0090] The proposal distribution refers to the probability distribution used to generate new parameter candidate values. It determines how to generate the next parameter candidate value from the current parameter value, thereby guiding the exploration of the MCMC model in the parameter space. Optionally, the proposal distribution includes distribution methods such as normal distribution and uniform distribution; the mean and variance of the proposal distribution need to be reasonably set to ensure the effectiveness and iteration efficiency of the iteration parameter values obtained during the iteration process.

[0091] The prior distribution refers to the probability distribution assumed for the iteration parameter value based on prior knowledge, expert experience, or preliminary estimation before the iteration of the parameter value of the first voltage capacity curve function. The prior distribution reflects the preliminary understanding of the iteration parameter value. In Bayesian statistics, the prior distribution is used in combination with the likelihood function to update the prior distribution to the posterior distribution, so that the obtained posterior distribution can integrate prior knowledge and iteration information and can more accurately reflect the uncertainty of the iteration parameter value. Optionally, the prior distribution includes distribution methods such as normal distribution and uniform distribution.

[0092] The prior probability of the first iteration parameter value refers to the probability magnitude corresponding to the first iteration parameter value in the prior distribution. In the case where the parameter corresponding to the first iteration parameter value is a discrete parameter, the prior probability of the first iteration parameter value represents the probability that the corresponding parameter is this specific value; in the case where the parameter corresponding to the first iteration parameter value is a continuous parameter, the prior probability of the first iteration parameter value represents the probability density of the corresponding parameter within a certain small interval near this specific value.

[0093] In an exemplary embodiment, substituting multiple data points, the first iteration parameter value, and the second iteration parameter value into the likelihood function respectively to obtain the likelihood value of the first iteration parameter value and the likelihood value of the second iteration parameter value respectively, includes: substituting multiple data points and the first iteration parameter value into the likelihood function to obtain the likelihood value of the first iteration parameter value; substituting multiple data points and the second iteration parameter value into the likelihood function to obtain the likelihood value of the second iteration parameter value.

[0094] It should be noted that the prior probability of the second iteration parameter value in this embodiment, as well as the prior probabilities of the third iteration parameter value and the fourth iteration parameter value mentioned below, are conceptually the same as the prior probability of the first iteration parameter value, except for the corresponding specific iteration parameter values. Therefore, they will not be elaborated here and below.

[0095] The likelihood function is a function of the iteration parameter value, which is used to measure the probability (i.e., the likelihood value) of the iteration parameter value occurring under given parameters. The likelihood value obtained based on the likelihood function can be used to match the degree of matching between the iteration parameter value and the given parameters.

[0096] The likelihood value of the first iteration parameter value represents the probability of the first iteration parameter value occurring under the corresponding parameters.

[0097] It should be noted that the likelihood values of the second iteration parameter value in this embodiment, as well as the likelihood values of the third iteration parameter value and the fourth iteration parameter value mentioned below, are conceptually the same as the likelihood value of the first iteration parameter value, except for the corresponding specific iteration parameter values. Therefore, they will not be elaborated here and below.

[0098] The first acceptance rate of the second iteration parameter value represents the probability of the second iteration parameter value being accepted as a new iteration parameter value. Since the second iteration parameter value is generated based on the first iteration parameter value and the proposed distribution of the parameter value, the second iteration parameter value is a candidate iteration parameter value generated based on the first iteration parameter value. Therefore, it is necessary to determine the first acceptance rate of the second iteration parameter value to determine whether to accept the second iteration parameter value, and further determine the parameter value of the first voltage capacity curve function based on the acceptance result of the second iteration parameter value.

[0099] It should be noted that the second acceptance rate of the third iteration parameter value and the third acceptance rate of the fourth iteration parameter value mentioned below are conceptually the same as the first acceptance rate of the second iteration parameter value, except for the corresponding specific iteration parameter values. Therefore, they will not be elaborated below.

