Power battery insulation monitoring method, system and device

Abstract

The application relates to the technical field of battery insulation monitoring, and particularly provides a power battery insulation monitoring method, system and device, aiming to solve the problem of how to accurately predict the risk of insulation deterioration of a power battery before the power battery occurs insulation deterioration, so as to perform fault early warning on the power battery. The application performs data statistics on long-time and large-data insulation resistance values, and analyzes whether the power battery has the risk of insulation deterioration according to the data statistics result; if the power battery has the risk of insulation deterioration, an alarm information is output. Based on the data statistics and analysis on long-time and large-data insulation resistance values, the application can perform insulation deterioration prediction in advance before the power battery occurs insulation deterioration, so that the user can timely perform battery maintenance before the power battery occurs insulation deterioration, and prevent the power battery from occurring failure faults.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery insulation monitoring, and in particular to a power battery insulation monitoring method, system and device. BACKGROUND

[0002] The power battery in the high-voltage system of an electric vehicle needs to have good electrical insulation performance to ensure the safety of the power battery and passengers in the electric vehicle. For example, when the power battery has insulation degradation, the cells inside the power battery may short circuit, and the short-circuited cells may have a temperature rise due to excessive short-circuit current. If the temperature of the short-circuited cells is too high, it may also cause short circuits and temperature rises in other surrounding cells, thereby causing the temperature of the entire power battery to rise, increasing the risk of thermal runaway of the power battery.

[0003] By monitoring the insulation performance of the power battery, a fault warning can be given before the battery fails due to thermal runaway and other battery failure faults, reminding the user to maintain the power battery and prevent the battery from failing. However, the traditional power battery insulation performance monitoring method can only monitor that the power battery has insulation degradation and give an alarm when the power battery is actually in an insulation degradation state. At this time, the power battery may have already experienced thermal runaway and other battery failure faults due to insulation degradation, so this method cannot reliably and effectively warn the power battery of a fault.

[0004] Accordingly, there is a need in the art for a new power battery insulation monitoring scheme to solve the above problems. SUMMARY

[0005] In order to overcome the above-mentioned defects, the present application is proposed to provide a power battery insulation monitoring method, system and device that solves or at least partially solves the problem of how to accurately predict the risk of power battery insulation degradation before the power battery has insulation degradation, so as to give a fault warning for the power battery.

[0006] In a first aspect, a power battery insulation monitoring method is provided, which comprises:

[0007] Taking the current time as a time starting point, obtaining each insulation resistance value of the power battery received within a preset first time length before the current time;

[0008] Statistically analyzing the obtained insulation resistance values, and analyzing whether the power battery has a risk of insulation degradation according to the statistical result; if so, outputting a first alarm information.

[0009] The step of "statistically analyzing the obtained insulation resistance values, and analyzing whether the power battery has a risk of insulation degradation according to the statistical result" specifically comprises:

[0010] performing mean and variance calculation on the insulation resistance values received within the preset first time length respectively to obtain an insulation resistance value mean res avg and an insulation resistance value variance res std;

[0011] arranging the insulation resistance values received within the preset first time length in order from small to large and calculating the cumulative percentage corresponding to each arranged insulation resistance value;

[0012] obtaining an insulation resistance value res Npct corresponding to the cumulative percentage with a percentage value of a preset value N;

[0013] analyzing whether the power battery has a risk of insulation degradation according to the res avg, res std and res Npct, specifically including:

[0014] if the power battery is in a low insulation resistance state;

[0015] The res avg set is a set value of the preset insulation resistance value mean, and the res std set is a set value of the preset insulation resistance value variance.

[0016] Before the step of "performing data statistics on the obtained insulation resistance value", the method further includes:

[0017] Step S1: determining whether the number of obtained insulation resistance values reaches a preset number threshold; if yes, performing the step of "performing data statistics on the obtained insulation resistance value"; if no, going to step S2;

[0018] Step S2: extending the current preset first time length according to a preset extension time, and re-obtaining the insulation resistance values received within the extended preset first time length before the current time point as a time starting point, and then going to step S1.

[0019] After analyzing that the power battery has a risk of insulation degradation, the method further includes:

[0020] obtaining each insulation resistance value of the power battery received within a preset second time length before the current time point as a time starting point and arranging the insulation resistance values in order from early to late to obtain an insulation resistance value array;

[0021] performing regression fitting calculation on the insulation resistance values in the insulation resistance value array to obtain an insulation resistance fitting value corresponding to each insulation resistance value in the insulation resistance value array, and arranging the insulation resistance fitting values corresponding to each insulation resistance value in the order of the receiving time to obtain an insulation resistance fitting value array;

[0022] performing first-order difference calculation on the insulation resistance fitting values in the insulation resistance fitting value array to obtain a first-order difference corresponding to each insulation resistance fitting value;

[0023] comparing each first-order difference with a preset difference threshold value respectively, and analyzing the risk level of insulation degradation of the power battery according to the comparison result and outputting corresponding second alarm information;

[0024] The preset second time length is much longer than the preset first time length, and the number of insulation resistance values received within the preset second time length is much larger than the number of insulation resistance values received within the preset first time length.

[0025] The step of "comparing each first-order difference with a preset difference threshold value respectively, and analyzing the risk level of insulation degradation of the power battery according to the comparison result" specifically comprises:

[0026] counting a first number count_down of the first-order differences whose difference values are less than a preset lower limit of the difference value, and a second number count_up of the first-order differences whose difference values are greater than a preset upper limit of the difference value;

[0027] If count_down < 2 and count_up = 0, the risk level of insulation degradation of the power battery is high risk;

[0028] If count_down > 2 and count_up = 0, the risk level of insulation degradation of the power battery is medium risk;

[0029] If count_down = 2 and count_up = 0 and resA_std = 0, the risk level of insulation degradation of the power battery is low risk;

[0030] The resA_std is a variance of the insulation resistance fitting values corresponding to all insulation resistance values within the preset second time length.

[0031] The method further comprises: when the risk level of insulation degradation of the power battery is low risk, outputting reminding information of insulation resistance value detection failure of the power battery.

[0032] In a second aspect, a power battery insulation monitoring system is provided, and the system comprises:

[0033] a data acquisition device configured to acquire each insulation resistance value of the power battery received within a preset first time length before a current time point as a time starting point;

[0034] a data analysis device configured to perform data statistics on the insulation resistance values acquired by the data acquisition device, analyze whether the power battery has a risk of insulation deterioration according to a data statistics result, and output first alarm information if so.

[0035] The data analysis device comprises a mean / variance calculation module, a percentile calculation module, and a data analysis module.

[0036] The mean / variance calculation module is configured to perform mean and variance calculation on the insulation resistance values received within the preset first time length respectively, to obtain an insulation resistance value mean res_avg and an insulation resistance value variance res_std.

[0037] The percentile calculation module is configured to arrange the insulation resistance values received within the preset first time length in ascending order and calculate a cumulative percentage corresponding to each arranged insulation resistance value, and acquire an insulation resistance value res_Npct corresponding to a cumulative percentage with a percentage value of a preset value N.

[0038] The data analysis module is configured to analyze whether the power battery has a risk of insulation deterioration according to the res_avg, res_std, and res_Npct, and specifically comprises: if the power battery is in a low insulation resistance state;

[0039] The res_avg_set is a preset insulation resistance value mean setting value, and the res_std_set is a preset insulation resistance value variance setting value.

