Battery state of health prediction method, electronic device, and readable storage medium

Abstract

A battery state-of-health prediction method, an electronic device, and a readable storage medium, related to the field of battery technology, comprising: collecting a cycle time, a storage time, and an average temperature within a target time period of a target battery according to preset target operating conditions; determining a cycle capacity loss of the target battery according to the cycle time, the average temperature, and a first preset relationship; determining a storage capacity loss of the target battery according to the storage time, the average temperature, and a second preset relationship; obtaining a cycle state-of-health standard deviation according to the cycle capacity loss and a third preset relationship; obtaining a storage capacity loss and a fourth preset relationship; and determining a predicted state-of-health of the target battery under the target operating conditions according to the cycle capacity loss, the storage capacity loss, the cycle state-of-health standard deviation, and the storage state-of-health standard deviation.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This disclosure claims the benefit of priority to Chinese Patent Application No. 202211348958.0, filed October 31, 2022, entitled "BATTERY STATE OF HEALTH PREDICTION METHOD, ELECTRONIC DEVICE, AND READABLE STORAGE MEDIUM," which is incorporated herein in its entirety.

[0002] FIELD Embodiments of the present disclosure relate to the technical field of batteries, and in particular to a method, electronic device, and computer-readable storage medium for predicting battery health state. [Background technology]

[0003] Currently, the battery health state is generally evaluated in the following way: first, a battery simulation model is configured to simulate the temperature changes of the battery at various positions inside the actual battery, and the battery capacity is obtained by screening and calculation to obtain the actual battery life at different positions; second, a decay base model is established based on the existing battery usage data and life data, and the life data of the target battery is obtained by the decay base model according to the usage data of the target battery; third, the correlation degree between the calendar life capacity fade in the battery, the AC internal resistance variation, the DC internal resistance change, and the temperature / voltage is analyzed, and the true life state of the cell is inferred from the accelerated aging test of the cell by a fitting formula.

[0004] However, the first method only considers the effect of temperature field on cell life and ignores the inconsistent capacity decay of the cells themselves caused by the factory manufacturing process. The second method builds a model based on existing battery data and does not, in principle, evaluate life. The evaluation accuracy is insufficient and it is difficult to disseminate. The third method is mainly aimed at predicting life from single-cell accelerated testing and does not consider the effects of temperature field differences and cell consistency differences within the battery. Summary of the Invention

[0005] An objective of one embodiment of the present disclosure is to provide a new technical solution for accurately assessing the battery state of health.

[0006] According to a first aspect of the present disclosure, there is provided a method for predicting a battery state of health, the method comprising: Obtaining a cycle time, a storage time, and an average temperature during a target period of the target battery according to a preset target operating condition; determining a cycle capacity loss of the target battery according to a cycle time, an average temperature, and a first preset relationship; and determining a storage capacity loss of the target battery according to a storage time, an average temperature, and a second preset relationship; obtaining a standard deviation of the cycle health state according to the cycle capacity loss and a third preset relationship, and obtaining a standard deviation of the storage health state according to the storage capacity loss and a fourth preset relationship; Determining a predicted state of health of the target battery under the target operating conditions according to the cycle capacity loss, the storage capacity loss, the standard deviation of the cycle state of health, and the standard deviation of the storage state of health.

[0007] In one embodiment, a predicted health state of the target battery is determined according to the cycle capacity loss, the storage capacity loss, the standard deviation of the cycle health state, and the standard deviation of the storage health state, which comprises: Obtaining a median value of the state of health of cells of the target battery under the target operating conditions according to the cycle capacity loss and the storage capacity loss; Obtaining a distribution standard deviation of the state of health of the cells of the target battery under the target operating conditions according to the standard deviation of the cycle state of health and the standard deviation of the storage state of health; Obtaining a predicted state of health of the target battery according to the median state of health and the standard deviation of the state of health distribution.

[0008] In one embodiment, the method comprises: obtaining first state-of-health data and second state-of-health data, the first state-of-health data being obtained by performing charge / discharge cycle tests on a first number of first cells at corresponding cycle temperatures, and the second state-of-health data being obtained by performing storage tests on a second number of second cells at corresponding storage temperatures, the first state-of-health data referring to data indicative of a state-of-health of the first cells after performing the charge / discharge cycle tests for a preset cycle time, and the second state-of-health data referring to data indicative of a state-of-health of the second cells after performing the storage tests for a storage cycle time; determining first median data of a first number of first cells according to the first state-of-health data, and second median data of a second number of second cells according to the second state-of-health data, wherein the first median data refers to data indicating an average value of the state of health of the first number of first cells after performing a charge / discharge cycle test for a preset cycle time, and the second median data refers to data indicating an average value of the state of health of the second number of second cells after performing a storage test for a preset storage time; The method further includes obtaining a first preset relationship according to the cycle temperature and the first median data, and obtaining a second preset relationship according to the storage temperature and the second median data.

