Vehicle battery fault diagnosis method, device and equipment

Abstract

The invention discloses a vehicle battery fault diagnosis method, device and equipment. The method comprises the following steps: acquiring battery information of a vehicle battery, wherein the battery comprises a plurality of battery units; according to the battery information, internal resistance characteristic values corresponding to the multiple battery units are determined, and the internal resistance characteristic values are used for representing internal resistance original characteristic values of the corresponding battery units; determining a variation coefficient of the battery according to the internal resistance characteristic values corresponding to the plurality of battery units; according to the method, the internal resistance characteristic value representing the internal resistance original characteristic value of the battery unit is obtained through calculation according to the battery information of the battery, the variation coefficient is determined through the internal resistance characteristic value, the variation degree of the battery unit can be measured, and the diagnosis result of the battery is obtained according to the variation coefficient of the battery. Therefore, a relatively accurate battery diagnosis result is obtained, and the accuracy of battery diagnosis can be improved.

Description

Technical Field

[0001] The present application belongs to the field of battery technology, and in particular, relates to a vehicle battery fault diagnosis method, device and equipment. Background Art

[0002] With the rapid development of the economy, the new energy vehicle market has shown explosive growth and has become a key force in promoting the green development of the automotive industry. As the core component of electric vehicles, the safety of power batteries directly determines the safety, power performance and endurance of electric vehicles. Real-time fault warning and safety monitoring of battery packs throughout their life cycle are of great practical significance.

[0003] Battery internal resistance is one of the key parameters for measuring battery performance. Currently, the measurement of battery internal resistance mainly adopts the hybrid power pulse characteristic (HPPC) measurement method. Under constant current charging conditions, when the current undergoes a step change, the battery internal resistance is calculated based on the ratio of voltage to current before and after the change.

[0004] In the above method, if affected by other interference factors, such as the ambient temperature of the battery, the remaining power, etc., the internal resistance value obtained may not be accurate enough, thereby affecting the accuracy of battery diagnosis. Summary of the invention

[0005] The embodiments of the present application provide a vehicle battery fault diagnosis method, device and equipment, which can improve the accuracy of battery diagnosis.

[0006] In a first aspect, an embodiment of the present application provides a vehicle battery fault diagnosis method, the method comprising:

[0007] Acquiring battery information of a vehicle battery, wherein the battery includes a plurality of battery cells;

[0008] Determine, according to the battery information, internal resistance characteristic values ​​corresponding to each of the plurality of battery cells, wherein the internal resistance characteristic values ​​are used to characterize the DC internal resistance of the corresponding battery cell;

[0009] Determining a coefficient of variation of the battery according to internal resistance characteristic values ​​corresponding to each of the plurality of battery cells;

[0010] A diagnosis result of the battery is obtained according to the coefficient of variation of the battery.

[0011] In one example, the battery information includes battery sub-information of a plurality of battery cells corresponding to a plurality of sampling points respectively collected under a target operating condition, the battery sub-information includes current values ​​and voltage values ​​of the corresponding battery cells, and the target operating condition includes a driving operating condition or a charging operating condition;

[0012] The step of determining the internal resistance characteristic values ​​corresponding to each of the plurality of battery cells according to the battery information includes:

[0013] For each of the battery cells, according to the current value of the battery cell at each of the two adjacent sampling points, a current change value corresponding to each of the two adjacent sampling points is calculated, and according to the voltage value of the battery cell at each of the two adjacent sampling points, a voltage change value corresponding to each of the two adjacent sampling points is calculated;

[0014] For each of the two adjacent sampling points, according to the voltage change value and the current change value corresponding to the two adjacent sampling points, obtain the original characteristic value of the internal resistance corresponding to each of the two adjacent sampling points;

[0015] Each of the battery cells is processed as follows:

[0016] The internal resistance characteristic value corresponding to the battery cell is calculated according to the original characteristic values ​​of the internal resistance corresponding to the two adjacent sampling points.

[0017] The calculating the internal resistance characteristic value corresponding to the battery cell according to the original characteristic values ​​of the internal resistance corresponding to the two adjacent sampling points respectively includes:

[0018] Take every two adjacent sampling points as a sampling point set, and obtain M sampling point sets;

[0019] Screening the M sampling point sets to obtain N sampling point sets, where N is less than or equal to M and is a positive integer;

[0020] Multiplying the original characteristic value of the internal resistance of the battery unit corresponding to the N sampling point sets by the target current value corresponding to each of the sampling point sets to obtain a first value corresponding to each of the N sampling point sets, and adding the first values ​​to obtain a second value; the N sampling point sets include a plurality of target sampling points; the target current value is a current value corresponding to the target sampling point in the sampling point set at a previous sampling time;

[0021] Taking the sum of the current values ​​of the battery cell corresponding to the plurality of target sampling points as the third value;

[0022] The second value is divided by the third value to obtain an internal resistance characteristic value corresponding to the battery cell.

[0023] In one example, the screening of the M sampling point sets to obtain N sampling point sets includes:

[0024] Each sampling point set is processed as follows:

[0025] If the current change value corresponding to the sampling point set is greater than a preset change value, and the interval between sampling times of two sampling points in the sampling point set is less than or equal to a preset threshold, the sampling point set is used as one of the N sampling point sets.

[0026] In one example, obtaining a diagnosis result of the battery according to the coefficient of variation of the battery includes:

[0027] Determining a Gini coefficient of the battery according to a maximum internal resistance characteristic value among the internal resistance characteristic values ​​of the plurality of battery cells;

[0028] A diagnosis result of the battery is obtained according to the coefficient of variation of the battery and the Gini coefficient.

[0029] In one example, determining the Gini coefficient of the battery according to the maximum internal resistance characteristic value among the internal resistance characteristic values ​​of the plurality of battery cells includes:

[0030] If the maximum internal resistance characteristic value is less than the first characteristic value, 0 is used as the Gini coefficient of the battery; the first characteristic value is determined according to the quantile, and the quantile is determined according to the sorting result of the internal resistance characteristic values ​​of the plurality of battery cells;

[0031] If the maximum internal resistance characteristic value is greater than or equal to the first characteristic value, the difference between 1 and the first probability value is taken as the Gini coefficient of the battery.

[0032] In one example, the first probability value is calculated as follows:

[0033] Binning the internal resistance characteristic values ​​of the plurality of battery cells to obtain the number of internal resistance characteristic values ​​that each bin falls into;

[0034] Obtaining a probability value corresponding to each box according to the number of internal resistance characteristic values ​​that each box falls into and the total number of the plurality of battery cells;

[0035] The first probability value is calculated according to the probability value corresponding to each box.

[0036] In one example, obtaining a diagnosis result of the battery according to the coefficient of variation of the battery and the Gini coefficient includes:

[0037] If the coefficient of variation of the battery is greater than or equal to the first fault coefficient of variation and less than the second fault coefficient of variation, and the Gini coefficient of the battery is less than the first fault Gini coefficient and greater than or equal to the second fault Gini coefficient, then the diagnosis result of the battery is a battery fault, and the fault level of the battery is a primary fault;

[0038] If the coefficient of variation of the battery is greater than or equal to the first fault coefficient of variation and less than the second fault coefficient of variation, and the Gini coefficient of the battery is less than the second fault Gini coefficient and greater than the third fault Gini coefficient, then the diagnosis result of the battery is a battery fault, and the fault level of the battery is a secondary fault;

[0039] If the coefficient of variation of the battery is greater than or equal to the second fault coefficient of variation, and the Gini coefficient of the battery is less than the second fault Gini coefficient, and greater than the third fault Gini coefficient, then the diagnosis result of the battery is a battery failure, and the fault level of the battery is a level 3 failure, and the fault level of the level 2 failure is greater than the fault level of the level 1 failure and less than the fault level of the level 3 failure.

