New energy battery pack diagnostic method, device, electronic equipment and storage medium

Abstract

The present invention discloses a diagnostic method, device, electronic device and storage medium for a new energy battery pack. The method includes: obtaining the data to be diagnosed of the target new energy battery pack, obtaining reference data to be diagnosed, and then dividing the reference data to be diagnosed into n target data to be diagnosed, determining the target abnormality degree value corresponding to each diagnosis type in n diagnosis types based on the n target data to be diagnosed, obtaining n target abnormality degree values, then determining the priority corresponding to each diagnosis type in n diagnosis types based on the n target abnormality degree values, obtaining n priorities, and finally diagnosing the target new energy battery pack based on the n priorities and the n target data to be diagnosed, and obtaining a target diagnosis result. The implementation of the present invention improves the accuracy of remote diagnosis of new energy battery packs.

Description

Technical Field

[0001] The present invention relates to the field of vehicle diagnosis technology, and in particular to a diagnosis method, device, electronic equipment and storage medium for a new energy battery pack. Background Art

[0002] New energy battery packs are the core energy storage devices of new energy vehicles. They directly determine the vehicle's range and power performance, and provide stable and reliable electricity for the vehicle's operation. The performance of the battery pack affects key performance indicators such as the vehicle's acceleration, climbing ability, and maximum speed. Remote diagnosis of new energy battery packs can monitor the status of the battery pack in real time, detect potential problems and hidden faults in advance, and take preventive measures to avoid sudden failures that lead to serious situations such as vehicle breakdowns. However, the accuracy of the diagnostic results of new energy battery packs is not high at present. Summary of the invention

[0003] Embodiments of the present invention provide a diagnostic method, device, electronic device and storage medium for a new energy battery pack, which improve the accuracy of remote diagnosis of the new energy battery pack.

[0004] In a first aspect, an embodiment of the present invention provides a method for diagnosing a new energy battery pack, the method comprising:

[0005] Acquire the data to be diagnosed of the target new energy battery pack and obtain reference data to be diagnosed;

[0006] The reference data to be diagnosed is divided into n target data to be diagnosed; each target data to be diagnosed corresponds to a diagnosis type; n is a positive integer;

[0007] Determine the target abnormality degree value corresponding to each diagnosis type in the n diagnosis types based on the n target to-be-diagnosed data, and obtain n target abnormality degree values;

[0008] Determine the priority corresponding to each of the n diagnosis types based on the n target abnormality values, and obtain n priorities; the target abnormality value is positively correlated with the priority;

[0009] The target new energy battery pack is diagnosed based on n priorities and n target data to be diagnosed, and a target diagnosis result is obtained.

[0010] In a second aspect, an embodiment of the present invention provides a diagnostic device for a new energy battery pack, the device comprising: an acquisition unit and a processing unit;

[0011] An acquisition unit, used to acquire the to-be-diagnosed data of the target new energy battery pack, and obtain reference to-be-diagnosed data;

[0012] A processing unit, used for dividing the reference data to be diagnosed into n target data to be diagnosed; each target data to be diagnosed corresponds to a diagnosis type; n is a positive integer;

[0013] Determine the target abnormality degree value corresponding to each diagnosis type in the n diagnosis types based on the n target to-be-diagnosed data, and obtain n target abnormality degree values;

[0014] Determine the priority corresponding to each of the n diagnosis types based on the n target abnormality values, and obtain n priorities; the target abnormality value is positively correlated with the priority;

[0015] The target new energy battery pack is diagnosed based on n priorities and n target data to be diagnosed, and a target diagnosis result is obtained.

[0016] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor so that the electronic device executes the method of the first aspect.

[0017] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method of the first aspect.

[0018] In a fifth aspect, an embodiment of the present invention provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, so that a computer executes the method of the first aspect.

[0019] The implementation of the present invention has the following beneficial effects:

[0020] It can be seen that the diagnostic method for the new energy battery pack described in the embodiment of the present invention first obtains the data to be diagnosed of the target new energy battery pack to obtain reference data to be diagnosed, and then divides the reference data to be diagnosed into n target data to be diagnosed, wherein each target data to be diagnosed corresponds to a diagnosis type, and the target abnormality degree value corresponding to each of the n diagnosis types is determined based on the n target data to be diagnosed to obtain n target abnormality degree values, and then the priority corresponding to each of the n diagnosis types is determined based on the n target abnormality degree values ​​to obtain n priorities, and finally the target new energy battery pack is diagnosed based on the n priorities and the n target data to be diagnosed to obtain a target diagnosis result, thereby improving the accuracy of remote diagnosis of the new energy battery pack. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0022] Figure 1 is a schematic diagram of a new energy vehicle diagnostic system provided by an embodiment of the present invention;

[0023] Figure 2 is a flow chart of a new energy battery pack diagnosis method provided by an embodiment of the present invention;

[0024] Figure 3 It is a structural schematic diagram of a diagnostic device for a new energy battery pack provided in an embodiment of the present invention;

[0025] Figure 4 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the implementation of the present invention will be clearly and completely described below in conjunction with the drawings in the implementation of the present invention. Obviously, the described implementation is only a part of the implementation of the present application, not all of the implementations. Based on the implementation in the present application, all other implementations obtained by ordinary technicians in the field without creative work are within the scope of protection of this application.

[0027] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0028] Reference to "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0029] See also Figure 1 , Figure 11 is a schematic diagram of a new energy vehicle diagnostic system provided by an embodiment of the present invention. The new energy vehicle diagnostic system 10 includes a target new energy vehicle 101 and a vehicle diagnostic device 102 .

