Battery consistency calculation method and device, electronic equipment and computer program product

Abstract

The invention relates to a battery consistency calculation method and device, electronic equipment and a computer program product, and the method comprises the steps: obtaining the battery data of a vehicle, and carrying out the data processing of the battery data; dividing the battery data after data processing into non-overlapping time windows according to time scales to construct coarse graining sequences, and calculating a sample entropy of each coarse graining sequence; and according to a preset allocation weight, allocating to each coarse graining sequence of each coarse sequence, calculating a weighted composite multi-scale entropy of the coarse graining sequence based on the sample entropy value of each coarse graining sequence, and evaluating the battery consistency of the vehicle based on the weighted composite multi-scale entropy. Therefore, the problems that in the prior art, a technical scheme for identifying the consistency of the battery cells is complex in calculation, large in calculation overhead, large in required data size, not suitable for online data and needing to add additional measuring equipment are solved, the specific battery cells with the consistency problem can be accurately identified, calculation is carried out by directly utilizing the online data, and external equipment does not need to be used.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a battery consistency calculation method, device, electronic device and computer program product. Background Art

[0002] Driven by the country's strong promotion and the development trend of the automobile industry, the popularity of automobiles has been greatly promoted. In recent years, the vehicle occupancy rate and growth rate have increased significantly, and the components of various electric vehicles have developed rapidly. Among them, the battery directly provides the kinetic energy for the operation of the power vehicle. Therefore, the battery system is the most important component for electric vehicles, and the quality of the power battery also directly affects the quality of electric vehicles. The power battery is composed of multiple cells connected in parallel and in series. The consistency between the cells directly affects the performance and safety of the entire battery pack. Therefore, it is very necessary to study the consistency of the cells.

[0003] The current calculation method for consistency is relatively simple. It uses simple statistics such as standard deviation to quantify consistency, and then sets a certain threshold to judge consistency problems by comparing two values, or some are measured by external equipment. In actual engineering implementation, the standard deviation method is more common, but it requires manual setting of thresholds, which has a certain degree of subjectivity. In addition, the consistency of the battery cell is long-term. Only using the standard deviation calculates the current consistency situation and does not extend to different time scales. Moreover, the standard deviation cannot reflect which specific battery cell has consistency problems. The equipment is only suitable for laboratory conditions and is not suitable for consistency judgment of online data, which needs to be solved urgently. Summary of the invention

[0004] The present application provides a battery consistency calculation method, device, electronic device and computer program product to solve the problems in the prior art that the technical solutions for identifying battery cell consistency are complex in calculation, have high calculation overhead, require a large amount of data, are not suitable for online data, and require the addition of additional measurement equipment.

[0005] The first aspect of the present application provides a battery consistency calculation method, comprising the following steps: acquiring battery data of a vehicle and performing data processing on the battery data; dividing the processed battery data into non-overlapping time windows according to a time scale to construct a coarse-grained sequence, and calculating the sample entropy of each coarse-grained sequence; allocating the sample entropy to each coarse sequence according to a preset allocation weight, and calculating the weighted composite multi-scale entropy of the coarse-grained sequence based on the sample entropy of each coarse-grained sequence, and evaluating the battery consistency of the vehicle based on the weighted composite multi-scale entropy.

[0006] Optionally, the data processing of the battery data includes: identifying and deleting duplicate data and data with field values ​​outside a normal range in the battery data; using quantile and box plot technology to identify abnormal values ​​of fields in the battery data, and deleting the entire row of data containing the abnormal values.

[0007] Optionally, the calculating of the sample entropy of each coarse-grained sequence includes: constructing a multidimensional vector, calculating the distance between the multidimensional vectors; counting the ratio of similar vector pairs according to a similarity coefficient threshold, and calculating the sample entropy of each coarse-grained sequence based on the ratio of the similar vector pairs using a preset sample entropy calculation formula, wherein the preset sample entropy calculation formula is:

[0008]

[0009] Among them, m is the pattern dimension, r is the similarity coefficient threshold, N is the data length, and p is the m (r) is the ratio of similar vector pairs.

[0010] Optionally, calculating the weighted composite multi-scale entropy of the coarse-grained sequence based on the sample entropy of each coarse-grained sequence includes: calculating the weighted composite multi-scale entropy of the coarse-grained sequence using a preset weighted composite multi-scale entropy calculation formula, wherein the preset weighted composite multi-scale entropy calculation formula is:

[0011]

[0012] Among them, w1, w2…w k are the preset allocation weights, τ is the scale factor, is the coarse-grained sequence, and M is the sequence length.