[0009] In one embodiment, the method comprises: According to the first health state data, a first equation is obtained that represents a corresponding relationship between the health state of the first cell and a cycle time, and according to the second health state data, a second equation is obtained that represents a corresponding relationship between the health state of the second cell and a storage time; determining a reference cycle time corresponding to the plurality of preset health states according to a first preset relationship, and determining a reference storage time corresponding to the plurality of preset health states according to a second preset relationship; obtaining a first reference health state corresponding to the first cell according to a reference cycle time and a first equation, and obtaining a second reference health state corresponding to the second cell according to a reference storage time and a second equation; The method further includes obtaining a third preset relationship according to the first reference health state, and obtaining a fourth preset relationship according to the second reference health state.

[0010] In one embodiment, a third preset relationship is obtained according to the first reference health state, and a fourth preset relationship is obtained according to the second reference health state, which includes: determining a first standard deviation of a first reference health state corresponding to each of the preset health states at each of the cycle temperatures, and determining a second standard deviation of a second reference health state corresponding to each of the preset health states at each of the storage temperatures; A third preset relationship is obtained according to the first standard deviation, and a fourth preset relationship is obtained according to the second standard deviation.

[0011] In one embodiment, the first preset relationship is: Q1=1-(c1*T1 d1 +e1)*t1 f1 and Q1 is the cycle capacity loss, T1 is the cycle temperature, t1 is the cycle time, c1, d1, e1, f1 are fitting coefficients, The second predefined relationship is Q2=1-(c2*T2 d2 +e2)*t2 f2 and Q2 is the storage capacity loss, T2 is the storage temperature, t2 is the storage time, and c2, d2, e2 and f2 are the fitting coefficients.

[0012] In one embodiment, the third preset relationship is: SD1=A1*exp(SOH1 B1 +C1)+D1 SOH1=1-Q1 and where SD1 is the standard deviation of the cycle health state, Q1 is the cycle capacity loss, A1, B1, C1 and D1 are fit coefficients, The fourth predefined relationship is SD2=A2*exp(SOH2 B2 +C2)+D2 SOH2=1-Q2 and SD2 is the standard deviation of the storage health status, Q2 is the storage capacity loss, and A2, B2, C2, and D2 are fit coefficients.

[0013] In one embodiment, an average temperature of the target battery during the target period is obtained, which includes: obtaining a cell temperature for each of the cells contained in the target battery during the target cycle; and determining the average value of the cell temperatures as the average temperature.

[0014] According to a second aspect of the present disclosure, there is also provided an electronic device, the electronic device including a memory and a processor, the memory configured to store a computer program, and the processor configured to execute the computer program to perform the method according to the first aspect of the present disclosure.

[0015] According to a third aspect of the present disclosure, there is also provided a computer-readable storage medium configured to store thereon a computer program which, when executed by a processor, performs a method according to the first aspect of the present disclosure.

[0016] According to this embodiment, the cycle capacity loss and storage capacity loss of the target battery are determined according to the cycle time, storage time, and average temperature of the target battery, and then the standard deviation of the cycle health state and the standard deviation of the storage health state of the target battery are obtained according to the cycle capacity loss and storage capacity loss. The predicted health state of the target battery is predicted according to the cycle capacity loss, storage capacity loss, standard deviation of the cycle health state, and standard deviation of the storage health state. In this embodiment, from the perspective of the cell, cycle and storage consistency deviations caused by the process for manufacturing the cell are fully taken into account, so that the predicted health state of the target battery can be predicted more accurately and with lower computational costs.

[0017] Other features and advantages of the embodiments of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure, which proceeds with reference to the accompanying drawings.

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of embodiments of the present disclosure. [Brief explanation of the drawings]

[0019] [Figure 1] FIG. 1 is a schematic block diagram of a hardware configuration of an electronic device that can be configured to implement embodiments of the present disclosure. [Figure 2] FIG. 1 is a schematic flow diagram of a method for predicting a battery state of health, according to one embodiment. [Figure 3] FIG. 4 is a schematic flow diagram of a method for predicting a battery state of health, according to another embodiment. [Figure 4] 1 is a block schematic diagram of an electronic device, according to one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0020] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the drawings. It should be noted that the relative configurations, formulas, and numerical values ​​of the components and steps described in these examples do not limit the scope of the present invention unless otherwise specified.

[0021] The following description of at least one exemplary embodiment is merely an illustration of a practice and is in no way intended to limit the invention, its application, or uses.

[0022] Techniques, methods, and apparatus known to those skilled in the art may not be described in detail but should, where appropriate, be considered part of this specification.

[0023] In all examples shown and described herein, any particular values ​​should be construed as examples only and not as limitations, and therefore, other examples of exemplary embodiments may have different values.

