Battery health state determination method and device, equipment and medium

Abstract

The invention provides a battery health state determination method and device, equipment and a medium, and relates to an energy storage technology. The method comprises the steps of determining a target feature point corresponding to a current voltage inflection point according to voltage differential capacity data of a to-be-detected battery; the voltage differential capacity data is obtained through complete discharge data of the battery to be measured, and the voltage inflection point is a high voltage inflection point HVTP or a low voltage inflection point LVTP; and determining the health state of the to-be-detected battery according to the battery capacity corresponding to the target feature point and the capacity of the initial voltage inflection point. According to the invention, the health state of the battery can be efficiently calculated.

Description

Technical Field

[0001] This application relates to energy storage technologies, and in particular, to a method, device, equipment, and medium for determining the state of health of a battery. Background Art

[0002] With the wide application of energy storage batteries, the assessment of the state of health (SOH) of batteries has become very important. SOH is a key indicator for measuring the current health status of energy storage batteries, which can reflect the degree of performance degradation of energy storage batteries.

[0003] Currently, in known technologies, SOH is mainly evaluated by accurately measuring the amount of electricity released and stored by a battery during a complete charge-discharge cycle, or by estimating the SOH of a battery based on the relationship between the battery voltage and the state of charge (SOC) of the battery. Among them, the latter depends on the voltage change characteristics of the battery at different SOCs, and infers the SOH of the battery by comparing the characteristic curves of the aged battery and the new battery.

[0004] When determining the SOH of a battery through the above process, there are defects of low efficiency. Summary of the Invention

[0005] This application provides a method, device, equipment, and medium for determining the state of health of a battery, so as to improve the efficiency of determining the state of health of a battery.

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

[0007] Determine a target feature point corresponding to a current voltage inflection point according to the voltage differential capacity data of the battery to be measured; the voltage differential capacity data is obtained from the complete discharge data of the battery to be measured, and the voltage inflection point is a high voltage inflection point HVTP or a low voltage inflection point LVTP;

[0008] Determine the state of health of the battery to be measured according to the battery capacity corresponding to the target feature point and the capacity of the initial voltage inflection point.

[0009] In a possible implementation manner, the voltage differential capacity data is used to indicate the change rate of the voltage corresponding to different capacities with respect to the capacity, the voltage differential capacity data includes at least two feature points, and the feature points are the maximum value points or minimum value points of the change rate; the target feature point is determined according to the at least two feature points.

[0010] In a possible implementation manner, the determining a target feature point corresponding to a current voltage inflection point according to the voltage differential capacity data of the battery to be measured includes:

[0011] For each feature point, determine the target value of the feature point; the target value is related to the capacity change values on both sides of the feature point and / or the change rate change value of the voltage relative to the capacity;

[0012] Determine the target feature point from the at least two feature points according to the target value.

[0013] In a possible implementation manner, the determining the target value of the feature point includes:

[0014] Obtain a first change rate change value and a second change rate change value on both sides of the feature point under the same target capacity change value;

[0015] Determine the target value according to the target capacity change value, the first change rate change value, and the second change rate change value.

[0016] In a possible implementation manner, the determining the target value according to the target capacity change value, the first change rate change value, and the second change rate change value includes:

[0017] Calculate the ratio of twice the sum of the target capacity change value and the first change rate change value to the product of the target capacity change value and the first change rate change value to obtain a first value;

[0018] Calculate the ratio of twice the sum of the target capacity change value and the second change rate change value to the product of the target capacity change value and the second change rate change value to obtain a second value;

[0019] Calculate the sum of the first value and the second value to obtain the target value.

[0020] In a possible implementation manner, the determining the target value of the feature point includes:

[0021] Obtain a first capacity change value and a second capacity change value on both sides of the feature point under the same target change rate change value;

[0022] Determine the target value of the feature point according to the target change rate change value, the first capacity change value, and the second capacity change value.

[0023] In a possible implementation manner, the determining the target value of the feature point according to the target change rate change value, the first capacity change value, and the second capacity change value includes:

[0024] Calculate the ratio of twice the sum of the target change rate change value and the first capacity change value to the product of the target change rate change value and the first capacity change value to obtain a third value;

[0025] Calculate the ratio of twice the sum of the target rate-of-change value and the second capacity change value to the product of the target rate-of-change value and the second capacity change value to obtain a fourth value;

[0026] Calculate the sum of the third value and the fourth value to obtain the target value of the feature point.

[0027] In a possible implementation manner, the determining the target feature point from the at least two feature points according to the target value includes:

[0028] Determine the feature point with the smallest target value among the at least two feature points as the target feature point.

[0029] In a possible implementation manner, the determining the target value of the feature point includes:

[0030] Obtain the rate-of-change amount or capacity change amount within a preset range on both sides of the feature point;

[0031] Perform curvature calculation according to the rate-of-change amount or the capacity change amount to obtain the target value.

[0032] In a possible implementation manner, the determining the target feature point from the at least two feature points according to the target value includes:

[0033] Determine the feature point with the largest target value among the at least two feature points as the target feature point.

[0034] In a possible implementation manner, the method further includes:

[0035] When the complete discharge data is obtained, perform filtering processing on the complete discharge data by using a preset filtering method to obtain first intermediate data; the preset filtering method is used to eliminate the spike noise in the complete discharge data;

[0036] Determine the voltage differential capacity data of the battery under test according to the first intermediate data.

[0037] In a possible implementation manner, the determining the voltage differential capacity data of the battery under test according to the first intermediate data includes:

[0038] Perform at least one of dynamic smoothing processing and multi-scale analysis processing on the first intermediate data to obtain at least one second intermediate data;

[0039] Obtain the voltage differential capacity data according to the at least one second intermediate data.

[0040] In a possible implementation manner, when performing the dynamic smoothing process and the multi-scale analysis process on the first intermediate data, obtaining the voltage differential capacity data according to the at least one second intermediate data includes:

[0041] Performing weighted average processing on the two obtained second intermediate data to obtain voltage capacity data;

[0042] Performing differential processing on the voltage capacity data to obtain the voltage differential capacity data.

[0043] In a possible implementation manner, performing dynamic smoothing processing on the first intermediate data to obtain second intermediate data includes:

[0044] Obtaining a target internal resistance according to an optimization objective; the optimization objective is expressed as: minR ||D(v)−D(I)∙ R|| 2 , where D(v) is used to represent the first derivative function, D(I) is used to represent the second derivative function, and R is used to represent the internal resistance of the battery under test; the first derivative function is used to calculate the mean difference of the voltage of the battery under test between two adjacent preset windows, and the second derivative function is used to calculate the mean difference of the current of the battery under test between two adjacent preset windows;

[0045] Determining the second intermediate data according to the target internal resistance.