[0100] Exemplarily, the current iteration parameter value corresponding to the current iteration number is denoted as θ, and the prior probability of the current iteration parameter value is denoted as p(θ). When the prior distribution of the parameter value is a normal distribution with a mean of 0 and a variance of σ p 2 then the prior probability of the current iteration parameter value is expressed as:

[0101] (5)

[0102] Exemplarily, if the proposed distribution of the parameter values is denoted as q, then the probability distribution of generating a new candidate iteration parameter value θ' based on the current iteration parameter value θ is a normal distribution with a mean of θ and a variance of σ q 2 , then the probability of generating a new candidate iteration parameter value θ' based on the current iteration parameter value θ is expressed as:

[0103] (6)

[0104] Exemplarily, if the iteration parameter value at iteration number t is denoted as θ(t), then the new candidate iteration parameter value at iteration number (t + 1) is denoted as θ(t + 1). Then, the new candidate iteration parameter value θ(t + 1) generated based on the proposed distribution of the parameter values on the basis of the iteration parameter value θ(t) at iteration number t can be further expressed as:

[0105] (7)

[0106] Exemplarily, if the likelihood function for determining the likelihood value of the current iteration parameter value θ is denoted as L(θ), the number of data points is denoted as N, the charging voltage data of the i-th data point is denoted as xi, the actual capacity data of the i-th data point is denoted as yi, the output value of the first voltage-capacity curve function corresponding to the current iteration parameter value θ is denoted as Q(xi, θ), and the standard deviation of the random error included in the charging data is denoted as σ0, then the formula for the likelihood function L(θ) can be expressed as:

[0107] (8)

[0108] Exemplarily, if the prior probability of the current iteration parameter value θ(t) is denoted as p[θ(t)], the likelihood value of the current iteration parameter value θ(t) is denoted as L[θ(t)], the prior probability of the new candidate iteration parameter value θ(t + 1) generated based on the current iteration parameter value θ(t) is denoted as p[θ(t + 1)], and the likelihood value of the new candidate iteration parameter value θ(t + 1) is denoted as L[θ(t + 1)], then if the acceptance rate of the new candidate iteration parameter value θ(t + 1) is denoted as r, the calculation formula for the acceptance rate r of the new candidate iteration parameter value θ(t + 1) can be expressed as:

[0109] (9)

[0110] In this embodiment, by presetting the first iteration parameter value, the proposal distribution of the parameter value, and the prior distribution of the parameter value, based on the iterative idea of the MCMC model, a second iteration parameter value is generated based on the first iteration parameter value and the proposal distribution of the parameter value. Further, the first acceptance rate of the second iteration parameter value is determined through the likelihood function and the prior distribution of the parameter value. Thus, the parameter value of the first voltage-capacity curve function is determined based on the first iteration parameter value and the first acceptance rate. Based on the iterative idea of the MCMC model, through its unique probability framework and parameter exploration mechanism, high-value feature information can be extracted from the data points obtained under low sampling frequency conditions, so as to improve the accuracy of the parameter value of the finally determined first voltage-capacity curve function. Furthermore, a high-precision capacity increment curve function can be determined based on the second voltage-capacity curve function with high accuracy.

[0111] In an exemplary embodiment, when the proposal distribution of the parameter value is a normal distribution, the generation of the second iteration parameter value based on the first iteration parameter value and the proposal distribution of the parameter value includes: centering on the first iteration parameter value, generating a second iteration parameter value within the range of the square of the variance corresponding to the normal distribution of the proposal distribution based on the step size parameter of the proposal distribution.

[0112] Among them, the step size parameter of the proposal distribution is used to control the change amplitude of the second iteration parameter value relative to the first iteration parameter value. Specifically, the larger the step size parameter, the greater the gap between the second iteration parameter value and the first iteration parameter value, the lower the accuracy of the iteration parameter value but the higher the iteration efficiency; on the contrary, the smaller the step size parameter, the smaller the gap between the second iteration parameter value and the first iteration parameter value, the higher the accuracy of the iteration parameter value but the lower the iteration efficiency. Therefore, the step size parameter of the proposal distribution can be determined according to actual iteration requirements.

[0113] In an exemplary embodiment, the prior distribution of the parameter value is a normal distribution.

[0114] In this embodiment, by using the normal distribution as the prior distribution, the parameter value can not only take values near the mean of the parameter value, but also take values within a certain range of the mean. The specific range is determined by the variance of the normal distribution. Thus, the parameter value can vary within a larger range, which can avoid overly strict setting restrictions on the parameter space. Furthermore, the iterative parameter value can search for the possibility of new iterative parameter values within a wider parameter control range, which can improve the flexibility and adaptability of the iterative process.

[0115] In an exemplary embodiment, the determination of the parameter value of the first voltage-capacity curve function based on the first iteration parameter value and the first acceptance rate includes:

[0116] When the first acceptance rate meets the preset numerical condition and the number of iterations is greater than the preset number of iterations, determine the parameter values of the first voltage-capacity curve function based on the first iteration parameter value and the second iteration parameter value.

