Method, system and device for identifying internal resistance abnormity of battery pack module and medium

Abstract

The invention relates to a method, a system, equipment and a medium for identifying internal resistance abnormity of a battery pack module, and the method comprises the following steps: obtaining whole vehicle operation monitoring data based on a battery big data platform, and carrying out the processing according to a preset rule to obtain sorted charging data; screening the sorted charging data based on the static SOC value of each module of the battery pack at the charging starting moment to obtain effective charging data; based on the effective charging data, a preset direct-current internal resistance calculation formula is adopted to calculate the direct-current internal resistance value of each module of the battery pack; and calculating corresponding internal resistance abnormity based on the DC internal resistance value of each module of the battery pack, and identifying and outputting an internal resistance abnormity module number. The method can be widely applied to the technical field of battery big data platforms or battery management systems.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery big data platforms or battery management systems (BMS), and in particular relates to a method, system, device and medium for identifying abnormal internal resistance of battery pack modules, which are used for health status monitoring and management of battery packs in fields such as electric vehicles and energy storage systems. Background Art

[0002] With the development of new energy vehicles and renewable energy technologies, batteries have been widely used as key energy storage components. However, over time, batteries can experience aging and performance degradation. In particular, individual modules within a battery pack may experience increased internal resistance, which not only affects the efficiency of the entire battery system but also poses potential safety risks.

[0003] Although existing battery monitoring systems can provide basic status information, their ability to detect and accurately identify internal resistance anomalies early is limited, and they often need to rely on manual inspection or wait until a fault occurs before taking action. Summary of the Invention

[0004] In response to the above problems, the purpose of the present invention is to provide a method, system, device and medium for identifying abnormal internal resistance of battery pack modules, which can effectively identify abnormal internal resistance of battery pack modules based on the screened partial charging data, and can detect and deal with potential problems of battery pack modules at an early stage, thereby improving the safety and reliability of the battery system.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for identifying abnormal internal resistance of a battery pack module, comprising the following steps:

[0007] Obtain vehicle operation monitoring data based on the battery big data platform and process it according to preset rules to obtain sorted charging data;

[0008] The sorted charging data is filtered based on the static SOC value of each module of the battery pack at the start of charging to obtain valid charging data;

[0009] Based on the effective charging data, the DC internal resistance value of each module of the battery pack is calculated using the preset DC internal resistance calculation formula;

[0010] Based on the DC internal resistance value of each module, the corresponding internal resistance abnormality is calculated, and the internal resistance abnormality module number is identified and output.

[0011] Furthermore, the vehicle operation monitoring data is obtained based on the battery big data platform, and sorted charging data is obtained by processing according to preset rules, including:

[0012] Obtain vehicle operation monitoring data of the battery pack during the evaluated time period, including the voltage value v and current value i of each module at each time t;

[0013] The acquired vehicle operation monitoring data is first sorted by time and then by vehicle number to obtain sorted charging data.

[0014] Furthermore, the sorted charging data is screened based on the static SOC value of each module at the start of charging to obtain valid charging data, including:

[0015] Get the static voltage value V of each module in the battery pack before charging starts n0 , and use the SOC-OCV test data of each module to look up the table to obtain the static voltage value V n0 Corresponding SOC value SOC n0 , as the static SOC value of the corresponding module;

[0016] Determine whether the maximum value of the SOC value SOC0 corresponding to the static voltage value V0 of each module meets <25%. If so, continue to calculate the DC internal resistance value. If not, discard the charging data and return to the previous step to judge the next charging data.

[0017] Furthermore, the DC internal resistance value R of each module n When performing calculations, the formula is:

[0018]

[0019] Where, It represents the average voltage value of module n within 5 minutes after the start of charging, i represents the charging current value, V n0 Indicates the static voltage value V of module number n n0 .

[0020] Furthermore, the calculation based on the DC internal resistance value of each module to obtain the corresponding internal resistance abnormality, and the identification and output of the internal resistance abnormal module number include:

[0021] Based on the DC internal resistance value of each module, calculate the internal resistance abnormality of each module respectively;

[0022] The internal resistance abnormality of each module is compared with the preset abnormality threshold, and the internal resistance abnormal module number is identified and output based on the comparison result.

[0023] Furthermore, the calculation formula for the abnormal degree of internal resistance of each module is:

[0024]

[0025] Where, is the current-weighted average value of the DC internal resistance of each module in the battery pack; σ is {R1, R2···R n The standard deviation of the sequence, b is the denoising parameter, usually the normal internal resistance value accuracy

[0026] Furthermore, the method of comparing the internal resistance abnormality of each module with a preset abnormality threshold, and identifying and outputting the internal resistance abnormality module number according to the comparison result, includes:

[0027] It is determined whether the internal resistance abnormality of each module is greater than the preset abnormality threshold. If so, it is considered that the module has internal resistance abnormality; otherwise, it is considered that the module is in normal state.

