A lithium battery pack multi-fault diagnosis method, system, device and medium based on local weighted Manhattan distance

Abstract

The application discloses a lithium battery pack multi-fault diagnosis method and system based on a local weighted Manhattan distance, a device, and a medium, and relates to the technical field of battery applications.The method comprises the following steps: acquiring voltage curves of each single battery in a target battery pack in a charging stage and a discharging stage; calculating each voltage curve in the charging stage by using a traditional Manhattan distance algorithm to obtain a first deviation value; calculating each voltage curve in the discharging stage by using a local weighted Manhattan distance algorithm to obtain a second deviation value; wherein the deviation value is deviation data between each voltage curve and a normal voltage curve in the charging or discharging stage; comparing the first deviation value and the second deviation value with a set evaluation threshold range respectively to determine whether there is a faulty single battery in the target battery pack and the positioning of the faulty single battery.The application can realize the detection of a mixed fault of low SOC and low capacity, an internal resistance fault, a connection fault and an external short-circuit fault.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery application, in particular to a lithium battery pack multi-fault diagnosis method, system, device and medium based on local weighted Manhattan distance. BACKGROUND

[0002] At present, the research on lithium ion battery fault diagnosis method mainly includes three categories: knowledge-based fault diagnosis method, data-driven fault diagnosis method and model-based fault diagnosis method. Among them, the knowledge-based fault diagnosis method is a method of using expert knowledge and rules to identify and locate system faults, which can be divided into expert system, graph theory and fuzzy logic. Expert system is a computer program based on knowledge, aiming to simulate and apply the knowledge and reasoning ability of experts in the field, but it has limitations such as difficulty in knowledge acquisition and poor adaptability. Graph theory generates fault diagnosis pattern graph according to the fault propagation and influence in the battery system, and the result is easy to understand, but it has problems such as difficulty in knowledge acquisition and modeling, complex calculation and data accuracy. Fuzzy logic can handle information containing uncertainty and fuzziness in battery fault diagnosis, but due to the complexity of the fault mechanism of the power battery system, the knowledge is difficult to express and acquire, and it is a great challenge to establish an effective knowledge system. The data-driven fault diagnosis method and the model-based fault diagnosis method both have the problems of studying only a single charging stage and no mixed faults. SUMMARY

[0003] The purpose of the present application is to provide a lithium battery pack multi-fault diagnosis method, system, device and medium based on local weighted Manhattan distance, which can detect mixed faults of low SOC and low capacity, internal resistance faults, connection faults and external short circuit faults.

[0004] To achieve the above purpose, the present application provides the following scheme:

[0005] A lithium battery pack multi-fault diagnosis method based on local weighted Manhattan distance, comprising:

[0006] Obtaining the voltage curves of each single battery in the target battery pack in the charging stage and the discharging stage;

[0007] Using a traditional Manhattan distance algorithm to calculate each voltage curve in the charging stage to obtain a first deviation value; the first deviation value is the deviation data between each voltage curve and the normal voltage curve in the charging stage;

[0008] Using a local weighted Manhattan distance algorithm to calculate each voltage curve in the discharging stage to obtain a second deviation value; the second deviation value is the deviation data between each voltage curve and the normal voltage curve in the discharging stage;

[0009] The first deviation value and the second deviation value are compared with a set evaluation threshold range respectively to determine whether there is a fault single battery in the target battery pack and the location of the fault single battery.

[0010] Optionally, the expression of the traditional Manhattan distance algorithm is:

[0011] d (i,j) = |V 1,i -V 1,j | + |V 2,i -V 2,j | + … + |V n,i -V n,j

[0012] wherein d (i,j) is the Manhattan distance between the single battery i (i = 1, 2, …, m) and the single battery j (j = 1, 2, …, m) in the battery pack, n represents the first voltage sampling time, and m represents the total number of single batteries.

[0013] Optionally, the expression of the weighting coefficient in the local weighted Manhattan distance algorithm is:

[0014]

[0015] wherein C i represents the rated capacity of the single battery i in the battery pack, C j represents the rated capacity of the single battery j, a represents a discharge rate factor affecting the discharge rate, b represents a temperature coefficient factor affecting the temperature, I d represents the discharge current under the dynamic working condition, represents the temperature of the single battery i of the battery pack at the time t, represents the temperature of the single battery j of the battery pack at the time t, n represents the first voltage sampling time, and k represents the second voltage sampling time.

