Early warning method, device and equipment for insulation fault of battery pack and storage medium

Abstract

The invention provides a battery pack insulation fault early warning method and device, equipment and a storage medium. Relates to the technical field of batteries. The method comprises the following steps: determining a first insulation resistance value of a target vehicle battery pack in each first time period in a first historical period; performing linear fitting on the first insulation resistance values of the first time periods to determine a fitting function corresponding to the target vehicle; determining predicted fault time corresponding to the battery pack based on the fitting function and a preset reference insulation resistance value; and based on the predicted fault time, whether insulation fault early warning is carried out on the target vehicle is judged. Therefore, the insulation fault can be effectively recognized in advance, later maintenance is facilitated, the early warning accuracy is improved, and as the insulation risk is judged through the insulation value in a certain historical period, false alarms caused by some special working conditions, environments, software and hardware states and the like are avoided.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a method, device, equipment and storage medium for early warning of insulation failure of a battery pack. Background Art

[0002] To meet the driving needs of users, the power and energy characteristics of electric vehicles are getting better and better at this stage. Some car companies have begun to launch 800V voltage platform electric vehicles. In these current electric vehicles, the peak discharge current may be as high as thousands of amperes. Therefore, high-voltage safety requirements are becoming more and more stringent in this field.

[0003] In order to prevent the risk of electric shock to users and ensure the safety of high-voltage systems, the battery management system needs to perform insulation testing on the entire vehicle and battery pack at all times to ensure that the insulation resistance is above a certain threshold. At the same time, if the insulation resistance is detected to be lower than a certain threshold, an alarm will be triggered and the user will be restricted from using the electric vehicle until the insulation value returns to normal.

[0004] However, these real-time insulation alarms can only be diagnosed when insulation failure has already occurred, which seriously affects the user experience. Summary of the invention

[0005] The present application provides a battery pack insulation failure early warning method, device, equipment and storage medium, aiming to solve at least one of the technical problems in the related art to a certain extent.

[0006] In a first aspect, the present application provides a battery pack insulation failure early warning method, comprising:

[0007] Determine a first insulation resistance value of a battery pack of a target vehicle in each first time period in a first historical period;

[0008] Performing linear fitting on the first insulation resistance values ​​of each first time period to determine a fitting function corresponding to the target vehicle;

[0009] Determining a predicted failure time corresponding to the battery pack based on the fitting function and a preset reference insulation resistance value;

[0010] Based on the predicted fault time, determine whether to issue an insulation fault warning to the target vehicle.

[0011] In a second aspect, the present application provides an early warning device for insulation failure of a battery pack, comprising:

[0012] A first determination module determines a first insulation resistance value of a battery pack of a target vehicle in each first time period in a first historical period;

[0013] A second determination module is used to perform linear fitting on the first insulation resistance values ​​of each first time period to determine a fitting function corresponding to the target vehicle;

[0014] A third determination module, configured to determine a predicted failure time corresponding to the battery pack based on the fitting function and a preset reference insulation resistance;

[0015] A judgment module is used to judge whether to issue an insulation fault warning to the target vehicle based on the predicted fault time.

[0016] In a third aspect, the present application provides an electronic device comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute instructions to implement a method for early warning of insulation failure of a battery pack.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium, which, when instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform a method for early warning of battery pack insulation failure.

[0018] In a fifth aspect, the present application provides a computer program product, including a computer program, wherein the computer program is executed by a processor to provide an early warning method for battery pack insulation failure.

[0019] The battery pack insulation fault warning method, device, equipment and storage medium provided by the present application first determine the first insulation resistance of the target vehicle battery pack in each first time period in the first historical period, then perform linear fitting on the first insulation resistance of each first time period to determine the fitting function corresponding to the target vehicle, then determine the predicted fault time corresponding to the battery pack based on the fitting function and the preset reference insulation resistance, and finally determine whether to issue an insulation fault warning to the target vehicle based on the predicted fault time. In this way, insulation faults can be effectively identified in advance, making it easier to contact users for later maintenance, and improving the accuracy of warnings. Since the insulation risk is judged by the insulation value of a certain historical period, false alarms caused by some special working conditions, environments, software and hardware status, etc. are avoided.