[0030] In this embodiment, the vehicle diagnostic device 102 obtains the target new energy battery pack to be diagnosed in the target new energy vehicle 101, obtains the reference data to be diagnosed, and then divides the reference data to be diagnosed into n target data to be diagnosed, wherein each target data to be diagnosed corresponds to a diagnosis type. Then the vehicle diagnostic device 102 determines the target abnormality level value corresponding to each diagnosis type in the n diagnosis types based on the n target data to be diagnosed, obtains n target abnormality level values, and then determines the priority level corresponding to each diagnosis type in the n diagnosis types based on the n target abnormality level values, obtains n priorities, wherein the target abnormality level value is positively correlated with the priority level, and finally diagnoses the target new energy battery pack based on the n priorities and the n target data to be diagnosed, obtains the target diagnosis result, and improves the accuracy of remote diagnosis of the new energy battery pack.

[0031] See also Figure 2 , Figure 2 : is a flow chart of a new energy battery pack diagnostic method provided by an embodiment of the present invention, including but not limited to the following steps:

[0032] S201: Acquire the data to be diagnosed of the target new energy battery pack to obtain reference data to be diagnosed.

[0033] In this embodiment, during the operation of the target new energy battery pack, its internal states and performances can be reflected by multiple parameters. These parameters are used to determine whether the target new energy battery pack is working normally and whether there are potential problems. The reference data to be diagnosed is usually a more complex data set containing a variety of different types of information. Specifically, the reference data to be diagnosed includes the collected various parameter data that can reflect the health status, working performance and other aspects of the battery pack. For example, it may include the voltage value of each battery cell (collected by a voltage sensor), the charge and discharge current value (obtained by a current sensor), the temperature data of the battery cell and the battery pack as a whole (monitored by a temperature sensor), the battery's state of charge, health status, etc. Integrating these acquired data together constitutes the reference data to be diagnosed.

[0034] S202: Divide the reference data to be diagnosed into n target data to be diagnosed.

[0035] In this embodiment, each target data to be diagnosed corresponds to a diagnostic type, and n is a positive integer. The reference data to be diagnosed is a relatively complex data set containing multiple different types of information. In order to facilitate more targeted and efficient diagnosis and analysis of the battery pack in the future, it is classified and divided according to the diagnostic type. Each type of data divided is defined as a target data to be diagnosed, and a total of n such categories are divided, where n is a positive integer, indicating the number of divided target data to be diagnosed. Specifically, the diagnostic type can include current diagnostic type, voltage diagnostic type, temperature diagnostic type, charging power diagnostic type, charge state diagnostic type, and health state diagnostic type. The current diagnosis type can include charging current data and discharging current data. By screening out all data related to the current in the battery charging process from the reference data to be diagnosed, the charging current data can be obtained. This data can be obtained through the current sensor on the charging line, reflecting the current size and changes of the battery during charging. The current data generated by the battery during the discharge process can be found from the reference data to be diagnosed. It is generally collected by the current sensor in the discharge line, including the discharge current information of the battery when the vehicle starts, accelerates, drives at a constant speed, climbs, and other electrical equipment is working; the voltage diagnosis type can include single cell voltage data and battery pack total voltage data. The single cell voltage data can reflect the working status of each single cell, and the battery pack total voltage data is used to evaluate the battery Whether the overall voltage output of the battery pack is normal. The change in total voltage can reflect the overall performance and working status of the battery pack; the temperature diagnosis type can include single cell temperature data and overall temperature distribution data of the battery pack. The single cell temperature data can find out whether the temperature of the single cell is too high or too low. The overall temperature distribution data of the battery pack is used to determine whether there is local overheating or uneven temperature inside the battery pack; the charging power diagnosis type is used to check whether the charging power is within the normal range. Abnormal charging power may indicate a failure of the charging equipment, an internal battery failure, or a problem with the charging line; the charge state diagnosis type is used to determine whether the charge state is consistent with the actual charging and discharging conditions of the battery pack; the health status diagnosis type is used to evaluate the health of the battery and determine whether the battery is aging or its performance is declining.

[0036] S203: Determine a target abnormality level value corresponding to each diagnosis type in n diagnosis types based on n target data to be diagnosed, and obtain n target abnormality level values.

