Method, apparatus and storage medium for determining battery state of health value

Abstract

The application relates to a battery health state value determination method, device, equipment and storage medium. The method comprises the following steps: obtaining a current battery internal resistance of a to-be-tested battery and a first correlation relationship; determining a current battery capacity of the to-be-tested battery according to the current battery internal resistance and the first correlation relationship, wherein the first correlation relationship is obtained by processing a test data set of the to-be-tested battery in a production stage and / or an operation data set in a use stage, the test data set and the operation data set both comprise battery internal resistances and battery capacities corresponding to different battery voltages of the to-be-tested battery; and determining an actual health state value of the to-be-tested battery at a current time according to the current battery capacity, a rated battery capacity of the to-be-tested battery and a standard health state value corresponding to current use data of the to-be-tested battery. The method can improve the accuracy of battery health state determination.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile battery, and particularly relates to a battery health state value determination method and device, equipment and a storage medium. BACKGROUND

[0002] In recent years, with the continuous development of new energy vehicles, the health state of the battery in the new energy vehicle has attracted more attention. For example, after a long time of operation, the health state of the battery will decrease, and at this time, the user will obviously feel that the charging time of the vehicle is prolonged, the discharging performance is attenuated, and the like. Therefore, accurately grasping the health state SOH (State of Health) of the battery is of great significance for evaluating the use performance of the battery.

[0003] In the prior art, in order to more accurately evaluate the SOH of the battery, the ratio between the current battery capacity of the battery and the rated battery capacity is generally taken as the SOH of the battery. However, since the existing battery health state determination method ignores the influence of the internal resistance of the battery on the health state of the battery, the accuracy of the determination of the health state of the battery is reduced. SUMMARY

[0004] Therefore, it is necessary to provide a battery health state value determination method, device, equipment and storage medium capable of improving the accuracy of the determination of the health state of the battery in view of the above technical problems.

[0005] In a first aspect, the present application provides a battery health state value determination method, comprising:

[0006] obtaining a current battery internal resistance of a to-be-tested battery and a first correlation relationship; wherein the first correlation relationship is obtained by processing a test data set of the to-be-tested battery in a production stage and / or a running data set of the to-be-tested battery in a use stage, and the test data set and the running data set both include the battery internal resistance and the battery capacity of the to-be-tested battery corresponding to a plurality of battery voltages;

[0007] determining a current battery capacity of the to-be-tested battery according to the current battery internal resistance and the first correlation relationship;

[0008] determining an actual health state value of the to-be-tested battery at a current time according to the current battery capacity, a rated battery capacity of the to-be-tested battery and a standard health state value corresponding to current use data of the to-be-tested battery.

[0009] In one of the embodiments, the processing of the test data set of the to-be-tested battery in the production stage and the running data set in the use stage comprises:

[0010] input each group of test data in the test data set into the standard regression function to obtain an offline regression parameter corresponding to each group of test data, and construct an offline regression parameter set of the battery to be tested according to each offline regression parameter; input each group of running data in the running data set into the standard regression function to obtain an online regression parameter corresponding to each group of running data, and construct an online regression parameter set of the battery to be tested according to each online regression parameter; and construct a target regression function according to the offline regression parameter set and the online regression parameter set; wherein the target regression function is used to represent a first correlation between the battery resistance and the battery capacity of the battery to be tested.

[0011] In one of the embodiments, the target regression function is constructed according to the offline regression parameter set and the online regression parameter set, including:

[0012] The offline regression parameter set and the online regression parameter set are fused to obtain a target parameter set; and the target parameter set is used to process the standard regression function to obtain the target regression function.

[0013] In one of the embodiments, the offline regression parameter set and the online regression parameter set are fused to obtain a target parameter set, including:

[0014] According to the available voltage range of the power supply to be tested, a parameter division voltage is determined; according to the parameter division voltage, a to-be-fused offline parameter is extracted from the offline regression parameter set; wherein the battery voltage corresponding to the to-be-fused offline parameter is less than the parameter division voltage; according to the parameter division voltage, a to-be-fused online parameter is extracted from the online regression parameter set; wherein the battery voltage corresponding to the to-be-fused online parameter is greater than the parameter division voltage; and the to-be-fused offline parameter and the to-be-fused online parameter are spliced to obtain the target parameter set.

[0015] In one of the embodiments, the actual health state value of the battery to be tested at the current time is determined according to the current battery capacity, the rated battery capacity of the battery to be tested, and the standard health state value corresponding to the current use data of the battery to be tested, including:

[0016] The ratio between the current battery capacity and the rated battery capacity of the battery to be tested is taken as a current correction coefficient; and the standard health state value corresponding to the current use data of the battery to be tested is corrected by using the current correction coefficient to obtain the actual health state value of the battery to be tested at the current time.

[0017] In one of the embodiments, the method further includes:

[0018] The current use data of the battery to be tested is taken as an index word to search from the standard health state values corresponding to each candidate use data to obtain the standard health state value corresponding to the current use data.

[0019] In one of the embodiments, the method further comprises:

[0020] According to the vehicle type and vehicle use information of the vehicle to which the to-be-tested battery belongs, a health state threshold of the to-be-tested battery is determined; according to the actual health state value and the health state threshold, battery early warning information of the to-be-tested battery is determined; and the battery early warning information is output to the vehicle.

[0021] In a second aspect, the application further provides a determination device of a battery health state value, comprising:

[0022] An internal resistance acquisition module is configured to acquire a current battery internal resistance of a to-be-tested battery and a first correlation; wherein the first correlation is obtained by processing a test data set of the to-be-tested battery in a production stage and / or a running data set in a use stage, and the test data set and the running data set both include battery internal resistances and battery capacities of the to-be-tested battery corresponding to a plurality of battery voltages;

[0023] A capacity determination module is configured to determine a current battery capacity of the to-be-tested battery according to the current battery internal resistance and the first correlation;

[0024] A state determination module is configured to determine an actual health state value of the to-be-tested battery at a current time according to the current battery capacity, a rated battery capacity of the to-be-tested battery, and a standard health state value corresponding to current use data of the to-be-tested battery.