[0040] The data analysis device is configured to perform the following operations:

[0041] Step S1: determining whether the number of acquired insulation resistance values reaches a preset number threshold; if so, performing the step of “performing data statistics on the acquired insulation resistance values”; if not, proceeding to step S2;

[0042] Step S2: extending the current preset first time length according to a preset extension time, reacquiring insulation resistance values received within the extended preset first time length before the current time point as a time starting point, and then proceeding to step S1.

[0043] The system further comprises a battery insulation deterioration risk analysis device, which comprises a data acquisition module, a regression fitting calculation module, a first-order difference calculation module, and an insulation deterioration risk level analysis module.

[0044] The data acquisition module is configured to take the current time as a time starting point, acquire each insulation resistance value received within a preset second time length before the current time, and arrange the insulation resistance values in the order of receiving time from early to late to obtain an insulation resistance value array.

[0045] The regression fitting calculation module is configured to perform regression fitting calculation on the insulation resistance values in the insulation resistance value array to obtain an insulation resistance fitting value corresponding to each insulation resistance value in the insulation resistance value array, and arrange the insulation resistance fitting values corresponding to each insulation resistance value in the order of receiving time from early to late to obtain an insulation resistance fitting value array.

[0046] The first-order difference calculation module is configured to perform first-order difference calculation on the insulation resistance fitting values in the insulation resistance fitting value array to obtain a first-order difference corresponding to each insulation resistance fitting value.

[0047] The insulation deterioration risk level analysis module is configured to compare each first-order difference with a preset difference threshold, analyze the risk level of insulation deterioration of the power battery according to the comparison result, and output corresponding second alarm information.

[0048] The preset second time length is much longer than the preset first time length, and the number of insulation resistance values received within the preset second time length is much larger than the number of insulation resistance values received within the preset first time length.

[0049] The insulation deterioration risk level analysis module is configured to perform the following operations:

[0050] count the first number of first-order differences whose difference values are less than a preset lower limit of difference value (count_down), and the second number of first-order differences whose difference values are greater than a preset upper limit of difference value (count_up);

[0051] If count_down < 2 and count_up = 0, the risk level of insulation deterioration of the power battery is high risk.

[0052] If count_down > 2 and count_up = 0, the risk level of insulation deterioration of the power battery is medium risk.

[0053] If count_down = 2 and count_up = 0 and resA_std = 0, the risk level of insulation deterioration of the power battery is low risk.

[0054] The resA_std is a variance of the insulation resistance fitting value corresponding to all insulation resistance values in the preset second time length.

[0055] The insulation deterioration risk level analysis module is configured to perform the following operations:

[0056] When the risk level of insulation deterioration of the power battery is low risk, output the prompt information of the insulation resistance value detection fault of the power battery.

[0057] In a third aspect, a storage device is provided, wherein a plurality of program codes are stored in the storage device, and the program codes are adapted to be loaded and run by a processor to execute the power battery insulation monitoring method of any one of the above aspects.

[0058] In a fourth aspect, a control device is provided, comprising a processor and a storage device, wherein the storage device is adapted to store a plurality of program codes, and the program codes are adapted to be loaded and run by the processor to execute the power battery insulation monitoring method of any one of the above aspects.

[0059] Scheme 1: A power battery insulation monitoring method, characterized in that the method comprises:

[0060] Taking a current time as a time starting point, acquiring each insulation resistance value of the power battery received within a preset first time length before the current time;

[0061] Performing data statistics on the acquired insulation resistance values, and analyzing whether the power battery has a risk of insulation deterioration according to the data statistics result; if yes, outputting first alarm information.

[0062] Scheme 2: The power battery insulation monitoring method according to scheme 1, characterized in that the step of "performing data statistics on the acquired insulation resistance values, and analyzing whether the power battery has a risk of insulation deterioration according to the data statistics result" specifically comprises:

[0063] Performing mean value and variance calculation on the insulation resistance values received within the preset first time length respectively, to obtain an insulation resistance value mean res_avg and an insulation resistance value variance res_std;

[0064] Arranging the insulation resistance values received within the preset first time length in order from small to large, and calculating a cumulative percentage corresponding to each arranged insulation resistance value;

[0065] acquiring an insulation resistance value res_Npct corresponding to a cumulative percentage of a preset percentage value N;

[0066] analyzing whether the power battery has a risk of insulation deterioration according to the res_avg, the res_std, and the res_Npct, and specifically comprising:

[0067] if the res_avg is greater than the res_avg_set and the res_std is greater than the res_std_set, and the res_Npct is greater than the res_Npct_set, then the power battery has the risk of insulation deterioration.

[0068] wherein the res_avg_set is a set value of the preset mean value of the insulation resistance value, and the res_std_set is a set value of the preset variance of the insulation resistance value.

[0069] Scheme 3: The power battery insulation monitoring method according to the scheme 1, characterized in that, before the step of “performing data statistics on the acquired insulation resistance value”, the method further comprises:

[0070] Step S1: judging whether the number of the acquired insulation resistance value reaches a preset number threshold; if yes, performing the step of “performing data statistics on the acquired insulation resistance value”; if no, turning to step S2;

[0071] Step S2: extending the preset first time length according to a preset extension time, and re-acquiring the insulation resistance value received within the extended preset first time length before the current time point as a time starting point, and then turning to step S1.

[0072] Scheme 4: The power battery insulation monitoring method according to any one of the schemes 1 to 3, characterized in that, after analyzing that the power battery has the risk of insulation deterioration, the method further comprises:

[0073] acquiring each insulation resistance value of the power battery received within a preset second time length before the current time point as a time starting point, and arranging the insulation resistance values in the order from early to late according to the receiving time to obtain an insulation resistance value array;

[0074] performing regression fitting calculation on the insulation resistance values in the insulation resistance value array to obtain an insulation resistance fitting value corresponding to each insulation resistance value in the insulation resistance value array, and arranging the insulation resistance fitting value corresponding to each insulation resistance value in the order from early to late according to the receiving time to obtain an insulation resistance fitting value array;

[0075] performing first-order difference calculation on the insulation resistance fitting values in the insulation resistance fitting value array to obtain a first-order difference corresponding to each insulation resistance fitting value;

[0076] respectively, with a preset differential threshold value, and analyzing the risk level of insulation degradation of the power battery according to the comparison result and outputting corresponding second alarm information;

[0077] wherein the preset second time length is much longer than the preset first time length, and the number of insulation resistance values received within the preset second time length is much larger than the number of insulation resistance values received within the preset first time length.

[0078] Scheme 5, the power battery insulation monitoring method according to scheme 4, characterized in that the step of "respectively comparing each first-order difference with a preset differential threshold value, and analyzing the risk level of insulation degradation of the power battery according to the comparison result and outputting corresponding second alarm information" specifically includes:

[0079] counting a first number count_down of first-order differences with a differential value less than a preset lower limit of differential value, and a second number count_up of first-order differences with a differential value greater than a preset upper limit of differential value;

[0080] if count_down < 2 and count_up = 0, the risk level of insulation degradation of the power battery is high risk;

[0081] if count_down > 2 and count_up = 0, the risk level of insulation degradation of the power battery is medium risk;

[0082] if count_down = 2 and count_up = 0 and resA_std = 0, the risk level of insulation degradation of the power battery is low risk;

[0083] wherein the resA_std is the insulation resistance fitting value variance obtained by calculating the variance of the insulation resistance fitting values corresponding to all insulation resistance values within the preset second time length.