[0013] The second aspect of the present application provides a battery consistency calculation device, including: a processing module, used to obtain battery data of a vehicle and perform data processing on the battery data; a first calculation module, used to divide the data-processed battery data into non-overlapping time windows according to a time scale to construct a coarse-grained sequence, and calculate the sample entropy of each coarse-grained sequence; a second calculation module, used to allocate each coarse-grained sequence according to a preset allocation weight, and calculate the weighted composite multi-scale entropy of the coarse-grained sequence based on the sample entropy of each coarse-grained sequence, and evaluate the battery consistency of the vehicle based on the weighted composite multi-scale entropy.

[0014] Optionally, the processing module is further used to: identify and delete duplicate data and data with field values ​​outside the normal range in the battery data; use quantile and box plot technology to identify abnormal values ​​of fields in the battery data, and delete the entire row of data containing the abnormal values.

[0015] Optionally, the first calculation module is further used to: construct a multidimensional vector, calculate the distance between the multidimensional vectors; count the ratio of similar vector pairs according to a similarity coefficient threshold, and calculate the sample entropy of each coarse-grained sequence based on the ratio of the similar vector pairs using a preset sample entropy calculation formula, wherein the preset sample entropy calculation formula is:

[0016]

[0017] Among them, m is the pattern dimension, r is the similarity coefficient threshold, N is the data length, and p is the m (r) is the ratio of similar vector pairs.

[0018] Optionally, the second calculation module is further used to calculate the weighted composite multi-scale entropy of the coarse-grained sequence using a preset weighted composite multi-scale entropy calculation formula, wherein the preset weighted composite multi-scale entropy calculation formula is:

[0019]

[0020] Among them, w1, w2…w k are the preset allocation weights, τ is the scale factor, is the coarse-grained sequence, and M is the sequence length.

[0021] The third aspect of the present application provides an electronic device, comprising: 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 battery consistency calculation method as described in the above embodiment.

[0022] The fourth aspect of the present application provides a computer program product on which a computer program is stored. The program is executed by a processor to implement the battery consistency calculation method as described in the above embodiment.

[0023] In the above implementation, the battery data of the vehicle is obtained, and the battery data is processed. The processed battery data is divided into non-overlapping time windows according to the time scale to construct a coarse-grained sequence, and the sample entropy of each coarse-grained sequence is calculated, and the sample entropy is allocated to each coarse sequence according to the preset allocation weight. The weighted composite multi-scale entropy of the coarse-grained sequence is calculated based on the sample entropy of each coarse-grained sequence, and the battery consistency of the vehicle is evaluated based on the weighted composite multi-scale entropy. Thus, the problem that the technical solution for identifying the consistency of the battery cell in the prior art is complex in calculation, has high computational overhead, requires a large amount of data, is not applicable to online data, and requires the addition of additional measurement equipment is solved. The specific battery cell with consistency problems can be accurately identified, and online data can be directly used for calculation without the need for external equipment, avoiding the problems of complex calculation, high computational overhead, and large amount of data required.

[0024] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0026] Figure 1 A flowchart of a battery consistency calculation method provided according to an embodiment of the present application;

[0027] Figure 2 is a flow chart of a battery consistency calculation method according to an embodiment of the present application;

[0028] Figure 3 is a schematic diagram of WMMSE of a vehicle at different time scales according to an embodiment of the present application;

[0029] Figure 4 is an exemplary diagram of a battery consistency calculation device according to an embodiment of the present application;

[0030] Figure 5 Schematic diagram of the structure of an electronic device according to an embodiment of the present application. Description of the drawings:

[0032] 10 - battery consistency calculation device; 100 - processing module; 200 - first calculation module; 300 - second calculation module; 501 - memory; 502 - processor and 503 - communication interface. DETAILED DESCRIPTION

