A test system and method for BMS function of new energy vehicles

Abstract

The present invention relates to the technical field of power battery testing, and in particular to a testing system and method for BMS functions for new energy vehicles. The method comprises the following steps: constructing battery pack structural model data according to battery pack CAD design drawing data; collecting vehicle operating condition charge and discharge sample data; performing adaptive current excitation sequence processing according to vehicle operating condition charge and discharge sample data to generate multi-operating condition disturbance pulse excitation sequence data; performing disturbance magnetic field simulation on battery pack structural model data through multi-operating condition disturbance pulse excitation sequence data to obtain operating condition test magnetic disturbance response data; performing battery pack BMS monitoring data collection based on operating condition test magnetic disturbance response data, and performing BMS current distribution monitoring performance evaluation to generate BMS current monitoring performance evaluation data. The present invention achieves a comprehensive evaluation of BMS performance by simulating current excitation and magnetic field response under multiple operating conditions, thereby improving the accuracy and efficiency of the test.

Description

Technical Field

[0001] The present invention relates to the technical field of power battery testing, and in particular to a testing system and method for a BMS function for a new energy vehicle. Background Art

[0002] As a core component of power batteries, one of the main functions of the battery management system (BMS) is to monitor the status of the battery pack in real time, including parameters such as voltage, current, and temperature, to ensure the safe operation of the battery and extend its service life. Among them, the uniformity of current distribution is an important indicator for evaluating the health status of the battery pack and predicting the battery life. Uneven current distribution can cause local overcharging or over-discharging of the battery pack, accelerate battery aging, and even cause safety accidents. Therefore, it is crucial to accurately evaluate the current distribution monitoring function of the BMS. Traditional testing methods are usually carried out under static or quasi-static conditions, which makes it difficult to simulate the dynamic current changes of the battery under actual driving conditions. In actual use of the battery, the current will fluctuate violently with changes in vehicle acceleration, deceleration, climbing and other operating conditions. These dynamic current changes will have a significant impact on the performance and life of the battery. The traditional test method for the BMS function of new energy vehicles cannot effectively evaluate the current distribution monitoring performance of the BMS under dynamic conditions, and cannot guarantee the reliability of the BMS in actual use. Summary of the invention

[0003] Based on this, the present invention provides a testing system and method for the BMS function of a new energy vehicle to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for testing the BMS function of a new energy vehicle comprises the following steps:

[0005] Step S1: obtaining CAD design drawing data of a battery pack; constructing a three-dimensional model of the battery pack according to the CAD design drawing data of the battery pack to generate battery pack structural model data; performing virtual current element processing using the battery pack structural model data to generate virtual current field data of the battery pack;

[0006] Step S2: collecting vehicle operating condition charge and discharge sample data; performing adaptive current excitation sequence processing according to the vehicle operating condition charge and discharge sample data, and performing multi-operating condition disturbance combination to generate multi-operating condition disturbance pulse excitation sequence data;

[0007] Step S3: Perform disturbance magnetic field simulation on the virtual current field data of the battery pack through multi-operating condition disturbance pulse excitation sequence data to generate multi-operating condition simulation magnetic disturbance matrix data; perform dynamic current excitation application on the test battery pack through multi-operating condition disturbance pulse excitation sequence data to obtain operating condition test magnetic disturbance response data; perform magnetic field distribution characteristic offset analysis based on the multi-operating condition simulation magnetic disturbance matrix data and the operating condition test magnetic disturbance response data, and perform single cell current distribution anomaly matching to obtain current abnormal single cell identification data;

[0008] Step S4: Based on the multi-operating condition disturbance pulse excitation sequence data, the current abnormal single battery identification data is processed with a single battery pulse current instruction, and the battery pack BMS monitoring data is collected to obtain BMS monitoring current excitation response data; the BMS current distribution monitoring performance of the BMS monitoring current excitation response data is evaluated through the operating condition test magnetic disturbance response data to generate BMS current monitoring performance evaluation data.

[0009] The present invention constructs an accurate three-dimensional model by acquiring the battery pack CAD design drawing data, and generates virtual current field data, so that the test model is highly consistent with the actual battery pack. At the same time, the real vehicle working condition charging and discharging sample data is collected, and adaptive current excitation sequence processing and multi-condition disturbance combination are performed. The generated multi-condition disturbance pulse excitation sequence data can truly reflect the current changes of the battery under various driving conditions, overcoming the limitation that traditional static or quasi-static tests cannot simulate dynamic current changes, thereby significantly improving the authenticity and accuracy of the test results. The multi-condition disturbance pulse excitation sequence data is used to perform dynamic current excitation on the virtual current field of the battery pack and the actual battery pack to simulate the violent fluctuations of the current of the battery during actual use. Through disturbance magnetic field simulation and actual testing, the working condition test magnetic disturbance response data and BMS monitoring current excitation response data are collected, and the magnetic field distribution characteristic offset analysis and single battery current distribution abnormal matching are performed, which can accurately evaluate the response speed, accuracy and stability of the BMS to the current distribution change under dynamic conditions, realize the dynamic evaluation of the BMS current distribution monitoring performance, and ensure the reliability of the BMS in actual use. Not only the current change under a single working condition is considered, but also the current change of the vehicle under various complex working conditions such as acceleration, deceleration, and climbing is simulated through a combination of multi-working condition disturbances, making the test more comprehensive and closer to the actual use scenario. At the same time, by marking the abnormal current single battery and processing the single battery pulse current command, the BMS's monitoring and protection capabilities for abnormal single battery current can be further verified. This multi-working condition and multi-angle test method effectively improves the reliability of the test and can comprehensively evaluate the current distribution monitoring performance of the BMS under various working conditions. Testing based on the virtual current field model can avoid frequent physical testing, greatly shorten the test cycle, and improve test efficiency. At the same time, the virtual current field model and the multi-working condition disturbance pulse excitation sequence data can be reused to ensure the repeatability of the test and facilitate the performance comparison and optimization of different BMS products. In addition, computer simulation can detect potential problems in advance, reducing the risks and costs in actual testing. Therefore, the testing method of the BMS function for new energy vehicles of the present invention accurately constructs a three-dimensional model of the battery pack and a virtual current field, combines the actual vehicle operating condition data, simulates the current changes of the battery under various driving conditions, and realizes a comprehensive evaluation of the dynamic current distribution monitoring performance of the BMS. It not only improves the authenticity and accuracy of the test, but also simulates the current changes of the vehicle under various complex operating conditions such as acceleration, deceleration, and climbing through a combination of multi-operating condition disturbances, making the test more comprehensive and closer to the actual usage scenario, and effectively improving the reliability of the BMS function test.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Acquire battery pack CAD design drawing data;

[0012] Step S12: extracting battery pack structural parameters according to the battery pack CAD design drawing data to generate battery pack structural parameter data, wherein the battery pack structural parameter data includes single cells, connecting pieces, and internal circuit structure of the battery pack;

[0013] Step S13: constructing a three-dimensional model of the battery pack based on the single battery, the connecting piece and the internal circuit structure of the battery pack to generate battery pack structural model data;

[0014] Step S14: Calculating the theoretical current value of the single battery based on the preset battery pack working state parameters and the battery pack structure model data to generate the single battery theoretical current value data;

[0015] Step S15: performing virtual current element processing on the battery pack structure model data according to the internal circuit structure of the battery pack and the theoretical current value data of the single battery to obtain virtual current element parameters;

[0016] Step S16: Perform virtual current field processing on the battery pack structure model data using virtual current element parameters to generate battery pack virtual current field data.