[0040] In one example, after obtaining a diagnosis result of the battery according to the coefficient of variation of the battery, the method further includes:

[0041] In the case where the diagnosis result indicates that the battery has a fault, determining a fault interval, wherein the lower boundary of the fault interval is a first boundary value, and the upper boundary of the fault interval is a maximum value among the internal resistance characteristic values ​​corresponding to the plurality of battery cells, wherein the first boundary value is determined according to the quantile of the binning process;

[0042] A battery cell whose corresponding internal resistance characteristic value among the plurality of battery cells falls within the fault interval is determined as a battery cell with a fault.

[0043] In a second aspect, an embodiment of the present application provides a vehicle battery fault diagnosis device, the device comprising:

[0044] An acquisition module, used to acquire battery information of a vehicle battery, wherein the battery includes a plurality of battery cells;

[0045] A first determination module, configured to determine, based on the battery information, internal resistance characteristic values ​​corresponding to each of the plurality of battery cells, wherein the internal resistance characteristic values ​​are used to characterize a DC internal resistance of the corresponding battery cell;

[0046] A second determination module, configured to determine a coefficient of variation of the battery according to internal resistance characteristic values ​​corresponding to each of the plurality of battery cells;

[0047] The diagnosis module is used to obtain a diagnosis result of the battery according to the coefficient of variation of the battery.

[0048] In a third aspect, an embodiment of the present application provides an electronic device, the device comprising: a processor and a memory storing computer program instructions;

[0049] When the processor executes the computer program instructions, the vehicle battery fault diagnosis method as described in the first aspect is implemented.

[0050] In a fourth aspect, an embodiment of the present application provides a computer storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the vehicle battery fault diagnosis method described in the first aspect is implemented.

[0051] In a fifth aspect, an embodiment of the present application provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the vehicle battery fault diagnosis method as described in the first aspect.

[0052] The vehicle battery fault diagnosis method, device and equipment of the embodiments of the present application calculate the internal resistance characteristic value that characterizes the DC internal resistance of the battery cell based on the battery information of the battery, and determine the coefficient of variation through the internal resistance characteristic value, which can measure the degree of variation of the battery cell, thereby obtaining a more accurate battery diagnosis result, which can improve the accuracy of battery diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0054] Figure 1 It is a flow chart of an embodiment of a vehicle battery fault diagnosis method provided by the present application;

[0055] Figure 2 is another flow chart of an embodiment of the vehicle battery fault diagnosis method provided by the present application;

[0056] Figure 3 It is another flow chart of an embodiment of the vehicle battery fault diagnosis method provided by the present application;

[0057] Figure 4 is another flow chart of an embodiment of the vehicle battery fault diagnosis method provided by the present application;

[0058] Figure 5 is a structural schematic diagram of an embodiment of a vehicle battery fault diagnosis device provided by the present application;

[0059] Figure 6 It is a schematic diagram of the structure of an embodiment of the electronic device provided by the present application. DETAILED DESCRIPTION

[0060] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0061] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0062] Battery internal resistance is one of the key parameters for measuring battery performance, which directly affects the battery's discharge efficiency, energy density, cycle life and safety. The abnormal increase in internal resistance may be due to a short circuit, overheating or other faults inside the battery, which may lead to a decline in battery performance or even cause a safety accident. The measurement of battery internal resistance mainly includes DC internal resistance measurement and AC internal resistance measurement, mainly using electrochemical impedance spectroscopy (EIS) and hybrid pulse power characteristics (HPPC) measurement methods. Among them, the measurement method of electrochemical impedance spectroscopy relies on precise measurement equipment and strict measurement conditions. High-precision internal resistance measurement equipment is often expensive and not conducive to real-time online monitoring and analysis in the cloud system. HPPC calculates the battery internal resistance based on the ratio of voltage to current before and after the change under constant current charging conditions and under the condition of a step change in current. On the one hand, this method is greatly affected by ambient temperature and state of charge SOC, resulting in the internal resistance value obtained being inaccurate, thereby affecting the accuracy of battery diagnosis. On the other hand, there are certain requirements for the working conditions of the vehicle.

[0063] In addition, abnormal battery connections, such as poor contact, wiring harness problems, or loose fasteners, can cause uneven current distribution in the battery pack, affecting the battery's charge and discharge efficiency and life. In extreme cases, abnormal connections may cause local overheating of the battery or even fire. Therefore, monitoring the battery connection status is critical to preventing battery failure and ensuring safe system operation.

[0064] In order to solve the technical problems in the related art, the embodiments of the present application provide a vehicle battery fault diagnosis method, device and equipment. The vehicle battery fault diagnosis method provided by the embodiments of the present application is first introduced below.

[0065] Figure 1 A schematic flow chart of an embodiment of the vehicle battery fault diagnosis method provided by the present application is shown. The vehicle battery fault diagnosis method of the present application can be applied to electronic devices.

[0066] like Figure 1 As shown, the vehicle battery fault diagnosis method provided by the embodiment of the present application includes the following steps S101 to S104, wherein:

[0067] S101. Obtain battery information of a vehicle battery, where the battery includes a plurality of battery cells.

[0068] In this embodiment, the electronic device may be a cloud server (hereinafter referred to as the cloud). The cloud is used as an example for explanation below. The cloud is connected to multiple vehicles for communication. The cloud receives battery information reported by the vehicles. The battery includes multiple battery cells, which may also be referred to as single cells. The cloud performs fault diagnosis on the battery information reported by each vehicle.

[0069] Among them, the vehicle reports battery information after power-on, so that the cloud can obtain the vehicle's battery information in time for real-time diagnosis, or report according to a pre-set reporting time, such as reporting battery information at 24:00 every day; or, the cloud sends a diagnostic command, and the vehicle reports the battery information after receiving the diagnostic command.

[0070] S102: Determine internal resistance characteristic values ​​corresponding to each of the plurality of battery cells according to the battery information, where the internal resistance characteristic values ​​are used to characterize the direct current internal resistance of the corresponding battery cell.

[0071] In this embodiment, the internal resistance characteristic values ​​corresponding to each of the multiple battery cells are determined based on the battery information. Specifically, the characteristic values ​​that meet the conditions are screened for each battery cell, so that the characteristic values ​​are aggregated to obtain the internal resistance characteristic value corresponding to the battery cell. The internal resistance characteristic value is used to characterize the DC internal resistance of the corresponding battery cell. The DC internal resistance is an important parameter used to describe battery performance, and is related to the battery's operating status, life, charging speed, and discharge capacity.

[0072] S103: Determine the coefficient of variation of the battery according to the internal resistance characteristic values ​​corresponding to each of the plurality of battery cells.

[0073] In this embodiment, the coefficient of variation is calculated based on the internal resistance characteristic values ​​corresponding to each of the multiple battery cells. The coefficient of variation (CV) is calculated based on the standard deviation and the mean. The larger the coefficient of variation, the higher the degree of variation. Conversely, the smaller the coefficient of variation, the lower the variation. The coefficient of variation is used to evaluate battery abnormalities.

[0074] Specifically, determining the coefficient of variation of the battery according to the internal resistance characteristic values ​​corresponding to each of the plurality of battery cells includes:

[0075] The standard deviation and the mean are calculated based on the internal resistance characteristic values ​​corresponding to the multiple battery cells; the ratio of the standard deviation to the mean is used as the coefficient of variation of the battery.

[0076] In this embodiment, the standard deviation and mean are calculated according to the internal resistance characteristic values ​​corresponding to the multiple battery cells, and the standard deviation and mean are substituted into formula (1) to calculate the coefficient of variation of the battery. Formula (1) is expressed as:

[0077]

[0078] Among them, DCRcv is the coefficient of variation of the battery, std is the standard deviation, and mean is the mean.