[0046] In a possible implementation manner, performing multi-scale analysis processing on the first intermediate data to obtain second intermediate data includes:

[0047] Decomposing the first intermediate data into different frequency scales by multiple preset methods, and performing analysis processing on the first intermediate data of different frequency scales to obtain second sub-intermediate data at each frequency scale; the multiple preset methods include wavelet transform method and Fourier transform method;

[0048] Performing weighted average processing on the second sub-intermediate data at each frequency scale to obtain the second intermediate data.

[0049] In a possible implementation manner, determining the health state of the battery under test according to the battery capacity corresponding to the target feature point and the capacity of the initial voltage inflection point includes:

[0050] Calculating a capacity difference according to the target capacity corresponding to the full charge state and the battery capacity;

[0051] Calculating the remaining capacity according to the capacity difference and the capacity of the initial voltage inflection point;

[0052] Calculate the health state of the battery under test according to the remaining capacity.

[0053] In a second aspect, the present application provides a device for determining the health state of a battery, the device comprising:

[0054] A first determination module, configured to determine a target feature point corresponding to a current voltage inflection point according to voltage differential capacity data of a battery under test; the voltage differential capacity data is obtained from complete discharge data of the battery under test, and the voltage inflection point is a high voltage inflection point HVTP or a low voltage inflection point LVTP;

[0055] A second determination module, configured to determine the health state of the battery under test according to the battery capacity corresponding to the target feature point and the capacity of the initial voltage inflection point.

[0056] In a third aspect, the present application provides an electronic device, comprising a processor and a memory communicatively connected to the processor;

[0057] The memory stores computer-executable instructions;

[0058] The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of the first aspects.

[0059] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method according to any one of the first aspects.

[0060] In a fifth aspect, the present application provides a computer program product, comprising a computer program, and when the computer program is executed by a processor, it implements the method according to any one of the first aspects.

[0061] The present application provides a method, a device, an equipment, and a medium for determining the health state of a battery. In the method of the present application, an electronic device determines a target feature point corresponding to a current voltage inflection point of a battery under test according to voltage differential capacity data of the battery under test, and determines the health state of the battery under test according to the battery capacity corresponding to the target feature point and the capacity of the initial voltage inflection point. Through the method of the present application, on the one hand, since the difference between the capacity corresponding to the voltage inflection point of the battery and the capacity after the battery is fully discharged remains constant, the accuracy of the health state of the battery under test obtained by the present application is guaranteed. On the other hand, the method of the present application does not rely on the calculation of the state of charge of the battery, and the calculation complexity is low, so that the calculation efficiency is guaranteed. Description of the Drawings

[0062] The accompanying drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0063] Figure 1 It is an application scenario diagram of a method for determining the state of health of a battery provided by an embodiment of the present application;

[0064] Figure 2 It is a flowchart of a method for determining the state of health of a battery provided by an embodiment of the present application Figure 1 ;

[0065] Figure 3 It is a flowchart of a method for determining the state of health of a battery provided by an embodiment of the present application Figure 2 ;

[0066] Figure 4 It is an example diagram of a process for determining target feature points provided by an embodiment of the present application;

[0067] Figure 5 It is a structural schematic diagram of a device for determining the state of health of a battery provided by an embodiment of the present application;

[0068] Figure 6 It is a structural schematic diagram of an electronic device provided by an embodiment of the present application.

[0069] Through the above accompanying drawings, specific embodiments of the present application have been shown, and there will be more detailed descriptions hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. Detailed Description of the Embodiments

[0070] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0071] With the emerging applications of renewable energy, energy storage batteries are also widely used. For example, there is a strong demand for high-performance batteries in the fields of electric vehicles, electric aviation, and marine navigation. Compared with traditional uses, these advanced power systems place higher requirements on battery performance, that is, not only the power and energy density need to be greatly improved, but also they need to withstand more extreme temperature fluctuations. These factors together accelerate the aging process of the battery.

[0072] In view of this, it is particularly important to accurately monitor the State Of Health (SOH) of the battery and determine the replacement time in a timely manner. It should be understood that SOH is a key indicator to measure the current health status of the energy storage battery, which is usually defined as the ratio of the current maximum capacity of the battery to the maximum capacity at the original factory, and is used to reflect the degree of performance degradation of the energy storage battery.

[0073] Currently, the known technologies for determining the SOH of a battery mainly measure the amount of electricity released and stored by the battery during a complete charge-discharge cycle accurately, compare the actual electricity with the nominal capacity, and evaluate the SOH by the ratio. Or, estimate the SOH based on the relationship between the battery voltage and the State of Charge (SOC), which changes with battery aging, and infer the SOH by comparing the voltage change characteristic curves of the aged battery and the new battery at different SOCs.

[0074] As an example, a known technology provides a method for estimating the health status of a lithium iron phosphate battery. This method requires the battery of the target vehicle to enter the fast charge state, and then obtain the battery charge state value of the battery in real time. When it is detected that the battery charge state value meets the target battery charge state value, the obtained voltage drop value of the battery is input into the aging model, so that the aging model outputs the battery health state value, and based on the battery health state value, calculate and obtain the total battery health state value of the battery.

[0075] It can be understood that for the first method mentioned above, it is necessary to conduct charge-discharge experiments on the battery to accurately measure the amount of electricity released and stored by the battery during a complete charge-discharge cycle. On the one hand, there is a defect of low efficiency, and on the other hand, it will also cause additional losses to the battery.

[0076] For the second method mentioned above, the accuracy of SOC directly affects the evaluation accuracy of SOH. Especially in the application scenario of lithium iron phosphate batteries, even a small deviation of SOC may be amplified due to the cumulative effect, and then significantly affect the calculation result of SOH. Conversely, the uncertainty of SOH will be feedback to the prediction model of SOC, forming a two-way interference. This cross-influence mechanism may lead to a vicious circle in the estimation of SOH, and even cause the failure of the entire state evaluation system in severe cases.

[0077] As can be seen from the above content, the known technologies are often inefficient and cause additional losses to the battery when determining the SOH of the battery. In addition, when calculating the SOH through SOC, there are extremely high requirements for the battery working conditions and the accuracy of SOC.

[0078] Therefore, the present application provides a battery health status determination method, device, equipment, and medium to solve the above problems. Specifically, the method of the present application is applicable to lithium iron phosphate batteries, and the battery health status determination method in the present application is executed by any electronic device.

