Battery performance evaluation method and device, computer equipment and readable storage medium

Abstract

The invention relates to a battery performance evaluation method and device, computer equipment and a readable storage medium, and relates to the technical field of batteries. The method comprises the steps of obtaining battery parameters of a target battery at multiple moments in response to a performance evaluation instruction for the target battery; aiming at the parameter change trend of each battery parameter, determining a target change trend interval to which the parameter change trend belongs; based on a first mapping relation between the change trend interval and the battery electric quantity, determining a target battery electric quantity matched with the target change trend interval; and performing performance evaluation on the target battery according to the electric quantity of the target battery to obtain a performance evaluation result of the target battery. By adopting the method, the battery performance evaluation efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of batteries, and in particular, to a method and device for evaluating battery performance, a computer device, a computer-readable storage medium, and a computer program product. Background Art

[0002] As a device for storing and releasing electrical energy, the safety issue of batteries has attracted increasing attention. Therefore, the evaluation of battery performance is becoming more and more important.

[0003] In traditional technologies, the evaluation of battery performance is usually achieved based on mathematical models such as long short-term memory networks and least squares support vector machines. However, mathematical models are often complex and require repeated training for optimization, which takes a long time, so to a certain extent, it will affect the efficiency of the entire battery performance evaluation. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a method and device for evaluating battery performance, a computer device, a computer-readable storage medium, and a computer program product that can improve the efficiency of battery performance evaluation.

[0005] In a first aspect, the present application provides a method for evaluating battery performance, including: in response to a performance evaluation instruction for a target battery, obtaining battery parameters of the target battery at multiple moments; determining a target change trend interval to which the parameter change trend belongs for each battery parameter; determining a target battery power that matches the target change trend interval based on a first mapping relationship between the change trend interval and the battery power; and evaluating the performance of the target battery according to the target battery power to obtain a performance evaluation result of the target battery.

[0006] In one embodiment, the battery parameter includes a target expansion force; determining a target change trend interval to which the parameter change trend belongs for each battery parameter includes: grouping the target expansion forces to obtain at least one expansion force parameter group; and determining a target change trend interval to which the parameter change trend belongs for each target expansion force in each expansion force parameter group.

[0007] In one embodiment, the battery parameter further includes a target voltage; for the parameter change trend of each target expansion force in each expansion force parameter group, determining the target change trend interval to which the parameter change trend belongs includes: for the parameter change trend of each target expansion force in each expansion force parameter group, determining a candidate change trend interval that matches the parameter change trend; when there are multiple candidate change trend intervals, based on the second mapping relationship between the voltage and the change trend interval, screening out the candidate change trend interval that matches the target voltage from the multiple candidate change trend intervals as the target change trend interval to which the parameter change trend belongs.

[0008] In one embodiment, based on the first mapping relationship between the change trend interval and the battery power, determining the target battery power that matches the target change trend interval includes: based on the first mapping relationship between the change trend interval and the analysis function of the battery power, determining the target power analysis function that matches the target change trend interval; through the target power analysis function, performing power analysis on the target battery to obtain the target battery power.

[0009] In one embodiment, for the parameter change trend of each battery parameter, determining the target change trend interval to which the parameter change trend belongs includes: for the parameter change trend of each battery parameter, when the parameter change trend is monotonically increasing, determining that the target change trend interval to which the parameter change trend belongs is a monotonically increasing interval; when the parameter change trend is monotonically decreasing, determining that the target change trend interval to which the parameter change trend belongs is a monotonically decreasing interval.

[0010] In one embodiment, the method further includes: when the parameter change trend is non-monotonic, determining the parameter extreme value in each battery parameter; based on the power analysis function that matches the parameter extreme value, performing power analysis on the target battery to obtain the target battery power.

[0011] In one embodiment, the target battery includes a battery cell and an end plate; the method further includes: obtaining the target distance between the battery cell and the end plate; based on the target distance, performing sensor quantity matching to obtain the target sensor quantity that matches the target distance; obtaining the battery parameters of the target battery at multiple moments, including: through the target sensors with the target sensor quantity, obtaining the battery parameters of the target battery at multiple moments.

[0012] Second aspect, the present application further provides a battery performance evaluation device, including: a parameter acquisition module, configured to acquire battery parameters of a target battery at multiple moments in response to a performance evaluation instruction for the target battery; an interval determination module, configured to determine a target change trend interval to which the parameter change trend belongs for each battery parameter; a power determination module, configured to determine a target battery power that matches the target change trend interval based on a first mapping relationship between the change trend interval and the battery power; and a battery evaluation module, configured to perform performance evaluation on the target battery according to the target battery power to obtain a performance evaluation result of the target battery.

[0013] Third aspect, the present application further provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented: acquiring battery parameters of a target battery at multiple moments in response to a performance evaluation instruction for the target battery; determining a target change trend interval to which the parameter change trend belongs for each battery parameter; determining a target battery power that matches the target change trend interval based on a first mapping relationship between the change trend interval and the battery power; and performing performance evaluation on the target battery according to the target battery power to obtain a performance evaluation result of the target battery.

[0014] Fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: acquiring battery parameters of a target battery at multiple moments in response to a performance evaluation instruction for the target battery; determining a target change trend interval to which the parameter change trend belongs for each battery parameter; determining a target battery power that matches the target change trend interval based on a first mapping relationship between the change trend interval and the battery power; and performing performance evaluation on the target battery according to the target battery power to obtain a performance evaluation result of the target battery.

[0015] Fifth aspect, the present application further provides a computer program product, including a computer program. When the computer program is executed by a processor, the following steps are implemented: acquiring battery parameters of a target battery at multiple moments in response to a performance evaluation instruction for the target battery; determining a target change trend interval to which the parameter change trend belongs for each battery parameter; determining a target battery power that matches the target change trend interval based on a first mapping relationship between the change trend interval and the battery power; and performing performance evaluation on the target battery according to the target battery power to obtain a performance evaluation result of the target battery.

