A battery pack micro-fault quantitative diagnosis method and system based on voltage maximum and minimum

Abstract

The application belongs to the field of lithium batteries, and provides a battery pack micro-failure quantitative diagnosis method and system based on voltage maximum and minimum values. The method comprises the following steps: obtaining voltage sequences of each monomer in a battery pack; based on the voltage sequences of each monomer in the battery pack, extracting the monomer highest voltage sequence and the monomer lowest voltage sequence of the battery pack; based on the monomer highest voltage sequence and the monomer lowest voltage sequence of the battery pack, calculating the maximum and minimum value sequence and the sequence difference sequence of the battery pack; according to the maximum and minimum value sequence and the sequence difference sequence of the battery pack, calculating the sample entropy of the battery pack and the sequence and the sample entropy of the battery pack difference sequence within a set time interval; analyzing the sample entropy of the battery pack and the sequence and the sample entropy of the battery pack difference sequence, judging whether the battery pack has a failure, and the failure type of the failure. After the failure is determined, the battery failure is quantitatively evaluated. The application realizes rapid and accurate diagnosis of whether the battery pack has a failure, and realizes quantitative evaluation of the battery failure.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of lithium batteries, and particularly relates to a battery pack micro-fault quantitative diagnosis method and system based on voltage maximum and minimum values. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.

[0003] The current new energy automobile industry is booming, and the most concerned is the electric vehicle with lithium ion battery as the main energy storage element. The safety problem of lithium ion power battery is the main factor restricting the large-scale promotion and application of electric vehicles. In the lithium ion battery pack, the most serious fault is the early short circuit fault. This fault will change the internal chemical properties of the battery and soon evolve into an internal short circuit fault, which will lead to thermal runaway of the battery, causing the electric vehicle to catch fire, which is very serious and the consequences are unbearable. Therefore, it is of great significance to invent an effective, timely and highly applicable battery pack micro-fault quantitative diagnosis method to ensure the safe use and normal operation of electric vehicles. However, the micro-short circuit fault has complex causes, the fault mechanism is difficult to trace, and the fault performance is very hidden, which is difficult to detect from the small amplitude abnormal change of voltage, and has been a bottleneck problem in the field of fault diagnosis.

[0004] The current power battery fault diagnosis technology mainly has the following deficiencies:

[0005] (1) The data sample is too large, and the redundancy is too high, and the diagnosis efficiency is low

[0006] Chinese invention patent (application number CN201911024438.2) proposes a battery early fault diagnosis method based on sample entropy, which mainly includes the following steps: using the battery pack monomer voltage time sequence collected by the sensor, calculating the sample entropy at each time, and setting a sliding window to optimize the detection effect, so as to realize the short circuit fault and open circuit fault detection of the battery pack monomer. Although this method can realize the fault diagnosis of the battery pack monomer, and the accuracy is high and misdiagnosis rarely occurs. However, when applied to the diagnosis of vehicle-mounted battery pack, this method needs to calculate the sample entropy of each battery monomer, and adjust the related parameters of each monomer respectively to ensure the robustness and accuracy of the diagnosis. Therefore, this method has large calculation amount, complicated steps and low diagnosis efficiency, and cannot be practically applied.

[0007] (2) Only the battery monomer fault can be detected, and the battery pack fault cannot be diagnosed

[0008] A method for diagnosing and separating short circuit and abuse faults of lithium ion batteries is proposed in Chinese invention patent (CN202010978479.1). The method establishes an equivalent circuit model and a thermal model of the battery, and through calculating the residual error between normal battery monomers and fault battery monomers, the short circuit fault and abuse fault of the battery monomers are diagnosed and separated by using a threshold value. However, the method is based on the establishment of monomer equivalent circuit model of different battery types, and can only diagnose battery monomer faults, and cannot identify battery pack faults.

[0009] (3) Only qualitative detection of battery faults, lack of quantitative diagnosis

[0010] A method and system for online diagnosis of power battery pack faults are proposed in Chinese invention patent (CN202210872840.1). The method analyzes and reconstructs the voltage time series of each monomer of the battery pack using fuzzy entropy, and finally compares the fuzzy entropy values of each monomer with the preset fuzzy entropy threshold value to realize qualitative diagnosis of the fault monomers and fault types in the battery pack. However, the method cannot quantitatively detect the fault degree. SUMMARY

[0011] To solve the technical problems existing in the above background art, the present application provides a battery pack micro-fault quantitative diagnosis method based on voltage maximum and minimum, which collects the highest monomer voltage and the lowest monomer voltage of the battery pack, and calculates the sample entropy of the time series of the two, to quickly and accurately diagnose whether the battery pack has failed, and realize quantitative evaluation of battery faults.