[0079] S104. Obtaining a diagnosis result of the battery according to the coefficient of variation of the battery.

[0080] In this embodiment, the battery's coefficient of variation is compared with the fault coefficient of variation, and the battery diagnosis result is obtained based on the comparison result. Specifically, if the battery's coefficient of variation is greater than or equal to the fault coefficient of variation, the battery diagnosis result is that the battery has a fault; if the battery's coefficient of variation is less than the fault coefficient of variation, the battery diagnosis result is that the battery currently has no fault. It should be noted that after obtaining the battery information of the vehicle battery, the solution of determining the internal resistance characteristic values ​​corresponding to each battery cell according to the battery information is continued to be executed, so as to determine again whether the battery has a fault.

[0081] In this embodiment, by obtaining the battery information of the vehicle, the battery includes multiple battery cells, and the internal resistance characteristic values ​​corresponding to each of the multiple battery cells are determined according to the battery information. The internal resistance characteristic value is used to characterize the DC internal resistance of the battery cell. The coefficient of variation of the battery is determined by the characteristic internal resistance corresponding to each of the multiple battery cells, and the diagnosis result of the battery is determined according to the coefficient of variation. Through the above steps, the internal resistance characteristic value characterizing the DC internal resistance of the battery cell is calculated according to the battery information of the battery. As the battery is used for a longer time, its performance may gradually decline, and the coefficient of variation may increase accordingly. By determining the coefficient of variation through the internal resistance characteristic value, the degree of variation of the battery cell can be measured, thereby obtaining a more accurate diagnosis result of the battery, which can improve the accuracy of battery diagnosis.

[0082] Figure 2 Another flow chart of an embodiment of the vehicle battery fault diagnosis method provided by the present application is shown. The vehicle battery fault diagnosis method of the present application can be applied to electronic devices.

[0083] like Figure 2 As shown, the vehicle battery fault diagnosis method provided by the embodiment of the present application includes the following steps S201 to S205, wherein:

[0084] S201. Obtain battery information of a vehicle battery, where the battery includes a plurality of battery cells.

[0085] S202: Determine internal resistance characteristic values ​​corresponding to each of the plurality of battery cells according to the battery information, where the internal resistance characteristic values ​​are used to characterize the direct current internal resistance of the corresponding battery cell.

[0086] S203: Determine the coefficient of variation of the battery according to the internal resistance characteristic values ​​corresponding to each of the plurality of battery cells.

[0087] For details of steps S201 to S203, please refer to the records in steps S101 to S103, which will not be described in detail here.

[0088] S204: Determine the Gini coefficient of the battery according to the maximum internal resistance characteristic value among the internal resistance characteristic values ​​of the plurality of battery cells.

[0089] In this embodiment, the maximum internal resistance characteristic value is compared with the first characteristic value, and the Gini coefficient of the battery is determined according to the comparison result.

[0090] S205. Obtain the battery diagnosis result according to the battery's coefficient of variation and Gini coefficient.

[0091] In this embodiment, the battery's coefficient of variation is compared with the fault coefficient of variation, and the Gini coefficient is compared with the fault Gini coefficient, and the battery diagnosis result is obtained based on the above comparison results. The coefficient of variation can compare the degree of variation of different internal resistance characteristic values. The larger the value of the coefficient of variation, the greater the degree of variation, indicating that there is a problem with the battery. Conversely, the smaller the degree of variation. A Gini coefficient of 0 indicates that there is no difference between the internal resistance characteristic values, indicating that there is no abnormality in the battery.

[0092] In one example, based on the coefficient of variation and the Gini coefficient of the battery, a battery diagnosis result is obtained, including:

[0093] If the battery's coefficient of variation is greater than or equal to the first fault coefficient of variation and less than the second fault coefficient of variation, and the battery's Gini coefficient is less than the first fault Gini coefficient and greater than or equal to the second fault Gini coefficient, then the battery's diagnosis result is a battery failure, and the battery's failure level is a level one failure; if the battery's coefficient of variation is greater than or equal to the first fault coefficient of variation and less than the second fault coefficient of variation, and the battery's Gini coefficient is less than the second fault Gini coefficient and greater than the third fault Gini coefficient, then the battery's diagnosis result is a battery failure, and the battery's failure level is a level two failure; if the battery's coefficient of variation is greater than or equal to the second fault coefficient of variation, and the battery's Gini coefficient is less than the second fault Gini coefficient and greater than the third fault Gini coefficient, then the battery's diagnosis result is a battery failure, and the battery's failure level is a level three failure, and the failure level of the level two failure is greater than the failure level of the level one failure and less than the failure level of the level three failure.

[0094] In this embodiment, the coefficient of variation of the battery is compared with the first fault coefficient of variation and the second fault coefficient of variation, and the Gini coefficient of the battery is compared with the first fault Gini coefficient and the second fault Gini coefficient. Based on the above comparison results, it is determined whether the battery is a faulty battery, and three levels of fault levels are set, including level one fault, level two fault and level three fault, which represent different fault levels according to the degree of fault.

[0095] Specifically, if the battery's coefficient of variation is greater than or equal to the first fault coefficient of variation and less than the second fault coefficient of variation, and the battery's Gini coefficient is less than the first fault Gini coefficient and greater than or equal to the second fault Gini coefficient, then the battery's diagnosis result is a battery fault, and the battery's fault level is a primary fault, expressed as:

[0096] throld dcr1 ≤DCR cv <throld dcr2 ,throld gini1 ≤Gini<throld gini2

[0097] Among them, throlddcr1 is the first fault variation coefficient, DCR cv is the coefficient of variation of the battery, throld dcr2 is the second fault variation coefficient, throld gini1 is the first fault Gini coefficient, Gini is the battery Gini coefficient, throld gini2 is the second fault Gini coefficient.

[0098] Specifically, if the battery's coefficient of variation is greater than or equal to the first fault coefficient of variation and less than the second fault coefficient of variation, and the battery's Gini coefficient is less than the second fault Gini coefficient and greater than the third fault Gini coefficient, then the battery's diagnosis result is a battery fault, and the battery's fault level is a secondary fault, expressed as:

[0099] throld dcr1 ≤DCR cv <throld dcr2 ,throld gini3 <Gini<throld gini2

[0100] Among them, throld gini3 is the third fault Gini coefficient, and the third fault Gini coefficient can be set to 0.

[0101] Specifically, if the battery's coefficient of variation is greater than or equal to the second fault coefficient of variation, and the battery's Gini coefficient is less than the second fault Gini coefficient, and greater than the third fault Gini coefficient, then the battery's diagnosis result is a battery fault, and the battery's fault level is a level 3 fault, and the level of the level 2 fault is greater than the level of the level 1 fault and less than the level of the level 3 fault, expressed as:

[0102] throld dcr2 ≤DCR cv ,throld gini3 <Gini<throld gini2

[0103] In the embodiment of the present application, the internal resistance characteristic value is calculated based on the characteristics of the battery data itself, the coefficient of variation is determined by the internal resistance characteristic value, and the battery diagnosis result is obtained in combination with the Gini coefficient. The coefficient of variation can be used to measure the degree of variation of the battery cell, and the Gini coefficient can be used to evaluate the difference in the internal resistance characteristic values ​​of the battery cells. The combination of these two can obtain more accurate diagnostic results and improve the accuracy of battery diagnosis.