[0037] In this embodiment, exemplarily, taking the first diagnostic type as an example, the target abnormality value corresponding to the first diagnostic type is determined. First, the first target data to be diagnosed corresponding to the first diagnostic type is determined, wherein the first diagnostic type is any one of the n diagnostic types, and the first target data to be diagnosed is the target data to be diagnosed corresponding to the first diagnostic type. Exemplarily, m values ​​in the first target data to be diagnosed are determined. Specifically, after the first target data to be diagnosed is determined, the data set contains multiple data points, and the m values ​​are the specific values ​​included in the first target data to be diagnosed. Exemplarily, the maximum threshold and the minimum threshold corresponding to the first diagnostic type are determined. Specifically, each diagnostic type has its corresponding normal data range, which is determined by the design specifications, performance requirements and a large amount of experimental and empirical data of the battery. The maximum threshold and the minimum threshold are used to define the boundary values ​​of this normal range. For the first diagnostic type, the upper and lower limits of this normal range, i.e., the maximum threshold and the minimum threshold, need to be clarified. Data beyond this range may be considered as abnormal data. Exemplarily, the abnormality value corresponding to each value in the m values ​​is determined based on the maximum threshold and the minimum threshold, and m abnormality values ​​are obtained. Exemplarily, first determine the difference between the maximum threshold and the minimum threshold to obtain the threshold difference. Specifically, calculate the distance between the maximum threshold and the minimum threshold that define the normal range. This distance is called the threshold difference, which represents the width of the normal range and is an important reference for the subsequent calculation of the abnormality level. Exemplarily, if the first value is greater than the maximum threshold, determine the first difference between the first value and the maximum threshold, and determine the abnormality level corresponding to the first value based on the threshold difference and the first difference, wherein the first value is any one of the m values. Specifically, if the first value exceeds the upper limit of the normal range (i.e., greater than the maximum threshold), it is necessary to calculate the distance between the first value and the maximum threshold, which is the first difference. Then, compare the first difference with the previously calculated threshold difference to determine the abnormality level of the first value relative to the normal range. Exemplarily, if the first value is less than or equal to the maximum threshold and greater than or equal to the minimum threshold, the abnormality level value corresponding to the first value is determined to be 0. Specifically, if the first value is within the normal range, that is, it is less than or equal to the maximum threshold and greater than or equal to the minimum threshold, then it means that the value is normal and does not deviate from the normal range, so the corresponding abnormality level value is set to 0.Exemplarily, if the first value is less than the minimum threshold, the second difference between the first value and the minimum threshold is determined, and the abnormality level value corresponding to the first value is determined based on the threshold difference and the second difference. Specifically, when the first value is lower than the lower limit of the normal range (i.e., less than the minimum threshold), it is necessary to calculate the distance between the first value and the minimum threshold to obtain the second difference. Similar to the case where the first value is greater than the maximum threshold, the second difference is compared with the threshold difference to determine the abnormality level value corresponding to the first value. For example, the normal discharge current range may be 100A-200A. If the discharge current is detected to be 220A, the abnormality level value is (220-200) / 200=0.1. If the discharge current is too low, such as the discharge current is detected to be 80A, the abnormality level value is (100-80) / 100=0.2. If the discharge current is detected to be 150A, the abnormality level value is determined to be 0.

[0038] It can be seen that by calculating the threshold difference and determining the abnormality value based on the relationship between the data and the threshold (greater than the maximum threshold, within the threshold range, and less than the minimum threshold), it is possible to finely distinguish different degrees of abnormalities. It is not just a simple judgment of whether the data exceeds the normal range, but also the degree of abnormality can be quantified according to the magnitude of the excess. The calculated abnormality value can be used as an important basis for determining the diagnostic priority. The higher the abnormality value, the more serious the problem usually means, and the higher the priority of the corresponding diagnostic type.

[0039] Exemplarily, the target abnormality level value corresponding to the first diagnosis type is determined based on m abnormality level values. Specifically, the weight corresponding to each abnormality level value in the m abnormality level values ​​is first determined to obtain m weights, wherein the sum of the m weights is 1. After the abnormality level values ​​have been obtained, considering that the importance of the numerical value corresponding to each abnormality level value in the entire first diagnosis type may be different, it is necessary to assign a weight to each abnormality level value. These weights are used to measure the relative importance of the abnormality level value of each data point to the overall abnormality level assessment. In order to ensure the rationality and normalization of weight allocation, the sum of all weights is required to be 1. Exemplarily, the reference abnormality level value is determined based on m weights and m abnormality level values. Specifically, after the weight corresponding to each abnormality level value is determined, all abnormality level values ​​are comprehensively considered by weighted calculation to obtain a reference abnormality level value that can represent the overall situation. The reference abnormality level value is calculated based on each abnormality level value and its weight. It integrates the abnormal information of all data points and more comprehensively reflects the overall abnormality level of the first target to-be-diagnosed data corresponding to the first diagnosis type. Exemplarily, when the internal resistance is small, it means that the conductivity inside the battery is relatively good. For the same abnormal conditions of parameters such as voltage and current, the impact on the overall performance and safety of the battery may be relatively small. At this time, the target adjustment parameter may reduce the abnormality value. On the contrary, when the internal resistance is large, it indicates that there may be some problems inside the battery (such as aging of electrode materials, drying of electrolyte, etc.), which reduces the performance of the battery and increases the possibility and severity of abnormalities. The target adjustment parameter will increase the abnormality value. Therefore, the internal resistance corresponding to the target new energy battery pack is obtained, and then the target adjustment parameter corresponding to the internal resistance is determined. Specifically, it can be a mapping relationship between the preset internal resistance and the adjustment parameter. Based on the mapping relationship, the target adjustment parameter corresponding to the internal resistance can be determined. Exemplarily, the reference abnormality value is adjusted according to the target adjustment parameter to obtain the target abnormality value corresponding to the first diagnostic type. Specifically, the specific calculation formula is as follows: target abnormality value = reference abnormality value × (1 + target adjustment parameter); the target abnormality value can be obtained according to the above formula.

[0040] It can be seen that by assigning weights to the m abnormality values ​​to determine the reference abnormality value, the relative importance of each data point in the overall diagnosis can be fully considered, and the abnormality value can be dynamically adjusted according to the internal resistance, so that the diagnosis result can better adapt to the actual state changes of the battery. During the use of the battery, the internal resistance will change with factors such as battery aging and working environment. By presetting the mapping relationship between the internal resistance and the adjustment parameters, the target abnormality value can be updated in time according to the real-time change of the internal resistance. This dynamic adjustment mechanism can more keenly capture the changes in the battery state, provide a basis that is more in line with the actual situation for diagnosis, and help to timely discover and deal with potential faults.

