CN112363059A - Battery fault diagnosis method and system based on GM (1, 1) gray model - Google Patents
Battery fault diagnosis method and system based on GM (1, 1) gray model Download PDFInfo
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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Abstract
The utility model provides a battery fault diagnosis method and system based on GM (1, 1) grey model, according to the fluctuation characteristic of voltage when the battery charges and discharges, establish battery GM (1, 1) voltage prediction model based on grey system theory, utilize the newest current operating mode to set for the time battery voltage data and predict, obtain the predicted value, calculate the difference between voltage measured value and predicted value, according to the latent trouble of difference change diagnosis battery, realize the early diagnosis to the many trouble of battery under different operating modes, and can accurate prediction out fault type and fault time, it is lower to the data density requirement, only need 1 second to gather battery voltage once. The method is simple and easy to implement, good in robustness and high in practical value.
Description
Technical Field
The disclosure belongs to the field of fault diagnosis of new energy automobile power batteries, and relates to a battery fault diagnosis method and system based on a GM (1, 1) gray model.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
To promote sustainable energy development and increasing energy demand, the development of electric vehicles is receiving wide attention from the world. The lithium battery (lithium ion power battery) has the outstanding advantages of high specific energy/specific power, long cycle life, no memory effect and the like, so that the lithium battery becomes one of the core technologies of the current electric automobile, and the performance of the lithium battery directly influences the performance, safety and economy of the electric automobile. However, the safety problem caused by battery failure becomes a key technical difficulty faced by the development of global electric vehicles, and the development of the electric vehicles is severely restricted. In a power battery pack, the occurrence of internal faults of the battery has great randomness, early tiny faults are latent in the voltage of the battery (generally the cut-off voltage of the battery is not exceeded), the detection is extremely difficult, the harm is extremely great, and the explosion or the combustion of the battery is easily caused. However, the prior art has certain defects in battery internal fault diagnosis.
According to the inventor's knowledge, the existing literature (Nicholas Williard, Wei He, Christopher Hendricks and Michael Pecht. Lessons left from the 787 dreamline off lithium-battery reliability [ J ] Energies,2013,6: 4682-. Although the method can diagnose obvious faults of the battery to a certain extent, early tiny faults which do not trigger the threshold value cannot be diagnosed, and the selection of the threshold value is also extremely challenging.
A multi-fault diagnosis method based on Sample Entropy is proposed in the prior art (Yunlong Shang, Gaopeng Lu, Yongzhe Kang, Zhongkai Zhou, Bin Duan, Chenghui Zhang. A Multi-fault diagnosis method based on modified Sample Entrol for quality-on basis string [ J ]. Journal of Power Source, 2020,446: 227275). The method can simultaneously diagnose various types of early-stage faults of the battery and predict the fault time, but a large amount of voltage sample data is needed, and the requirement on the sampling density of the data is high (usually, one data is collected for 0.01 s).
Disclosure of Invention
The battery fault diagnosis method and system based on the GM (1, 1) gray model can realize early diagnosis of multiple faults of the battery under different working conditions, can accurately predict fault types and fault time, have low requirements on data density, and only need to acquire the voltage of the battery once within set time. Simple and easy to operate, good in robustness and great in practical value.
According to some embodiments, the following technical scheme is adopted in the disclosure:
a battery fault diagnosis method based on a GM (1, 1) gray model comprises the following steps:
according to the fluctuation characteristics of the voltage during the charge and discharge of the battery, a battery GM (1, 1) voltage prediction model is established based on a grey system theory, the latest current working condition is used for setting secondary battery voltage data for prediction to obtain a predicted value, the difference between the voltage measured value and the predicted value is calculated, and the potential fault of the battery is diagnosed according to the difference value change.
As an alternative embodiment, the specific process of establishing a battery GM (1, 1) voltage prediction model based on the gray system theory according to the fluctuation characteristics of the voltage during the charging and discharging of the battery includes:
coding the voltage data of the battery according to time to form a system characteristic data sequence of the corresponding battery voltage;
and acquiring the current set secondary voltage data of the battery which is latest in time from the system characteristic data sequence of the battery voltage, and performing gray processing on the acquired data.