[0025] In a third aspect, the application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor realizes the following steps when executing the computer program:

[0026] A current battery internal resistance of a to-be-tested battery and a first correlation are acquired; wherein the first correlation is obtained by processing a test data set of the to-be-tested battery in a production stage and / or a running data set in a use stage, and the test data set and the running data set both include battery internal resistances and battery capacities of the to-be-tested battery corresponding to a plurality of battery voltages;

[0027] A current battery capacity of the to-be-tested battery is determined according to the current battery internal resistance and the first correlation;

[0028] An actual health state value of the to-be-tested battery at a current time is determined according to the current battery capacity, a rated battery capacity of the to-be-tested battery, and a standard health state value corresponding to current use data of the to-be-tested battery.

[0029] In a fourth aspect, the application further provides a computer readable storage medium, which stores a computer program, and the computer program realizes the following steps when executed by a processor:

[0030] obtaining a current battery internal resistance of the to-be-tested battery and a first correlation relationship; the first correlation relationship is obtained by processing a test data set of the to-be-tested battery in a production stage and / or a running data set of the to-be-tested battery in a use stage; the test data set and the running data set each include battery internal resistances corresponding to a plurality of battery voltages and battery capacities of the to-be-tested battery;

[0031] determining a current battery capacity of the to-be-tested battery according to the current battery internal resistance and the first correlation relationship;

[0032] determining an actual health state value of the to-be-tested battery at a current time according to the current battery capacity, a rated battery capacity of the to-be-tested battery, and a standard health state value corresponding to current use data of the to-be-tested battery.

[0033] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0034] obtaining a current battery internal resistance of the to-be-tested battery and a first correlation relationship; the first correlation relationship is obtained by processing a test data set of the to-be-tested battery in a production stage and / or a running data set of the to-be-tested battery in a use stage; the test data set and the running data set each include battery internal resistances corresponding to a plurality of battery voltages and battery capacities of the to-be-tested battery;

[0035] determining a current battery capacity of the to-be-tested battery according to the current battery internal resistance and the first correlation relationship;

[0036] determining an actual health state value of the to-be-tested battery at a current time according to the current battery capacity, a rated battery capacity of the to-be-tested battery, and a standard health state value corresponding to current use data of the to-be-tested battery.

[0037] The above method, device, equipment and storage medium for determining a battery health state value, by processing a test data set of a to-be-tested battery in a production stage and / or a running data set of the to-be-tested battery in a use stage, a first correlation relationship between a battery internal resistance and a battery capacity of the to-be-tested battery is obtained in advance, and then a current battery capacity of the to-be-tested battery is determined according to the current battery internal resistance and the first correlation relationship; subsequently, an actual health state value of the to-be-tested battery at a current time is determined according to the current battery capacity, a rated battery capacity of the to-be-tested battery, and a standard health state value corresponding to current use data of the to-be-tested battery. Compared with the related art in which only the temperature or the battery capacity of the battery is used to determine the health state of the battery, the first correlation relationship between the battery internal resistance and the battery capacity is introduced in the above method, and the influence of the battery internal resistance on the health state of the battery is considered when the health state of the to-be-tested battery is determined, thereby improving the accuracy of the determination of the health state of the battery. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the present application or the related art. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other related drawings can also be obtained on the basis of these drawings without creative labor.

[0039] Figure 1 A flowchart of a method for determining a battery state of health value in an embodiment;

[0040] Figure 2 A flowchart of determining a correlation in an embodiment;

[0041] Figure 3 A flowchart of determining a target parameter set in an embodiment;

[0042] Figure 4 A flowchart of determining an actual state of health value in an embodiment;

[0043] Figure 5 A flowchart of information output in an embodiment;

[0044] Figure 6 A flowchart of a method for determining a battery state of health value in another embodiment;

[0045] Figure 7 A block diagram of a device for determining a battery state of health value in an embodiment;

[0046] Figure 8 A block diagram of a device for determining a battery state of health value in another embodiment;

[0047] Figure 9 An internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0048] In order to make the purposes, technical solutions and advantages of the present application clearer, the following will further describe the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0049] In recent years, with the continuous development of new energy vehicles, the health status of the batteries in new energy vehicles has also attracted more attention. For example, after a long time of operation, the health status of the battery will decrease, and at that time, users will obviously feel that the charging time of the vehicle is prolonged, the discharging performance is attenuated, and the like. Therefore, accurately grasping the state of health (SOH) of the battery has great significance for the evaluation of the use performance of the battery.

[0050] In the prior art, in order to more accurately evaluate the SOH of the battery, the ratio between the current battery capacity and the rated battery capacity of the battery is generally taken as the SOH of the battery. However, since the existing battery health state determination method ignores the influence of the battery internal resistance on the battery health state, the accuracy of the battery health state determination is reduced.

[0051] Based on this, in an exemplary embodiment, a battery health state value determination method is provided, which is applied to a health state determination device in a vehicle, as shown in Figure 1 The specific steps include the following steps:

[0052] S101, obtaining the current battery internal resistance and the first correlation of the to-be-tested battery.

[0053] The to-be-tested battery refers to a vehicle battery in the vehicle that has a health state determination requirement; the current battery internal resistance refers to the internal resistance of the to-be-tested battery at the current time.

[0054] Optionally, in the case of detecting a health state determination event, the current battery internal resistance of the to-be-tested battery can be calculated based on the current flowing condition in the to-be-tested battery. The health state determination event can include, but is not limited to, a battery health detection operation triggered by the driver, or a battery health detection event triggered automatically when a preset time is reached.

[0055] Further, the first correlation can be directly obtained from the battery management system (BMS) deployed in the vehicle. The first correlation is obtained by processing the test data set and / or the operation data set of the to-be-tested battery in the production stage. The test data set and the operation data set both include the battery internal resistance and the battery capacity corresponding to multiple battery voltages of the to-be-tested battery.