[0084] Scheme 6, the power battery insulation monitoring method according to scheme 5, characterized in that the method further includes:

[0085] when the risk level of insulation degradation of the power battery is low risk, outputting the reminder information of insulation resistance value detection failure of the power battery.

[0086] Scheme 7, a power battery insulation monitoring system, characterized in that the system includes:

[0087] data acquisition device configured to acquire each insulation resistance value of the power battery received within a preset first time length before the current time point with the current time point as the time starting point;

[0088] The data analysis device is configured to statistically analyze the insulation resistance values obtained by the data acquisition device, analyze whether the power battery has a risk of insulation deterioration according to the statistical analysis result, and output first alarm information if the power battery has the risk.

[0089] In scheme 8, the data analysis device includes a mean / variance calculation module, a percentile calculation module, and a data analysis module.

[0090] The mean / variance calculation module is configured to calculate the mean and variance of the insulation resistance values received within the preset first time length, respectively, to obtain an insulation resistance value mean res_avg and an insulation resistance value variance res_std.

[0091] The percentile calculation module is configured to arrange the insulation resistance values received within the preset first time length in ascending order and calculate the cumulative percentage corresponding to each arranged insulation resistance value, and obtain an insulation resistance value res_Npct corresponding to a cumulative percentage with a preset number N.

[0092] The data analysis module is configured to analyze whether the power battery has a risk of insulation deterioration according to the res_avg, res_std, and res_Npct, and specifically includes: if the power battery has the risk of insulation deterioration.

[0093] The res_avg_set is a preset set value of the insulation resistance value mean, and the res_std_set is a preset set value of the insulation resistance value variance.

[0094] In scheme 9, the data analysis device is further configured to perform the following operations.

[0095] Step S1: determining whether the number of obtained insulation resistance values reaches a preset number threshold; if yes, performing the step of “statistically analyzing the obtained insulation resistance values”; if no, proceeding to step S2.

[0096] Step S2: extending the current preset first time length according to a preset extension time, and reacquiring the insulation resistance values received within the extended preset first time length before the current time point, and then proceeding to step S1.

[0097] Scheme 10, the power battery insulation monitoring system according to scheme 7, characterized in that the system further comprises a battery insulation deterioration risk level analysis device, the battery insulation deterioration risk level analysis device comprises a data acquisition module, a regression fitting calculation module, a first-order difference calculation module and a insulation deterioration risk analysis module;

[0098] The data acquisition module is configured to, taking the current time as a time starting point, acquire each insulation resistance value received within a preset second time length before the current time and arrange the insulation resistance values in the order of receiving time from early to late to obtain an insulation resistance value array;

[0099] The regression fitting calculation module is configured to perform regression fitting calculation on the insulation resistance values in the insulation resistance value array to obtain an insulation resistance fitting value corresponding to each insulation resistance value in the insulation resistance value array, and arrange the insulation resistance fitting values corresponding to each insulation resistance value in the order of receiving time from early to late to obtain an insulation resistance fitting value array;

[0100] The first-order difference calculation module is configured to perform first-order difference calculation on the insulation resistance fitting values in the insulation resistance fitting value array to obtain a first-order difference corresponding to each insulation resistance fitting value;

[0101] The insulation deterioration risk level analysis module is configured to compare each first-order difference with a preset difference threshold respectively, analyze the risk level of insulation deterioration of the power battery according to the comparison result and output corresponding second alarm information;

[0102] Wherein, the preset second time length is much longer than the preset first time length and the number of insulation resistance values received within the preset second time length is much larger than the number of insulation resistance values received within the preset first time length.

[0103] Scheme 11, the power battery insulation monitoring system according to scheme 10, characterized in that the insulation deterioration risk level analysis module is further configured to perform the following operations:

[0104] Count the first number count_down of first-order differences whose difference values are less than a preset lower limit of difference value, and the second number count_up of first-order differences whose difference values are greater than a preset upper limit of difference value;

[0105] If count_down < 2 and count_up = 0, the risk level of insulation deterioration of the power battery is high risk;

[0106] If count_down > 2 and count_up = 0, the risk level of insulation deterioration of the power battery is medium risk;

[0107] If count_down = 2 and count_up = 0 and resA_std = 0, the risk level of insulation deterioration of the power battery is low risk;

[0108] The resA_std is a variance of the insulation resistance fitting value corresponding to all insulation resistance values in the preset second time length.

[0109] Scheme 12, the power battery insulation monitoring system according to scheme 11, characterized in that, further comprising the insulation deterioration risk level analysis module is configured to perform the following operations:

[0110] When the risk level of insulation deterioration of the power battery is low risk, output the prompt information of the insulation resistance value detection fault of the power battery.

[0111] Scheme 13, a storage device, wherein a plurality of program codes are stored, characterized in that, the program codes are suitable for being loaded and run by a processor to execute the power battery insulation monitoring method in any one of schemes 1 to 6.

[0112] Scheme 14, a control device, comprising a processor and a storage device, the storage device is suitable for storing a plurality of program codes, characterized in that, the program codes are suitable for being loaded and run by the processor to execute the power battery insulation monitoring method in any one of schemes 1 to 6.

[0113] The above one or more technical schemes of the present application have at least one or more of the following beneficial effects:

[0114] In the technical solution of the present application, the insulation resistance values received within a long time range (e.g. 10 hours) are extracted, the insulation resistance values are statistically analyzed, and whether the power battery has a risk of insulation deterioration is analyzed according to the statistical analysis result; if the power battery has a risk of insulation deterioration, an alarm information is output. An example: the battery management system in the electric vehicle detects the insulation resistance value of the power battery in real time, and sends the detected insulation resistance value to the background server connected to the electric vehicle network, and the background server receives and stores the respective insulation resistance value of each power battery. After the background server determines that the power battery has a risk of insulation deterioration by performing the above operation, an alarm information is output to the electric vehicle to remind the user of the electric vehicle that the power battery has a risk of insulation deterioration. The embodiment of the present application can make early insulation deterioration prediction before the power battery has insulation deterioration by statistically analyzing the insulation resistance values with long time and large data volume and analyzing whether the power battery has a risk of insulation deterioration according to the statistical analysis result, so that the user can timely perform battery maintenance before the power battery has insulation deterioration, and prevent the power battery from failing.

[0115] Further, in the technical solution of the present application, when it is determined that the power battery has a risk of insulation deterioration, the risk level of the power battery can be further analyzed by performing the following steps. Specifically, first, the insulation resistance received in a time range longer than the above long time range (e.g. 50 hours) is extracted, and the insulation resistance fitting value corresponding to each insulation resistance value is calculated by regression fitting calculation of the insulation resistance values. Then, the first-order difference of the insulation resistance fitting value is calculated, the first-order difference corresponding to each insulation resistance fitting value is obtained, each first-order difference is compared with a preset difference threshold value, and according to the comparison result, the insulation resistance value corresponding to the sudden change (sudden drop) of the insulation resistance of the power battery can be accurately found, and then according to the number of insulation resistance values corresponding to the sudden change of the insulation resistance, the risk level (high risk, medium risk and low risk) of the power battery is evaluated and the corresponding second alarm information is output. The embodiment of the present application performs regression fitting calculation on the insulation resistance values with long time and large data volume, and the change trend of the insulation resistance can be represented by the regression fitting calculation result; the first-order difference calculation result of the regression fitting calculation and the comparison of each first-order difference with the preset difference threshold value can accurately evaluate the risk level of the power battery. BRIEF DESCRIPTION OF DRAWINGS