[0012] To achieve the above purpose, the present application adopts the following technical solutions:

[0013] The first aspect of the present application provides a battery pack micro-fault quantitative diagnosis method based on voltage maximum and minimum.

[0014] A battery pack micro-fault quantitative diagnosis method based on voltage maximum and minimum, comprising:

[0015] Obtaining the voltage sequence of each monomer in the battery pack;

[0016] Based on the voltage sequence of each monomer in the battery pack, the highest monomer voltage sequence and the lowest monomer voltage sequence of the battery pack are extracted;

[0017] Based on the highest monomer voltage sequence and the lowest monomer voltage sequence of the battery pack, the maximum and minimum value sequence and the maximum and minimum value difference sequence of the battery pack are calculated;

[0018] According to the maximum and minimum value sequence and the maximum and minimum value difference sequence of the battery pack, the sample entropy of the battery pack and the sequence, and the sample entropy of the battery pack difference sequence every set time are calculated;

[0019] By analyzing the sample entropy of the battery pack and the sequence and the sample entropy of the battery pack difference sequence, it can be determined whether the battery pack has failed and the type of failure.

[0020] Furthermore, after confirming a battery malfunction, the following steps are also included:

[0021] A battery Rint equivalent model is constructed. Through theoretical derivation of the relevant parameters of the battery Rint equivalent model, a quantitative relationship between battery parameters and short-circuit current and short-circuit resistance is established. The battery parameters include the terminal voltage of a normal battery and the terminal voltage of a faulty battery.

[0022] Based on the difference between the terminal voltage of a normal battery and the terminal voltage of a faulty battery, the short-circuit current and short-circuit resistance are calculated to achieve a quantitative assessment of battery faults.

[0023] Furthermore, for a battery pack α consisting of m individual cells and a total of g sampling points, the voltage sequence of each individual cell in the battery pack is defined as:

[0024]

[0025] Assume the voltage of the i-th cell at time t is u. i (t), then the highest single-cell voltage of the battery pack at time t. Defined as:

[0026]

[0027] The sequence of highest voltages of individual cells in the battery pack is as follows:

[0028]

[0029] Assume the voltage of the i-th cell at time t is u. i (t), then the lowest single-cell voltage of the battery pack at time t. Defined as:

[0030]

[0031] The lowest voltage sequence of the individual cells is then:

[0032]

[0033] Furthermore, the extreme values ​​and sequences are obtained by adding the highest voltage of a single cell to the lowest voltage of a single cell at each time step; the extreme value difference sequences are obtained by subtracting the lowest voltage of a single cell from the highest voltage of a single cell at each time step.

[0034] Furthermore, the calculation process for the sample entropy of the battery pack and sequence includes:

[0035] According to the maximum vector distance between different group maximum value and sequence, the probability of two maximum value and sequence vector matching under z dimension and the probability of two maximum value and sequence vector matching under z+1 dimension are calculated;

[0036] According to the maximum vector distance between different group maximum value and sequence, the probability of two maximum value and sequence vector matching under z dimension and the probability of two maximum value and sequence vector matching under z+1 dimension are calculated;

[0037] According to the probability of two maximum value and sequence vector matching under z dimension and the probability of two maximum value and sequence vector matching under z+1 dimension, the sample entropy of the battery group and sequence is calculated.

[0038] Further, the calculation process of the sample entropy of the battery group difference sequence includes:

[0039] According to the maximum vector distance between different group maximum value and sequence, the probability of two maximum value and sequence vector matching under z dimension and the probability of two maximum value and sequence vector matching under z+1 dimension are calculated;

[0040] According to the maximum vector distance between different group maximum value and sequence, the probability of two maximum value and sequence vector matching under z dimension and the probability of two maximum value and sequence vector matching under z+1 dimension are calculated;

[0041] According to the probability of two maximum value and sequence vector matching under z dimension and the probability of two maximum value and sequence vector matching under z+1 dimension, the sample entropy of the battery group difference sequence is calculated.

[0042] Further, the analysis of the sample entropy of the battery group and sequence and the sample entropy of the battery group difference sequence, the determination of whether the battery group fails, and the fault type of the fault occurring specifically includes:

[0043] If the sample entropy of the battery group and sequence is less than zero, and the sample entropy of the battery group difference sequence is greater than zero, the battery group fails;

[0044] If the sample entropy of the battery group and sequence is greater than or equal to zero, and the sample entropy of the battery group difference sequence is greater than zero, the battery cell fails;

[0045] If the sample entropy of the battery group and sequence is greater than or equal to zero, and the sample entropy of the battery group difference sequence is less than or equal to zero, no fault occurs.