[0041] It should be noted that it is also necessary to determine the maximum abnormality value among the m abnormality values. If the maximum abnormality value is greater than the first abnormality threshold, a first alarm message is generated based on the maximum abnormality value and the first diagnostic type, and the target user is prompted based on the first alarm message that a fault related to the first diagnostic type has occurred. Specifically, when the determined maximum abnormality value exceeds the first abnormality threshold, it means that the current abnormal situation is already serious, and the first alarm message is generated based on the maximum abnormality value and the specific information of the first diagnostic type to which it belongs. The content of the alarm message will include the severity of the abnormality (reflected by the maximum abnormality value) and the specific diagnostic type involved (such as battery voltage abnormality, etc.), so that the target user who receives the alarm message can quickly understand which aspect has a serious problem. After the first alarm message is generated, this information needs to be conveyed to the target user, and the first alarm message is displayed to the target user in a specific manner (such as displaying an alarm prompt on the vehicle dashboard, sending an alarm text message or push notification to the target user's mobile phone, etc.), so that they know that a fault related to the first diagnostic type has occurred, so that they can take corresponding measures.

[0042] It can be seen that among the numerous diagnostic data of the target new energy battery pack, there may be multiple parameters that slightly deviate from the normal range, but the parameter corresponding to the maximum abnormality value is often the most likely to cause serious consequences. When the maximum abnormality value is greater than the first abnormality threshold, the first alarm information is generated in time to ensure that the alarm process can be quickly initiated when the most serious abnormal situation occurs.

[0043] It should be noted that if there is an abnormality value greater than the second abnormality value threshold among the n target abnormality values, then k target abnormality values ​​greater than the second abnormality value threshold among the n target abnormality values ​​are determined, where k is an integer less than n, and the second abnormality value threshold is less than the first abnormality value threshold, and then k diagnostic types corresponding to the k target abnormality values ​​are determined. Specifically, since each target abnormality value is obtained corresponding to a specific diagnostic type, after selecting k target abnormality values ​​greater than the second abnormality value threshold, it is necessary to clarify which diagnostic types these k values ​​correspond to. Exemplarily, a second alarm message is generated based on k target abnormality level values ​​and k diagnostic types, and based on the second alarm message, the target user is prompted that a fault related to the k diagnostic types has occurred. Specifically, after determining the k target abnormality level values ​​that exceed the second abnormality level value threshold and their corresponding k diagnostic types, a special alarm message is generated based on this information, i.e., the second alarm message. The content of the second alarm message includes key information about these abnormal conditions, such as which specific diagnostic types have abnormalities (reflected by the k diagnostic types) and the approximate severity of these abnormalities (reflected by the k target abnormality level values), so that the target user can quickly understand in which aspects of the battery pack there are more serious abnormalities.

[0044] It can be seen that determining the diagnosis types corresponding to k target abnormality degree values ​​and generating a second alarm message can accurately inform the user of the specific aspects of the abnormality. The target user can focus on the relevant diagnosis types according to the alarm information. The second alarm message prompts the user to have faults related to the k diagnosis types, which can detect potential fault hazards in advance before the problem deteriorates to the most serious level.

[0045] S204: Determine the priority corresponding to each of the n diagnosis types based on the n target abnormality degree values, and obtain n priorities.

[0046] In this embodiment, the target abnormality level value is positively correlated with the priority. After obtaining the target abnormality level values ​​corresponding to each of the n diagnostic types, a priority order is determined for each diagnostic type based on the size of these values ​​and the degree of impact of the problems they reflect on the performance and safety of the battery pack. The larger the target abnormality level value, the more serious the problem in the data involved in the diagnostic type, and the greater the impact on the battery pack, then the higher the priority corresponding to it. In this way, for the n diagnostic types, their respective priorities are determined, and finally n priorities are obtained. In this way, in the subsequent diagnostic process, those more important problems that are more likely to cause serious consequences can be dealt with first according to the priority level, thereby improving the diagnostic efficiency and ensuring the safety of the target new energy battery pack.

[0047] It can be seen that determining the priority according to the target abnormality level value can ensure that the diagnosis work is carried out in order according to the severity and urgency of the problem. The target abnormality level value is positively correlated with the priority, so that the diagnosis process can first focus on the diagnosis type with a higher degree of abnormality. For new energy battery packs, safety is of paramount importance. High-priority diagnosis types often involve serious problems that may cause safety accidents, such as battery thermal runaway and short circuit. By giving priority to these problems, timely measures can be taken to eliminate safety hazards, and serious accidents such as battery explosion due to overcharging can be prevented, thereby enhancing the safety of the battery pack system.

[0048] S205: Diagnose the target new energy battery pack based on the n priorities and the n target data to be diagnosed, and obtain a target diagnosis result.

[0049] In this embodiment, according to the priority order of the n determined diagnostic types and the target data to be diagnosed corresponding to each priority, starting from the diagnostic type with the highest priority and its corresponding target data to be diagnosed, the target new energy battery pack is analyzed and diagnosed in depth. During the diagnosis process, methods such as rule-based diagnostic methods, data-driven diagnostic methods, and detection using professional diagnostic equipment are used to conduct detailed investigations on the specific problems reflected in each target data to be diagnosed, find out the impact of these problems on the performance of the battery pack, and finally summarize and integrate all the diagnostic results to form a target diagnostic result for the entire target new energy battery pack. This target diagnostic result can comprehensively reflect information such as the health status of the target new energy battery pack, the types of possible problems, and the severity of the problems.