[0016] Optionally, the expression of the local weighted Manhattan distance algorithm is:

[0017] d dis(i,j) = w (i,j) |V n,i -V n,j | + … + w (i,j) |V k,i -V k,j

[0018] wherein d dis(i,j) represents the Manhattan distance between the single battery i and the single battery j in the battery pack, w (i,j) represents the weight relationship established between the single battery i and the single battery j in the battery pack, and V n,iVi(n) represents the sampling voltage of the single battery i at the n moment, V n,j Vj(n) represents the sampling voltage of the single battery j at the n moment, V k,i Vi(k) represents the sampling voltage of the single battery i at the k moment, V k,j Vj(k) represents the sampling voltage of the single battery j at the k moment.

[0019] Optionally, the evaluation threshold range is (0, 0.17).

[0020] The application further provides a lithium battery pack multi-fault diagnosis system based on a locally weighted Manhattan distance, comprising:

[0021] A data sampling unit is configured to acquire voltage curves of each single battery in a target battery pack in a charging stage and a discharging stage;

[0022] A charging stage deviation calculation unit is configured to calculate each voltage curve in the charging stage by using a traditional Manhattan distance algorithm to obtain a first deviation value; the first deviation value is deviation data between each voltage curve and a normal voltage curve in the charging stage;

[0023] A discharging stage deviation calculation unit is configured to calculate each voltage curve in the discharging stage by using a locally weighted Manhattan distance algorithm to obtain a second deviation value; the second deviation value is deviation data between each voltage curve and the normal voltage curve in the discharging stage;

[0024] A fault diagnosis unit is configured to compare the first deviation value and the second deviation value with a set evaluation threshold range respectively, and determine whether there is a fault single battery in the target battery pack and the positioning of the fault single battery.

[0025] The application further provides an electronic device comprising a memory and a processor, wherein the memory is configured to store a computer program, and the processor is configured to run the computer program to enable the electronic device to execute the lithium battery pack multi-fault diagnosis method based on the locally weighted Manhattan distance.

[0026] The application further provides a computer readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the lithium battery pack multi-fault diagnosis method based on the locally weighted Manhattan distance.

[0027] According to the embodiments of the application, the following technical effects are achieved:

[0028] The application discloses a lithium battery pack multi-fault diagnosis method, system, device and medium based on a local weighted Manhattan distance, the method comprises the following steps: acquiring voltage curves of each single battery in a target battery pack in a charging stage and a discharging stage; calculating each voltage curve in the charging stage by using a traditional Manhattan distance algorithm to obtain a first deviation value; calculating each voltage curve in the discharging stage by using a local weighted Manhattan distance algorithm to obtain a second deviation value; wherein the deviation value is the deviation data between each voltage curve and a normal voltage curve in the charging or discharging stage; comparing the first deviation value and the second deviation value with a set evaluation threshold range respectively to determine whether there is a fault single battery in the target battery pack and the positioning of the fault single battery. The application can realize the detection of the mixed fault, internal resistance fault, connection fault and external short circuit fault of the battery with low SOC and low capacity. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0030] Figure 1 It is a flowchart of the lithium battery pack multi-fault diagnosis method of the present application.

[0031] Figure 2 It is a schematic diagram of the Euclidean distance and Manhattan distance in the present embodiment.

[0032] Figure 3 It is a charging stage curve Manhattan distance calculation diagram in the present embodiment. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0034] The present application aims to provide a lithium battery pack multi-fault diagnosis method, system, device and medium based on a local weighted Manhattan distance, which can realize the detection of the mixed fault, internal resistance fault, connection fault and external short circuit fault of the battery with low SOC and low capacity.

[0035] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail with reference to the drawings and specific embodiments.

[0036] As Figure 1 shown, the present application provides a lithium battery pack multi-fault diagnosis method based on local weighted Manhattan distance, comprising:

[0037] Step 100: Obtain the voltage curves of each single battery in the target battery pack in the charging stage and the discharging stage.