[0079] It should be noted that the voltage-capacity characteristics of lithium iron phosphate batteries show a unique three-stage behavior pattern, which is interspersed with a "platform area" where the voltage changes slowly and an "inflection point area" where the voltage jumps rapidly. In these inflection point areas, the most significant voltage jump is defined as the voltage inflection point, which is subdivided into high voltage inflection point (High Voltage Threshold Point, HVTP) and low voltage inflection point (Low Voltage Threshold Point, LVTP) according to different voltage levels.

[0080] During the battery aging process, the difference between the capacity values ​​corresponding to HVTP and LVTP and the capacity value at zero SOC remains constant. Specifically, the SOH state of the battery can be indirectly reflected by calculating the capacity value at full SOC minus the capacity value corresponding to HVTP or LVTP. In addition, it is worth noting that for battery cells of the same production batch, the power QHVTP and QLVTP corresponding to HVTP and LVTP are fixed and unchanged, and this parameter can be determined by pre-calibration.

[0081] On this basis, the method of the present application proposes to determine the target characteristic point corresponding to the current voltage inflection point based on the voltage differential capacity data of the battery to be tested, and determine the health status of the battery to be tested based on the target characteristic point, that is, the battery capacity corresponding to the current voltage inflection point and the capacity of the initial voltage inflection point.

[0082] Through the above process, under the premise of effectively ensuring the accuracy of the SOH of the battery to be tested, there is no need to perform additional charge and discharge experiments on the battery to be tested, which reduces the loss of the battery to be tested and the experimental time. There is no need to calculate the SOC of the battery to be tested and calculate the SOH based on the SOC, which has the advantage of low computational complexity and is beneficial to improving computational efficiency.

[0083] It can be understood that the method of the present application is applicable to any scenario of determining the SOH of a lithium iron phosphate battery. For example, Figure 1 An application scenario diagram of a battery health status determination method provided in an embodiment of the present application, such as Figure 1 As shown, the method of the present application can be used for electric vehicles or hybrid vehicles using lithium iron phosphate batteries.

[0084] Specifically, during the operation of an electric vehicle or hybrid vehicle, the electronic device interacts with the BMS of the electric vehicle or hybrid vehicle to obtain voltage differential capacity data of the battery to be tested, and determine the target feature point corresponding to the current voltage inflection point based on the voltage differential capacity data. Further, the health status of the battery to be tested is determined based on the battery capacity corresponding to the target feature point and the capacity at the initial voltage inflection point.

[0085] It is understandable that the electronic device can be integrated into the BMS of the corresponding electric vehicle or hybrid vehicle, or can be independently provided, which is not limited in this embodiment. When the electronic device is independently provided, it communicates with the BMS of the electric vehicle or hybrid vehicle through a wireless connection.

[0086] In conjunction with the accompanying drawings, some implementations of the battery health status determination method of the present application are described in detail below. In the event that the embodiments do not conflict with each other, the following embodiments and features in the embodiments may be combined with each other.

[0087] The present application provides a method for determining a battery health status. Figure 2 A flow chart of a method for determining a battery health status provided in an embodiment of the present application Figure 1 ,like Figure 2 As shown, a method for determining a battery health status provided in an embodiment of the present application includes the following contents:

[0088] S201, determining a target characteristic point corresponding to a current voltage inflection point according to voltage differential capacity data of a battery to be tested.

[0089] Among them, the voltage differential capacity data is obtained through the complete discharge data of the battery to be tested, and the voltage inflection point is the high voltage inflection point HVTP or the low voltage inflection point LVTP, which is the point where the voltage jump is most significant.

[0090] Specifically, in this embodiment, the voltage differential capacity data is used to indicate the rate of change of the voltage corresponding to different capacities relative to the capacity, and the voltage differential capacity data includes at least two characteristic points, which are the maximum or minimum points of the rate of change; the target characteristic point is determined based on at least two characteristic points.

[0091] More specifically, in this embodiment, the voltage differential capacity data is expressed in the form of a curve, the horizontal axis of the curve is the capacity, and the vertical axis is the rate of change of the voltage relative to the capacity. In practical applications, the voltage differential capacity data can also be expressed in the form of a digital sequence, that is, the voltage differential capacity data is arranged into a digital sequence in the order of capacity, and each capacity corresponds to a rate of change of the voltage relative to the capacity. In practical applications, the voltage differential capacity data can also be expressed in the form of a table, a matrix, etc., which is not limited in this embodiment.

[0092] In this embodiment, the complete discharge data of the battery under test is obtained during its operation. After the battery under test is put into use, its discharge process from 100% SOC to 0% SOC is traced to capture the behavioral characteristics of the battery under test under real load conditions. Whenever an electronic device obtains a piece of complete discharge data of the battery under test, it obtains voltage differential capacity data based on the complete discharge data and executes the calculation logic of this application to determine the SOH of the battery under test. In practical applications, when the electronic device obtains user requirements, it can also determine the voltage differential capacity data based on the latest piece of complete discharge data obtained, and then determine the SOH of the battery under test. This embodiment does not limit this.

[0093] It can be understood that the complete discharge data refers to a set of data in which a series of parameters related to the battery state change with time or capacity during the entire process of the battery under test from a fully charged state to complete discharge to a specified cut-off condition. Specifically, it includes voltage data, current data, and capacity data. More specifically, the voltage data includes the voltage values of the battery under test at each moment during the discharge process, the current data is used to indicate the magnitude and direction of the discharge current, and the capacity data is used to indicate the amount of electricity released by the battery under test during the discharge process, which is calculated by ampere-hour integration.

[0094] In practical applications, the voltage differential capacity data can also be used to indicate the change rate of voltage with respect to capacity corresponding to different voltages. This embodiment does not limit this.

[0095] S202. Determine the health state of the battery under test according to the battery capacity corresponding to the target characteristic point and the capacity of the initial voltage inflection point.

[0096] It can be understood that when the battery under test leaves the factory, it will be charged with a constant current to a fully saturated state to ensure that the active substances inside the battery under test react fully and reach the maximum energy storage capacity. Subsequently, it is completely discharged to zero state with a constant current, and the maximum capacity value during this process will be recorded, and a detailed voltage-capacity curve will be collected.

[0097] In this embodiment, through the analysis of this voltage-capacity curve, it is possible to offline calibrate and lock the charge QHVTP corresponding to the high voltage inflection point and QLVTP corresponding to the low voltage inflection point at this time, that is, the capacity of the initial voltage inflection point mentioned in this embodiment.

