A battery performance prediction method, device, equipment, computer readable storage medium and computer program product

Abstract

This application provides a battery performance prediction method, apparatus, device, computer-readable storage medium, and computer program product, comprising: determining a first cell parameter based on cell consistency factors and / or at least one cell parameter aging variable information; determining probability distribution information of the first cell parameter based on batch information of at least one cell in the battery system; assigning values ​​to the cells in the battery system based on the probability distribution information, so as to predict battery performance using the assigned battery system. This can improve the accuracy of battery performance prediction.

Description

Technical Field

[0001] This application relates to the field of batteries, and more particularly to a method, apparatus, device, computer-readable storage medium, and computer program product for predicting battery performance. Background Technology

[0002] Currently, battery performance prediction under extreme operating conditions is achieved using battery systems in the Beginning of Life (BOL) state. However, in actual use, the cells in a battery system will age, and the battery system will not remain in the BOL state indefinitely. Using only a BOL-state battery system for simulation prediction will result in performance predictions that differ from reality, leading to low accuracy in battery performance prediction. Furthermore, the performance differences between individual cells in a battery system will also affect the overall performance prediction of the battery system, resulting in performance predictions that differ from reality and causing low accuracy in battery performance prediction. Summary of the Invention

[0003] This application provides a battery performance prediction method, apparatus, device, computer-readable storage medium, and computer program product. These can improve the accuracy of battery performance prediction.

[0004] The technical solution of this application is implemented as follows: This application provides a battery performance prediction method, the method comprising: The first cell parameter is determined based on cell consistency factors and / or at least one cell parameter aging variable information; Based on batch information of at least one cell in the battery system, the probability distribution information of the parameters of the first cell is determined; The battery cells in the battery system are assigned values ​​based on the probability distribution information, so as to predict battery performance using the assigned battery system.

[0005] This application provides a battery performance prediction device, the device comprising: The determining unit is used to determine the first cell parameter based on cell consistency factors and / or at least one cell parameter aging variable information; and to determine the probability distribution information of the first cell parameter based on batch information of at least one cell in the battery system. The prediction unit is used to assign values ​​to the cells in the battery system based on the probability distribution information, so as to predict the battery performance using the assigned battery system.

[0006] This application provides a battery performance prediction device, the device comprising: Memory is used to store executable instructions for a computer; The processor, when executing computer-executable instructions stored in the memory, implements the above-described battery performance detection method.

[0007] This application provides a computer-readable storage medium storing computer-executable instructions or computer programs, which, when executed by a processor, implement the aforementioned battery performance detection method.

[0008] This application provides a computer program product, including computer-executable instructions or a computer program, which, when executed by a processor, implements the above-described battery performance detection method.

[0009] This application provides a battery performance prediction method, apparatus, device, computer-readable storage medium, and computer program product. The method includes: determining a first cell parameter based on cell consistency factors and / or at least one cell parameter aging variable information; determining the normal distribution information of the first cell parameter based on batch information of at least one cell in the battery system; and assigning values ​​to the cells in the battery system based on the normal distribution information, so as to predict battery performance using the assigned battery system. By adopting the above implementation scheme, referring to at least one cell parameter aging variable information and / or cell consistency factors, the first cell parameter can comprehensively consider battery aging factors and cell consistency factors; generating the normal distribution of the first cell parameter based on cell batch information and assigning values ​​to all cells in the battery system can ensure the consistency of cells within the battery system. This allows for comprehensive consideration of the performance performance after cell aging and the performance differences between individual cells when using the battery system as a whole for battery performance prediction, improving the consistency between the performance prediction results and the actual results, thereby improving the accuracy of battery performance prediction. Attached Figure Description

[0010] Figure 1 This is a schematic diagram of the first process of a battery performance prediction method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the second process of a battery performance prediction method provided in an embodiment of this application; Figure 3 A schematic diagram of the third process of a battery performance prediction method provided in an embodiment of this application; Figure 4 This is a schematic diagram of the fourth process of a battery performance prediction method provided in an embodiment of this application; Figure 5 A schematic diagram of the fifth process of a battery performance prediction method provided in an embodiment of this application; Figure 6 A schematic diagram of the fifth process of a battery performance prediction method provided in an embodiment of this application; Figure 7 A schematic diagram of simulation results for an exemplary battery system model provided in this application embodiment; Figure 8 This is a schematic diagram of the structure of a battery performance prediction device provided in an embodiment of this application; Figure 9 This is a schematic diagram of the structure of a battery performance prediction device provided in an embodiment of this application.

[0011] It should be noted that the terms "first" and "second" mentioned above are only used to distinguish between different options and do not represent the degree of superiority or inferiority of the options or their priority in the implementation process. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0014] In the following description, the terms "first, second, third" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0015] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0016] Unless otherwise defined, all technical and scientific terms used in the embodiments of this application have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in the embodiments of this application is for the purpose of describing the embodiments of this application only and is not intended to limit this application.