[0117] Among them, the number of iterations can be 500 times, 700 times, 1000 times or other numbers. When the number of iterations is less than or equal to the preset number of iterations, it means that the iteration process of the parameter values of the first voltage-capacity curve function has not ended. On the contrary, when the number of iterations is greater than the preset number of iterations, it means that the iteration process of the parameter values of the first voltage-capacity curve function has ended. At this time, the second iteration parameter value and the first iteration parameter value for which the first acceptance rate meets the preset numerical condition can be determined as the parameter values of the first voltage-capacity curve function.

[0118] Optionally, meeting the preset numerical condition can mean that the first acceptance rate is greater than or equal to the preset acceptance rate, or it can mean that the first acceptance rate is greater than a randomly generated random number. Exemplarily, when the preset numerical condition means that the first acceptance rate is greater than a randomly generated random number, since the value range of the second iteration parameter value is (0, 1), accordingly, the value range of the randomly generated random number should also be (0, 1).

[0119] Optionally, the number of second iteration parameter values can be multiple. Thus, based on the first iteration parameter value and all the second iteration parameter values for which the first acceptance rate meets the preset numerical condition, determine the parameter values of the first voltage-capacity curve function.

[0120] Exemplarily, represent the random number as u, the value range of u is (0, 1), represent the candidate iteration parameter value generated based on the current iteration parameter value θ(t) as θ', and represent the iteration parameter value at the new number of iterations as θ'(t + 1). If the acceptance rate of the candidate iteration parameter value θ' generated based on the current iteration parameter value θ(t) is greater than or equal to the random number, then use the candidate iteration parameter value θ' as the iteration parameter value θ'(t + 1) at the new number of iterations. If the acceptance rate of the candidate iteration parameter value θ' is less than the random number, still use the current iteration parameter value θ(t) as the iteration parameter value θ'(t + 1) at the new number of iterations. Specifically, the iteration parameter value θ'(t + 1) at the new number of iterations can be expressed as:

[0121] (10)

[0122] In an exemplary embodiment, the above determination of the parameter values of the first voltage-capacity curve function based on the first iteration parameter value and the first acceptance rate further includes:

[0123] When the first acceptance rate does not meet the preset numerical condition and the number of iterations is less than or equal to the preset number of times, a third iteration parameter value is generated based on the first iteration parameter value and the proposal distribution of the parameter value.

[0124] Based on the prior distribution of the parameter value, the prior probability of the third iteration parameter value is determined.

[0125] Multiple data points, the first iteration parameter value, and the third iteration parameter value are respectively substituted into the likelihood function to obtain the likelihood value of the first iteration parameter value and the likelihood value of the third iteration parameter value respectively.

[0126] Based on the prior probability of the first iteration parameter value, the likelihood value of the first iteration parameter value, the prior probability of the third iteration parameter value, and the likelihood value of the third iteration parameter value, the second acceptance rate of the third iteration parameter value is determined.

[0127] When the second acceptance rate meets the preset numerical condition and the number of iterations is greater than the preset number of times, based on the first iteration parameter value and the third iteration parameter value, the parameter value of the first voltage capacity curve function is determined.

[0128] Among them, the third iteration parameter value is a candidate iteration parameter value different from the second iteration parameter value generated based on the first iteration parameter value.

[0129] Specifically, since the first acceptance rate is determined based on the prior probability of the first iteration parameter value, the likelihood value of the first iteration parameter value, the prior probability of the second iteration parameter value, and the likelihood value of the second iteration parameter value, therefore, when the first acceptance rate does not meet the preset numerical condition, it indicates that the matching degree between the second iteration parameter value and the given parameter may be relatively low or the probability density in the prior distribution is relatively low. Thus, at this time, the second iteration parameter value will not be determined as the parameter value of the first voltage capacity function curve, but a third iteration parameter value is regenerated based on the first iteration parameter value and the proposal distribution of the parameter value.

[0130] Optionally, there can be multiple third iteration parameter values. Thus, based on the first iteration parameter value and all the third iteration parameter values for which the second acceptance rate meets the preset numerical condition, the parameter value of the first voltage capacity curve function is determined.

[0131] It should be noted that the implementation process of generating the third iteration parameter value in this embodiment and the implementation process of generating the fourth iteration parameter value in the following text are the same as the implementation process of generating the second iteration parameter value in the above text, only the corresponding specific iteration parameter values are different. Therefore, they will not be elaborated here and in the following text.