[0016] The above battery performance evaluation method, device, computer device, computer-readable storage medium, and computer program product, in response to a performance evaluation instruction for a target battery, first obtain battery parameters of the target battery at multiple moments and determine the parameter change trend of each battery parameter. Then, query the target change trend interval to which the parameter change trend belongs, and based on the first mapping relationship between the change trend interval and the battery power, determine the target battery power that matches the target change trend interval. The battery power can directly reflect the battery's endurance or energy storage capacity. Therefore, in this solution, the target battery is evaluated based on the battery power to obtain the performance evaluation result of the target battery. Compared with the current method of implementing battery performance evaluation based on a complex mathematical model, this solution simplifies the determination process of the battery power by dividing the change trend interval of the battery parameters and establishing a local mapping relationship, transforming the analysis problem of the battery power into a simple local mapping problem, effectively simplifying the determination process of the battery power, thereby achieving a rapid evaluation of the battery power and improving the efficiency of battery performance evaluation based on the battery power. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is an application environment diagram of the battery performance evaluation method in an embodiment;

[0019] Figure 2 It is a flowchart of the battery performance evaluation method in an embodiment;

[0020] Figure 3 It is a calibration diagram of battery parameters during charging / discharging in an embodiment;

[0021] Figure 4 It is a line graph showing the relationship between the expansion force and the battery SOC during charging in an embodiment;

[0022] Figure 5 It is a line graph showing the relationship between the battery voltage and the battery SOC during charging in an embodiment;

[0023] Figure 6 It is a flowchart of determining the target change trend interval in an embodiment;

[0024] Figure 7 It is a flowchart of the SOC estimation process of the energy storage battery pack during charging in a specific embodiment;

[0025] Figure 8 Schematic diagram of estimating the battery power during the tenth charging process in a specific embodiment;

[0026] Figure 9 Schematic diagram of the balance degree of each of the three battery modules in a specific embodiment;

[0027] Figure 10 Schematic diagram of sensor arrangement in an embodiment;

[0028] Figure 11 Schematic diagram of the composition structure of the battery module in an embodiment;

[0029] Figure 12 Schematic diagram of the energy storage battery module in an embodiment;

[0030] Figure 13 Block diagram of the structure of the battery performance evaluation device in an embodiment;

[0031] Figure 14 Internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0032] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0033] The battery performance evaluation method provided by the embodiments of the present application can be applied to an application environment as shown in Figure 1 . Among them, the terminal 102 communicates with the server 104 through a network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or can be placed in the cloud or other network servers. The terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers and Internet of Things devices. The server 104 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0034] Specifically, in response to a performance evaluation instruction for a target battery initiated by the terminal 102, the server 104 first obtains the battery parameters of the target battery at multiple moments. And for the parameter change trend of each battery parameter, determine the target change trend interval to which the parameter change trend belongs. Then, based on the first mapping relationship between the change trend interval and the battery power, determine the target battery power that matches the target change trend interval. Finally, according to the target battery power, perform a performance evaluation on the target battery to obtain the performance evaluation result of the target battery.

[0035] In an exemplary embodiment, as Figure 2 shown, a battery performance evaluation method is provided. Taking the method applied to the Figure 1 server 104 as an example, the method includes the following steps:

[0036] Step S202, in response to a performance evaluation instruction for a target battery, obtain battery parameters of the target battery at multiple moments.

[0037] Among them, the target battery refers to the battery that needs to be evaluated for performance, such as a storage battery, especially a storage battery pack. A storage battery pack is a battery system used for storing electrical energy, mainly for long-term energy storage and charge and discharge, and is widely used in fields such as power grid dispatching, peak load shaving, and power management. A storage battery pack usually consists of multiple monolithic battery cells connected in parallel and series to form a complete battery module. Of course, in actual applications, this embodiment is also applicable to other types of battery packs. The performance evaluation instruction refers to an instruction for evaluating the performance of the target battery. Multiple moments can be understood as the moments when battery parameter collection is performed, which can be determined according to actual situations. It should be noted that since this embodiment needs to determine the change trend of battery parameters, it is necessary to collect the parameter values of battery parameters at each moment. That is to say, the battery parameters in this embodiment are essentially an array sequence. Battery parameters refer to the relevant parameters of the target battery during charging / discharging, including but not limited to expansion force, battery voltage, temperature, current, operation duration, etc.

[0038] Exemplarily, after the server receives a performance evaluation instruction for a storage battery pack initiated by a terminal device, it can first instruct a parameter sensor to collect battery parameters of the storage battery pack at multiple moments. Of course, it can also be that the parameter sensor automatically collects battery parameters without the server's instruction. In this case, the performance evaluation instruction can be sent to both the server and the parameter sensor at the same time. After the parameter sensor finishes collecting, these battery parameters can be transmitted to the server for the server to analyze the parameter change trend.

[0039] Step S204, for the parameter change trend of each battery parameter, determine the target change trend interval to which the parameter change trend belongs.

[0040] Among them, since the battery parameters are parameters at multiple moments, during the actual operation of the battery, its parameters are not constant and will change with various internal and external influencing factors of the battery, which leads to a change trend of the battery parameters. In this embodiment, the target change trend interval to which this change trend belongs will be matched to simplify the global problem into a local problem.