[0104] In one example, the battery information includes battery sub-information of multiple battery cells corresponding to multiple sampling points collected under a target operating condition, the battery sub-information includes current values ​​and voltage values ​​of the corresponding battery cells, and the target operating condition includes a driving operating condition or a charging operating condition; according to the battery information, determining the internal resistance characteristic values ​​corresponding to each of the multiple battery cells includes:

[0105] For each battery cell, the current change value corresponding to each two adjacent sampling points is calculated according to the current value of the battery cell at each two adjacent sampling points, and the voltage change value corresponding to each two adjacent sampling points is calculated according to the voltage value of the battery cell at each two adjacent sampling points; for each two adjacent sampling points, the original characteristic value of the internal resistance corresponding to each of the two adjacent sampling points is obtained according to the voltage change value and the current change value corresponding to the two adjacent sampling points;

[0106] Each battery cell is processed as follows:

[0107] According to the original characteristic values ​​of the internal resistance corresponding to two adjacent sampling points, the characteristic value of the internal resistance corresponding to the battery cell is calculated.

[0108] In this embodiment, the battery information includes battery information of multiple battery cells corresponding to multiple sampling points collected under the target operating condition, wherein the target operating condition is a driving condition or a charging condition, and the battery information includes the current value and voltage value of the corresponding battery cell.

[0109] Specifically, sampling is performed at a preset time interval, such as sampling at 10s intervals. For each battery cell, according to the current value of the battery cell at each two adjacent sampling points, the current change value corresponding to each two adjacent sampling points is calculated. Specifically, the current value of each two adjacent sampling points is substituted into formula (2) to calculate the current change value corresponding to each two adjacent sampling points. Formula (2) is expressed as:

[0110] deltI(t)=I(t)-I(t-1) (2)

[0111] Wherein, deltI(t) is the current change value, I(t) is the current value of the sampling point at time t among the adjacent sampling points, and I(t-1) is the current value of the sampling point at time t-1 among the adjacent sampling points.

[0112] It should be noted that the batteries are connected in series, and at the same time, the current value of each battery cell in the battery is the same.

[0113] Further, according to the voltage value of the battery unit at every two adjacent sampling points, the current change value corresponding to every two adjacent sampling points is calculated. Specifically, the voltage value of every two adjacent sampling points is substituted into formula (3) to calculate the voltage change value corresponding to every two adjacent sampling points. Formula (3) is expressed as:

[0114] deltUi(t)=Ui(t)-Ui(t-1) (3)

[0115] Wherein, deltIUi(t) is the voltage change value, Ui(t) is the voltage value of the battery cell i at the sampling point at time t among the adjacent sampling points, and Ui(t-1) is the voltage value of the battery cell i at the sampling point at time t-1 among the adjacent sampling points, where i is the number of the battery cell.

[0116] Furthermore, for every two adjacent sampling points, according to the voltage change value and current change value corresponding to the two adjacent sampling points, the original characteristic value of the internal resistance corresponding to the two adjacent sampling points is calculated according to formula (4), and formula (4) is expressed as:

[0117]

[0118] Among them, DCRi(t) is the original characteristic value of the internal resistance, deltIUi(t) is the voltage change value, and deltI(t) is the current change value.

[0119] Furthermore, each battery cell is processed as follows: the internal resistance characteristic value corresponding to the battery cell is calculated based on the original characteristic values ​​of the internal resistance corresponding to two adjacent sampling points. During the calculation process, screening and aggregation operations are involved. The adjacent sampling points are screened according to the screening conditions to obtain a sampling point set, and then an aggregation operation is performed to obtain the internal resistance characteristic value corresponding to the battery cell.

[0120] In one example, the internal resistance characteristic value corresponding to the battery cell is calculated based on the original characteristic values ​​of the internal resistance corresponding to two adjacent sampling points, including:

[0121] Every two adjacent sampling points are taken as a sampling point set to obtain M sampling point sets; the M sampling point sets are screened to obtain N sampling point sets, where N is less than or equal to M and is a positive integer; the original characteristic value of the internal resistance corresponding to the battery cell in the N sampling point sets is multiplied by the target current value corresponding to each sampling point set to obtain the first value corresponding to each of the N sampling point sets, and the first values ​​are added to obtain the second value; the N sampling point sets include multiple target sampling points; the target current value is the current value corresponding to the target sampling point in the sampling point set at the previous sampling moment; the sum of the current values ​​corresponding to the battery cell at the multiple target sampling points is taken as the third value; the second value is divided by the third value to obtain the internal resistance characteristic value corresponding to the battery cell.

[0122] In this embodiment, every two adjacent sampling points are taken as a sampling point set to obtain M sampling point sets, and the M sampling point sets are screened according to the screening conditions to obtain N sampling point sets. The purpose of the screening is to find a sampling set with relatively abnormal changes, that is, an abnormal sampling point, and eliminate the normal sampling set. The internal resistance characteristic value corresponding to the abnormal sampling set and the current value of the target sampling point in the abnormal set can better reflect the abnormal situation of the battery unit.

[0123] Among them, N is less than or equal to M, and N is a positive integer. If the number of sampling point sets obtained by screening is 0, it means that there is no data to be analyzed, which also indirectly indicates that there is no abnormality in the battery at present.

[0124] Specifically, the original characteristic value of the internal resistance of the battery cell corresponding to N sampling point sets is multiplied by the target current value corresponding to each sampling point set to obtain the first value corresponding to each of the N sampling point sets, and the first values ​​corresponding to each of the N sampling point sets are added to obtain the second value, wherein the N sampling point sets include multiple target sampling points, one sampling point set includes two target sampling points, the sampling times of the two target sampling points are different, and the current value corresponding to the target sampling point at the previous sampling time is used as the target current value.

[0125] Further, the sum of the current values ​​corresponding to the battery cell at multiple target sampling points is taken as the third value, the N sampling point sets include multiple target sampling points, the N sampling point sets are also referred to as target sampling point sets, and the sampling points included in any one of the N sampling point sets are referred to as target sampling points. Further, the second value is divided by the third value to calculate the internal resistance characteristic value corresponding to the battery cell. For a specific calculation method of the internal resistance characteristic value, see formula (5):

[0126]

[0127] Among them, DCRwi is the characteristic value of internal resistance, DCR i (t) is the original characteristic value of internal resistance, I(t) is the target current value, represents the second value, Represents the third value.

[0128] In one example, M sampling point sets are screened to obtain N sampling point sets, including:

[0129] Each set of sampling points is processed as follows:

[0130] If the current change value corresponding to the sampling point set is greater than the preset change value, and the interval between the sampling times of two sampling points in the sampling point set is less than or equal to the preset threshold, the sampling point set is taken as one of the N sampling point sets.

[0131] In this embodiment, the current change value corresponding to the sampling point set is compared with the preset change value, and the interval between the sampling times of two sampling points in the sampling point set is compared with the preset threshold value. According to the above two comparison results, it is determined whether to use the sampling point set as one of the N sampling point sets. Specifically, if the current change value corresponding to the sampling point set is greater than the preset change value, and the interval between the sampling times of two sampling points in the sampling point set is less than or equal to the preset threshold value, the sampling point set is used as one of the N sampling point sets.

[0132] Specifically, the screening condition in the above text is expressed as: T(t)-T(t-1)≤throltime, deltI(t)>throldcurrent, wherein throltime represents a preset threshold, throlcurrent represents a preset change value, and the current change value corresponding to the sampling point set is greater than the preset change value. When the parameter value of the sampling point set satisfies the above screening condition, it means that the time interval between the two sampling points is relatively small, and the current change of the two sampling points is relatively large, which belongs to abnormal data, namely, an abnormal sampling set. The sampling point set is taken as one of the N sampling point sets, namely, the target sampling point set.

[0133] In this embodiment, by adding a screening process, the abnormal sampling points can be located, so that the data corresponding to the abnormal sampling points are used to calculate the internal resistance characteristic values, which can fully reflect the abnormality or fault condition of the battery unit.

