Lithium ion battery abnormity identification method and system based on abnormal state quantity and cumulative probability method

Abstract

The invention provides a lithium ion battery abnormity identification method and system based on an abnormal state quantity and a cumulative probability method. The method comprises the following steps: firstly, measuring a voltage signal of each single battery in a battery pack in a charge-discharge cycle; performing multilayer decomposition on the voltage signal of each battery by applying wavelet transform, extracting multilayer approximation coefficients to generate an approximation coefficient set, and calculating an abnormal state quantity; taking the abnormal state quantity of each battery of the normal battery pack as a training set, and calculating a normal threshold value of the battery by using a cumulative probability method; and comparing the abnormal state quantity of each single battery of the tested battery pack with a normal threshold value, and judging an abnormal battery. The method has high calculation efficiency and accuracy, fault detection and early warning of the single batteries in the battery pack can be realized, and the overall reliability and safety of the battery pack are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of batteries, and in particular relates to a lithium-ion battery abnormality identification method and system based on abnormal state quantity and cumulative probability method. Background Art

[0002] Lithium-ion batteries have become core energy storage components in electric vehicles, smart grids, and consumer electronics due to their high energy density, long life, and low self-discharge rate. However, in long-term use, the battery performance will gradually decline due to factors such as the number of charge and discharge cycles, temperature, current rate, storage time, and charge and discharge depth, which is manifested as capacity loss and power decline. As the number of cycles increases, the structural changes of the electrode materials and the loss of active lithium will cause the battery capacity to decay. At the same time, the impact of temperature changes on internal electrochemical reactions will also accelerate performance degradation. In the field of new energy vehicles, the health of lithium-ion batteries is directly related to vehicle safety, so it is crucial to accurately assess the health status of the battery and identify potential faults. To this end, model-based and data-driven fault diagnosis technologies are widely used to monitor the battery status in real time, extract key features, and realize battery fault warning. However, these methods have large computational complexity and slow reaction speed, and are not suitable for large-scale battery abnormality identification. Therefore, there is currently a lack of a simple and effective large-scale lithium-ion battery abnormality discrimination method. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a lithium-ion battery abnormality identification method and system based on abnormal state quantity and cumulative probability method, which can efficiently and accurately realize the identification and early warning of battery abnormalities.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] In a first aspect, the present invention provides a method for identifying abnormalities in a lithium-ion battery based on abnormal state quantity and cumulative probability method, comprising the following steps:

[0006] S1: measuring the voltage signal of each single cell in the battery pack during the charge and discharge cycle, wherein the battery pack includes a normal battery pack and a test battery pack;

[0007] S2: Apply wavelet transform to perform multi-layer decomposition on the voltage signal of each battery, extract the approximate coefficients of each layer to generate an approximate coefficient set, and calculate the abnormal state quantity of the battery according to the approximate coefficient set;

[0008] S3: Taking the abnormal state quantity of each battery in the normal battery group as the training set, the normal threshold value of the battery is calculated using the cumulative probability method;

[0009] S4: Compare the abnormal state quantity of each single battery in the test battery pack with the normal threshold value of the battery to identify the abnormal battery.

[0010] Optionally, step S1 specifically includes:

[0011] Measure the voltage signal U of each single cell in the battery pack during the charge and discharge cycle i，k =[U i，k (1), U i，k (2) U i，k (3),…,U i，k (n)]; where U i，k (n) represents the nth voltage signal sample collected from the i-th battery in the k-th charge and discharge cycle.

[0012] Optionally, in step S2, the application of wavelet transform to perform multi-layer decomposition on the voltage signal of each battery is specifically as follows:

[0013] The voltage signal of each single cell in the battery pack during the charge and discharge cycle is decomposed by multi-layer wavelet decomposition to the Lth layer, and the approximate coefficients of each layer are extracted. The wavelet transform formula is as follows:

[0014]

[0015] Where, WT U (m, k) is the coefficient of wavelet transform, m is the scale parameter, q is the position index, U n is the nth charge and discharge voltage signal, t n is the voltage signal collection point, ψ(2 -m t n -q) is the wavelet basis function, is the normalization factor, and N is the total number of sampling points of the voltage signal.