[0098] In this embodiment, the electronic device obtains the target capacity corresponding to the fully charged state, and calculates the SOH based on the target capacity and the capacity at the initial voltage inflection point. Specifically, the electronic device calculates the capacity difference according to the target capacity corresponding to the fully charged state and the battery capacity at the current voltage inflection point; calculates the remaining capacity according to the capacity difference and the capacity at the initial voltage inflection point; and calculates the health state of the battery under test according to the remaining capacity.

[0099] Among them, the target capacity corresponding to the fully charged state is obtained based on the complete discharge data of this time, specifically, it is the capacity when the battery under test is in the fully charged state.

[0100] As an example, when the voltage inflection point is HVTP, after obtaining the capacity difference, the electronic device calculates the remaining capacity by calling the formula Q = QHVTP + △Q. Among them, QHVTP is used to represent the capacity at the initial high voltage inflection point, △Q is used to represent the capacity difference, and Q is used to represent the remaining capacity.

[0101] Furthermore, the electronic device calculates the SOH of the battery under test by calling the formula SOH = Q / Qnew ∙ 100%, where Qnew is used to represent the initial maximum capacity value of the battery under test.

[0102] It can be understood that the health state of the battery is essentially closely related to the quantity and activity of the internal active substances. As the battery is used, the active substances will gradually be consumed, resulting in a decrease in the battery capacity. By recording the capacity corresponding to the initial voltage inflection point (QHVTP and LVTP) and comparing the capacity at the current voltage inflection point in subsequent measurements, the change of the active substances can be keenly captured. Because the inflection point of the voltage-capacity curve is closely related to the electrochemical reaction process inside the battery, the change of the capacity at the inflection point directly reflects the degree and quantity change of the active substances participating in the reaction. Therefore, through the above process, the SOH of the battery under test can be accurately evaluated.

[0103] In the method provided in this embodiment, the electronic device determines the target feature point corresponding to the current voltage inflection point of the battery under test according to the voltage differential capacity data of the battery under test to determine the current voltage inflection point. Furthermore, the electronic device determines the health state of the battery under test according to the battery capacity corresponding to the target feature point and the capacity at the initial voltage inflection point.

[0104] Through the method of this embodiment, on the premise of ensuring the accuracy of SOH calculation, the SOH of the battery under test can be calculated directly according to the capacity corresponding to the current voltage inflection point obtained from the voltage differential capacity data, without performing discharge tests on the battery under test, calculating the SOC of the battery under test, etc. Therefore, on the one hand, the loss to the battery under test is effectively reduced, and on the other hand, the calculation complexity is low, which is beneficial to ensuring the calculation efficiency.

[0105] In a possible design,Figure 3 Flow schematic of a method for determining the state of health of a battery provided by an embodiment of the present application Figure 2 Based on the foregoing embodiment, the embodiment of the present application further elaborates in detail on the method for determining voltage differential capacity data and the method for determining target feature points in the foregoing embodiment. As Figure 3 shown, the method of this embodiment includes:

[0106] S301. When complete discharge data is obtained, perform filtering processing on the complete discharge data using a preset filtering method to obtain first intermediate data.

[0107] Among them, the preset filtering method is used to eliminate spike noise in the complete discharge data.

[0108] It can be understood that the instantaneous change of voltage may mask feature points, thereby affecting the accurate assessment of the battery state. Therefore, in this embodiment, whenever the electronic device obtains the complete discharge data of the battery to be measured, it performs filtering processing on the complete discharge data using a preset filtering method to eliminate the voltage fluctuation interference caused by current and obtain first intermediate data.

[0109] Specifically, in this embodiment, the preset filtering method can be median filtering or interquartile range filtering method, which is used to remove spike noise in the voltage signal. More specifically, median filtering replaces the value of the center point by selecting the median within the window, which can effectively eliminate the voltage spike noise caused by large current. Interquartile range filtering calculates the upper and lower boundaries through the upper and lower quartiles. Any value less than the lower bound or greater than the upper bound is regarded as an outlier. After removing the outliers, the goal of eliminating spike noise is achieved.

[0110] It can be understood that in practical applications, the preset filtering method can also be mean filtering, Gaussian filtering, etc. The corresponding filtering method can be adopted in combination with specific working conditions, and this is not limited in this embodiment.

[0111] S302. Determine the voltage differential capacity data of the battery to be measured according to the first intermediate data.

[0112] In this embodiment, the electronic device determines the voltage differential capacity data of the battery to be measured according to the first intermediate data after filtering processing. It can be understood that the first intermediate data contains the capacity and voltage information of the battery to be measured during the complete discharge process. The electronic device extracts the battery capacity and battery voltage corresponding to different time points from the first intermediate data. Further, since the obtained data is discrete, the method of discrete difference is used in this embodiment to approximately calculate the change rate of voltage with respect to capacity, thereby obtaining the voltage differential capacity data.

[0113] As a preferred example, when the electronic device determines the voltage differential capacity data according to the first intermediate data, it specifically performs at least one of dynamic smoothing processing and multi-scale analysis processing on the first intermediate data to obtain at least one second intermediate data; then, based on the at least one second intermediate data, the voltage differential capacity data is obtained.

[0114] Among them, the dynamic smoothing processing is used to make the voltage-capacity curve corresponding to the first intermediate data smoother, and the multi-scale analysis processing is used to further remove the noise components and retain the useful information. Through this setting, clearer and more accurate second intermediate data can be obtained. Based on this second intermediate data, more accurate voltage differential capacity data can be obtained, thus providing a solid foundation for further battery SOH evaluation and prediction.

[0115] More specifically, in the case of performing dynamic smoothing processing and multi-scale analysis processing on the first intermediate data, weighted average processing is performed on the two obtained second intermediate data to obtain voltage-capacity data; differential processing is performed on the voltage-capacity data to obtain voltage differential capacity data.

[0116] In this embodiment, the electronic device specifically performs dynamic smoothing processing and multi-scale analysis processing on the first intermediate data to obtain two second intermediate data. After weighted average processing is performed on the two obtained second intermediate data, voltage-capacity data is obtained. Furthermore, by performing differential processing on the voltage-capacity data, voltage differential capacity data is obtained. Through this process, the advantages of various filtering methods can be fully utilized to improve the overall quality of the voltage-capacity data.

