Accurate diagnosis method and system for undersampling battery pack faults based on data compensation

Abstract

The present invention belongs to the technical field of battery pack fault diagnosis and provides a method and system for accurately diagnosing undersampling battery pack faults based on data compensation. The method includes obtaining the voltages of all series-connected battery cells in the battery pack to be diagnosed; constructing a corresponding voltage sequence based on the voltages of all series-connected battery cells, compensating and mapping the constructed multi-channel voltage sequence to the same battery cell to obtain a single-channel voltage sequence; determining whether the single-channel voltage sequence contains abnormal information, and if so, tracing the corresponding voltage sequence that caused the abnormal information based on the voltage value corresponding to the abnormal information, and determining the faulty battery cell and its fault type based on the voltage sequence. The information loss under undersampling conditions improves the BMS's ability to identify battery cell faults.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery pack fault diagnosis, and in particular relates to a data compensation-based undersampling battery pack fault diagnosis method and system. Background Art

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

[0003] Voltage sensors are essential components of electric vehicle BMSs (battery management systems). Oversampling voltage signals (sampling frequency that satisfies the Nyquist sampling theorem) is a prerequisite for timely and accurate diagnosis of battery faults. However, the size and cost of electric vehicle battery packs limit the number and performance of voltage sensors that can be deployed. Communication conditions in daisy-chain connection schemes, such as the LTC6811, restrict the transmission speed of sampling results. Noise interference generated by high-frequency parallel sampling limits the sampling accuracy of sensors. All of these factors hinder the implementation of high-frequency parallel sampling technology in BMSs. Under current technology, simultaneously and frequently acquiring voltage information from all battery cells is unrealistic.

[0004] Considering these issues, most electric vehicle battery management systems prioritize pack-level voltage monitoring, relying solely on undersampling for single-cell voltage monitoring. (Undersampling refers to sampling at a frequency lower than the Nyquist sampling theorem. This sampling method causes high-frequency components to be mixed with low-frequency components, rendering the signal unrecoverable.) For example, the battery pack of the Tesla Model S, for example, features a battery monitoring board (BMB) for detecting battery voltage and temperature located only on the power supply unit at the pack level. Consequently, the vehicle's BMS lacks the ability to handle single-cell battery failures.

[0005] Although the BMS installed in the earlier General Motors EV1 model can detect the voltage and current parameters of each battery, its sampling frequency is low and its ability to distinguish noise is poor.

[0006] Undersampling or not sampling the cell voltage reduces design costs and ensures sampling quality, but correspondingly, fault information is difficult to capture completely. The frequency of misdiagnosis and missed diagnosis increases as the frequency decreases, making it difficult to identify traditional battery failures.

[0007] For example, the invention patent with publication number CN110703109A and invention name is Battery Fault Diagnosis Method Based on Sample Entropy. Under undersampling conditions, there is a large error between the complexity of the sampling point voltage sequence calculated by this method and the complexity of the actual voltage sequence, which causes misjudgment of the battery status, resulting in a sharp increase in the misdiagnosis rate and missed diagnosis rate as the sampling frequency decreases. Summary of the Invention

[0008] In order to solve at least one technical problem existing in the above background technology, the present invention provides an accurate diagnosis method for undersampling battery pack faults based on data compensation, which makes up for the information missing due to undersampling through an algorithm and improves the BMS's ability to identify battery cell faults.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions:

[0010] A first aspect of the present invention provides an accurate diagnosis method for undersampling battery pack faults based on data compensation, comprising:

[0011] Obtain the voltage of all battery cells connected in series in the battery pack to be diagnosed;

[0012] Construct a corresponding voltage sequence based on the voltages of all battery cells connected in series, and then compensating and mapping the constructed multi-channel voltage sequence to the same battery cell to obtain a single-channel voltage sequence;

[0013] Determine whether there is abnormal information in the single-channel voltage sequence. If so, trace the voltage value corresponding to the abnormal information to the corresponding voltage sequence that caused the abnormal information, determine the type of fault based on the voltage sequence, and issue a fault alarm.