[0020] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0022] Figure 1 is a flow chart of a method for early warning of insulation failure of a battery pack according to the first embodiment of the present application;

[0023] Figure 2 is a schematic diagram of an application scenario of a vehicle battery pack insulation failure according to the first embodiment of the present application;

[0024] Figure 3 is a flow chart of a method for early warning of insulation failure of a battery pack according to the second embodiment of the present application;

[0025] Figure 4 is a block diagram of a battery pack insulation failure warning device according to the present application;

[0026] Figure 5 A block diagram of an exemplary electronic device suitable for implementing the embodiments of the present application is shown.

[0027] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0028] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be construed as limiting the present application. On the contrary, the embodiments of the present application include all changes, modifications and equivalents that fall within the spirit and connotation of the appended claims.

[0029] It should be noted that the executor of the battery pack insulation failure warning method of this embodiment may be a battery pack insulation failure warning device, which may be implemented by software and / or hardware, and may be configured in an electronic device, which is not limited here.

[0030] Figure 1 is a flow chart of a method for early warning of battery pack insulation failure according to the first embodiment of the present application, such as Figure 1 As shown, the method includes:

[0031] S101: Determine a first insulation resistance value of a target vehicle battery pack in each first time period within a first historical period.

[0032] The target vehicle may be a vehicle that needs to be tested for insulation fault.

[0033] The first historical period may be a time period of a specified length adjacent to the current time, such as 5 days. For example, if the current time is Saturday, Monday to Friday of this week may be taken as the first historical period. Specifically, when performing an insulation fault test on a target vehicle, sample data of the first historical period may be obtained to test the target vehicle.

[0034] The first time period may be each preset time period in the first historical period.

[0035] For example, one hour may be used as a time period, that is, as the first time period. If the first historical period is 5 days, the first historical period includes 5x24=120 first time periods.

[0036] It should be noted that the above examples are only for illustrative purposes and are not intended to be limitations of the present disclosure. For example, the first historical period may be 7 days, 8 days, or half a month, and the first time period may be half an hour, 2 hours, or 3 hours, which are not limited here.

[0037] The first insulation resistance may be a median value of the insulation resistance of the battery pack detected within the first time period.

[0038] like Figure 2 As shown, Figure 2 A schematic diagram of an application scenario of insulation failure of a vehicle battery pack is shown. Among them, the battery provides energy input for the external load and provides the power performance of the whole vehicle. The target vehicle may include a battery management system (Battery Management System, referred to as BMS), and the battery management system may include an insulation detection module to detect the insulation value of the battery in real time, and can be forwarded to the telematics box (TBOX) through the vehicle control unit (VCU), and then uploaded to the cloud through the network protocol. The cloud can calculate the uploaded insulation detection data and judge whether the battery pack of the target vehicle has an insulation detection abnormality.

[0039] Among them, the battery management system is a system used to monitor, control and protect battery packs. It is mainly used in electric vehicles, hybrid vehicles, energy storage systems and other fields. Its functions include battery status monitoring, battery balancing control, temperature management, current management, voltage management, fault diagnosis and safety protection.

[0040] S102: Performing linear fitting on the first insulation resistance values ​​in each first time period to determine a fitting function corresponding to the target vehicle.

[0041] As a possible implementation method, the fitting function can be first set to m=kn+c, where k and c are the fitting parameters to be obtained, n is any first time period in the first historical period, and m is the first insulation resistance corresponding to the first time period n.

[0042] Optionally, the least squares method can be used to calculate the fit:

[0043]

[0044] For example, if the first time period is in hours and the first historical period is M days, then x = [1, 2, 3...M*24], x T The transposition of the pointing quantity x, y is the vector of the first insulation resistance corresponding to the vector x, thereby the fitting parameters k and c can be calculated, and then the fitting function m=kn+c can be determined.

[0045] S103: Determine the predicted failure time corresponding to the battery pack based on the fitting function and the preset reference insulation resistance.

[0046] Specifically, the fitting parameters and the degree of fit may be first determined according to the fitting function, and then the predicted failure time corresponding to the battery pack may be determined based on the fitting parameters, the degree of fit, the reference insulation resistance and the number of the first time period.

[0047] The reference insulation resistance value may be the lower limit of the insulation threshold value specified in the national standard, or may be set according to actual experience, and is not limited here. It is understandable that if the insulation resistance value is lower than the reference insulation resistance value, it indicates that insulation failure may have occurred, or there is a risk of insulation failure.

[0048] The predicted failure time indicates that the battery pack may fail after this period of time. For example, if the predicted failure time is 48 hours, it means that the battery pack may have an insulation failure after 48 hours.