[0017] In the implementation of this application, the collection and processing of relevant data should strictly comply with the requirements of relevant national laws and regulations, obtain the informed consent or separate consent of the personal information subject, and carry out subsequent data use and processing within the scope of laws and regulations and the authorization of the personal information subject.

[0018] Before providing a further detailed description of the embodiments of this application, the nouns and terms involved in the embodiments of this application will be explained, and the nouns and terms involved in the embodiments of this application shall be interpreted as follows.

[0019] 1) BOL (Best in Late Stage) Status: BOL status refers to the performance of a power battery during its initial use phase, typically based on parameters such as the battery's rated capacity, internal resistance, and open-circuit voltage. At this stage, the battery performance is optimal, and capacity decay and internal resistance growth have not yet occurred significantly.

[0020] 2) End of Life (EOL) State: The EOL state refers to the state in which the battery performance deteriorates to the point that it can no longer meet the needs of the device. It is usually defined as the battery capacity decaying to below 70%-80% of its initial capacity. At this time, the battery range is significantly reduced, the charging frequency increases, and it may be accompanied by problems such as longer charging time, increased heat generation, and frequent BMS alarms.

[0021] 3) State of Health (SOH): SOH reflects the current health of the battery, usually expressed as the ratio of parameters such as battery capacity and internal resistance to their initial values. Newly manufactured batteries have an SOH of 100%, which gradually decreases with use. SOH is used to assess the degree of battery aging and predict remaining lifespan.

[0022] 4) Cell consistency: Cell consistency refers to the similarity in key parameters (such as DC resistance, nominal capacity, open-circuit voltage, etc.) among multiple cells within the same battery pack. A battery pack with good consistency can fully utilize its maximum power, current, and usable capacity, and its performance degradation is more uniform. Poor consistency can lead to a decline in battery system performance, a shortened lifespan, and even safety issues.

[0023] Before describing the solution provided in this application in detail, in order to enable those skilled in the art to better understand the solution provided in this application, the following examples illustrate the application scenarios of this application.

[0024] The battery performance prediction method provided in this application can be applied to scenarios where battery performance needs to be predicted, such as electric vehicles operating under extreme conditions like high temperature, low temperature, and high-rate charging and discharging; energy storage systems operating under extreme conditions like frequent charging and discharging and high power output during grid adjustments and renewable energy grid connection; or specialized equipment in aerospace, deep-sea exploration, and other applications operating under extreme conditions like high temperature, high pressure, and strong vibration. The battery proposed in this application can be a power battery (such as a lithium-ion battery, solid-state battery, sodium-ion battery, fuel cell, etc.), where the power battery is the core component providing continuous power to electrified equipment (such as electric vehicles, energy storage systems, special equipment, etc.), achieving energy conversion through electrochemical energy storage and release.

[0025] Example 1 This application provides a battery performance prediction method, such as... Figure 1 As shown, the method may include: S101. Determine the first cell parameter based on cell consistency factors and / or at least one cell parameter aging variable information.

[0026] In one embodiment, the cell parameters involved in the cell consistency factors may include at least one of the following: cell capacity and DC resistance; wherein, cell capacity is the cell's ability to store electrical energy, and DC resistance is the cell's internal resistance during charging and discharging, reflecting energy loss and heat generation characteristics.

[0027] It should be noted that the DC resistance can be the DC internal resistance (DCR) (the internal resistance of the battery under DC conditions) or R in the second-order RC model. S (Internal resistance in ohms, unit is mΩ).

[0028] In one embodiment, the aging variable information of at least one cell parameter can characterize the degree of change of at least one cell parameter after cell aging, that is, the degree of influence of cell aging on at least one cell parameter. The higher the aging variable information of the cell parameter, the higher the degree of influence of cell aging on the corresponding cell parameter; correspondingly, the lower the aging variable information of the cell parameter, the lower the degree of influence of cell aging on the corresponding cell parameter.

[0029] In one embodiment, the parameter type of at least one cell parameter can be determined based on the cell model. The cell model can be a second-order RC model, a third-order RC model, etc.

[0030] For example, if a second-order RC model is applied to the cell model, then at least one corresponding cell parameter may include: R S R SEI(SEI film resistance, measured in mΩ, reflects the resistive characteristics of the solid electrolyte interface inside the cell), R CT (Charge transfer resistance, measured in mΩ, reflects the resistance characteristics of the charge transfer process within the battery cell), C SEI (SEI film capacitance, measured in mF, reflects the capacitive characteristics of the SEI film), C Cl (Double-layer capacitor, measured in mF, reflects the characteristics of the double-layer capacitor inside the battery cell).

[0031] In one embodiment, the number of first cell parameters is one or more, and the specific selection can be made according to the actual situation. This application does not impose specific limitations on the embodiments.

[0032] In one embodiment, one or more cell parameters can be selected as the first cell parameter from cell parameters and / or at least one cell parameter involved in cell consistency.