[0014] A second aspect of the present invention provides an accurate diagnosis system for undersampling battery pack faults based on data compensation, comprising:

[0015] A data acquisition module is used to obtain the voltage of all battery cells connected in series in the battery pack to be diagnosed;

[0016] The compensation mapping module is used to construct a corresponding voltage sequence according to the voltages of all battery cells connected in series, and to compensate and map the constructed multi-channel voltage sequence to the same battery cell to obtain a single-channel voltage sequence;

[0017] The fault diagnosis module is used to determine whether there is abnormal information in the single-channel voltage sequence. If so, it traces the voltage value corresponding to the abnormal information to the corresponding voltage sequence that causes the abnormal information, determines the type of fault that has occurred based on the voltage sequence, and issues a fault alarm.

[0018] A third aspect of the present invention provides a computer-readable storage medium.

[0019] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned method for accurately diagnosing under-sampling battery pack faults based on data compensation.

[0020] A fourth aspect of the present invention provides a computer device.

[0021] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned method for accurately diagnosing undersampling battery pack faults based on data compensation are implemented.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] (1) The present invention increases the number of battery cell voltage samples under undersampling conditions through data compensation, reduces the misdiagnosis rate and fault omission rate of fault diagnosis, and greatly improves the effect of battery fault diagnosis under undersampling.

[0024] (2) The diagnosable fault types of the diagnosis algorithm are expanded by fusion of multi-cell battery data.

[0025] (3) The present invention does not require any additional hardware and can be applied to the battery management systems of almost all electric vehicles.

[0026] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0028] Figure 1 This is a sensor-multiplexer type single-unit voltage acquisition system according to an embodiment of the present invention;

[0029] Figure 2 This is a flow chart of the method for accurately diagnosing under-sampling battery pack faults based on data compensation of the present invention;

[0030] Figure 3(a)-Figure 3(b) is the voltage curve of the series monomers according to the embodiment of the present invention;

[0031] Figure 4(a)-Figure 4(b) This is the direct diagnosis result of the sample entropy method according to the embodiment of the present invention;

[0032] Figure 5 It is a voltage sequence formed by directly combining the single-unit voltages according to the sampling time in the embodiment of the present invention;

[0033] Figure 6 is a sequence of battery cell voltages after mapping according to an embodiment of the present invention;

[0034] Figure 7 This is the sample entropy diagnosis result of the voltage sequence after compensation mapping in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0036] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0037] 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.

[0038] Common battery cell voltage sampling methods for BMSs include integrated chips (such as the LTC6811, MC33771C, BQ76940, and BQ20Z655) and sensor-multiplexer combination systems. These chips are highly packaged, resulting in small differences in sampling time between different battery cells, making them unsuitable for this application.

[0039] The acquisition method of the sensor and the multiplexer is highly flexible, and the voltage of each cell is acquired in sequence and at the same interval. This is the main hardware basis for the application of the present invention. Its basic principle is as follows: Figure 1 Shown is a sensor-multiplexer type single-unit voltage acquisition system.

[0040] Example 1

[0041] In such Figure 1 In this single-cell voltage sampling method, different battery cells are selected sequentially by the multiplexer, and the voltages across them are sampled differentially by the sensor. The multiplexer's selection is controlled by the BMS's control signal. Under normal operating conditions, the voltage of each cell is sampled sequentially, and the time required to complete each sampling is the inter-cell voltage sampling interval.

[0042] like Figure 2 As shown, this embodiment provides an accurate diagnosis method for undersampling battery pack faults based on data compensation, comprising the following steps:

[0043] Step 1: Obtain the voltage of all battery cells connected in series in the battery pack to be diagnosed;

[0044] The battery pack of an electric vehicle is composed of multiple groups of battery cells connected in series and parallel. The battery cells in the same series branch have the same current passing through them at the same time, and the inconsistency is relatively small.

[0045] During battery operation, the voltages of the series-connected cells are similar and the current change trends are similar, which makes data compensation for the series-connected cells possible.

[0046] In step 1, assume that the voltage sequences collected by the series-connected cells of n common voltage sensors are:

[0047] V 1,1 ,V 1,n+1 ,V 1,2n+1 …

[0048] V 2,2 ,V 2,n+2 ,V 2,2n+2 …

[0049] V i,j ,V i,n+j ,V i,2n+j …

[0050] V n,n ,V n,n+n ,V n,2n+n …

[0051] Among them, V i,j It represents the voltage information collected by the i-th battery cell in the j-th sampling cycle of the sensor.

