CN116027110B - Online early warning method, medium and equipment for new energy automobile - Google Patents
Online early warning method, medium and equipment for new energy automobile Download PDFInfo
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- CN116027110B CN116027110B CN202310132172.3A CN202310132172A CN116027110B CN 116027110 B CN116027110 B CN 116027110B CN 202310132172 A CN202310132172 A CN 202310132172A CN 116027110 B CN116027110 B CN 116027110B
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Abstract
The invention relates to the field of data processing, and discloses a new energy automobile online early warning method, medium and equipment. The method comprises the following steps: acquiring a charge-discharge state sequence and an insulation resistance sequence of the new energy automobile; determining the maximum insulation resistance of the vehicle type of the new energy vehicle in a reasonable range; screening a first insulation resistor sub-sequence corresponding to a charging working condition and a second insulation resistor sub-sequence corresponding to a discharging working condition according to the charging and discharging state sequences and the insulation resistor sequences; clustering is carried out on each insulation resistor sub-sequence, the minimum center value is selected from the center values corresponding to the classes obtained after clustering, and the ratio of the insulation resistor number of the class where the minimum center value is located in the insulation resistor number in the insulation resistor sub-sequence is calculated; aiming at each working condition, on-line early warning judgment is carried out on the working condition according to the maximum insulation resistance, the minimum central value and the duty ratio. The method can be applied to online early warning scenes of various types of new energy automobiles.
Description
Technical Field
The invention relates to the technical field of new energy automobile monitoring, in particular to a new energy automobile online early warning method, medium and equipment.
Background
Along with the rapid increase of the storage quantity of the new energy automobile, the degree of importance of the new energy automobile owner on the safety of the automobile is rapidly increased. And the state monitoring data of the new energy vehicles are required to be uploaded by a plurality of whole-vehicle enterprises in China. The insulation resistance data is used as one of important monitoring data of the new energy automobile, and plays a vital role in effectively evaluating the safety state of the automobile. How to use insulation resistance data to perform online early warning on new energy automobiles is the subject of research.
Disclosure of Invention
In order to solve at least one technical problem, the invention provides a new energy automobile online early warning method, medium and equipment.
According to a first aspect, the new energy automobile online early warning method provided by the embodiment of the invention comprises the following steps:
acquiring a charge-discharge state sequence and an insulation resistance sequence of the new energy automobile; the charging and discharging state sequence comprises working conditions corresponding to each time frame of a preset time interval, the working conditions are charging working conditions or discharging working conditions, and the insulation resistance sequence comprises insulation resistances corresponding to each time frame;
determining the maximum insulation resistance of the vehicle type of the new energy vehicle in a reasonable range;
screening a first insulation resistor subsequence corresponding to the charging working condition and a second insulation resistor subsequence corresponding to the discharging working condition according to the charging and discharging state sequences and the insulation resistor sequences;
clustering is carried out on each insulation resistor sub-sequence, the minimum center value is selected from the center values corresponding to the classes obtained after clustering, and the ratio of the insulation resistor number of the class where the minimum center value is located in the insulation resistor number in the insulation resistor sub-sequence is calculated;
and aiming at each working condition, carrying out on-line early warning judgment on the working condition according to the maximum insulation resistance, the minimum central value and the duty ratio.
According to a second aspect, a computer readable storage medium is provided, on which a computer program is stored which, when executed in a computer, causes the computer to perform the method provided in the first aspect.
According to a third aspect, a computing device provided by an embodiment of the present invention includes a memory and a processor, where the memory stores executable code, and the processor implements the method provided by the first aspect when executing the executable code.
The embodiment of the invention has the following technical effects:
according to the embodiment of the invention, the charge and discharge state sequence and the insulation resistance sequence of the new energy automobile are firstly obtained, then the maximum insulation resistance of the automobile type of the new energy automobile in a reasonable range is determined, and the first insulation resistance sub-sequence corresponding to the charge working condition and the second insulation resistance sub-sequence corresponding to the discharge working condition are screened out according to the charge and discharge state sequence and the insulation resistance sequence, namely the two insulation resistance sub-sequences are screened out according to the working condition. Clustering is carried out on each insulation resistor sub-sequence, the minimum center value is selected from the center values corresponding to the classes obtained after clustering, and the ratio of the insulation resistor number of the class where the minimum center value is located in the insulation resistor number in the insulation resistor sub-sequence is calculated; and aiming at each working condition, carrying out on-line early warning judgment on the working condition according to the maximum insulation resistance, the minimum central value and the duty ratio. Therefore, the embodiment of the invention can realize online early warning according to the insulation resistance data. In addition, the insulation resistance is processed based on the clustering thought in the embodiment of the invention, so that the difference caused by different vehicle models is avoided, and the universality of early warning is improved. That is, the method provided by the embodiment of the invention can be applied to online early warning scenes of various types of new energy automobiles, and has a wider application range.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an online early warning method for a new energy automobile in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the invention, are within the scope of the invention.