[0046] The second aspect of the application provides a battery pack micro-fault quantitative diagnosis system based on voltage maximum value.

[0047] A battery pack micro-fault quantitative diagnosis system based on voltage maximum value, comprising:

[0048] A data acquisition module configured to acquire the voltage sequence of each cell in the battery pack;

[0049] The data processing module is configured to extract the highest voltage sequence and the lowest voltage sequence of each cell in the battery pack based on the voltage sequence of each cell.

[0050] The first calculation module is configured to: calculate the maximum and minimum values ​​of the battery pack and the difference between the maximum and minimum values ​​based on the highest and lowest voltage sequences of individual cells in the battery pack.

[0051] The second calculation module is configured to: calculate the sample entropy of the battery pack and sequence and the sample entropy of the battery pack difference sequence at set intervals based on the maximum and minimum values ​​and the sequence of maximum and minimum values ​​of the battery pack.

[0052] The fault diagnosis module is configured to analyze the sample entropy of the battery pack and sequence and the sample entropy of the battery pack difference sequence to determine whether the battery pack has failed and the type of failure.

[0053] A third aspect of the present invention provides a computer-readable storage medium.

[0054] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the quantitative diagnosis method for minor battery pack faults based on voltage extrema as described in the first aspect above.

[0055] A fourth aspect of the present invention provides a computer device.

[0056] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the quantitative diagnosis method for minor battery pack faults based on voltage maximum values ​​as described in the first aspect above.

[0057] Compared with the prior art, the beneficial effects of the present invention are:

[0058] (1) This invention only requires collecting the maximum and minimum voltage values ​​of individual cells in the battery pack to achieve early diagnosis of minor faults, reducing the computing power requirements of the battery management system, greatly reducing fault diagnosis time, and improving diagnostic efficiency and practicality. In the embodiments, compared with traditional methods, this invention shortens the computation time by 55.67%.

[0059] (2) This invention can identify battery pack faults and individual battery cell faults by calculating the sample entropy of the battery pack maximum and minimum value sequence and the maximum and minimum value difference sequence, and proposing fault criteria.

[0060] (3) This invention establishes an equivalent model of normal batteries and faulty batteries, establishes a quantitative relationship between fault resistance and voltage residual, and realizes quantitative assessment of battery faults. Attached Figure Description

[0061] The accompanying drawings, which form a part of this specification, are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification. The embodiments of the application, and their

[0062] Figure 1 is a fault battery Rint equivalent circuit model shown in the present application;

[0063] Figure 2 is a normal battery Rint equivalent circuit model shown in the present application;

[0064] Figure 3 is a battery pack udds working condition voltage curve diagram shown in the present application;

[0065] Figure 4 is a battery pack maximum and minimum voltage sequence diagram shown in the present application;

[0066] Figure 5 is a battery pack each single sample entropy diagnosis result diagram based on the present application;

[0067] Figure 6 is a battery pack maximum and minimum sample entropy diagnosis result diagram based on the present application;

[0068] Figure 7 is a traditional method and maximum and minimum method operation time comparison diagram shown in the present application;

[0069] Figure 8 is a battery single fault diagnosis result diagram based on the sum and difference sequence shown in the present application;

[0070] Figure 9 is a battery pack fault diagnosis result diagram based on the sum and difference sequence shown in the present application;

[0071] Figure 10 is a battery pack micro fault quantitative diagnosis method based on the voltage maximum and minimum shown in the present application. DETAILED DESCRIPTION

[0072] The application will be further described below with reference to the drawings and embodiments.

[0073] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the application. Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the application pertains.

[0074] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0075] It should be noted that the flowchart and block diagrams in the drawings illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagrams can represent a module, a segment, or a portion of code, which includes one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession can in fact be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the flowchart and / or block diagrams and combinations of blocks in the flowchart and / or block diagrams can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and

[0076] Embodiment one

[0077] As Figure 10 shown, the embodiment provides a battery pack micro-failure quantitative diagnosis method based on voltage maximum and minimum value. The embodiment takes the method applied to a server as an example. It can be understood that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server and is realized through interaction of the terminal and the server. The server can be a physical server, a server cluster composed of multiple physical servers or a distributed system, and can also be a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communication, middleware services, domain name services, security services CDN, and big data and artificial intelligence platforms and other basic cloud computing services. The terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch and the like, but is not limited thereto. The terminal and the server can be directly or indirectly connected through wired or wireless communication, which is not limited in the present application. In the embodiment, the method includes the following steps:

[0078] obtaining a voltage sequence of each single cell in the battery pack;

[0079] Based on the voltage sequence of each monomer in the battery pack, the highest monomer voltage sequence and the lowest monomer voltage sequence of the battery pack are extracted;

[0080] Based on the highest monomer voltage sequence and the lowest monomer voltage sequence of the battery pack, the maximum and minimum value sequence and the maximum and minimum value difference sequence of the battery pack are calculated;

[0081] According to the maximum and minimum value sequence and the maximum and minimum value difference sequence of the battery pack, the sample entropy of the battery pack and the sequence and the sample entropy of the battery pack difference sequence within a set time are calculated;

[0082] The sample entropy of the battery pack and the sequence and the sample entropy of the battery pack difference sequence are analyzed to determine whether the battery pack has failed and the type of failure.

[0083] After determining the failure, it further includes: constructing a battery Rint equivalent model, establishing a quantitative relationship between the battery parameters and the short-circuit current and the short-circuit resistance through theoretical derivation of the related parameters of the constructed battery Rint equivalent model, wherein the battery parameters include the terminal voltage of the normal battery and the terminal voltage of the failed battery; according to the difference between the terminal voltage of the normal battery and the terminal voltage of the failed battery, the short-circuit current and the short-circuit resistance are calculated to realize quantitative evaluation of the battery failure.

[0084] Invention principle: In actual vehicle-mounted batteries, a battery pack often contains hundreds of battery monomers, and due to the inconsistency between the battery monomers, the highest monomer voltage at each moment is changing. Similarly, the lowest monomer voltage of the entire battery pack is also a key parameter reflecting the running state of the entire battery pack. Therefore, by analyzing the highest and lowest monomer voltage of the entire battery pack at each moment during operation, whether the battery pack has a failure can be diagnosed.

[0085] When a monomer failure occurs, the voltage of a certain battery monomer will fluctuate, resulting in abnormal values of the highest voltage or the lowest voltage at the same time. When a battery pack failure occurs, the voltage of all monomers in the entire battery pack fluctuates, resulting in abnormal values of the highest voltage and the lowest voltage at the same time. Based on the above analysis, by calculating the sample entropy of the difference sequence and the sum sequence, the battery pack failure and the monomer failure can be distinguished. In addition, the voltage fluctuation amplitude corresponding to different degrees of short-circuit failure is different. By establishing a quantitative relationship between the fault degree and the voltage residual, quantitative evaluation of the failure can be realized.

[0086] Specifically, the specific scheme of the embodiment can be implemented by referring to the following contents:

[0087] The highest monomer voltage and the lowest monomer voltage of the battery pack are collected, and the sample entropy of the time sequence of the two is calculated to quickly and accurately diagnose whether the battery pack has failed. To complete the above steps, the following definitions are made:

[0088] (1) The original voltage sequence of the battery pack:

[0089] For a battery pack α consisting of m cells and with a total number of sampling points g, its original voltage sequence can be represented by the following vector:

[0090]

[0091] (2) The highest voltage sequence of the battery pack

[0092] Let the voltage of the i-th cell at time t be u i (t), then the highest cell voltage of the battery pack at time t is defined as:

[0093]

[0094] Further, the highest voltage sequence of the battery pack is:

[0095]

[0096] (3) The lowest voltage sequence of the battery pack

[0097] Let the voltage of the i-th cell at time t be u i (t), then the lowest cell voltage of the battery pack at time t is defined as:

[0098] Similarly, the lowest voltage sequence of the battery pack is:

[0099]

[0100] (4) The maximum and minimum sequence:

[0101] The maximum and minimum sequence of the battery pack adds the highest cell voltage and the lowest cell voltage at each time:

[0102]

[0103]

[0104]

[0105] (5) The maximum and minimum difference sequence:

[0106] The maximum and minimum difference sequence of the battery pack subtracts the highest cell voltage from the lowest cell voltage at each time:

[0107]

[0108]

[0109] ​​After the above definitions are clear, the quantitative diagnosis strategy of the slight failure of the battery pack can be realized by the following steps.