[0033] For example, one or more cell parameters can be selected as the first cell parameter from at least one cell parameter in descending order of sensitivity information, or one or more cell parameters can be selected as the first cell parameter from cell parameters involved in cell consistency factors, or one or more cell parameters can be selected as the first cell parameter from at least one cell parameter and cell parameters involved in cell consistency factors.

[0034] S102. Based on the batch information of at least one cell in the battery system, determine the probability distribution information of the parameters of the first cell.

[0035] Optionally, the batch information of at least one cell can be the batch information of all cells in the battery system, or it can be the batch information of some cells in the battery system. The specific selection can be made according to the actual situation, and this application embodiment does not make specific limitations.

[0036] In one embodiment, the batch information of at least one battery cell may include at least one of the following: material batch, process parameters (such as coating speed, formation temperature, etc.), production date, production line, serial number, etc. The specific selection can be made according to actual circumstances, and this application embodiment does not impose specific limitations.

[0037] In one embodiment, the type of probability distribution may include: normal distribution, gamma distribution, beta distribution, log-normal distribution, Weibull distribution, Cauchy distribution, etc. The specific type can be selected according to the actual situation, and this application embodiment does not impose specific limitations.

[0038] In one embodiment, batch information of at least one battery cell can determine key parameters of the probability distribution information to construct the probability distribution information.

[0039] For example, batch information of at least one battery cell can be used to determine the standard deviation parameter of a normal distribution, or batch information of at least one battery cell can be used as the scale parameter of a gamma distribution, etc. The specific choice can be made according to the actual situation, and the embodiments of this application do not make specific limitations.

[0040] It should be noted that the batch information of at least one battery cell can be quantified into a numerical value as a key parameter value for probability distribution information. The specific value can be selected according to the actual situation, and this application embodiment does not impose any specific limitations.

[0041] S103. Assign values ​​to the cells in the battery system based on probability distribution information, so as to predict battery performance using the assigned battery system.

[0042] In one embodiment, a third cell parameter value can be selected from the probability distribution information of the first cell parameter, and the third cell parameter value can be assigned to the first cell parameter of the cell model in the battery system model.

[0043] In one embodiment, a cell model can first be established, which can be a second-order RC model, a third-order RC model, etc. Then, a corresponding circuit model can be established based on the predicted number of cells connected in series and / or in parallel in the battery system.

[0044] In one embodiment, the battery performance under extreme conditions is predicted using the assigned battery system model.

[0045] In one embodiment, the operating conditions can be formed by temperature and state of charge (SOC).

[0046] In one embodiment, the predicted battery performance may include at least one of the following: discharge power, discharge current and duration, minimum voltage (system minimum voltage, individual cell minimum voltage), etc. The specific parameters can be selected based on actual conditions, and this application does not impose specific limitations.

[0047] It is understood that, in the above embodiments, by referring to at least one cell parameter aging variable information and / or cell consistency factors, the first cell parameter can comprehensively consider both battery aging factors and cell consistency factors. Generating a normal distribution of the first cell parameter based on cell batch information and assigning values ​​to all cells in the battery system ensures cell consistency within the system. This allows for comprehensive consideration of cell performance after aging and performance differences between individual cells when using the battery system for battery performance prediction, improving the consistency between performance prediction results and actual results, thereby enhancing the accuracy of battery performance prediction. Improving the accuracy of battery performance prediction can also further optimize the formulation of subsequent battery available energy boundaries and power utilization strategies.

[0048] Example 2 Based on the above embodiments, such as Figure 2 As shown, before S101, the following steps can also be implemented: S201. Determine at least one first cell parameter value for at least one cell parameter in the initial state of the cell.

[0049] In one embodiment, the initial state of the battery cell is BOL (Best in Least Own) state, i.e., 100% SOH (State of Health). SOH represents the battery's current performance as a percentage of its brand-new state, reflecting the degree of battery aging. 100% SOH indicates that the battery is in a brand-new state, with parameters such as capacity, internal resistance, and charge / discharge efficiency exactly matching the design values.

[0050] In one embodiment, a BOL state cell model is established. Then, at least one cell parameter of the BOL state cell model is calibrated to obtain at least one first cell parameter value.

[0051] For example, a second-order RC model can be used and a cell model can be built based on AMEsim.

[0052] In one embodiment, at least one cell parameter of a cell model can be calibrated using a cell calibration method. This cell calibration method may include methods such as Hybrid Pulse Power Characterization (HPPC) for calibrating the cell parameters of the cell model.

[0053] It should be noted that the HPPC method mainly measures the battery's voltage response by applying a mixed pulse current, thereby calibrating the cell model parameters.

[0054] In one embodiment, at least one first cell parameter value is described in the cell model by a map to cover all operating conditions of temperature and SOC combinations.