[0017] The present invention starts with obtaining the CAD design drawing data of the battery pack and extracts accurate structural parameters, including key information such as single cells, connecting pieces, and the internal circuit structure of the battery pack. These accurate structural parameters are the basis for building a high-fidelity virtual current field model, ensuring the accuracy and reliability of subsequent simulations. Based on the extracted structural parameters, a three-dimensional model of the battery pack is constructed, and the digital visualization of the battery pack structure is realized, so that the internal structure of the battery pack is clear at a glance, which is easy to understand and analyze. At the same time, the three-dimensional model also provides the necessary spatial geometric information for the subsequent virtual current element processing. Next, by presetting the working state parameters of the battery pack and combining the three-dimensional model data, the theoretical current value of the single cell is calculated. These theoretical current values ​​are the key inputs for the virtual current element processing, so that the virtual current field can accurately reflect the current distribution of the battery pack under different working conditions. Using the internal circuit structure information of the battery pack and the theoretical current value of the single cell, the three-dimensional model is processed by virtual current element, and the abstract current distribution is converted into specific circuit element parameters, which provides refined current distribution data for virtual current field processing, thereby improving the accuracy of current distribution simulation. Finally, these virtual current element parameters are used to process the virtual current field of the battery pack structure model, generating virtual current field data of the battery pack that can be used for dynamic current excitation and BMS performance evaluation. By constructing this high-precision virtual current field model, the current distribution of the battery pack under various working conditions can be effectively simulated.

[0018] Preferably, step S15 comprises the following steps:

[0019] Step S151: extracting battery characteristic parameters of a single battery using a preset battery single battery characteristic database, and obtaining a battery single battery internal resistance parameter and a battery single battery capacity parameter;

[0020] Step S152: performing Monte Carlo normal distribution assignment according to the battery cell internal resistance parameter and the battery cell capacity parameter, and obtaining random internal resistance value data of the single cell and random capacity value data of the single cell respectively;

[0021] Step S153: using the single cell theoretical current value data to calculate the single cell terminal voltage of the single cell random internal resistance value data and the single cell random capacity value data, to generate the single cell terminal voltage data;

[0022] Step S154: calculating the connection sheet resistance value according to the battery pack structure model data to generate connection sheet resistance value data;

[0023] Step S155: establishing a circuit equation group through the internal circuit structure of the battery pack, and solving the single cell current value and the connecting piece current value by using the single cell terminal voltage data and the connecting piece resistance value data, and obtaining the actual single cell current value and the connecting piece current value respectively;

[0024] Step S156: Setting a virtual current element for the battery pack structure model data according to the actual current value of the single battery and the current value of the connecting piece to obtain virtual current element parameters.

[0025] Starting from the level of single cells, the present invention uses a preset battery cell characteristic database to extract the internal resistance and capacity parameters of single cells. These basic parameters are the key to building an accurate model, ensuring that the model can accurately reflect the inherent characteristics of single cells. In order to further improve the authenticity of the model, the scheme introduces the Monte Carlo normal distribution assignment method to assign random values ​​to the internal resistance and capacity of single cells, simulating the discreteness of single cell parameters in actual production, so that the virtual current field model can better reflect the non-ideal characteristics of the actual battery pack, thereby enhancing the scope of application and prediction ability of the model. The terminal voltage of the single cell is calculated by comprehensively considering the theoretical current value, random internal resistance and random capacity of the single cell. This step fully considers the actual working characteristics of the single cell, making the terminal voltage calculation more accurate. At the same time, the scheme also calculates the resistance value of the connecting piece according to the battery pack structure model data, providing the necessary parameters for the subsequent solution of the circuit equation group, ensuring the accuracy of the current distribution calculation. A circuit equation group is established based on the internal circuit structure of the battery pack, and the actual current value of the single cell and the current value of the connecting piece are solved using the terminal voltage of the single cell and the resistance value of the connecting piece. This step closely combines circuit theory with the actual model, realizes the precise calculation of the current distribution inside the battery pack, and provides accurate input data for the virtual current element setting. Finally, by applying the actual current value of the single cell and the current value of the connecting piece to the battery pack structure model, the virtual current element is set up and the precise virtual current element parameters are obtained. By introducing the characteristic parameters of the single cell, considering the randomness of the parameters, and solving the circuit equations, this scheme realizes the refined modeling of the virtual current element processing, so that the virtual current field model can more accurately reflect the current distribution inside the battery pack, including the differences between the single cells, the influence of the connecting piece resistance, and the dynamic changes of the battery working state.

[0026] Preferably, step S2 comprises the following steps:

[0027] Step S21: collecting vehicle operating condition charge and discharge sample data through preset vehicle operating condition type data to obtain vehicle operating condition charge and discharge sample data;

[0028] Step S22: Processing the battery pack charge and discharge current change curve on the vehicle operating condition charge and discharge sample data to generate operating condition current curve data;

[0029] Step S23: Calculating the local excitation current intensity of the battery pack structure model data based on the operating current curve data to generate local excitation parameter data;

[0030] Step S24: performing adaptive pulse current excitation sequence processing according to the local excitation parameter data, thereby obtaining initial pulse excitation sequence data;

[0031] Step S25: using a preset battery pack disturbance factor database to select a random disturbance factor to obtain random disturbance factor data;

[0032] Step S26: applying a disturbance rule to the initial pulse excitation sequence data through random disturbance factor data, and performing multi-operating condition disturbance combination to generate multi-operating condition disturbance pulse excitation sequence data.

[0033] The present invention starts with collecting vehicle operating condition charge and discharge sample data, collects current, voltage, and temperature data of new energy vehicles under typical operating conditions such as acceleration, deceleration, climbing, and low-temperature starting, and obtains battery pack current change data under different operating conditions. These raw data are the basis for constructing actual operating condition simulation, ensuring that the subsequently generated excitation sequence can reflect the operating status of the vehicle under real conditions. Next, the collected vehicle operating condition charge and discharge sample data are processed to extract the charge and discharge current change curve of the battery pack. Based on the operating condition current curve data and combined with the battery pack structure model data, the local excitation current intensity of the battery pack at different positions is calculated to generate local excitation parameter data. This step takes into account the influence of the internal structure of the battery pack on the current distribution, so that the generated excitation sequence is more targeted and can simulate the current changes at different positions inside the battery pack. A random perturbation factor is introduced. Through the preset battery pack perturbation factor database, random perturbation factor data is selected to simulate various uncertain factors that appear in actual operation, such as temperature changes, aging effects, contact resistance fluctuations, etc. Finally, the random perturbation factor data is used to impose perturbation rules on the initial pulse excitation sequence data, transforming the idealized pulse excitation sequence into a complex excitation sequence that is closer to the actual situation and contains various perturbation factors.

[0034] Preferably, step S3 comprises the following steps:

[0035] Step S31: performing thermal simulation analysis of the battery pack according to the virtual current field data of the battery pack, identifying potential thermal risk areas, and obtaining distribution data of potential thermal risk areas;

[0036] Step S32: determining the deployment position of the Hall effect sensor based on the battery pack structure model data by using the potential thermal risk area distribution data to generate sensor deployment position data;

[0037] Step S33: using the sensor deployment position data to test the battery pack Hall effect sensor array deployment to obtain a Hall effect sensor array;

[0038] Step S34: performing disturbance magnetic field simulation on the virtual current field data of the battery pack through the multi-operating condition disturbance pulse excitation sequence data to generate multi-operating condition simulation magnetic disturbance matrix data;

[0039] Step S35: Initializing the battery pack test environment through the preset battery test state data, applying dynamic current excitation through the multi-operating condition disturbance pulse excitation sequence data, and collecting the internal magnetic field response data of the battery pack through the Hall effect sensor array to obtain the operating condition test magnetic disturbance response data;

[0040] Step S36: Analyze the magnetic field distribution characteristic offset according to the multi-operating condition simulation magnetic disturbance matrix data and the operating condition test magnetic disturbance response data to generate dynamic magnetic field characteristic offset data; match the single-cell current distribution anomaly with the dynamic magnetic field characteristic offset data through the battery pack structure model data to obtain current abnormal single-cell identification data.