[0033] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0034] The battery consistency calculation method, device, electronic device and computer program product of the embodiment of the present application are described below with reference to the accompanying drawings. In view of the problem that the technical solution for identifying the consistency of the battery cell in the prior art mentioned in the above background technology is complex in calculation, has high calculation overhead, requires a large amount of data, is not applicable to online data, and requires additional measurement equipment, the present application provides a battery consistency calculation method, in which the battery data of the vehicle is obtained, and the battery data is processed, and the battery data after data processing is divided into non-overlapping time windows according to the time scale to construct a coarse-grained sequence, and the sample entropy of each coarse-grained sequence is calculated, and each coarse-grained sequence is allocated to each coarse sequence according to a preset allocation weight, and the weighted composite multi-scale entropy of the coarse-grained sequence is calculated based on the sample entropy of each coarse-grained sequence, and the battery consistency of the vehicle is evaluated based on the weighted composite multi-scale entropy. Thus, the problem that the technical solution for identifying the consistency of the battery cell in the prior art is complex in calculation, has high calculation overhead, requires a large amount of data, is not applicable to online data, and requires additional measurement equipment is solved, and the specific battery cell with consistency problems can be accurately identified, and online data can be directly used for calculation without the need to apply external equipment, avoiding the problem of complex calculation, high calculation overhead, and large amount of data required.

[0035] Specifically, Figure 1 A flowchart of a battery consistency calculation method provided in an embodiment of the present application.

[0036] like Figure 1 As shown, the battery consistency calculation method includes the following steps:

[0037] In step S101 , the battery data of the vehicle is acquired and the battery data is processed.

[0038] Among them, battery data includes voltage data (such as real-time voltage, average voltage, voltage change rate, etc.), capacity data, temperature data, SOC and SOH data, etc.

[0039] In step S102, the processed battery data is divided into non-overlapping time windows according to the time scale to construct a coarse-grained sequence, and the sample entropy of each coarse-grained sequence is calculated.

[0040] Optionally, in some embodiments, data processing is performed on the battery data, including: identifying and deleting duplicate data and data with field values ​​outside a normal range in the battery data; using quantile and box plot techniques to identify outliers in fields in the battery data, and deleting entire rows of data containing outliers.

[0041] Specifically, the battery data fields are cleaned and preprocessed using database query languages, PYTHON data processing and analysis. The fields required include VIN code, time, cell voltage list, temperature list, etc. Data collection requires database support (DBEAVER, SQLSERVER), and data processing and analysis require support from PYTHON's pandas, numpy, and sklearn.

[0042] Dirty data in battery data needs to be cleaned. Dirty data includes duplicate data in the database, values ​​outside the normal value range of the field, and outliers. The first two types of dirty data are processed by direct deletion. For outliers, outliers can be identified through quantiles and box plots, and the entire row of the identified outlier fields is also deleted. Data preprocessing is performed based on the field caliber defined in the database, mainly including offset removal and unit conversion. Among them, offset removal mainly refers to correcting the field value offset set in the field caliber, and unit conversion mainly refers to the different values ​​of field values ​​due to different unit settings.

[0043] In step S103, a preset allocation weight is allocated to each coarse sequence and each coarse-grained sequence is assigned a weighted composite multi-scale entropy of the coarse-grained sequence based on the sample entropy of each coarse-grained sequence, and the battery consistency of the vehicle is evaluated based on the weighted composite multi-scale entropy.

[0044] The preset allocation weight may be a threshold value preset by a user, a threshold value obtained through a finite number of experiments, or a threshold value obtained through a finite number of computer simulations, which is not specifically limited here.

[0045] The battery consistency of a vehicle is evaluated based on the weighted composite multi-scale entropy: if the weighted composite multi-scale entropies of different batteries at different time scales of a vehicle are closer, the battery pack consistency is better; if the weighted composite multi-scale entropy of a battery deviates from that of other cells, it means that the battery has consistency problems.

[0046] In some embodiments, calculating the sample entropy of each coarse-grained sequence includes: constructing a multidimensional vector, calculating the distance between the multidimensional vectors; counting the ratio of similar vector pairs according to a similarity coefficient threshold, and calculating the sample entropy of each coarse-grained sequence based on the ratio of the similar vector pairs using a preset sample entropy calculation formula, wherein the preset sample entropy calculation formula is:

[0047]

[0048] Among them, m is the pattern dimension, r is the similarity coefficient threshold, N is the data length, and p is the m (r) is the ratio of similar vector pairs.

[0049] In some embodiments, the weighted composite multi-scale entropy of the coarse-grained sequence is calculated based on the sample entropy of each coarse-grained sequence, including: using a preset weighted composite multi-scale entropy calculation formula to calculate the weighted composite multi-scale entropy of the coarse-grained sequence, wherein the preset weighted composite multi-scale entropy calculation formula is:

[0050]

[0051] Among them, w1, w2…w k are the preset allocation weights, τ is the scale factor, is the coarse-grained sequence, and M is the sequence length.