[0134] In one example, determining the Gini coefficient of a battery according to a maximum internal resistance characteristic value among internal resistance characteristic values ​​of a plurality of battery cells includes:

[0135] If the maximum internal resistance eigenvalue is less than the first eigenvalue, 0 is used as the Gini coefficient of the battery; the first eigenvalue is determined according to the quantile, and the quantile is determined according to the sorting result of the internal resistance eigenvalues ​​of multiple battery cells; if the maximum internal resistance eigenvalue is greater than or equal to the first eigenvalue, the difference between 1 and the first probability value is used as the Gini coefficient of the battery.

[0136] In this embodiment, the maximum internal resistance characteristic value among the internal resistance characteristic values ​​of the plurality of battery cells is compared with the first characteristic value, and the Gini coefficient is determined according to the comparison result. Specifically, if the maximum internal resistance characteristic value is less than the first characteristic value, 0 is taken as the Gini coefficient of the battery, which is expressed as: max(DCRw i )<X, Gini=0, where max(DCRw i ) is the maximum internal resistance eigenvalue, X is the first eigenvalue, and the Gini coefficient is 0.

[0137] If the maximum internal resistance characteristic value is greater than or equal to the first characteristic value, the difference between 1 and the first probability value is taken as the Gini coefficient of the battery, expressed as: max(DCRw i )≥X, Gini=1-Y, where max(DCRwi) is the maximum internal resistance eigenvalue, X is the first eigenvalue, and the Gini coefficient is 1 minus the difference between the first probability value Y.

[0138] Specifically, the first eigenvalue is determined in the following manner: sort the internal resistance eigenvalues ​​of multiple battery cells from small to large to obtain a sorting result, determine the 75th% value from the sorting result, that is, obtain the 75th quantile, determine the 25th% value from the sorting result, that is, obtain the 25th quantile, and further determine the first eigenvalue based on the two quantiles.

[0139] Furthermore, the first eigenvalue is expressed as: X = Q75 + 1.5 × (Q75 - Q25), then the above Gini coefficient is expressed as: max(DCRw i )<Q75+1.5×(Q75-Q25), Gini=0, where Q75 is the 75th percentile, Q25 is the 25th percentile, max(DCRw i ) is the maximum internal resistance characteristic value, and Gini is the Gini coefficient.

[0140] In one example, the first probability value is calculated as follows:

[0141] The internal resistance characteristic values ​​of multiple battery cells are binned to obtain the number of internal resistance characteristic values ​​that each box falls into; based on the number of internal resistance characteristic values ​​that each box falls into and the total number of multiple battery cells, the probability value corresponding to each box is obtained; and based on the probability value corresponding to each box, a first probability value is calculated.

[0142] Specifically, the first probability value is obtained by binning, and the binning is set as follows: bin = [min(DCRw i ), Buuu], [Buuu, max(DCRw i )],min(DCRw i ) is the minimum internal resistance characteristic value among the internal resistance characteristic values ​​of multiple battery cells, Buuu is the first boundary value, max(DCRw i ) is the maximum internal resistance characteristic value among the internal resistance characteristic values ​​of multiple battery cells.

[0143] Specifically, the first boundary value can also be calculated based on the above two quantiles. The first boundary value is expressed as: Buuu=Q75+4.5×(Q75-Q25), where Q75 is the 75th quantile and Q25 is the 25th quantile.

[0144] The internal resistance characteristic values ​​of multiple battery cells are divided into boxes using a box plot to obtain the number of internal resistance characteristic values ​​that each box falls into. According to the number of internal resistance characteristic values ​​that each box falls into and the total number of multiple battery cells, the probability value corresponding to each box is obtained, which is expressed by formula (6):

[0145]

[0146] Among them, n k is the number of internal resistance characteristic values ​​that the box falls into, and M is the total number of multiple battery cells.

[0147] Furthermore, the first probability value is calculated according to the probability value corresponding to each box, and is expressed by formula (7):

[0148]

[0149] Where Y is the first probability value, then the above Gini coefficient is expressed as: max(DCRw i )≥X,

[0150] In this embodiment, the Gini coefficient is determined by comparing the maximum internal resistance characteristic value with the first characteristic value, and the Gini coefficient is used as one of the abnormality evaluation indicators, so that the battery can be diagnosed accurately.

[0151] In the embodiment of the present application, the internal resistance characteristic value is calculated based on the characteristics of the battery data itself, and the characteristics of the fault data itself caused by abnormal battery internal resistance and abnormal connection are calculated using a box plot and a coefficient of variation, which can improve the accuracy of battery diagnosis. At the same time, the fault level can be further divided through the Gini coefficient so that the user can better understand the degree of battery failure.

[0152] Figure 3 Another flow chart of an embodiment of the vehicle battery fault diagnosis method provided by the present application is shown. The vehicle battery fault diagnosis method of the present application can be applied to electronic devices.

[0153] like Figure 3 As shown, the vehicle battery fault diagnosis method provided in the embodiment of the present application includes the following steps S301 to S306.

[0154] S301. Obtain battery information of a vehicle battery, where the battery includes a plurality of battery cells.

[0155] S302: Determine internal resistance characteristic values ​​corresponding to each of the plurality of battery cells according to the battery information, where the internal resistance characteristic values ​​are used to characterize the direct current internal resistance of the corresponding battery cell.

[0156] S303: Determine the coefficient of variation of the battery according to the internal resistance characteristic values ​​corresponding to each of the plurality of battery cells.

[0157] S304: Obtain a diagnosis result of the battery according to the coefficient of variation of the battery.

[0158] For details of steps S301 to S304, please refer to the records in steps S101 to S104, which will not be described in detail here.

[0159] S305. When the diagnosis result indicates that the battery is faulty, determine the fault interval, the lower boundary of the fault interval is a first boundary value, the upper boundary of the fault interval is the maximum value of the internal resistance characteristic values ​​corresponding to the multiple battery cells, and the first boundary value is determined according to the quantile of the binning processing.

[0160] In this embodiment, when the diagnosis result indicates that the battery has a fault, a fault interval is determined. The fault interval is composed of a first boundary value and a maximum value among the internal resistance characteristic values ​​corresponding to a plurality of battery cells. The first boundary value is the lower boundary of the fault interval, and the maximum value among the internal resistance characteristic values ​​is the upper boundary of the fault interval. The fault interval is expressed as: [Buuu, max(DCRw i )], Buuu is the first boundary value, max(DCRw i ) is the maximum value of the internal resistance characteristic value. Wherein, Buuu=Q75+4.5*(Q75-Q25), where Q75 is the 75th percentile and Q25 is the 25th percentile.

[0161] S306: Determine a battery cell whose corresponding internal resistance characteristic value among the multiple battery cells falls within the fault interval as a faulty battery cell.

[0162] In this embodiment, if the corresponding internal resistance characteristic value of multiple battery cells falls into the battery cell of the fault interval, it indicates that there is a faulty battery cell. The battery cell whose corresponding internal resistance characteristic value among multiple battery cells falls into the fault interval is judged as a faulty battery cell, and the number of the faulty battery cell is obtained. The cloud sends the number of the faulty battery cell to the corresponding terminal, such as a maintenance terminal or a user terminal, so that the battery can be replaced or repaired in time.

[0163] In this embodiment, based on the characteristics of the battery data itself, the box plot and the coefficient of variation are used to calculate the characteristics of the fault data itself caused by abnormal battery internal resistance and abnormal connection, which can improve the accuracy of battery diagnosis. At the same time, the Gini coefficient can be used to further divide the fault level so that users can better understand the degree of battery failure. In addition, the fault interval can accurately locate the faulty battery cell from the battery, so that the faulty battery cell can be directly replaced later.

[0164] The following is an example of the vehicle battery fault diagnosis method provided in the embodiment of the present application. Figure 4Another flow chart of an embodiment of the vehicle battery fault diagnosis method provided by the present application is shown as follows: Figure 4 As shown, the vehicle battery fault diagnosis method includes:

[0165] S401, driving condition data screening: screening single cell voltage measurement values ​​and current measurement values ​​under driving conditions.