As a further limitation, the step of performing gray processing on the acquired set secondary voltage data specifically includes: performing primary grey accumulation generation processing on the acquired set voltage data to obtain a primary grey accumulation generation sequence of the battery voltage;
and performing adjacent mean value generation operation on the obtained gray primary accumulation generation sequence of the battery voltage to obtain an adjacent mean value generation sequence of the gray primary accumulation generation sequence of the battery voltage.
As an alternative embodiment, the specific process of predicting by using the latest current working condition setting sub-battery voltage data to obtain the predicted value includes: and calculating the gray action amount required by the battery voltage for gray prediction tracking according to the obtained set secondary voltage data, obtaining an equal-dimensional successive compensation gray univariate first-order time response sequence corresponding to the battery voltage, and performing gray prediction tracking on the battery voltage.
As an alternative embodiment, the diagnosing the latent battery fault according to the difference value change specifically includes: and judging whether the battery has a fault, and if the variation of the output difference value in a certain time exceeds a first set value, judging that the battery has the fault.
As a further limitation, if the difference value has a sudden rising trend and is greater than a second set value, the fault is determined to be an open-circuit fault or an overvoltage fault;
and if the sudden drop trend of the difference value is smaller than a third set value, judging that the fault is a short-circuit fault or an undervoltage fault.
The battery fault diagnosis system based on the GM (1, 1) gray model comprises:
the prediction model building module is configured to build a battery GM (1, 1) voltage prediction model based on a grey system theory according to the fluctuation characteristics of the voltage during the charging and discharging of the battery;
the prediction module is configured to predict the secondary battery voltage data by using the latest current working condition to obtain a predicted value;
and the difference value analysis module is configured to calculate the difference between the voltage measured value and the predicted value and diagnose the potential fault of the battery according to the change of the difference value.
As an alternative embodiment, the prediction model construction module comprises:
the sequence acquisition module is configured to encode the voltage data of the battery according to time to form a corresponding system characteristic data sequence of the battery voltage;
the grey processing module is configured to acquire current set secondary voltage data of the battery, which is latest in time, from the system characteristic data sequence of the battery voltage and perform grey processing on the acquired data;
and the prediction root module is configured to calculate the gray acting amount required by the battery voltage for gray prediction tracking according to the obtained set secondary voltage data, obtain an equal-dimensional successive compensation gray univariate first-order time response sequence corresponding to the battery voltage, and perform gray prediction tracking on the battery voltage.
A computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to execute the GM (1, 1) gray model-based battery fault diagnosis method.
A terminal device comprising a processor and a computer readable storage medium, the processor being configured to implement instructions; the computer readable storage medium stores a plurality of instructions adapted to be loaded by a processor and to perform the GM (1, 1) gray model based battery fault diagnosis method.
Compared with the prior art, the beneficial effect of this disclosure is:
the early multi-fault diagnosis method of the lithium ion battery based on the GM (1, 1) gray model can accurately diagnose the early faults of the battery under the condition of no model, can quickly and accurately diagnose and predict the fault types and time of the lithium ion battery, and can predict the faults of the battery in advance under the condition that the battery is not obviously abnormal.
The method and the device have the advantages that the diagnosis sensitivity to the fault is kept by setting the length of the secondary voltage data, the calculation cost is low, and the real-time implementation is convenient.
The method has low requirement on data acquisition density, and only needs 1 second to acquire the battery voltage once, thereby greatly saving the data storage space.
The method and the device can diagnose various early faults of the battery only by comparing the predicted value with the measured value, and greatly reduce the calculation cost.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a flowchart of a battery fault diagnosis method based on a GM (1, 1) gray model according to this embodiment;
FIG. 2 is a graph of voltage prediction based on GM (1, 1) gray model in this example;
fig. 3 is a diagram of the multiple-fault diagnosis of the battery based on the GM (1, 1) gray model according to the present embodiment.