[0041] The target change trend interval is the change trend interval to which the parameter change trend selected from the pre-calibrated change trend intervals belongs. The pre-calibrated change trend intervals are the change trend intervals pre-calibrated based on the test parameters of the target battery. That is, the server will pre-consider the battery parameters during the first n complete charge and discharge processes of the target battery as test parameters, and based on the change trends of these test parameters, calibrate the corresponding change trend intervals, thereby establishing a mapping relationship between the parameter change trend and the change trend interval. Refer to Figure 3 , the server obtains the battery parameters (expansion force and battery pressure) during the first n charging processes of the target battery as test parameters, and analyzes the change trends of these test arrays to form a line chart. Figure 3 shows the charging stage and the discharging stage, and the variation of different battery parameters over time. Based on this line chart, the server can calibrate the change trend intervals. For example, during charging, the parameter change trend corresponding to the A1 interval is monotonically increasing, the parameter change trend corresponding to the A2 interval is monotonically decreasing, and the parameter change trend corresponding to the A3 interval is monotonically increasing. During discharging, the parameter change trend corresponding to the A4 interval is monotonically decreasing, the parameter change trend corresponding to the A5 interval is monotonically increasing, and the parameter change trend corresponding to the A6 interval is monotonically decreasing. It should be noted that the number of intervals can be set according to the actual situation, and this embodiment does not limit this. In this way, only a small number of test parameters are required to calibrate the change trend intervals, thereby realizing the subsequent estimation of the battery power, without the need for a large amount of data to drive, and also saving the repetitive iterative optimization work of large models of the neural network type. The implementation difficulty is low and the practicability is high. It can be understood that the static stage in the figure refers to the stage when the target battery is not officially charging / discharging.

[0042] In some embodiments, for the parameter change trend of each battery parameter, determining the target change trend interval to which the parameter change trend belongs includes: for the parameter change trend of each battery parameter, when the parameter change trend is monotonically increasing, determining the target change trend interval to which the parameter change trend belongs as a monotonically increasing interval; when the parameter change trend is monotonically decreasing, determining the target change trend interval to which the parameter change trend belongs as a monotonically decreasing interval.

[0043] Among them, the monotonically increasing interval refers to the interval showing a monotonically increasing trend, and the monotonically decreasing interval refers to the interval showing a monotonically decreasing trend.

[0044] Exemplarily, when the parameter change trend of each battery parameter is monotonically increasing, determining the target change trend interval to which the parameter change trend belongs as a monotonically increasing interval, such as the A1 interval, the A3 interval, and the A5 interval. When the parameter change trend of each battery parameter is monotonically decreasing, determining the target change trend interval to which the parameter change trend belongs as a monotonically decreasing interval, such as the A2 interval, the A4 interval, and the A6 interval.

[0045] Exemplarily, after the server obtains the battery parameters of the energy storage battery pack at multiple moments, it can analyze the change trend of these battery parameters. Then, by matching with each pre-calibrated parameter change interval, the target change trend interval matching the parameter change trend can be obtained.

[0046] Step S206, based on the first mapping relationship between the change trend interval and the battery power, determine the target battery power matching the target change trend interval.

[0047] Among them, in addition to the corresponding relationship between the parameter change trend and the change trend interval, there is also a mapping relationship between the change trend interval and the battery power. The battery power specifically refers to the remaining power (State of Charge, SOC) of the target battery currently, which can be expressed in the form of a percentage. It can be understood that SOC is an important parameter in the battery management system, and the accurate estimation of SOC is crucial for preventing overcharging / overdischarging of the battery, extending the battery life, improving the battery usage efficiency, and realizing early warning, etc. Therefore, in this embodiment, the parameter of battery power is selected to evaluate the battery performance. Before that, it is necessary to achieve the accurate evaluation of the battery power, that is, based on the first mapping relationship between the change trend interval and the battery power, determine the target battery power matching the target change trend interval.

[0048] The target battery power is the battery power matching the target change trend interval, and it can also be understood as the remaining power of the target battery currently. The first mapping relationship reflects the corresponding relationship between the change trend interval and the battery power.

[0049] Exemplarily, the server will pre-store the first mapping relationship between the change trend interval and the battery power. In this way, after obtaining the target change trend interval of each battery parameter, the first mapping relationship can be queried to obtain the target battery power matching the target change trend interval.

[0050] In some embodiments, based on the first mapping relationship between the change trend interval and the battery power, determining the target battery power matching the target change trend interval includes: based on the first mapping relationship between the change trend interval and the analysis function of the battery power, determine the target power analysis function matching the target change trend interval; through the target power analysis function, perform power analysis on the target battery to obtain the target battery power.

[0051] Among them, the expression of the analysis function of the battery power can be , where i represents the i-th interval, and F(K) can be understood as a mathematical function with the battery parameter K as a variable. The specific function expression can be set according to the actual situation, as long as it can reflect the relationship between the change trend interval, the battery parameter, and the battery power.

[0052] For example, in the A1 interval, , in the A2 interval, , in the A3 interval, , in the A4 interval, , in the A5 interval, , in the A6 interval, . The specific value of K will be explained in detail later. F() in different intervals can be the same or different.

[0053] Figure 4 shows a broken line graph of the relationship between the expansion force and the battery SOC during charging, which can also be understood as a schematic diagram of the analysis function of the battery power. It is established when calibrating the change trend intervals. The three intervals among them are: A1 interval: the SOC range is 1% - 30%; A2 interval: the SOC range is 31% - 59%; A3 interval: the SOC range is 60% - 100%. Figure 4 The A1 interval, A2 interval, and A3 interval in Figure 3 correspond to the A1 interval, A2 interval, and A3 interval in

[0054] Figure 5 shows a broken line graph of the relationship between the battery voltage and the battery SOC during charging. It should be noted that the battery voltage will be used to assist in estimating the battery power, and the specific process will be explained later. It is also established when calibrating the change trend intervals. It includes voltage value V1 and voltage value V2. Figure 5 V1 in Figure 3 corresponds to KP1 (peak expansion force) in Figure 3 and V2 corresponds to KP2 (valley expansion force) in

[0055] It should be noted that based on the above content, a broken line graph of the relationship between the expansion force and the battery SOC during discharging, and a broken line graph of the relationship between the battery voltage and the battery SOC during discharging can be derived. Therefore, they are not shown in this embodiment.