[0110] 1. A method for diagnosing slight failure of a battery pack

[0111] (1) Real-time detection of the voltage of each single cell in the battery pack, extraction of the highest voltage sequence of the single cell and the lowest voltage sequence of the single cell of the battery pack;

[0112] (2) Calculation of the sample entropy of the battery pack and the sequence in a certain time interval the sample entropy of the difference sequence of the battery pack

[0113] The calculation method of the sample entropy is as follows:

[0114] First, construct the battery pack and difference sequence vector containing k points:

[0115]

[0116]

[0117] Taking the voltage difference sequence as an example, the distance between different groups of difference sequence vectors can be calculated by the following formula:

[0118]

[0119] Where i≠j, 0≤l≤z-1. Then calculate the matching probability of the two difference sequence vectors:

[0120]

[0121]

[0122] Where represents the number of . Using the same method, the matching probability of the two difference sequence vectors under the z+1 dimension can be obtained as:

[0123]

[0124]

[0125] Where represents the number of . On the basis of the above steps, the sample entropy value of the difference sequence of the battery pack can be calculated as:

[0126]

[0127] Similarly, the sample entropy values of the highest and lowest value sequences of the battery pack can be obtained as:

[0128]

[0129] The sample entropy value of the sum sequence is set to a negative value, so that the sample entropy values of the sum and difference sequences can be observed simultaneously in the same graph, which facilitates the determination of the fault range of the battery pack in the next step.

[0130] (3) Analyze the sample entropy of the battery pack and the sum and difference sequences to determine whether the battery pack has failed and to determine the range of the failure. The specific determination method is as follows:

[0131] If and , the battery pack has failed;

[0132] If and , the battery cell has failed;

[0133] If and , no failure has occurred.

[0134] 2. Quantitative method for small faults of the battery pack

[0135] The process of establishing a model for the battery, performing formula derivation, and finally determining the quantitative relationship between the fault degree and the voltage residual error is as follows:

[0136] First, to analyze the parameter changes when the battery has a short circuit fault, the Rint equivalent circuit model of the battery is established as shown in Figure 1 and Figure 2 .

[0137] For the Rint equivalent model of the fault battery established in Figure 1 , where is the open-circuit terminal voltage of the fault battery, R0 is the equivalent DC internal resistance of the battery, I0 is the equivalent current inside the battery, R ISC represents the internal short-circuit resistance, I ISC is the internal short-circuit current, represents the terminal voltage of the fault battery, and I represents the current of the entire battery pack. The equivalent circuit relationship of this model can be established as follows:

[0138]

[0139] At the same time, for a normal battery cell, the Rint equivalent model shown in Figure 2 is established. Where is the open-circuit terminal voltage of the normal battery, R0 is the equivalent internal resistance of the battery, and I represents the current of the entire battery pack. The equivalent relationship between the several variables can be described by the following formula:

[0140]

[0141] For faulty and normal battery cells connected in series within the same battery pack, their equivalent internal resistance R0 and the current flowing through the cell I can be considered approximately equal, while I0, I ISC These parameters belong to the internal structure of the battery.

[0142] Parameters are difficult to measure directly. Therefore, among the measurable macroscopic parameters, the only difference between a faulty battery cell and a normal battery cell is the battery voltage. and There are differences.

[0143] Will and By taking the difference, a quantitative relationship between faulty and normal batteries can be obtained:

[0144]

[0145] Considering that the open-circuit voltage of the faulty battery will not change significantly in the immediate period immediately following a short-circuit fault, therefore, we assume... The following quantitative relationship can be obtained:

[0146]

[0147] Based on the above equations, the fault current I ISC and fault resistor R ISC It can be calculated using the following formula:

[0148]

[0149]

[0150] Therefore, the short-circuit current I can be estimated based on the voltage difference Δu between the faulty battery and the normal battery cell. ISC Short-circuit resistance R ISC This enables quantitative assessment of battery failures.

[0151] Specific examples:

[0152] The following uses a comparative approach and specific examples to illustrate the methods and effects of the embodiments:

[0153] Battery pack α consists of seven cells: A1, A2, A3, A4, A5, A6, and A7. Under UDDS conditions, a short-circuit fault was caused in cell A5 during the period from 299s to 332s. The short-circuit resistances were R1 = 0.04Ω, R2 = 0.05Ω, R3 = 0.06Ω, R4 = 0.08Ω, and R5 = 0.1Ω.

[0154] During the test period, the measured voltage trends of each individual cell are as follows: Figure 3As shown in FIG. 1, the maximum voltage and the minimum voltage of the battery pack are extracted. Figure 3 As shown in FIG. 1 and FIG. 2, the battery cell corresponding to the maximum voltage and the minimum voltage is not fixed but changes constantly during the non-fault period of the battery pack.