The specific implementation mode is as follows:
the present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, the method for diagnosing early multiple faults of a battery based on a GM (1, 1) gray model comprises the following steps:
s1, encoding voltage data of the battery according to time (t is 1,2,3 …, k-1) to form a system characteristic data sequence V of the battery voltage(0)Wherein k is the predicted time;
s2, slave system characteristic data sequence V(0)Acquiring the current latest 5-time voltage data of the battery: v (k-5) to v (k-1), where k is the predicted time and k is>5；
S3, obtaining the data sequence V of the battery voltage in the step S2(0)1-AGO (Gray primary accumulation generation processing) is carried out to obtain a Gray primary accumulation generation sequence V of the battery voltage(1)；
S4, the product obtained in the step S3Generating a sequence V by gray one-time accumulation of battery voltages(1)Performing adjacent mean value generation operation to obtain a gray primary accumulation generation sequence V of the battery voltage(1)Is generated by the adjacent mean generation sequence Z(1)；
S5, calculating the gray action amount a required by the battery voltage for gray prediction and tracking by using the data obtained in the steps S2-S4VAnd bV；
S6, gray action amount a required for gray prediction and tracking according to the battery voltage obtained in the step S5VAnd bVObtaining the equal-dimensional successive compensation gray univariate first-order time response sequence of the battery voltage
Gray prediction tracking of the battery voltage is carried out;
s7, obtaining an equal-dimensional successive compensation gray univariate first-order time response sequence of the battery voltage according to the step S6
Generated by accumulation and reduced to the original series value of the corresponding variable
And S8, performing difference comparison on the obtained voltage predicted value and the obtained voltage measured value, diagnosing a fault point of the battery by describing the change of the voltage difference value, and further judging the fault type and determining the fault time.
In the step S1, the battery voltage raw data sequence V(0)The formula of (1) is:
V(0)＝(v(1),v(2),…,v(k-1)) (1)
in the formula, k is a positive integer and is sampling time; and v (j) is the battery voltage value of the battery at the moment j, wherein j is 1,2, …, k-1.
In the step S3, the sequence V is generated by accumulating gray once(1)The formula is as follows:
in the step S4, the sequence Z is generated immediately after the mean value(1)：
In the formula, v (i)(1)1, 2., 5 is the ith data in formula (2).
In the step S5, the ash action amount aVAnd bVThe specific calculation expression of (2) is:
wherein y and B are intermediate variables, and y and B are derived from the following formulae, respectively:
in the formula, v(0)(i) Wherein, k-1 is V in formula (1) · k-4, k-3(0)The ith data of (1).
In the formula, BTA transposed matrix representing the matrix B, B-1Represents the inverse matrix of B.
z(1)(i) Wherein, i is k-4, k-3, k-1 is Z in formula (3)(1)The ith data of (1).
In step S6, an equal-dimensional iterative gray univariate first-order prediction model of battery voltage
The specific calculation expression is as follows:
in the formula, v(0)(1) Is V in formula (1)(0)1 st data of (1).
In step S7, the gray tracking primitive sequence value of the battery voltage
Comprises the following steps:
in step S8, the difference detection method is
Fig. 1 is a flow chart of a battery fault diagnosis method based on a GM (1, 1) gray model according to this embodiment;
FIG. 2 shows a voltage prediction chart based on the GM (1, 1) gray model in this embodiment;
as shown in fig. 3, it is a battery multiple fault diagnosis diagram based on GM (1, 1) gray model of the present embodiment;
as can be seen from the comparison of the measured value and the predicted value in the graph, the method based on the GM (1, 1) gray model can well predict the development rule and the change trend of the battery voltage, and the potential fault of the battery can be effectively diagnosed through the difference detection of the predicted value and the measured value; if the difference value has a sudden rising trend which is more than 0.1, the fault can be judged to be an open-circuit fault or an overvoltage fault; if the sudden drop trend of the difference value is less than-0.1, the fault can be judged to be a short-circuit fault or an undervoltage fault; if the difference does not fluctuate significantly, it can be determined that the battery is not faulty.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (10)
1. A battery fault diagnosis method based on a GM (1, 1) gray model is characterized in that: the method comprises the following steps:
according to the fluctuation characteristics of the voltage during the charge and discharge of the battery, a battery GM (1, 1) voltage prediction model is established based on a grey system theory, the latest current working condition is used for setting secondary battery voltage data for prediction to obtain a predicted value, the difference between the voltage measured value and the predicted value is calculated, and the potential fault of the battery is diagnosed according to the difference value change.