[0024] It should be noted that like reference numbers and letters in the following drawings refer to like items, and thus, once an item is defined in one drawing, there is no need to further describe it in subsequent drawings.

[0025] <Hardware configuration> FIG. 1 is a schematic structural diagram of an electronic device that can be configured to implement embodiments of the present disclosure.

[0026] The electronic device 1000 may be a smartphone, a portable computer, a desktop computer, a tablet computer, a server, etc., and is not limited thereto herein.

[0027] The electronic device 1000 may include, but is not limited to, a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600, a speaker 1700, a microphone 1800, etc. The processor 1100 may be a central processing unit (CPU), a graphics processor (GPU), a microprocessor (MCU), etc. for executing a computer program that may be written in an instruction set of an architecture such as x86, Arm, RISC, MIPS, SSE, etc. The memory 1200 may include, for example, a read-only memory (ROM), a random access memory (RAM), a non-volatile memory such as a hard disk, etc. The interface device 1300 may include, for example, a USB interface, a serial interface, a parallel interface, etc. The communication device 1400 may perform wired communication using, for example, optical fiber or cable, or may perform wireless communication, and the wireless communication may include, among others, WiFi communication, Bluetooth communication, 2G / 3G / 4G / 5G communication, etc. The display device 1500 may be, for example, a liquid crystal display, a touch display, etc. The input device 1600 may include, for example, a touch screen, a keyboard, a somatosensory input, etc. The speaker 1700 is configured to output an audio signal. The microphone 1800 is configured to collect an audio signal.

[0028] As applied to the embodiments of the present disclosure, the memory 1200 of the electronic device 1000 is configured to store a computer program, and the computer program is configured to control the processor 1100 to operate to perform a method according to the embodiments of the present disclosure. A person skilled in the art can design a computer program according to the solutions disclosed in the present disclosure. How a computer program controls a processor to operate is well known in the art and will not be described in detail herein. The electronic device 1000 can be equipped with an intelligent operating system (e.g., a system such as Windows, Linux, Android, or IOS) and application software.

[0029] Those skilled in the art will understand that although multiple units of electronic device 1000 are shown in FIG. 1, electronic device 1000 of an embodiment of the present disclosure may relate to only some of those units, e.g., processor 1100, memory 1200, etc.

[0030] Various embodiments and examples according to the present invention are described below with reference to the accompanying drawings.

[0031] <Method embodiment> 2 is a schematic flow diagram of a method for predicting a battery state of health according to one embodiment that can be implemented by an electronic device, such as the electronic device 1000 shown in FIG.

[0032] As shown in FIG. 2, the method for predicting a battery state of health of this embodiment may include steps S2100 to S2400 as follows.

[0033] In step S2100, the cycle time, storage time, and average temperature during the target period of the target battery are obtained according to the preset target operating conditions.

[0034] In this embodiment, the target operating conditions can be decomposed to obtain the cycle time, storage time, and average temperature of the target battery.

[0035] The target period can be preset according to the application scenario or specific requirements, for example, the target period can be within the past day.

[0036] The target battery of this embodiment may include at least one cell. When the target battery includes multiple cells, the multiple cells may be connected in series and / or in parallel.

[0037] In one embodiment, an average temperature of the target battery during the target cycle is obtained, which may include obtaining a cell temperature of each of the cells included in the target battery during the target cycle, and determining the average value of the cell temperatures as the average temperature of the target battery during the target cycle.

[0038] In this embodiment, the cell temperature of each of the cells during the target cycle can be obtained according to the thermal simulation.

[0039] In this embodiment, cycle refers to the charge / discharge cycle of the target battery, and cycle time refers to the time of the charge and discharge cycle.

[0040] According to this embodiment, the predicted health state of the target battery is predicted according to the average value of the cell temperature of the cells in the target battery during the target period, and the prediction result can be made more accurate.

[0041] In step S2200, a cycle capacity loss of the target battery is determined according to the cycle time, the average temperature, and the first preset relationship, and a storage capacity loss of the target battery is determined according to the storage time, the average temperature, and the second preset relationship.

[0042] In this embodiment, the cycle capacity loss is the capacity loss of the target battery due to charge-discharge cycling, and the storage capacity loss is the capacity loss of the target battery due to storage. The first preset relationship is the corresponding relationship between cycle time, cycle temperature, and capacity loss of the battery due to charge / discharge cycling. The second preset relationship is the corresponding relationship between storage time, storage temperature, and capacity loss of the battery due to storage.

[0043] In one embodiment, the first preset relationship is: Q1=1-(c1*T1 d1 +e1)*t1 f1 can be expressed as Q1 is the cycle capacity loss, T1 is the cycle temperature, t1 is the cycle time, and c1, d1, e1, f1 are fit coefficients.

[0044] In this embodiment, the average temperature can be the cycle temperature, and the cycle time and cycle temperature can be substituted into a first preset relationship to obtain the cycle capacity loss.