[0132] It should be noted that in this embodiment, the implementation process of determining the prior probability of the third iteration parameter value, and the determination of the prior probability of the fourth iteration parameter value in the following text are the same as the implementation process of determining the prior probability of the second iteration parameter value in the above text. Only the corresponding specific iteration parameter values are different. Therefore, they will not be elaborated here and in the following text.

[0133] It should be noted that in this embodiment, the implementation process of separately obtaining the likelihood value of the first iteration parameter value and the likelihood value of the third iteration parameter value, and the implementation process of separately obtaining the likelihood value of the second iteration parameter value and the likelihood value of the fourth iteration parameter value in the following text are the same as the implementation process of separately obtaining the likelihood value of the first iteration parameter value and the likelihood value of the second iteration parameter value in the above text. Only the corresponding specific iteration parameter values are different. Therefore, they will not be elaborated here and in the following text.

[0134] It should be noted that in this embodiment, the implementation process of determining the second acceptance rate of the third iteration parameter value, and the implementation process of determining the third acceptance rate of the fourth iteration parameter value in the following text are the same as the implementation process of determining the first acceptance rate of the second iteration parameter value in the above text. Only the corresponding specific iteration parameter values are different. Therefore, they will not be elaborated here and in the following text.

[0135] In this embodiment, when the first acceptance rate of the second iteration parameter value is relatively low and the iteration process has not ended, based on the first iteration parameter value and the proposal distribution of the parameter value, a new candidate iteration parameter value, that is, the third iteration parameter value, is generated. Thus, it can be ensured that the parameter values of the finally determined first voltage capacity function curve are all highly accurate and reliable.

[0136] In an exemplary embodiment, the above determination of the parameter value of the first voltage capacity curve based on the first iteration parameter value and the first acceptance rate further includes:

[0137] When the first acceptance rate meets the preset numerical condition and the number of iterations is less than or equal to the preset number of times, based on the second iteration parameter value and the proposal distribution of the parameter value, a fourth iteration parameter value is generated.

[0138] Based on the prior distribution of the parameter value, the prior probability of the fourth iteration parameter value is determined.

[0139] Multiple data points, the second iteration parameter value, and the fourth iteration parameter value are respectively substituted into the likelihood function to separately obtain the likelihood value of the second iteration parameter value and the likelihood value of the fourth iteration parameter value.

[0140] Based on the prior probability of the second iteration parameter value, the likelihood value of the second iteration parameter value, the prior probability of the third iteration parameter value, and the likelihood value of the fourth iteration parameter value, the third acceptance rate of the fourth iteration parameter value is determined.

[0141] When the third acceptance rate meets the preset numerical condition and the number of iterations is greater than the preset number, based on the first iteration parameter value, the second iteration parameter value, and the fourth iteration parameter value, determine the parameter value of the first voltage-capacity curve function.

[0142] Among them, the fourth iteration parameter value is a new candidate iteration parameter value generated based on the accepted second iteration parameter value.

[0143] Optionally, there can be multiple fourth iteration parameter values. Thus, based on the first iteration parameter value, all the second iteration parameter values for which the first acceptance rate meets the preset numerical condition, and all the fourth iteration parameter values for which the third acceptance rate meets the preset numerical condition, determine the parameter value of the first voltage-capacity curve function.

[0144] In this embodiment, when the second iteration parameter value is accepted, the fourth iteration parameter value is generated. And when the third acceptance rate of the fourth iteration parameter value also meets the preset numerical condition, the first iteration parameter value, the second iteration parameter value, and the fourth iteration parameter value are determined as the parameter values of the first voltage-capacity curve function. Thus, under the iterative idea of the MCMC model, by continuously accepting and generating new iteration parameter values, this embodiment can fully explore the parameter space to ensure that the multiple candidate iteration parameter values obtained in the iterative process of this embodiment can gradually converge to the true parameter value distribution. At the same time, it can also avoid the bad situation of the iterative process falling into the local optimal solution, ensuring the global optimality of the parameter value. Furthermore, the parameter value of the first voltage-capacity curve function determined from the charging data collected under the low sampling frequency condition also has sufficient accuracy and reliability, enabling this embodiment to accurately determine the battery capacity increment curve function under the low sampling frequency condition.

[0145] It should be noted that since the parameter value of the first voltage-capacity curve function includes the peak position, peak area, and peak width of the first capacity increment peak, and the peak position, peak area, and peak width of the second capacity increment peak, correspondingly, the first iteration parameter value, the second iteration parameter value, the third iteration parameter value, and the fourth iteration parameter value each include 6 iteration parameter values corresponding to the parameter value of the first voltage-capacity curve function.