[0056] It can be understood that, in order to ensure the reliability of the data set, the available battery range of the to-be-tested battery can be uniformly divided to obtain multiple different test voltages, and each test voltage is taken as the battery voltage required for determining the data set; or the open circuit voltage of the to-be-tested battery in the use state can be taken as the battery voltage required for determining the data set.

[0057] Further, the static test can be performed on the battery under test at different battery voltages when the battery under test is in the production stage, and then the resistance data and the capacity data corresponding to each battery voltage are obtained, i.e., a test data set; correspondingly, the capacity data can be calculated by the BMS of the vehicle online at different battery voltages when the battery under test is in the use stage, and the resistance data corresponding to the capacity data calculated by the application layer software of the BMS are obtained, and then a running data set is obtained.

[0058] Finally, the first correlation between the battery resistance and the battery capacity of the battery under test can be constructed based on the test data set in the production stage and / or the running data set in the use stage.

[0059] For example, an offline regression equation containing the regression relationship between the resistance and the capacity can be constructed for the test data set, and an online regression equation containing the regression relationship between the resistance and the capacity can be constructed for the running data set; then, the regression coefficient representing the regression relationship between the resistance and the capacity of the battery under test is determined based on the equation coefficients of the offline regression equation and the equation coefficients of the online regression equation, and the first correlation is constructed based on the regression coefficient.

[0060] S102, determining the current battery capacity of the battery under test according to the current battery resistance and the first correlation.

[0061] Optionally, after the current battery resistance and the first correlation are obtained, since the first correlation can represent the correlation between the battery resistance and the battery capacity, the current battery capacity of the battery under test at the current time can be calculated by substituting the current battery resistance into the first correlation.

[0062] S103, determining the actual health state value of the battery under test at the current time according to the current battery capacity, the rated battery capacity of the battery under test, and the standard health state value corresponding to the current use data of the battery under test.

[0063] The rated battery capacity refers to the standard battery capacity of the battery under test when it is shipped; the current use data is used to represent the use of the battery under test at the current time, such as the number of charge and discharge times and the use time of the battery under test; the standard health state value is used to represent the standard health state corresponding to the running data of the battery under test at the current time; and the actual health state value is used to represent the actual health state of the battery under test at the current time.

[0064] Optionally, the standard state of health value of the battery to be tested at the current time can be determined in advance according to the current use data of the battery to be tested; subsequently, the current battery capacity, the rated battery capacity, and the standard state of health value can be input into the trained state determination model, and the actual state of health value of the battery to be tested at the current time can be output by the state determination model according to the current battery capacity, the rated battery capacity, the standard state of health value, and the model parameters.

[0065] In the method for determining the battery state of health value, the first correlation between the battery internal resistance and the battery capacity of the battery to be tested is obtained in advance by processing the test data set of the battery to be tested at the production stage and / or the operation data set at the use stage, and then the current battery capacity of the battery to be tested and the first correlation are determined according to the current battery internal resistance of the battery to be tested; subsequently, the actual state of health value of the battery to be tested at the current time is determined according to the current battery capacity, the rated battery capacity of the battery to be tested, and the standard state of health value corresponding to the current use data of the battery to be tested. Compared with the related art in which the state of health of the battery is determined only according to the temperature or the battery capacity of the battery, the first correlation between the battery internal resistance and the battery capacity is introduced by using the above method, and the influence of the battery internal resistance on the state of health of the battery is considered when the state of health of the battery to be tested is determined, thereby improving the accuracy of the determination of the state of health of the battery.

[0066] In order to ensure the reliability of the first correlation between the battery internal resistance and the battery capacity of the battery to be tested, on the basis of the above embodiment, in this embodiment, an optional manner for determining the first correlation is provided, as shown in Figure 2 The method comprises the following steps:

[0067] S201, input each group of test data in the test data set into a standard regression function to obtain offline regression parameters corresponding to each group of test data, and construct an offline regression parameter set of the battery to be tested according to the offline regression parameters.

[0068] The standard regression function refers to a function capable of representing the correlation between data; the test data refer to a data group containing the battery capacity and the battery internal resistance in the production stage of the battery to be tested; the offline regression parameter is used to represent a regression parameter calculated by the standard regression function based on the test data; and the offline regression parameter set is used to represent a set of regression parameters corresponding to each group of test data.

[0069] Optionally, for each group of test data in the test data set, the battery capacity and the battery internal resistance in the group of test data can be substituted into the standard regression function to calculate a group of offline regression parameters corresponding to the group of test data; further, the offline regression parameters corresponding to each group of test data are counted to construct the offline regression parameter set of the battery to be tested.

[0070] For example, in the case where the test data set contains n groups of test data, the battery internal resistance in each group of test data can be taken as x i , the battery capacity as y i , and substituted into the following formula (1) to obtain the offline regression parameter set a off ={a off1 , a off2 , a of3 ...a offn} and b off ={b off1 , b off2 , b of3 ...b offn}. Wherein, represents the mean of the battery internal resistance in the test data set; represents the mean of the battery capacity in the test data set.

[0071] (1)

[0072] S202, each group of running data in the running data set is input into the standard regression function to obtain the online regression parameter corresponding to each group of running data, and the online regression parameter set of the battery under test is constructed according to the online regression parameters.

[0073] Wherein, the running data refers to the data set containing the battery capacity and the battery internal resistance in the test phase of the battery under test; the online regression parameter is used to represent the regression parameter calculated by the standard regression function based on the running data; and the online regression parameter set is used to represent the set of regression parameters corresponding to each group of running data.

[0074] Optionally, for each group of running data in the running data set, the battery capacity and the battery internal resistance in the group of running data can be substituted into the standard regression function to calculate a group of online regression parameters corresponding to the group of running data; further, the online regression parameters corresponding to each group of running data are counted to obtain the online regression parameter set of the battery under test.

[0075] For example, in the case where the test data set contains n groups of test data, the battery internal resistance in each group of test data can be taken as x j , the battery capacity as y j , and substituted into the following formula (2) to obtain the online regression parameter set a on ={a on1 , a on2 , a of3 ...a onn} and b on ={b on1 , b on2 , b of3 ...b onn} wherein, represents the mean value of the battery internal resistance in the running data set; represents the mean value of the battery capacity in the running data set.