[0041] The present invention identifies potential thermal risk areas by performing thermal simulation analysis on the virtual current field of the battery pack, and uses these areas as the priority deployment positions of the Hall effect sensor. This effectively solves the blindness of sensor deployment and ensures that the sensor can more effectively capture the current changes in the thermal risk area, thereby improving the sensitivity and accuracy of current anomaly detection. The redundancy of sensor deployment is avoided and resource utilization is improved. The battery pack is dynamically current excited using multi-operating condition disturbance pulse excitation sequence data, and the deployed Hall effect sensor array is used to collect the internal magnetic field response data of the battery pack. This dynamic test method is closer to the real battery working environment, can more comprehensively reflect the current distribution of the battery under different working conditions, and can more effectively discover potential current anomaly problems compared to traditional static test methods. By comparing the multi-operating condition simulation magnetic disturbance matrix data and the operating condition test magnetic disturbance response data, the magnetic field distribution characteristic offset analysis is performed, and the single battery current distribution anomaly matching is performed in combination with the battery pack structure model data. This detection method based on magnetic field disturbance can more sensitively capture subtle current changes in the battery pack and accurately locate single cells with abnormal current.

[0042] Preferably, step S34 includes the following steps:

[0043] Step S341: constructing a finite element electromagnetic field simulation model according to the battery pack virtual current field data and the battery pack structure model data to obtain an electromagnetic field virtual simulation model;

[0044] Step S342: mapping the Hall effect monitoring points of the electromagnetic field virtual simulation model through the sensor deployment position data, and setting finite element parameters to obtain finite element analysis parameters;

[0045] Step S343: performing single-operating-condition pulse excitation signal selection on the multi-operating-condition disturbance pulse excitation sequence data to obtain single-operating-condition pulse excitation signal data;

[0046] Step S344: simulating the magnetic field distribution inside the battery pack by using the single-operating pulse excitation signal data and finite element analysis parameters on the electromagnetic field virtual simulation model to generate simulated battery pack magnetic field distribution data;

[0047] Step S345: performing excitation signal sequence traversal on the multi-operating condition disturbance pulse excitation sequence data, and repeating step S344, thereby obtaining multiple sets of single-operating condition disturbance magnetic field distribution data;

[0048] Step S346: extract the vertical magnetic field component at the Hall sensor position according to multiple groups of single-operating-condition disturbance magnetic field distribution data, and generate multi-operating-condition magnetic disturbance simulation matrix data.

[0049] The present invention constructs a finite element electromagnetic field simulation model based on the virtual current field data and structural model data of the battery pack, and maps the deployment position of the Hall effect sensor in the model. This enables the simulation process to more accurately reflect the actual battery pack structure and sensor layout, thereby improving the reliability of the simulation results. The single-operating-condition pulse excitation signal is selected for the multi-operating-condition disturbance pulse excitation sequence data, and the electromagnetic field virtual simulation model is used to simulate the magnetic field distribution inside the battery pack using the finite element analysis parameters. This method of simulating each operating condition one by one can more finely capture the changing law of the magnetic field inside the battery pack under different operating conditions, avoiding the complexity and errors caused by directly simulating the superposition of multiple operating conditions. By traversing the multi-operating-condition disturbance pulse excitation sequence data and repeating the single-operating-condition magnetic field distribution simulation, multiple groups of single-operating-condition disturbance magnetic field distribution data are finally obtained, which effectively covers the working state of the battery under different operating conditions. The vertical magnetic field component of the Hall sensor position is extracted from multiple groups of single-operating-condition disturbance magnetic field distribution data, and multi-operating-condition magnetic disturbance simulation matrix data is generated. These data are used as baseline data for comparison with the magnetic field response data collected in actual tests, so as to identify abnormal current distribution inside the battery pack.

[0050] Preferably, step S36 includes the following steps:

[0051] Step S361: extracting magnetic field response features from the working condition test magnetic disturbance response data to obtain standard magnetic disturbance response matrix data;

[0052] Step S362: Correlating the multi-operating condition simulation magnetic disturbance matrix data with the magnetic disturbance of the same operating condition through the standard magnetic disturbance response matrix data, and calculating the correlation coefficient to obtain the mutual information correlation coefficient;

[0053] Step S363: Based on the mutual information correlation coefficient, the standard magnetic disturbance response matrix data and the multi-operating condition simulated magnetic disturbance matrix data are screened for Hall monitoring spatiotemporal magnetic field anomalies, and magnetic disturbance difference characteristics are calculated to generate dynamic magnetic field characteristic offset data;

[0054] Step S364: matching the sensor monitoring position according to the dynamic magnetic field characteristic offset data, and identifying the single battery with abnormal current distribution through the battery pack structure model data to obtain the identification data of the single battery with abnormal current distribution.

[0055] The present invention extracts the magnetic field response characteristics of the working condition test magnetic disturbance response data to obtain the standard magnetic disturbance response matrix data, which provides a benchmark for subsequent comparative analysis. Then, by calculating the mutual information correlation coefficient of the standard magnetic disturbance response matrix data and the multi-condition simulation magnetic disturbance matrix data, the association between the simulated data and the measured data is established, providing a quantitative index for abnormal screening. Based on the mutual information correlation coefficient, the standard magnetic disturbance response matrix data and the multi-condition simulation magnetic disturbance matrix data are screened for Hall monitoring spatiotemporal magnetic field anomalies, which can effectively eliminate the interference of environmental noise and system errors and improve the accuracy of abnormal detection. And by calculating the magnetic disturbance difference characteristics, dynamic magnetic field characteristic offset data is generated, which provides key information for accurately locating current anomalies. Sensor monitoring position matching is performed according to the dynamic magnetic field characteristic offset data, and the current distribution abnormal single battery is identified in combination with the battery pack structure model data. This positioning method based on position matching can associate magnetic field anomaly information with specific battery cells, thereby accurately locating the specific location where the current anomaly occurs.

[0056] Preferably, step S361 includes the following steps:

[0057] Filtering the working condition test magnetic disturbance response data and amplifying the magnetic field strength to generate amplified working condition magnetic disturbance response data;

[0058] Arrange the time-space magnetic field response data according to the magnetic disturbance response data of the amplified working condition to obtain the time-space magnetic disturbance response data;

[0059] Extracting the magnetic field signal spectrum characteristics from the magnetic disturbance response data under time and space conditions to obtain the magnetic disturbance response spectrum characteristic data;

[0060] The spatial variation rate of magnetic field intensity is calculated for the magnetic disturbance response data under time and space conditions to obtain the spatial variation data of magnetic field intensity;

[0061] According to the spatial variation data of magnetic field intensity and the characteristic data of magnetic disturbance response spectrum, current density inversion is performed to obtain the response current density distribution data;

[0062] Perform current density standard deviation processing on the response current density distribution data to obtain the response current uniformity index data;

[0063] The magnetic disturbance response spectrum characteristic data, the magnetic field intensity spatial variation data and the response current uniformity index data are processed into multi-dimensional magnetic field response characteristic vectors to generate standard magnetic disturbance response matrix data.

[0064] The present invention filters and amplifies the original magnetic disturbance response data, effectively removes noise interference, highlights weak magnetic field change signals, improves the signal-to-noise ratio of the signal, and lays the foundation for subsequent feature extraction. The amplified magnetic disturbance response data is sorted in time and space dimensions, and the spectrum characteristics and spatial change rate of the magnetic field signal are extracted. The spectrum characteristics reflect the frequency components of the magnetic field signal and can reveal the dynamic characteristics of the current change; while the spatial change rate reflects the spatial distribution of the magnetic field intensity, which can be used to identify the unevenness of the current distribution. Through the current density inversion technology, the magnetic field response data is converted into current density distribution data, and the standard deviation of the current density is calculated to obtain the response current uniformity index data. Current density inversion can more intuitively reflect the current distribution inside the battery, while the current density standard deviation can quantify the uniformity of the current distribution. The magnetic disturbance response spectrum feature data, the magnetic field intensity spatial change data, and the response current uniformity index data are processed with multi-dimensional magnetic field response feature vectors to generate standard magnetic disturbance response matrix data. This multi-dimensional feature fusion method comprehensively considers the time domain, frequency domain and spatial characteristics of the magnetic field signal, as well as the uniformity of current distribution, so that the generated standard magnetic disturbance response matrix data is more informative and representative, and can more comprehensively reflect the current distribution state inside the battery, thereby significantly improving the sensitivity and accuracy of current anomaly detection.