In a first aspect, an embodiment of the present invention provides an online early warning method for a new energy automobile, referring to fig. 1, the method includes steps S110 to S150:
s110, acquiring a charge and discharge state sequence and an insulation resistance sequence of the new energy automobile;
the charging and discharging state sequence comprises working conditions corresponding to each time frame of a preset time interval, the working conditions are charging working conditions or discharging working conditions, and the insulation resistance sequence comprises insulation resistances corresponding to each time frame;
specifically, a charge-discharge state sequence and an insulation resistance sequence can be obtained from the new energy automobile monitoring platform. For example, a charge-discharge state sequence and an insulation resistance sequence are acquired from the new energy automobile monitoring platform every 5 minutes, the charge-discharge state sequence is a sequence formed by respective charge-discharge states corresponding to respective time frames in the 5-minute interval, and the insulation resistance sequence is a sequence formed by respective insulation resistances corresponding to respective time frames in the 5-minute interval. Wherein one time frame corresponds to one charge-discharge state and one insulation resistance.
The charging and discharging states comprise a charging state and a discharging state, wherein the charging state is also called a charging working condition, and the discharging state is also called a discharging working condition.
S120, determining the maximum insulation resistance of the vehicle type of the new energy automobile in a reasonable range;
specifically, the maximum insulation resistance of the new energy automobile in a reasonable range can be determined according to the automobile enterprise standard, and the maximum insulation resistance is represented by R_max.
In one embodiment, before executing S130, the method provided by the embodiment of the present invention may further include the following steps:
detecting whether a null value item exists in the insulation resistance sequence;
if the null value item exists, judging whether the duration corresponding to the continuous null value item is longer than the first duration;
if the time frame is longer than the first time length, deleting each time frame corresponding to the null value item;
and if the time length is smaller than or equal to the first time length, filling the insulation resistance of each null value item by using the insulation resistance before and after the null value item.
It can be appreciated that due to various effects of network delay and platform hardware resources, there is inevitably a certain abnormal situation in the data, and such abnormal situation may greatly affect the accuracy of the subsequent early warning, so that the abnormal data is cleaned before executing S130.
Specifically, whether null items exist in the insulation resistance sequence is detected first. The null item refers to a time frame in which there is no specific data, for example, one or more time frames in a preset time interval have no corresponding insulation resistance in the insulation resistance sequence, when the one or more time frames are null items. If null items exist and a plurality of continuous null items exist, judging whether the total duration of each time frame corresponding to the continuous null items is greater than the first duration. If the first duration is longer than the first duration, the null entries are too many, and even though the data after being filled is possibly at a large risk of error, so that the continuous null entries need to be deleted. If the total duration of each time frame of the continuous null value items is less than or equal to the first duration, the null value items are reserved, and the null value items are filled in an interpolation mode. For example, for a null entry, the null entry may be filled with an average of insulation resistances corresponding to a previous time frame and insulation resistances corresponding to a subsequent time frame of the null entry.
The first duration may be set as needed.
S130, screening out a first insulation resistor sub-sequence corresponding to the charging working condition and a second insulation resistor sub-sequence corresponding to the discharging working condition according to the charging and discharging state sequences and the insulation resistor sequences;
that is, the insulation resistances corresponding to the charging condition in the insulation resistance sequence are extracted, a sub-sequence, namely a first insulation resistance sub-sequence, is formed according to the time sequence, and the insulation resistances corresponding to the discharging condition in the insulation resistance sequence are extracted, and a sub-sequence, namely a second insulation resistance sub-sequence, is formed according to the time sequence. It can be seen that two subsequences can be obtained by extraction according to the charge-discharge state.
In one embodiment, before executing S140, the method provided by the embodiment of the present invention may further include: and performing empirical mode decomposition on each insulation resistor sub-sequence, and removing a high-frequency part in the insulation resistor sub-sequence after the empirical mode decomposition.