[0045] In one embodiment, the second preset relationship is: Q2=1-(c2*T2 d2 +e2)*t2 f2 can be expressed as Q2 is the storage capacity loss, T2 is the storage temperature, t2 is the storage time, and c2, d2, e2 and f2 are the fitting coefficients.

[0046] In this embodiment, the average temperature can be the storage temperature, and the storage time and storage temperature can be substituted into a second preset relationship to obtain the storage capacity loss.

[0047] In step S2300, a standard deviation of the cycle health state is obtained according to the cycle capacity loss and the third preset relationship, and a standard deviation of the storage health state is obtained according to the storage capacity loss and the fourth preset relationship.

[0048] In this embodiment, the third preset relationship is a corresponding relationship between the cycle health state and the standard deviation, and the fourth preset relationship is a corresponding relationship between the storage health state and the standard deviation.

[0049] The cycle health state can be the predicted health state of the target battery after a cycle-induced capacity loss, specifically the difference between 1 and the cycle capacity loss, and the storage health state can be the predicted health state of the target battery after a storage-induced capacity loss, specifically the difference between 1 and the storage capacity loss.

[0050] The cycle health state standard deviation corresponding to the cycle health state can be a distribution standard deviation indicating that the cells of the target battery reached the cycle health state through a manufacturing process. The storage health state standard deviation corresponding to the storage health state can be a distribution standard deviation indicating that the cells of the target battery reached the storage health state through a manufacturing process.

[0051] The state of health (SOH) of a battery may be a percentage of the battery's fully charged capacity relative to its rated capacity.

[0052] In one embodiment, the third preset relationship is: SD1=A1*exp(SOH1 B1 +C1)+D1 SOH1=1-Q1 can be expressed as SD1 is the standard deviation of the cycle health states, Q1 is the cycle capacity loss, SOH1 is the cycle health states, and A1, B1, C1 and D1 are fit coefficients.

[0053] In one embodiment, the fourth preset relationship is: SD2=A2*exp(SOH2 B2 +C2)+D2 SOH2=1-Q2 can be expressed as SD2 is the standard deviation of the storage health status, Q2 is the storage capacity loss, SOH2 is the storage health status, and A2, B2, C2, and D2 are fit coefficients.

[0054] In step S2400, a predicted state of health of the target battery under the target operating conditions is determined according to the cycle capacity loss, the storage capacity loss, the standard deviation of the cycle state of health, and the standard deviation of the storage state of health.

[0055] In one embodiment of the present disclosure, a predicted health state of the target battery is determined according to the cycle capacity loss, the storage capacity loss, the standard deviation of the cycle health state, and the standard deviation of the storage health state, which may include steps S2410 to S2430 shown below.

[0056] In step S2410, a median state of health of cells of the target battery under target operating conditions is obtained according to cycle capacity loss and storage capacity loss.

[0057] In this embodiment, the median state of health SOH3 is SOH3=1-Q1-Q2 can be expressed as:

[0058] In step S2420, a distribution standard deviation of the state of health of the cells of the target battery under the target operating conditions is obtained according to the standard deviation of the cycle state of health and the standard deviation of the storage state of health.

[0059] The state-of-health distribution standard deviation can reflect the consistency deviation of the state-of-health of each cell in the target battery due to the manufacturing process.

[0060] In this embodiment, the standard deviation of the cycle health state and the standard deviation of the storage health state may be weighted and summed to obtain the distribution standard deviation of the health state.

[0061] In one example, the distribution standard deviation SD3 of the health state is: SD3=SD1*λ1+SD2*λ2 can be expressed as SD1 is the standard deviation of the cycle health state, SD2 is the standard deviation of the storage health state, and λ1 and λ2 are pre-set weights.

[0062] In another example, the distribution standard deviation SD3 of the health state is

[0063]

number

[0064] In step S2430, a predicted state of health of the target battery is obtained according to the median state of health and the distribution standard deviation of the state of health.

[0065] In one embodiment, a predicted state of health of the target battery can be obtained according to the median state of health, the distribution standard deviation of the state of health, and a fifth preset relationship.

[0066] The fifth predefined relationship is SOH=SOH3±n*SD3 can be expressed as SOH represents the predicted state of health of the target battery, SOH3 is the median state of health, SD3 is the standard deviation of the state of health distribution, and n is the number of standard deviations corresponding to a pre-set confidence interval.

[0067] The confidence interval can be preset according to the application scenario or specific needs, for example, the confidence interval can be 68%, 95%, or 99%.

[0068] n is the number of standard deviations that corresponds to the confidence interval. For example, for a 68% confidence interval, the corresponding number of standard deviations, n, may be 1. As another example, for a 95% confidence interval, the corresponding number of standard deviations, n, may be 2. As another example, for a 99% confidence interval, the corresponding number of standard deviations, n, may be 3.