[0052] Specifically, Figure 2 As shown, first obtain the online data uploaded by the vehicle: obtain the data uploaded by the vehicle in accordance with the national standard 32960 from the vehicle manufacturer's data platform, and obtain the relevant fields and corresponding value ranges.

[0053] Data cleaning and preprocessing: Data cleaning and preprocessing are performed on the fields that are needed. Dirty data in the database needs to be cleaned. Dirty data includes duplicate data in the database, values ​​outside the normal value range of the field, and abnormal values. For the first two types of dirty data mentioned above, they are processed by direct deletion. For the last type of abnormal value, the abnormal value can be identified through quantiles and box plots, and the entire row of the identified abnormal value field is also deleted. For the field caliber defined in the database, offsets or unit conversions can be performed.

[0054] The processed battery data is further divided into non-overlapping time windows according to the time scale to construct a coarse-grained sequence. First, let the battery data be X i =x1,x2,…,x N , where N is the data length, the battery data sequence is divided into non-overlapping windows based on the time scale, where the time scale is called the scale factor τ, and then the coarse-grained sequence is established as follows:

[0055]

[0056] Further calculation of sample entropy of coarse-grained sequence: For the coarse-grained sequence {y(1), y(2), …, y(M)}, where M is the sequence length, let M = j, then the sample entropy of the coarse-grained sequence is calculated as follows:

[0057] 1) Given a pattern dimension of m, the original sequence is composed of an m-dimensional vector:

[0058] Y(i)=[y(i),y(i+1),…,y(i+m-1)](i=1,2,…M+m-1);

[0059] 2) Define the distance between Y(i) and Y(j):

[0060]

[0061] 3) Given a similarity coefficient threshold r, count the number of d(i,j) < r and the ratio of this number to the total number N - m + 1, denoted as where the method for determining the similarity coefficient threshold r is the standard deviation of the original sequence.

[0062]

[0063] Furthermore, calculate the average value for all i:

[0064]

[0065] Repeat steps 1) - 3) for m + 1, denoted as

[0066]

[0067] Then the sample entropy of this coarse-grained sequence can be obtained.:

[0068]

[0069] When N takes a finite value, the estimated value of the sample entropy is:

[0070]

[0071] Using the above formula, the subsequence entropy at each scale can be calculated, and then a function with the scale τ as the independent variable and the sample entropy SaEn as the dependent variable can be established to obtain the multi-scale entropy-scale factor curve:

[0072] MSE(τ) = SaEn(y (τ) , m, r);

[0073] For each scale factor, calculate the sample entropy of each coarse sequence and then, through the preset distribution weights for k entropy values, calculate the weighted composite multi-scale entropy WMMSE at the scale factor τ:

[0074]

[0075] Thus, the embodiments of the present application integrate the information of all coarse-grained sequences and can amplify or reduce the importance of the analysis time scale according to the weights, avoiding the entropy fluctuations caused by the moving average.

[0076] In the actual verification process, as Figure 3 shown Figure 3 The WMMSE of a certain vehicle at different time scales shows that the No. 28 battery cell is obviously deviated from other battery cells, indicating that there is a consistency problem with the No. 28 battery cell.

[0077] In summary, the advantages of the embodiments of the present application are as follows:

[0078] 1. Directly use online data for calculation without external equipment, which is more suitable for project implementation;

[0079] 2. Compared with traditional methods, it takes into account the consistency analysis under multiple time scales, rather than being limited to the calculation of consistency at the moment.

[0080] 3. Data requirements do not require a complete charge and discharge cycle, any data fragment can be calculated;

[0081] 4. Assign weights to different time scales. If you are more concerned about long-term consistency, you can assign a higher weight to the long-term scale. The same is true for the short-term. This is more flexible than directly using the arithmetic average method.

[0082] 5. It is also a method that considers multiple time scales. There is no need to establish other correlation models. The calculation is simpler and the computational overhead is smaller.