[0052] Step 2: Construct a corresponding voltage sequence based on the voltages of all battery cells connected in series, and compensate and map the constructed multi-channel voltage sequence to the same battery cell to obtain a single-channel voltage sequence;

[0053] It should be noted that the mapping cell in this embodiment can be selected arbitrarily. By mapping the voltage compensation of other battery cells to the voltage of the selected battery cell, which is used as the voltage sampling value of this battery cell at a certain moment, the fusion of voltage information of multiple cells can be achieved.

[0054] Assume that the voltage sequence after mapping is:

[0055]

[0056] Compensating and mapping the constructed multi-channel voltage sequence to the same battery cell includes:

[0057] Select the battery cell to be mapped;

[0058] Determine whether there is a voltage difference between the battery cell to be mapped and other series-connected cells at the same time. If so, determine the type of voltage difference. If it is caused by the open-circuit voltage difference of the battery cells with different states of charge, use the dynamic compensation method to adjust the compensation value to eliminate the voltage difference. If it is caused by the difference in the ohmic internal resistance and polarization internal resistance of the cells during operation, use the direct compensation method to eliminate the voltage difference.

[0059] This embodiment divides the voltage difference between series-connected battery cells into two parts: the open circuit voltage difference caused by the different SOC (state of charge) of the battery cells, and the operating voltage difference caused by the differences in the ohmic internal resistance and polarization internal resistance of the cells.

[0060] For the open-circuit voltage difference, this embodiment adopts a dynamic compensation method to adjust the compensation value in real time. For the other part of the voltage difference, this embodiment adopts a direct compensation method to compensate from a priori perspective, and finally obtains a mapped voltage sequence that is more consistent with the true value.

[0061] After that, any fault diagnosis method for the single cell can be used for detection.

[0062] The dynamic compensation method is used to adjust the compensation value to eliminate the voltage difference by adding a voltage dynamic compensation variable to the single voltage (source voltage) sampling value to be mapped;

[0063] The direct compensation method is used to eliminate the voltage difference and make it approach the target voltage value through the voltage difference compensation variable. The above two compensation amounts will change dynamically under the control of the source voltage and the target cell voltage at the latest moment.

[0064] The voltage dynamic compensation variable is incrementally controlled, that is, in each sampling period, the compensation value increases or decreases with the relationship between the source voltage and the target voltage;

[0065] The voltage difference compensation variable is regulated by position, that is, the compensation value is directly determined by the relationship between the source voltage and the target voltage, and has Markov properties.

[0066] The specific expression is:

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075] Where c is the compensation constant, k is a constant integer, and sign is the sign function, whose values ​​are as follows:

[0076]

[0077] D i is the open circuit voltage dynamic compensation variable, and its value accompanies each mapping iteration. The iteration method is:

[0078]

[0079] d is the compensation constant whose value is less than c, V 1,k×n+1 is the voltage information collected by the first battery cell in the k×n+1th sampling cycle of the sensor, It is the mapped value of the voltage information collected by the i-th battery cell in the k×n+1-th sampling cycle of the sensor.

[0080] After data compensation, the original n-channel voltage sequence is mapped to a single channel, and the sequence length is extended to a standardized sequence that is n times the original channel length (n is the number of battery cells).

[0081] The advantage of this solution is that it uses an algorithm to compensate for small differences between cells, mapping the collected voltages of all series-connected cells to the voltages of the same cell, thereby obtaining a higher-frequency voltage sequence. This mapping complements the voltage fluctuation characteristics during normal operation, reducing the probability of false fault alarms.

[0082] Troubleshooting this mapped sequence has the following two advantages:

[0083] 1. Fault information is concentrated, and surrounding information is clear. The "hidden valley" caused by undersampling is reduced, and the missed diagnosis rate of fault diagnosis is reduced.

[0084] 2. The voltage information is complete, the "false mutation" caused by undersampling is reduced, and the misdiagnosis rate of fault diagnosis is reduced.

[0085] Step 3: Determine whether there is abnormal information in the single-channel voltage sequence. If so, trace the voltage value corresponding to the abnormal information to the corresponding voltage sequence that caused the abnormal information, determine the type of fault based on the voltage sequence, and issue a fault alarm.