[0065] Preferably, step S4 comprises the following steps:

[0066] Step S41: performing single-cell pulse current instruction processing on current abnormal single-cell identification data based on multi-operating condition disturbance pulse excitation sequence data, and collecting battery pack BMS monitoring data, thereby obtaining BMS monitoring current excitation response data;

[0067] Step S42: calculating the current abnormality monitoring response time according to the BMS monitoring current excitation response data to obtain abnormality monitoring response time data;

[0068] Step S43: Counting the false alarm rate and missed alarm rate of the monitoring function by monitoring the current excitation response data and the magnetic disturbance response data of the working condition test through the BMS, and obtaining the false alarm rate and missed alarm rate data;

[0069] Step S44: performing multi-operating condition magnetic field response analysis on the operating condition test magnetic disturbance response data through the BMS monitoring current excitation response data to obtain operating condition adaptability data;

[0070] Step S45: Perform BMS current distribution monitoring performance evaluation according to the abnormal monitoring response time data, the false alarm rate and missed alarm rate data, and the operating condition adaptability data to generate BMS current monitoring performance evaluation data.

[0071] The present invention processes the single-cell pulse current instruction for the identified current abnormal single cell based on the multi-operating condition disturbance pulse excitation sequence data, and collects the monitoring data of the BMS. This directional excitation method can more accurately test the monitoring ability of the BMS for the current change of a single battery, avoid the interference caused by the global excitation, and improve the accuracy of the test results. The abnormal monitoring response time is calculated according to the BMS monitoring current excitation response data, and the false alarm rate and the missed alarm rate of the monitoring function are statistically calculated. The response time reflects the rapidity of the BMS detecting current abnormalities, and the false alarm rate and the missed alarm rate reflect the accuracy and reliability of the BMS detection. These indicators can comprehensively evaluate the performance of the BMS in current abnormality detection. The multi-operating condition magnetic field response analysis of the operating condition test magnetic disturbance response data is also performed through the BMS monitoring current excitation response data to obtain the operating condition adaptability data. This reflects the current monitoring performance of the BMS under different operating conditions, and can evaluate the adaptability of the BMS to complex operating conditions. By performing multi-operating condition magnetic field response analysis, the adaptability of the BMS under different operating conditions is evaluated. Good adaptability to working conditions means that the BMS can maintain stable and accurate monitoring performance in various complex operating environments, thereby ensuring the reliability of the battery system. Combining the abnormal monitoring response time, false alarm rate, missed alarm rate and working condition adaptability data, the final generated BMS current monitoring performance evaluation data provides a comprehensive, objective and quantitative evaluation of the BMS performance.

[0072] The present invention also provides a testing system for the BMS function of a new energy vehicle, which executes the testing method for the BMS function of a new energy vehicle as described above. The testing system for the BMS function of a new energy vehicle comprises:

[0073] A virtual battery model building module is used to obtain the battery pack CAD design drawing data; construct a three-dimensional model of the battery pack according to the battery pack CAD design drawing data to generate the battery pack structure model data; use the battery pack structure model data to perform virtual current element processing to generate the battery pack virtual current field data;

[0074] The excitation sequence generation module is used to collect vehicle operating condition charge and discharge sample data; perform adaptive current excitation sequence processing according to the vehicle operating condition charge and discharge sample data, and perform multi-operating condition disturbance combination to generate multi-operating condition disturbance pulse excitation sequence data;

[0075] The current anomaly diagnosis module is used to simulate the disturbance magnetic field of the virtual current field data of the battery pack through the multi-operating condition disturbance pulse excitation sequence data to generate multi-operating condition simulation magnetic disturbance matrix data; to test the dynamic current excitation of the battery pack through the multi-operating condition disturbance pulse excitation sequence data to obtain the operating condition test magnetic disturbance response data; to analyze the magnetic field distribution characteristic offset according to the multi-operating condition simulation magnetic disturbance matrix data and the operating condition test magnetic disturbance response data, and to match the single battery current distribution anomaly to obtain the current abnormal single battery identification data;

[0076] The BMS performance evaluation module is used to process the single-cell pulse current command of the current abnormal single-cell identification data based on the multi-operating condition disturbance pulse excitation sequence data, and collect the battery pack BMS monitoring data to obtain the BMS monitoring current excitation response data; the BMS current distribution monitoring performance is evaluated on the BMS monitoring current excitation response data through the operating condition test magnetic disturbance response data, and the BMS current monitoring performance evaluation data is generated. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 A schematic diagram of the steps of a method for testing the BMS function of a new energy vehicle according to the present invention;

[0078] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0079] Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0080] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0081] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0082] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0083] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0084] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for testing the BMS function of a new energy vehicle, comprising the following steps:

[0085] Step S1: obtaining CAD design drawing data of a battery pack; constructing a three-dimensional model of the battery pack according to the CAD design drawing data of the battery pack to generate battery pack structural model data; performing virtual current element processing using the battery pack structural model data to generate virtual current field data of the battery pack;

[0086] Step S2: collecting vehicle operating condition charge and discharge sample data; performing adaptive current excitation sequence processing according to the vehicle operating condition charge and discharge sample data, and performing multi-operating condition disturbance combination to generate multi-operating condition disturbance pulse excitation sequence data;

[0087] Step S3: Perform disturbance magnetic field simulation on the virtual current field data of the battery pack through multi-operating condition disturbance pulse excitation sequence data to generate multi-operating condition simulation magnetic disturbance matrix data; perform dynamic current excitation application on the test battery pack through multi-operating condition disturbance pulse excitation sequence data to obtain operating condition test magnetic disturbance response data; perform magnetic field distribution characteristic offset analysis based on the multi-operating condition simulation magnetic disturbance matrix data and the operating condition test magnetic disturbance response data, and perform single cell current distribution anomaly matching to obtain current abnormal single cell identification data;

[0088] Step S4: Based on the multi-operating condition disturbance pulse excitation sequence data, the current abnormal single battery identification data is processed with a single battery pulse current instruction, and the battery pack BMS monitoring data is collected to obtain BMS monitoring current excitation response data; the BMS current distribution monitoring performance of the BMS monitoring current excitation response data is evaluated through the operating condition test magnetic disturbance response data to generate BMS current monitoring performance evaluation data.

[0089] In the embodiment of the present invention, reference Figure 1 As shown, it is a schematic diagram of the steps of a method for testing the BMS function of a new energy vehicle of the present invention. In this embodiment, the method for testing the BMS function of a new energy vehicle comprises the following steps:

[0090] Step S1: obtaining CAD design drawing data of a battery pack; constructing a three-dimensional model of the battery pack according to the CAD design drawing data of the battery pack to generate battery pack structural model data; performing virtual current element processing using the battery pack structural model data to generate virtual current field data of the battery pack;

[0091] In an embodiment of the present invention, the CAD design drawings of the target new energy vehicle battery pack are obtained. These drawings usually contain key information such as battery module layout, Busbar connection method, sampling resistor position, etc. . Use three-dimensional modeling software (such as SolidWorks, CATIA, etc.) to accurately build a three-dimensional model of the battery pack according to the CAD drawing data. This model should include all key components such as battery cells, modules, Busbar, connectors, heat sinks, and BMS sampling harnesses. Then, based on the three-dimensional model, virtual current element processing is performed using finite element analysis software (such as ANSYS Maxwell, COMSOL Multiphysics, etc.). The specific operation method is to divide the conductive parts such as battery cells and Busbar into fine grid units, and apply a current density consistent with the actual working state to each unit. By solving the electromagnetic field equation, the virtual current field distribution data inside the battery pack can be obtained. These data are presented in three dimensions, which can clearly show the density and flow direction of the current at different positions. For example, in a battery pack consisting of 100 single cells, after constructing a three-dimensional model using CAD drawing data, each single cell is divided into 1,000 grid cells in the finite element software, and the busbar is divided into 500 grid cells. Then, based on the rated capacity and charge and discharge current of the battery pack, the corresponding current density is applied to each cell, and finally the current distribution inside the entire battery pack is calculated, including areas with higher current density and the current flow direction.