[0049] The number of each first time period can be determined based on the length of the first historical period and the length of the first time period. For example, if the first historical period is 5 days and the first time period is 1 hour, the number of first time periods is 120.

[0050] In the disclosed embodiment, the goodness of fit can be evaluated using the correlation coefficient (R-squared, R2), which represents the degree of linear correlation between the fitting function and the actual observation value, and the value range is between 0 and 1. The closer to 1, the better the fitting effect.

[0051] Specifically, we can first calculate the fitting degree R according to the fitting function. 2 :

[0052]

[0053] Among them, y fit (i) is the fitted value y corresponding to the i-th x, y fit (i)=kx(i)+c is the fitting function, k and c are the fitting parameters in the fitting function, avg(y) refers to the average value of the fitting value y set, y(i) is the i-th value in the y set, and the larger the fitting value is, the closer the fitted curve is to the actual value.

[0054]

[0055] Wherein, T is the predicted fault time, Y is the reference insulation resistance, k and c are fitting parameters (refer to the above steps), and N is the number of each first time period.

[0056] S104: Based on the predicted fault time, determine whether to issue an insulation fault warning to the target vehicle.

[0057] It should be noted that, when the predicted fault time and the degree of fit meet certain conditions, it can be determined that the target vehicle may have an insulation failure, and thus an insulation failure warning can be performed.

[0058] As a possible implementation method, if the predicted fault time is less than a preset fault time threshold, it can be determined that an insulation fault warning needs to be issued to the target vehicle; otherwise, an insulation fault warning does not need to be issued to the target vehicle.

[0059] For example, if the preset fault time threshold is 3 days and the predicted fault time is 2 days, which is less than the fault time threshold, an insulation fault warning needs to be issued for the target vehicle.

[0060] It should be noted that if the predicted failure time is less than the preset failure time threshold, it means that the battery pack is about to experience insulation failure and requires timely repair to avoid failure after 2 days. Therefore, it is necessary to issue an insulation failure warning to the target vehicle to inform the user or staff to perform maintenance.

[0061] As a possible implementation method, the fit of the fitting function can be determined first, and then when the predicted fault time is less than the time threshold and the fit is greater than the fit threshold, an insulation fault warning is issued to the target vehicle.

[0062] The implementation method of determining the fitting degree of the fitting function may refer to the above steps.

[0063] The time threshold may be a threshold for predicting failure time.

[0064] In the embodiment of the present disclosure, the time threshold may be 7 days, or may also be 6 days, 8 days, etc., which are not limited here. The fitting degree threshold may be 0.9, 0.91, 0.92, which may be adjusted according to actual experience, which are not limited here.

[0065] For example, if the preset time threshold is 7 days, the fit threshold is 0.9, the predicted failure time is 6 days, and the fit is 0.92, then the vehicle needs to be warned, indicating that an insulation failure is about to occur.

[0066] Optionally, the predicted failure time and fit corresponding to the battery pack may be calculated according to a specified period, and then based on the predicted failure time and fit, it may be determined whether to issue an insulation failure warning to the target vehicle.

[0067] Optionally, the specified period can be half a day, four hours, five hours, etc., which is not limited here. It should be noted that the predicted failure time and fit of the battery pack can be determined multiple times a day, and then based on the predicted failure time, it is determined whether to issue an insulation fault warning to the target vehicle, thereby realizing a sliding window warning, achieving a higher monitoring frequency, and improving the safety of the battery pack and the vehicle.

[0068] The battery pack insulation fault warning method provided by the present application first determines the first insulation resistance of the battery pack of the target vehicle in each first time period in the first historical period, then performs linear fitting on the first insulation resistance of each first time period to determine the fitting function corresponding to the target vehicle, then determines the predicted fault time corresponding to the battery pack based on the fitting function and the preset reference insulation resistance, and finally determines whether to issue an insulation fault warning to the target vehicle based on the predicted fault time. In this way, insulation faults can be effectively identified in advance, which is convenient for contacting users for later maintenance, and the accuracy of early warning is improved. Since the insulation risk is judged by the insulation value of a certain historical period, false alarms caused by some special working conditions, environment, software and hardware status, etc. are avoided.

[0069] Figure 3 is a flow chart of a method for early warning of battery pack insulation failure according to the second embodiment of the present application, such as Figure 3 As shown, the method includes:

[0070] S201: Obtain test samples of the battery pack of each test vehicle in each second time period within a second historical period.