[0166] In this embodiment, the execution subject is the cloud big data platform. The vehicle reports the original monitoring data of the battery pack to the cloud big data platform. The battery pack includes multiple single cells (that is, the battery in the above text includes multiple battery units). The cloud big data platform cleans the original monitoring data of the battery pack reported by the vehicle, including but not limited to the cleaning steps such as removing abnormal values ​​and converting the data format, to obtain data set A. Then, according to the vehicle status, speed, and current data, the data set A is screened to obtain the driving condition data set B. The driving condition data set B includes: the battery sub-information of multiple single cells corresponding to each of the multiple sampling points collected under the driving condition, and the battery sub-information includes the current measurement value and voltage measurement value of the corresponding single cell (that is, the battery sub-information in the above text includes the current value and voltage value of the corresponding battery unit).

[0167] It should be noted that the screening may also be for the charging condition data screening: the single cell voltage measurement values ​​and current measurement values ​​under the charging condition are screened out. In this embodiment, the driving condition is taken as an example for explanation.

[0168] S402, calculating the original characteristics of the single cell: calculating the characteristic value of the single cell at each sampling time.

[0169] In this embodiment, for each single cell, the current change value corresponding to each two adjacent sampling points is calculated according to the current measurement value of the single cell at each two adjacent sampling points (that is, for each battery cell in the above text, the current change value corresponding to each two adjacent sampling points is calculated according to the current value of the battery cell at each two adjacent sampling points), and the formula is: deltI(t)=I(t)-I(t-1), deltI(t) is the current change value, I(t) is the current measurement value of the sampling point at time t among the adjacent sampling points, and I(t-1) is the current measurement value of the sampling point at time t-1 among the adjacent sampling points.

[0170] Further, according to the voltage measurement value of the single cell at every two adjacent sampling points, the voltage change value corresponding to every two adjacent sampling points is calculated (that is, according to the voltage value of the battery cell at every two adjacent sampling points in the above text, the voltage change value corresponding to every two adjacent sampling points is calculated), the formula is: deltUi(t)=Ui(t)-Ui(t-1), wherein deltIUi(t) is the voltage change value, Ui(t) is the voltage measurement value of the battery cell i at the sampling point at time t among the adjacent sampling points, and Ui(t-1) is the voltage measurement value of the battery cell i at the sampling point at time t-1 among the adjacent sampling points, wherein i is the number of the single cell.

[0171] Further, for every two adjacent sampling points, the original eigenvalues ​​corresponding to the two adjacent sampling points are obtained according to the voltage change values ​​and current change values ​​corresponding to the two adjacent sampling points (that is, for every two adjacent sampling points, the original eigenvalues ​​of the internal resistance corresponding to the two adjacent sampling points are obtained according to the voltage change values ​​and current change values ​​corresponding to the two adjacent sampling points), which is expressed as: Among them, DCRi(t) is the original eigenvalue (that is, the original eigenvalue of the internal resistance mentioned above), deltIUi(t) is the voltage change value, and deltI(t) is the current change value.

[0172] S403, single cell feature screening: screening the feature values ​​that meet the conditions.

[0173] In this embodiment, the original characteristic values ​​of the single cells are screened according to the screening conditions. If the sampling time interval between every two adjacent sampling points is less than or equal to the preset threshold and the current change value is greater than the preset change value, then every two adjacent sampling points are target sampling points (that is, if the current change value corresponding to the sampling point set is greater than the preset change value, and the interval between the sampling times of two sampling points in the sampling point set is less than or equal to the preset threshold, then the sampling point set is taken as one of the N sampling point sets).

[0174] The above filtering conditions are expressed as: T(t)-T(t-1)≤throltime, deltI(t)>throldcurrent, where throltime represents a preset threshold, and throlcurrent represents a preset change value. When the above filtering conditions are met, it means that the time interval between each two adjacent sampling points is relatively small, and the current changes between the first and second adjacent sampling points are relatively large, which are abnormal data. The original eigenvalues ​​corresponding to the first and second adjacent sampling points are eigenvalues ​​that meet the filtering conditions.

[0175] S404, calculation of monomer cell aggregation characteristics: calculation of monomer cell characteristics.

[0176] In this embodiment, the aggregate characteristic calculation is performed for each single cell, and the sum of the original characteristic value, the target current value and the current value corresponding to the target sampling point that meets the above screening conditions is substituted into the following formula, where the target current value refers to the current value corresponding to the target sampling point at the previous sampling moment among two adjacent sampling points (that is, the target current value in the above text is the current value corresponding to the target sampling point at the previous sampling moment in the sampling point set), and the single cell characteristic (that is, the internal resistance characteristic value corresponding to the battery unit in the above text) is calculated, and the formula is expressed as follows:

[0177]

[0178] Among them, DCRw i is the characteristic value (i.e. the internal resistance characteristic value mentioned above), DCR i (t) is the original characteristic value (i.e. the original characteristic value of internal resistance mentioned above), I(t) is the target current value, Represents the sum of the current values ​​(ie, the third value above).

[0179] S405, battery pack evaluation index calculation: use the coefficient of variation to calculate the battery pack fault warning abnormal evaluation index.

[0180] In this embodiment, the standard deviation and mean are calculated according to the characteristic values ​​corresponding to each of the multiple single battery cells (that is, the standard deviation and mean are calculated according to the internal resistance characteristic values ​​corresponding to each of the multiple battery cells in the above text), and the standard deviation and mean are substituted into formula (1) to calculate the coefficient of variation of the battery.

[0181] S406, fault classification warning threshold calculation: The fault warning index classification evaluation threshold is calculated using the box plot and the Gini coefficient.

[0182] In this embodiment, a box plot is used to divide the characteristic values ​​of a single core into boxes, and the probability value P in each box is counted. k , and record the binning results of each monomer, where bins is the binning setting, as shown below:

[0183] bin=[min(DCRw i ), Buuu], [Buuu, max(DCRw i )];

[0184] Buuu = Q75 + 4.5 * (Q75 - Q25);

[0185]

[0186] Among them, n k is the number of cells that fall into the k box, M is the total data of cells, Q75 is the 75th percentile, Q25 is the 25th percentile, Pk The probability value for each box body.

[0187] Further, calculate the Gini coefficient Gini:

[0188] If max(DCRw i ) >= Q75 + 1.5 * (Q75 - Q25), then

[0189] If max(DCRw i ) < Q75 + 1.5 * (Q75 - Q25), then Gini = 0.

[0190] Further, according to the calculated battery pack warning anomaly evaluation index DCRcv and the Gini coefficient Gini, combined with different warning thresholds, conduct risk warnings, where:

[0191] Level 1 warning: throld dcr1 ≤ DCR cv <throld dcr2 , throld gini1 ≤ Gini < throld gini2 ;

[0192] Level 2 warning: throld dcr1 ≤ DCR cv <throld dcr2 , 0 < Gini < throld gini2 ;

[0193] Level 3 warning: throld dcr2 ≤ DCR cv , 0 < Gini < throld gini2 ;

[0194] Among them, throlddcr1 is the first warning coefficient of variation, DCRcv is the coefficient of variation of the battery, throlddcr2 is the second warning coefficient of variation, throldgini1 is the first warning Gini coefficient, Gini is the Gini coefficient of the battery, and throldgini2 is the second warning Gini coefficient.

[0195] In the above warning levels, the warning level of the second-level warning is greater than the warning level of the first-level warning and less than the warning level of the third-level warning (that is, the fault level of the second-level fault in the above text is greater than the fault level of the first-level fault and less than the fault level of the third-level fault).