[0092] Step S2: collecting vehicle operating condition charge and discharge sample data; performing adaptive current excitation sequence processing according to the vehicle operating condition charge and discharge sample data, and performing multi-operating condition disturbance combination to generate multi-operating condition disturbance pulse excitation sequence data;

[0093] In an embodiment of the present invention, the charging and discharging current sample data of the vehicle under different driving conditions are collected. These data can be obtained through the vehicle CAN bus or BMS data interface, covering various typical working conditions such as acceleration, deceleration, constant speed, climbing, and braking energy recovery. The collected data should include key parameters such as current amplitude, duration, and rate of change. For example, the current data of an electric car driving on an urban road for 1 hour is collected, including the current changes under various working conditions such as starting acceleration, traffic light stop, constant speed driving, overtaking acceleration, deceleration braking, etc. Then, these current sample data are processed by adaptive current excitation sequence. The specific method is to generate a series of current pulse sequences with different amplitudes, frequencies, and waveforms according to the statistical characteristics of the current data (such as mean, variance, peak value, etc.). These pulse sequences can fully reflect the dynamic change characteristics of the current under actual working conditions. In order to further improve the comprehensiveness and reliability of the test, it is necessary to perform a multi-condition disturbance combination. The specific method is to randomly combine the current excitation sequences under different working conditions or sort them according to a specific rule to generate a pulse excitation sequence containing multiple working condition disturbances. For example, a high-amplitude pulse under acceleration conditions, a low-amplitude pulse under uniform speed conditions, and a negative current pulse under braking conditions are combined to generate an excitation sequence containing multiple current change modes.

[0094] Step S3: Perform disturbance magnetic field simulation on the virtual current field data of the battery pack through multi-operating condition disturbance pulse excitation sequence data to generate multi-operating condition simulation magnetic disturbance matrix data; perform dynamic current excitation application on the test battery pack through multi-operating condition disturbance pulse excitation sequence data to obtain operating condition test magnetic disturbance response data; perform magnetic field distribution characteristic offset analysis based on the multi-operating condition simulation magnetic disturbance matrix data and the operating condition test magnetic disturbance response data, and perform single cell current distribution anomaly matching to obtain current abnormal single cell identification data;

[0095] In an embodiment of the present invention, a current pulse sequence is used as an excitation source and applied to a virtual current field model. By solving the electromagnetic field equation, the changes in the magnetic field distribution inside the battery pack are calculated. Since the current pulse sequence contains disturbances of multiple working conditions, the simulated magnetic field distribution also has dynamic change characteristics. By superimposing the magnetic field distribution data at different times, a multi-condition simulated magnetic disturbance matrix data can be obtained, which contains the changes in the magnetic field distribution inside the battery pack under different working conditions. For example, a current pulse sequence containing three working conditions of acceleration, deceleration, and uniform speed is applied to the virtual current field model of the battery pack, and the magnetic field distribution at each pulse moment is calculated respectively, and then these magnetic field distribution data are combined into a matrix, each row of the matrix represents the magnetic field distribution at a moment, and each column represents the magnetic field intensity at a spatial position. At the same time, the same multi-condition disturbance pulse excitation sequence is applied to the actual test battery pack, and the magnetic field response data of the battery pack at different positions are collected through magnetic field measurement equipment such as Hall sensors or fluxgate sensors to obtain working condition test magnetic disturbance response data. Next, the multi-condition simulated magnetic disturbance matrix data and the working condition test magnetic disturbance response data are compared and analyzed. By calculating the offset between the two, the degree of difference between the magnetic field distribution of the actual battery pack and the theoretical model can be evaluated. If the magnetic field offset in some areas is too large, it indicates that there is an abnormal current distribution in the area. For example, by comparison, it is found that near a module of the battery pack, the actual measured magnetic field strength is 20% higher than the theoretical simulation value, and it is preliminarily judged that the module has problems such as uneven current distribution or short circuit. Then, combined with the single-cell current distribution model, the single-cell current distribution anomaly matching is performed for the area with abnormal magnetic field distribution. By analyzing the correlation between magnetic field anomalies and current anomalies, the single cell with abnormal current can be accurately located, and the current abnormal single cell identification data can be generated. For example, based on the distribution characteristics of the magnetic field abnormality area, combined with the series-parallel structure of the battery module and the Busbar connection method, it is inferred which single cell or module has current abnormality, and its number or location information is recorded in the current abnormal single cell identification data.

[0096] Step S4: Based on the multi-operating condition disturbance pulse excitation sequence data, the current abnormal single battery identification data is processed with a single battery pulse current instruction, and the battery pack BMS monitoring data is collected to obtain BMS monitoring current excitation response data; the BMS current distribution monitoring performance of the BMS monitoring current excitation response data is evaluated through the operating condition test magnetic disturbance response data to generate BMS current monitoring performance evaluation data.

[0097] In the embodiment of the present invention, according to the characteristics of the single cell with abnormal current (such as internal resistance, capacity, etc.), a series of current pulse instructions with specific amplitude, frequency and duration are generated, and these instructions will be used to simulate the current change of the single cell under abnormal state. For example, for a single cell with increased internal resistance, a series of current pulse instructions with small amplitude and short duration can be generated to simulate its current fluctuation during charging and discharging. Then, these single cell pulse current instructions are sent to the BMS, and the monitoring data of the BMS is collected at the same time, including parameters such as single cell voltage, current, temperature, etc. These data constitute the BMS monitoring current excitation response data. By analyzing the BMS monitoring current excitation response data, the detection capability and response speed of the BMS to the abnormal current of the single cell can be evaluated. For example, by analyzing the BMS monitoring data, it can be determined whether the BMS can detect the abnormal current of the single cell in a timely and accurate manner, and whether corresponding protection measures (such as cutting off the charging and discharging circuit, balancing the current, etc.) can be taken. At the same time, the BMS monitoring current excitation response data is used to evaluate the BMS current distribution monitoring performance. The specific method is to compare the current distribution monitored by the BMS with the actual magnetic field distribution to evaluate the accuracy and consistency of the BMS monitoring data. For example, if the BMS detects that the current of a certain module is too high, and the actual magnetic field distribution also shows that the magnetic field intensity near the module is high, it means that the current monitoring performance of the BMS is good. Conversely, if the BMS monitoring data is inconsistent with the actual magnetic field distribution, it means that the current monitoring performance of the BMS is defective. Finally, based on the BMS current monitoring performance evaluation results, the BMS current monitoring performance evaluation data is generated. The data includes key indicators such as BMS current monitoring accuracy, response time, false alarm rate, and missed alarm rate, which are used to evaluate the reliability and effectiveness of the BMS current monitoring function.

[0098] Preferably, step S1 comprises the following steps:

[0099] Step S11: Acquire battery pack CAD design drawing data;

[0100] Step S12: extracting battery pack structural parameters according to the battery pack CAD design drawing data to generate battery pack structural parameter data, wherein the battery pack structural parameter data includes single cells, connecting pieces, and internal circuit structure of the battery pack;

[0101] Step S13: constructing a three-dimensional model of the battery pack based on the single battery, the connecting piece and the internal circuit structure of the battery pack to generate battery pack structural model data;

[0102] Step S14: Calculating the theoretical current value of the single battery based on the preset battery pack working state parameters and the battery pack structure model data to generate the single battery theoretical current value data;

[0103] Step S15: performing virtual current element processing on the battery pack structure model data according to the internal circuit structure of the battery pack and the theoretical current value data of the single battery to obtain virtual current element parameters;

[0104] Step S16: Perform virtual current field processing on the battery pack structure model data using virtual current element parameters to generate battery pack virtual current field data.