[0116] The specific embodiments of the present application will be described below with reference to the accompanying drawings, in which:

[0117] Figure 1is a main step flow schematic diagram of a power battery insulation monitoring method according to an embodiment of the present application;

[0118] Figure 2 is an insulation resistance change curve schematic diagram when a power battery does not occur insulation degradation according to an embodiment of the present application;

[0119] Figure 3 is an insulation resistance change curve schematic diagram when a risk level of a power battery occurs insulation degradation is a high risk according to an embodiment of the present application;

[0120] Figure 4 is an insulation resistance change curve schematic diagram when a risk level of a power battery occurs insulation degradation is a medium risk according to an embodiment of the present application;

[0121] Figure 5 is an insulation resistance change curve schematic diagram when a risk level of a power battery occurs insulation degradation is a low risk according to an embodiment of the present application;

[0122] Figure 6 is a main structure schematic diagram of a power battery insulation monitoring system according to an embodiment of the present application;

[0123] Figure 7 is an application scenario schematic diagram of the present application;

[0124] List of reference signs:

[0125] 11: data acquisition device; 12: data analysis device; 21: electric vehicle; 22: background server. DETAILED DESCRIPTION

[0126] Some embodiments of the present application will be described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present application, and are not intended to limit the protection scope of the present application.

[0127] In the description of the present application, "module" and "processor" can include hardware, software or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and can also include a software portion such as program code, and can be a combination of software and hardware. The processor can be a central processor, a microprocessor, a digital signal processor or any other suitable processor. The processor has data and / or signal processing functions. The processor can be implemented in software, hardware or a combination of both. The non-transitory computer readable storage medium includes any suitable medium that can store program code, such as a magnetic disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or both A and B. The term "at least one of A or B" or "at least one of A and B" has a similar meaning to "A and / or B", and can include only A, only B, or both A and B. The singular form of the term "one", "this" can also include the plural form.

[0128] Some terms related to the present application are explained first.

[0129] The insulation resistance of the power battery refers to the resistance that can limit the leakage voltage of the power battery within a very small range, and is one of the important indicators of the insulation performance of the power battery.

[0130] The insulation degradation of the power battery refers to the irreversible decline of the insulation performance of the power battery. For example, the irreversible decline of the insulation resistance of the power battery.

[0131] The failure of the power battery mainly includes performance failure and safety failure of the power battery. The performance failure refers to the failure of the capacity and the life of the power battery to meet the preset use requirements, and the safety failure refers to the failure of the power battery due to improper use or other factors, which has a certain safety risk. For example, when the power battery occurs thermal runaway, the power battery has a safety risk, and the safety failure of the power battery.

[0132] The conventional power battery insulation performance monitoring method in the prior art can only monitor that the power battery has insulation degradation and alarm when the power battery is actually in the insulation degradation state, and at this time the power battery is likely to have thermal runaway and other battery failure faults due to insulation degradation, so that the power battery cannot be reliably and effectively warned of failure by this method.

[0133] In the embodiment of the present application, insulation resistance values received in a long time range (for example, 10 hours) are extracted, data statistics of the insulation resistance values are performed, and whether the power battery has a risk of insulation deterioration is analyzed according to the data statistics result; if the power battery has a risk of insulation deterioration, an alarm information is output. An example: the battery management system in the electric vehicle detects the insulation resistance value of the power battery in real time, and sends the detected insulation resistance value to the background server connected with the electric vehicle network, and the background server receives and stores the respective insulation resistance value corresponding to each power battery. After the background server judges that the power battery has a risk of insulation deterioration by performing the above operation, an alarm information is output to the electric vehicle to remind the user of the electric vehicle that the power battery has a risk of insulation deterioration. The embodiment of the present application can make early insulation deterioration prediction before the power battery has insulation deterioration by performing data statistics on the insulation resistance values with long time and large data volume and analyzing whether the power battery has a risk of insulation deterioration according to the data statistics result, so that the user can timely perform battery maintenance before the power battery has insulation deterioration, and prevent the power battery from having failure fault.

[0134] Further, when it is judged that the power battery has a risk of insulation deterioration, the embodiment of the present application can further analyze the risk level of the power battery by performing the following steps. Specifically, first, insulation resistances received in a time range longer than the above long time range (for example, 50 hours) are extracted, and the insulation resistance fitting values corresponding to each insulation resistance value are obtained by performing regression fitting calculation on the insulation resistance values. Then, the first-order difference of each insulation resistance fitting value is obtained by performing first-order difference calculation on the insulation resistance fitting values, each first-order difference is compared with a preset difference threshold value, and according to the comparison result, the insulation resistance value corresponding to the insulation resistance mutation (sudden drop) of the power battery can be accurately found, and then according to the number of insulation resistance values corresponding to the insulation resistance mutation, the risk level (high risk, medium risk and low risk) of the power battery is evaluated and the corresponding second alarm information is output. The embodiment of the present application performs regression fitting calculation on the insulation resistance values with long time and large data volume, and the change trend of the insulation resistance can be represented by the regression fitting calculation result; the first-order difference calculation result of the regression fitting calculation and the comparison of each first-order difference with the preset difference threshold value can accurately evaluate the risk level of the power battery.

[0135] Referring to the drawings Figure 7 , Figure 7Is an application scenario schematic diagram of an embodiment of the technical scheme of the present application. The electric vehicle 21 is provided with a communication device, a power battery, and a battery management system (BMS) capable of detecting the insulation resistance value of the power battery. The electric vehicle 21 establishes a communication connection with the background server 22 through the communication device (including but not limited to: WIFI communication device and 4G communication device (communication device based on the fourth generation mobile communication and its technology)). The battery management system detects the insulation resistance value of the power battery in real time, and the electric vehicle 21 sends the detected insulation resistance value to the background server 22 in real time through the communication device. The background server 22 analyzes the received insulation resistance value, and when it is analyzed that the power battery has a risk of insulation deterioration and the corresponding risk level, an alarm information (for example: the current power battery has a high risk of insulation deterioration) is sent to the electric vehicle 21 to remind the driver in the electric vehicle 21 to timely repair the power battery. Further, the background server 22 can also be in communication connection with the terminal (including but not limited to: mobile phone and tablet computer) of the user of the electric vehicle 21 and / or the service provider terminal (including but not limited to: computer equipment) of the electric vehicle. After analyzing that the power battery has a risk of insulation deterioration and the corresponding risk level, an alarm information is sent to the terminal of the user and / or an alarm information is sent to the service provider terminal of the electric vehicle according to the identity code (including but not limited to: ID number of the power battery) of the current power battery (for example: the power battery with ID number "Battery111" has a high risk of insulation deterioration), so as to remind the service provider of the electric vehicle to timely repair the power battery.

[0136] Referring to the accompanying Figure 1 , Figure 1 Is a main step flow schematic diagram of a power battery insulation monitoring method according to an embodiment of the present application. As shown in Figure 1 , the power battery insulation monitoring method in the embodiment of the present application mainly includes the following steps:

[0137] Step S101: Taking the current time as the time starting point, obtaining each insulation resistance value of the power battery received within a preset first time length before the current time.

[0138] The current time refers to the current time of the insulation monitoring of the power battery, rather than the current time of receiving the insulation resistance value.

[0139] An example: the current time is 8 pm on January 1, 2020, and the preset first time length is 2 hours. Therefore, "each insulation resistance value of the power battery received within a preset first time length before the current time" refers to each insulation resistance value of the power battery received within 2 hours between 6 pm and 8 pm.