Wherein, the empirical mode decomposition is Empirical ModeDecomposition, which is called EMD for short. By performing EMD and removing the high-frequency part, the influence of small-range fluctuation on the result can be reduced, so that the false alarm rate of early warning is reduced.
In one embodiment, before executing S140, the method provided by the embodiment of the present invention may further include: and removing each insulation resistor with the time difference between the first insulation resistor sub-sequence and the charging starting time being less than or equal to the second time length, and removing each insulation resistor with the time difference between the first insulation resistor sub-sequence and the charging ending time being less than or equal to the second time length.
The first insulation resistor subsequence is a first subsequence corresponding to the charging state.
In consideration of the influence of plugging and unplugging the charging gun in the charging process on the insulation resistance uploaded by the vehicle, insulation three-level alarm is triggered, and therefore, in each charging working condition, the insulation resistance data in a period of time near the charging starting time and the charging ending time are not selected for subsequent analysis. Therefore, the insulation resistances of the first insulation resistance sub-sequence, which are less than the second time period from the starting time and less than the second time period from the ending time, are deleted.
S140, clustering is carried out on each insulation resistor sub-sequence, a minimum center value is selected from the center values corresponding to the classes obtained after clustering, and the ratio of the insulation resistor number of the class where the minimum center value is located in the insulation resistor number in the insulation resistor sub-sequence is calculated;
that is, the sub-sequences of each working condition are clustered to obtain a plurality of classes. The clustering algorithm is an unsupervised learning algorithm, and the unlabeled sample data is input and divided into a plurality of classes according to the distance between the data. The partitioning principle is intra-class sample minimization and inter-class distance maximization.
For example, clustering is performed on each insulation resistor in the first insulation resistor sub-sequence to obtain K classes, the central value of each class is calculated to obtain K central values, and the minimum value is selected from the K central values to obtain the minimum central value.
The ratio of the number of insulation resistances of the class in which the minimum center value is located to the number of insulation resistances in the insulation resistance sub-sequence is denoted as r_per. For example, for the first insulation resistor sub-sequence, the class of the minimum center value includes 30 insulation resistor values, and the corresponding first insulation resistor sub-sequence includes 100 insulation resistors, the duty ratio r_per is 0.3.
The clustering algorithm may adopt a K-means algorithm, and of course, other clustering algorithms may achieve the same effect, for example, a learning vectorization clustering algorithm, a nearest neighbor node algorithm, and the like.
S150, aiming at each working condition, carrying out online early warning judgment on the working condition according to the maximum insulation resistance, the minimum central value and the duty ratio.
That is, for each working condition, according to the maximum insulation resistance, the minimum central value and the duty ratio, judging whether the condition needing early warning exists, and if the condition needing early warning exists, giving an alarm.
In one embodiment, S150 may specifically include at least one of:
(1) If the minimum central value is larger than or equal to the first product, zeroing the duty ratio, and carrying out early warning judgment on the increase trend of the abnormal insulation resistance duty ratio; wherein the first product is a product between the maximum insulation resistance and a first coefficient;
for example, the maximum insulation resistance is denoted by r_max, the first coefficient is a, and the minimum center value is denoted by r_kmin. If r_kmin > =a×r_max, the duty ratio r_per is set to 0, so as to prepare for early warning and judging the increase trend of the duty ratio of the subsequent abnormal insulation resistance.
In one embodiment, the early warning judgment of the abnormal insulation resistance duty ratio increasing trend may include the following steps:
determining the current accumulated number of the corresponding insulation resistor sub-sequences according to the working condition;
judging whether the current accumulated number is larger than a second number or not;
if the number of the insulating resistor sub-sequences is larger than the second number, curve fitting is carried out according to the latest second number of the insulating resistor sub-sequences to obtain a fitted curve, the abscissa of the fitted curve is the sequence corresponding to each insulating resistor sub-sequence of the working condition, the sequence is larger than or equal to 1 and smaller than or equal to the second number, and the ordinate of the fitted curve is the corresponding duty ratio of each insulating resistor sub-sequence of the working condition;
determining the duty ratio increase rate of the working condition according to the fitting curve;
if the duty ratio increase rate of the working condition is greater than a preset threshold value, the early warning type is an abnormal insulation resistance duty ratio increase trend, and the early warning type is used for warning.