[0105] In an embodiment of the present invention, the CAD design drawing data of the target new energy vehicle battery pack is obtained. These drawing data usually exist in the form of electronic files, such as .dwg, .step or .iges formats. The drawing data should contain the complete structural information of the battery pack, such as the size and layout of the battery cell, the shape and connection method of the connecting piece, the direction and connection point of the Busbar, the location of the sampling resistor, and the layout of the BMS wiring harness. Use CAD software or a special parameter extraction tool to automatically or manually extract key dimensions, positions, and connection relationships from the drawings. The battery pack structural parameter data includes the size parameters (length, width, height), material properties, and rated capacity of the single cell; the size parameters (length, width, thickness), material properties, and resistivity of the connecting piece (Busbar); and the internal circuit structure information of the battery pack, such as the series and parallel mode of the single cell, the connection relationship of the Busbar, the location and resistance of the sampling resistor, etc. For example, use the measurement tool of the CAD software to measure the length, width, and height of each single cell and record them in a table; read the material type of the Busbar, and consult the material manual to obtain its resistivity. Create a 3D model of a single cell according to the size parameters of the single cell, and combine multiple single cells into a battery module according to the series-parallel connection method. Then, create a 3D model of the connecting piece according to the shape and size parameters of the connecting piece, and place it between the battery modules to simulate the actual connection method. Finally, create a 3D model of the Busbar according to the direction and connection point of the Busbar, and connect it with the battery module and the connecting piece to form a complete 3D structural model of the battery pack. The generated battery pack structural model data should contain information such as the geometry, size, position and connection relationship of all components. For example, in SolidWorks, first create a cylinder to represent a single cell, then copy multiple cylinders and arrange them in series and parallel to form a battery module, then create a cuboid to represent the Busbar, assemble it with the battery module, and finally generate a complete 3D model of the battery pack. Calculate the theoretical current value of each single cell according to the charge and discharge current of the battery pack and the series and parallel connection method of the single cell. For example, if the charge and discharge current of the battery pack is 100A, and it is composed of 10 series and 10 parallel single cells, the theoretical current value of each single cell is 10A. Then, according to the temperature and SOC of the single cell, the battery model (such as an equivalent circuit model or an electrochemical model) is used to correct the current of the single cell to obtain more accurate theoretical current value data of the single cell. For example, according to the temperature characteristics of the single cell, the current value is temperature compensated; according to the SOC of the single cell, the current value is SOC corrected. The final generated single cell theoretical current value data should contain the current value of each single cell under different working conditions. The conductive components (such as single cells, connecting pieces, busbars, etc.) in the battery pack structure model are discretized into a finite number of units, each of which is called a current element.Then, according to the theoretical current value data of the single cell and the distribution law of the current in the circuit, the current density of each current element is determined. For example, for a single cell, it can be divided into multiple small cubic units, and the current density of each unit is equal to the current of the single cell divided by the cross-sectional area of ​​the unit. For the connecting piece and the busbar, the current density of each unit can be calculated according to the flow direction and cross-sectional area of ​​the current. The virtual current element parameters include the position coordinates, current density vector, unit volume and other information of each current element. For example, for a busbar divided into 1000 units, the virtual current element parameters of each unit include the center coordinates (x, y, z) of the unit, the current density vector (Jx, Jy, Jz) and the volume dV of the unit. The virtual current element parameters are input into the electromagnetic field simulation software (such as ANSYS Maxwell, COMSOL Multiphysics, etc.), and the current distribution and magnetic field distribution inside the battery pack are calculated by solving the Maxwell equations. The simulation software will calculate the electric field and magnetic field generated by each unit according to the current density and position of the current element, and superimpose the electric field and magnetic field of all units to obtain the electric field and magnetic field distribution of the entire battery pack. The generated virtual current field data of the battery pack includes information such as current density distribution, magnetic field intensity distribution, and magnetic flux density distribution.

[0106] Preferably, step S15 comprises the following steps:

[0107] Step S151: extracting battery characteristic parameters of a single battery using a preset battery single battery characteristic database, and obtaining a battery single battery internal resistance parameter and a battery single battery capacity parameter;

[0108] Step S152: performing Monte Carlo normal distribution assignment according to the battery cell internal resistance parameter and the battery cell capacity parameter, and obtaining random internal resistance value data of the single cell and random capacity value data of the single cell respectively;

[0109] Step S153: using the single cell theoretical current value data to calculate the single cell terminal voltage of the single cell random internal resistance value data and the single cell random capacity value data, to generate the single cell terminal voltage data;

[0110] Step S154: calculating the connection sheet resistance value according to the battery pack structure model data to generate connection sheet resistance value data;

[0111] Step S155: establishing a circuit equation group through the internal circuit structure of the battery pack, and solving the single cell current value and the connecting piece current value by using the single cell terminal voltage data and the connecting piece resistance value data, and obtaining the actual single cell current value and the connecting piece current value respectively;

[0112] Step S156: Setting a virtual current element for the battery pack structure model data according to the actual current value of the single battery and the current value of the connecting piece to obtain virtual current element parameters.

[0113] In an embodiment of the present invention, a preset battery cell characteristic database is used to extract battery characteristic parameters for each single cell in a battery pack. The battery cell characteristic database stores various characteristic parameters of battery cells of different models and batches, such as internal resistance, capacity, open circuit voltage, charge and discharge curves, etc. According to the model and batch information of the single cells used in the battery pack, the corresponding battery characteristic parameters are retrieved from the database. Then, the internal resistance parameters and capacity parameters of the battery cell are extracted. The internal resistance parameters usually include DC internal resistance and AC internal resistance, and the capacity parameters usually include rated capacity and actual capacity. Assuming that the internal resistance and capacity parameters of the battery cell obey a normal distribution, the internal resistance parameters and capacity parameters extracted in step S151 are taken as the mean, and a normal distribution random number is generated with a certain standard deviation (usually determined based on data provided by the battery manufacturer or empirical values). A random internal resistance value and a random capacity value are generated for each single cell. For example, for a single cell with an internal resistance of 0.5 mΩ and a capacity of 50 Ah in step S151, assuming that the standard deviation of the internal resistance is 0.05 mΩ and the standard deviation of the capacity is 1 Ah, a normal distribution N(0.5, ) of random internal resistance and a normal distribution N(50, ) random capacity value. According to the circuit principle, the terminal voltage of a single battery is equal to the open circuit voltage minus the internal resistance voltage drop, that is, U=Voc IR, where U is the terminal voltage, Voc is the open circuit voltage, I is the current, and R is the internal resistance. First, according to the SOC and temperature of the single cell, the open circuit voltage Voc of the single cell is determined using a battery model (such as an equivalent circuit model or an electrochemical model). Then, the internal resistance drop IR of each single cell is calculated using the theoretical current value data of the single cell and the random internal resistance value data of the single cell. Finally, the open circuit voltage Voc is subtracted from the internal resistance drop I R, get the terminal voltage value of each single cell, and generate the terminal voltage data of the single cell. Extract the geometric parameters of the connecting piece, such as length, width, thickness, etc., from the battery pack structure model data. Then, according to the material properties of the connecting piece (such as resistivity), use the resistance calculation formula R=ρ L / A calculates the resistance value of the connecting piece, where R is the resistance value, ρ is the resistivity, L is the length, and A is the cross-sectional area. The circuit equation group is established through the internal circuit structure of the battery pack. According to the series-parallel structure and connection method of the battery pack, the Kirchhoff current law (KCL) and Kirchhoff voltage law (KVL) equation groups are established. This equation group describes the relationship between the current and voltage inside the battery pack. Then, the single cell terminal voltage data obtained in step S153 and the connecting piece resistance value data obtained in step S154 are substituted into the circuit equation group to solve the single cell current value and the connecting piece current value. This is a linear or nonlinear equation group solution problem, which can be solved using numerical methods (such as Gaussian elimination method, Newton iteration method, etc.). Finally, the actual current value of each single cell and the current value of each connecting piece are obtained. The conductive components (such as single cells, connecting pieces, Busbar, etc.) in the battery pack structure model are discretized into a finite number of current elements. Then, according to the actual current value of the single cell and the current value of the connecting piece obtained in step S155, the current density of each current element is determined. For a single cell, it is divided into multiple current elements, and the current density of each current element is equal to the actual current of the single cell divided by the cross-sectional area of ​​the unit. For a connecting piece, the current density of each current element is calculated based on the current value and cross-sectional area of ​​the connecting piece. For a busbar, the current density of each current element is determined based on the direction of current flow and the geometric shape of the busbar. Finally, the position coordinates, current density vector, unit volume and other information of each current element are combined into virtual current element parameters.