[0155] (1) Fault time positioning of the battery based on the maximum and minimum voltage

[0156] As shown in FIG. 1, after extracting the maximum and minimum voltage sequence of the battery pack, the original abnormal voltage fluctuation is retained during the time period of 299s to 332s. As shown in FIG. 2, during the fault period, the cell corresponding to the maximum and minimum voltage no longer changes and remains consistent with the fault cell. Therefore, the maximum and minimum voltage sequence can eliminate the repeated normal battery operation data under the premise of retaining the abnormal operation information of the battery pack, thereby reducing the data redundancy of the entire battery pack. Figure 4 Figure 4 As shown in FIG. 1 and FIG. 2, both methods can diagnose that there is a fault in the battery pack during the time period of 299s to 332s through the sample entropy of non-zero amplitude, but cannot determine the specific fault range (i.e., cannot determine whether it is a single battery cell or the entire battery pack that has failed). Fortunately, the number of algorithm runs is reduced from seven to two, and the voltage sequence used for calculation is less, greatly reducing the computational load and having great application value. In actual vehicle-mounted batteries, the battery pack often has several hundred cells. If the traditional diagnosis method based on the voltage of the battery cell is used, the voltage sequence of several hundred cells stored in the BMS needs to be diagnosed separately. However, the present embodiment only needs to diagnose the extracted maximum and minimum voltage sequence twice to diagnose whether the battery pack has failed.

[0157] To further illustrate the advantage of the present embodiment in reducing fault diagnosis time, the same battery pack with different numbers of cells is diagnosed using the traditional method for battery cells (taking sample entropy as an example) and the method based on the maximum and minimum voltage, and the corresponding operation time is calculated. As shown in FIG. 3, the traditional method for battery cells requires 7 times of operation, while the method based on the maximum and minimum voltage only requires 2 times of operation, which greatly reduces the operation time. Figure 5 Figure 6 As shown in FIG. 3, the traditional method for battery cells requires 7 times of operation, while the method based on the maximum and minimum voltage only requires 2 times of operation, which greatly reduces the operation time.

[0158] To further illustrate the advantage of the present embodiment in reducing fault diagnosis time, the same battery pack with different numbers of cells is diagnosed using the traditional method for battery cells (taking sample entropy as an example) and the method based on the maximum and minimum voltage, and the corresponding operation time is calculated. As shown in FIG. 3, the traditional method for battery cells requires 7 times of operation, while the method based on the maximum and minimum voltage only requires 2 times of operation, which greatly reduces the operation time. Figure 7 ​​As shown, the method reduces the calculation time by 55.67% in a battery pack containing 7 monomers; in a battery pack containing 30 monomers, the operation time of the method is only 10.11% of the traditional battery monomer diagnosis method. At present, the vehicle-mounted battery pack on the market contains 100 monomers, and the operation time of the method for diagnosing such a battery pack is only 3.03% of the traditional method. It can be seen that in the battery pack with a large number of battery monomers, this advantage is more significant. Compared with the traditional diagnosis method, the method based on the maximum value of voltage proposed in the application greatly reduces the operation time required for fault diagnosis.

[0159] (2) Battery pack and monomer fault diagnosis based on maximum value and difference sequence

[0160] After determining the fault period of the battery pack, the sample entropy of the maximum value and difference sequence proposed in this embodiment is further calculated to determine the specific type of fault, as shown in Figure 8

[0161] According to the criterion proposed in the foregoing method, the Figure 8 , Figure 9 are analyzed. In Figure 8 , it can be seen that between the fault period 299s to 333s, and Therefore Figure 8 the fault shown is a battery monomer fault.

[0162] Similarly, in Figure 9 , the fault period has and Therefore Figure 9 the fault shown is a battery pack fault.

[0163] (3) Quantitative evaluation of battery fault

[0164] According to (1) (2), it can be concluded that in the battery pack a, when a monomer short circuit fault occurs, the fault monomer is A5. By establishing the internal equivalent circuit of the fault battery in the foregoing method, the voltage difference between the normal battery monomer and the fault battery monomer is obtained, and further according to formulas (23), (24) the short circuit resistance R ISC , the results in Table 1 are obtained. Under the premise of fixing the value of R0, the method realizes the quantitative estimation of the short circuit fault, and the mean square error (MSE) of the predicted resistance value is less than 0.007%.

[0165] Table 1 Fault quantitative diagnosis results

[0166]

[0167] Example two

[0168] ​The embodiment provides a battery pack micro-failure quantitative diagnosis system based on voltage maximum and minimum values.