[0050] It can be seen that diagnosing the target new energy battery pack in order of priority can ensure that the diagnostic process proceeds in an orderly manner. During the diagnostic process, starting from the high-priority target data to be diagnosed, in-depth analysis of key issues can be carried out, and those issues that may cause safety accidents can be dealt with first.

[0051] It should be noted that in this embodiment, the target abnormality value corresponding to each diagnosis type in the n diagnosis types can also be determined based on the n target data to be diagnosed according to the following method to obtain n target abnormality values. For example, the fault frequency of the second diagnosis type in multiple historical time periods is obtained to obtain multiple fault frequencies, wherein the second diagnosis type is any one of the n diagnosis types, and then fitting is performed based on the multiple fault frequencies to obtain a fault frequency straight line. Specifically, after obtaining multiple fault frequencies, the multiple fault frequencies are fitted using a fitting method. After the fitting is completed, a fault frequency straight line can be obtained. The fault frequency straight line obtained by the fitting method can simply and intuitively highlight the change trend of the fault frequency. For example, the slope corresponding to the fault frequency straight line is determined to obtain the target slope. Specifically, any two points in the fault frequency straight line are selected to calculate the slope corresponding to the fault frequency straight line to obtain the target slope. For example, the reference abnormality value corresponding to the second diagnosis type is determined based on the target slope. Specifically, it can be a mapping relationship between a preset slope and an abnormality value. Based on the mapping relationship, the reference abnormality value corresponding to the target slope can be determined. Exemplarily, the second target data to be diagnosed corresponding to the second diagnostic type among the n target data to be diagnosed is determined, and the target optimization factor corresponding to the second target data to be diagnosed is determined. Specifically, since the reference data to be diagnosed has been divided into n target data to be diagnosed, and each target data to be diagnosed corresponds to a diagnostic type, the second target data to be diagnosed corresponding to the second diagnostic type can be determined, and then the average value of the values ​​in the second target data to be diagnosed is determined, and then the target optimization factor corresponding to the average value is determined, which can be a mapping relationship between a preset value and an optimization factor, and the target optimization factor corresponding to the second target data to be diagnosed can be determined based on the mapping relationship. Exemplarily, the reference abnormality degree value is optimized according to the target optimization factor to obtain the target abnormality degree value corresponding to the second diagnostic type. The specific calculation formula is as follows: target abnormality degree value = reference abnormality degree value × (1 + target optimization factor); the target abnormality degree value can be obtained according to the above formula. According to the determination method of the target abnormality degree value corresponding to the second diagnostic type, the target abnormality degree value corresponding to each diagnostic type in the n diagnostic types can be determined to obtain n target abnormality degree values.

[0052] It can be seen that by fitting multiple fault frequencies corresponding to each diagnostic type in different diagnostic types to obtain a fault frequency straight line and determining the target slope, the complex historical fault frequency changes corresponding to each diagnostic type can be simplified into an intuitive numerical feature. The reference abnormality degree value is optimized based on the target optimization factor, which can fully consider the impact of actual factors on the diagnosis and can more comprehensively and objectively reflect the abnormality degrees of different diagnostic types.

[0053] In summary, the implementation of the present invention has the following beneficial effects:

[0054] It can be seen that the diagnostic method for the new energy battery pack described in the embodiment of the present invention first obtains the data to be diagnosed of the target new energy battery pack to obtain reference data to be diagnosed, and then divides the reference data to be diagnosed into n target data to be diagnosed, wherein each target data to be diagnosed corresponds to a diagnosis type, and the target abnormality degree value corresponding to each of the n diagnosis types is determined based on the n target data to be diagnosed to obtain n target abnormality degree values, and then the priority corresponding to each of the n diagnosis types is determined based on the n target abnormality degree values ​​to obtain n priorities, and finally the target new energy battery pack is diagnosed based on the n priorities and the n target data to be diagnosed to obtain a target diagnosis result, thereby improving the accuracy of remote diagnosis of the new energy battery pack.

[0055] See also Figure 3 , Figure 3 3 is a schematic structural diagram of a diagnostic device for a new energy battery pack provided in an embodiment of the present invention. The diagnostic device 300 for a new energy battery pack includes: an acquisition unit 301 and a processing unit 302;

[0056] The acquisition unit 301 is used to acquire the to-be-diagnosed data of the target new energy battery pack to obtain the reference to-be-diagnosed data;

[0057] The processing unit 302 is used to divide the reference data to be diagnosed into n target data to be diagnosed; each target data to be diagnosed corresponds to a diagnosis type; n is a positive integer;

[0058] Determine the target abnormality degree value corresponding to each diagnosis type in the n diagnosis types based on the n target to-be-diagnosed data, and obtain n target abnormality degree values;

[0059] Determine the priority corresponding to each of the n diagnosis types based on the n target abnormality values, and obtain n priorities; the target abnormality value is positively correlated with the priority;

[0060] The target new energy battery pack is diagnosed based on n priorities and n target data to be diagnosed, and a target diagnosis result is obtained.