[0028] In a second aspect, the present invention provides a system for identifying abnormal internal resistance of a battery pack module, comprising:

[0029] The data acquisition and sorting module is used to obtain vehicle operation monitoring data based on the battery big data platform and process it according to preset rules to obtain sorted charging data;

[0030] A data screening module is used to screen the sorted charging data based on the static SOC value of each module of the battery pack at the start of charging to obtain valid charging data;

[0031] A DC internal resistance calculation module is used to calculate the DC internal resistance of each module of the battery pack based on valid charging data using a preset DC internal resistance calculation formula;

[0032] The identification and output module is used to calculate the corresponding internal resistance abnormality based on the DC internal resistance value of each module, identify and output the internal resistance abnormality module number.

[0033] In a third aspect, the present invention provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions that, when executed by a computing device, enable the computing device to execute the method for identifying abnormal internal resistance of a battery pack module.

[0034] In a fourth aspect, the present invention provides a computing device comprising: one or more processors and a memory, wherein the memory stores one or more programs and is configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method for identifying abnormal internal resistance of a battery pack module.

[0035] The present invention has the following advantages due to the adoption of the above technical solution:

[0036] 1. The present invention calculates the static voltage value of each module and obtains the corresponding static SOC value by looking up the table, thereby ensuring an accurate assessment of the battery pack module status before charging begins, providing a basis for subsequent internal resistance calculation.

[0037] 2. The charge and discharge data are screened according to the static SOC value. This screening mechanism ensures that the internal resistance test is only performed when the battery is close to fully discharged, thereby improving the accuracy of the test results.

[0038] 3. The present invention improves the calculation formula of the DC internal resistance value. This calculation method effectively quantifies the internal resistance characteristics of the battery module and provides a reliable basis for abnormality detection.

[0039] 4. The present invention not only considers the influence of current change on internal resistance in calculating the abnormality of internal resistance of each module, but also reduces noise interference by adding denoising parameters, thereby improving the stability of the calculation results.

[0040] Therefore, the present invention can be widely applied to the technical fields of battery big data platforms or battery management systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. Throughout the drawings, the same reference numerals are used to denote the same components. In the drawings:

[0042] Figure 1 This is a flow chart of a method for identifying abnormal internal resistance of a battery pack module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of the present invention.

[0044] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0045] In some embodiments of the present invention, a method for identifying abnormal internal resistance in battery pack modules is provided. This method can identify abnormal internal resistance modules based on partial battery charging data. The method includes the following steps: acquiring vehicle operation monitoring data; filtering data based on the static SOC value of each module at the start of charging; calculating the DC internal resistance value and the degree of internal resistance abnormality based on the filtered charging data, and identifying and outputting the module number with abnormal internal resistance. This method can calculate the internal resistance of power battery modules and identify abnormalities based on battery charging data from the Internet of Vehicles or other battery monitoring data platforms.

[0046] Correspondingly, in other embodiments of the present invention, a system, device, and medium for identifying abnormal internal resistance of a battery pack module are provided.

[0047] Example 1

[0048] like Figure 1 As shown, the present invention provides a method for identifying abnormal internal resistance of a battery pack module, which includes the following steps:

[0049] 1) Obtain vehicle operation monitoring data based on the battery big data platform and process it according to preset rules to obtain sorted charging data;

[0050] 2) Filtering the sorted charging data based on the static SOC value of each module of the battery pack at the start of charging to obtain valid charging data;

[0051] 3) Based on the effective charging data, the DC internal resistance value of each module of the battery pack is calculated using the preset DC internal resistance calculation formula;

[0052] 4) Based on the DC internal resistance value of each module in the battery pack, the corresponding internal resistance abnormality is calculated, and the module number with abnormal internal resistance is identified and output.

[0053] Furthermore, the above step 1) includes the following steps:

[0054] 1.1) Obtain vehicle operation monitoring data for the battery pack during the evaluation period, including the voltage value v and current value i of each module in the battery pack at each time t;

[0055] 1.2) Sort the acquired vehicle operation monitoring data by time and then by vehicle number to obtain sorted charging data.

[0056] In this embodiment, the vehicle operation monitoring data is first sorted by time and then by vehicle number, which can ensure that the data sorted by time is distributed in blocks, and the data of the same vehicle are grouped together, which can facilitate subsequent calculations.