[0016] Optionally, in step S2, the recursive calculation formula of the approximate coefficient is:

[0017]

[0018] In the formula, h * (p-2v) is the low channel filter in wavelet decomposition, C L (v) is the approximate coefficient of the battery voltage after decomposition at the Lth layer, denoted by A i，L , represents the approximate coefficient of the Lth layer of the i-th battery, v is the position index, C L-1 (p) represents the approximate coefficient of the voltage signal in the L-1 layer, p is the position index in the previous layer, 2v is the downsampling position of the voltage signal in the current layer, To sum all position indexes p of the previous layer;

[0019] Then the approximate coefficient set corresponding to the i-th battery is A i =[A i，1 , Ai，2 , A i，3 , ..., A i,L ]; Integrate the approximate coefficient sets of all single cells in the battery pack to obtain the approximate coefficient set of all cells α = [A1, A2, A3, ..., A i ].

[0020] Optionally, in step S2, the abnormal state quantity of the battery is calculated according to the approximate coefficient set, specifically:

[0021] The abnormal state quantity is calculated for the approximate coefficient set α of all batteries as follows:

[0022]

[0023] D=(αP e P e T )(αP e P e T ) T =αP e P e T α T

[0024] Where α is the data matrix of all battery approximation coefficients, is the reconstructed data matrix represented by principal component analysis, E is the residual matrix, T is the principal component score matrix, P T is the principal component loading matrix, T e is the residual score matrix, P e T is the residual loading matrix, α T It means to transpose the data matrix of all battery approximate coefficients, D is the abnormal state quantity, and the abnormal state quantities of all batteries are extracted to form an abnormal state quantity set F = [F1, F2, F3, ..., Fi], F i It represents the abnormal state quantity of the ith battery, and its value reflects the abnormal condition of the ith battery.

[0025] Optionally, step S3 specifically includes:

[0026] Extract the battery abnormal state quantities of b normal batteries from the abnormal state quantity set F of all batteries, and record it as the normal battery abnormal state quantity set F j =[F1, F2, ..., F b ],F b represents the abnormal state quantity of the bth normal battery, and the abnormal state quantity set F of the normal battery j For the training set, the normal battery threshold is calculated using the cumulative probability method. The formula is:

[0027]

[0028] X d1 =G -1 (0.01)

[0029] X d2 =G -1 (0.99)

[0030] In the formula, G(s) represents the cumulative distribution function, f(s) represents the probability density function, represents the integral of the probability density function from negative infinity to x, where x represents the specific value of the abnormal state quantity, G -1 (0.01) is the inverse function of the cumulative distribution function G(s), which indicates the threshold corresponding to the cumulative probability of 0.01, denoted as Xd1, which represents the lower limit of the normal threshold, G -1 (0.99) is the inverse function of the cumulative distribution function G(s), which indicates the threshold corresponding to the cumulative probability of 0.99, denoted as X d2 , represents the upper limit of the normal threshold; determine the normal threshold range Φ=[X d1 , X d2 ], X d1 <X d2 .

[0031] Optionally, step S4 specifically includes:

[0032] The abnormal state quantity F of the i-th single cell i Compared with the normal threshold Φ, if X d1 <F i <X d2 , then the single cell is determined to be a normal battery; otherwise, the single cell is determined to be an abnormal battery.

[0033] In a second aspect, the present invention provides a lithium-ion battery abnormality identification system based on abnormal state quantity and cumulative probability method, comprising:

[0034] A measuring unit, used to measure the voltage signal of each single cell in the battery pack during the charge and discharge cycle, wherein the battery pack includes a normal battery pack and a test battery pack;

[0035] An abnormal state quantity calculation unit is used to perform multi-layer decomposition on the voltage signal of each battery by applying wavelet transform, extract the approximate coefficients of each layer to generate an approximate coefficient set, and calculate the abnormal state quantity of the battery according to the approximate coefficient set;

[0036] A normal threshold value calculation unit, used to calculate the normal threshold value of the battery by using the abnormal state quantity of each battery in the normal battery group as a training set and using the cumulative probability method;

[0037] The distinguishing unit is used to compare the abnormal state quantity of each single battery in the test battery group with the normal threshold value of the battery to distinguish the abnormal battery.

[0038] The beneficial effects of the present invention are:

[0039] (1) The present invention extracts an approximate coefficient set from the battery charge and discharge voltage signal through wavelet decomposition technology, which can effectively retain the key information of the signal, and then calculates the abnormal state quantity, thereby enhancing the accuracy and efficiency of battery abnormality diagnosis.