[0069] According to this embodiment, the cycle capacity loss and storage capacity loss of the target battery are determined according to the cycle time, storage time, and average temperature of the target battery. Then, the standard deviation of the cycle health state and the standard deviation of the storage health state of the target battery are obtained according to the cycle capacity loss and storage capacity loss. The predicted state of health of the target battery is predicted according to the cycle capacity loss, storage capacity loss, standard deviation of the cycle health state, and standard deviation of the storage health state. In this embodiment, from the perspective of the cell, the cycle and storage consistency deviations caused by the process for manufacturing the cell are fully taken into account, so that the predicted state of health of the target battery can be predicted more accurately and with lower computational costs.

[0070] In an embodiment of the present disclosure, before performing step S2200, the method further includes steps S3100 to S3300 shown in FIG.

[0071] In step S3100, first state of health data and second state of health data are obtained, the first state of health data being obtained by performing a charge / discharge cycle test of a first number of first cells at a corresponding cycle temperature, and the second state of health data being obtained by performing a storage test of a second number of second cells at a corresponding storage temperature.

[0072] The first state of health data refers to data indicative of the state of health of a first cell after performing a charge / discharge cycle test for a preset cycle time, and the second state of health data refers to data indicative of the state of health of a second cell after performing a storage test for a preset storage time.

[0073] In this embodiment, the first cell and the second cell can be the same type of cell, and the production batches of any two of the first cell and the second cell can be the same or different. In one example, the first number of first cells and the second number of second cells can be all batches covering the model of the cell.

[0074] Furthermore, at least one cycle temperature and at least one storage temperature can be preset according to application scenarios or specific requirements. For example, the cycle temperatures can include 25°C, 35°C, and 45°C, and the storage temperatures can include 25°C, 45°C, and 60°C. In this case, the first number of first cells can be divided into three parts in advance, and the first part of the first cells can be set in an environment of 25°C to perform charge / discharge cycles with a discharge depth of 100% and a charge / discharge current of 0.5, the second part of the first cells can be set in an environment of 35°C to perform charge / discharge cycles with a discharge depth of 100% and a charge / discharge current of 0.5, and the third part of the first cells can be set in an environment of 45°C to perform charge / discharge cycles with a discharge depth of 100% and a charge / discharge current of 0.5. The second number of second cells can be pre-divided into three portions, and a first portion of the second cells can be set in a 25°C environment to perform a storage test at 100% state of charge (SOC), a second portion of the second cells can be set in a 45°C environment to perform a storage test at 100% state of charge, and a third portion of the second cells can be set in a 60°C environment to perform a storage test at 100% state of charge.

[0075] When the charge / discharge cycle test is completed, first state of health data for each of the first cells can be obtained. When the storage test is completed, second state of health data for each of the second cells can be obtained.

[0076] In this embodiment, the preset cycle time and the preset storage time can be preset to multiple times according to application scenarios or specific requirements, for example, the preset cycle time can include 1 day, 2 days, ..., 200 days, and the preset storage time can include 1 day, 2 days, ..., 300 days.

[0077] In step S3200, first median data of a first number of first cells is determined according to the first state-of-health data, and second median data of a second number of second cells is determined according to the second state-of-health data, where the first median data refers to data indicative of an average state-of-health value of the first number of first cells after performing a charge / discharge cycle test for a preset cycle time, and the second median data refers to data indicative of an average state-of-health value of the second number of second cells after performing a storage test for a preset storage time.

[0078] In this embodiment, to obtain first median data, for each of the preset cycle times, an average value of the state of health of a first number of first cells after performing a charge / discharge cycle test for the preset cycle time is determined, and to obtain second median data, for each of the preset storage times, an average value of the state of health of a second number of second cells after performing a storage test for the storage time is determined.

[0079] In step S3300, a first preset relationship is obtained according to the cycle temperature and the first median data, and a second preset relationship is obtained according to the storage temperature and the second median data.

[0080] In this embodiment, the initial form of the first preset relationship is established according to the first median data, i.e., Q1=1-g1*t1 f1 and g1 is the first fitting coefficient.

[0081] The initial form of the second preset relationship is established according to the second median data, i.e., Q2=1-g2*t2 f2 and g2 is the second fitting coefficient.

[0082] The first conformance factor is different at different cycle temperatures, and the second conformance factor is different at different storage temperatures.

[0083] Therefore, a third equation representing the corresponding relationship between the first conformance coefficient g1 and the cycle temperature T1 can be established according to the first health state data of each of the first cells and the corresponding cycle temperature, and a fourth equation representing the corresponding relationship between the second conformance coefficient g2 and the storage temperature T2 can be established according to the second health state data of each of the second cells and the corresponding storage temperature.