[0146] In an exemplary embodiment, the above method further includes:

[0147] Obtain the charging voltage of the battery to be measured.

[0148] Based on the charging voltage of the battery to be measured and the capacity increment curve function, obtain the target capacity increment of the battery to be measured at the charging voltage.

[0149] Among them, the battery to be measured is a battery that has a monitoring requirement for battery health and needs to determine the capacity increment.

[0150] In an exemplary embodiment, when the battery to be measured is in a fault state, the above method further includes: determining the fault phenomenon of the battery to be measured based on the target capacity increment of the battery to be measured under the charging voltage; the fault phenomenon is a battery aging phenomenon and / or an internal short circuit fault phenomenon.

[0151] In this embodiment, by obtaining the charging voltage of the battery to be measured, the target capacity increment of the battery to be measured under the charging voltage can be obtained based on the charging voltage of the battery to be measured and the capacity increment curve function. Thus, based on the target capacity increment of the battery to be measured under the charging voltage, the battery health status of the battery to be measured can be monitored. Specifically, when the battery to be measured is in a fault state, it can be determined whether the battery to be measured has a battery aging phenomenon or an internal short circuit fault phenomenon based on the target capacity increment. Obviously, based on the accurately determined capacity increment curve function, a convenient and efficient means is provided for the battery fault diagnosis of the battery to be measured.

[0152] In an exemplary embodiment, determining the parameter value of the first voltage-capacity curve function based on the first iteration parameter value and the first acceptance rate includes: determining the parameter value of the first voltage-capacity curve function based on the first iteration parameter value, the second iteration parameter value whose first acceptance rate meets the preset numerical condition, the third iteration parameter value whose second acceptance rate meets the preset numerical condition, and the fourth iteration parameter value whose third acceptance rate meets the preset numerical condition.

[0153] In an exemplary embodiment, the above determining the parameter value of the first voltage-capacity curve function based on the first iteration parameter value, the second iteration parameter value whose first acceptance rate meets the preset numerical condition, the third iteration parameter value whose second acceptance rate meets the preset numerical condition, and the fourth iteration parameter value whose third acceptance rate meets the preset numerical condition includes: among the first iteration parameter value, the second iteration parameter value whose first acceptance rate meets the preset numerical condition, the third iteration parameter value whose second acceptance rate meets the preset numerical condition, and the fourth iteration parameter value whose third acceptance rate meets the preset numerical condition, determining the target iteration parameter value whose corresponding iteration number is within the preset number interval; the preset number interval belongs to the range of the preset number; and determining the average value of the target iteration parameter value as the parameter value of the first voltage-capacity curve function.

[0154] Among them, the preset number of times interval belongs to the range of the preset number of times, which means that the maximum value of the preset number of times interval is less than the preset number of times. Optionally, since the candidate iteration parameter values obtained in the early iterations of the iteration process are greatly affected by the artificially set initial state, the stability of the candidate iteration parameter values obtained in the early iterations is low. Therefore, the preset number of times interval is the middle and later section interval of the range of the preset number of times, that is to say, the preset number of times interval does not include the front-end interval of the range of the preset number of times.

[0155] Optionally, the preset number of times interval can be determined according to the size of the preset number of times. Exemplarily, the minimum value of the preset number of times interval can be a preset ratio of the preset number of times. Further exemplarily, the preset ratio can be 0.5.

[0156] Exemplarily, represent the iteration parameter value obtained in each iteration process as θ0. When the maximum value of the preset number of times interval is the number of iterations, represent the average value of the target iteration parameter value as θ'', represent the number of iterations as T', and represent the minimum value of the preset number of times interval as B. Then the average value of the target iteration parameter value represented as θ'' can be expressed as:

[0157] (10)

[0158] In this embodiment, since after the iteration process is completed, the candidate iteration erased values obtained in the early iterations are discarded. Thus, no matter how the initial states such as the first iteration parameter, the prior distribution of the parameter value, and the proposal distribution of the parameter value are set, they will not have too much impact on the parameter value of the final first voltage capacity curve function. Furthermore, this embodiment can be applied to the iteration process with different initial states, improving the stability and reliability of the iteration process, and also ensuring that the parameter value of the first voltage capacity curve function has sufficient accuracy and reliability.

[0159] It should be noted that for the method for determining the battery capacity increment curve function provided in this application, accurate capacity increment curves can also be determined by collecting battery charging data at a high sampling frequency, rather than being limited to collecting battery charging data at a frequency lower than the preset frequency.