[0169] A battery pack micro-failure quantitative diagnosis system based on voltage maximum and minimum values comprises the following.

[0170] A data acquisition module configured to acquire voltage sequences of each monomer in a battery pack.

[0171] A data processing module configured to extract monomer maximum voltage sequences and monomer minimum voltage sequences of the battery pack based on the voltage sequences of each monomer in the battery pack.

[0172] A first calculation module configured to calculate maximum and minimum value sequences and sequence and maximum and minimum value difference sequences of the battery pack based on the monomer maximum voltage sequences and the monomer minimum voltage sequences of the battery pack.

[0173] A second calculation module configured to calculate sample entropy of the battery pack and sequence and sample entropy of the battery pack difference sequence in every set time according to the maximum and minimum value sequences and the sequence and maximum and minimum value difference sequences of the battery pack.

[0174] A fault diagnosis module configured to analyze the sample entropy of the battery pack and sequence and the sample entropy of the battery pack difference sequence, determine whether the battery pack has a fault, and determine a fault type of the fault.

[0175] As one or more implementation manners, the system further comprises the following after fault determination.

[0176] A quantitative evaluation module configured to construct a battery Rint equivalent model, establish a quantitative relationship between battery parameters and short-circuit current and short-circuit resistance through theoretical derivation of related parameters of the constructed battery Rint equivalent model, wherein the battery parameters comprise an end voltage of a normal battery and an end voltage of a fault battery; and calculate the short-circuit current and the short-circuit resistance according to a difference between the end voltage of the normal battery and the end voltage of the fault battery, so as to realize quantitative evaluation of the battery fault.

[0177] It should be noted that the data acquisition module, the data processing module, the first calculation module, the second calculation module, the fault diagnosis module and the quantitative evaluation module are the same as the examples and application scenarios realized in the first embodiment, but are not limited to the content disclosed in the first embodiment. It should be noted that the modules as a part of the system can be executed in a computer system such as a group of computer executable instructions.

[0178] Embodiment three

[0179] The embodiment provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize steps in the battery pack micro-failure quantitative diagnosis method based on voltage maximum and minimum values according to the first embodiment.

[0180] Embodiment four

[0181] The embodiment provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor realizes steps in the battery pack micro-failure quantitative diagnosis method based on voltage maximum and minimum values according to the first embodiment when executing the program.

[0182] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can adopt a hardware embodiment, a software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage and optical storage) containing computer usable program codes.

[0183] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system) and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device for realizing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for performing the functions specified in the flow(s) or block(s).

[0184] These computer program instructions can also be stored in a computer readable storage medium capable of guiding the computer or other programmable data processing devices to work in a specific way, so that the instructions stored in the computer readable storage medium produce a product including instruction means, which realizes the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for performing the functions specified in the flow(s) or block(s).

[0185] These computer program instructions can also be loaded into the computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable devices to produce a computer implemented process, so that the instructions executed on the computer or other programmable devices provide a process for realizing the functions specified in the flowcharts and / or block diagrams. Figure 1one or more processes and / or blocks Figure 1 Figure 1 steps of the functions specified in the one or more blocks.

[0186] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The program can be stored in a computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiment methods can be included. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM), a random access memory (RAM), or the like.

[0187] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A quantitative diagnosis method for minor faults in battery packs based on voltage extrema, characterized in that, include: Obtain the voltage sequence of each cell in the battery pack; Based on the voltage sequence of each cell in the battery pack, the highest voltage sequence and the lowest voltage sequence of each cell in the battery pack are extracted. Based on the highest and lowest voltage sequences of individual cells in the battery pack, calculate the maximum and minimum values ​​of the battery pack and the sequence of differences between the maximum and minimum values. Based on the maximum and minimum values ​​of the battery pack and the difference between the maximum and minimum values, calculate the sample entropy of the battery pack and the sequence and the sample entropy of the difference between the battery packs at set time intervals. By analyzing the sample entropy of the battery pack and the sequence and the sample entropy of the battery pack difference sequence, it can be determined whether the battery pack has failed and the type of failure.

2. The quantitative diagnosis method for minor battery pack faults based on voltage extrema as described in claim 1, characterized in that, After confirming the battery malfunction, the following is also included: A battery Rint equivalent model is constructed. Through theoretical derivation of the relevant parameters of the battery Rint equivalent model, a quantitative relationship between battery parameters and short-circuit current and short-circuit resistance is established. The battery parameters include the terminal voltage of a normal battery and the terminal voltage of a faulty battery. Based on the difference between the terminal voltage of a normal battery and the terminal voltage of a faulty battery, the short-circuit current and short-circuit resistance are calculated to achieve a quantitative assessment of battery faults.