[0038] Step 200: Calculate each voltage curve in the charging stage by using the traditional Manhattan distance algorithm to obtain the first deviation value; the first deviation value is the deviation data between each voltage curve and the normal voltage curve in the charging stage.

[0039] Step 300: Calculate each voltage curve in the discharging stage by using the local weighted Manhattan distance algorithm to obtain the second deviation value; the second deviation value is the deviation data between each voltage curve and the normal voltage curve in the discharging stage.

[0040] Step 400: Compare the first deviation value and the second deviation value with the set evaluation threshold range respectively to determine whether there is a fault single battery in the target battery pack and the positioning of the fault single battery.

[0041] Based on the above technical solution, the following specific implementation is provided.

[0042] Constructing a lithium battery pack multi-fault diagnosis strategy: based on the Manhattan distance evaluation of the battery pack voltage sequence, an evaluation method of local weighted Manhattan distance is proposed, and the fault type of single fault and mixed fault is detected by the abnormal value comprehensive analysis method based on voltage ratio. This embodiment will specifically illustrate its theory and give the diagnosis idea, and propose the overall flow of the lithium ion battery pack multi-fault diagnosis method.

[0043] As a more specific processing step, first, curve Manhattan distance calculation is performed.

[0044] Manhattan distance is to represent the distance between two points by the sum of the absolute values of the coordinate differences of the related points in two-dimensional space. The greater the distance, the greater the difference between the samples, and the more dissimilar the samples; on the contrary, the smaller the distance, the smaller the sample difference, and the higher the similarity between the samples. The Manhattan distance between two points a and b in two-dimensional space is calculated by formula (1):

[0045] d (a,b) =|x a -x b |+|y a -y b | (1)

[0046] Where d (a,b) represents the point (x a , ya ) and point (x b ,y b ).

[0047] The Manhattan distance indicates the positions of two points relative to the coordinate axes without compression transformation of the original distance, reflecting the true difference between the two points. As shown in Figure 2 , A represents the Manhattan distance, B represents the Euclidean distance, and C represents the equivalent Manhattan distance.

[0048] To quantify the distance between two curves of the same length in two-dimensional space, the traditional Manhattan distance is improved to quantify the Manhattan distance between two curves of the same length.

[0049] Suppose l1 and l2 are two curves of the same length in two-dimensional space, and the distance between l1 and l2 is calculated by the curve Manhattan distance, with the formula: d = |x1-x'1|+|y1-y'1|+…+|x n -x′ n |+|y n -y′ n |.

[0050] In the formula, (x n ,y n ) and (x' n ,y' n ) are n points that make up the curves l1 and l2, respectively; d represents the Manhattan distance between the curves l1 and l2. For lithium-ion battery pack multi-fault diagnosis, suppose that the terminal voltage curve data of each cell in the charging and discharging stages of the lithium-ion battery pack obtained is as shown in formula (2), and the specific detection positioning steps are as follows:

[0051]

[0052] In the formula, [V 1,m ,…V n,m ] T is the voltage sequence of the mth battery in the charging stage of the lithium-ion battery pack, and [V n,m ,…V k,m ] T is the voltage sequence of the mth battery in the discharging stage of the lithium-ion battery pack; V n,m is the sampling voltage of the mth battery at time n in the charging stage, and V k,m is the sampling voltage of the mth battery at time k in the discharging stage. m is the total number of cells in the battery pack.

[0053] According to the terminal voltage curve data of each cell, the Manhattan distance between the cells in the charging stage is calculated as follows:

[0054] d (i,j) =|V 1,i -V1,j |+|V 2,i -V 2,j |+…+|V n,i -V n,j | (3)

[0055] Where, d (i,j) Let n be the Manhattan distance between individual cells i (i = 1, 2, ..., m) and j (j = 1, 2, ..., m) within the battery pack, where n represents the first voltage sampling time and m represents the total number of individual cells. The Manhattan matrix of the lithium battery pack during the charging phase is established as shown in equation (4).

[0056]

[0057] In equation (4), d' (i,j) d' is the normalized Manhattan distance between cells i and j in a lithium-ion battery pack, where cell i and cell j are both cells within the battery pack, and d' (i,j) =d' (j,i) .