[0056] Step S208, according to the target battery power, perform a performance evaluation on the target battery to obtain a performance evaluation result of the target battery.

[0057] Among them, the performance evaluation result can be used to characterize the performance status of the target battery, such as the service life, usage efficiency, etc. of the target battery.

[0058] Exemplarily, after estimating the remaining power of the target battery, i.e., the energy storage battery pack, the server can perform a performance evaluation on the energy storage battery pack based on the remaining power, so as to obtain evaluation results such as the service life and usage efficiency of the target battery. For example, the actual released electric energy of the energy storage battery pack from 100% SOC to the remaining power can be calculated. If the actual released electric energy is significantly lower than the calibrated release, it indicates that there is an energy release attenuation and the battery may need to be replaced.

[0059] In this embodiment, in response to a performance evaluation instruction for the target battery, first, battery parameters of the target battery at multiple moments are obtained, and the parameter change trend of each battery parameter is determined. Then, the target change trend interval to which the parameter change trend belongs is queried, and based on the first mapping relationship between the change trend interval and the battery power, the target battery power matching the target change trend interval is determined. The battery power can directly reflect the battery's endurance or energy storage capacity. Therefore, in this embodiment, the target battery is evaluated based on the battery power to obtain the performance evaluation result of the target battery. Compared with the current method of implementing battery performance evaluation based on a complex mathematical model, in this embodiment, by dividing the change trend interval of the battery parameters and establishing a local mapping relationship, the problem of analyzing the battery power is transformed into a simple local mapping problem, effectively simplifying the determination process of the battery power, thereby realizing the rapid evaluation of the battery power and further improving the efficiency of battery performance evaluation based on the battery power.

[0060] In some embodiments, as Figure 6 shown, the battery parameter includes the target expansion force. For the parameter change trend of each battery parameter, determining the target change trend interval to which the parameter change trend belongs includes:

[0061] Step S602, grouping the target expansion forces to obtain at least one expansion force parameter group.

[0062] It should be noted that the swelling force is also an important parameter in the battery management system. During the charge and discharge process of the energy storage battery module, the positive and negative electrode materials, electrolyte, and separator inside will undergo volume changes. Especially for negative electrode materials such as graphite, when charging, the insertion of lithium ions will cause changes in the lattice structure, resulting in volume expansion; when discharging, the lithium ions return to the positive electrode and the volume shrinks. The force generated by this repeated volume change is the swelling force. Since the change law of the swelling force is different when the energy storage battery pack is charged at different temperatures and charging rates, currently, it is usually chosen to utilize the law of the swelling force's performance in different states of the energy storage battery to link other electrical and thermal parameters such as battery power to evaluate the performance of the energy storage battery pack. In traditional technologies, there are usually the following ways to use the swelling force to predict the battery power: One is the dynamic force look-up table method. This method divides the swelling force into dynamic force and static force. According to the current and charge-discharge conditions, the dynamic force part is calculated using the dynamic force model, and then the dynamic force is subtracted from the total swelling force to obtain the static force. By looking up the table, the static force is corresponded to the SOC, thereby estimating the SOC of the battery. However, this method is difficult to apply to the battery module level, and since the swelling force in the energy storage battery and the battery power do not have a one-to-one correspondence relationship, directly looking up the table is prone to situations of incorrect estimation. The other is the swelling force modeling method, that is, directly equivalent the battery module to a mechanical model, such as a rod-spring system, a spring-damper system. The equivalent mechanical model is used to estimate the swelling force, directly constructing the direct relationship between the SOC of the battery module and the swelling force, and then combining the Kalman filtering method to predict the SOC, and modifying the model parameters according to the error threshold conditions. However, equivalent modeling requires a large amount of work to identify parameters, and the model accuracy is uncertain. The existing methods mainly target single battery cells, and the SOC estimation and imbalance evaluation methods at the battery module level are not involved.

[0063] In summary, the traditional methods for estimating the battery power based on the swelling force all have defects. Therefore, in this embodiment, according to the change trend of the swelling force, the corresponding change trend intervals are matched, and thus according to the mapping relationship between each change trend interval and the battery power, the final battery power is determined. It has higher practicability, is simpler, and requires fewer resources.

[0064] Among them, the target swelling force refers to the swelling force of the target battery at multiple moments. The swelling force parameter group refers to the parameter group obtained after grouping each target swelling force.

[0065] For example, assuming that each target swelling force is , where N represents the number of target swelling forces. Grouping each target swelling force into groups of three, the swelling force parameter group can be obtained, where includes , and so on.

[0066] Exemplarily, after the server obtains each target expansion force of the energy storage battery pack at multiple moments, it can group each target expansion force according to a specified quantity to obtain each expansion force parameter group. It should be noted that since the subsequent parameter change trend analysis is performed for each expansion force parameter group, in order to improve the accuracy and reliability of the parameter change trend analysis, the number of parameters in each expansion force parameter group should not be less than three.

[0067] In some embodiments, the interval between multiple moments can be , which means that parameter acquisition is performed once when the battery change amount is 1%. Thus, the target expansion force can specifically be , and the expansion force parameter group can specifically be .

[0068] Step S604: Determine the target change trend interval to which the parameter change trend belongs for each target expansion force in each expansion force parameter group.

[0069] Exemplarily, after the grouping is completed, for each group of expansion force parameter groups, the change trend interval matching can be performed separately. That is, obtain the parameter change trend of each expansion force parameter group respectively and match it with the pre-calibrated change trend interval, so as to obtain the target change trend interval corresponding to each expansion force parameter group.