That is, for the current working condition, the total number of the insulation resistor sub-sequences which are screened at present is determined, which may also be called the current accumulated number. For example, a sequence of charge and discharge states and a sequence of insulation resistances are collected every 5 minutes during the past one hour, thereby obtaining a first sequence of insulation resistances and a second sequence of insulation resistances, which total 12 first sequences of insulation resistances and 12 second sequences of insulation resistances are accumulated for the one hour. If the current operating condition is a charging operating condition, the current accumulated number of the first insulation resistor sub-sequence is 12. If the current accumulated number is larger than the second number, the current accumulated number of the first insulation resistor sub-sequence is enough to perform curve fitting, and if the current accumulated number is smaller than or equal to the second number, the current accumulated number of the first insulation resistor sub-sequence is insufficient to perform curve fitting.
The specific fitting process is as follows: and taking a second number of insulation resistor sub-sequences closest to the current moment, then establishing a coordinate system, wherein the horizontal axis of the coordinate system represents the sequence of each insulation resistor sub-sequence, the sequence of the insulation resistor sub-sequence which is closer to the current moment is larger, the sequence of the insulation resistor sub-sequence which is farther from the current moment is smaller, and the sequence is changed from 1 to the second number. The vertical axis of the coordinate system represents the above-mentioned duty cycle. Marking coordinate points (sequence and duty ratio) corresponding to a second number of insulation resistor sub-sequences closest to the current moment in a coordinate system to obtain a second number of discrete points, and then performing curve fitting based on the second number of discrete points to obtain a fitting curve under the working condition. The linear fitting can be carried out, and the duty ratio increase rate can be obtained by convenient calculation from the fitted curve.
If the duty ratio increase rate of the working condition is larger than a preset threshold, the situation that early warning is needed exists, the early warning type is an abnormal insulation resistance duty ratio increase trend, and then an alarm is given according to the early warning type.
(2) If the minimum central value is smaller than or equal to the second product, the early warning type is 'insulation resistance low', and the alarm is given according to the early warning type; wherein the second product is a product between the maximum insulation resistance and a second coefficient, the second coefficient being smaller than the first coefficient;
for example, the maximum insulation resistance is represented by r_max, the first coefficient is a, the second coefficient is represented by c, the minimum center value is represented by r_kmin, if r_kmin < = c r_max, the situation that needs to be pre-warned is considered to exist, the pre-warning type is low in insulation resistance, and the pre-warning is performed according to the pre-warning type.
(3) If the minimum central value is larger than the second product and smaller than the first product, determining the insulation resistance jump times according to the continuous degree of each time frame in the class of the minimum central value, and performing early warning judgment according to the insulation resistance jump times, the minimum central value and the duty ratio.
Specifically, the jump times of the insulation resistors are determined according to the discontinuous times of the time frames corresponding to the insulation resistors in the class where the minimum center value is located. For example, there are 20 insulation resistances in the class of the minimum center value, and two adjacent time frames in 20 time frames corresponding to the 20 insulation resistances are not continuous, and one insulation resistance jump is considered. In this way, the number of insulation resistance jumps is determined.
In one embodiment, the early warning judgment according to the insulation resistance jump number, the minimum center value and the duty ratio may include at least one of the following:
(3-1) if the minimum central value is smaller than the first product and larger than the third product, the duty ratio is larger than 0.5, and the insulation resistance jump number is smaller than or equal to the first number, the early warning type is an insulation resistance continuously lower state, and an alarm is given according to the early warning type;
(3-2) if the minimum center value is smaller than the first product and larger than a third product, and the insulation resistance jump number is larger than the first number, the early warning type is 'insulation resistance lower state jump', and an alarm is given according to the early warning type;
(3-3) if the minimum central value is smaller than or equal to the third product and larger than the second product, the duty ratio is larger than 0.5, and the insulation resistance jump times are smaller than or equal to the first number, the early warning type is an insulation resistance continuously low state, and an alarm is given according to the early warning type;
(3-4) if the minimum central value is smaller than or equal to the third product and larger than the second product, and the insulation resistance jump number is larger than the first number, the early warning type is insulation resistance low state jump, and an alarm is given according to the early warning type;
wherein the third product is a product between the maximum insulation resistance and a third coefficient that is less than the first coefficient and greater than the second coefficient.
For example, the third coefficient is denoted by b. The third coefficient is smaller than the first coefficient and larger than the second coefficient.