[0084] The third formula in this embodiment is: g1=c1*T1 d1 +e1 can be expressed as:

[0085] The fourth formula in this embodiment is: g2=c2*T2 d2 +e2 can be expressed as:

[0086] When predicting the predicted state of health of the target battery, a median cycle capacity loss of the target battery due to charge-discharge cycles and a median storage capacity loss of the target battery due to storage can be determined according to the first preset relationship and the second preset relationship established in this embodiment, and further, a median predicted state of health of the target battery is determined according to the median cycle capacity loss and the median storage capacity loss.

[0087] In an embodiment of the present disclosure, before performing step S2200, the method further includes steps S3400 to S3700 shown in FIG.

[0088] In step S3400, a first equation representing a corresponding relationship between the health state of the first cell and the cycle time is obtained according to the first health state data, and a second equation representing a corresponding relationship between the health state of the second cell and the storage time is obtained according to the second health state data.

[0089] In this embodiment, a first equation of the first cell can be obtained according to the first health state data of each of the first cells. The first equation can be:

[0090] SOH1=a1*t1 b1 can be expressed as SOH1 is the state of health of the first cell, t1 is the cycle time, and a1 and b1 are fitness factors.

[0091] In this embodiment, a second equation for the second cell can be obtained according to the second health state data of each of the second cells. The second equation can be: SOH2=a2*t2 b2 can be expressed as SOH2 is the state of health of the first cell, t2 is the storage time, and a2 and b2 are fitness coefficients.

[0092] In step S3500, a reference cycle time corresponding to a plurality of preset health states is determined according to a first preset relationship, and a reference storage time corresponding to a plurality of preset health states is determined according to a second preset relationship.

[0093] The preset health states in this embodiment can be preset according to application scenarios or specific requirements, for example, the preset health states can include 95%, 90%, 85%, 80%, 75%, 70%, 65%, and 60%.

[0094] Each of the preset health states can be substituted into a first preset relationship to obtain a reference cycle time corresponding to each of the preset health states, and each of the preset health states can be substituted into a second preset relationship to obtain a reference cycle time corresponding to each of the preset health states.

[0095] In step S3600, a first reference health state corresponding to the first cell is obtained according to the reference cycle time and a first equation, and a second reference health state corresponding to the second cell is obtained according to the reference storage time and a second equation.

[0096] In this embodiment, each of the reference cycle times can be substituted into the first equation for each of the first cells, respectively, to obtain a first reference state of health for each of the first cells after charge / discharge cycle testing during each of the reference cycle times, and each of the reference storage times can be substituted into the second equation for each of the second cells, respectively, to obtain a second reference state of health for each of the second cells after storage testing during each of the reference storage times.

[0097] In step S3700, a third preset relationship is obtained according to the first reference health state, and a fourth preset relationship is obtained according to the second reference health state.

[0098] In this embodiment, the third preset relationship is adapted according to the first reference health state, and the fourth preset relationship is adapted according to the second reference health state, thereby shortening the test time of the first cell and the second cell and reducing the test cost, and then the reference cycle time and the reference storage time corresponding to the preset health states are obtained according to the first preset relationship and the second preset relationship, and then the first reference health state corresponding to the first cell is obtained according to the reference cycle time and the first equation, and the second reference health state corresponding to the second cell is obtained according to the reference storage time and the second equation.

[0099] In one embodiment of the present disclosure, a third preset relationship is obtained according to the first reference health state, and a fourth preset relationship is obtained according to the second reference health state, which may include steps S3710 to S3720 shown in FIG. 3 .

[0100] In step S3710, a first standard deviation of a first reference health state corresponding to each of the preset health states at each of the cycle temperatures is determined, and a second standard deviation of a second reference health state corresponding to each of the preset health states at each of the storage temperatures is determined.

[0101] In this embodiment, the first reference health states of a first number of first cells can be grouped according to the preset health states and cycle temperatures, and when the number of preset health states is 8 and the cycle temperature is 3, 8*3=24 groups of first reference health states can be obtained, and each group of first reference health states can be determined by the reference cycle time corresponding to the same preset health state, and the cycle temperature corresponding to the first cells is the same.

[0102] Additionally, the standard deviation of each group of the first baseline health state can be determined as a first standard deviation.

[0103] In this embodiment, the second reference health states of the second number of second cells can be grouped according to the preset health states and cycle temperatures, and when the number of preset health states is 8 and the storage temperature is 3, 8*3=24 groups of second reference health states can be obtained, and each group of second reference health states can be determined by the reference cycle time corresponding to the same preset health state, and the storage temperature corresponding to the second cells is the same.

[0104] Additionally, the standard deviation of each group of the second reference health state can be determined as a second standard deviation.

[0105] In step S3720, a third preset relationship is obtained according to the first standard deviation, and a fourth preset relationship is obtained according to the second standard deviation.

[0106] In this embodiment, a third preset relationship representing a corresponding relationship between the cycle health state and the standard deviation can be obtained according to the standard deviation of each group of the first reference health state and the corresponding preset health state, and a fourth preset relationship representing a corresponding relationship between the storage health state and the standard deviation can be obtained according to the standard deviation of each group of the second reference health state and the corresponding preset health state.