[0086] In step 3, the following methods are available to determine whether there is abnormal information in the single-channel voltage sequence:

[0087] 1. Sample entropy method: The complexity of the mapped sequence is evaluated by calculating the sample entropy of the sequence. When a fault occurs, the faulty cell will have obviously abnormal voltage characteristics, which will cause the complexity of the sequence to increase significantly. Based on this, the fault can be diagnosed.

[0088] 2. The differential threshold method performs fault diagnosis by comparing the difference between adjacent values ​​in the mapped sequence with a preset threshold. If the absolute value of the differential exceeds the threshold, it means that a battery cell has experienced unexpected voltage fluctuations, which often indicates a fault.

[0089] 3. Square difference method, similar to the sample entropy method, a large square difference represents a large fluctuation of the sequence and is an important feature of the occurrence of a fault.

[0090] The advantage of this technical solution is that when a battery cell fails, its voltage becomes abnormal, and the mapped voltage value also retains this abnormal information, which can be identified by the fault diagnosis algorithm. Under this mapping, any series battery cell failure will be reflected in the mapped voltage sequence. During further fault tracing, the fault cell can be identified by finding the set of sampling points that caused the abnormality in the mapped voltage sequence.

[0091] In step 3, the fault types include battery cell micro short circuit fault, battery pack overall micro short circuit fault, cell poor contact fault, battery pack overall poor contact fault, sensor failure fault and battery cell leakage fault, etc.

[0092] The voltage anomaly caused by the above faults can be easily confused with environmental noise and load state fluctuations, and its accurate diagnosis depends on high-frequency and precise voltage sampling.

[0093] The advantage of the above technical solution is that by aggregating multi-channel voltage sampling data, the voltage sampling information density is increased at the algorithm level, reducing the difficulty of fault identification.

[0094] Furthermore, battery cell leakage faults evolve slowly, with the faulty cell voltage characteristically exhibiting a slow overall decrease but normal local fluctuations. This makes it difficult to detect by monitoring the cell's own voltage. However, the solution proposed in this embodiment aggregates multi-channel voltage information and performs data compensation. This allows the faulty cell's voltage anomaly to be clearly reflected in the mapped sequence, shortening the warning time for this type of fault.

[0095] Case Study

[0096] The voltage data of 8 series-connected cells in an existing electric vehicle battery pack (obtained through simulation experiments) are as follows. Each cell is sampled alternately, and the voltage sampling frequency is 0.1 Hz. Figure 3(a)-Figure 3(b) Shown is the plotted voltage curve.

[0097] In the area circled in box ① of Figure 3(a), the series cell 4 fails, which corresponds to the 200th to 400th sampling cycles of the sensor. In the area circled in box ②, the cell 1 fails, which occurs in the 1280th to 1600th sampling cycles of the sensor. Figure 3(a)-Figure 3(b)The result curve of the sample entropy method for 8-segment monomer diagnosis is depicted.

[0098] Figure 4(a)-Figure 4(b) This is the direct diagnostic result of the sample entropy method according to an embodiment of the present invention. Boxes ③ and ④ in Figure 4(a) plot the diagnostic results of the sample entropy method when a fault occurs. When the first fault occurs, the sample entropy value of cell 4's voltage sequence does not increase significantly, demonstrating that the sample entropy method is ineffective for diagnosing faults that cause gently fluctuating parameters. When the second fault occurs, the sample entropy value of cell 1's voltage sequence increases significantly. Within the circled area of ​​boxes ⑤-⑧, even though no cells have failed, the sample entropy of each cell in each section shows a sharp increase.

[0099] The sample entropy anomalies in boxes 5-8 completely mask the sample entropy anomalies during the faults in boxes 3 and 4. Analysis of the voltage sequence characteristics reveals that the sample entropy anomalies in boxes 5 and 6 are caused by voltage fluctuations at the sampling points due to undersampling. At low sampling frequencies, the voltage acquisition intervals are large, and a gently rising voltage signal may exhibit significant differences between the two sampling points, leading to an abnormal increase in sequence complexity. Therefore, the sample entropy method cannot be directly applied to battery fault diagnosis under undersampling conditions.

[0100] Figure 5 The voltage sequence is plotted by directly combining the individual voltages according to the sampling time.

[0101] Due to the differences between cells, the voltage sequence has a large oscillation with the number of cells as the period, which cannot be directly used for fault detection. The compensation mapping method proposed in this invention can alleviate this problem. The voltage sequence after compensation mapping is as follows: Figure 6 shown.