[0114] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S2 in the embodiment are shown in the flowchart. In this embodiment, step S2 includes:

[0115] Step S21: collecting vehicle operating condition charge and discharge sample data through preset vehicle operating condition type data to obtain vehicle operating condition charge and discharge sample data;

[0116] In an embodiment of the present invention, the type of vehicle operating condition that needs to be simulated is determined. The vehicle operating condition type data generally includes urban operating conditions, high-speed operating conditions, suburban operating conditions, mountainous operating conditions, etc. Each operating condition can be further subdivided into different driving stages such as acceleration, deceleration, constant speed, climbing, braking, etc. According to the preset vehicle operating condition type, the vehicle operating condition charge and discharge sample data is collected. Data collection can be carried out through actual road testing, bench testing or simulation. Through the vehicle's CAN bus, BMS data interface or current sensor and other equipment, the charging and discharging current, voltage, temperature and other data of the vehicle under different operating conditions are collected.

[0117] Step S22: Processing the battery pack charge and discharge current change curve on the vehicle operating condition charge and discharge sample data to generate operating condition current curve data;

[0118] In an embodiment of the present invention, current data is extracted from sample data and sorted according to timestamps. Then, the current data is filtered, denoised, and other processes are performed to eliminate measurement errors and noise interference. Next, as needed, the current data can be normalized so that its amplitude is within a certain range. Finally, the processed current data is represented in the form of a curve to generate operating current curve data. For example, current data is extracted from the collected urban congestion operating condition sample data, a low-pass filter is used to remove high-frequency noise, and then the current data is plotted as a curve of current change over time, to obtain current curve data for urban congestion operating conditions. The same processing is performed on each operating condition type to generate multiple operating condition current curve data.

[0119] Step S23: Calculating the local excitation current intensity of the battery pack structure model data based on the operating current curve data to generate local excitation parameter data;

[0120] In an embodiment of the present invention, the current value in the operating current curve data is used as the total current of the battery pack. Then, according to the circuit structure inside the battery pack and the series-parallel relationship of the single cells, the total current is distributed to each single cell and the connecting piece, and the current of each single cell and the connecting piece is calculated. Next, using the battery pack structure model data generated in step S16, each single cell and the connecting piece are discretized into multiple units, and the current density of each unit, that is, the local excitation current intensity, is calculated based on the current distribution. Generate local excitation parameter data, including information such as the position coordinates and current density vector of each unit. For example, for a battery pack with 10 series and 10 parallels, according to the operating current curve data, at a certain moment the total current is 100A, then according to the series-parallel relationship, the current of each single cell is calculated to be 10A, and then according to the battery pack structure model, each single cell and the connecting piece are divided into multiple units, the current density of each unit is calculated, and the local excitation parameter data is generated.

[0121] Step S24: performing adaptive pulse current excitation sequence processing according to the local excitation parameter data, thereby obtaining initial pulse excitation sequence data;

[0122] In an embodiment of the present invention, the characteristics of the operating current curve data, such as current amplitude, frequency, rise / fall time, etc., are analyzed. Then, based on these characteristics, the continuous operating current curve data is discretized into a series of pulse currents. The amplitude, width, interval and other parameters of the pulse current can be adaptively adjusted according to the characteristics of the actual operating conditions. In order to ensure the excitation effect, the amplitude of the pulse current should be able to cover the range of the actual operating current, the pulse width should be short enough to capture the rapid changes in the current, and the pulse interval should be able to reflect the duration of the operating condition.

[0123] Step S25: using a preset battery pack disturbance factor database to select a random disturbance factor to obtain random disturbance factor data;

[0124] In an embodiment of the present invention, a preset battery pack disturbance factor database is used to select random disturbance factors. The battery pack disturbance factor database stores various factors that affect the performance of the battery pack, such as temperature change, vibration shock, electromagnetic interference, etc. Each disturbance factor has its corresponding probability distribution function, which describes the possibility and intensity of the occurrence of the disturbance factor. Some disturbance factors are randomly selected from the database, and random disturbance factor data is generated according to their probability distribution function. For example, temperature change and vibration shock are selected from the database as disturbance factors, and random temperature disturbance data that obeys the normal distribution is generated according to the historical data of temperature change, and random vibration acceleration data is generated according to the spectral characteristics of vibration shock.

[0125] Step S26: applying a disturbance rule to the initial pulse excitation sequence data through random disturbance factor data, and performing multi-operating condition disturbance combination to generate multi-operating condition disturbance pulse excitation sequence data.

[0126] In an embodiment of the present invention, a disturbance rule is applied to the initial pulse excitation sequence data through random disturbance factor data. The disturbance rule can be preset according to the characteristics of the actual working conditions. For example, the temperature disturbance factor can be superimposed on the amplitude of the pulse current, and the vibration disturbance factor can be superimposed on the frequency of the pulse current. By applying the random disturbance factor data to the initial pulse excitation sequence data according to the preset disturbance rule, a pulse excitation sequence containing disturbance factors can be generated. Then, the disturbance pulse excitation sequences under different working conditions are combined to generate multi-working condition disturbance pulse excitation sequence data. The combination method can be a simple time sequence superposition, or it can be combined according to the switching law of the actual working condition.

[0127] Preferably, step S3 comprises the following steps:

[0128] Step S31: performing thermal simulation analysis of the battery pack according to the virtual current field data of the battery pack, identifying potential thermal risk areas, and obtaining distribution data of potential thermal risk areas;

[0129] Step S32: determining the deployment position of the Hall effect sensor based on the battery pack structure model data by using the potential thermal risk area distribution data to generate sensor deployment position data;

[0130] Step S33: using the sensor deployment position data to test the battery pack Hall effect sensor array deployment to obtain a Hall effect sensor array;

[0131] Step S34: performing disturbance magnetic field simulation on the virtual current field data of the battery pack through the multi-operating condition disturbance pulse excitation sequence data to generate multi-operating condition simulation magnetic disturbance matrix data;

[0132] Step S35: Initializing the battery pack test environment through the preset battery test state data, applying dynamic current excitation through the multi-operating condition disturbance pulse excitation sequence data, and collecting the internal magnetic field response data of the battery pack through the Hall effect sensor array to obtain the operating condition test magnetic disturbance response data;

[0133] Step S36: Analyze the magnetic field distribution characteristic offset according to the multi-operating condition simulation magnetic disturbance matrix data and the operating condition test magnetic disturbance response data to generate dynamic magnetic field characteristic offset data; match the single-cell current distribution anomaly with the dynamic magnetic field characteristic offset data through the battery pack structure model data to obtain current abnormal single-cell identification data.