[0076] (2)

[0077] S203, constructing a target regression function according to the offline regression parameter set and the online regression parameter set.

[0078] The target regression function is used to represent the first correlation between the battery internal resistance and the battery capacity of the battery to be measured.

[0079] Optionally, after determining the offline regression parameter set and the online regression parameter set, the standard regression function can be trained based on the offline regression parameter set and the online regression parameter set to obtain the target regression function.

[0080] It can be understood that, in order to improve the reliability of the correlation between the battery internal resistance and the battery capacity, when constructing the correlation between the battery internal resistance and the battery capacity, the test data set in the production stage and the running data set in the use stage of batteries of various materials can be used, and the target regression function corresponding to each type of battery of different materials can be constructed by referring to the above steps S201-S203; accordingly, the corresponding target regression function can be called based on the material of the battery to be measured.

[0081] In the embodiments of the present application, by constructing the target regression function according to the offline regression parameter set and the online regression parameter set, the target regression function can represent the correlation between the battery internal resistance and the battery capacity of the battery to be measured in the production stage and the use stage, thereby ensuring the reliability of the target regression function corresponding to the battery to be measured.

[0082] In order to ensure the accuracy of the target regression function, on the basis of the above embodiments, in the present embodiment, an optional way of determining the target regression function is provided, specifically, the offline regression parameter set and the online regression parameter set are fused to obtain a target parameter set; the target parameter set is used to process the standard regression function to obtain the target regression function.

[0083] The target parameter set is used to represent the parameter set for training the regression function.

[0084] Optionally, in order to shorten the training time of the target regression function, the offline regression parameter set and the online regression parameter set can be fused to obtain a target parameter set for training the target regression function.

[0085] Exemplarily, the same number of regression parameters can be randomly selected from the offline regression parameter set and the online regression parameter set respectively to construct the target parameter set; or the offline regression parameter set and the online regression parameter set can be simultaneously input into the trained fusion model, and the fusion model outputs the target parameter set according to the offline regression parameter set, the online regression parameter set and the model parameter.

[0086] Further, after obtaining the target parameter set, the parameters in the target parameter set can be sequentially substituted into the standard regression function as function coefficients to train the target regression function.

[0087] In the embodiments of the present application, the fusion processing of the offline regression parameter set and the online regression parameter set can ensure the comprehensiveness of the target parameter set and the efficiency of determining the target regression function.

[0088] In order to ensure the accuracy of the target parameter set, on the basis of the above embodiments, in the present embodiment, an optional way of determining the target parameter set is provided, as shown in the following. Figure 3 The specific steps include the following.

[0089] S301, determining a parameter division voltage according to an available voltage range of the to-be-tested power supply.

[0090] The available voltage range refers to the voltage range that can be accepted by the to-be-tested power supply; and the parameter division voltage refers to the battery voltage used for dividing the parameter set.

[0091] It can be understood that the change curve of the battery internal resistance is affected by many factors, and it is difficult to capture the influencing factors of the change curve in the low-voltage condition of the battery use stage. Therefore, the front part of the offline regression parameter set corresponding to the production stage can be fused with the rear part of the online regression parameter set corresponding to the use stage to obtain the target parameter set.

[0092] Based on this, the parameter division voltage can be selected from the available voltage range of the to-be-tested power supply. For example, the median of the available voltage range can be taken as the parameter division voltage.

[0093] S302, extracting to-be-fused offline parameters from the offline regression parameter set according to the parameter division voltage.

[0094] The to-be-fused offline parameters refer to the offline regression parameters selected from the offline regression parameter set and needing parameter fusion; and the battery voltage corresponding to the to-be-fused offline parameters is less than the parameter division voltage.

[0095] Optionally, the parameter division voltage can be taken as a boundary voltage value, and the offline regression parameters with a battery voltage less than the parameter division voltage can be extracted from the offline regression parameter set as the to-be-fused offline parameters.

[0096] S303, extracting the to-be-fused online parameters from the online regression parameter set according to the parameter division voltage.

[0097] The to-be-fused online parameters refer to the online regression parameters selected from the online regression parameter set and needing parameter fusion; and the battery voltage corresponding to the to-be-fused online parameters is greater than the parameter division voltage.

[0098] Optionally, the parameter division voltage can be taken as a boundary voltage value, and the online regression parameters with the battery voltage greater than the parameter division voltage are extracted from the online regression parameter set as the to-be-fused online parameters.

[0099] S304, performing splicing processing on the to-be-fused offline parameters and the to-be-fused online parameters to obtain a target parameter set.

[0100] Optionally, the to-be-fused offline parameters and the to-be-fused online parameters can be directly spliced to obtain the target parameter set.

[0101] In the embodiment of the present application, by splicing the front part of the offline regression parameter set and the rear part of the online regression parameter set, the target parameter set is obtained, which can ensure the adaptability of the target parameter set to the battery to be measured, and further improve the accuracy of the target parameter set.

[0102] In order to ensure the accuracy of the actual health state value, on the basis of the above-mentioned embodiments, in the present embodiment, an optional way of determining the actual health state value is provided, as shown in the following. Figure 4 The specific steps include the following.

[0103] S401, taking the ratio between the current battery capacity and the rated battery capacity of the battery to be measured as a current correction coefficient.

[0104] The current correction coefficient refers to the coefficient used for correcting the standard health state value at the current time.

[0105] Optionally, after the rated battery capacity of the battery to be measured is determined, the ratio between the current battery capacity and the rated battery capacity of the battery to be measured can be directly taken as the current correction coefficient.

[0106] S402, using the current correction coefficient to correct the standard health state value corresponding to the current use data of the battery to be measured to obtain the actual health state value of the battery to be measured at the current time.

[0107] It can be understood that, in order to improve the efficiency of the health state value determination, before each battery is put into use, the standard health state value corresponding to each use data can be set according to the use data of each battery in the production stage, and then the standard health state value corresponding to each use data is configured in the BMS of the vehicle.