[0102] It is easy to find that after compensation mapping, the periodic oscillation problem of the voltage sequence is significantly alleviated. The sample entropy is calculated using the mapped voltage sequence, and the results are as follows: Figure 7 shown.

[0103] Fault range in Figure 6 In the figure circled in boxes 9 and 10, the sample entropy method clearly indicates both faults and has no significant effect on the voltage sequence during normal operation. This demonstrates that the adaptive data compensation method proposed in this paper can be effectively combined with traditional diagnostic methods such as sample entropy to achieve battery fault diagnosis under undersampling conditions.

[0104] Example 2

[0105] This embodiment provides an accurate diagnosis system for undersampling battery pack faults based on data compensation, including:

[0106] A data acquisition module is used to obtain the voltage of all battery cells connected in series in the battery pack to be diagnosed;

[0107] The compensation mapping module is used to construct a corresponding voltage sequence according to the voltages of all battery cells connected in series, and to compensate and map the constructed multi-channel voltage sequence to the same battery cell to obtain a single-channel voltage sequence;

[0108] The fault diagnosis module is used to determine whether there is abnormal information in the single-channel voltage sequence. If so, it traces the voltage value corresponding to the abnormal information to the corresponding voltage sequence that causes the abnormal information, determines the type of fault that has occurred based on the voltage sequence, and issues a fault alarm.

[0109] The step of compensating and mapping the constructed multi-channel voltage sequence to the same battery cell includes:

[0110] Select the battery cell to be mapped;

[0111] Determine whether there is a voltage difference between the battery cell to be mapped and other series-connected cells. If so, determine the type of voltage difference. If the open-circuit voltage difference is caused by different states of charge of the battery cells, use the dynamic compensation method to adjust the compensation value to eliminate the voltage difference. If the operating voltage difference is caused by differences in the ohmic internal resistance and polarization internal resistance of the cells, use the direct compensation method to eliminate the voltage difference.

[0112] The dynamic compensation method is used to adjust the compensation value to eliminate the voltage difference by adding a voltage dynamic compensation variable to the battery cell voltage sampling value to be mapped so that it approaches the target voltage value; the direct compensation method is used to eliminate the voltage difference by adding a voltage difference compensation variable to the battery cell voltage sampling value to be mapped so that it approaches the target voltage value;

[0113] The voltage dynamic compensation variable is controlled by increment mode, and the voltage difference compensation variable is controlled by position mode. The determination of whether there is abnormal information in the single-channel voltage sequence includes:

[0114] By calculating the sample entropy of the sequence, the complexity of the single-channel voltage sequence is evaluated based on the sample entropy. When a fault occurs, the faulty cell will have obviously abnormal voltage characteristics, which will cause the complexity of the sequence to increase significantly. Based on this, the fault can be diagnosed.

[0115] Alternatively, the difference between adjacent values ​​of the single-channel voltage sequence is compared with a preset threshold. If the absolute value of the difference exceeds the threshold, it means that a battery cell has experienced an unexpected voltage fluctuation, and this fluctuation indicates a fault.

[0116] Example 3

[0117] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the above-mentioned method for accurately diagnosing undersampling battery pack faults based on data compensation are implemented.

[0118] Example 4

[0119] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for accurately diagnosing undersampling battery pack faults based on data compensation as described above are implemented.

[0120] Those skilled in the art will appreciate 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 hardware embodiments, software embodiments, or embodiments 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 and optical storage, etc.) containing computer-usable program code.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0125] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. An accurate diagnosis method for undersampling battery pack faults based on data compensation, characterized in that: include: Obtain the voltage of all battery cells connected in series in the battery pack to be diagnosed; Construct a corresponding voltage sequence based on the voltages of all battery cells connected in series, and then compensating and mapping the constructed multi-channel voltage sequence to the same battery cell to obtain a single-channel voltage sequence; Determine whether there is abnormal information in the single-channel voltage sequence. If so, trace the voltage value corresponding to the abnormal information to the corresponding voltage sequence that caused the abnormal information, determine the type of fault based on the voltage sequence, and issue a fault alarm.