[0160] Specifically, in an exemplary embodiment, based on the method for determining the battery capacity increment curve function (mcmc-LFF), Gaussian filtering method (Gaussian Filtering), and Kalman filtering method (KalmanFiltering) provided in this embodiment, the charging data of the battery is collected at different first frequencies to determine the capacity increment curve function, such as Figure 3As shown in (a), it is the variation of the estimated error value (Mean Absolute Error, MAE) of the capacity increment curve function with the charge cycle after collecting the charging data of the battery at the first frequency of 1 Hz by three different methods; as Figure 3 shown in (b), it is the variation of the estimated error value of the capacity increment curve function with the charge cycle after collecting the charging data of the battery at the first frequency of 0.1 Hz by three different methods. Obviously, whether under the condition of high sampling frequency or low sampling frequency, the estimated error value of the capacity increment curve function determined in this application is always around 0.1. At the same time, as can be seen from Figure 3 (b), under the condition of low sampling frequency, the estimated error value of the capacity increment curve function determined in this application is significantly smaller than that of the battery capacity increment curve function obtained by other methods. That is to say, the method for determining the battery capacity increment curve function provided in this embodiment has higher advantages under the condition of low sampling frequency.

[0161] Furthermore, in an exemplary embodiment, as Figure 4 shown, based on the method for determining the battery capacity increment curve function provided in this embodiment, as the number of chains increases, the MAE of the capacity increment curve function obtained in this embodiment will stabilize around approximately 0.117; as Figure 5 shown, they are respectively the original data of the actual capacity increment curve function, the capacity increment curve function with 1000 chains, the capacity increment curve function with 2000 chains, the capacity increment curve function with 3000 chains, and the capacity increment curve function with 4000 chains. The variation of the incremental capacity (Ah-V -1 ) at different charging voltages (Voltage(V)) can be seen. As the number of chains increases, the fitting degree between the obtained capacity increment curve function and the actual capacity increment curve function plotted based on the voltage-capacity data obtained from the charging data of the battery will also increase accordingly. It can be seen that the method for determining the battery capacity increment curve function provided in this application is not only true and effective but also has effective benefits in terms of the number of chains.

[0162] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence 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 limit for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0163] Based on the same inventive concept, an embodiment of the present application also provides a device for determining a battery capacity increment curve function for implementing the battery capacity increment curve function determination method described above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the battery capacity increment curve function determination device provided below can refer to the limitations on the battery capacity increment curve function determination method in the above text, and will not be repeated here.

[0164] In an exemplary embodiment, as Figure 6 shown, a device for determining a battery capacity increment curve function is provided, including: a collection module 602, a first determination module 604, and a second determination module 606, where:

[0165] The collection module 602 is configured to collect charging data of the battery at a first frequency, and the first frequency is less than or equal to a preset frequency.

[0166] The first determination module 604 is configured to determine parameter values of a first voltage-capacity curve function based on the charging data to obtain a second voltage-capacity curve function.

[0167] The second determination module 606 is configured to determine a capacity increment curve function based on the second voltage-capacity curve function.

[0168] In an exemplary embodiment, the above first determination module 604 is configured to determine capacity data based on the charging current data and charging time data in the charging data; determine voltage-capacity data including multiple data points based on the charging voltage data and capacity data in the charging data; and determine parameter values of a first voltage-capacity curve function based on the multiple data points to obtain a second voltage-capacity curve function.

[0169] In an exemplary embodiment, the first determination module 604 is configured to generate a second iteration parameter value based on a first iteration parameter value and a proposal distribution of the parameter value; determine a prior probability of the first iteration parameter value and a prior probability of the second iteration parameter value respectively based on a prior distribution of the parameter value; substitute a plurality of data points, the first iteration parameter value, and the second iteration parameter value into a likelihood function respectively to obtain a likelihood value of the first iteration parameter value and a likelihood value of the second iteration parameter value respectively; determine a first acceptance rate of the second iteration parameter value based on the prior probability of the first iteration parameter value, the likelihood value of the first iteration parameter value, the prior probability of the second iteration parameter value, and the likelihood value of the second iteration parameter value; determine a parameter value of the first voltage-capacity curve function based on the first iteration parameter value and the first acceptance rate to obtain a second voltage-capacity curve function.

[0170] In an exemplary embodiment, the first determination module 604 is configured to determine a parameter value of the first voltage-capacity curve function based on the first iteration parameter value and all the second iteration parameter values for which the first acceptance rate meets a preset numerical condition when the first acceptance rate meets the preset numerical condition and the number of iterations is greater than a preset number.