[0058] like Figure 3 As shown, assume curves a, b, and c are three voltage curves representing the charging stages, and the Manhattan distance D between curves a and b is D = ΔU. ab The Manhattan distance D′ between curves b and c can be expressed as D′=ΔU bc1 +|ΔU bc2 |+|ΔU bc3 |

[0059] Then, a locally weighted Manhattan distance is constructed: Due to the influence of discharge rate and temperature on battery terminal voltage under dynamic operating conditions, there are significant differences in the voltage between individual cells in the battery pack, causing normal batteries to exhibit abnormal conditions similar to faulty batteries during the discharge phase. Therefore, this embodiment employs a locally weighted Manhattan distance method during the discharge phase, assigning different weights to different battery cells to reduce voltage anomalies caused by discharge rate and temperature, and using the curved Manhattan distance for accurate fault diagnosis.

[0060] Assume α and β are the discharge rate factor and temperature coefficient factor, respectively, affecting the discharge rate and temperature. Where C... i C j I represents the rated capacity of a single cell in a battery pack. d The current represents the discharge current under dynamic operating conditions; t represents the duration of the discharge process. The temperature of a single cell i in the battery pack at time t is shown in equation (7):

[0061]

[0062] In the formula, the capacity difference and temperature difference between the monomer batteries are quantified as the form of distance difference, and then the weighting coefficient W is derived, where a, b need to satisfy 0≤a, b≤1. As can be seen from the above, the weighted Manhattan distance of the discharge stage is obtained by combining the discharge rate and temperature factors:

[0063] d dis(i,j) = w (i,j) |V n,i -V n,j |+…+w (i,j) |V k,i -V k,j | (8)

[0064] where d dis(i,j) represents the Manhattan distance between monomer battery i and monomer battery j in the battery pack, w (i,j) represents the weight relationship established between monomer battery i and monomer battery j in the battery pack, V n,i represents the sampling voltage of monomer battery i at time n, V n,j represents the sampling voltage of monomer battery j at time n, V k,i represents the sampling voltage of monomer battery i at time k, V k,j represents the sampling voltage of monomer battery j at time k.

[0065] The local weighted Manhattan matrix of the lithium ion battery pack is established, as shown in formula (9):

[0066]

[0067] where d' dis(i,j) is the normalized local weighted Manhattan distance between i and j cells in the lithium ion battery pack. In addition to the above voltage distance evaluation of charging and discharging, the following steps need to be clarified:

[0068] For the batteries in the lithium ion battery pack, the distance standardization between two monomers will be mapped within a smaller range, and the standard Manhattan distance is usually between (0, 1).

[0069] (1) The Manhattan distance of the normal battery in the charging stage is usually around 0.1. The selection of the threshold value is related to the normalized Manhattan distance of the normal battery. In order to reduce the omission of faults and ensure timely warning, it is necessary to ensure that the threshold value is not less than the Manhattan distance of the normal battery. After multiple calculations and tests, the threshold value range is set between (0, 0.17).

[0070] (2) Due to the influence of factors such as discharge rate and temperature, the Manhattan distance range in the discharge stage is much larger than that of the normal battery. The method of local weighted Manhattan distance is adopted for evaluation, which ensures that the threshold value in the discharge stage can be consistent with that in the charging stage.

[0071] For the batteries in lithium-ion battery pack, the voltage curve trends of them are similar in the charging phase. Due to the influence of temperature, production process and other factors, the characteristics of battery data are inconsistent, and this inconsistency will be further intensified in dynamic conditions.

[0072] Therefore, the embodiment proposes an evaluation method based on local weighted Manhattan distance, which solves the voltage discharge rate difference of the battery in the discharge phase, so as to achieve the same threshold evaluation standard as the charging phase. By comprehensively considering the charging and discharging phases, the integrity of the Manhattan distance evaluation is ensured. The deviation of the complete charging and discharging voltage curve of the faulty battery cell from the normal curve is quantified, and the faulty cell in the battery pack is positioned and detected.

[0073] When there are different faults in the battery pack, the faulty cell will show a voltage deviation phenomenon different from the normal. By using different Manhattan distance methods to evaluate the battery pack in the charging and discharging phases, the deviation is quantified, which can quickly and reliably locate and detect the fault.