[0108] Further, the standard health state value corresponding to the current use data can be determined based on the standard health state value corresponding to each use data.

[0109] For example, the current use data of the battery to be tested is taken as an index word to search from the standard health state values corresponding to each candidate use data, to obtain the standard health state value corresponding to the current use data.

[0110] Optionally, the data in each preset dimension in the current use data of the battery to be tested can be spliced to form a search word, and then the search word is used to search from the standard health state values corresponding to each candidate use data, to further obtain the standard health state value corresponding to the current use data.

[0111] Further, the standard health state value corresponding to the current use data of the battery to be tested can be weighted by using the current correction coefficient to realize the correction of the standard health state value, and further obtain the actual health state value of the battery to be tested at the current time.

[0112] In the embodiment of the present application, the current correction coefficient is introduced, and the standard health state value corresponding to the current use data of the battery to be tested is corrected by using the current correction coefficient to obtain the actual health state value of the battery to be tested at the current time, which can ensure the accuracy of the actual health state value.

[0113] In order to ensure the reliability of the battery to be tested, on the basis of the above-mentioned embodiments, in the present embodiment, an optional information output mode is provided, as shown in Figure 5 The specific steps include the following steps:

[0114] S501, according to the vehicle type and vehicle use information of the vehicle to which the battery to be tested belongs, the health state threshold of the battery to be tested is determined.

[0115] The vehicle type refers to the vehicle model of the vehicle to which the battery to be tested belongs; the vehicle use information refers to the use information of the vehicle to which the battery to be tested belongs, which can include but is not limited to vehicle charging data and vehicle driving data; the health state threshold refers to the threshold for measuring the health state of the vehicle to which the battery to be tested belongs.

[0116] Optionally, the vehicle type and vehicle use information can be input into the trained threshold determination model, and the threshold determination model can output the health state threshold of the battery to be tested according to the vehicle type, vehicle use information and model parameters.

[0117] S502, determining the battery warning information of the battery to be measured according to the actual health state value and the health state threshold.

[0118] The battery warning information is used to represent information for warning the user.

[0119] Optionally, the actual health state value and the health state threshold can be compared in size relationship, and then the battery warning information of the battery to be measured is determined according to the comparison result.

[0120] For example, when the actual health state value is greater than the health state threshold, it can be determined that the health state of the battery to be measured is good, at this time the battery warning information is empty, and the actual health state value is directly fed back to the user; when the actual health state value is less than or equal to the health state threshold, it can be determined that the health state of the battery to be measured is poor, at this time the battery warning information capable of warning the driver can be generated based on the actual health state value.

[0121] S503, outputting the battery warning information to the vehicle.

[0122] Optionally, the battery warning information can be output to the display screen or the vehicle-mounted prompting device such as broadcast in the vehicle to feed back the battery warning information to the user; then the user can timely process the battery to be measured based on the battery warning information.

[0123] In the embodiment of the application, by determining the battery warning information of the battery to be measured according to the actual health state value and the health state threshold, the battery warning information can be output in time when the battery to be measured is abnormal, thereby ensuring the reliability of the operation of the battery to be measured.

[0124] Figure 6 For another flowchart of the method for determining the battery health state value in the embodiment, on the basis of the above-mentioned embodiment, the embodiment provides an optional example of the method for determining the battery health state value. In combination with Figure 6 , the specific implementation process is as follows:

[0125] S601, inputting each set of test data in the test data set into the standard regression function to obtain the offline regression parameter corresponding to each set of test data, and constructing the offline regression parameter set of the battery to be measured according to the offline regression parameters.

[0126] S602, inputting each set of running data in the running data set into the standard regression function to obtain the online regression parameter corresponding to each set of running data, and constructing the online regression parameter set of the battery to be measured according to the online regression parameters.

[0127] S603, determining the parameter division voltage according to the available voltage range of the power supply to be measured.

[0128] S604, extracting the offline parameters to be fused from the offline regression parameter set according to the parameter division voltage.

[0129] The battery voltage corresponding to the offline parameters to be fused is less than the parameter division voltage.

[0130] S605, extracting the online parameters to be fused from the online regression parameter set according to the parameter division voltage.

[0131] The battery voltage corresponding to the online parameters to be fused is greater than the parameter division voltage.

[0132] S606, performing splicing processing on the offline parameters to be fused and the online parameters to be fused to obtain a target parameter set.

[0133] S607, processing the standard regression function using the target parameter set to obtain a target regression function.

[0134] The target regression function is used to represent a first correlation between the battery internal resistance and the battery capacity of the battery to be measured.

[0135] S608, determining the current battery capacity of the battery to be measured based on the target regression function and the current battery internal resistance of the battery to be measured.

[0136] S609, taking the ratio between the current battery capacity and the rated battery capacity of the battery to be measured as a current correction coefficient.

[0137] S610, taking the current use data of the battery to be measured as an index word to search for the standard health state value corresponding to the current use data from the standard health state values corresponding to each candidate use data.

[0138] S611, correcting the standard health state value corresponding to the current use data of the battery to be measured using the current correction coefficient to obtain the actual health state value of the battery to be measured at the current time.

[0139] S612, determining the health state threshold of the battery to be measured according to the vehicle type and vehicle use information of the vehicle to which the battery to be measured belongs.

[0140] S613, determining the battery warning information of the battery to be measured according to the actual health state value and the health state threshold.

[0141] S614, outputting the battery warning information to the vehicle.

[0142] The specific process of S601-S614 can refer to the description of the above method embodiments, which has similar implementation principles and technical effects, and will not be repeated here.

[0143] It should be understood that although the steps in the flowcharts involved in the embodiments described above are shown in sequence according to the arrows, the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in sequence, and the steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the steps or stages is not necessarily sequential, but can be alternately executed with other steps or steps or stages in at least part of other steps.

[0144] Based on the same inventive concept, the embodiments of the present application also provide a battery state of health value determination device for implementing the battery state of health value determination method described above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more battery state of health value determination device embodiments provided below can refer to the limitations of the battery state of health value determination method described above, which will not be repeated here.