[0083] According to the battery consistency calculation method proposed in the embodiment of the present application, the battery data of the vehicle is obtained, and the battery data is processed. The processed battery data is divided into non-overlapping time windows according to the time scale to construct a coarse-grained sequence, and the sample entropy of each coarse-grained sequence is calculated, and the sample entropy is distributed to each coarse sequence according to the preset distribution weight. Each coarse-grained sequence is calculated based on the sample entropy of each coarse-grained sequence. The weighted composite multi-scale entropy of the coarse-grained sequence is calculated, and the battery consistency of the vehicle is evaluated based on the weighted composite multi-scale entropy. As a result, the problem that the technical solution for identifying the consistency of the battery cell in the prior art is complex in calculation, has high calculation overhead, requires a large amount of data, is not applicable to online data, and requires the addition of additional measurement equipment is solved. The specific battery cell with consistency problems can be accurately identified, and online data can be directly used for calculation without the need for external equipment, avoiding the problems of complex calculation, high calculation overhead, and large amount of data required.

[0084] Next, a battery consistency calculation device proposed according to an embodiment of the present application is described with reference to the accompanying drawings.

[0085] Figure 4 It is a block diagram of a battery consistency calculation device according to an embodiment of the present application.

[0086] like Figure 4 As shown, the battery consistency calculation device 10 includes: a processing module 100 , a first calculation module 200 and a second calculation module 300 .

[0087] Among them, the processing module 100 is used to obtain the battery data of the vehicle and perform data processing on the battery data; the first calculation module 200 is used to divide the battery data after data processing into non-overlapping time windows according to the time scale to construct a coarse-grained sequence, and calculate the sample entropy of each coarse-grained sequence; the second calculation module 300 is used to allocate each coarse-grained sequence according to a preset allocation weight, and calculate the weighted composite multi-scale entropy of the coarse-grained sequence based on the sample entropy of each coarse-grained sequence, and evaluate the battery consistency of the vehicle based on the weighted composite multi-scale entropy.

[0088] Optionally, in some embodiments, the processing module 100 is further used to: identify and delete duplicate data in the battery data and data with field values ​​outside the normal range; use quantile and box plot technology to identify abnormal values ​​of fields in the battery data, and delete the entire row of data containing the abnormal value.

[0089] Optionally, in some embodiments, the first calculation module 200 is further used to: construct a multidimensional vector, calculate the distance between the multidimensional vectors; count the ratio of similar vector pairs according to a similarity coefficient threshold, and calculate the sample entropy of each coarse-grained sequence based on the ratio of the similar vector pairs using a preset sample entropy calculation formula, wherein the preset sample entropy calculation formula is:

[0090]

[0091] Among them, m is the pattern dimension, r is the similarity coefficient threshold, N is the data length, and p is the m (r) is the ratio of similar vector pairs.

[0092] Optionally, in some embodiments, the second calculation module 300 is further used to calculate the weighted composite multi-scale entropy of the coarse-grained sequence using a preset weighted composite multi-scale entropy calculation formula, wherein the preset weighted composite multi-scale entropy calculation formula is:

[0093]

[0094] Among them, w1, w2…w k are the preset allocation weights, τ is the scale factor, is the coarse-grained sequence, and M is the sequence length.

[0095] It should be noted that the above explanation of the battery consistency calculation method embodiment is also applicable to the battery consistency calculation device of this embodiment, and will not be repeated here.

[0096] According to the battery consistency calculation device proposed in the embodiment of the present application, the battery data of the vehicle is obtained, and the battery data is processed. The battery data after data processing is divided into non-overlapping time windows according to the time scale to construct a coarse-grained sequence, and the sample entropy of each coarse-grained sequence is calculated, and the sample entropy is distributed to each coarse sequence according to the preset distribution weight. Each coarse-grained sequence is calculated based on the sample entropy of each coarse-grained sequence, and the weighted composite multi-scale entropy of the coarse-grained sequence is calculated, and the battery consistency of the vehicle is evaluated based on the weighted composite multi-scale entropy. As a result, the problem that the technical solution for identifying the consistency of the battery cell in the prior art is complex in calculation, has high calculation overhead, requires a large amount of data, is not applicable to online data, and requires the addition of additional measurement equipment is solved. The specific battery cell with consistency problems can be accurately identified, and online data can be directly used for calculation without the need for external equipment, avoiding the problems of complex calculation, high calculation overhead, and large amount of data required.

[0097] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0098] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0099] When the processor 502 executes the program, the battery consistency calculation method provided in the above embodiment is implemented.

[0100] Furthermore, the electronic device further comprises:

[0101] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0102] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0103] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0104] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0105] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0106] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0107] An embodiment of the present application also provides a computer program product, on which a computer program is stored, and when the program is executed by a processor, the above battery consistency calculation method is implemented.