[0055] For example, the process of calibrating at least one electromagnetic energy parameter using the HPPC method to cover different operating conditions can be achieved through the following stages: Pre-treatment stage: First, the cell model is fully charged (100% SOC) and kept at constant temperature at different temperatures (e.g., -20°C, 0°C, 25°C, 45°C) to ensure uniform battery temperature. Then, the battery is adjusted to the target SOC (e.g., 100%, 80%, 60%, 40%, 20%, 0%) through constant current discharge. Each SOC point needs to be left to stand for more than 1 hour to stabilize the voltage.

[0056] Pulse testing phase: First, discharge at 1C or a higher rate (e.g., 2C) for 10 seconds and record the voltage transient response. Then, allow the battery to rest for 40 seconds after discharging and observe the voltage recovery. Finally, charge at the same rate for 10 seconds and record the voltage change. Repeat the above steps to cover all target temperatures and SOC points.

[0057] Data processing and parameter extraction stage: Five cell parameters of the second-order RC model are extracted from the HPPC experimental data obtained through the above testing stage.

[0058] The stage of constructing MAP diagrams for different temperature and SOC combinations: The calibrated cell parameter values ​​are organized into MAP diagrams according to temperature and SOC.

[0059] It should be noted that the calibration results of at least one cell parameter are organized into a matrix form according to temperature (horizontal axis / vertical axis) and SOC (vertical axis / horizontal axis) to obtain at least one MAP diagram, where each cell parameter corresponds to one MAP diagram.

[0060] For example, for R S The MAP diagram can be found in Table 1. Table 1 is only an exemplary MAP diagram, and the selection of temperature and SOC in the MAP diagram can be changed.

[0061] Table 1

[0062] S202. Determine at least one second cell parameter value for at least one cell parameter under the cell aging state.

[0063] In one embodiment, the cell aging state can be EOL state or other states that have not reached 100% SOH state, wherein EOL state can be defined as 80% SOH state.

[0064] In one embodiment, at least one second cell parameter value can be determined for a cell aging state (such as EOL state); alternatively, at least one second cell parameter value can be determined for each of multiple cell aging states (such as 80% SOH, 60% SOH, etc.). The specific choice can be made according to the actual situation, and this application embodiment does not impose specific limitations.

[0065] S203. Using at least one first cell parameter value and at least one second cell parameter value, determine at least one cell parameter aging variable information respectively.

[0066] In one embodiment, the first cell parameter value and the second cell parameter value can be input into formula (1) to obtain cell parameter aging variable information.

[0067] M=(C2-C1) / C1 / 100(S1-S2) (1) Where M represents the cell parameter aging variable information, C2 represents the second cell parameter value, C1 represents the first cell parameter value, S1 represents the SOH percentage corresponding to the initial state of the battery, and S2 represents the SOH percentage corresponding to the aging state of the battery.

[0068] For example, the cell parameter values ​​for BOL and EOL states under a specific SOC and temperature combination can be found in Table 2.

[0069] Table 2

[0070] It should be noted that Δ / 1%SOH in Table 2 represents the sensitivity information for each cell parameter.

[0071] For example, referring to Table 2, the five cell parameters are sorted in descending order of sensitivity information as follows: C1, R2, R0, R1, C2. If a first cell parameter is selected based on the sensitivity information, then C1 is selected as the first cell parameter.

[0072] It is understood that, based on determining at least one first cell parameter value in the BOL state, the above embodiments also determine at least one second cell parameter value in the battery aging state. The at least one second cell parameter value and the at least one first cell parameter value are used to calculate at least one cell parameter aging variable information. This cell parameter aging variable information reflects the perception of at least one cell parameter on the cell aging condition. Thus, the first cell parameter selected based on the sensitivity information also considers the cell aging factor. Consequently, the battery system model established based on this takes into account the performance of the cell after aging when predicting battery performance, which can improve the consistency between the performance prediction results and the actual situation, thereby improving the accuracy of battery performance prediction.

[0073] Example 3 Based on the above embodiments, regarding S202, see... Figure 3 It may also include the following steps: S301. Determine the second cell parameter value based on the correspondence between the cell capacity change information and the cell parameter value change information; and / or, calibrate the second cell parameter value using a cell model in an aging state.

[0074] It should be noted that, regarding the determination of the second cell parameter value, one approach is to pre-set the correspondence between cell capacity change information and cell parameter value change information to directly determine the second cell parameter value; this method is simple to implement. Another approach is to directly calibrate the second cell parameter value using a cell model in an aged state; this method offers higher accuracy. Either of these two methods can be chosen, or a combination of both can be used (e.g., for at least one cell parameter, part of the first method and part of the second method can be used). The specific choice can be made based on the actual situation, and this application does not impose specific limitations.

[0075] In one embodiment, for the method of determining the second cell parameter value based on the correspondence, the correspondence between the cell capacity change information and the cell parameter value change information can be predetermined. This correspondence can be determined using methods such as machine learning, experimental observation, and theoretical derivation.