[0070] For example, when the energy storage battery pack is working, starting from the t moment, continuously record the battery expansion force at the moment . Considering Figure 3 , there will be two situations during charging. Situation 1: If is monotonically decreasing, then it corresponds to the A2 interval, and the energy storage battery module is in the of . Situation 2: If is monotonically increasing, then it corresponds to the A1 or A3 interval. At this time, it is necessary to further combine the battery voltage to determine which specific interval it corresponds to. Similarly, there will be two situations during discharging. Situation 1: If is monotonically increasing, then it corresponds to the A5 interval, and the energy storage battery module is in the of . Situation 2: If is monotonically decreasing, then it corresponds to the A4 or A6 interval. At this time, it is necessary to further combine the battery voltage to determine which specific interval it corresponds to.

[0071] Accordingly, in some embodiments, the battery parameters further include a target voltage; for the parameter change trend of each target expansion force in each expansion force parameter group, determining the target change trend interval to which the parameter change trend belongs includes: for the parameter change trend of each target expansion force in each expansion force parameter group, determining a candidate change trend interval that matches the parameter change trend; in the case where there are multiple candidate change trend intervals, based on the second mapping relationship between the voltage and the change trend interval, screening out the candidate change trend interval that matches the target voltage from the multiple candidate change trend intervals as the target change trend interval to which the parameter change trend belongs.

[0072] Among them, the candidate change trend interval refers to multiple change trend intervals that match the number change trend. The second mapping relationship reflects the corresponding relationship between the battery voltage and the change trend interval.

[0073] Taking Case 2 during charging as an example, that is monotonically increasing, then it corresponds to the A1 or A3 interval. At this time, it is necessary to further analyze the battery voltage. Assume the target voltage is , if , then it is considered that this expansion force parameter group belongs to the A1 interval, and the energy storage battery module is in of ; if , then it is considered that this expansion force parameter group belongs to the A3 interval, and the energy storage battery module is in of .

[0074] Similarly, taking Case 2 during discharging as an example, that is monotonically decreasing, then it corresponds to the A4 or A6 interval. Combining Figure 3 , further analyzing the battery voltage, if is greater than V4, then it is considered that this expansion force parameter group belongs to the A4 interval, and the energy storage battery module is in of ; if is less than V3, then it is considered that this expansion force parameter group belongs to the A6 interval, and the energy storage battery module is in of . Among them, V3 corresponds to the expansion force valley value KP3 during discharging, and V4 corresponds to the expansion force peak value KP4 during discharging.

[0075] In some embodiments, when the parameter change trend is non-monotonic, the method further includes: when the parameter change trend is non-monotonic, determining the parameter extreme values in each battery parameter; based on the power analysis function that matches the parameter extreme values, performing power analysis on the target battery to obtain the target battery power.

[0076] Among them, this embodiment further considers the case where the parameter change trend is non-monotonic, and mainly calculates the battery power according to the parameter extreme values. The parameter extreme values refer to the extreme values of the expansion force, including the peak value of the expansion force and the valley value of the expansion force.

[0077] Exemplarily, when the parameter change trend is non-monotonic, a peak / valley search can be performed on the expansion force parameter group. If the peak value of the expansion force / valley value of the expansion force is searched, the power analysis function corresponding to the peak value of the expansion force / valley value of the expansion force is further queried, and through this function, the power of the target battery is calculated to obtain the target battery power.

[0078] For example, taking charging as an example, if it is non-monotonic, then further search whether there is a peak point or a valley point. If it is a peak point, then . Among them, , represents the peak number. If it is a valley point, then . Among them, , represents the valley number. If the expansion force parameter group contains both a peak point and a valley point at the same time, the mapping relationship of the data pair in the A2 interval between the peak point and the valley point is updated. Other situations are regarded as abnormal situations, and the SOC is not updated and waits for the next round of calculation and confirmation.

[0079] Similarly, taking discharging as an example, if it is non-monotonic, then further search whether there is a peak point or a valley point. If it is a valley point, then . Among them, . If it is a peak point, then . Among them, . If the expansion force parameter group contains both a peak point and a valley point at the same time, the mapping relationship of the data pair in the A5 interval between the peak point and the valley point is updated. Other situations are regarded as abnormal situations, and the SOC is not updated and waits for the next round of calculation and confirmation.

[0080] In a specific embodiment, Figure 7 shows a schematic diagram of the SOC estimation process of the energy storage battery pack during charging. First, obtain the expansion force of the energy storage battery pack , and the battery voltage . Then judge whether it is monotonically decreasing. If so, it corresponds to the A2 interval, and the energy storage battery module is in of . If not, then further judge whether it is monotonically increasing. If it is monotonically increasing, then judge Whether it is less than , if it is less, it is considered that the expansion force parameter group belongs to the A1 interval, and the energy storage battery module is in of . Otherwise, it is further judged Whether it is greater than , if it is greater, it is considered that the expansion force parameter group belongs to the A3 interval, and the energy storage battery module is in of . If it is not greater, the SOC is not updated and waits for the next round of confirmation.

[0081] If is not monotonically increasing, that is is in a non-monotonic state, then it is further judged Whether it is a peak value, if so, then . If not, then judge Whether it is a valley value, if so, then . If is neither a peak value nor a valley value, the SOC is not updated and waits for the next round of confirmation. The SOC estimation process of the energy storage battery pack during discharge is the same as that during charging, so it will not be elaborated here.

[0082] In a specific embodiment, Figure 8 shows a schematic diagram of the battery power estimation during the tenth charging process. It can be seen that the SOC estimated value is proportional to the charging time. In addition, in order to ensure the rigor of the battery power estimation, the battery module imbalance degree analysis is also carried out in this embodiment. The imbalance degree Inconsistency can characterize the deviation degree of the battery cell performance in the battery module, and its expression is:

[0083]

[0084] Among them, PH represents the peak value (maximum value) of the expansion force of the current battery module, and PL represents the valley value (minimum value) of the expansion force of the current battery module. represents the peak value of the expansion force in the initial state of the battery module, represents the valley value of the expansion force in the initial state of the battery module. The meaning of the expression is to calculate the relative change between the current expansion force range and the initial expansion force range. If the value of Inconsistency is close to 0, it means that the current expansion force range is basically the same as the initial expansion force range, and the expansion force distribution of the battery module is relatively balanced. Otherwise, it means that the expansion force distribution of the battery module is unbalanced.