[0140] In one embodiment, the insulation resistance value of the power battery detected and sent by the insulation resistance detection device such as the battery management system can be directly received, or the insulation resistance value of the power battery sent by other devices connected to the insulation resistance detection device in network can be received, and these devices can receive / store the insulation resistance value of the power battery detected by the insulation resistance detection device. One example: directly receiving the insulation resistance value of the power battery sent by the battery management system in the electric vehicle; another example: receiving the insulation resistance value of the power battery sent by the vehicle control device in the electric vehicle, the vehicle control device is connected to the battery management system in network and can receive / store the insulation resistance value of the power battery detected by the battery management system.

[0141] In one embodiment, after obtaining the insulation resistance value received within the preset first time length before the current time, the step of judging the number of received insulation resistance values and determining whether to re-obtain the insulation resistance value according to the judgment result can also be included. This step specifically includes:

[0142] Step S1021: After obtaining the insulation resistance value received within the preset first time length before the current time, it is judged whether the number of obtained insulation resistance values reaches a preset number threshold; if yes, the insulation resistance value does not need to be re-obtained, and step S103 is continued to be executed. If no, the insulation resistance value needs to be re-obtained, and step S1022 is turned to.

[0143] Step S1022: The current preset first time length is extended according to a preset extension time, and the insulation resistance value received within the extended preset first time length before the current time is re-obtained with the current time as the time starting point, and then step S1021 is turned to.

[0144] One example: assuming that the current time is 8 pm on January 1, 2020, the preset first time length is 2 hours, the preset extension time is 1 hour, and the preset number threshold is 30, the step of judging whether to re-obtain the insulation resistance value specifically includes:

[0145] Step 1: After obtaining each insulation resistance value received within 2 hours between 6 pm and 8 pm, it is judged that the number of insulation resistance values received in this period is less than 30, then the preset first time length is delayed by 1 hour, and each insulation resistance value received within 2 hours between 5 pm and 8 pm is re-obtained.

[0146] Step 2: After obtaining each insulation resistance value received within 3 hours between 5 pm-8 pm, if the number of insulation resistance values received within this time is still less than 30, the preset first time length is delayed for 1 hour again, and each insulation resistance value received within 4 hours between 4 pm-8 pm is obtained again.

[0147] Step 3: After obtaining each insulation resistance value received within 4 hours between 4 pm-8 pm, if the number of insulation resistance values received within this time is greater than 30, the insulation resistance value does not need to be obtained again.

[0148] The embodiment of the present application can accurately analyze whether the power battery has the risk of insulation deterioration by statistically analyzing the insulation resistance values with long time and large data volume. However, in actual application, due to different insulation resistance detection frequencies or insulation resistance transmission frequencies of different insulation resistance detection devices, the number of received insulation resistance values for power batteries using different insulation resistance detection devices is greatly different within the same preset first time length. If the number of received insulation resistance values is too small, the accuracy of the data statistical analysis result will be seriously affected, and then the analysis accuracy of the insulation deterioration risk will be affected. Therefore, in order to overcome this problem, the embodiment of the present application further judges whether the number of obtained insulation resistance values is sufficient (the number of insulation resistance values is greater than or equal to a preset number threshold) after obtaining the insulation resistance values received within the preset first time length, whether the insulation deterioration analysis of the power battery can have high accuracy; if the number is small (the number of insulation resistance values is less than the preset number threshold), the preset first time length is extended and the insulation resistance values are obtained again until the number of insulation resistance values is sufficient to ensure that the insulation deterioration analysis of the power battery has high accuracy.

[0149] Step S102: statistically analyzing the obtained insulation resistance values.

[0150] In one embodiment, by statistically analyzing n insulation resistance values {res1, res2,..., resi,..., resn} within a preset first time length before the current time, the mean, variance and percentile of the insulation resistance values within this time can be obtained.

[0151] 1. Mean

[0152] The mean res_avg of the insulation resistance values is calculated according to the method shown in the following formula (1):

[0153]

[0154] resi represents the i-th insulation resistance value received within a preset first time length before the current moment.

[0155] 2. Variance

[0156] The variance res_std of the insulation resistance value is calculated according to the method shown in the following formula (2):

[0157]

[0158] 3. Percentile

[0159] In the embodiment, the percentile of the insulation resistance value can be obtained according to the following steps: first, arrange the insulation resistance values received within a preset first time length before the current moment in ascending order and calculate the cumulative percentage corresponding to each arranged insulation resistance value; then, obtain the insulation resistance value res_Npct corresponding to the cumulative percentage with a preset value N. An example: obtain the insulation resistance value res_90pct corresponding to the cumulative percentage of 90%.

[0160] Step S103: Analyze whether the power battery has a risk of insulation deterioration according to the data statistics result. Specifically, if it is analyzed that the power battery has a risk of insulation deterioration, go to step S104; if it is analyzed that the power battery does not have a risk of insulation deterioration (for example: the power battery is in the insulation resistance value change state shown in FIG. 6), go to step S106. Figure 2

[0161] Specifically, after data statistics of n insulation resistance values {res1, res2,..., resi,..., resn} within a preset first time length before the current moment are obtained, the mean value res_avg, the variance res_std and the percentile res_Npct of the insulation resistance values within the time are obtained, if it is determined that the power battery has a risk of insulation deterioration.

[0162] res_avg_set represents a preset set value of the mean value of the insulation resistance value, and res_std_set represents a preset set value of the variance of the insulation resistance value. An example: when the voltage range of the high-voltage system of the electric vehicle is 300-500V, the value range of res_avg_set is 500-30000 Ohm, and the value range of res_std_set is 500-10000 Ohm.

[0163] ​The embodiment of the present application obtains data quantification information (mean, variance and percentile) capable of representing the change state of the insulation resistance value in this period of time by statistically analyzing the insulation resistance value of a long time and a large data volume, and then compares the data quantification information with a threshold value, and judges whether the power battery has a risk of insulation deterioration according to the comparison result. Compared with the method of directly comparing the insulation resistance value detected once with the resistance threshold value and judging whether the power battery has insulation deterioration according to the comparison result (if the insulation resistance value is less than the resistance threshold value, it is judged that insulation deterioration occurs), the present application can effectively overcome the problem of misjudgment of insulation deterioration caused by large fluctuation of the insulation resistance value in the normal charging and discharging process of the power battery, and the problem that reliable insulation deterioration early warning cannot be performed before the battery failure fault occurs when the set value of the resistance threshold value is low. Specifically, since the insulation resistance value fluctuates greatly in the normal charging and discharging process of the power battery, when the insulation resistance value is monitored to be less than the resistance threshold value, the insulation resistance value may be a normal fluctuation value of the insulation resistance, and actually the power battery does not have insulation deterioration, but it is misjudged as insulation deterioration because the insulation resistance value is less than the resistance threshold value. Further, when the set value of the resistance threshold value is low, if the insulation resistance value is monitored to be less than the resistance threshold value, the power battery may have a battery failure fault such as thermal runaway due to insulation deterioration at this time, and thus reliable insulation deterioration early warning cannot be performed before the battery failure fault occurs by this method.

[0164] The first step of the present scheme triggers a relatively high threshold condition, and res_avg_set takes a value in the range of 500-30000, so that some batteries with good insulation but large fluctuations in the use process also enter the suspected problem data pool, and therefore a second stage of further judgment is needed to determine the insulation deterioration risk level and lock the problem battery.