[0145] In an exemplary embodiment, as shown in Figure 7 a battery state of health value determination device 1 is provided, comprising: an internal resistance acquisition module 10, a capacity determination module 20 and a state determination module 30, wherein:

[0146] The internal resistance acquisition module 10 is configured to acquire a current battery internal resistance of a battery to be tested and a first correlation relationship;

[0147] The capacity determination module 20 is configured to determine a current battery capacity of the battery to be tested according to the current battery internal resistance and the first correlation relationship; wherein the first correlation relationship is obtained by processing a test data set of the battery to be tested in a production stage and / or a running data set in a use stage, and the test data set and the running data set both include battery internal resistances and battery capacities corresponding to a plurality of battery voltages of the battery to be tested;

[0148] The state determination module 30 is configured to determine an actual state of health value of the battery to be tested at a current time according to the current battery capacity, a rated battery capacity of the battery to be tested and a standard state of health value corresponding to current use data of the battery to be tested.

[0149] In an exemplary embodiment, the battery state of health value determination device 1 further comprises a function construction module 40, as shown in Figure 8 The function construction module 40 comprises:

[0150] The offline parameter determination unit 41 is configured to input each group of test data in the test data set into a standard regression function to obtain offline regression parameters corresponding to each group of test data, and construct an offline regression parameter set of the battery to be measured according to the offline regression parameters.

[0151] The online parameter determination unit 42 is configured to input each group of running data in the running data set into a standard regression function to obtain online regression parameters corresponding to each group of running data, and construct an online regression parameter set of the battery to be measured according to the online regression parameters.

[0152] The function construction unit 43 is configured to construct a target regression function according to the offline regression parameter set and the online regression parameter set; wherein the target regression function is used to represent a first correlation between the battery resistance and the battery capacity of the battery to be measured.

[0153] In an exemplary embodiment, the function construction unit 43 comprises:

[0154] The fusion sub-unit is configured to perform fusion processing on the offline regression parameter set and the online regression parameter set to obtain a target parameter set.

[0155] The function sub-unit is configured to process the standard regression function by using the target parameter set to obtain the target regression function.

[0156] In an exemplary embodiment, the fusion sub-unit is specifically configured to:

[0157] According to the available voltage range of the power supply to be measured, a parameter division voltage is determined; according to the parameter division voltage, offline parameters to be fused are extracted from the offline regression parameter set; wherein the battery voltage corresponding to the offline parameters to be fused is less than the parameter division voltage; according to the parameter division voltage, online parameters to be fused are extracted from the online regression parameter set; wherein the battery voltage corresponding to the online parameters to be fused is greater than the parameter division voltage; the offline parameters to be fused and the online parameters to be fused are spliced to obtain the target parameter set.

[0158] In an exemplary embodiment, the state determination module 30 is specifically configured to:

[0159] The ratio between the current battery capacity and the rated battery capacity of the battery to be measured is taken as a current correction coefficient; the standard state of health value corresponding to the current use data of the battery to be measured is corrected by using the current correction coefficient to obtain an actual state of health value of the battery to be measured at the current time.

[0160] In an exemplary embodiment, the state determination module 30 further comprises a query unit, wherein the query unit is specifically configured to:

[0161] The current use data of the battery to be tested is taken as an index word to search from the standard health state values corresponding to each candidate use data to obtain a standard health state value corresponding to the current use data.

[0162] In an exemplary embodiment, the battery health state value determination apparatus further comprises an output module, wherein the output module is specifically configured to:

[0163] According to the vehicle type and vehicle use information of the vehicle to which the battery to be tested belongs, a health state threshold of the battery to be tested is determined; according to the actual health state value and the health state threshold, battery warning information of the battery to be tested is determined; and the battery warning information is output to the vehicle.

[0164] Each module in the battery health state value determination apparatus described above can be realized by software, hardware and combinations thereof in whole or in part. Each module described above can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0165] In an exemplary embodiment, a computer device is provided, which can be a terminal, and the internal structure diagram thereof can be as shown in Figure 9 The computer device comprises a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to perform wired or wireless communication with external terminals, and the wireless communication can be realized through WIFI, mobile cellular network, near field communication (NFC) or other technologies. The computer program is executed by the processor to implement a battery health state value determination method. The display unit of the computer device is configured to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or can be a key, trackball or touchpad arranged on the shell of the computer device, or can be an external keyboard, touchpad or mouse, etc.

[0166] Those skilled in the art can understand that,Figure 9 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0167] In one exemplary embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program:

[0168] obtaining a current battery internal resistance and a first correlation relationship of a to-be-tested battery;

[0169] determining a current battery capacity of the to-be-tested battery according to the current battery internal resistance and the first correlation relationship; wherein the first correlation relationship is obtained by processing a test data set of the to-be-tested battery in a production stage and / or a running data set of the to-be-tested battery in a use stage, and the test data set and the running data set each include battery internal resistances and battery capacities of the to-be-tested battery corresponding to a plurality of battery voltages;

[0170] determining an actual health state value of the to-be-tested battery at a current time according to the current battery capacity, a rated battery capacity of the to-be-tested battery, and a standard health state value corresponding to current use data of the to-be-tested battery.

[0171] In one embodiment, the processor further implements the following steps when executing the computer program:

[0172] inputting each group of test data in the test data set into a standard regression function to obtain an offline regression parameter corresponding to each group of test data, and constructing an offline regression parameter set of the to-be-tested battery according to the offline regression parameters; inputting each group of running data in the running data set into the standard regression function to obtain an online regression parameter corresponding to each group of running data, and constructing an online regression parameter set of the to-be-tested battery according to the online regression parameters; and constructing a target regression function according to the offline regression parameter set and the online regression parameter set; wherein the target regression function is used to represent the first correlation relationship between the battery internal resistance and the battery capacity of the to-be-tested battery.

[0173] In one embodiment, the processor further implements the following steps when executing the computer program:

[0174] performing fusion processing on the offline regression parameter set and the online regression parameter set to obtain a target parameter set; and processing the standard regression function using the target parameter set to obtain the target regression function.