[0061] In some possible implementations, in determining the target abnormality level value corresponding to each diagnosis type in the n diagnosis types based on the n target to-be-diagnosed data, and obtaining the n target abnormality level values, the processing unit 302 is specifically configured to:

[0062] Determine first target data to be diagnosed corresponding to a first diagnosis type; the first diagnosis type is any one of the n diagnosis types, and the first target data to be diagnosed is the target data to be diagnosed corresponding to the first diagnosis type;

[0063] Determine m values ​​in the first target to-be-diagnosed data;

[0064] Determine a maximum threshold and a minimum threshold corresponding to the first diagnosis type;

[0065] Determine the abnormality degree value corresponding to each value in the m values ​​based on the maximum threshold and the minimum threshold, and obtain m abnormality degree values;

[0066] A target abnormality level value corresponding to the first diagnosis type is determined based on the m abnormality level values.

[0067] In some possible implementations, in determining the abnormality level value corresponding to each of the m values ​​based on the maximum threshold and the minimum threshold to obtain the m abnormality level values, the processing unit 302 is specifically configured to:

[0068] Determine the difference between the maximum threshold and the minimum threshold to obtain a threshold difference;

[0069] If the first value is greater than the maximum threshold, a first difference between the first value and the maximum threshold is determined, and an abnormality degree value corresponding to the first value is determined based on the threshold difference and the first difference; the first value is any one of the m values;

[0070] If the first value is less than or equal to the maximum threshold and greater than or equal to the minimum threshold, then the abnormality level value corresponding to the first value is determined to be 0;

[0071] If the first value is less than the minimum threshold, a second difference between the first value and the minimum threshold is determined, and the abnormality level value corresponding to the first value is determined based on the threshold difference and the second difference.

[0072] In some possible implementations, in determining the target abnormality level value corresponding to the first diagnosis type based on the m abnormality level values, the processing unit 302 is specifically configured to:

[0073] Determine the weight corresponding to each of the m abnormality degree values ​​to obtain m weights; the sum of the m weights is 1;

[0074] Determine a reference abnormality degree value based on the m weights and the m abnormality degree values;

[0075] Get the internal resistance of the target new energy battery pack;

[0076] Determine a target adjustment parameter corresponding to the internal resistance;

[0077] The reference abnormality level value is adjusted according to the target adjustment parameter to obtain a target abnormality level value corresponding to the first diagnosis type.

[0078] In some possible implementations, the processing unit 302 is further specifically configured to:

[0079] Determine the maximum abnormality degree value among the m abnormality degree values;

[0080] If the maximum abnormality level value is greater than a first abnormality level threshold, generating a first alarm message based on the maximum abnormality level value and the first diagnosis type;

[0081] Based on the first alarm information, a target user is prompted that a fault related to the first diagnosis type occurs.

[0082] In some possible implementations, the processing unit 302 is further specifically configured to:

[0083] If there is an abnormality value greater than the second abnormality value threshold value among the n target abnormality values, k target abnormality values ​​greater than the second abnormality value threshold value among the n target abnormality values ​​are determined; k is an integer less than n; the second abnormality value threshold value is less than the first abnormality value threshold value;

[0084] Determine k diagnosis types corresponding to k target abnormality values;

[0085] generating second alarm information based on k target abnormality degree values ​​and k diagnosis types;

[0086] Based on the second alarm information, the target user is prompted that faults related to k diagnosis types occur.

[0087] In some possible implementations, in determining the target abnormality level value corresponding to each diagnosis type in the n diagnosis types based on the n target to-be-diagnosed data, and obtaining the n target abnormality level values, the processing unit 302 is specifically configured to:

[0088] Obtaining the fault frequency of the second diagnosis type in multiple historical time periods to obtain multiple fault frequencies; the second diagnosis type is any one of the n diagnosis types;

[0089] Fitting is performed based on multiple fault frequencies to obtain a fault frequency straight line;

[0090] Determine the slope corresponding to the fault frequency straight line and obtain the target slope;

[0091] Determining a reference abnormality degree value corresponding to the second diagnosis type based on the target slope;

[0092] Determining second target data to be diagnosed corresponding to a second diagnosis type among the n target data to be diagnosed;

[0093] Determining a target optimization factor corresponding to the second target data to be diagnosed;

[0094] The reference abnormality level value is optimized according to the target optimization factor to obtain a target abnormality level value corresponding to the second diagnosis type.

[0095] See also Figure 4 , Figure 4 Schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, the electronic device 400 includes a transceiver 401, a processor 402 and a memory 403. They are connected via a bus 404. The memory 403 is used to store computer programs and data, and the transceiver 401 can transmit the data stored in the memory 403 to the processor 402. The above program includes instructions for executing the following steps:

[0096] Acquire the data to be diagnosed of the target new energy battery pack and obtain reference data to be diagnosed;

[0097] The reference data to be diagnosed is divided into n target data to be diagnosed; each target data to be diagnosed corresponds to a diagnosis type; n is a positive integer;

[0098] Determine the target abnormality degree value corresponding to each diagnosis type in the n diagnosis types based on the n target to-be-diagnosed data, and obtain n target abnormality degree values;

[0099] Determine the priority corresponding to each of the n diagnosis types based on the n target abnormality values, and obtain n priorities; the target abnormality value is positively correlated with the priority;

[0100] The target new energy battery pack is diagnosed based on n priorities and n target data to be diagnosed, and a target diagnosis result is obtained.