[0117] In this embodiment, when performing dynamic smoothing processing on the first intermediate data to obtain the second intermediate data, specifically, the target internal resistance is obtained according to the optimization objective, and then the second intermediate data is determined according to the target internal resistance. Specifically, the optimization objective is expressed as: minR ||D(v)−D(I) ∙R||2, where D(v) is used to represent the first derivative function, D(I) is used to represent the second derivative function, and R is used to represent the internal resistance of the battery under test; the first derivative function is used to calculate the mean difference of the voltage of the battery under test between two adjacent preset windows, and the second derivative function is used to calculate the mean difference of the current of the battery under test between two adjacent preset windows.

[0118] It can be understood that since Vocv≈V - I∙R, if the internal resistance can be known, the true OCV curve can be known. Given that Vocv is smooth, the goal is to find R such that V - I∙R is as smooth as possible. In this embodiment, a derivative function D is first defined to calculate the mean difference of the voltage between two adjacent preset windows, which is expressed as: D(V)(i)=mean(V(i:i + s)) − mean(V(i - s:i)), where the two adjacent preset windows are the window from i to i + s and the window from i - s to i. On this basis, the optimization goal is expressed as minR ||D(v)−D(I) ∙R|| 2 , which is used to make the obtained OCV curve as smooth as possible and the pressure difference between the front and back as small as possible.

[0119] Solve the optimization goal to obtain the target internal resistance. Specifically, the target internal resistance R=(D(I)TD(I))−1D(I)

[0120] It can be understood that the first intermediate data may contain noise generated by factors such as the accuracy of the measurement device, external electromagnetic interference, or instantaneous reactions inside the battery. Through dynamic smoothing processing, with the goal of finding a suitable internal resistance R to make V - I∙R close to the smooth Vocv curve, the influence of these noises can be effectively reduced. Because noise often manifests as high-frequency fluctuations in the data, and the optimization process will make the reconstructed voltage curve smoother, filtering out these high-frequency noise components, making the data better reflect the true characteristics of the battery, which is conducive to improving the accuracy of calculating SOH.

[0121] In this embodiment, multi-scale analysis processing is performed on the first intermediate data to obtain the second intermediate data. Specifically, the first intermediate data is decomposed into different frequency scales through a variety of preset methods, and the first intermediate data at different frequency scales is analyzed and processed to obtain the second sub-intermediate data at each frequency scale; then weighted average processing is performed on the second sub-intermediate data at each frequency scale to obtain the second intermediate data. Among them, the variety of preset methods include wavelet transform method and Fourier transform method.

[0122] In this embodiment, the possible multi-scale characteristics of the voltage signal are fully considered, which can identify and remove noise components at different frequencies while retaining useful information, so as to obtain the second intermediate data that conforms to the actual situation.

[0123] S303, for each feature point, determine the target value of the feature point.

[0124] Among them, the target value is related to the capacity change value on both sides of the feature point and / or the change rate change value of the voltage relative to the capacity.

[0125] S304. Determine a target feature point from at least two feature points according to a target value.

[0126] In this embodiment, the feature point is specifically a peak or a valley on the voltage differential capacity curve of voltage differential capacity data. The abscissa of the voltage differential capacity curve is used to represent the capacity, and the ordinate is used to represent the change rate of the voltage with respect to the capacity. The electronic device determines the target value of the peak or valley according to the capacity change value on both sides of the peak or valley and / or the change rate of the voltage with respect to the capacity.

[0127] It can be understood that when calculating the SOH of the battery under test according to HVTP, the electronic device calculates the target value of each peak. When calculating the SOH of the battery under test according to LVTP, the electronic device calculates the target value of each valley.

[0128] In this embodiment, the electronic device determines a target peak or a target valley from at least two peaks or at least two valleys according to the target value of each peak or valley, and calculates the SOH of the battery under test according to the target peak or the target valley.

[0129] As a design, for each feature point (peak or valley), the electronic device determines the target feature point through the ratio of the perimeter and the area on both sides of the feature point, and the curve amplitude at the target feature point is the highest.

[0130] Specifically, the electronic device obtains a first change rate change value and a second change rate change value at the same target capacity change value on both sides of the feature point; determines the target value according to the target capacity change value, the first change rate change value, and the second change rate change value.

[0131] More specifically, the electronic device calculates the ratio of twice the sum of the target capacity change value and the first change rate change value to the product of the target capacity change value and the first change rate change value to obtain a first value; calculates the ratio of twice the sum of the target capacity change value and the second change rate change value to the product of the target capacity change value and the second change rate change value to obtain a second value; calculates the sum of the first value and the second value to obtain the target value.

[0132] Similarly, the electronic device can also obtain a first capacity change value and a second capacity change value at the same target change rate change value on both sides of the feature point; determines the target value of the feature point according to the target change rate change value, the first capacity change value, and the second capacity change value.

[0133] More specifically, the electronic device calculates the ratio of twice the sum of the target change rate change value and the first capacity change value to the product of the target change rate change value and the first capacity change value to obtain a third value; calculates the ratio of twice the sum of the target change rate change value and the second capacity change value to the product of the target change rate change value and the second capacity change value to obtain a fourth value; calculates the sum of the third value and the fourth value to obtain the target value of the feature point.

[0134] On this basis, the electronic device determines the feature point with the smallest target value among at least two feature points as the target feature point.

[0135] It can be understood that when analyzing battery-related curves (such as voltage differential capacity curves, etc.), feature points (peaks or valleys) often contain important information about changes in the internal state of the battery. By determining the target feature point through the ratio of the perimeter and area on both sides of the feature point (reflected by the relationship between the change rate and the capacity), the rationality lies in that the change situations of the curves on both sides of different feature points are different, and this change can be quantified through the combined relationship between the change rate and the capacity.

[0136] Specifically, the perimeter on both sides of the feature point is twice the sum of the corresponding side target capacity change value and the first change rate change value, or twice the sum of the target change rate change value and the first capacity change value, and the area on both sides of the feature point is the product of the corresponding side target capacity change value and the first change rate change value, or the product of the target change rate change value and the first capacity change value.

[0137] In the above process, by considering the change rate and capacity change situations on both sides of the feature point, rather than relying solely on a single amplitude or other simple indicators, the feature point can be evaluated more comprehensively. Different feature points may be similar in amplitude, but there are differences in the change trends on both sides. This multi-factor evaluation method can more accurately screen out the target feature point that best represents the key state changes of the battery. The target feature point is often closely related to the electrochemical reaction inside the battery, the change of active substances, etc.