[0040] (2) The present invention uses the cumulative probability method to establish a threshold range (upper and lower limits) using normal battery data, which can accurately and efficiently distinguish normal batteries from abnormal batteries. The threshold setting based on the statistical method makes fault identification more reliable and can provide early warning when the battery is abnormal.

[0041] (3) The present invention is simple and effective, and is suitable for identifying abnormal batteries in large-scale battery packs. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of a lithium-ion battery abnormality identification method based on abnormal state quantity and cumulative probability method;

[0043] Figure 2 is a first group of battery charging voltage signal segment curve graphs;

[0044] Figure 3 is a normal threshold value T determined based on the first group of normal batteries;

[0045] Figure 4 is a second group of battery charging voltage signal segment curve graphs;

[0046] Figure 5 It is an abnormal identification diagram for the second group of batteries based on the normal threshold T. DETAILED DESCRIPTION

[0047] The present invention will now be described in further detail with reference to the accompanying drawings.

[0048] In one embodiment, the present invention proposes a lithium-ion battery abnormality identification method based on abnormal state quantity and cumulative probability method. The overall process is as follows: Figure 1 As shown, the following steps are included:

[0049] S1: Measure the voltage signal of each single cell in the battery pack during the charge and discharge cycle.

[0050] Measure the voltage signal U of each single cell in the battery pack during the charge and discharge cycle i,k =[U i，k (1), U i，k (2) Ui，k (3),…,U i，k (n)]; where U i，k (n) represents the nth voltage signal sample collected from the i-th battery in the k-th charge and discharge cycle.

[0051] S2: Apply wavelet transform to perform multi-layer decomposition on the charging voltage of each battery, extract the approximate coefficients of the multiple layers to generate an approximate coefficient set, and calculate the abnormal state quantity.

[0052] The voltage data U collected during the charging cycle of each single battery i，k =[U i，k (1), U i，k (2) U i，k (3),…,U i，k (n)] perform multi-layer wavelet decomposition, decompose to the Lth layer, extract the approximate coefficients of each layer, and the wavelet transform formula is as follows:

[0053]

[0054] Where, WT U (m, k) is the coefficient of wavelet transform, m is the scale parameter, q is the position index, U n is the nth charge and discharge voltage signal, t n is the voltage signal collection point, ψ(2 -m t n -q) is the wavelet basis function, is the normalization factor, and N is the total number of sampling points representing the voltage signal.

[0055] The recursive calculation formula for the approximate coefficient is:

[0056]

[0057] In the formula, h * (p-2v) is the low channel filter in wavelet decomposition, C L (v) is the approximate coefficient of the battery voltage after decomposition at the Lth layer, denoted by A i，L , represents the approximate coefficient of the Lth layer of the i-th battery, v is the position index, C L-1 (p) represents the approximate coefficient of the voltage signal in the L-1 layer, p is the position index in the previous layer, 2v is the downsampling position of the voltage signal in the current layer, is to sum all position indexes p of the previous layer.

[0058] The approximate coefficient set corresponding to the i-th battery is A i =[A i，1 , A i，2 , A i，3 , ..., Ai，L ], where A i，L Represents the L-th layer approximation coefficient of the ith battery.

[0059] The approximate coefficient sets of all single cells in the battery pack are integrated to obtain the approximate coefficient set of all cells α = [A1, A2, A3, ..., A i ], where A i Represents the set of approximate coefficients for the ith battery.

[0060] The abnormal state quantity is calculated for the approximate coefficient set α of all batteries as follows:

[0061]

[0062] D=(αP e P e T )(αP e P e T ) T =αP e P e T α T

[0063] Where α is the data matrix of all battery approximation coefficients, is the reconstructed data matrix represented by principal component analysis, E is the residual matrix, T is the principal component score matrix, P T is the principal component loading matrix, T e is the residual score matrix, P e T is the residual loading matrix, α T It means to transpose the data matrix of all battery approximate coefficients, D is the abnormal state quantity, and the abnormal state quantities of all batteries are extracted to form an abnormal state quantity set F = [F1, F2, F3, ..., F i ],F i It represents the abnormal state quantity of the ith battery, and its value can reflect the abnormal condition of the ith battery.

[0064] S3: Taking the abnormal state quantity of each battery in the normal battery pack as a training set, and using the cumulative probability method to calculate the battery normal threshold.