3. The quantitative diagnosis method for minor battery pack faults based on voltage extrema as described in claim 1, characterized in that, For a battery pack α consisting of m individual cells and a total of g sampling points, the voltage sequence of each individual cell in the battery pack is defined as: Assume the voltage of the i-th cell at time t is u. i (t), then the highest single-cell voltage of the battery pack at time t. Defined as: The sequence of highest voltages of individual cells in the battery pack is as follows: Assume the voltage of the i-th cell at time t is u. i (t), then the lowest single-cell voltage of the battery pack at time t. Defined as: The lowest voltage sequence of the individual cells is then:

4. The method for quantitative diagnosis of minor battery pack faults based on voltage extrema as described in claim 1, characterized in that, The extreme values ​​and sequences are obtained by adding the highest voltage of a single cell to the lowest voltage of a single cell at each time step; the extreme value difference sequences are obtained by subtracting the lowest voltage of a single cell from the highest voltage of a single cell at each time step.

5. The method for quantitative diagnosis of minor battery pack faults based on voltage extrema as described in claim 1, characterized in that, The calculation process for the sample entropy of the battery pack and sequence includes: Calculate the maximum vector distance between the extreme values ​​and sequences of different battery packs; Based on the maximum vector distance between different sets of extrema and sequences, calculate the probability of matching two extrema and sequence vectors in the z-dimensional dimension and the probability of matching two extrema and sequence vectors in the z+1-dimensional dimension. The sample entropy of the battery pack and the sequence is calculated based on the probability of matching the two extrema and the sequence vector in the z-dimension and the probability of matching the two extrema and the sequence vector in the z+1-dimension.

6. The method for quantitative diagnosis of minor battery pack faults based on voltage extrema as described in claim 1, characterized in that, The calculation process of the sample entropy of the battery pack difference sequence includes: Calculate the maximum vector distance between the extreme value difference sequences of different battery packs based on the extreme value difference sequence; Based on the maximum vector distance between different sets of maximum and minimum difference sequences, calculate the probability of matching two maximum and minimum difference sequence vectors in the z-dimensional dimension and the probability of matching two maximum and minimum difference sequence vectors in the z+1-dimensional dimension. The sample entropy of the battery pack difference sequence is calculated based on the probability of matching two extreme value difference sequence vectors in the z-dimensional dimension and the probability of matching two extreme value difference sequence vectors in the z+1-dimensional dimension.

7. The method for quantitative diagnosis of minor battery pack faults based on voltage extrema as described in claim 1, characterized in that, The analysis of the sample entropy of the battery pack and the sequence, and the difference between the sample entropy of the battery pack and the sequence, determines whether the battery pack has failed, and the specific types of failures include: If the sample entropy of the battery pack and the sequence is less than zero, and the sample entropy of the battery pack difference sequence is greater than zero, then a battery pack failure occurs. If the sample entropy of the battery pack and the sequence is greater than or equal to zero, and the sample entropy of the battery pack difference sequence is greater than zero, then a single battery cell failure occurs. If the sample entropy of the battery pack and sequence is greater than or equal to zero, and the sample entropy of the battery pack difference sequence is less than or equal to zero, then no fault occurs.

8. A quantitative diagnostic system for minor battery pack faults based on voltage extrema, characterized in that, include: The data acquisition module is configured to acquire the voltage sequence of each cell in the battery pack. The data processing module is configured to extract the highest voltage sequence and the lowest voltage sequence of each cell in the battery pack based on the voltage sequence of each cell. The first calculation module is configured to: calculate the maximum and minimum values ​​of the battery pack and the difference between the maximum and minimum values ​​based on the highest and lowest voltage sequences of individual cells in the battery pack. The second calculation module is configured to: calculate the sample entropy of the battery pack and sequence and the sample entropy of the battery pack difference sequence at set intervals based on the maximum and minimum values ​​and the sequence of maximum and minimum values ​​of the battery pack. The fault diagnosis module is configured to analyze the sample entropy of the battery pack and sequence and the sample entropy of the battery pack difference sequence to determine whether the battery pack has failed and the type of failure.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the quantitative diagnosis method for minor battery pack faults based on voltage extrema as described in any one of claims 1-7.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the quantitative diagnosis method for minor battery pack faults based on voltage maximum values ​​as described in any one of claims 1-7.

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