[0196] S407. Fault location: Based on the battery pack evaluation index and the threshold, locate the faulty battery cell.

[0197] In this embodiment, according to DCRw iSelect the one that falls into [Buuu,max(DCRw i )] range (i.e. the fault interval mentioned above), and the single cell falling into the above range is regarded as the faulty cell (i.e. the battery cell whose corresponding internal resistance characteristic value among multiple battery cells falls into the fault interval is judged as the faulty battery cell).

[0198] In the embodiment of the present application, the advantage of the big data of the cloud monitoring platform is brought into play. According to the driving condition data of the vehicle on the cloud monitoring platform, cloud service calculation and analysis can be performed in a daily batch processing mode, without the need for additional measuring equipment, which is more in line with the big data security monitoring scenario. According to the daily driving condition data of the vehicle, no special working condition requirements are required, and the coverage rate of triggering calculations for all vehicles can be better achieved, and the risk of underreporting is low. There is no need to adopt the scheme of selecting constant current charging condition data, and there is no need to sample the scheme of calculating the DC internal resistance under the condition of step change of current. Instead, starting from the monitoring characteristics of the monitoring data itself, the box plot and the coefficient of variation are used to calculate the characteristics of the fault data itself caused by abnormal battery internal resistance and abnormal connection, and the Gini coefficient is used to further classify and classify the faults for early warning. Using driving condition data to avoid the calculation requirements of special working conditions can better achieve the coverage rate of triggering calculations for all vehicles, with good robustness. Fault identification of abnormal internal resistance and abnormal connection can be achieved at the same time, with strong adaptability.

[0199] The vehicle battery fault diagnosis method provided in the embodiment of the present application can be executed by a vehicle battery fault diagnosis device. In the embodiment of the present application, the vehicle battery fault diagnosis method is executed by a vehicle battery fault diagnosis device as an example to illustrate the vehicle battery fault diagnosis device provided in the embodiment of the present application.

[0200] Figure 5 The schematic diagram of the structure of the vehicle battery fault diagnosis device provided by the embodiment of the present application is shown. Figure 5 As shown, the vehicle battery fault diagnosis device 50 of the present application includes:

[0201] The acquisition module 501 is used to acquire battery information of a vehicle battery, where the battery includes a plurality of battery cells.

[0202] The first determination module 502 is used to determine the internal resistance characteristic value corresponding to each of the plurality of battery cells according to the battery information, where the internal resistance characteristic value is used to characterize the direct current internal resistance of the corresponding battery cell.

[0203] The second determination module 503 is further used to determine the battery's coefficient of variation according to the internal resistance characteristic values ​​corresponding to each of the plurality of battery cells.

[0204] The diagnosis module 504 is used to obtain a diagnosis result of the battery according to the coefficient of variation of the battery.

[0205] In one example, the first determination module 502 is further used to calculate, for each battery cell, a current change value corresponding to each two adjacent sampling points based on the current value of the battery cell at each two adjacent sampling points, and to calculate a voltage change value corresponding to each two adjacent sampling points based on the voltage value of the battery cell at each two adjacent sampling points; for each two adjacent sampling points, obtain the original characteristic values ​​of the internal resistance corresponding to each of the two adjacent sampling points based on the voltage change values ​​and current change values ​​corresponding to the two adjacent sampling points; and perform the following processing on each battery cell: calculate the internal resistance characteristic value corresponding to the battery cell based on the original characteristic values ​​of the internal resistance corresponding to each of the two adjacent sampling points.

[0206] In one example, the first determination module 502 is further used to take every two adjacent sampling points as a sampling point set to obtain M sampling point sets; screen the M sampling point sets to obtain N sampling point sets, where N is less than or equal to M and is a positive integer; multiply the original characteristic value of the internal resistance corresponding to the battery cell in the N sampling point sets by the target current value corresponding to each sampling point set to obtain the first value corresponding to each of the N sampling point sets, and add the first values ​​to obtain the second value; the N sampling point sets include multiple target sampling points; the target current value is the current value corresponding to the target sampling point in the sampling point set at the previous sampling moment; the sum of the current values ​​corresponding to the battery cell at multiple target sampling points is taken as the third value; the second value is divided by the third value to obtain the internal resistance characteristic value corresponding to the battery cell.

[0207] In one example, the first determination module 502 is further used to perform the following processing on each sampling point set: if the current change value corresponding to the sampling point set is greater than a preset change value, and the interval between the sampling times of two sampling points in the sampling point set is less than or equal to a preset threshold, then the sampling point set is used as one of the N sampling point sets.

[0208] In one example, the diagnosis module 504 is further used to determine the Gini coefficient of the battery according to the maximum internal resistance characteristic value among the internal resistance characteristic values ​​of multiple battery cells; and obtain the diagnosis result of the battery according to the coefficient of variation and the Gini coefficient of the battery.

[0209] In one example, the diagnostic module 504 is also used to use 0 as the Gini coefficient of the battery if the maximum internal resistance eigenvalue is less than the first eigenvalue; the first eigenvalue is determined based on the quantile, and the quantile is determined based on the sorting result of the internal resistance eigenvalues ​​of multiple battery cells; if the maximum internal resistance eigenvalue is greater than or equal to the first eigenvalue, the difference between 1 and the first probability value is used as the Gini coefficient of the battery.

[0210] In one example, the diagnosis module 504 includes a binning module and a determination submodule;

[0211] A binning module is used to bin the internal resistance characteristic values ​​of multiple battery cells to obtain the number of internal resistance characteristic values ​​that each bin falls into;

[0212] The determination submodule is used to obtain the probability value corresponding to each box according to the number of internal resistance characteristic values ​​falling into each box and the total number of multiple battery cells; and calculate the first probability value according to the probability value corresponding to each box.

[0213] In one example, the diagnostic module 504 is also used to: if the battery's coefficient of variation is greater than or equal to the first fault coefficient of variation and less than the second fault coefficient of variation, and the battery's Gini coefficient is less than the first fault Gini coefficient and greater than or equal to the second fault Gini coefficient, then the battery's diagnostic result is a battery failure, and the battery's failure level is a level one failure; if the battery's coefficient of variation is greater than or equal to the first fault coefficient of variation and less than the second fault coefficient of variation, and the battery's Gini coefficient is less than the second fault Gini coefficient and greater than the third fault Gini coefficient, then the battery's diagnostic result is a battery failure, and the battery's failure level is a level two failure; if the battery's coefficient of variation is greater than or equal to the second fault coefficient of variation, and the battery's Gini coefficient is less than the second fault Gini coefficient and greater than the third fault Gini coefficient, then the battery's diagnostic result is a battery failure, and the battery's failure level is a level three failure, and the level of the level two failure is greater than the level of the level one failure and less than the level three failure.

[0214] In one example, the diagnostic module 504 is also used to determine a fault interval when the diagnostic result indicates that the battery is faulty, the lower boundary of the fault interval is a first boundary value, the upper boundary of the fault interval is a maximum value among the internal resistance characteristic values ​​corresponding to the multiple battery cells, and the first boundary value is determined based on the quantile of the binning processing; the battery cells whose corresponding internal resistance characteristic values ​​among the multiple battery cells fall into the fault interval are determined to be faulty battery cells.

[0215] The vehicle battery fault diagnosis device provided in the embodiment of the present application can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar and will not be repeated here.

[0216] Figure 6 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0217] The electronic device may include a processor 601 and a memory 602 storing computer program instructions.

[0218] Specifically, the processor 601 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0219] The memory 602 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 602 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 602 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 602 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 602 is a non-volatile solid-state memory.

[0220] In some embodiments, the memory 602 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage media device, an optical storage media device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0221] The processor 601 implements any one of the vehicle battery fault diagnosis methods in the above embodiments by reading and executing computer program instructions stored in the memory 602 .