The above four cases are summarized in the following table 1, in which r_kmin is the minimum center value, r_per is the above-mentioned duty ratio, N1 is the number of insulation resistance transitions, d is the first number, r_max is the maximum insulation resistance, a is the first coefficient, and b is the third coefficient.
TABLE 1
As can be seen from table 1, when the number of insulation resistance transitions is larger than the first number, that is, when the number of insulation resistance transitions is relatively large, there is a case where early warning is required regardless of the ratio. And when the duty ratio is large, the insulation resistance jump times are small, and the situation that early warning is needed is considered to exist. The larger the duty ratio, the greater the persistence is reflected to some extent. The larger the number of hops, the higher the degree of hopping is reflected. Therefore, the early warning type is divided into continuous and jump. The minimum central value has lower and lower conditions, so that the insulation resistance is also divided into lower and lower conditions, and the early warning type is also divided into lower and lower conditions. This can be divided into four cases in table 1 above. In other cases than the four cases in table 1, no alarm is considered necessary.
Wherein a, b, c are all data greater than 0 and less than 1, and d is an integer greater than 0. For example, a is set to 0.9, b is set to 0.4, c is set to 0.01, d is set to 10, the second number is set to 40, and the preset threshold is set to 1.
In one embodiment, for the case of (3), after performing early warning judgment according to the insulation resistance jump number, the minimum center value and the duty ratio, the method may further include:
determining the current accumulated number of the corresponding insulation resistor sub-sequences for each working condition;
judging whether the current accumulated number is larger than a second number or not;
if the number of the insulating resistor sub-sequences is larger than the second number, curve fitting is carried out according to the latest second number of the insulating resistor sub-sequences to obtain a fitted curve, the abscissa of the fitted curve is the sequence corresponding to each insulating resistor sub-sequence of the working condition, the sequence is larger than or equal to 1 and smaller than or equal to the second number, and the ordinate of the fitted curve is the corresponding duty ratio of each insulating resistor sub-sequence of the working condition;
determining the duty ratio increase rate of the working condition according to the fitting curve;
if the duty ratio increase rate of the working condition is greater than a preset threshold value, the early warning type is an abnormal insulation resistance duty ratio increase trend, and the early warning type is used for warning.
It can be understood that the above situation is early warning judgment of the increasing trend of the abnormal insulation resistance ratio. The procedure is the same as the step of early warning and judging the increasing trend of the abnormal insulation resistance ratio in the case of (1), and will not be described in detail here.
Therefore, the online monitoring and safety early warning of the insulation resistance of the new energy automobile can be realized according to the flow.
In the prior art, a direct deleting processing mode is generally adopted for abnormal data of insulation resistors, which is easy to cause omission of key data, and in the embodiment of the invention, whether the total duration of each time frame corresponding to the continuous null value items is longer than the first duration is judged, the continuous null value items are deleted when the total duration is longer than the first duration, and the null value items are filled through front and rear insulation resistors when the total duration is shorter than the first duration, so that the problem that the key data is omitted due to direct deletion is avoided, and the early warning accuracy is improved.
In the prior art, an early warning threshold value needs to be determined for each vehicle type in each region, and the defects of poor operability and large influence of human factors on the setting of the threshold value are overcome. In the embodiment of the invention, the corresponding maximum insulation resistance is only required to be determined according to the vehicle type, the region is not distinguished, and the parameters required to be set by people are fewer, so that the operability can be improved, and the influence of human factors on parameter setting can be reduced.
In the prior art, the insulation resistance adopts a mode of fixed time frame quantity, and the insulation resistance near the starting charging time and the ending charging time under the charging working condition is removed in the embodiment of the invention, so that false alarm caused by rapid change of data is avoided, namely, false alarm possibly caused by the mode of fixed time frame quantity when charging and discharging are alternated is avoided.
In addition, the insulation resistance is processed based on the clustering thought in the embodiment of the invention, so that the difference caused by different vehicle models is avoided, and the universality of early warning is improved.
The method provided by the embodiment of the invention can be applied to a hardware device, the hardware device is directly connected with a new energy automobile, and the vehicle data are collected in real time and used as an independent alarm device to ensure the safety of users. The method provided by the embodiment of the invention can also be applied to a safety early-warning cloud platform, the safety early-warning cloud platform receives the data uploaded by the vehicle, and the safety early-warning cloud platform feeds back alarm information to enterprises or vehicle owners of the new energy vehicles through the method provided by the embodiment of the invention.