[0057] Furthermore, the above step 2) includes the following steps:

[0058] 2.1) Obtain the static voltage value V of each module in the battery pack before charging begins n0 , and use the SOC-OCV test data of each module to look up the table to obtain the static voltage value V n0 Corresponding SOC value SOC n0 , as the static SOC value of the corresponding module.

[0059] In this embodiment, by calculating the static voltage value of each module and obtaining the corresponding static SOC value by looking up the table, an accurate assessment of the state of the battery pack module before charging begins is ensured, providing a basis for subsequent internal resistance calculation.

[0060] 2.2) Determine whether the maximum value of the SOC value SOC0 corresponding to the static voltage value V0 of each module satisfies <25%. If so, proceed to step 3) to continue the calculation. If not, discard the charging data and return to step 2.1) to determine the next charging data.

[0061] In this embodiment, the charge and discharge data are screened according to the static SOC value. This screening mechanism ensures that the internal resistance test is performed only when the battery is nearly fully discharged, thereby improving the accuracy of the test results.

[0062] Furthermore, in the above step 3), the DC internal resistance value R of each module is n When performing calculations, the formula is:

[0063]

[0064] Where, It represents the average voltage value of module n within 5 minutes after the start of charging, i represents the charging current value, V n0 Indicates the static voltage value V of module number n n0 .

[0065] In this embodiment, the DC internal resistance value of each module is calculated using charging data within 5 minutes after the start of charging. This calculation method effectively quantifies the internal resistance characteristics of the battery module and provides a reliable basis for abnormality detection.

[0066] Furthermore, the above step 4) includes the following steps:

[0067] 4.1) Based on the DC internal resistance value of each module, calculate the internal resistance abnormality of each module.

[0068] In this embodiment, the calculation formula for the internal resistance abnormality of each module is:

[0069]

[0070] Where, is the current-weighted average value of the DC internal resistance of each module in the battery pack; σ is {R1, R2···R n The standard deviation of the sequence, b is the denoising parameter, usually the normal internal resistance value accuracy If the normal internal resistance of the module is 1.1mΩ, then b = 0.01mΩ.

[0071] This method not only takes into account the impact of current changes on internal resistance, but also reduces noise interference by adding denoising parameters, thereby improving the stability of the calculation results.

[0072] 4.2) Compare the internal resistance abnormality of each module with the preset abnormality threshold, and identify and output the internal resistance abnormal module number based on the comparison result.

[0073] In this embodiment, the abnormality threshold is set to 5, and the internal resistance abnormality of each module is determined to be greater than 5 to determine the internal resistance abnormal module, that is, to determine α n >5, if module number n satisfies α n If the value is greater than 5, it is considered that module n has an internal resistance abnormality. This classification helps to quickly locate the problem module, improve maintenance efficiency and battery pack safety.

[0074] Example 2

[0075] The above-mentioned embodiment 1 provides a method for identifying abnormal internal resistance of a battery pack module. Correspondingly, this embodiment provides a system for identifying abnormal internal resistance of a battery pack module. The system provided by this embodiment can implement the method for identifying abnormal internal resistance of a battery pack module in embodiment 1. The system can be implemented by software, hardware, or a combination of software and hardware. For example, the system may include integrated or separate functional modules or functional units to execute the corresponding steps in each method of embodiment 1. Since the system of this embodiment is basically similar to the method embodiment, the description process of this embodiment is relatively simple. For relevant matters, please refer to the partial description of embodiment 1. The embodiment of the system provided by this embodiment is merely illustrative.

[0076] The system for identifying abnormal internal resistance of a battery pack module provided in this embodiment includes:

[0077] The data acquisition and sorting module is used to obtain vehicle operation monitoring data based on the battery big data platform and process it according to preset rules to obtain sorted charging data;

[0078] A data screening module is used to screen the sorted charging data based on the static SOC value of each module of the battery pack at the start of charging to obtain valid charging data;

[0079] A DC internal resistance calculation module is used to calculate the DC internal resistance of each module of the battery pack based on valid charging data using a preset DC internal resistance calculation formula;

[0080] The identification and output module is used to calculate the corresponding internal resistance abnormality based on the DC internal resistance value of each module in the battery pack, identify and output the internal resistance abnormality module number.

[0081] Example 3

[0082] This embodiment provides a processing device corresponding to the method for identifying abnormal internal resistance of a battery pack module provided in this embodiment 1. The processing device can be a processing device for a client, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the method of embodiment 1.

[0083] The processing device includes a processor, a memory, a communication interface, and a bus. The processor, memory, and communication interface are connected via the bus to facilitate communication between them. The memory stores a computer program executable on the processor. When the processor executes the computer program, it executes the method for identifying abnormal internal resistance of a battery pack module provided in Example 1.

[0084] Preferably, the memory may be a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0085] Preferably, the processor may be a central processing unit (CPU), a digital signal processor (DSP), or other general-purpose processors of various types, which are not limited here.