[0222] In one example, the electronic device may further include a communication interface 606 and a bus 610. Figure 6 As shown, the processor 601, the memory 602, and the communication interface 606 are connected via a bus 610 and communicate with each other.

[0223] The communication interface 606 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0224] Bus 610 includes hardware, software or both, and the parts of online data flow billing equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-end bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 610 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0225] The electronic device can execute the vehicle battery fault diagnosis method in the embodiment of the present application, thereby realizing the combination Figure 1 and Figure 5 A vehicle battery fault diagnosis method and apparatus are described.

[0226] In addition, in combination with the vehicle battery fault diagnosis method in the above embodiment, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, the vehicle battery fault diagnosis method in the above embodiment is implemented.

[0227] In combination with the vehicle battery fault diagnosis method in the above embodiment, an embodiment of the present application may provide a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device implements the vehicle battery fault diagnosis method in the above embodiment.

[0228] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0229] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0230] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0231] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0232] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A vehicle battery fault diagnosis method, characterized in that: include: Acquiring battery information of a vehicle battery, wherein the battery includes a plurality of battery cells; Determine, according to the battery information, internal resistance characteristic values ​​corresponding to each of the plurality of battery cells, wherein the internal resistance characteristic values ​​are used to characterize the DC internal resistance of the corresponding battery cell; Determining a coefficient of variation of the battery according to internal resistance characteristic values ​​corresponding to each of the plurality of battery cells; A diagnosis result of the battery is obtained according to the coefficient of variation of the battery.

2. The method according to claim 1, characterized in that The battery information includes battery sub-information of a plurality of battery cells corresponding to a plurality of sampling points respectively collected under a target operating condition, the battery sub-information includes current values ​​and voltage values ​​of the corresponding battery cells, and the target operating condition includes a driving operating condition or a charging operating condition; The step of determining the internal resistance characteristic values ​​corresponding to each of the plurality of battery cells according to the battery information includes: For each of the battery cells, according to the current value of the battery cell at each of the two adjacent sampling points, a current change value corresponding to each of the two adjacent sampling points is calculated, and according to the voltage value of the battery cell at each of the two adjacent sampling points, a voltage change value corresponding to each of the two adjacent sampling points is calculated; For each of the two adjacent sampling points, according to the voltage change value and the current change value corresponding to the two adjacent sampling points, obtain the original characteristic value of the internal resistance corresponding to each of the two adjacent sampling points; Each of the battery cells is processed as follows: The internal resistance characteristic value corresponding to the battery cell is calculated according to the original characteristic values ​​of the internal resistance corresponding to the two adjacent sampling points.

3. The method according to claim 2, characterized in that The step of calculating the internal resistance characteristic value corresponding to the battery cell according to the original characteristic values ​​of the internal resistance corresponding to the two adjacent sampling points includes: Take every two adjacent sampling points as a sampling point set, and obtain M sampling point sets; Screening the M sampling point sets to obtain N sampling point sets, where N is less than or equal to M and is a positive integer; Multiplying the original characteristic value of the internal resistance of the battery unit corresponding to the N sampling point sets by the target current value corresponding to each of the sampling point sets to obtain a first value corresponding to each of the N sampling point sets, and adding the first values ​​to obtain a second value; the N sampling point sets include a plurality of target sampling points; the target current value is a current value corresponding to the target sampling point in the sampling point set at a previous sampling time; Taking the sum of the current values ​​of the battery cell corresponding to the plurality of target sampling points as the third value; The second value is divided by the third value to obtain an internal resistance characteristic value corresponding to the battery cell.

4. The method according to claim 3, characterized in that The screening of the M sampling point sets to obtain N sampling point sets includes: Each sampling point set is processed as follows: If the current change value corresponding to the sampling point set is greater than a preset change value, and the interval between sampling times of two sampling points in the sampling point set is less than or equal to a preset threshold, the sampling point set is used as one of the N sampling point sets.

5. The method according to claim 1, characterized in that Obtaining a diagnosis result of the battery according to the coefficient of variation of the battery includes: Determining a Gini coefficient of the battery according to a maximum internal resistance characteristic value among the internal resistance characteristic values ​​of the plurality of battery cells; A diagnosis result of the battery is obtained according to the coefficient of variation of the battery and the Gini coefficient.

6. The method according to claim 5, characterized in that The determining the Gini coefficient of the battery according to the maximum internal resistance characteristic value among the internal resistance characteristic values ​​of the plurality of battery cells comprises: If the maximum internal resistance characteristic value is less than the first characteristic value, 0 is used as the Gini coefficient of the battery; the first characteristic value is determined according to the quantile, and the quantile is determined according to the sorting result of the internal resistance characteristic values ​​of the plurality of battery cells; If the maximum internal resistance characteristic value is greater than or equal to the first characteristic value, the difference between 1 and the first probability value is taken as the Gini coefficient of the battery.

7. The method according to claim 6, characterized in that The first probability value is calculated in the following way: Binning the internal resistance characteristic values ​​of the plurality of battery cells to obtain the number of internal resistance characteristic values ​​that each bin falls into; Obtaining a probability value corresponding to each box according to the number of internal resistance characteristic values ​​that each box falls into and the total number of the plurality of battery cells; The first probability value is calculated according to the probability value corresponding to each box.

8. The method according to claim 5, characterized in that The step of obtaining a diagnosis result of the battery according to the coefficient of variation of the battery and the Gini coefficient includes: If the coefficient of variation of the battery is greater than or equal to the first fault coefficient of variation and less than the second fault coefficient of variation, and the Gini coefficient of the battery is less than the first fault Gini coefficient and greater than or equal to the second fault Gini coefficient, then the diagnosis result of the battery is a battery fault, and the fault level of the battery is a primary fault; If the coefficient of variation of the battery is greater than or equal to the first fault coefficient of variation and less than the second fault coefficient of variation, and the Gini coefficient of the battery is less than the second fault Gini coefficient and greater than the third fault Gini coefficient, then the diagnosis result of the battery is a battery fault, and the fault level of the battery is a secondary fault; If the variation coefficient of the battery is greater than or equal to the second fault variation coefficient, and the Gini coefficient of the battery is less than the second fault Gini coefficient, and greater than the third fault Gini coefficient, then the diagnosis result of the battery is a battery fault, and the fault level of the battery is a level 3 fault; The fault level of the secondary fault is greater than the fault level of the primary fault and less than the fault level of the tertiary fault.

9. The method according to claim 1, characterized in that: After obtaining the diagnosis result of the battery according to the coefficient of variation of the battery, the method further includes: In the case where the diagnosis result indicates that the battery has a fault, determining a fault interval, wherein the lower boundary of the fault interval is a first boundary value, and the upper boundary of the fault interval is a maximum value among the internal resistance characteristic values ​​corresponding to the plurality of battery cells, wherein the first boundary value is determined according to the quantile of the binning process; A battery cell whose corresponding internal resistance characteristic value among the plurality of battery cells falls within the fault interval is determined as a battery cell with a fault.

10. A vehicle battery fault diagnosis device, characterized in that: The device comprises: An acquisition module, used to acquire battery information of a vehicle battery, wherein the battery includes a plurality of battery cells; A first determination module, configured to determine, based on the battery information, internal resistance characteristic values ​​corresponding to each of the plurality of battery cells, wherein the internal resistance characteristic values ​​are used to characterize a DC internal resistance of the corresponding battery cell; A second determination module, configured to determine a coefficient of variation of the battery according to internal resistance characteristic values ​​corresponding to each of the plurality of battery cells; The diagnosis module is used to obtain a diagnosis result of the battery according to the coefficient of variation of the battery.

11. An electronic device, characterized in that: The electronic device comprises: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the vehicle battery fault diagnosis method as described in any one of claims 1 to 9 is implemented.

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