In a second aspect, embodiments of the present application provide a computer readable medium having stored thereon computer instructions which, when executed by a processor, cause the processor to perform the method provided in the first aspect.
Specifically, a system or apparatus provided with a storage medium on which a software program code realizing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or apparatus may be caused to read out and execute the program code stored in the storage medium.
In this case, the program code itself read from the storage medium may realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code form part of the present invention.
Examples of the storage medium for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (e.g., CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a nonvolatile memory card, and a ROM. Alternatively, the program code may be downloaded from a server computer by a communication network.
Further, it should be apparent that the functions of any of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing an operating system or the like operating on the computer to perform part or all of the actual operations based on the instructions of the program code.
Further, it is understood that the program code read out by the storage medium is written into a memory provided in an expansion board inserted into a computer or into a memory provided in an expansion module connected to the computer, and then a CPU or the like mounted on the expansion board or the expansion module is caused to perform part and all of actual operations based on instructions of the program code, thereby realizing the functions of any of the above embodiments.
It may be appreciated that, for explanation, specific implementation, beneficial effects, examples, etc. of the content in the computer readable medium provided in the embodiments of the present application, reference may be made to corresponding parts in the method provided in the first aspect, and details are not repeated herein.
In a third aspect, an embodiment of the present specification provides a computing device including a memory having executable code stored therein and a processor that, when executing the executable code, performs a method according to any one of the embodiments of the present specification.
It may be appreciated that, for explanation, specific implementation, beneficial effects, examples, etc. of the content in the computing device provided in the embodiments of the present application, reference may be made to corresponding parts in the method provided in the first aspect, which are not repeated herein.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments in part.
Those skilled in the art will appreciate that in one or more of the examples described above, the functions described in the present invention may be implemented in hardware, software, a pendant, or any combination thereof. When implemented in software, these functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present application. As used in this specification, the terms "a," "an," "the," and/or "the" are not intended to be limiting, but rather are to be construed as covering the singular and the plural, unless the context clearly dictates otherwise. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method or apparatus comprising such elements.
It should also be noted that the positional or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the positional or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or element in question must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Unless specifically stated or limited otherwise, the terms "mounted," "connected," and the like are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.
Claims (8)
1. The online early warning method for the new energy automobile is characterized by comprising the following steps of:
acquiring a charge-discharge state sequence and an insulation resistance sequence of the new energy automobile; the charging and discharging state sequence comprises working conditions corresponding to each time frame of a preset time interval, the working conditions are charging working conditions or discharging working conditions, and the insulation resistance sequence comprises insulation resistances corresponding to each time frame;
determining the maximum insulation resistance of the vehicle type of the new energy vehicle in a reasonable range;
screening a first insulation resistor subsequence corresponding to the charging working condition and a second insulation resistor subsequence corresponding to the discharging working condition according to the charging and discharging state sequences and the insulation resistor sequences;
clustering is carried out on each insulation resistor sub-sequence, the minimum center value is selected from the center values corresponding to the classes obtained after clustering, and the ratio of the insulation resistor number of the class where the minimum center value is located in the insulation resistor number in the insulation resistor sub-sequence is calculated;
aiming at each working condition, carrying out online early warning judgment on the working condition according to the maximum insulation resistance, the minimum central value and the duty ratio;
and aiming at each working condition, carrying out on-line early warning judgment on the working condition according to the maximum insulation resistance, the minimum central value and the duty ratio, wherein the on-line early warning judgment comprises the following steps:
if the minimum central value is larger than or equal to the first product, zeroing the duty ratio, and carrying out early warning judgment on the increase trend of the abnormal insulation resistance duty ratio; wherein the first product is a product between the maximum insulation resistance and a first coefficient;
if the minimum central value is smaller than or equal to the second product, the early warning type is 'insulation resistance low', and the alarm is given according to the early warning type; wherein the second product is a product between the maximum insulation resistance and a second coefficient, the second coefficient being smaller than the first coefficient;
if the minimum central value is larger than the second product and smaller than the first product, determining the insulation resistance jump times according to the continuous degree of each time frame in the class of the minimum central value, and performing early warning judgment according to the insulation resistance jump times, the minimum central value and the duty ratio;
the early warning judgment is carried out according to the insulation resistance jump times, the minimum central value and the duty ratio, and the early warning judgment comprises the following steps:
if the minimum central value is smaller than the first product and larger than the third product, the duty ratio is larger than 0.5, and the insulation resistance jump times are smaller than or equal to the first quantity, the early warning type is an insulation resistance continuously lower state, and the early warning type is used for giving an alarm;
if the minimum central value is smaller than the first product and larger than the third product, and the insulation resistance jump times are larger than the first quantity, the early warning type is insulation resistance lower state jump, and an alarm is given according to the early warning type;
if the minimum central value is smaller than or equal to the third product and larger than the second product, the duty ratio is larger than 0.5, and the insulation resistance jump number is smaller than or equal to the first number, the early warning type is an insulation resistance continuous low state, and the early warning type is used for warning;
if the minimum central value is smaller than or equal to the third product and larger than the second product, and the insulation resistance jump times are larger than the first quantity, the early warning type is insulation resistance low state jump, and the early warning type is used for warning;
wherein the third product is a product between the maximum insulation resistance and a third coefficient that is less than the first coefficient and greater than the second coefficient.