[0175] In one embodiment, the processor further implements the following steps when executing the computer program:

[0176] According to the available voltage range of the to-be-tested power supply, a parameter division voltage is determined; according to the parameter division voltage, to-be-fused offline parameters are extracted from the offline regression parameter set; wherein the battery voltage corresponding to the to-be-fused offline parameters is less than the parameter division voltage; according to the parameter division voltage, to-be-fused online parameters are extracted from the online regression parameter set; wherein the battery voltage corresponding to the to-be-fused online parameters is greater than the parameter division voltage; the to-be-fused offline parameters and the to-be-fused online parameters are spliced to obtain a target parameter set.

[0177] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0178] The ratio between the current battery capacity and the rated battery capacity of the to-be-tested battery is taken as a current correction coefficient; the standard state of health value corresponding to the current use data of the to-be-tested battery is corrected by using the current correction coefficient to obtain an actual state of health value of the to-be-tested battery at the current time.

[0179] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0180] The current use data of the to-be-tested battery is taken as an index word to search from the standard state of health values corresponding to each candidate use data to obtain a standard state of health value corresponding to the current use data.

[0181] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0182] According to the vehicle type and vehicle use information of the vehicle to which the to-be-tested battery belongs, a state of health threshold of the to-be-tested battery is determined; according to the actual state of health value and the state of health threshold, battery warning information of the to-be-tested battery is determined; and the battery warning information is output to the vehicle.

[0183] In one embodiment, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, and the computer program, when executed by a processor, implements the following steps:

[0184] The current battery resistance and the first correlation relationship of the to-be-tested battery are obtained;

[0185] According to the current battery resistance and the first correlation relationship, a current battery capacity of the to-be-tested battery is determined; wherein the first correlation relationship is obtained by processing a test data set of the to-be-tested battery in a production stage and / or a running data set in a use stage, and the test data set and the running data set both include battery resistances and battery capacities corresponding to a plurality of battery voltages of the to-be-tested battery;

[0186] According to the current battery capacity, the rated battery capacity of the to-be-tested battery, and a standard state of health value corresponding to the current use data of the to-be-tested battery, an actual state of health value of the to-be-tested battery at the current time is determined.

[0187] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0188] Each set of test data in the test data set is input into the standard regression function to obtain an offline regression parameter corresponding to each set of test data, and an offline regression parameter set of the battery under test is constructed according to each offline regression parameter; each set of running data in the running data set is input into the standard regression function to obtain an online regression parameter corresponding to each set of running data, and an online regression parameter set of the battery under test is constructed according to each online regression parameter; and a target regression function is constructed according to the offline regression parameter set and the online regression parameter set; wherein the target regression function is used to represent a first correlation between the battery resistance and the battery capacity of the battery under test.

[0189] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0190] The offline regression parameter set and the online regression parameter set are fused to obtain a target parameter set; and the target parameter set is used to process the standard regression function to obtain the target regression function.

[0191] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0192] According to the available voltage range of the power supply under test, a parameter division voltage is determined; according to the parameter division voltage, a to-be-fused offline parameter is extracted from the offline regression parameter set; wherein the battery voltage corresponding to the to-be-fused offline parameter is less than the parameter division voltage; according to the parameter division voltage, a to-be-fused online parameter is extracted from the online regression parameter set; wherein the battery voltage corresponding to the to-be-fused online parameter is greater than the parameter division voltage; and the to-be-fused offline parameter and the to-be-fused online parameter are spliced to obtain a target parameter set.

[0193] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0194] The ratio between the current battery capacity and the rated battery capacity of the battery under test is taken as a current correction coefficient; and the current correction coefficient is used to correct the standard state of health value corresponding to the current use data of the battery under test to obtain an actual state of health value of the battery under test at the current time.

[0195] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0196] The current use data of the battery under test is taken as an index word to search for a standard state of health value corresponding to the current use data from the standard state of health values corresponding to each candidate use data.

[0197] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0198] According to the vehicle type and vehicle use information of the vehicle to which the to-be-tested battery belongs, a health state threshold of the to-be-tested battery is determined; according to the actual health state value and the health state threshold, battery warning information of the to-be-tested battery is determined; and the battery warning information is output to the vehicle.

[0199] In one embodiment, a computer program product is provided, comprising a computer program which, when executed by the processor, implements the following steps:

[0200] A current battery internal resistance of the to-be-tested battery and a first correlation relationship are obtained;

[0201] According to the current battery internal resistance and the first correlation relationship, a current battery capacity of the to-be-tested battery is determined; wherein the first correlation relationship is obtained by processing a test data set of the to-be-tested battery in a production stage and / or a running data set of the to-be-tested battery in a use stage, and the test data set and the running data set both include battery internal resistances and battery capacities corresponding to a plurality of battery voltages of the to-be-tested battery;

[0202] According to the current battery capacity, a rated battery capacity of the to-be-tested battery, and a standard health state value corresponding to current use data of the to-be-tested battery, an actual health state value of the to-be-tested battery at a current time is determined.

[0203] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0204] Each group of test data in the test data set is input into the standard regression function to obtain an offline regression parameter corresponding to each group of test data, and an offline regression parameter set of the to-be-tested battery is constructed according to the offline regression parameters; each group of running data in the running data set is input into the standard regression function to obtain an online regression parameter corresponding to each group of running data, and an online regression parameter set of the to-be-tested battery is constructed according to the online regression parameters; and a target regression function is constructed according to the offline regression parameter set and the online regression parameter set; wherein the target regression function is used to represent the first correlation relationship between the battery internal resistance and the battery capacity of the to-be-tested battery.

[0205] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0206] The offline regression parameter set and the online regression parameter set are fused to obtain a target parameter set; and the target parameter set is used to process the standard regression function to obtain the target regression function.

[0207] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0208] According to the available voltage range of the to-be-tested power supply, a parameter division voltage is determined; according to the parameter division voltage, to-be-fused offline parameters are extracted from the offline regression parameter set; wherein the battery voltage corresponding to the to-be-fused offline parameters is less than the parameter division voltage; according to the parameter division voltage, to-be-fused online parameters are extracted from the online regression parameter set; wherein the battery voltage corresponding to the to-be-fused online parameters is greater than the parameter division voltage; the to-be-fused offline parameters and the to-be-fused online parameters are spliced to obtain a target parameter set.