[0085] In a specific embodiment, Figure 9Shows the balance of each of the three battery modules during constant - power charging of a 25Ah (ampere - hour) pouch battery. Among them, Case 1 indicates good battery consistency. At this time, the power levels of the 16 cells in the battery are the same, and there is no imbalance phenomenon, Inconsistency = 0%. Case 2 indicates medium consistency. At this time, the SOC of 4 cells among the 16 cells is 5% lower than that of the other 12 cells, and there is an imbalance phenomenon, Inconsistency = 5.4%. Case 3 indicates poor consistency. At this time, the SOC of 4 cells among the 16 cells is 10% lower than that of the other 12 cells, and there is a relatively obvious imbalance phenomenon, Inconsistency = 13.8%.

[0086] In some embodiments, the target battery includes battery cells and end plates; the method further includes: obtaining a target distance between the battery cells and the end plates; based on the target distance, performing sensor - quantity matching to obtain a target sensor quantity that matches the target distance.

[0087] Among them, the battery cell is the basic component unit of the battery module. The end plates include left and right end plates, which are two fixing plate components of the battery module. The target distance refers to the distance between the separator corresponding to the right - most battery cell and the right end plate. The size of the target distance can reflect the distribution range of the expansion force. The larger the distance, the more uniform the distribution of the expansion force, and a single sensor can meet the measurement requirements; the smaller the distance, the more uneven the distribution of the expansion force, and multiple sensors are required to cover it.

[0088] Exemplarily, the server will pre - obtain the distance between the separator corresponding to the right - most battery cell and the right end plate, and based on this distance, calculate the required number of sensors. Specifically, when this distance exceeds the distance upper limit, only one sensor can be arranged, which can be arranged at the center position of the end plate, such as Figure 10 sensor S1 in. When this distance is between the distance upper limit and the distance lower limit, four sensors can be selected and installed at the four corner positions of the end plate. That is, Figure 10 sensors S2 - S5 in. If this distance is lower than the distance lower limit, sensors need to be arranged at both the center position and the four corner positions of the end plate.

[0089] Figure 11 Shows a schematic diagram of the composition structure of the battery module. It can be seen that the energy - storage battery module includes end plate 1 (left end plate), battery cells 1 - battery cell N, the separators between the battery cells, the module fixing belt for fixing the entire battery module, end plate 2 (right end plate), and the target sensor S for detecting the expansion force (only S1, S2, S4 are shown in the figure). Between the separator of battery cell N and end plate 2, except for the contact between sensor S and the two, there is no other contact. For ease of understanding, Figure 12The schematic diagram of the energy storage battery module is shown, where the fixing band is a bolt, and other structures are the same as those in Figure 11 the structure, which will not be elaborated here.

[0090] In some embodiments, the respective target sensors can be connected through a circuit board and a flexible material to improve the convenience of sensor installation and wiring.

[0091] In some embodiments, battery parameters of the target battery at multiple moments are obtained, including: obtaining the battery parameters of the target battery at multiple moments through the target sensors with the number of target sensors.

[0092] Exemplarily, after the target sensor detects the expansion force, it will output a corresponding electrical signal. After being processed by the conditioning circuit, it will be converted into a voltage signal. This voltage signal will be transmitted to the analog-to-digital converter for conversion to obtain a digital signal. By performing a scaling change on this digital signal, the final force value, i.e., the expansion force value, can be output. It should be noted that when there is only one sensor (S1), the total expansion force K = K1; when the sensors include S2 to S5, the total expansion force K = K2 + K3 + K4 + K5; when the sensors include S1 to S5, the total expansion force K = K1 + K2 + K3 + K4 + K5.

[0093] In this embodiment, based on the distance between the battery cell and the end plate, the number of sensors is determined. This ensures the reliability and accuracy of the number of sensors, thereby ensuring the accuracy of battery parameter acquisition and laying a data foundation for the overall battery performance evaluation.

[0094] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments, and the execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0095] Based on the same inventive concept, an embodiment of the present application further provides a battery performance evaluation device for implementing the battery performance evaluation method involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the battery performance evaluation device provided below can refer to the limitations on the battery performance evaluation method in the above text and will not be elaborated here.

[0096] In an exemplary embodiment, as Figure 13 shown, a battery performance evaluation device is provided, including: a parameter acquisition module 1302, configured to acquire battery parameters of a target battery at multiple moments in response to a performance evaluation instruction for the target battery; an interval determination module 1304, configured to determine a target change trend interval to which the parameter change trend belongs for the parameter change trend of each battery parameter; a power determination module 1306, configured to determine a target battery power that matches the target change trend interval based on a first mapping relationship between the change trend interval and the battery power; and a battery evaluation module 1308, configured to perform a performance evaluation on the target battery according to the target battery power to obtain a performance evaluation result of the target battery.

[0097] In an exemplary embodiment, the battery parameter includes a target expansion force; the interval determination module 1304 is further configured to: group the target expansion forces to obtain at least one expansion force parameter group; and determine a target change trend interval to which the parameter change trend belongs for the parameter change trend of each target expansion force in each expansion force parameter group.

[0098] In an exemplary embodiment, the battery parameter further includes a target voltage; the interval determination module 1304 is further configured to: determine a candidate change trend interval that matches the parameter change trend for the parameter change trend of each target expansion force in each expansion force parameter group; in the case where there are multiple candidate change trend intervals, based on a second mapping relationship between the voltage and the change trend interval, screen out a candidate change trend interval that matches the target voltage from the multiple candidate change trend intervals as the target change trend interval to which the parameter change trend belongs.