[0138] As an example, Figure 4 is an example diagram of a process for determining a target feature point provided by an embodiment of the present application. Specifically, Figure 4 In (1), the discharge voltage curve obtained by the electronic device from the acquired complete discharge data, where the abscissa of this curve is used to represent the capacity and the ordinate is used to represent the discharge voltage. Figure 4 In (2), the discharge voltage curve obtained by the electronic device from the voltage-capacity data obtained by performing the multi-layer fusion smoothing filtering process mentioned above on the complete discharge data, where the abscissa of this curve is used to represent the capacity and the ordinate is used to represent the smoothed filtering voltage, that is, the discharge voltage obtained after the multi-layer fusion smoothing filtering process. Figure 4(3) in the figure is the voltage differential capacity data obtained by the electronic device through the voltage capacity data, and the obtained voltage differential capacity curve has a horizontal axis used to represent the capacity and a vertical axis used to represent the rate of change of the voltage relative to the capacity.

[0139] More specifically, the characteristic point is the peak or valley value on the voltage differential capacity curve. When searching for HVTP through the peak value and calculating the remaining capacity based on HVTP, for each peak value, the electronic device determines the target value corresponding to the peak value based on the first change rate change value h1 and the second change rate change value h2 under the same target capacity change value w1 (w1= w2) on both sides. Among them, the target value is expressed as 2(h1+w1)∕(h1∙w1)+2(h2+w2)∕(h2∙w2), and the electronic device takes the peak value with the lowest target value as the true peak, that is, the target characteristic point, and calculates the SOH of the battery to be tested based on the target characteristic point and QHVTP.

[0140] It is understandable that from the characteristics of battery curves (such as voltage-capacity curves, voltage differential capacity curves, etc.), the characteristic points (peaks or valleys) reflect the key positions of battery state changes. The change rate or capacity change on both sides of the characteristic point is closely related to the curvature of the curve. Curvature is an important indicator to describe the curvature of the curve. By calculating the curvature within a preset range on both sides of the characteristic point, the curvature of the curve near the characteristic point can be quantified.

[0141] Therefore, as another design, the electronic device obtains the change rate change or capacity change within a preset range on both sides of the feature point; calculates the curvature according to the change rate change or capacity change to obtain the target value.

[0142] On this basis, the electronic device determines the feature point with the largest target value among the at least two feature points as the target feature point.

[0143] It is understandable that a feature point with a larger curvature of the curve means that the state change near the point is more drastic, and is more likely to represent an important physical or chemical change inside the battery, such as a change in the intensity of the electrode reaction, a change in the internal structure of the battery, etc. Therefore, in this embodiment, the feature point with the largest target value (curvature) is selected as the target feature point.

[0144] In the above process, by calculating the curvature to select the target feature points, it is possible to more accurately highlight those feature points with large curve curvature and drastic state changes. Compared with selecting feature points based on a single indicator such as amplitude, this method takes into account the local shape and change trend of the curve, and can more comprehensively reflect the state changes of the battery, thereby improving the accuracy of feature point selection.

[0145] In addition, during the actual battery data acquisition process, there may be noise interference, resulting in the appearance of some false feature points. The method based on curvature calculation pays more attention to the overall change trend of the curve and has a certain anti-interference ability for local small fluctuations caused by noise. Since noise usually does not cause obvious bending changes in the curve, by screening the feature points with the largest curvature, noise interference can be effectively reduced and the quality of the feature points can be improved.

[0146] In practical applications, the electronic device can also determine the target feature points in other ways, as long as it can find the feature points corresponding to the voltage inflection point. For example, the target feature points can be determined by the slope similarity on both sides of the feature point, and the target feature point is the feature point with the highest slope similarity on both sides.

[0147] In summary, in the method provided by this application, during the battery discharge process, the voltage-capacity curve is detected and analyzed in real time. It is not necessary to determine the battery operating conditions and the accuracy of SOC. During the discharge process, only the voltage-capacity curve needs to be processed by multi-layer fusion smoothing filtering to obtain the voltage differential capacity data, and then the HVTP or LVTP can be identified according to the voltage differential capacity data, and the remaining capacity of the battery can be calculated for further obtaining the battery SOH.

[0148] Through the method of this application, the drawback of over-relying on the SOC estimation accuracy in the traditional SOH calculation method can be avoided, and the charging conditions of the battery can be unrestricted. The SOH can be calculated only through the discharge section data.

[0149] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0150] Furthermore, it should be noted that although each step in the flowchart is displayed in sequence according to the arrow indication, these steps are not necessarily executed in the order indicated by the arrow. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0151] From the perspective of the method flow, a method for determining the state of health of a battery is introduced through the above embodiments. The following embodiments introduce a device for determining the state of health of a battery from the perspective of virtual modules or virtual units. For details, see the following embodiments.

[0152] An embodiment of the present application further provides a device for determining the state of health of a battery, which is used to implement the method in the above method embodiment. Figure 5 The following is a schematic structural diagram of a device for determining the state of health of a battery provided by an embodiment of the present application. As Figure 5 shown, in this embodiment, the device for determining the state of health of a battery may include:

[0153] A first determination module 51, configured to determine a target feature point corresponding to a current voltage inflection point according to voltage differential capacity data of a battery to be measured; the voltage differential capacity data is obtained from the complete discharge data of the battery to be measured, and the voltage inflection point is a high voltage inflection point HVTP or a low voltage inflection point LVTP;

[0154] A second determination module 52, configured to determine the state of health of the battery to be measured according to the battery capacity corresponding to the target feature point and the capacity of the initial voltage inflection point.

[0155] In a possible implementation manner of the embodiment of the present application, the voltage differential capacity data is used to indicate the change rate of the voltage corresponding to different capacities with respect to the capacity. The voltage differential capacity data includes at least two feature points, and the feature points are maximum change rate points or minimum change rate points; the target feature point is determined according to at least two feature points.

[0156] In a possible implementation manner of the embodiment of the present application, the first determination module 51 is specifically configured to:

[0157] For each feature point, determine a target value of the feature point; the target value is related to the capacity change value on both sides of the feature point and / or the change value of the voltage change rate with respect to the capacity;

[0158] According to the target value, determine the target feature point from at least two feature points.

[0159] In a possible implementation manner of the embodiment of the present application, the first determination module 51 is specifically configured to:

[0160] Obtain a first change rate change value and a second change rate change value on both sides of the feature point under the same target capacity change value;

[0161] According to the target capacity change value, the first change rate change value, and the second change rate change value, determine the target value.