2. The method of claim 1, wherein before the step of screening the first insulation resistor sub-sequence corresponding to the charging condition and the second insulation resistor sub-sequence corresponding to the discharging condition according to the charging and discharging state sequences and the insulation resistor sequence, the method further comprises:
detecting whether a null value item exists in the insulation resistance sequence;
if the null value item exists, judging whether the duration corresponding to the continuous null value item is longer than the first duration;
if the time frame is longer than the first time length, deleting each time frame corresponding to the null value item;
and if the time length is smaller than or equal to the first time length, filling the insulation resistance of each null value item by using the insulation resistance before and after the null value item.
3. The method of claim 1, wherein prior to clustering each insulation resistor sub-sequence, the method further comprises: and performing empirical mode decomposition on each insulation resistor sub-sequence, and removing a high-frequency part in the insulation resistor sub-sequence after the empirical mode decomposition.
4. The method of claim 1, wherein prior to clustering each insulation resistor sub-sequence, the method further comprises: and removing each insulation resistor with the time difference between the first insulation resistor sub-sequence and the charging starting time being less than or equal to the second time length, and removing each insulation resistor with the time difference between the first insulation resistor sub-sequence and the charging ending time being less than or equal to the second time length.
5. The method of claim 1, wherein the performing the abnormal insulation resistance duty cycle increasing trend early warning determination comprises:
determining the current accumulated number of the corresponding insulation resistor sub-sequences according to the working condition;
judging whether the current accumulated number is larger than a second number or not;
if the number of the insulating resistor sub-sequences is larger than the second number, curve fitting is carried out according to the latest second number of the insulating resistor sub-sequences to obtain a fitted curve, the abscissa of the fitted curve is the sequence corresponding to each insulating resistor sub-sequence of the working condition, the sequence is larger than or equal to 1 and smaller than or equal to the second number, and the ordinate of the fitted curve is the corresponding duty ratio of each insulating resistor sub-sequence of the working condition;
determining the duty ratio increase rate of the working condition according to the fitting curve;
if the duty ratio increase rate of the working condition is greater than a preset threshold value, the early warning type is an abnormal insulation resistance duty ratio increase trend, and the early warning type is used for warning.
6. The method of claim 1, wherein after the early warning determination based on the insulation resistance jump number, the minimum center value, and the duty cycle, the method further comprises:
determining the current accumulated number of the corresponding insulation resistor sub-sequences for each working condition;
judging whether the current accumulated number is larger than a second number or not;
if the number of the insulating resistor sub-sequences is larger than the second number, curve fitting is carried out according to the latest second number of the insulating resistor sub-sequences to obtain a fitted curve, the abscissa of the fitted curve is the sequence corresponding to each insulating resistor sub-sequence of the working condition, the sequence is larger than or equal to 1 and smaller than or equal to the second number, and the ordinate of the fitted curve is the corresponding duty ratio of each insulating resistor sub-sequence of the working condition;
determining the duty ratio increase rate of the working condition according to the fitting curve;
if the duty ratio increase rate of the working condition is greater than a preset threshold value, the early warning type is an abnormal insulation resistance duty ratio increase trend, and the early warning type is used for warning.
7. A computer readable storage medium, having stored thereon a computer program which, when executed in a computer, causes the computer to perform the method of any of claims 1-6.
8. A computing device comprising a memory and a processor, the memory having executable code stored therein, the processor, when executing the executable code, implementing the method of any of claims 1-6.
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