[0165] In one embodiment, after analyzing that the power battery is in a low insulation resistance state, a step of analyzing the risk level of insulation deterioration of the power battery can also be included. The step specifically includes:

[0166] Step S1031: Taking the current time as a time starting point, obtaining each insulation resistance value of the power battery received within a preset second time length before the current time, and arranging the insulation resistance values in the order from early to late to obtain an insulation resistance value array {resA1, resA2,..., resAj,..., resAm}. resAj represents the jth insulation resistance value in the insulation resistance value array, that is, the jth insulation resistance value received within the preset second time length before the current time.

[0167] The current time in the embodiment is the same as the current time in step S101. The preset second time length is much longer than the preset first time length in step S101, and the number of insulation resistance values received in the preset second time length is also much larger than the number of insulation resistance values received in the preset first time length (m >> n).

[0168] Step S1032: regression fitting calculation is performed on the insulation resistance values in the insulation resistance value array to obtain an insulation resistance fitting value corresponding to each insulation resistance value in the insulation resistance value array, and the insulation resistance fitting values corresponding to each insulation resistance value are arranged in the order of the receiving time from early to late to obtain an insulation resistance fitting value array {resA_1, resA_2,..., resA_j,..., resA_m}. resA_j represents the insulation resistance fitting value corresponding to the jth insulation resistance value in the insulation resistance fitting value array, that is, the insulation resistance fitting value corresponding to the jth insulation resistance value received in the preset second time length before the current time.

[0169] In the embodiment, a regression algorithm such as a tree regression algorithm, a ridge regression algorithm (Ridge Regression or Tikhonov Regularization), a linear regression algorithm (Linear Regression), etc. can be used to perform regression fitting calculation on each insulation resistance value received in the preset second time length before the current time.

[0170] Step S1033: first-order difference calculation is performed on the insulation resistance fitting values in the insulation resistance fitting value array to obtain a first-order difference {resA_diff_1, resA_diff_2,..., resA_diff_j,..., resA_diff_m} corresponding to each insulation resistance fitting value. resA_diff_j represents the first-order difference corresponding to the jth insulation resistance fitting value in the insulation resistance fitting value array.

[0171] Step S1034: each first-order difference is compared with a preset difference threshold, and the risk level of insulation degradation of the power battery is analyzed according to the comparison result and the corresponding second alarm information is output.

[0172] The embodiment of the application performs regression fitting calculation on long-time and large-data insulation resistance values. The change trend of the insulation resistance can be represented by the regression fitting calculation result, so that the risk level of insulation degradation of the power battery can be accurately evaluated after the first-order difference calculation on the regression fitting calculation result and the comparison of each first-order difference with the preset difference threshold.

[0173] In one implementation, the risk level of insulation degradation in the power battery can be analyzed by following these steps:

[0174] First, count the first number of first-order differences whose difference value is less than the preset lower limit of the difference value, count_down, and count the second number of first-order differences whose difference value is greater than the preset upper limit of the difference value, count_up.

[0175] Then, based on the first count_down and the second count_up, the risk level of insulation degradation in the power battery is determined and analyzed. Specifically:

[0176] If count_down < 2 and count_up = 0, the risk level of insulation degradation in the power battery is high. count_up = 0 indicates that among the insulation resistance values ​​(insulation resistance value array) received within the preset second time period prior to the current moment, there are no cases where the resistance difference between two adjacent insulation resistance values ​​is large (no large fluctuations in the trend of insulation resistance value changes). count_down < 2 indicates that there are relatively few cases where the resistance values ​​between two adjacent insulation resistance values ​​are very close (no relatively stable phases in the trend of insulation resistance value changes). Therefore, it can be concluded that the insulation resistance value of the power battery exhibits a small low-resistance fluctuation state during this period (e.g., ...). Figure 3 (The change process of the insulation resistance value is shown on the right side of the image). Furthermore, in this embodiment, a second alarm message indicating a high risk of insulation degradation in the power battery can also be output.

[0177] If count_down > 2 and count_up = 0, the risk level of insulation degradation in the power battery is medium. count_down > 2 indicates that there are many instances where the resistance values ​​between two adjacent insulation resistance values ​​are very close, and given that the power battery is known to be in a low insulation resistance state, the insulation resistance value of the power battery during this period mainly shows a gradual decrease (e.g., ...). Figure 4 The insulation resistance value change process shown is not presented as a gradual increase in resistance value. Furthermore, in this embodiment, a second alarm message indicating a medium risk of insulation degradation in the power battery can also be output.

[0178] If count_down = 2, count_up = 0, and resA_std = 0, then the risk level of insulation degradation in the power battery is low. Here, resA_std is the variance of the fitted insulation resistance values ​​obtained after calculating the variance of all insulation resistance values ​​received within a preset second time period prior to the current moment. resA_std = 0 indicates that the value of each fitted insulation resistance value is the same (e.g., ...).Figure 5 In addition, in the embodiment, the second alarm information that the risk of insulation deterioration of the power battery is low can be output.

[0179] The first quantity count_down of the first-order difference whose difference value is less than the preset lower limit of the difference value and the second quantity count_up of the first-order difference whose difference value is greater than the preset upper limit of the difference value are counted, so that the resistance fluctuation state (or the development trend of the insulation resistance value) of the insulation resistance value of the power battery can be analyzed, and the risk level of the insulation deterioration of the power battery can be determined.

[0180] In one embodiment, when it is analyzed that the risk level of the insulation deterioration of the power battery is low, it is possible that the insulation resistance detection device such as the battery management system fails to detect the insulation resistance value of the power battery. Therefore, when it is analyzed that the risk level of the insulation deterioration of the power battery is low, the reminding information of the insulation resistance value detection failure of the power battery can be output to remind the user or the electric vehicle service provider to timely repair the insulation resistance detection device such as the battery management system.

[0181] Step S104: output the first alarm information.

[0182] The first alarm information refers to the reminding information that the power battery has the risk of insulation deterioration. One example: the first alarm information can be "the current power battery has the risk of insulation deterioration, please repair in time".

[0183] Step S106: do not output the alarm information.

[0184] It should be noted that although the above embodiment describes the steps in a specific order, those skilled in the art can understand that, in order to achieve the effect of the present application, the different steps do not necessarily have to be executed in such an order, they can be executed simultaneously (in parallel) or in other orders, and these changes are within the protection scope of the present application.

[0185] Referring to the accompanying Figure 6 , Figure 6 is the main structure diagram of the power battery insulation monitoring system according to one embodiment of the present application. As shown in Figure 6 , the power battery insulation monitoring system in the embodiment mainly includes a data acquisition device 11 and a data analysis device 12. For the sake of simplicity, although the processor and the memory are not shown in the figure, it should be understood that the data acquisition device 11 and the data analysis device 12 are respectively connected to the processor and the memory. Figure 6As shown in FIG. 1, it can be understood by those skilled in the art that the power battery insulation monitoring system can be part of a processor and / or a memory. For example, in some embodiments, one or more of the data acquisition device 11 and the data analysis device 12 can be part of a processor. In some embodiments, these devices can respectively correspond to a part of electronic circuitry in the processor for signal or data processing, or to relevant program code stored in a computer readable medium such as a memory. In some embodiments, the data acquisition device 11 and the data analysis device 12 can also not be part of the current processor, but part of another processor outside the current processor. In some embodiments, one or more of the data acquisition device 11 and the data analysis device 12 can be combined into one device.

[0186] Specifically, the data acquisition device 11 can be configured to acquire each insulation resistance value of the power battery received within a preset first time length before the current time point, taking the current time point as the time starting point. In one implementation, the description of the function can be referred to step S101.