[0071] The second historical period can be a historical time period for obtaining the detection sample data. It should be noted that by making a preliminary judgment on the detection samples of the second historical period, it is convenient to determine the target vehicle later. For example, the second historical period can be one week, or two weeks, or ten days, which is not limited here.

[0072] Among them, the length of the second historical period is greater than or equal to the first historical period.

[0073] For example, if the second historical period is 15 days, the first historical period can be 15 days, or can also be 7 days or 5 days, which is not limited here.

[0074] It should be noted that since the test vehicles may be in large quantities, such as 5,000 vehicles, if the insulation data of each vehicle is directly obtained for analysis, a large amount of cloud computing power and computing resources will be consumed. Therefore, the test samples of each test vehicle in the second historical period can be obtained first for screening. After the target vehicle is screened, a shorter historical period can be selected to analyze the insulation data of the target vehicle more finely, thereby saving computing power.

[0075] The second time period may be a time period into which the second historical period is divided.

[0076] Optionally, in the embodiment of the present disclosure, the second time period may be longer than the first time period.

[0077] For example, the first time period is 1 hour, and the second time period may be 1 day.

[0078] For example, we can first obtain the second insulation resistance values ​​corresponding to 5,000 test vehicles of the same model every day (the second time period is 1 day) within 30 days (the second historical period is 30 days), and then screen out 50 target vehicles that need to be focused on. At this time, we can obtain the first insulation resistance values ​​corresponding to each target vehicle every hour (the first time period is 1 hour) within 5 days (the first historical period is 5 days). In this way, computing resources can be saved, and since the first time period is smaller than the second time period, the granularity is finer, which improves the accuracy of the early warning.

[0079] Each test sample is the second insulation resistance value of the battery pack of any test vehicle in each second time period, and the models of the test vehicles are the same.

[0080] The second insulation resistance may be a median of the insulation resistance in the second time period.

[0081] For example, if the second time period is one day, the second insulation resistance value may be a median value of the insulation resistance values ​​of the battery pack on that day.

[0082] The test vehicle may be a vehicle for which it is necessary to determine whether to perform insulation testing.

[0083] S202: When the number of detection samples is less than a first threshold, each test vehicle is taken as a target vehicle.

[0084] The first threshold may be a threshold for the number of detected samples.

[0085] For example, the first threshold may be 400, which is not limited here.

[0086] It should be noted that if the number of test samples is small, each test vehicle can be focused on as a target vehicle, and then insulation testing can be performed.

[0087] S203: When the number of detection samples is greater than or equal to the first threshold, determine the target vehicle from each test vehicle according to the second insulation resistance value corresponding to each second time period.

[0088] It should be noted that if the number of detection samples is greater than or equal to the first threshold, it means that the number of detection samples is large. In order to save computing power, further screening can be performed from each test vehicle to further determine the vehicles that need to be focused on.

[0089] Optionally, the second threshold corresponding to the second insulation resistance value may be calculated first according to each second insulation resistance value.

[0090] As a possible implementation method, the preset first parameter and the second parameter can be first multiplied by each second insulation resistance value respectively to determine a first value of each second insulation resistance value corresponding to the first parameter, and a second value corresponding to the second parameter, wherein the first parameter is smaller than the second parameter, and then the second threshold value can be calculated based on the first value, the second value, the preset third parameter and the first formula.

[0091] As an example, the first parameter may be 0.25, and the second parameter may be 0.75, that is, the lower quarter quantile value and the upper quarter quantile value of the second insulation resistance may be determined first.

[0092] For example, if the second insulation resistance is Q, the first parameter is 0.25, and the second parameter is 0.75, then it can be determined that the first value Q1=0.25Q, and the second value Q2 is 0.75Q.

[0093] Optionally, the first formula may be:

[0094] Y=Q1-Q3*(Q2-Q1)

[0095] Among them, Y is the second threshold, Q3 is the preset third parameter, Q2 is the second value, and Q1 is the first value.

[0096] Optionally, Q3 may be 1.5, which is not limited here.

[0097] Furthermore, when the second insulation resistance corresponding to any second time period is less than the corresponding second threshold, the detected sample can be determined to be a target sample corresponding to the test vehicle, and the number of target samples corresponding to each test vehicle can be determined.

[0098] For example, if the second insulation resistance corresponding to the second time period X1 is 6 and the second threshold is 7, the second insulation resistance corresponding to the second time period X1 can be used as the target sample.