[0065] Extract the battery abnormal state quantities of b normal batteries from the abnormal state quantity set F of all batteries, and record it as the normal battery abnormal state quantity set F j =[F1, F2, ..., F b ],F b represents the abnormal state quantity of the bth normal battery, and the abnormal state quantity set F of the normal battery jFor the training set, using the cumulative probability method, the formula is:

[0066]

[0067] X d1 =G -1 (0.01)

[0068] X d2 =G -1 (0.99)

[0069] In the formula, G(s) represents the cumulative distribution function, f(s) represents the probability density function, represents the integral of the probability density function from negative infinity to x, where x represents the specific value of the abnormal state quantity, G -1 (0.01) is the inverse function of the cumulative distribution function G(s), which indicates the threshold corresponding to the cumulative probability of 0.01, denoted as X d1 , represents the lower limit of normal threshold, G -1 (0.99) is the inverse function of the cumulative distribution function G(s), which indicates the threshold corresponding to the cumulative probability of 0.99, denoted as X d2 , represents the upper limit of the normal threshold; determine the normal threshold range Φ=[X d1 , X d2 ], X d1 <X d2 .

[0070] S4: Compare the abnormal state quantity of each single cell of the test battery pack with the normal threshold value to identify the abnormal cell.

[0071] The abnormal state quantity F of the i-th single cell i Compared with the normal threshold Φ, if the abnormal state quantity F of battery i i Located at the normal threshold Φ=[X d1 , X d2 ], that is, X d1 <F i <X d2 , then the single cell is determined to be a normal battery; otherwise, the single cell is determined to be an abnormal battery.

[0072] Next, in order to demonstrate the specific application process and effect of the lithium-ion battery abnormality identification method based on abnormal state quantity and cumulative probability method, a specific example is used to illustrate the following.

[0073] The battery data used in this example is a cycle aging experiment of cylindrical lithium-ion batteries with the same specifications under a battery testing system in a laboratory. The specific process is as follows:

[0074] (1) Based on the laboratory battery testing system, the voltage signals of each single cell in the two battery groups during the charge and discharge cycle are collected and recorded as two groups of data. The first group of data is the voltage signals of 6 normal batteries, that is, the normal battery group, and the second group is the voltage signals of 8 batteries with certain faults, that is, the test battery group, where the test battery group has the following faults: insufficient SOC (2_1), internal resistance fault (2_2) and low initial capacity (2_7). The charging voltage signal segment curve of the normal battery group is as follows: Figure 2 shown.

[0075] (2) Perform three-layer wavelet transform on the voltage signal of each battery in the normal battery pack and extract the approximate coefficients of each layer. Some results are shown in Table 1.

[0076] Table 1 Approximate coefficients of each battery in a normal battery pack (only some data are listed)

[0077] Battery ID Approximation coefficient 1 Approximation coefficient 2 Approximation coefficient 3 Approximation coefficient 4 … 1_1 11.23702277 11.17490344 11.11964304 11.07124158 … 1_2 11.26021587 11.21057697 11.16716062 11.12936576 … 1_3 11.2806159 11.21892083 11.16192803 11.1104153 … 1_4 11.25183666 11.20856172 11.16818592 11.12982538 … 1_5 11.22754754 11.17013047 11.11854703 11.07385788 … 1_6 11.23412363 11.17529235 11.12317858 11.07721664 …

[0078] (3) The principal component analysis method is used to process the approximate coefficient set of each battery in the normal battery pack, and the abnormal state quantity corresponding to each battery is calculated, as shown in Table 2.

[0079] Table 2 Abnormal state quantity corresponding to each battery in a normal battery pack

[0080] Battery ID Abnormal state quantity 1_1 -0.169001119 1_2 0.10128876 1_3 -0.147066832 1_4 -0.098563283 1_5 -0.127280911 1_6 -0.155929509

[0081] (4) Taking the abnormal state quantity of each battery in the normal battery pack as the training set, the normal threshold range T = [-0.1690011191489867, 0.09506586951893053] is calculated by using the cumulative probability method. Figure 2 .

[0082] (5) Perform three-layer wavelet transform on the voltage signals of each battery in the test battery pack and extract the approximate coefficients of each layer. Some results are shown in Table 3.