[0107] When predicting the predicted health state of the target battery, the consistency deviation of the target battery when reaching a cycle health state through the manufacturing process and the consistency deviation of the target battery when reaching a storage health state through the manufacturing process can be determined according to the third preset relationship and the fourth preset relationship established in this embodiment, and further, the consistency deviation of the target battery when reaching a health state through the manufacturing process is determined according to the consistency deviation of the target battery when reaching a cycle health state through the manufacturing process and the consistency deviation of the target battery when reaching a storage health state through the manufacturing process.

[0108] In another embodiment of the present disclosure, a third preset relationship is obtained according to the first standard deviation and a fourth preset relationship is obtained according to the second standard deviation, which may further include determining a standard deviation of a first reference health state corresponding to each of the preset health states and a standard deviation of a second reference health state corresponding to each of the preset health states, and obtaining a third preset relationship according to the standard deviation of the first reference health state corresponding to each of the preset health states and obtaining a fourth preset relationship according to the standard deviation of the second reference health state corresponding to each of the preset health states.

[0109] <Device embodiment> FIG. 4 is a schematic structural diagram of a hardware structure of an electronic device according to another embodiment.

[0110] As shown in FIG. 4, the electronic device 4000 includes a processor 4100 and a memory 4200, where the memory 4200 is configured to store an executable computer program, and the processor 4100 is configured to perform a method as in any of the method embodiments described above under the control of the computer program.

[0111] The electronic device 4000 can be an electronic product such as a smartphone, a portable computer, a desktop computer, a tablet computer, a server, or a computer cluster.

[0112] Each module of the electronic device 4000 described above may be realized by the processor 4100 in this embodiment executing a computer program stored in the memory 4200, or may be realized by other circuit configurations not limited to those described herein.

[0113] Computer-Readable Storage Medium Embodiments The present embodiment provides a computer-readable storage medium configured to store executable instructions that, when executed by a processor, perform a method described in any method embodiment herein.

[0114] The present invention may be a system, method, and / or computer program product that may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of the present invention.

[0115] A computer-readable storage medium may be any tangible device capable of retaining and storing instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, machine-encoded devices such as punch cards or in-groove raised structures with instructions stored thereon, and any suitable combination thereof. As used herein, computer-readable storage medium should not be construed as a transient signal per se, such as an electric wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted over an electrical wire.

[0116] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing / processing devices or to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing / processing device.

[0117] Computer program instructions for carrying out the operations of the present invention can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state-setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and traditional procedural programming languages ​​such as "C" or similar programming languages. The computer-readable program instructions can be executed entirely on the user's computer, partially on the user's computer as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. When a remote computer is involved, the remote computer can be connected to the user's computer by any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, electronic circuitry, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), is personalized by utilizing state information in computer-readable program instructions that can execute the computer-readable program instructions to implement various aspects of the present invention.

[0118] Various aspects of the present invention are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0119] These computer-readable program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, thereby generating a machine such that the instructions, when executed by the processor of the computer or other programmable data processing apparatus, generate an apparatus for performing the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams. Such computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other apparatus to operate in a particular manner, such that the computer-readable medium having the instructions stored thereon comprises an article of manufacture comprising instructions that implement various aspects of the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0120] The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to generate a computer-implemented process such that a series of operational steps are executed on the computer, other programmable data processing apparatus, or other device, such that the instructions, when executed on the computer, other programmable data processing apparatus, or other device, perform the functions / acts specified in one or more blocks in the flowcharts and / or block diagrams.

[0121] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowcharts or block diagrams may represent a module, program segment, or portion of instructions, which in turn comprises one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two successive blocks may in fact be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functionality involved. It should also be noted that each block in the block diagrams and / or flowchart diagrams, and combinations of blocks in the block diagrams and / or flowchart diagrams, may be implemented using a dedicated hardware-based system that performs the specified functions or functions, or a combination of dedicated hardware and computer instructions. Equivalents between hardware implementations, software implementations, and combinations of software and hardware implementations are readily apparent to those skilled in the art.

[0122] While various embodiments of the present invention have been described above, the above description is illustrative, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the illustrated embodiments. The terms used herein are chosen to best explain the principles, practical applications, or technical improvements in the marketplace of the various embodiments, or to enable those skilled in the art to understand the various embodiments disclosed herein. The scope of the present invention is defined by the appended claims.