[0171] In an exemplary embodiment, the first determination module 604 is configured to generate a third iteration parameter value based on the first iteration parameter value and the proposal distribution of the parameter value when the first acceptance rate does not meet the preset numerical condition and the number of iterations is less than or equal to the preset number; determine a prior probability of the third iteration parameter value based on the prior distribution of the parameter value; substitute a plurality of data points, the first iteration parameter value, and the third iteration parameter value into the likelihood function respectively to obtain a likelihood value of the first iteration parameter value and a likelihood value of the third iteration parameter value respectively; determine a second acceptance rate of the third iteration parameter value based on the prior probability of the first iteration parameter value, the likelihood value of the first iteration parameter value, the prior probability of the third iteration parameter value, and the likelihood value of the third iteration parameter value; determine a parameter value of the first voltage-capacity curve function based on the first iteration parameter value and all the third iteration parameter values for which the second acceptance rate meets the preset numerical condition when the second acceptance rate meets the preset numerical condition and the number of iterations is greater than the preset number.

[0172] In an exemplary embodiment, the above-mentioned first determination module 604 is configured to generate a fourth iteration parameter value based on a second iteration parameter value and a proposal distribution of the parameter value when the first acceptance rate meets a preset numerical condition and the number of iterations is less than or equal to a preset number; determine a prior probability of the fourth iteration parameter value based on a prior distribution of the parameter value; substitute multiple data points, the second iteration parameter value, and the fourth iteration parameter value into the likelihood function respectively to obtain a likelihood value of the second iteration parameter value and a likelihood value of the fourth iteration parameter value respectively; determine a third acceptance rate of the fourth iteration parameter value based on the prior probability of the second iteration parameter value, the likelihood value of the second iteration parameter value, the prior probability of the third iteration parameter value, and the likelihood value of the fourth iteration parameter value; and determine a parameter value of the first voltage capacity curve function based on the first iteration parameter value, the second iteration parameter value, and the fourth iteration parameter value when the third acceptance rate meets the preset numerical condition and the number of iterations is greater than the preset number.

[0173] In an exemplary embodiment, the above-mentioned second determination module 606 is further configured to obtain a charging voltage of a battery to be measured; and obtain a target capacity increment of the battery to be measured at the charging voltage based on the charging voltage of the battery to be measured and a capacity increment curve function.

[0174] Each module in the above-mentioned battery capacity increment curve function determination device can be implemented in whole or in part by software, hardware, and a combination thereof. Each of the above modules can be embedded in a processor in a computer device in a hardware form or be independent of the processor, or can be stored in a memory in the computer device in a software form so that the processor can call and execute operations corresponding to the above modules.

[0175] In an exemplary embodiment, a computer device is provided. The computer device can be a server, and its internal structural diagram can be as Figure 7 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store charging data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for determining a battery capacity increment curve function.

[0176] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structural diagram may be as shown in Figure 8 . The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with external terminals in a wired or wireless manner. The wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for determining a battery capacity increment curve function. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0177] Those skilled in the art can understand that Figure 8 the structure shown in

[0178] is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0179] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps in the above-mentioned method embodiments.

[0180] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, it implements the steps in the above-mentioned method embodiments.

[0181] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data that have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0182] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., and are not limited thereto. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., and are not limited thereto.

[0183] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this application.

[0184] The above-described embodiments merely represent several implementation manners of this application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of this application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of this application shall be subject to the appended claims.

Claims

1. A method for determining a battery capacity increment curve function, characterized in that, The method includes: Collecting charging data of the battery at a first frequency, where the first frequency is less than or equal to a preset frequency; Determining parameter values of a first voltage-capacity curve function according to the charging data to obtain a second voltage-capacity curve function; Determining a capacity increment curve function according to the second voltage-capacity curve function.

2. The method according to claim 1, wherein The determining parameter values of the first voltage-capacity curve function according to the charging data to obtain the second voltage-capacity curve function includes: Determining capacity data based on charging current data and charging time data in the charging data; Determining voltage-capacity data including a plurality of data points based on charging voltage data in the charging data and the capacity data; Determining parameter values of the first voltage-capacity curve function based on the plurality of data points to obtain the second voltage-capacity curve function.