[0209] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0210] The ratio between the current battery capacity and the rated battery capacity of the to-be-tested battery is taken as a current correction coefficient; the standard state of health value corresponding to the current use data of the to-be-tested battery is corrected by using the current correction coefficient to obtain the actual state of health value of the to-be-tested battery at the current time.

[0211] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0212] The current use data of the to-be-tested battery is taken as an index word to search from the standard state of health values corresponding to each candidate use data to obtain the standard state of health value corresponding to the current use data.

[0213] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0214] According to the vehicle type and vehicle use information of the vehicle to which the to-be-tested battery belongs, a state of health threshold of the to-be-tested battery is determined; according to the actual state of health value and the state of health threshold, battery warning information of the to-be-tested battery is determined; and the battery warning information is output to the vehicle.

[0215] It should be noted that the data involved in the present application (including but not limited to the internal resistance data of the vehicle) are all data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data need to comply with relevant regulations.

[0216] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, 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. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0217] The technical features of the above embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist, they should be considered as the scope of the present application.

[0218] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A method for determining the state of health of a battery, characterized in that, The method includes: Obtain the current internal resistance of the battery under test and a first correlation relationship; wherein, the first correlation relationship is obtained by processing the test dataset of the battery under test in the production stage and / or the running dataset in the use stage, and both the test dataset and the running dataset include the internal resistance and battery capacity of the battery under test at multiple battery voltages; Based on the current battery internal resistance and the first correlation, determine the current battery capacity of the battery under test; Based on the current battery capacity, the rated battery capacity of the battery under test, and the standard health status value corresponding to the current usage data of the battery under test, determine the actual health status value of the battery under test at the current moment. The process of processing the test dataset from the production phase and the operational dataset from the usage phase of the battery under test includes: Based on the available voltage range of the power supply under test, determine the parameter division voltage; Based on the voltage division according to the parameters, the offline parameters to be fused are extracted from the offline regression parameter set; wherein, the offline regression parameter set is the set of regression parameters corresponding to each test data in the test dataset; the battery voltage corresponding to the offline parameter to be fused is less than the voltage division according to the parameters; Based on the voltage division according to the parameters, online parameters to be fused are extracted from the online regression parameter set; wherein, the online regression parameter set is the set of regression parameters corresponding to each running data in the running dataset; the battery voltage corresponding to the online parameter to be fused is greater than the voltage division according to the parameters; The offline parameters to be fused and the online parameters to be fused are concatenated to obtain the target parameter set; Using the target parameter set, the standard regression function is processed to obtain the target regression function; wherein, the target regression function is used to characterize the first correlation between the battery internal resistance and the battery capacity of the battery under test.

2. The method according to claim 1, characterized in that, The method further includes: Each set of test data in the test dataset is input into the standard regression function to obtain the offline regression parameters corresponding to each set of test data, and the offline regression parameter set of the battery under test is constructed based on each offline regression parameter. Each set of running data in the running dataset is input into the standard regression function to obtain the online regression parameters corresponding to each set of running data. Based on each online regression parameter, the online regression parameter set of the battery under test is constructed.

3. The method according to claim 1, characterized in that, The step of determining the actual health status value of the battery under test at the current moment based on the current battery capacity, the rated battery capacity of the battery under test, and the standard health status value corresponding to the current usage data of the battery under test includes: The ratio between the current battery capacity and the rated battery capacity of the battery under test is used as the current correction factor; The standard health status value corresponding to the current usage data of the battery under test is corrected using the current correction coefficient to obtain the actual health status value of the battery under test at the current moment.

4. The method according to claim 1, characterized in that, The method further includes: Using the current usage data of the battery under test as an index term, the standard health status value corresponding to each candidate usage data is searched to obtain the standard health status value corresponding to the current usage data.

5. The method according to claim 1, characterized in that, The method further includes: Based on the vehicle type and vehicle usage information of the vehicle to which the battery under test belongs, determine the health status threshold of the battery under test; Based on the actual health status value and the health status threshold, determine the battery warning information of the battery under test; The battery warning information is output to the vehicle.

6. The method according to claim 1, characterized in that, Determining the current battery capacity of the battery under test based on the current battery internal resistance and the first correlation includes: Substituting the current battery internal resistance into the first correlation relationship, the current battery capacity of the battery under test is calculated.

7. A device for determining the state of health of a battery, characterized in that, The device includes: An internal resistance acquisition module is used to acquire the current internal resistance of the battery under test and a first correlation relationship; wherein, the first correlation relationship is obtained by processing the test dataset of the battery under test in the production stage and / or the running dataset in the use stage, and both the test dataset and the running dataset include the internal resistance and battery capacity of the battery under test at multiple battery voltages. A capacity determination module is used to determine the current battery capacity of the battery under test based on the current battery internal resistance and the first correlation relationship; The status determination module is used to determine the actual health status value of the battery under test at the current moment based on the current battery capacity, the rated battery capacity of the battery under test, and the standard health status value corresponding to the current usage data of the battery under test. The function construction module is used to determine the parameter division voltage based on the available voltage range of the power supply under test; extract offline parameters to be fused from the offline regression parameter set based on the parameter division voltage; extract online parameters to be fused from the online regression parameter set based on the parameter division voltage; concatenate the offline parameters to be fused and the online parameters to be fused to obtain the target parameter set; and use the target parameter set to process the standard regression function to obtain the target regression function. Wherein, the offline regression parameter set is the set of regression parameters corresponding to each test data in the test dataset; the battery voltage corresponding to the offline parameter to be fused is less than the parameter division voltage; the online regression parameter set is the set of regression parameters corresponding to each running data in the running dataset; the battery voltage corresponding to the online parameter to be fused is greater than the parameter division voltage; the target regression function is used to characterize the first correlation between the battery internal resistance and battery capacity of the battery under test.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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