[0101] In some possible implementations, in terms of determining a target abnormality level value corresponding to each of n diagnostic types based on n target to-be-diagnosed data to obtain n target abnormality level values, the program includes instructions for executing the following steps:

[0102] Determine first target data to be diagnosed corresponding to a first diagnosis type; the first diagnosis type is any one of the n diagnosis types, and the first target data to be diagnosed is the target data to be diagnosed corresponding to the first diagnosis type;

[0103] Determine m values ​​in the first target to-be-diagnosed data;

[0104] Determine a maximum threshold and a minimum threshold corresponding to the first diagnosis type;

[0105] Determine the abnormality degree value corresponding to each value in the m values ​​based on the maximum threshold and the minimum threshold, and obtain m abnormality degree values;

[0106] A target abnormality level value corresponding to the first diagnosis type is determined based on the m abnormality level values.

[0107] In some possible implementations, in terms of determining the abnormality level value corresponding to each of the m values ​​based on the maximum threshold and the minimum threshold to obtain the m abnormality level values, the program includes instructions for performing the following steps:

[0108] Determine the difference between the maximum threshold and the minimum threshold to obtain a threshold difference;

[0109] If the first value is greater than the maximum threshold, a first difference between the first value and the maximum threshold is determined, and an abnormality degree value corresponding to the first value is determined based on the threshold difference and the first difference; the first value is any one of the m values;

[0110] If the first value is less than or equal to the maximum threshold and greater than or equal to the minimum threshold, then the abnormality level value corresponding to the first value is determined to be 0;

[0111] If the first value is less than the minimum threshold, a second difference between the first value and the minimum threshold is determined, and the abnormality level value corresponding to the first value is determined based on the threshold difference and the second difference.

[0112] In some possible implementations, in determining a target abnormality level value corresponding to the first diagnosis type based on the m abnormality level values, the program includes instructions for performing the following steps:

[0113] Determine the weight corresponding to each of the m abnormality degree values ​​to obtain m weights; the sum of the m weights is 1;

[0114] Determine a reference abnormality degree value based on the m weights and the m abnormality degree values;

[0115] Get the internal resistance of the target new energy battery pack;

[0116] Determine a target adjustment parameter corresponding to the internal resistance;

[0117] The reference abnormality level value is adjusted according to the target adjustment parameter to obtain a target abnormality level value corresponding to the first diagnosis type.

[0118] In some possible implementations, the above program includes instructions for performing the following steps:

[0119] Determine the maximum abnormality degree value among the m abnormality degree values;

[0120] If the maximum abnormality level value is greater than a first abnormality level threshold, generating a first alarm message based on the maximum abnormality level value and the first diagnosis type;

[0121] Based on the first alarm information, a target user is prompted that a fault related to the first diagnosis type occurs.

[0122] In some possible implementations, the above program includes instructions for performing the following steps:

[0123] If there is an abnormality value greater than the second abnormality value threshold value among the n target abnormality values, k target abnormality values ​​greater than the second abnormality value threshold value among the n target abnormality values ​​are determined; k is an integer less than n; the second abnormality value threshold value is less than the first abnormality value threshold value;

[0124] Determine k diagnosis types corresponding to k target abnormality values;

[0125] generating second alarm information based on k target abnormality degree values ​​and k diagnosis types;

[0126] Based on the second alarm information, the target user is prompted that faults related to k diagnosis types occur.

[0127] In some possible implementations, in terms of determining a target abnormality level value corresponding to each of n diagnostic types based on n target to-be-diagnosed data to obtain n target abnormality level values, the program includes instructions for executing the following steps:

[0128] Obtaining the fault frequency of the second diagnosis type in multiple historical time periods to obtain multiple fault frequencies; the second diagnosis type is any one of the n diagnosis types;

[0129] Fitting is performed based on multiple fault frequencies to obtain a fault frequency straight line;

[0130] Determine the slope corresponding to the fault frequency straight line and obtain the target slope;

[0131] Determining a reference abnormality degree value corresponding to the second diagnosis type based on the target slope;

[0132] Determining second target data to be diagnosed corresponding to a second diagnosis type among the n target data to be diagnosed;

[0133] Determining a target optimization factor corresponding to the second target data to be diagnosed;

[0134] The reference abnormality level value is optimized according to the target optimization factor to obtain a target abnormality level value corresponding to the second diagnosis type.

[0135] It should be understood that the electronic devices in this application may include smart phones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, PDAs, laptops, mobile Internet devices MID (Mobile Internet Devices, MID for short) or wearable devices or servers, edge computing nodes, etc. The above electronic devices are only examples, not exhaustive, and include but are not limited to the above electronic devices.

[0136] An embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement part or all of the steps of any new energy battery pack diagnostic method recorded in the above method embodiment.

[0137] An embodiment of the present invention also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute part or all of the steps of any new energy battery pack diagnostic method recorded in the above method embodiment.

[0138] It should be noted that, for the above-mentioned various method implementations, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the implementations described in the specification are all optional implementations, and the actions and modules involved are not necessarily required by this application.

[0139] In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device implementation described above is only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0141] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0142] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software program module.

[0143] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of each implementation method of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0144] A person skilled in the art can understand that all or part of the steps in the various methods of the above-mentioned embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0145] The above is a detailed introduction to the implementation mode of the present invention. Specific examples are used in this article to illustrate the principles and implementation modes of the present application. The description of the above implementation modes is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation mode and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A diagnostic method for a new energy battery pack, characterized in that: include: Acquire the data to be diagnosed of the target new energy battery pack and obtain reference data to be diagnosed; Dividing the reference data to be diagnosed into n target data to be diagnosed; Each target data to be diagnosed corresponds to a diagnosis type; n is a positive integer; Determine a target abnormality degree value corresponding to each diagnosis type in n diagnosis types based on the n target to-be-diagnosed data, and obtain n target abnormality degree values; Determine the priority corresponding to each of the n diagnosis types based on the n target abnormality degree values ​​to obtain n priorities; The target abnormality degree value is positively correlated with the priority; The target new energy battery pack is diagnosed based on the n priorities and the n target data to be diagnosed to obtain a target diagnosis result.