[0187] The data analysis device 12 can be configured to perform data statistics on the insulation resistance values acquired by the data acquisition device 11, analyze whether the power battery has a risk of insulation deterioration according to the data statistics result, and output a first alarm information if yes. The data analysis device 12 includes a mean / variance calculation module, a percentile calculation module and a data analysis module. The mean / variance calculation module can be configured to perform mean and variance calculation on the insulation resistance values received within the preset first time length respectively, to obtain the insulation resistance value mean res_avg and the insulation resistance value variance res_std. The percentile calculation module can be configured to arrange the insulation resistance values received within the preset first time length in ascending order and calculate the cumulative percentage corresponding to each arranged insulation resistance value; and acquire the insulation resistance value res_Npct corresponding to the cumulative percentage with the percentage value being a preset value N. The data analysis module can be configured to analyze whether the power battery has a risk of insulation deterioration according to res_avg, res_std and res_Npct, specifically including: if the power battery has a risk of insulation deterioration; res_avg_set is a set value of the preset insulation resistance value mean, and res_std_set is a set value of the preset insulation resistance value variance. In one implementation, the description of the function can be referred to steps S102 and S103.

[0188] In one embodiment, the power battery insulation monitoring system can further comprise a battery insulation deterioration risk level analysis device, which can comprise a data acquisition module, a regression fitting calculation module, a first-order difference calculation module, and an insulation deterioration risk level analysis module.

[0189] Specifically, the data acquisition module can be configured to, taking the current time as a time starting point, acquire each insulation resistance value received within a preset second time length before the current time and arrange the insulation resistance values in the order of receiving time from early to late to obtain an insulation resistance value array. The regression fitting calculation module can be configured to perform regression fitting calculation on the insulation resistance values in the insulation resistance value array to obtain an insulation resistance fitting value corresponding to each insulation resistance value in the insulation resistance value array, and arrange the insulation resistance fitting values corresponding to each insulation resistance value in the order of receiving time from early to late to obtain an insulation resistance fitting value array. The first-order difference calculation module can be configured to perform first-order difference calculation on the insulation resistance fitting values in the insulation resistance fitting value array to obtain a first-order difference corresponding to each insulation resistance fitting value. The insulation deterioration risk level analysis module can be configured to compare each first-order difference with a preset difference threshold respectively, analyze the risk level of insulation deterioration of the power battery according to the comparison result, and output corresponding second alarm information. The preset second time length is much longer than the preset first time length, and the number of insulation resistance values received within the preset second time length is much larger than the number of insulation resistance values received within the preset first time length. In one embodiment, the description of the specific implementation function can be referred to steps S1031-S1034.

[0190] In one embodiment, the insulation deterioration risk level analysis module can be configured to perform the following operations

[0191] a first number count_down of the first-order differences whose difference values are less than a preset lower limit of the difference values, and a second number count_up of the first-order differences whose difference values are greater than a preset upper limit of the difference values; if count_down < 2 and count_up = 0, the risk level of the power battery to occur insulation deterioration is a higher risk; if count_down > 2 and count_up = 0, the risk level of the power battery to occur insulation deterioration is a medium risk; if count_down = 2 and resA_std = 0, the risk level of the power battery to occur insulation deterioration is a lower risk; further, when the risk level of the power battery to occur insulation deterioration is a lower risk, the reminding information of the battery management system to occur insulation resistance value detection failure can be further output. Wherein, resA_std is a variance of the insulation resistance fitting values corresponding to all the insulation resistances in a preset second time length. In an embodiment, the description of the specific implementation function can be referred to the description of step S103.

[0192] The power battery insulation monitoring system described above is used to execute Figure 1 The power battery insulation monitoring method embodiment shown above, the technical principles, the technical problems solved and the technical effects generated are similar. Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and related description of the power battery insulation monitoring system can refer to the description of the power battery insulation monitoring method embodiment, which will not be repeated here.

[0193] Based on the above method embodiment, the application further provides a storage device embodiment. In the storage device embodiment, the storage device stores a plurality of program codes, and the program codes are suitable for being loaded and run by the processor to execute the power battery insulation monitoring method of the above method embodiment. For the convenience of description, only the parts related to the embodiments of the application are shown, and the specific technical details not disclosed are referred to the method part of the embodiments of the application.

[0194] Based on the above method embodiment, the application further provides a control device embodiment. In the control device embodiment, the device includes a processor and a storage device, and the storage device stores a plurality of program codes, and the program codes are suitable for being loaded and run by the processor to execute the power battery insulation monitoring method of the above method embodiment. For the convenience of description, only the parts related to the embodiments of the application are shown, and the specific technical details not disclosed are referred to the method part of the embodiments of the application.

[0195] Those skilled in the art can understand that all or part of the processes in the method of the above-mentioned embodiment of the present application can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium can include any entity or device, medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code. It should be noted that the content included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0196] Further, it should be understood that, since the setting of each module is only for illustrating the functional units of the system of the present application, the physical device corresponding to the module can be the processor itself, or a part of software in the processor, a part of hardware, or a part of combination of software and hardware. Therefore, the number of each module in the figure is only illustrative.

[0197] Those skilled in the art can understand that each module in the system can be adaptively split or combined. Such splitting or combining of the specific module does not cause the technical solution to deviate from the principles of the present application, therefore, the technical solution after splitting or combining will fall within the protection scope of the present application.

[0198] In the embodiment of the present application, insulation resistance values received in a long time range (for example, 10 hours) are extracted, data statistics are performed on the insulation resistance values, and whether the power battery has a risk of insulation deterioration is analyzed according to the data statistics results; if the power battery has a risk of insulation deterioration, an alarm information is output. An example: a battery management system in an electric vehicle detects insulation resistance values of the power battery in real time, and sends the detected insulation resistance values to a background server connected to a network of the electric vehicle, and the background server receives and stores the respective insulation resistance values of each power battery. After the background server determines that the power battery has a risk of insulation deterioration by performing the above operation, the background server outputs an alarm information to the electric vehicle to remind the user of the electric vehicle that the power battery has a risk of insulation deterioration. The embodiment of the present application can analyze whether the power battery has a risk of insulation deterioration according to data statistics of long-time and large-data-volume insulation resistance values, and can make a prediction of insulation deterioration in advance before the power battery has insulation deterioration, so that the user can timely perform battery maintenance before the power battery has insulation deterioration, and prevent the power battery from having a failure fault.

[0199] So far, the technical solutions of the present application have been described in combination with one embodiment shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after the changes or replacements will fall within the protection scope of the present application.

Claims

1. A method of monitoring the insulation of a power cell, characterized by The method comprises: acquiring each insulation resistance value of the power battery received within a preset first time length before the current time point as a time starting point; statistically processing the acquired insulation resistance values, and analyzing whether the power battery has a risk of insulation deterioration according to a data statistical result; if yes, outputting first alarm information; wherein, the statistically processing the acquired insulation resistance values and the analyzing whether the power battery has a risk of insulation deterioration according to a data statistical result comprise: respectively performing mean value and variance calculation on the insulation resistance values received within the preset first time length to obtain an insulation resistance value mean res_avg and an insulation resistance value variance res_std; arranging the insulation resistance values received within the preset first time length in an order from small to large and calculating a cumulative percentage corresponding to each arranged insulation resistance value; acquiring an insulation resistance value res_Npct corresponding to a cumulative percentage with a percentage value of a preset value N; According to the res_avg, res_std and res_Npct, it is analyzed whether the power battery has a risk of insulation deterioration, specifically comprising: If res_avg < res_avg_set and res_std < res_std_set and res_Npct < res_Npct_set, the power battery has a risk of insulation deterioration; the res_avg_set is a set value of a preset average insulation resistance value, and the res_std_set is a set value of a preset insulation resistance value variance. the res_avg, the res_std and the res_Npct are data quantification information representing an insulation resistance value change state, and the res_avg_set and the res_std_set represent threshold values of the data quantification information.