[0076] In one embodiment, cell capacity change information can be characterized as the degree of SOH decrease.

[0077] In one embodiment, the cell capacity change information in the above correspondence is for the initial state of the cell, and correspondingly, the cell parameter value change information in the above correspondence is for the first cell parameter value.

[0078] For example, for C SEI The corresponding relationship between the changes in cell capacity and cell parameter values ​​can be as follows: for every 1% decrease in SOH, C SEI The value increases by 7%. When the battery aging condition is 80% SOH and C is in BOL state... SEI When the value is 230, it can be determined that the SOH decreased by 20%, and the corresponding C SEI If the value increases by 140%, then the C in the 80% SOH state... SEI The answer is 230 * (1 + 1.4) = 552.

[0079] In another embodiment, regarding the method of calibrating the second cell parameter value, a cell model in an aging state is first established. Then, at least one cell parameter of the aging state cell model is calibrated to obtain at least one second cell parameter value for the at least one cell parameter.

[0080] It should be noted that the process of obtaining at least one second cell parameter value of at least one cell parameter from at least one cell parameter of the calibrated cell aging state cell model is consistent with the process of obtaining at least one third cell parameter value of at least one cell parameter from at least one cell parameter of the calibrated BOL state cell model in S201. Please refer to the relevant description in S201, which will not be repeated here.

[0081] It is understandable that at least one second cell parameter value can be determined by at least one of the two methods mentioned above, which can increase the diversity of determining the second cell parameter value.

[0082] Example 4 Based on the above embodiments, such as Figure 4 As shown, for S102, the following steps may also be included: S401. Obtain the third cell parameter value of the first cell parameter under the initial state of the cell.

[0083] In one embodiment, if at least one cell parameter aging variable information determination process has been performed first, then a third cell parameter value corresponding to the first cell parameter can be selected from at least one first cell parameter value; otherwise, a BOL state cell model can be established here, and the third cell parameter value of the first cell parameter can be calibrated. The specific selection can be made according to the actual situation, and this application embodiment does not impose specific limitations.

[0084] S402. Determine the standard deviation based on the batch information of at least one battery cell, and use the parameter value of the third battery cell as the mean to determine the normal distribution information of the parameters of the first battery cell.

[0085] In one embodiment, the normal distribution information of the first cell parameters can be generated based on batch information of at least one cell and the parameter value of a third cell. Specifically, the standard deviation (σ) can be determined based on the batch information of the cells, i.e., the batch information of the cells determines the width of the normal distribution, and the mean (μ) is used as the third cell parameter value, i.e., the third cell parameter value determines the center position of the normal distribution.

[0086] In one embodiment, the horizontal axis of the normal distribution information can be probability, and the vertical axis can be numerical value.

[0087] In one embodiment, normal distribution information is assigned to the first cell parameter of the cell model. The target cell parameter value of the first cell parameter of the cell model can be adjusted in real time based on the normal distribution information.

[0088] Understandably, generating a normal distribution of the first cell's parameters based on batch information of at least one cell and the parameter values ​​of the third cell can intuitively reflect the central tendency and dispersion of the first cell's parameters. The normal distribution takes into account the fluctuation range of the first cell's parameters, making subsequent battery performance predictions closer to reality and reducing prediction errors caused by parameter dispersion.

[0089] Example 5 Based on the above embodiments, such as Figure 5 As shown, for S103, the following steps may also be included: S501. Based on the cell performance requirement information, find the target cell parameter values ​​from the normal distribution information.

[0090] In one embodiment, the cell performance requirement information can be such that it can be met by any number of cells in the battery system. For example, it could be met by 99% of the cells in the battery system, or by 95% of the cells.

[0091] For example, for cell performance requirements that can be met by 99% of the cells in a battery system, the 99th percentile value can be found from the normal distribution information as the target cell parameter value for the first cell parameter.

[0092] S502, Assign target cell parameter values ​​to the first cell parameter of the cell in the battery system.

[0093] In one embodiment, the battery system described above, after being assigned a value, can be used to predict battery performance under extreme operating conditions.

[0094] Understandably, different target cell parameter values ​​can be assigned to the initial cell parameters of cells in a battery system based on different cell performance requirements, thus satisfying diverse performance needs. Furthermore, by utilizing normal distribution information, combined with the initial performance parameters of the cells and batch consistency data, the performance of the battery system under extreme operating conditions can be predicted more accurately.

[0095] Example 6 Based on the above embodiments, such as Figure 6 As shown, for S103, the following steps may also be included: S601, Obtain the first temperature and SOC combination for battery performance prediction.

[0096] In one embodiment, this application can predict battery performance under different temperature and SOC combinations. When assigning values ​​to cells in a battery system, the first temperature and SOC combination for subsequent battery performance prediction can be determined first.

[0097] S602. Find the first probability distribution information corresponding to the combination of the first temperature and SOC from at least one probability distribution information.