[0099] In an exemplary embodiment, the power determination module 1306 is further configured to: determine a target power analysis function that matches the target change trend interval based on a first mapping relationship between the change trend interval and the analysis function of the battery power; and perform a power analysis on the target battery through the target power analysis function to obtain the target battery power.

[0100] In an exemplary embodiment, the interval determination module 1304 is further configured to: for the parameter change trend of each battery parameter, when the parameter change trend is monotonically increasing, determine that the target change trend interval to which the parameter change trend belongs is a monotonically increasing interval; when the parameter change trend is monotonically decreasing, determine that the target change trend interval to which the parameter change trend belongs is a monotonically decreasing interval.

[0101] In an exemplary embodiment, the device is further configured to: when the parameter change trend is non-monotonic, determine the parameter extreme value in each battery parameter; based on the power analysis function matching the parameter extreme value, perform power analysis on the target battery to obtain the target battery power.

[0102] In an exemplary embodiment, the target battery includes a battery cell and an end plate; the device is further configured to: obtain the target distance between the battery cell and the end plate; based on the target distance, perform sensor quantity matching to obtain the target sensor quantity matching the target distance; the parameter acquisition module 1302 is further configured to: obtain the battery parameters of the target battery at multiple moments through the target sensors with the target sensor quantity.

[0103] Each module in the above battery performance evaluation device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the computer device in hardware form or be independent of it, or can be stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to the above respective modules.

[0104] In an exemplary embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 14 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store battery performance evaluation data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, a battery performance evaluation method is implemented.

[0105] Those skilled in the art can understand, Figure 14The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0106] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented: in response to a performance evaluation instruction for a target battery, obtain battery parameters of the target battery at multiple moments; for the parameter change trend of each battery parameter, determine the target change trend interval to which the parameter change trend belongs; based on the first mapping relationship between the change trend interval and the battery power, determine the target battery power that matches the target change trend interval; according to the target battery power, perform a performance evaluation on the target battery to obtain a performance evaluation result of the target battery.

[0107] In one embodiment, when the processor executes the computer program, the following steps are further implemented: group each target expansion force to obtain at least one expansion force parameter group; for the parameter change trend of each target expansion force in each expansion force parameter group, determine the target change trend interval to which the parameter change trend belongs.

[0108] In one embodiment, when the processor executes the computer program, the following steps are further implemented: for the parameter change trend of each target expansion force in each expansion force parameter group, determine the candidate change trend interval that matches the parameter change trend; in the case where there are multiple candidate change trend intervals, based on the second mapping relationship between the voltage and the change trend interval, screen out the candidate change trend interval that matches the target voltage from the multiple candidate change trend intervals as the target change trend interval to which the parameter change trend belongs.

[0109] In one embodiment, when the processor executes the computer program, the following steps are further implemented: based on the first mapping relationship between the change trend interval and the analysis function of the battery power, determine the target power analysis function that matches the target change trend interval; through the target power analysis function, perform a power analysis on the target battery to obtain the target battery power.

[0110] In one embodiment, when the processor executes the computer program, the following steps are further implemented: for the parameter change trend of each battery parameter, when the parameter change trend is monotonically increasing, determine that the target change trend interval to which the parameter change trend belongs is a monotonically increasing interval; when the parameter change trend is monotonically decreasing, determine that the target change trend interval to which the parameter change trend belongs is a monotonically decreasing interval.

[0111] In one embodiment, when the processor executes a computer program, the following steps are further implemented: when the parameter change trend is non-monotonic, determine the parameter extreme values in each battery parameter; based on the power analysis function matching the parameter extreme values, perform power analysis on the target battery to obtain the target battery power.

[0112] In one embodiment, when the processor executes a computer program, the following steps are further implemented: obtain the target spacing between the battery cell and the end plate; based on the target spacing, perform sensor quantity matching to obtain the target sensor quantity matching the target spacing; obtain the battery parameters of the target battery at multiple moments, including: obtain the battery parameters of the target battery at multiple moments through the target sensors with the target sensor quantity.

[0113] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: in response to a performance evaluation instruction for a target battery, obtain the battery parameters of the target battery at multiple moments; for the parameter change trend of each battery parameter, determine the target change trend interval to which the parameter change trend belongs; based on the first mapping relationship between the change trend interval and the battery power, determine the target battery power matching the target change trend interval; according to the target battery power, perform a performance evaluation on the target battery to obtain the performance evaluation result of the target battery.

[0114] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: group each target expansion force to obtain at least one expansion force parameter group; for the parameter change trend of each target expansion force in each expansion force parameter group, determine the target change trend interval to which the parameter change trend belongs.

[0115] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: for the parameter change trend of each target expansion force in each expansion force parameter group, determine the candidate change trend intervals matching the parameter change trend; in the case where there are multiple candidate change trend intervals, based on the second mapping relationship between the voltage and the change trend interval, screen out the candidate change trend interval matching the target voltage from the multiple candidate change trend intervals as the target change trend interval to which the parameter change trend belongs.

[0116] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: based on the first mapping relationship between the change trend interval and the analysis function of the battery power, determine the target power analysis function matching the target change trend interval; through the target power analysis function, perform power analysis on the target battery to obtain the target battery power.

[0117] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: for the parameter change trend of each battery parameter, when the parameter change trend is monotonically increasing, determine that the target change trend interval to which the parameter change trend belongs is the monotonically increasing interval; when the parameter change trend is monotonically decreasing, determine that the target change trend interval to which the parameter change trend belongs is the monotonically decreasing interval.

[0118] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: when the parameter change trend is non-monotonic, determine the parameter extreme value in each battery parameter; based on the power analysis function matching the parameter extreme value, perform power analysis on the target battery to obtain the target battery power.