[0162] In a possible implementation manner of the embodiment of the present application, the first determination module 51 is specifically configured to:

[0163] Calculate the ratio of twice the sum of the target capacity change value and the first change rate change value to the product of the target capacity change value and the first change rate change value to obtain a first value;

[0164] Calculate the ratio of twice the sum of the target capacity change value and the second change rate change value to the product of the target capacity change value and the second change rate change value to obtain a second value;

[0165] Calculate the sum of the first value and the second value to obtain the target value.

[0166] In a possible implementation manner of the embodiment of the present application, the first determination module 51 is specifically configured to:

[0167] Obtain the first capacity change value and the second capacity change value on both sides of the feature point under the same target change rate change value;

[0168] Determine the target value of the feature point according to the target change rate change value, the first capacity change value, and the second capacity change value.

[0169] In a possible implementation manner of the embodiment of the present application, the first determination module 51 is specifically configured to:

[0170] Calculate the ratio of twice the sum of the target change rate change value and the first capacity change value to the product of the target change rate change value and the first capacity change value to obtain a third value;

[0171] Calculate the ratio of twice the sum of the target change rate change value and the second capacity change value to the product of the target change rate change value and the second capacity change value to obtain a fourth value;

[0172] Calculate the sum of the third value and the fourth value to obtain the target value of the feature point.

[0173] In a possible implementation manner of the embodiment of the present application, the first determination module 51 is specifically configured to:

[0174] Determine the feature point with the smallest target value among at least two feature points as the target feature point.

[0175] In a possible implementation manner of the embodiment of the present application, the first determination module 51 is specifically configured to:

[0176] Obtain the change rate change amount or the capacity change amount within a preset range on both sides of the feature point;

[0177] Perform curvature calculation according to the change rate change amount or the capacity change amount to obtain the target value.

[0178] In a possible implementation manner of the embodiment of the present application, the first determination module 51 is specifically configured to:

[0179] Determine the feature point with the largest target value among at least two feature points as the target feature point.

[0180] In a possible implementation manner of the embodiment of the present application, the first determination module 51 is further configured to:

[0181] When the complete discharge data is obtained, perform filtering processing on the complete discharge data by using a preset filtering method to obtain first intermediate data; the preset filtering method is used to eliminate the spike noise in the complete discharge data;

[0182] Determine the voltage differential capacity data of the battery to be measured according to the first intermediate data.

[0183] In a possible implementation manner of the embodiment of the present application, the first determination module 51 is specifically configured to:

[0184] Perform at least one of dynamic smoothing processing and multi-scale analysis processing on the first intermediate data to obtain at least one second intermediate data;

[0185] Obtain the voltage differential capacity data according to at least one second intermediate data.

[0186] In a possible implementation manner of the embodiment of the present application, the first determination module 51 is specifically configured to:

[0187] In the case of performing dynamic smoothing processing and multi-scale analysis processing on the first intermediate data, perform weighted average processing on the two obtained second intermediate data to obtain voltage capacity data;

[0188] Perform differential processing on the voltage capacity data to obtain voltage differential capacity data.

[0189] In a possible implementation manner of the embodiment of the present application, the first determination module 51 is specifically configured to:

[0190] Obtain the target internal resistance according to the optimization objective; the optimization objective is expressed as: minR ||D(v)−D(I)∙ R|| 2 , where D(v) is used to represent the first derivative function, D(I) is used to represent the second derivative function, and R is used to represent the internal resistance of the battery to be measured; the first derivative function is used to calculate the mean difference of the voltage of the battery to be measured between two adjacent preset windows, and the second derivative function is used to calculate the mean difference of the current of the battery to be measured between two adjacent preset windows;

[0191] Determine the second intermediate data according to the target internal resistance.

[0192] In a possible implementation manner of the embodiment of the present application, the first determination module 51 is specifically configured to:

[0193] Decompose the first intermediate data into different frequency scales through a variety of preset methods, analyze and process the first intermediate data of different frequency scales, and obtain the second sub-intermediate data at each frequency scale; the variety of preset methods include wavelet transform method and Fourier transform method.

[0194] Perform weighted average processing on the second sub-intermediate data at each frequency scale to obtain the second intermediate data.

[0195] In a possible implementation manner of the embodiment of the present application, the second determination module 52 is specifically configured to:

[0196] Calculate the capacity difference according to the target capacity corresponding to the full charge state and the battery capacity.

[0197] Calculate the remaining capacity according to the capacity difference and the capacity at the initial voltage inflection point.

[0198] Calculate the health state of the battery under test according to the remaining capacity.

[0199] It should be understood that the above device embodiments are illustrative, and the devices of the present application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0200] An electronic device is provided in an embodiment of the present application. Figure 6 This is a schematic structural diagram of an electronic device provided in an embodiment of the present application, as Figure 6 shown. Figure 6 The electronic device shown includes: a processor 61 and a memory 62. Among them, the processor 61 and the memory 62 are connected, such as connected through a bus 63. Optionally, the electronic device may further include a transceiver 64. It should be noted that in actual applications, the transceiver 64 is not limited to one, and the structure of this electronic device does not constitute a limitation to the embodiment of the present application.

[0201] The processor 61 may be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in connection with the disclosure of this application. The processor 61 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0202] The bus 63 may include a path for transmitting information between the above components. The bus 63 may be a peripheral component interconnect (PCI) bus, an extended industry standard architecture (EISA) bus, or the like. The bus 63 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 only a thick line is shown in the figure, but it does not mean that there is only one bus or one type of bus.

[0203] The memory 62 may be a read only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM), or other type of dynamic storage device that can store information and instructions. It may also be an electrically erasable programmable read only memory (EEPROM), a compact disc read only memory (CD-ROM), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0204] The memory 62 is used to store the application program code for executing the solution of this application, and is controlled by the processor 61 for execution. The processor 61 is used to execute the application program code stored in the memory 62 to implement the content shown in the foregoing method embodiments.

[0205] This application also provides a computer-readable storage medium, which may include: various media that can store program code, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs. Specifically, the computer-readable storage medium stores program instructions for implementing the methods in the above embodiments.

[0206] This application embodiment also provides a computer program product, including a computer program, which implements the technical solutions of the above method embodiments when executed by a processor. The implementation principle and technical effects are similar and will not be elaborated here.

[0207] In the above embodiments, the descriptions of the respective embodiments have their own focuses. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of 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 described in this specification.

[0208] Those skilled in the art will readily think of other implementation schemes of this application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptive changes of this application, which follow the general principles of this application and include the common general knowledge or conventional technical means in the technical field not disclosed in this application. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of this application are pointed out by the following claims.

[0209] It should be understood that this application is not limited to the exact structure already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is only limited by the appended claims.