[0083] Table 3 Approximate coefficients corresponding to each battery in the test battery pack

[0084] Battery ID Approximation coefficient 1 Approximation coefficient 2 Approximation coefficient 3 Approximation coefficient 4 … 2_1 11.29949565 11.27174171 11.24423526 11.21757733 … 2_2 11.05052336 11.02284012 10.99653575 10.97093849 … 2_3 11.20011179 11.16694849 11.13389124 11.10150575 … 2_4 11.21708236 11.18416654 11.15096787 11.11840561 … 2_5 11.21146086 11.17843897 11.14569993 11.11317302 … 2_6 11.20923347 11.17607016 11.14333112 11.1107335 … 2_7 11.27898956 11.26216041 11.24575554 11.23023454 … 2_8 11.20124317 11.1677263 11.13417409 11.10168253 …

[0085] (6) Calculate the abnormal state quantity of each battery in the test battery pack, see Table 4. Based on the normal threshold range T calculated by (4), all batteries in the test battery pack are identified for abnormality. The abnormal state quantity of abnormal batteries with insufficient SOC (2_1), internal resistance failure (2_2) and low initial capacity (2_7) exceeds the normal threshold range T. Therefore, the abnormal batteries with insufficient SOC (2_1), internal resistance failure (2_2) and low initial capacity (2_7) can be accurately identified, such as Figure 5 shown.

[0086] Table 4 Abnormal state quantity corresponding to each battery in the test battery pack

[0087] Battery ID Abnormal state quantity 2_1 0.421987814 2_2 -0.390074886 2_3 -0.044416787 2_4 0.007066674 2_5 -0.006874444 2_6 -0.015044747 2_7 0.675218228 2_8 -0.051308958

[0088] In another embodiment, the present invention proposes a lithium-ion battery abnormality identification system based on abnormal state quantity and cumulative probability method, which corresponds to a lithium-ion battery abnormality identification method based on abnormal state quantity and cumulative probability method, including:

[0089] A measuring unit, used to measure the voltage signal of each single cell in the battery pack during the charge and discharge cycle, wherein the battery pack includes a normal battery pack and a test battery pack;

[0090] An abnormal state quantity calculation unit is used to perform multi-layer decomposition on the voltage signal of each battery by applying wavelet transform, extract the approximate coefficients of each layer to generate an approximate coefficient set, and calculate the abnormal state quantity of the battery according to the approximate coefficient set;

[0091] A normal threshold value calculation unit, used to calculate the normal threshold value of the battery by using the abnormal state quantity of each battery in the normal battery group as a training set and using the cumulative probability method;

[0092] The distinguishing unit is used to compare the abnormal state quantity of each single battery in the test battery group with the normal threshold value of the battery to distinguish the abnormal battery.

[0093] In this embodiment, the specific functions and execution processes of each unit of the system are the same as the steps of the method in the above-mentioned embodiment, so they are not repeated here.

[0094] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A lithium-ion battery abnormality identification method based on abnormal state quantity and cumulative probability method, characterized in that: The following steps are involved: S1: measuring the voltage signal of each single cell in the battery pack during the charge and discharge cycle, wherein the battery pack includes a normal battery pack and a test battery pack; S2: Apply wavelet transform to perform multi-layer decomposition on the voltage signal of each battery, extract the approximate coefficients of each layer to generate an approximate coefficient set, and calculate the abnormal state quantity of the battery according to the approximate coefficient set; S3: Taking the abnormal state quantity of each battery in the normal battery group as the training set, the normal threshold value of the battery is calculated using the cumulative probability method; S4: Compare the abnormal state quantity of each single battery in the test battery pack with the normal threshold value of the battery to identify the abnormal battery.

2. A lithium-ion battery abnormality identification method based on abnormal state quantity and cumulative probability method as claimed in claim 1, characterized in that: Step S1 specifically includes: Measure the voltage signal U of each single cell in the battery pack during the charge and discharge cycle i，k =[U i，k (1), U i，k (2) U i，k (3), …, U i，k (n)]; where U i，k (n) represents the nth voltage signal sample collected from the i-th battery in the k-th charge and discharge cycle.

3. A lithium-ion battery abnormality identification method based on abnormal state quantity and cumulative probability method as claimed in claim 1, characterized in that: In step S2, the voltage signal of each battery is decomposed by multi-layers by applying wavelet transform, specifically: The voltage signal of each single cell in the battery pack during the charge and discharge cycle is decomposed by multi-layer wavelet decomposition to the Lth layer, and the approximate coefficients of each layer are extracted. The wavelet transform formula is as follows: Where, WT U (m, k) is the coefficient of wavelet transform, m is the scale parameter, q is the position index, U n is the nth charge and discharge voltage signal, t n is the voltage signal collection point, ψ(2 -m t n -q) is the wavelet basis function, is the normalization factor, and N is the total number of sampling points of the voltage signal.