[0098] It should be noted that a corresponding probability distribution can be generated for different combinations of temperature and SOC. That is, the number of probability distributions is the same as the number of temperature and SOC combinations. When there are multiple temperature and SOC combinations, a corresponding probability distribution will be generated for each combination.

[0099] S603. Assign values ​​to the cells in the battery system based on the first probability distribution information, so as to use the assigned battery system to perform the battery performance prediction process under the first temperature and SOC combination.

[0100] It should be noted that the target cell parameter values ​​assigned to the cell may not be the same for different combinations of temperature and SOC.

[0101] Understandably, generating a probability distribution for each temperature and SOC combination allows for the assignment of values ​​to cells in the battery system based on this distribution when predicting battery performance under different temperature and SOC combinations. This makes the assigned battery system more closely resemble the tested operating conditions, improving the accuracy of battery performance prediction. It also enables battery performance prediction under different operating conditions, enhancing the intelligence and versatility of battery performance prediction.

[0102] It should be noted that the battery performance prediction method involved in the above embodiments may include at least one of Embodiments 1 to 6. For example, Embodiment 1 may be implemented as an independent embodiment, Embodiment 1+2 may be implemented as an independent embodiment, Embodiment 1+2+3 may be implemented as an independent embodiment, Embodiment 1+4 may be implemented as an independent embodiment, Embodiment 1+2+4 may be implemented as an independent embodiment, Embodiment 1+2+3+4 may be implemented as an independent embodiment, Embodiment 1+5 may be implemented as an independent embodiment, Embodiment 1+2+5 may be implemented as an independent embodiment, Embodiment 1+2+3+5 may be implemented as an independent embodiment, Embodiment 1+2+3+4+5 may be implemented as an independent embodiment, Embodiment 1+6 may be implemented as an independent embodiment, Embodiment 1+2+6 may be implemented as an independent embodiment, Embodiment 1+2+3+6 may be implemented as an independent embodiment, Embodiment 1+2+3+4+6 may be implemented as an independent embodiment, Embodiment 1+2+3+4+5+6 may be implemented as an independent embodiment, etc., but not limited to these, and will not be listed in detail here.

[0103] Based on the above embodiments, a method for predicting battery performance under extreme operating conditions is proposed. This includes: 1. BOL cell model confirmed The BOL cell model uses a second-order RC model and is based on AMEsim. It has five parameters: Rs: Simulation parameter, unit: mΩ R SEI Simulation parameters, unit: mΩ R CT Simulation parameters, unit: mΩ C SEI Simulation parameters, unit: mF C cl Simulation parameters, unit: mF Each parameter is described by a map in the BOL cell model to cover all possible temperature and SOC conditions. The map parameters to be used can be calibrated using the HPPC method.

[0104] 2. EOL cell model confirmed The above five map parameters are calibrated using cells in EOL state (i.e., SOH of 80%). The map parameters can be calibrated using the HPPC method.

[0105] It should be noted that both the BOL cell model and the EOL cell model use a second-order RC model. Compared with the single-resistor model of the power transmission strategy, the prediction results are more accurate due to the increased number of cell parameter types.

[0106] 3. Battery system model establishment A battery system model is established based on the predicted number of series and parallel cells in the battery system. Each cell is assigned a normal distribution of parameters. The key cell parameters of interest may include at least one of the following: capacity and DC resistance (either DCR or Rs). The following are a set of model calibration and setup results to illustrate the voltage prediction method and results of the battery system model under extreme operating conditions: For a specific combination of SOC and temperature, the calibrated values ​​of cell parameters for BOL and EOL states can be found in Table 2.

[0107] Table 2

[0108] It should be noted that the introduction of the SOH concept can further predict the performance of the battery after aging, in addition to the BOL state.

[0109] In one embodiment, based on battery system consistency considerations, the R0 coefficients in Table 2 are assigned a normal distribution, with the mean and σ derived from cell batch information.

[0110] It should be noted that by introducing cell consistency factors, the worst and best performance of the battery system can be predicted, and the prediction results can be adjusted based on cell batch information.

[0111] In one embodiment, based on battery SOH considerations, for the C1 coefficient in Table 2, for every 1% decrease in SOH, the C1map value increases by 7%.

[0112] Figure 7 The simulation results for the above battery system model are shown, where the inputs are SOC, temperature, capacity quantiles, and SOH. Figure 7 The horizontal axis represents time, and the vertical axis represents voltage. Figure 6Line 1 in the diagram represents the voltage of a single battery cell simulated during the operation of the battery system model. Figure 7 Line 2 in the diagram represents the highest single-cell voltage Vmax during the operation of the battery system model. Figure 7 Line 3 represents the lowest single-cell voltage Vmin when the battery system model is operating.

[0113] It should be noted that, Figure 7 One-dimensional simulation is used, which only simulates the cell voltage, reducing the simulation dimensions and thus improving computational efficiency.