[0108] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0109] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0110] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0111] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer program product for use with an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer program product" can be any device that can contain, store, communicate, propagate or transmit a program for use with an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer program products (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). Furthermore, the computer program product may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example by optically scanning the paper or other medium and then editing, interpreting or, if necessary, processing in another suitable manner, and then storing it in a computer memory.

[0112] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0113] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer program product, which, when executed, includes one or a combination of the steps of the method embodiment.

[0114] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer program product.

[0115] The computer program product mentioned above may be a read-only memory, a disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A battery consistency calculation method, characterized in that: The following steps are involved: Obtain the battery data of the vehicle and process the battery data; The processed battery data is divided into non-overlapping time windows according to the time scale to construct a coarse-grained sequence, and the sample entropy of each coarse-grained sequence is calculated; A preset allocation weight is allocated to each coarse sequence and each coarse-grained sequence, and a weighted composite multi-scale entropy of the coarse-grained sequence is calculated based on the sample entropy of each coarse-grained sequence, and the battery consistency of the vehicle is evaluated based on the weighted composite multi-scale entropy.

2. The method according to claim 1, characterized in that The processing of the battery data includes: Identify and delete duplicate data and data whose field values ​​exceed a normal range in the battery data; Quantile and box plot techniques are used to identify outliers in fields in the battery data, and the entire row of data containing outliers is deleted.

3. The method according to claim 1, characterized in that The calculating of the sample entropy of each coarse-grained sequence includes: Constructing multidimensional vectors, and calculating distances between the multidimensional vectors; The ratio of similar vector pairs is counted according to the similarity coefficient threshold, and based on the ratio of the similar vector pairs, the sample entropy of each coarse-grained sequence is calculated using a preset sample entropy calculation formula, wherein the preset sample entropy calculation formula is: Among them, m is the pattern dimension, r is the similarity coefficient threshold, N is the data length, and p is the m (r) is the ratio of similar vector pairs.

4. The method according to claim 1, characterized in that The step of calculating the weighted composite multi-scale entropy of the coarse-grained sequence based on the sample entropy of each coarse-grained sequence comprises: The weighted composite multi-scale entropy of the coarse-grained sequence is calculated using a preset weighted composite multi-scale entropy calculation formula, wherein the preset weighted composite multi-scale entropy calculation formula is: Among them, w1, w2…w k are the preset allocation weights, τ is the scale factor, is the coarse-grained sequence, and M is the sequence length.

5. A battery consistency calculation device, characterized in that: include: A processing module, used for acquiring battery data of the vehicle and performing data processing on the battery data; A first calculation module is used to divide the processed battery data into non-overlapping time windows according to the time scale to construct a coarse-grained sequence, and calculate the sample entropy of each coarse-grained sequence; The second calculation module is used to allocate a preset allocation weight to each coarse sequence and each coarse-grained sequence, calculate the weighted composite multi-scale entropy of the coarse-grained sequence based on the sample entropy of each coarse-grained sequence, and evaluate the battery consistency of the vehicle based on the weighted composite multi-scale entropy.

6. The device according to claim 5, characterized in that The processing module is further used for: Identify and delete duplicate data and data whose field values ​​exceed a normal range in the battery data; Quantile and box plot techniques are used to identify outliers in fields in the battery data, and the entire row of data containing outliers is deleted.

7. The device according to claim 5, characterized in that The first computing module is further configured to: Constructing multidimensional vectors, and calculating distances between the multidimensional vectors; The ratio of similar vector pairs is counted according to the similarity coefficient threshold, and based on the ratio of the similar vector pairs, the sample entropy of each coarse-grained sequence is calculated using a preset sample entropy calculation formula, wherein the preset sample entropy calculation formula is: Among them, m is the pattern dimension, r is the similarity coefficient threshold, N is the data length, and p is the m (r) is the ratio of similar vector pairs.

8. The device according to claim 5, characterized in that The second computing module is further used for: The weighted composite multi-scale entropy of the coarse-grained sequence is calculated using a preset weighted composite multi-scale entropy calculation formula, wherein the preset weighted composite multi-scale entropy calculation formula is: Among them, w1, w2…w k are the preset allocation weights, τ is the scale factor, is the coarse-grained sequence, and M is the sequence length.

9. An electronic device, characterized in that: Including memory and processor; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the battery consistency calculation method as described in any one of claims 1-4.

10. A computer program product, wherein the computer program product stores a computer program, characterized in that: When the program is executed by a processor, the battery consistency calculation method as described in any one of claims 1 to 4 is implemented.

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