[0074] The embodiment improves the curve Manhattan distance in the charging phase, innovatively adds the local weighted Manhattan distance in the discharging phase, accurately quantifies the voltage curve change between the batteries, and solves the limitation of using only the charging phase voltage in fault diagnosis. The complete charging and discharging process is used for multi-fault positioning of the lithium-ion battery pack.

[0075] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts of each embodiment can be referred to each other.

[0076] The principles and implementation modes of the present application are described by applying specific examples in this paper, and the above embodiment description is only used to help understand the core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In view of the above, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for diagnosing multiple faults in lithium battery packs based on locally weighted Manhattan distance, characterized in that, include: Obtain the voltage curves of each individual cell in the target battery pack during the charging and discharging phases; The first deviation value is obtained by calculating the voltage curves during the charging stage using the traditional Manhattan distance algorithm; the first deviation value is the deviation data between each voltage curve and the normal voltage curve during the charging stage. The local weighted Manhattan distance algorithm is used to calculate the voltage curves during the discharge phase to obtain the second deviation value; the second deviation value is the deviation data between each voltage curve and the normal voltage curve during the discharge phase. The first deviation value and the second deviation value are compared with the set evaluation threshold range to determine whether there is a faulty single cell in the target battery pack and the location of the faulty single cell. The expression for the weighting coefficients in the locally weighted Manhattan distance algorithm is as follows: ， in, C i Indicates the individual cells in the battery pack i Rated capacity, C j Indication and single cell j Rated capacity, α The discharge rate factor represents the factor that affects the discharge rate. β The temperature coefficient factor represents the effect of temperature. I d This represents the discharge current under dynamic operating conditions. Indicates the individual cells of the battery pack i exist t The temperature at that moment Indicates the individual cells of the battery pack j exist t The temperature at that moment n denoted by k, where k represents the first voltage sampling time, and k represents the second voltage sampling time.

2. The method for multi-fault diagnosis of lithium battery packs based on locally weighted Manhattan distance according to claim 1, characterized in that, The expression for the traditional Manhattan distance algorithm is: ， in, d （i,j） For individual cells within the battery pack i ( i =1, 2, …, m ) and single cell j ( j =1, 2, …, m Manhattan distance between ) n Indicates the first voltage sampling time. m This indicates the total number of individual battery cells.

3. The method for multi-fault diagnosis of lithium battery packs based on locally weighted Manhattan distance according to claim 1, characterized in that, The expression for the locally weighted Manhattan distance algorithm is: ， in, d dis(i,j) Indicates the individual cells within the battery pack i and single cell battery j Manhattan distance between them w (i,j) Indicates the individual cells within the battery pack i and single cell battery j The weighted relationship established between them V n,i Indicates a single cell i exist n The sampling voltage at time t, V n,j Indicates a single cell j exist n The sampling voltage at time t, V k,i Indicates a single cell i exist k The sampling voltage at time t, V k,j Indicates a single cell j exist k The sampling voltage at that moment.

4. The method for multi-fault diagnosis of lithium battery packs based on locally weighted Manhattan distance according to claim 1, characterized in that, The set evaluation threshold range is (0, 0.17).

5. A multi-fault diagnosis system for lithium battery packs based on locally weighted Manhattan distance, using the method as described in any one of claims 1-4, characterized in that, include: The data sampling unit is used to acquire the voltage curves of each individual cell in the target battery pack during the charging and discharging phases. The charging stage deviation calculation unit is used to calculate the voltage curves of each stage of charging using the traditional Manhattan distance algorithm to obtain the first deviation value; the first deviation value is the deviation data between each voltage curve and the normal voltage curve during the charging stage. The discharge stage deviation calculation unit is used to calculate the voltage curves of each voltage curve in the discharge stage using the local weighted Manhattan distance algorithm to obtain the second deviation value; the second deviation value is the deviation data between each voltage curve and the normal voltage curve in the discharge stage. The fault diagnosis unit is used to compare the first deviation value and the second deviation value with a set evaluation threshold range to determine whether there is a faulty single cell in the target battery pack and the location of the faulty single cell.

6. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the lithium battery pack multi-fault diagnosis method based on local weighted Manhattan distance according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the lithium battery pack multi-fault diagnosis method based on locally weighted Manhattan distance as described in any one of claims 1-4.

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