4. A lithium-ion battery abnormality identification method based on abnormal state quantity and cumulative probability method as claimed in claim 3, characterized in that: In step S2, the recursive calculation formula of the approximate coefficient is: In the formula, h * (p-2v) is the low channel filter in wavelet decomposition, C L (v) is the approximate coefficient of the battery voltage after decomposition at the Lth layer, denoted by A i，L , represents the approximate coefficient of the Lth layer of the i-th battery, v is the position index, C L-1 (p) represents the approximate coefficient of the voltage signal in the L-1 layer, p is the position index in the previous layer, 2v is the downsampling position of the voltage signal in the current layer, To sum all position indexes p of the previous layer; Then the approximate coefficient set corresponding to the i-th battery is A i =[A i，1 , A i，2 , A i，3 …, A i，L ]; Integrate the approximate coefficient sets of all single cells in the battery pack to obtain the approximate coefficient set of all cells α = [A1, A2, A3, ..., A i ].

5. A lithium-ion battery abnormality identification method based on abnormal state quantity and cumulative probability method as claimed in claim 4, characterized in that: In step S2, the abnormal state quantity of the battery is calculated according to the approximate coefficient set, specifically: The abnormal state quantity is calculated for the approximate coefficient set α of all batteries as follows: D=(αP e P e T )(αP e P e T ) T =αP e P e T a T Where α is the data matrix of all battery approximation coefficients, is the reconstructed data matrix represented by principal component analysis, E is the residual matrix, T is the principal component score matrix, P T is the principal component loading matrix, T e is the residual score matrix, P e T is the residual loading matrix, α T It means to transpose the data matrix of all battery approximate coefficients, D is the abnormal state quantity, and the abnormal state quantities of all batteries are extracted to form an abnormal state quantity set F = [F1, F2, F3, ..., F i ],F i It represents the abnormal state quantity of the ith battery, and its value reflects the abnormal condition of the ith battery.

6. A lithium-ion battery abnormality identification method based on abnormal state quantity and cumulative probability method as claimed in claim 1, characterized in that: Step S3 specifically includes: Extract the battery abnormal state quantities of b normal batteries from the abnormal state quantity set F of all batteries, and record it as the normal battery abnormal state quantity set F j =[F1, F2, ..., F b ],F b represents the abnormal state quantity of the bth normal battery, and the abnormal state quantity set F of the normal battery j For the training set, the normal battery threshold is calculated using the cumulative probability method. The formula is: X d1 =G -1 (0.01) X d2 =G -1 (0.99) In the formula, G(s) represents the cumulative distribution function, f(s) represents the probability density function, represents the integral of the probability density function from negative infinity to x, where x represents the specific value of the abnormal state quantity, G 11 (0.01) is the inverse function of the cumulative distribution function G(s), which indicates the threshold corresponding to the cumulative probability of 0.01, denoted as X d1 , represents the lower limit of normal threshold, G -1 (0.99) is the inverse function of the cumulative distribution function G(s), which indicates the threshold corresponding to the cumulative probability of 0.99, denoted as X d2 , represents the upper limit of the normal threshold; determine the normal threshold range Φ=[X d1 , X d2 ], X d1 <X d2 .

7. A lithium-ion battery abnormality identification method based on abnormal state quantity and cumulative probability method as claimed in claim 6, characterized in that: Step S4 specifically includes: The abnormal state quantity F of the i-th single cell i Compared with the normal threshold Φ, if X d1 <F i <X d2 , then the single cell is determined to be a normal battery; otherwise, the single cell is determined to be an abnormal battery.

8. A lithium-ion battery abnormality identification system based on abnormal state quantity and cumulative probability method, characterized in that: include: A measuring unit, used to measure the voltage signal of each single cell in the battery pack during the charge and discharge cycle, wherein the battery pack includes a normal battery pack and a test battery pack; An abnormal state quantity calculation unit is used to perform multi-layer decomposition on the voltage signal of each battery by applying wavelet transform, extract the approximate coefficients of each layer to generate an approximate coefficient set, and calculate the abnormal state quantity of the battery according to the approximate coefficient set; A normal threshold value calculation unit, used to calculate the normal threshold value of the battery by using the abnormal state quantity of each battery in the normal battery group as a training set and using the cumulative probability method; The distinguishing unit is used to compare the abnormal state quantity of each single battery in the test battery group with the normal threshold value of the battery to distinguish the abnormal battery.

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