[0086] Example 4

[0087] The method for identifying abnormal internal resistance of a battery pack module in this embodiment 1 can be specifically implemented as a computer program product. The computer program product may include a computer-readable storage medium on which computer-readable program instructions are loaded for executing the method for identifying abnormal internal resistance of a battery pack module described in this embodiment 1.

[0088] Computer readable storage media can be tangible devices that hold and store instructions used by instruction execution devices. Computer readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any combination thereof.

[0089] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0090] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0091] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for identifying abnormal internal resistance of a battery pack module, characterized in that: The following steps are involved: Obtain vehicle operation monitoring data based on the battery big data platform and process it according to preset rules to obtain sorted charging data; The sorted charging data is filtered based on the static SOC value of each module of the battery pack at the start of charging to obtain valid charging data; Based on the effective charging data, the DC internal resistance value of each module of the battery pack is calculated using the preset DC internal resistance calculation formula; Based on the DC internal resistance value of each module, the corresponding internal resistance abnormality is calculated, and the internal resistance abnormality module number is identified and output.

2. The method for identifying abnormal internal resistance of a battery pack module according to claim 1, wherein: The method of obtaining vehicle operation monitoring data based on the battery big data platform and processing it according to preset rules to obtain sorted charging data includes: Obtain vehicle operation monitoring data of the battery pack during the evaluated time period, including the voltage value v and current value i of each module at each time t; The acquired vehicle operation monitoring data is first sorted by time and then by vehicle number to obtain sorted charging data.

3. The method for identifying abnormal internal resistance of a battery pack module according to claim 1, wherein: The sorted charging data is screened based on the static SOC value of each module at the start of charging to obtain valid charging data, including: Get the static voltage value V of each module in the battery pack before charging starts n0 , and use the SOC-OCV test data of each module to look up the table to obtain the static voltage value V n0 Corresponding SOC value SOC n0 , as the static SOC value of the corresponding module; Determine whether the maximum value of the SOC value SOC0 corresponding to the static voltage value V0 of each module meets <25%. If so, continue to calculate the DC internal resistance value. If not, discard the charging data and return to the previous step to judge the next charging data.

4. The method for identifying abnormal internal resistance of a battery pack module according to claim 1, wherein: The DC internal resistance value R of each module n When performing calculations, the formula is: Where, It represents the average voltage value of module n within 5 minutes after the start of charging, i represents the charging current value, V n0 Indicates the static voltage value V of module number n n0 .

5. The method for identifying abnormal internal resistance of a battery pack module according to claim 1, wherein: The calculation based on the DC internal resistance value of each module obtains the corresponding internal resistance abnormality, identifies and outputs the internal resistance abnormal module number, including: Based on the DC internal resistance value of each module, calculate the internal resistance abnormality of each module respectively; The internal resistance abnormality of each module is compared with the preset abnormality threshold, and the internal resistance abnormal module number is identified and output based on the comparison result.

6. The method for identifying abnormal internal resistance of a battery pack module according to claim 5, characterized in that: The calculation formula for the abnormality of the internal resistance of each module is: Where, is the current-weighted average value of the DC internal resistance of each module in the battery pack; σ is {R1, R2···R n The standard deviation of the sequence, b is the denoising parameter, usually the normal internal resistance value accuracy 7. The method for identifying abnormal internal resistance of a battery pack module according to claim 5, wherein: The method of comparing the internal resistance abnormality of each module with a preset abnormality threshold, and identifying and outputting the internal resistance abnormality module number according to the comparison result, includes: It is determined whether the internal resistance abnormality of each module is greater than the preset abnormality threshold. If so, it is considered that the module has internal resistance abnormality; otherwise, it is considered that the module is in normal state.

8. A system for identifying abnormal internal resistance of a battery pack module, characterized in that: include: The data acquisition and sorting module is used to obtain vehicle operation monitoring data based on the battery big data platform and process it according to preset rules to obtain sorted charging data; A data screening module is used to screen the sorted charging data based on the static SOC value of each module of the battery pack at the start of charging to obtain valid charging data; A DC internal resistance calculation module is used to calculate the DC internal resistance of each module of the battery pack based on valid charging data using a preset DC internal resistance calculation formula; The identification and output module is used to calculate the corresponding internal resistance abnormality based on the DC internal resistance value of each module, identify and output the internal resistance abnormality module number.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any one of the methods of claims 1 to 7 .

10. A computing device, characterized in that include: One or more processors and a memory, wherein the memory stores one or more programs and is configured to be executed by the one or more processors, wherein the one or more programs include instructions for executing any one of the methods according to claims 1 to 7.

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