[0099] Furthermore, for example, if vehicle W includes 6 samples, among which the second insulation resistance values ​​corresponding to samples 1, 2, and 3 are all less than the second threshold, then samples 1, 2, and 3 can be used as target samples, indicating that the number of target samples corresponding to vehicle W is 3.

[0100] Afterwards, if the number of target samples corresponding to any test vehicle is greater than a third threshold, any test vehicle can be determined as a target vehicle.

[0101] The third threshold may be a threshold of the number of target samples.

[0102] In the embodiment of the present disclosure, the third threshold may be 2, or 3, or 1, which is not limited here.

[0103] It should be noted that if the number of target samples corresponding to any test vehicle is greater than the third threshold, it means that the test vehicle needs to be regarded as a key focus vehicle, that is, a target vehicle.

[0104] S204: Determine a first insulation resistance value of the target vehicle battery pack in each first time period within a first historical period.

[0105] S205: Performing linear fitting on the first insulation resistance values ​​in each first time period to determine a fitting function corresponding to the target vehicle.

[0106] S206: Determine the predicted failure time corresponding to the battery pack based on the fitting function and a preset reference insulation resistance.

[0107] S207: Based on the predicted fault time, determine whether to issue an insulation fault warning to the target vehicle.

[0108] It should be noted that the specific implementation of steps S204-S207 can refer to the above embodiment and will not be described in detail here.

[0109] In the disclosed embodiment, first, detection samples of the battery pack of each test vehicle in each second time period within the second historical period are obtained, and then, when the number of detection samples is less than the first threshold, each test vehicle is taken as a target vehicle, and then, when the number of detection samples is greater than or equal to the first threshold, the target vehicle is determined from the test vehicles according to the second insulation resistance corresponding to each second time period, and then the first insulation resistance of the battery pack of the target vehicle in each first time period in the first historical period is determined, and then the first insulation resistance of each first time period is linearly fitted to determine the fitting function corresponding to the target vehicle, and then based on the fitting function and the preset reference insulation resistance, the predicted failure time corresponding to the battery pack is determined, and finally, based on the predicted failure time, it is determined whether to issue an insulation failure warning to the target vehicle.

[0110] Therefore, by monitoring and analyzing the insulation resistance of the battery pack, the failure time can be predicted in advance, and maintenance or replacement measures can be taken in time to avoid vehicle damage or safety accidents caused by battery failure. By predicting the failure time, maintenance plans can be arranged in a targeted manner to avoid unnecessary maintenance and replacement, reduce maintenance costs and downtime, and regularly monitor and predict failure times to detect potential problems with the battery pack in advance, and repair or replace damaged parts in time, thereby improving the reliability and stability of the entire vehicle. By performing data analysis and linear fitting modeling on a large number of insulation resistance samples of test vehicles, fitting parameters and fitting degrees can be obtained, and the characteristics and trends of the battery pack can be further understood, providing a basis for subsequent optimization design and improvement. The method of calculating the cluster first and then judging the trend reduces the computing power of the cloud computing. Using this method for a large number of models can save a lot of computing resources.

[0111] Figure 4 is a block diagram of a battery pack insulation failure warning device according to the present application, such as Figure 4 As shown, the early warning device 400 for battery pack insulation failure includes:

[0112] A first determination module 410 determines a first insulation resistance value of a target vehicle battery pack in each first time period in a first historical period;

[0113] A second determination module 420 is used to perform linear fitting on the first insulation resistance values ​​of each first time period to determine a fitting function corresponding to the target vehicle;

[0114] A third determination module 430, configured to determine a predicted failure time corresponding to the battery pack based on the fitting function and a preset reference insulation resistance value;

[0115] The judgment module 440 is used to judge whether to issue an insulation fault warning to the target vehicle based on the predicted fault time.

[0116] Optionally, the third determining module is specifically configured to:

[0117] According to the fitting function, determining fitting parameters and fitting degree;

[0118] Based on the fitting parameters, the fitting degree, the reference insulation resistance and the number of the first time periods, a predicted failure time corresponding to the battery pack is determined.