2. The GM (1, 1) gray model-based battery fault diagnosis method of claim 1, wherein: the specific process of establishing a battery GM (1, 1) voltage prediction model based on a grey system theory according to the fluctuation characteristics of the voltage during the charge and discharge of the battery comprises the following steps:
coding the voltage data of the battery according to time to form a system characteristic data sequence of the corresponding battery voltage;
and acquiring the current set secondary voltage data of the battery which is latest in time from the system characteristic data sequence of the battery voltage, and performing gray processing on the acquired data.
3. The GM (1, 1) gray model-based battery fault diagnosis method of claim 2, wherein: performing gray processing on the acquired set secondary voltage data, specifically comprising the following steps of: performing primary grey accumulation generation processing on the acquired set voltage data to obtain a primary grey accumulation generation sequence of the battery voltage;
and performing adjacent mean value generation operation on the obtained gray primary accumulation generation sequence of the battery voltage to obtain an adjacent mean value generation sequence of the gray primary accumulation generation sequence of the battery voltage.
4. The GM (1, 1) gray model-based battery fault diagnosis method of claim 1, wherein: the specific process of utilizing the latest current working condition to set the secondary battery voltage data for prediction to obtain the predicted value comprises the following steps: and calculating the gray action amount required by the battery voltage for gray prediction tracking according to the obtained set secondary voltage data, obtaining an equal-dimensional successive compensation gray univariate first-order time response sequence corresponding to the battery voltage, and performing gray prediction tracking on the battery voltage.
5. The GM (1, 1) gray model-based battery fault diagnosis method of claim 1, wherein: the specific steps of diagnosing the potential battery fault according to the difference change comprise: and judging whether the battery has a fault, and if the variation of the output difference value in a certain time exceeds a first set value, judging that the battery has the fault.
6. The GM (1, 1) Gray model-based battery failure diagnosis method of claim 5, characterized by: if the difference value has a sudden rising trend and is greater than a second set value, judging that the fault is an open-circuit fault or an overvoltage fault;
and if the sudden drop trend of the difference value is smaller than a third set value, judging that the fault is a short-circuit fault or an undervoltage fault.
7. The battery fault diagnosis system based on the GM (1, 1) gray model is characterized in that: the method comprises the following steps:
the prediction model building module is configured to build a battery GM (1, 1) voltage prediction model based on a grey system theory according to the fluctuation characteristics of the voltage during the charging and discharging of the battery;
the prediction module is configured to predict the secondary battery voltage data by using the latest current working condition to obtain a predicted value;
and the difference value analysis module is configured to calculate the difference between the voltage measured value and the predicted value and diagnose the potential fault of the battery according to the change of the difference value.
8. The GM (1, 1) gray model based battery fault diagnosis system of claim 7 wherein: the prediction model building module comprises:
the sequence acquisition module is configured to encode the voltage data of the battery according to time to form a corresponding system characteristic data sequence of the battery voltage;
the grey processing module is configured to acquire current set secondary voltage data of the battery, which is latest in time, from the system characteristic data sequence of the battery voltage and perform grey processing on the acquired data;
and the prediction root module is configured to calculate the gray acting amount required by the battery voltage for gray prediction tracking according to the obtained set secondary voltage data, obtain an equal-dimensional successive compensation gray univariate first-order time response sequence corresponding to the battery voltage, and perform gray prediction tracking on the battery voltage.
9. A computer-readable storage medium characterized by: a plurality of instructions are stored therein, the instructions being adapted to be loaded by a processor of a terminal device and to perform the GM (1, 1) grey model based battery fault diagnosis method according to any of claims 1-6.
10. A terminal device is characterized in that: the system comprises a processor and a computer readable storage medium, wherein the processor is used for realizing instructions; the computer readable storage medium stores a plurality of instructions adapted to be loaded by a processor and to perform the GM (1, 1) gray model based battery fault diagnosis method of any of claims 1-6.
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