2. The power cell insulation monitoring method of claim 1, wherein, Before the step of "statistically processing the acquired insulation resistance values", the method further comprises: step S1: judging whether the number of the acquired insulation resistance values reaches a preset number threshold; if yes, performing the step of "statistically processing the acquired insulation resistance values"; if no, turning to step S2; step S2: extending the current preset first time length according to a preset extension time, and re-acquiring insulation resistance values received within the extended preset first time length before the current time point as a time starting point, and then turning to step S1.

3. The method of claim 1 or 2, wherein After analyzing that the power battery has a risk of insulation deterioration, the method further comprises: acquiring each insulation resistance value of the power battery received within a preset second time length before the current time point as a time starting point and arranging the insulation resistance values in an order from early to late according to a receiving time to obtain an insulation resistance value array; performing regression fitting calculation on the insulation resistance values in the insulation resistance value array to obtain an insulation resistance fitting value corresponding to each insulation resistance value in the insulation resistance value array, and arranging the insulation resistance fitting values corresponding to each insulation resistance value in an order from early to late according to the receiving time to obtain an insulation resistance fitting value array; performing first-order difference calculation on the insulation resistance fitting values in the insulation resistance fitting value array to obtain a first-order difference corresponding to each insulation resistance fitting value; respectively comparing each first-order difference with a preset difference threshold, and analyzing a risk level of insulation deterioration of the power battery according to a comparison result and outputting corresponding second alarm information; The preset second time length is much longer than the preset first time length, and the number of insulation resistance values received in the preset second time length is much larger than the number of insulation resistance values received in the preset first time length. The comparing each first-order difference with the preset difference threshold respectively and analyzing the risk level of insulation degradation of the power battery according to the comparison result comprises: The first number count_down of the first-order differences whose difference values are less than the preset lower limit of the difference value and the second number count_up of the first-order differences whose difference values are greater than the preset upper limit of the difference value are counted; if count_down<2 and count_up=0, the risk level of insulation degradation of the power battery is high; if count_down>2 and count_up=0, the risk level of insulation degradation of the power battery is medium; if count_down=2 and count_up=0 and resA_std=0, the risk level of insulation degradation of the power battery is low; the resA_std is the insulation resistance fitting value variance obtained by performing variance calculation on the insulation resistance fitting values corresponding to all insulation resistance values in the preset second time length.

4. The power cell insulation monitoring method of claim 3, wherein, The method further comprises: When the risk level of insulation degradation of the power battery is low, outputting the prompt information of the insulation resistance value detection fault of the power battery.

5. A power cell insulation monitoring system, characterized by The system comprises: The data acquisition device is configured to acquire each insulation resistance value of the power battery received in a preset first time length before the current time point as the time starting point; The data analysis device is configured to perform data statistics on the insulation resistance values acquired by the data acquisition device and analyze whether the power battery has the risk of insulation degradation according to the data statistics result; if yes, output the first alarm information; The data analysis device comprises a mean / variance calculation module, a percentile calculation module and a data analysis module. The mean / variance calculation module is configured to perform mean and variance calculation on the insulation resistance values received in the preset first time length respectively to obtain the insulation resistance value mean res_avg and the insulation resistance value variance res_std. The percentile calculation module is configured to arrange the insulation resistance values received in the preset first time length in descending order and calculate the cumulative percentage corresponding to each arranged insulation resistance value; and acquire the insulation resistance value res_Npct corresponding to the cumulative percentage with the percentage value being the preset value N. The res_avg, res_std and res_Npct are data quantification information representing the insulation resistance value change state, and the res_avg_set and res_std_set represent the threshold of the data quantification information. The data analysis module is configured to analyze whether the power battery has a risk of insulation deterioration according to the res_avg, the res_std and the res_Npct, and specifically includes: if the power battery has a risk of insulation deterioration; the res_avg_set is a set value of a preset mean value of insulation resistance, and the res_std_set is a set value of a preset variance of insulation resistance. The data analysis device is configured to perform the following operations:

6. The power cell insulation monitoring system of claim 5, wherein, ​ Step S1: judging whether the number of acquired insulation resistance values reaches a preset number threshold; if yes, executing the step of "carrying out data statistics on the acquired insulation resistance values"; if no, turning to step S2; Step S2: extending the current preset first time length according to a preset extension time, and reacquiring the insulation resistance values received within the extended preset first time length before the current time point as a time starting point, and then turning to step S1.

7. The power cell insulation monitoring system of claim 5, wherein, The system further comprises a battery insulation deterioration risk level analysis device, which comprises a data acquisition module, a regression fitting calculation module, a first-order difference calculation module and an insulation deterioration risk analysis module. The data acquisition module is configured to acquire each insulation resistance value received within a preset second time length before the current time point as a time starting point and arrange the insulation resistance values in the order of receiving time from early to late to obtain an insulation resistance value array. The regression fitting calculation module is configured to perform regression fitting calculation on the insulation resistance values in the insulation resistance value array to obtain an insulation resistance fitting value corresponding to each insulation resistance value in the insulation resistance value array, and arrange the insulation resistance fitting values corresponding to each insulation resistance value in the order of receiving time from early to late to obtain an insulation resistance fitting value array. The first-order difference calculation module is configured to perform first-order difference calculation on the insulation resistance fitting values in the insulation resistance fitting value array to obtain a first-order difference corresponding to each insulation resistance fitting value. The insulation deterioration risk level analysis module is configured to compare each first-order difference with a preset difference threshold respectively, analyze the risk level of insulation deterioration of the power battery according to the comparison result and output corresponding second alarm information. The preset second time length is much longer than the preset first time length, and the number of insulation resistance values received within the preset second time length is much larger than the number of insulation resistance values received within the preset first time length. The insulation deterioration risk level analysis module is configured to perform the following operations: counting a first number count_down of first-order differences with a difference value less than a preset lower limit of difference value, and a second number count_up of first-order differences with a difference value greater than a preset upper limit of difference value; if count_down<2 and count_up=0, the risk level of insulation deterioration of the power battery is high; if count_down>2 and count_up=0, the risk level of insulation deterioration of the power battery is medium; if count_down=2 and count_up=0 and resA_std=0, the risk level of insulation deterioration of the power battery is low; the resA_std is a variance of the insulation resistance fitting values corresponding to all insulation resistance values within the preset second time length.

8. The power cell insulation monitoring system of claim 7, wherein, The insulation deterioration risk level analysis module is further configured to perform the following operations: When the risk level of the power battery developing insulation deterioration is a low risk, outputting a prompt information of an insulation resistance value detection fault of the power battery.

9. A storage device storing therein a plurality of pieces of program code, characterized by, The program code is adapted to be loaded and run by the processor to execute the power battery insulation monitoring method of any one of claims 1 to 4.

10. A control device comprising a processor and a storage device, the storage device being adapted to store a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by the processor to execute the power battery insulation monitoring method of any one of claims 1 to 4.

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