Claims

1. A method for determining a battery health state, characterized in that: The method comprises: Determine a target characteristic point corresponding to a current voltage inflection point according to voltage differential capacity data of the battery to be tested; the voltage differential capacity data is obtained through complete discharge data of the battery to be tested, and the voltage inflection point is a high voltage inflection point HVTP or a low voltage inflection point LVTP; The health state of the battery to be tested is determined according to the battery capacity corresponding to the target characteristic point and the capacity of the initial voltage inflection point.

2. The method according to claim 1, characterized in that: The voltage differential capacity data is used to indicate the rate of change of voltage corresponding to different capacities relative to capacity, and the voltage differential capacity data includes at least two characteristic points, which are the maximum or minimum points of the rate of change; the target characteristic point is determined based on the at least two characteristic points.

3. The method according to claim 2, characterized in that The step of determining the target characteristic point corresponding to the current voltage inflection point according to the voltage differential capacity data of the battery to be tested includes: For each characteristic point, determining a target value of the characteristic point; the target value is related to a capacity change value and / or a change rate change value of voltage relative to capacity on both sides of the characteristic point; According to the target value, the target feature point is determined from the at least two feature points.

4. The method according to claim 3, characterized in that The determining of the target value of the feature point includes: Obtaining a first change rate change value and a second change rate change value at both sides of the characteristic point under the same target capacity change value; The target value is determined according to the target capacity change value, the first change rate change value and the second change rate change value.

5. The method according to claim 4, characterized in that The determining the target value according to the target capacity change value, the first change rate change value, and the second change rate change value includes: Calculate a ratio of twice the sum of the target capacity change value and the first change rate change value to a product of the target capacity change value and the first change rate change value to obtain a first value; Calculate the ratio of twice the sum of the target capacity change value and the second change rate change value to the product of the target capacity change value and the second change rate change value to obtain a second value; The sum of the first value and the second value is calculated to obtain the target value.

6. The method according to claim 3, characterized in that The determining of the target value of the feature point includes: Obtaining a first capacity change value and a second capacity change value at both sides of the feature point under the same target change rate change value; A target value of the feature point is determined according to the target change rate change value, the first capacity change value, and the second capacity change value.

7. The method according to claim 6, characterized in that The determining a target value of the characteristic point according to the target change rate change value, the first capacity change value, and the second capacity change value includes: Calculate a ratio of twice the sum of the target change rate change value and the first capacity change value to the product of the target change rate change value and the first capacity change value to obtain a third value; Calculate a ratio of twice the sum of the target change rate change value and the second capacity change value to the product of the target change rate change value and the second capacity change value to obtain a fourth value; The sum of the third value and the fourth value is calculated to obtain a target value of the feature point.

8. The method according to claim 4 or 6, characterized in that: The step of determining the target feature point from the at least two feature points according to the target value includes: Determine the feature point with the smallest target value among the at least two feature points as the target feature point.

9. The method according to claim 3, characterized in that: The determining of the target value of the feature point includes: Obtaining a change rate or capacity change within a preset range on both sides of the feature point; The curvature is calculated according to the change in the rate of change or the change in the capacity to obtain the target value.

10. The method according to claim 9, characterized in that The step of determining the target feature point from the at least two feature points according to the target value includes: Determine the feature point with the largest target value among the at least two feature points as the target feature point.

11. The method according to any one of claims 1 to 7, characterized in that: The method further comprises: When the complete discharge data is acquired, a preset filtering method is used to filter the complete discharge data to obtain first intermediate data; the preset filtering method is used to eliminate spike noise in the complete discharge data; The voltage differential capacity data of the battery to be tested is determined according to the first intermediate data.

12. The method according to claim 11, characterized in that Determining the voltage differential capacity data of the battery to be tested according to the first intermediate data includes: Perform at least one of dynamic smoothing processing and multi-scale analysis processing on the first intermediate data to obtain at least one second intermediate data; Voltage differential capacity data is obtained according to the at least one second intermediate data.

13. The method according to claim 12, characterized in that In the case where the dynamic smoothing process and the multi-scale analysis process are performed on the first intermediate data, the voltage differential capacity data is obtained according to the at least one second intermediate data, including: Performing weighted average processing on the two obtained second intermediate data to obtain voltage capacity data; The voltage capacity data is differentially processed to obtain the voltage differential capacity data.

14. The method according to claim 12, characterized in that Performing dynamic smoothing processing on the first intermediate data to obtain second intermediate data includes: A target internal resistance is obtained according to an optimization target; the optimization target is expressed as: minR ||D(v)−D(I)∙ R||2, wherein D(v) is used to represent a first derivative function, D(I) is used to represent a second derivative function, and R is used to represent the internal resistance of the battery to be tested; the first derivative function is used to calculate the mean difference of the voltage of the battery to be tested between two adjacent preset windows, and the second derivative function is used to calculate the mean difference of the current of the battery to be tested between two adjacent preset windows; The second intermediate data is determined according to the target internal resistance.

15. The method according to claim 12, characterized in that Performing multi-scale analysis on the first intermediate data to obtain second intermediate data includes: Decomposing the first intermediate data into different frequency scales by a plurality of preset methods, analyzing and processing the first intermediate data at different frequency scales, and obtaining second sub-intermediate data at each frequency scale; the plurality of preset methods include a wavelet transform method and a Fourier transform method; A weighted average process is performed on the second sub-intermediate data at each frequency scale to obtain the second intermediate data.

16. The method according to any one of claims 1 to 7, characterized in that: The determining the health state of the battery to be tested according to the battery capacity corresponding to the target characteristic point and the capacity of the initial voltage inflection point includes: Calculating a capacity difference according to a target capacity corresponding to a fully charged state and the battery capacity; Calculating the remaining capacity according to the capacity difference and the capacity at the initial voltage inflection point; The health state of the battery to be tested is calculated according to the remaining capacity.

17. A battery health status determination device, characterized in that: The device comprises: A first determination module is used to determine a target characteristic point corresponding to a current voltage inflection point according to voltage differential capacity data of the battery to be tested; the voltage differential capacity data is obtained through complete discharge data of the battery to be tested, and the voltage inflection point is a high voltage inflection point HVTP or a low voltage inflection point LVTP; The second determination module is used to determine the health state of the battery to be tested according to the battery capacity corresponding to the target characteristic point and the capacity of the initial voltage inflection point.

18. An electronic device, characterized in that: The electronic device comprises a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 16.

19. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 16 when executed by a processor.

20. A computer program product, characterized in that The method comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 16.

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