2. The method for accurately diagnosing undersampling battery pack faults based on data compensation according to claim 1, characterized in that: Compensating and mapping the constructed multi-channel voltage sequence to the same battery cell includes: Select the battery cell to be mapped; Determine whether there is a voltage difference between the battery cell to be mapped and other series-connected cells at the same time. If so, determine the type of voltage difference. If it is caused by the open-circuit voltage difference of the battery cells with different states of charge, use the dynamic compensation method to adjust the compensation value to eliminate the voltage difference. If it is caused by the difference in the ohmic internal resistance and polarization internal resistance of the cells during operation, use the direct compensation method to eliminate the voltage difference.

3. The method for accurately diagnosing undersampling battery pack faults based on data compensation according to claim 2, characterized in that: The dynamic compensation method is used to adjust the compensation value to eliminate the voltage difference by adding a voltage dynamic compensation variable to the battery cell voltage sampling value to be mapped so that it approaches the target voltage value; the direct compensation method is used to eliminate the voltage difference by adding a voltage difference compensation variable to the battery cell voltage sampling value to be mapped so that it approaches the target voltage value; Among them, the voltage dynamic compensation variable is subject to incremental control, and the voltage difference compensation variable is subject to position control.

4. The method for accurately diagnosing undersampling battery pack faults based on data compensation according to claim 1, wherein: The determining whether there is abnormal information in the single-channel voltage sequence includes: By calculating the sample entropy of the sequence, the complexity of the single-channel voltage sequence is evaluated based on the sample entropy. When a fault occurs, the faulty cell will have obviously abnormal voltage characteristics, which will cause the complexity of the sequence to increase significantly. Based on this, the fault can be diagnosed. Alternatively, the difference between adjacent values ​​of the single-channel voltage sequence is compared with a preset threshold. If the absolute value of the difference exceeds the threshold, it means that a battery cell has experienced an unexpected voltage fluctuation, and this fluctuation indicates a fault.

5. The method for accurately diagnosing under-sampling battery pack faults based on data compensation according to claim 1, characterized in that: The fault types include battery single cell micro short circuit fault, battery pack overall micro short circuit fault, single cell poor contact fault, battery pack overall poor contact fault, sensor failure fault and battery single cell leakage fault.

6. An accurate diagnosis system for under-sampling battery pack faults based on data compensation, characterized in that: include: A data acquisition module is used to obtain the voltage of all battery cells connected in series in the battery pack to be diagnosed; The compensation mapping module is used to construct a corresponding voltage sequence according to the voltages of all battery cells connected in series, and to compensate and map the constructed multi-channel voltage sequence to the same battery cell to obtain a single-channel voltage sequence; The fault diagnosis module is used to determine whether there is abnormal information in the single-channel voltage sequence. If so, it traces the voltage value corresponding to the abnormal information to the corresponding voltage sequence that causes the abnormal information, and determines the faulty battery cell and its fault type based on the voltage sequence.

7. The under-sampling battery pack fault accurate diagnosis system based on data compensation according to claim 6, characterized in that: The compensating and mapping the constructed multi-channel voltage sequence to the same battery cell includes: Select the battery cell to be mapped; Determine whether there is a voltage difference between the battery cell to be mapped and other series-connected cells. If so, determine the type of voltage difference. If the open-circuit voltage difference is caused by different states of charge of the battery cells, use the dynamic compensation method to adjust the compensation value to eliminate the voltage difference. If the operating voltage difference is caused by differences in the ohmic internal resistance and polarization internal resistance of the cells, use the direct compensation method to eliminate the voltage difference.

8. The under-sampling battery pack fault accurate diagnosis system based on data compensation according to claim 6, characterized in that: The determining whether there is abnormal information in the single-channel voltage sequence includes: By calculating the sample entropy of the sequence, the complexity of the single-channel voltage sequence is evaluated based on the sample entropy. When a fault occurs, the faulty cell will have obviously abnormal voltage characteristics, which will cause the complexity of the sequence to increase significantly. Based on this, the fault can be diagnosed. Alternatively, the difference between adjacent values ​​of the single-channel voltage sequence is compared with a preset threshold. If the absolute value of the difference exceeds the threshold, it means that a battery cell has experienced an unexpected voltage fluctuation, and this fluctuation indicates a fault.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method for accurately diagnosing under-sampling battery pack faults based on data compensation as described in any one of claims 1 to 6 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for accurately diagnosing under-sampling battery pack faults based on data compensation are implemented as described in any one of claims 1 to 6.

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