[0119] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: obtain the target distance between the battery cell and the end plate; based on the target distance, perform sensor quantity matching to obtain the target sensor quantity matching the target distance; obtain the battery parameters of the target battery at multiple moments, including: obtaining the battery parameters of the target battery at multiple moments through the target sensors with the target sensor quantity.

[0120] In one embodiment, a computer program product is provided, including a computer program, which when executed by a processor, implements the following steps: in response to a performance evaluation instruction for a target battery, obtain the battery parameters of the target battery at multiple moments; for the parameter change trend of each battery parameter, determine the target change trend interval to which the parameter change trend belongs; based on the first mapping relationship between the change trend interval and the battery power, determine the target battery power matching the target change trend interval; according to the target battery power, perform performance evaluation on the target battery to obtain the performance evaluation result of the target battery.

[0121] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: group each target expansion force to obtain at least one expansion force parameter group; for the parameter change trend of each target expansion force in each expansion force parameter group, determine the target change trend interval to which the parameter change trend belongs.

[0122] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: for the parameter change trend of each target expansion force in each expansion force parameter group, determine the candidate change trend interval matching the parameter change trend; in the case where there are multiple candidate change trend intervals, based on the second mapping relationship between the voltage and the change trend interval, screen out the candidate change trend interval matching the target voltage from the multiple candidate change trend intervals as the target change trend interval to which the parameter change trend belongs.

[0123] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: determining a target power analysis function that matches the target change trend interval based on a first mapping relationship between the change trend interval and the power analysis function of the battery power; performing power analysis on the target battery through the target power analysis function to obtain the target battery power.

[0124] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: for the parameter change trend of each battery parameter, when the parameter change trend is monotonically increasing, determining that the target change trend interval to which the parameter change trend belongs is a monotonically increasing interval; when the parameter change trend is monotonically decreasing, determining that the target change trend interval to which the parameter change trend belongs is a monotonically decreasing interval.

[0125] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: when the parameter change trend is non-monotonic, determining the parameter extreme value in each battery parameter; performing power analysis on the target battery based on the power analysis function that matches the parameter extreme value to obtain the target battery power.

[0126] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: obtaining the target distance between the battery cell and the end plate; based on the target distance, performing sensor quantity matching to obtain the target sensor quantity that matches the target distance; obtaining the battery parameters of the target battery at multiple moments, including: obtaining the battery parameters of the target battery at multiple moments through the target sensors with the target sensor quantity.

[0127] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0128] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0129] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded in this application.

[0130] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A battery performance evaluation method, characterized in that: The method comprises: In response to a performance evaluation instruction for a target battery, obtaining battery parameters of the target battery at multiple time points; For each parameter change trend of the battery parameter, determining a target change trend interval to which the parameter change trend belongs; Determining a target battery power that matches the target change trend interval based on a first mapping relationship between the change trend interval and the battery power; According to the target battery power, a performance evaluation is performed on the target battery to obtain a performance evaluation result of the target battery.

2. The method according to claim 1, characterized in that The battery parameters include a target expansion force; and the parameter change trend of each battery parameter, determining a target change trend interval to which the parameter change trend belongs, includes: Grouping the target expansion forces to obtain at least one expansion force parameter group; With respect to the parameter change trend of each target expansion force in each expansion force parameter group, a target change trend interval to which the parameter change trend belongs is determined.

3. The method according to claim 2, characterized in that The battery parameter also includes a target voltage; the parameter change trend of each target expansion force in each expansion force parameter group, determining the target change trend interval to which the parameter change trend belongs, includes: For each parameter change trend of each target expansion force in each expansion force parameter group, determining a candidate change trend interval matching the parameter change trend; When there are multiple candidate change trend intervals, based on the second mapping relationship between voltage and change trend interval, a candidate change trend interval matching the target voltage is screened out from the multiple candidate change trend intervals as the target change trend interval to which the parameter change trend belongs.

4. The method according to claim 1, characterized in that: The determining, based on a first mapping relationship between a change trend interval and a battery power level, a target battery power level that matches the target change trend interval includes: Determining a target power analysis function that matches the target change trend interval based on a first mapping relationship between the change trend interval and the battery power analysis function; The target battery power analysis function is used to analyze the target battery power to obtain the target battery power.

5. The method according to claim 1, characterized in that The step of determining the target change trend interval to which the parameter change trend belongs for each of the battery parameters comprises: For the parameter change trend of each of the battery parameters, when the parameter change trend is monotonically increasing, determining that the target change trend interval to which the parameter change trend belongs is a monotonically increasing interval; When the parameter variation trend is monotonically decreasing, it is determined that the target variation trend interval to which the parameter variation trend belongs is a monotonically decreasing interval.

6. The method according to claim 1, characterized in that The method further comprises: When the parameter change trend is a non-monotonic change, determining the parameter extreme value of each of the battery parameters; Based on the power analysis function that matches the extreme value of the parameter, power analysis is performed on the target battery to obtain the target battery power.

7. The method according to claim 1, characterized in that The target battery includes a battery cell and an end plate; the method further includes: Obtaining a target distance between the battery cell and the end plate; Based on the target spacing, matching the number of sensors is performed to obtain the number of target sensors that matches the target spacing; The obtaining of battery parameters of the target battery at multiple times includes: The battery parameters of the target battery at multiple moments are acquired through the target sensors of the target sensor quantity.

8. A battery performance evaluation device, characterized in that: The device comprises: A parameter acquisition module, configured to acquire battery parameters of the target battery at multiple moments in response to a performance evaluation instruction for the target battery; An interval determination module, for determining, with respect to a parameter change trend of each battery parameter, a target change trend interval to which the parameter change trend belongs; A power determination module, configured to determine a target battery power that matches the target change trend interval based on a first mapping relationship between the change trend interval and the battery power; The battery evaluation module is used to perform performance evaluation on the target battery according to the power of the target battery to obtain a performance evaluation result of the target battery.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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