[0114] This application proposes a method for predicting the performance of battery systems, which is of great significance for predicting the power performance of battery systems under extreme operating conditions. Based on the simulation results of power performance under extreme conditions, the available energy boundary and power utilization strategy of the battery can be further optimized.

[0115] This application provides a battery performance prediction device 1. For example... Figure 8 As shown, the battery performance prediction device 1 includes: The determining unit 10 is used to determine the first cell parameter based on cell consistency factors and / or at least one cell parameter aging variable information; and to determine the probability distribution information of the first cell parameter based on batch information of at least one cell in the battery system. The prediction unit 11 is used to assign values ​​to the cells in the battery system based on the probability distribution information, so as to predict the battery performance using the assigned battery system.

[0116] Optionally, the determining unit 10 is further configured to determine at least one first cell parameter value of the at least one cell parameter in the initial state of the cell; determine at least one second cell parameter value of the at least one cell parameter in the aging state of the cell; and determine the aging variable information of the at least one cell parameter using at least one first cell parameter value and at least one second cell parameter value.

[0117] Optionally, the device further includes a calibration unit; The determining unit 10 is further configured to determine the second cell parameter value based on the correspondence between the cell capacity change information and the cell parameter value change information; The calibration unit is also used to calibrate the parameter values ​​of the second cell using a cell model in an aged state.

[0118] Optionally, the probability distribution information includes normal distribution information, and the device further includes: an acquisition unit; The acquisition unit is further configured to acquire the third cell parameter value of the first cell parameter in the initial state of the cell; The determining unit 10 is further configured to determine the standard deviation based on the batch information of at least one battery cell, and to determine the normal distribution information of the first battery cell parameter using the third battery cell parameter value as the mean.

[0119] Optionally, the device further includes: a lookup unit and an assignment unit; The search unit is also used to search for target cell parameter values ​​from the normal distribution information based on the cell performance requirement information; The assignment unit is also used to assign the target cell parameter value to the first cell parameter of the cell in the battery system.

[0120] Optionally, the number of probability distribution information and the temperature are consistent with the number of combinations of state of charge (SOC); The acquisition unit is also used to acquire a first temperature and SOC combination for predicting battery performance; The search unit is further configured to search for first probability distribution information corresponding to the combination of first temperature and SOC from at least one of the probability distribution information; The prediction unit 11 is further configured to assign values ​​to the cells in the battery system based on the first probability distribution information, so as to use the assigned battery system to execute the battery performance prediction process under the first temperature and SOC combination.

[0121] This application provides a battery performance prediction device that determines a first cell parameter based on cell consistency factors and / or at least one cell parameter aging variable information; generates normal distribution information of the first cell parameter based on batch information of cells in the battery system; and assigns values ​​to cells in the battery system based on the normal distribution information to predict battery performance using the assigned battery system. Therefore, the battery performance prediction device proposed in this embodiment, by referring to at least one cell parameter aging variable information and / or cell consistency factors, allows the first cell parameter to comprehensively consider both battery aging factors and cell consistency factors. Generating the normal distribution of the first cell parameter based on cell batch information and assigning values ​​to all cells in the battery system ensures cell consistency within the battery system. This allows for comprehensive consideration of the performance performance of cells after aging and the performance differences between individual cells when using the battery system as a whole for battery performance prediction, improving the consistency between the performance prediction results and the actual results, thereby improving the accuracy of battery performance prediction.

[0122] Figure 9 A schematic diagram of the composition structure of a battery performance prediction device 2 provided in this application embodiment. Figure 2 In practical applications, based on the same disclosed concept of the above embodiments, such as Figure 9 As shown, the battery performance prediction device 2 in this embodiment includes: a processor 20, a memory 21, and a communication bus 22.

[0123] The processor 20 described above can be at least one of the following: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), CPU, controller, microcontroller, and microprocessor. It is understood that, for different devices, the electronic device used to implement the above processor function can also be other types, and this embodiment does not impose specific limitations.

[0124] In this embodiment, the communication bus 22 is used to realize the connection communication between the processor 20 and the memory 21; when the processor 20 executes the running program stored in the memory 21, it implements the following battery performance prediction method: Based on cell consistency factors and / or at least one cell parameter aging variable information, a first cell parameter is determined; based on batch information of at least one cell in the battery system, probability distribution information of the first cell parameter is determined; based on the probability distribution information, values ​​are assigned to the cells in the battery system to predict battery performance using the assigned battery system.

[0125] Furthermore, the processor 20 is also configured to determine at least one first cell parameter value of the at least one cell parameter in the initial state of the cell; determine at least one second cell parameter value of the at least one cell parameter in the aging state of the cell; and determine the aging variable information of the at least one cell parameter using at least one first cell parameter value and at least one second cell parameter value.

[0126] Furthermore, the processor 20 is also used to determine the second cell parameter value based on the correspondence between the cell capacity change information and the cell parameter value change information; and / or to calibrate the second cell parameter value using a cell model in an aging state.