3. The method according to claim 2, wherein The determining parameter values of the first voltage-capacity curve function based on the plurality of data points to obtain the second voltage-capacity curve function includes: Generating a second iteration parameter value based on a first iteration parameter value and a proposal distribution of the parameter value; Respectively determining a prior probability of the first iteration parameter value and a prior probability of the second iteration parameter value based on a prior distribution of the parameter value; Substituting the plurality of data points, the first iteration parameter value, and the second iteration parameter value into a likelihood function respectively to obtain a likelihood value of the first iteration parameter value and a likelihood value of the second iteration parameter value; Determining a first acceptance rate of the second iteration parameter value based on the prior probability of the first iteration parameter value, the likelihood value of the first iteration parameter value, the prior probability of the second iteration parameter value, and the likelihood value of the second iteration parameter value; Determining parameter values of the first voltage-capacity curve function based on the first iteration parameter value and the first acceptance rate to obtain the second voltage-capacity curve function.

4. The method according to claim 3, wherein The determining parameter values of the first voltage-capacity curve function based on the first iteration parameter value and the first acceptance rate includes: When the first acceptance rate meets a preset numerical condition and the number of iterations is greater than a preset number, determining parameter values of the first voltage-capacity curve function based on the first iteration parameter value and the second iteration parameter value.

5. The method according to claim 4, wherein The determining parameter values of the first voltage-capacity curve function based on the first iteration parameter value and the first acceptance rate further includes: When the first acceptance rate does not meet the preset numerical condition and the number of iterations is less than or equal to the preset number, generating a third iteration parameter value based on the first iteration parameter value and the proposal distribution of the parameter value; Determining a prior probability of the third iteration parameter value based on the prior distribution of the parameter value; Substituting the plurality of data points, the first iteration parameter value, and the third iteration parameter value into the likelihood function respectively to obtain a likelihood value of the first iteration parameter value and a likelihood value of the third iteration parameter value; Determine a second acceptance rate of the third iterative parameter value based on the prior probability of the first iterative parameter value, the likelihood value of the first iterative parameter value, the prior probability of the third iterative parameter value, and the likelihood value of the third iterative parameter value; When the second acceptance rate meets a preset numerical condition and the number of iterations is greater than a preset number, determine the parameter value of the first voltage-capacity curve function based on the first iterative parameter value and the third iterative parameter value.

6. The method according to claim 5, wherein The determining the parameter value of the first voltage-capacity curve function based on the first iterative parameter value and the first acceptance rate further includes: When the first acceptance rate meets a preset numerical condition and the number of iterations is less than or equal to a preset number, generate a fourth iterative parameter value based on the second iterative parameter value and the proposed distribution of the parameter value; Determine the prior probability of the fourth iterative parameter value based on the prior distribution of the parameter value; Substitute multiple said data points, the second iterative parameter value, and the fourth iterative parameter value into the likelihood function respectively to obtain the likelihood value of the second iterative parameter value and the likelihood value of the fourth iterative parameter value respectively; Determine a third acceptance rate of the fourth iterative parameter value based on the prior probability of the second iterative parameter value, the likelihood value of the second iterative parameter value, the prior probability of the third iterative parameter value, and the likelihood value of the fourth iterative parameter value; When the third acceptance rate meets a preset numerical condition and the number of iterations is greater than a preset number, determine the parameter value of the first voltage-capacity curve function based on the first iterative parameter value, the second iterative parameter value, and the fourth iterative parameter value.

7. The method according to claim 1, characterized in that, The derivative result of the first voltage-capacity curve function includes a first capacity increment peak and a second capacity increment peak; the first voltage-capacity curve function includes an integrated first Lorentz function and an integrated second Lorentz function, the first Lorentz function corresponds to the first capacity increment peak, and the second Lorentz function corresponds to the second capacity increment peak; The parameter value of the first voltage-capacity curve function includes the peak position, peak area, and peak width of the first capacity increment peak, and the peak position, peak area, and peak width of the second capacity increment peak.

8. The method according to claim 1, wherein The method further includes: Obtain the charging voltage of the battery to be tested; Based on the charging voltage of the battery to be tested and the capacity increment curve function, obtain the target capacity increment of the battery to be tested at the charging voltage.

9. A device for determining a battery capacity increment curve function, characterized in that, The device includes: An acquisition module, configured to acquire charging data of the battery at a first frequency, where the first frequency is less than or equal to a preset frequency; A first determination module, configured to determine the parameter value of the first voltage-capacity curve function according to the charging data to obtain a second voltage-capacity curve function; A second determination module, configured to determine a capacity increment curve function according to the second voltage-capacity curve function.

10. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

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