[0119] Optionally, the first determining module further includes:

[0120] an acquisition unit, used to acquire detection samples of the battery packs of each test vehicle in each second time period within the second historical period,

[0121] Wherein, each of the test samples is the second insulation resistance value of the battery pack of any test vehicle in each second time period, the models of the test vehicles are the same, the length of the second time period is greater than the first time period, and the length of the second historical period is greater than or equal to the first historical period;

[0122] A first determining unit, configured to take each of the test vehicles as a target vehicle when the number of the detection samples is less than a first threshold;

[0123] A second determination unit is configured to determine the target vehicle from the test vehicles according to a second insulation resistance value corresponding to each second time period when the number of the detection samples is greater than or equal to the first threshold.

[0124] Optionally, the second determining unit includes:

[0125] a calculation subunit, configured to calculate, according to each second insulation resistance value, a second threshold value corresponding to the second insulation resistance value;

[0126] A first determination subunit, configured to determine that the detection sample is a target sample corresponding to the test vehicle when a second insulation resistance value corresponding to any second time period is less than a corresponding second threshold value;

[0127] A second determination subunit is used to determine the number of target samples corresponding to each of the test vehicles;

[0128] The third determination subunit is used to determine that any test vehicle is the target vehicle when the number of target samples corresponding to any test vehicle is greater than a third threshold.

[0129] Optionally, the computing subunit is specifically used for:

[0130] Multiplying each second insulation resistance by a preset first parameter and a preset second parameter respectively to determine a first value of each second insulation resistance corresponding to the first parameter and a second value corresponding to the second parameter, wherein the first parameter is smaller than the second parameter;

[0131] The second threshold is calculated based on the first value, the second value, a preset third parameter and a first formula.

[0132] Optionally, the judgment module is specifically used to:

[0133] Determining the fitness of the fitting function;

[0134] When the predicted fault time is less than a time threshold and the degree of fit is greater than a degree of fit threshold, an insulation fault warning is issued to the target vehicle.

[0135] Optionally, the device further includes:

[0136] A calculation module, used for calculating the predicted failure time and the degree of fit corresponding to the battery pack according to a specified period;

[0137] The early warning module is used to determine whether to issue an insulation fault early warning to the target vehicle based on the predicted fault time.

[0138] The battery pack insulation fault warning method provided by the present application first determines the first insulation resistance of the battery pack of the target vehicle in each first time period in the first historical period, then performs linear fitting on the first insulation resistance of each first time period to determine the fitting function corresponding to the target vehicle, then determines the predicted fault time corresponding to the battery pack based on the fitting function and the preset reference insulation resistance, and finally determines whether to issue an insulation fault warning to the target vehicle based on the predicted fault time. In this way, insulation faults can be effectively identified in advance, which is convenient for contacting users for later maintenance, and the accuracy of early warning is improved. Since the insulation risk is judged by the insulation value of a certain historical period, false alarms caused by some special working conditions, environment, software and hardware status, etc. are avoided.

[0139] According to an embodiment of the present application, the present application also provides an electronic device, a readable storage medium and a computer program product.

[0140] Figure 5 6 is a block diagram of an electronic device according to the present application. For example, the electronic device 600 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0141] Reference Figure 5, the electronic device 600 may include one or more of the following components: a processing component 602 , a memory 604 , a power component 606 , a multimedia component 608 , an audio component 610 , an input / output (I / O) interface 612 , a sensor component 614 , and a communication component 616 .

[0142] The processing component 602 generally controls the overall operation of the electronic device 600, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 602 may include one or more processors 620 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 602 may include one or more modules to facilitate the interaction between the processing component 602 and other components. For example, the processing component 602 may include a multimedia module to facilitate the interaction between the multimedia component 608 and the processing component 602.

[0143] The memory 604 is configured to store various types of data to support operations on the electronic device 600. Examples of such data include instructions for any application or method operating on the electronic device 600, contact data, phone book data, messages, pictures, videos, etc. The memory 604 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0144] The power supply component 606 provides power to the various components of the electronic device 600. The power supply component 606 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 600.

[0145] The multimedia component 608 includes a touch screen that provides an output interface between the electronic device 600 and the user. In some embodiments, the touch screen may include a liquid crystal display (LCD) and a touch panel (TP). The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor can not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 608 includes a front camera and / or a rear camera. When the electronic device 600 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0146] The audio component 610 is configured to output and / or input audio signals. For example, the audio component 610 includes a microphone (MIC), and when the electronic device 600 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 604 or sent via the communication component 616.

[0147] In some embodiments, the audio component 610 also includes a speaker for outputting audio signals.