2. The method according to claim 1, characterized in that The step of determining a target abnormality level value corresponding to each diagnosis type in the n diagnosis types based on the n target data to be diagnosed, and obtaining the n target abnormality level values, comprises: Determine first target data to be diagnosed corresponding to a first diagnosis type; the first diagnosis type is any one of the n diagnosis types, and the first target data to be diagnosed is the target data to be diagnosed corresponding to the first diagnosis type; Determine m values ​​of the first target data to be diagnosed; Determining a maximum threshold and a minimum threshold corresponding to the first diagnosis type; Determine the abnormality level value corresponding to each of the m values ​​based on the maximum threshold and the minimum threshold, and obtain m abnormality level values; The target abnormality level value corresponding to the first diagnosis type is determined based on the m abnormality level values.

3. The method according to claim 2, characterized in that The step of determining the abnormality level value corresponding to each of the m values ​​based on the maximum threshold and the minimum threshold to obtain the m abnormality level values ​​includes: Determine a difference between the maximum threshold and the minimum threshold to obtain a threshold difference; If the first value is greater than the maximum threshold, a first difference between the first value and the maximum threshold is determined, and an abnormality degree value corresponding to the first value is determined based on the threshold difference and the first difference; the first value is any one of the m values; If the first value is less than or equal to the maximum threshold and greater than or equal to the minimum threshold, determining that the abnormality level value corresponding to the first value is 0; If the first value is less than the minimum threshold, a second difference between the first value and the minimum threshold is determined, and an abnormality level value corresponding to the first value is determined based on the threshold difference and the second difference.

4. The method according to claim 3, characterized in that The determining the target abnormality level value corresponding to the first diagnosis type based on the m abnormality level values ​​includes: Determine the weight corresponding to each of the m abnormality degree values ​​to obtain m weights; the sum of the m weights is 1; Determining a reference abnormality degree value based on the m weights and the m abnormality degree values; Obtaining the internal resistance corresponding to the target new energy battery pack; determining a target adjustment parameter corresponding to the internal resistance; The reference abnormality level value is adjusted according to the target adjustment parameter to obtain the target abnormality level value corresponding to the first diagnosis type.

5. The method according to any one of claims 2 to 4, characterized in that: The method further comprises: Determine the maximum abnormality degree value among the m abnormality degree values; If the maximum abnormality level value is greater than a first abnormality level threshold, generating a first alarm message based on the maximum abnormality level value and the first diagnosis type; Based on the first alarm information, a target user is prompted that a fault related to the first diagnosis type occurs.

6. The method according to claim 5, characterized in that The method further comprises: If there is an abnormality value greater than a second abnormality value threshold value among the n target abnormality values, k target abnormality values ​​greater than the second abnormality value threshold value among the n target abnormality values ​​are determined; k is an integer less than n; the second abnormality value threshold value is less than the first abnormality value threshold value; Determine k diagnosis types corresponding to the k target abnormality degree values; generating second alarm information based on the k target abnormality degree values ​​and the k diagnosis types; The target user is prompted based on the second alarm information that a fault associated with the k diagnosis types occurs.

7. The method according to claim 1, characterized in that The step of determining a target abnormality level value corresponding to each diagnosis type in the n diagnosis types based on the n target data to be diagnosed, and obtaining the n target abnormality level values, comprises: Obtaining the fault frequency of the second diagnosis type in multiple historical time periods to obtain multiple fault frequencies; the second diagnosis type is any one of the n diagnosis types; Perform fitting based on the multiple fault frequencies to obtain a fault frequency straight line; Determine the slope corresponding to the fault frequency straight line to obtain a target slope; determining a reference abnormality degree value corresponding to the second diagnosis type based on the target slope; Determining second target data to be diagnosed corresponding to the second diagnosis type among the n target data to be diagnosed; Determining a target optimization factor corresponding to the second target data to be diagnosed; The reference abnormality level value is optimized according to the target optimization factor to obtain the target abnormality level value corresponding to the second diagnosis type.

8. A diagnostic device for a new energy battery pack, characterized in that: The device comprises: an acquisition unit and a processing unit; The acquisition unit is used to acquire the data to be diagnosed of the target new energy battery pack to obtain reference data to be diagnosed; The processing unit is used to divide the reference data to be diagnosed into n target data to be diagnosed; each target data to be diagnosed corresponds to a diagnosis type; n is a positive integer; Determine a target abnormality degree value corresponding to each diagnosis type in n diagnosis types based on the n target to-be-diagnosed data, and obtain n target abnormality degree values; Determine the priority corresponding to each of the n diagnosis types based on the n target abnormality degree values, and obtain n priorities; the target abnormality degree value is positively correlated with the priority; The target new energy battery pack is diagnosed based on the n priorities and the n target data to be diagnosed to obtain a target diagnosis result.

9. An electronic device, characterized in that: The method comprises a processor, a memory, a communication interface and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the one or more programs include instructions for executing the steps in the method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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