[0127] Furthermore, the probability distribution information includes normal distribution information. The processor 20 is further configured to acquire the third cell parameter value of the first cell parameter in the initial state of the cell; determine the standard deviation based on the batch information of at least one cell; and use the third cell parameter value as the mean to determine the normal distribution information of the first cell parameter.

[0128] Furthermore, the processor 20 is also used to find the target cell parameter value from the normal distribution information based on the cell performance requirement information; The target cell parameter value is assigned to the first cell parameter of the cell in the battery system.

[0129] Furthermore, the number of probability distribution information and the temperature are consistent with the number of combinations of state of charge (SOC); The processor 20 described above is further configured to obtain a first temperature and SOC combination for battery performance prediction; search for a first probability distribution information corresponding to the first temperature and SOC combination from at least one of the probability distribution information; assign values ​​to the cells in the battery system based on the first probability distribution information, so as to use the assigned battery system to execute the battery performance prediction process under the first temperature and SOC combination.

[0130] This application provides a storage medium storing a computer program thereon. The computer-readable storage medium stores one or more programs, which can be executed by one or more processors. The computer program implements the battery performance prediction method described above.

[0131] Based on the above embodiments, this application provides a computer program product, including a computer program that can be executed by one or more processors, and the computer program implements the battery performance prediction method described above.

[0132] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0133] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the related technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause an image display device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this disclosure.

[0134] The above description is merely a preferred embodiment of this application and is not intended to limit the scope of protection of this application.

Claims

1. A method for predicting battery performance, characterized in that, The method includes: The first cell parameter is determined based on cell consistency factors and / or at least one cell parameter aging variable information; Based on batch information of at least one cell in the battery system, the probability distribution information of the parameters of the first cell is determined; The battery cells in the battery system are assigned values ​​based on the probability distribution information, so as to predict battery performance using the assigned battery system.

2. The method according to claim 1, characterized in that, Before determining the first cell parameter based on at least one cell parameter aging variable information, the method further includes: Determine at least one first cell parameter value for at least one cell parameter in the initial state of the cell; Determine at least one second cell parameter value for the at least one cell parameter under the cell aging state; Using at least one first cell parameter value and at least one second cell parameter value, the aging variable information of the at least one cell parameter is determined respectively.

3. The method according to any one of claims 1-2, characterized in that, The step of determining at least one second cell parameter value for at least one cell parameter under the cell aging state includes: The second cell parameter value is determined based on the correspondence between the cell capacity change information and the cell parameter value change information; And / or, using an aged cell model to calibrate the parameter values ​​of the second cell.

4. The method according to any one of claims 1-3, characterized in that, The probability distribution information includes normal distribution information. The probability distribution information for determining the parameters of the first cell based on batch information of at least one cell in the battery system includes: Obtain the third cell parameter value of the first cell parameter in the initial state of the cell; The standard deviation is determined based on the batch information of at least one cell, and the normal distribution information of the first cell parameter is determined using the third cell parameter value as the mean.

5. The method according to any one of claims 1-4, characterized in that, Assigning values ​​to the cells in the battery system based on the probability distribution information includes: Based on the cell performance requirement information, the target cell parameter values ​​are found from the probability distribution information; Assign the target cell parameter value to the first cell parameter of the cell in the battery system.

6. The method according to any one of claims 1-5, characterized in that, The number of probability distribution information and the number of combinations of temperature and state of charge (SOC) are consistent; the process of assigning values ​​to the cells in the battery system based on the probability distribution information, so as to predict battery performance using the assigned battery system, includes: Obtain the first temperature and SOC combination corresponding to the battery performance prediction process; Search for the first probability distribution information corresponding to the combination of first temperature and SOC from at least one of the probability distribution information; The battery cells in the battery system are assigned values ​​based on the first probability distribution information, so as to use the assigned battery system to perform the battery performance prediction process under the first temperature and SOC combination.

7. A battery performance prediction device, characterized in that, The device includes: The determining unit is used to determine the first cell parameter based on cell consistency factors and / or at least one cell parameter aging variable information; and to determine the probability distribution information of the first cell parameter based on batch information of at least one cell in the battery system. The prediction unit is used to assign values ​​to the cells in the battery system based on the probability distribution information, so as to predict the battery performance using the assigned battery system.

8. A battery performance prediction device, characterized in that, The device includes: Memory is used to store executable instructions for a computer; A processor, when executing computer-executable instructions stored in the memory, implements the method of any one of claims 1 to 6.

9. A computer-readable storage medium storing computer-executable instructions or a computer program, characterized in that, When the computer-executable instructions or computer program are executed by a processor, they implement the method described in any one of claims 1 to 6.

10. A computer program product comprising computer-executable instructions or a computer program, characterized in that, When the computer-executable instructions or computer program are executed by a processor, they implement the method according to any one of claims 1 to 6.

Images