[0148] I / O interface 612 provides an interface between processing component 602 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0149] The sensor assembly 614 includes one or more sensors for providing various aspects of status assessment for the electronic device 600. For example, the sensor assembly 614 can detect the open / closed state of the electronic device 600, the relative positioning of components, such as the display and keypad of the electronic device 600, and the sensor assembly 614 can also detect the position change of the electronic device 600 or a component of the electronic device 600, the presence or absence of user contact with the electronic device 600, the orientation or acceleration / deceleration of the electronic device 600, and the temperature change of the electronic device 600. The sensor assembly 614 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 614 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 614 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0150] The communication component 616 is configured to facilitate wired or wireless communication between the electronic device 600 and other devices. The electronic device 600 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 616 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 616 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0151] In an exemplary embodiment, the electronic device 600 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned battery pack insulation failure early warning method.

[0152] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 604 including instructions, and the above instructions can be executed by the processor 920 of the electronic device 600 to complete the above method. Alternatively, the computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0153] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0154] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A battery pack insulation failure early warning method, characterized in that: include: Determine a first insulation resistance value of a battery pack of a target vehicle in each first time period in a first historical period; Performing linear fitting on the first insulation resistance values ​​of each first time period to determine a fitting function corresponding to the target vehicle; Determining a predicted failure time corresponding to the battery pack based on the fitting function and a preset reference insulation resistance value; Based on the predicted fault time, determine whether to issue an insulation fault warning to the target vehicle.

2. The method according to claim 1, characterized in that The determining, based on the fitting function and a preset reference insulation resistance, a predicted failure time corresponding to the battery pack includes: According to the fitting function, determining fitting parameters and fitting degree; Based on the fitting parameters, the fitting degree, the reference insulation resistance and the number of the first time periods, a predicted failure time corresponding to the battery pack is determined.

3. The method according to claim 1, characterized in that Also includes: Obtaining test samples of the battery packs of each test vehicle in each second time period within the second historical period, Wherein, each of the test samples is the second insulation resistance value of the battery pack of any test vehicle in each second time period, the models of the test vehicles are the same, the length of the second time period is greater than the first time period, and the length of the second historical period is greater than or equal to the first historical period; When the number of the detection samples is less than a first threshold, taking each of the test vehicles as a target vehicle; When the number of the detection samples is greater than or equal to the first threshold, the target vehicle is determined from the test vehicles according to the second insulation resistance corresponding to each of the second time periods.

4. The method according to claim 3, characterized in that The step of determining the target vehicle from the test vehicles according to the second insulation resistance value corresponding to each of the second time periods includes: According to each second insulation resistance value, calculating a second threshold value corresponding to the second insulation resistance value; When the second insulation resistance corresponding to any second time period is less than the corresponding second threshold value, determining that the detection sample is the target sample corresponding to the test vehicle; Determining the number of target samples corresponding to each of the test vehicles; When the number of target samples corresponding to any test vehicle is greater than a third threshold, the any test vehicle is determined to be the target vehicle.

5. The method according to claim 4, characterized in that The step of calculating, according to each second insulation resistance value, a second threshold value corresponding to the second insulation resistance value comprises: Multiplying each second insulation resistance by a preset first parameter and a preset second parameter respectively to determine a first value of each second insulation resistance corresponding to the first parameter and a second value corresponding to the second parameter, wherein the first parameter is smaller than the second parameter; The second threshold is calculated based on the first value, the second value, a preset third parameter and a first formula.

6. The method according to claim 1, characterized in that The determining whether to perform insulation fault warning on the target vehicle based on the predicted fault time includes: Determining the fitness of the fitting function; When the predicted fault time is less than a time threshold and the degree of fit is greater than a degree of fit threshold, an insulation fault warning is issued to the target vehicle.

7. The method according to claim 1, characterized in that Also includes: Calculating the predicted failure time corresponding to the battery pack according to a specified period; Based on the predicted fault time, determine whether to issue an insulation fault warning to the target vehicle.

8. A battery pack insulation failure warning device, characterized in that: include: A first determination module determines a first insulation resistance value of a battery pack of a target vehicle in each first time period in a first historical period; A second determination module is used to perform linear fitting on the first insulation resistance values ​​of each first time period to determine a fitting function corresponding to the target vehicle; A third determination module, configured to determine a predicted failure time corresponding to the battery pack based on the fitting function and a preset reference insulation resistance; A judgment module is used to judge whether to issue an insulation fault warning to the target vehicle based on the predicted fault time.

9. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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