Claims

1. 1. A method for predicting a battery state of health, comprising: Obtaining a cycle time, a storage time, and an average temperature during a target period of the target battery according to a preset target operating condition; determining a cycle capacity loss of the target battery according to the cycle time, the average temperature, and a first preset relationship, and determining a storage capacity loss of the target battery according to the storage time, the average temperature, and a second preset relationship; obtaining a standard deviation of a cycle health state according to the cycle capacity loss and a third preset relationship, and obtaining a standard deviation of a storage health state according to the storage capacity loss and a fourth preset relationship; determining a predicted state of health of the target battery under the target operating conditions according to the cycle capacity loss, the storage capacity loss, the standard deviation of the cycle state of health, and the standard deviation of the storage state of health; A method comprising:

2. determining the predicted state of health of the target battery under the target operating conditions according to the cycle capacity loss, the storage capacity loss, the standard deviation of the cycle state of health, and the standard deviation of the storage state of health; obtaining a median state of health of cells of the target battery under the target operating conditions according to the cycle capacity loss and the storage capacity loss; Obtaining a distribution standard deviation of the state of health of cells of the target battery under the target operating conditions according to the standard deviation of the cycle state of health and the standard deviation of the storage state of health; obtaining a predicted state of health of the target battery according to the median of the states of health and the distribution standard deviation of the states of health; The method of claim 1 , comprising:

3. The method comprises: acquiring first state-of-health data and second state-of-health data, the first state-of-health data being acquired by performing a charge / discharge cycle test of a first number of first cells at a corresponding cycle temperature, the second state-of-health data being acquired by performing a storage test of a second number of second cells at a corresponding storage temperature, the first state-of-health data referring to data indicative of a state-of-health of the first cells after performing a charge / discharge cycle test for a preset cycle time, and the second state-of-health data referring to data indicative of a state-of-health of the second cells after performing a storage test for a preset storage time; determining first median data of the first number of first cells according to the first state-of-health data and second median data of the second number of second cells according to the second state-of-health data, wherein the first median data refers to data indicative of an average value of the state-of-health of the first number of first cells after performing a charge / discharge cycle test for the preset cycle time, and the second median data refers to data indicative of an average value of the state-of-health of the second number of second cells after performing a storage test for the preset storage time; obtaining the first preset relationship according to the cycle temperature and the first median data, and obtaining the second preset relationship according to the storage temperature and the second median data; The method of claim 1 or 2, further comprising:

4. The method comprises: obtaining a first equation representing a corresponding relationship between the health state of the first cell and the cycle time according to the first health state data, and obtaining a second equation representing a corresponding relationship between the health state of the second cell and the storage time according to the second health state data; determining a reference cycle time corresponding to a plurality of preset health states according to the first preset relationship, and determining a reference storage time corresponding to a plurality of preset health states according to the second preset relationship; obtaining a first reference health state corresponding to the first cell according to the reference cycle time and the first equation, and obtaining a second reference health state corresponding to the second cell according to the reference storage time and the second equation; obtaining the third preset relationship according to the first reference health state, and obtaining the fourth preset relationship according to the second reference health state; The method of claim 3 further comprising:

5. obtaining the third preset relationship according to the first reference health state and obtaining the fourth preset relationship according to the second reference health state; determining a first standard deviation of a first reference health state corresponding to each of the preset health states at each of the cycle temperatures and determining a second standard deviation of a second reference health state corresponding to each of the preset health states at each of the storage temperatures; obtaining the third preset relationship according to the first standard deviation, and obtaining the fourth preset relationship according to the second standard deviation; The method of claim 4 comprising:

6. The first preset relationship is: Q1=1-(1*91 ｄ１ +e1)*t1 ｆ１ where Q1 is the cycle capacity loss, T1 is the cycle temperature, t1 is the cycle time, and c1, d1, e1, and f1 are compatibility coefficients; The second preset relationship is: Q2=1-(c2*T2 ｄ２ +e2)*t2 ｆ２ 5. The method of claim 1, wherein Q2 is the storage capacity loss, T2 is the storage temperature, t2 is the storage time, and c2, d2, e2 and f2 are compatibility factors.

7. The third preset relationship is: SD1=A1*exp(SOH1 Ｂ１ +C1)+D1 SOH1=1-Q1 where SD1 is the standard deviation of the cycle health state, Q1 is the cycle capacity loss, and A1, B1, C1 and D1 are fit coefficients; The fourth preset relationship is: SD2=A2*exp(SOH2 Ｂ２ +C2)+D2 SOH2 = 1 - Q2 7. The method of claim 1, wherein SD2 is the standard deviation of the storage health state, Q2 is the storage capacity loss, and A2, B2, C2, and D2 are fitness factors.

8. obtaining the average temperature of the target battery during the target period; obtaining a cell temperature of each of the cells included in the target battery during the target cycle; determining the average value of the cell temperatures as the average temperature; The method of any one of claims 1 to 7, comprising:

9. 9. An electronic device comprising a memory and a processor, wherein the memory is configured to store a computer program and wherein the processor is configured to execute the method of any one of claims 1 to 8 under the control of the computer program.

10. 9. A computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method of any one of claims 1 to 8.

Images