[0134] In an embodiment of the present invention, the virtual current field data is input as a heat source into the thermal simulation software (such as ANSYS Icepak, Fluent, etc.), and the material properties, boundary conditions, and ambient temperature parameters of the battery pack are set. The temperature distribution inside the battery pack is calculated by solving the heat transfer equation. Then, the potential thermal risk area is identified based on the temperature distribution results. The deployment position of the Hall effect sensor is determined based on the distribution data of the potential thermal risk area. The Hall effect sensor is used to measure the magnetic field distribution inside the battery pack, and the current anomaly usually causes the magnetic field anomaly. Therefore, the Hall effect sensor should be deployed in areas with high current density, prone to current anomalies, and near the potential thermal risk area. First, according to the distribution data of the potential thermal risk area, the area that needs to be monitored is determined. Then, in these areas, according to the battery pack structure model data, the appropriate sensor installation position is selected. The installation position should ensure that the sensor can accurately measure the magnetic field of the target area and will not interfere with the normal operation of the battery pack. According to the sensor deployment position data, a suitable Hall effect sensor is selected, and the layout and installation method of the sensor array are designed. The sensor array should be able to cover the key areas of the battery pack and provide sufficient spatial resolution. The sensor is installed at the specified position of the test battery pack and connected to the data acquisition system. The multi-condition disturbance pulse excitation sequence data is used to simulate the disturbance magnetic field of the virtual current field data of the battery pack. The multi-condition disturbance pulse excitation sequence data is input into the electromagnetic field simulation software as the excitation signal of the virtual current source. By solving the Maxwell equations, the change of the magnetic field distribution inside the battery pack over time is calculated. Since the excitation signal contains disturbances of multiple working conditions, the simulated magnetic field distribution also has dynamic characteristics. The magnetic field distribution data at different times are combined into a matrix to generate multi-condition simulated magnetic disturbance matrix data. Each row of the matrix represents the magnetic field distribution at a moment, and each column represents the magnetic field intensity at a spatial position. According to the preset battery test state data, the initial state of the battery pack is set, such as SOC, temperature, voltage, etc. Then, the multi-condition disturbance pulse excitation sequence data is input into the current control device to apply dynamic current excitation to the test battery pack. At the same time, the magnetic field response data inside the battery pack is collected using the Hall effect sensor array. The data acquisition system records the magnetic field intensity of each sensor in real time and saves the data in the database. For example, the SOC of the battery pack is set to 80% and the temperature is set to 25°C. Then, according to the preset multi-condition disturbance pulse excitation sequence, the current source is controlled to charge and discharge the battery pack. At the same time, the Hall effect sensor array is used to collect the magnetic field data inside the battery pack, and the collected data is filtered, amplified and stored. Generate the working condition test magnetic disturbance response data, which includes the magnetic field strength of each sensor at different times. Compare the working condition test magnetic disturbance response data with the multi-condition simulation magnetic disturbance matrix data, and calculate the difference between the two. The difference can be measured by a variety of indicators, such as root mean square error, correlation coefficient, etc.By analyzing the changes of the difference over time and space, the dynamic magnetic field characteristic offset data can be obtained. This data describes the deviation between the actual magnetic field distribution and the theoretical simulation value. For example, the difference between the actual measured magnetic field strength and the theoretical simulation value at a specific time and a specific location is calculated, and these differences are analyzed over time and space to obtain the dynamic magnetic field characteristic offset data. Then, using the battery pack structure model data, the dynamic magnetic field characteristic offset data is associated with the single cell current distribution to match the single cell current distribution anomaly. By analyzing the relationship between magnetic field anomalies and current anomalies, single cells with current anomalies can be identified, and current abnormal single cell identification data can be generated.

[0135] Preferably, step S34 includes the following steps:

[0136] Step S341: constructing a finite element electromagnetic field simulation model according to the battery pack virtual current field data and the battery pack structure model data to obtain an electromagnetic field virtual simulation model;

[0137] Step S342: mapping the Hall effect monitoring points of the electromagnetic field virtual simulation model through the sensor deployment position data, and setting finite element parameters to obtain finite element analysis parameters;

[0138] Step S343: performing single-operating-condition pulse excitation signal selection on the multi-operating-condition disturbance pulse excitation sequence data to obtain single-operating-condition pulse excitation signal data;

[0139] Step S344: simulating the magnetic field distribution inside the battery pack by using the single-operating pulse excitation signal data and finite element analysis parameters on the electromagnetic field virtual simulation model to generate simulated battery pack magnetic field distribution data;

[0140] Step S345: performing excitation signal sequence traversal on the multi-operating condition disturbance pulse excitation sequence data, and repeating step S344, thereby obtaining multiple sets of single-operating condition disturbance magnetic field distribution data;

[0141] Step S346: extract the vertical magnetic field component at the Hall sensor position according to multiple groups of single-operating-condition disturbance magnetic field distribution data, and generate multi-operating-condition magnetic disturbance simulation matrix data.

[0142] In an embodiment of the present invention, the battery pack structure model is imported into a finite element analysis software (such as ANSYSMaxwell, COMSOL Multiphysics, etc.). According to the material properties (such as conductivity, magnetic permeability) and geometric structure of the battery pack, the battery pack model is divided into many small grid units. Then, the virtual current field data is mapped to the grid unit as a current source. Set the boundary conditions of the simulation model, such as the electromagnetic shielding characteristics of the battery pack shell. Finally, an electromagnetic field virtual simulation model containing the battery pack geometry, material properties and current source information is obtained. According to the sensor deployment position data generated in step S32, the monitoring points of the Hall effect sensor are mapped in the simulation model. The three-dimensional coordinates of each sensor are corresponded to the grid nodes in the simulation model. These nodes will be used as observation points for subsequent magnetic field calculations. Then, according to the accuracy requirements and computing resources of the simulation, set the finite element analysis parameters, such as grid size, solver type, convergence accuracy, etc. Select a single-condition pulse excitation signal from the multi-condition disturbance pulse excitation sequence data generated in step S26. The multi-condition perturbation pulse excitation sequence data includes current excitation signals under various working conditions, such as acceleration, deceleration, and uniform speed. As needed, the pulse excitation signal of one of the working conditions can be selected as the single-condition pulse excitation signal data. The single-condition pulse excitation signal data is input into the simulation model as the excitation source, and the solution is performed using the preset finite element analysis parameters. The simulation software calculates the magnetic field strength and direction at each position inside the battery pack according to the excitation signal and the model parameters. For example, in ANSYS Maxwell, the single-condition pulse excitation signal data is loaded into the simulation model as current excitation, the solution time and step size are set, and the simulation calculation is run to obtain the magnetic field distribution data inside the battery pack, including the magnetic field strength and direction of each grid node. The multi-condition perturbation pulse excitation sequence data includes current excitation signals under various working conditions. Select the pulse excitation signal of each working condition in turn, repeat step S344, and simulate the magnetic field distribution inside the battery pack for the electromagnetic field virtual simulation model. For example, pulse excitation signals under conditions such as acceleration, deceleration, and uniform speed are selected in turn, and are input into the simulation model as excitation sources for simulation calculation. Each simulation generates a set of simulated battery pack magnetic field distribution data. Since Hall effect sensors usually measure the magnetic field component perpendicular to the sensor surface, it is necessary to extract the vertical magnetic field component at the sensor position from the simulation results. Extract the vertical magnetic field component at the sensor position from each set of single-condition disturbance magnetic field distribution data and arrange them in chronological order to form a matrix. Each row of the matrix represents the sensor measurement value at a moment, and each column represents the measurement data of a sensor. Finally, multi-condition magnetic disturbance simulation matrix data is generated, which contains the vertical magnetic field components at each sensor position under different conditions.

[0143] Preferably, step S36 includes the following steps:

[0144] Step S361: extracting magnetic field response features from the working condition test magnetic disturbance response data to obtain standard magnetic disturbance response matrix data;

[0145] Step S362: Correlating the multi-operating condition simulation magnetic disturbance matrix data with the magnetic disturbance of the same operating condition through the standard magnetic disturbance response matrix data, and calculating the correlation coefficient to obtain the mutual information correlation coefficient;

[0146] Step S363: Based on the mutual information correlation coefficient, the standard magnetic disturbance response matrix data and the multi-operating condition simulated magnetic disturbance matrix data are screened for Hall monitoring spatiotemporal magnetic field anomalies, and magnetic disturbance difference characteristics are calculated to generate dynamic magnetic field characteristic offset data;

[0147] Step S364: matching the sensor monitoring position according to the dynamic magnetic field characteristic offset data, and identifying the single battery with abnormal current distribution through the battery pack structure model data to obtain the identification data of the single battery with abnormal current distribution.

[0148] In an embodiment of the present invention, a magnetic field response feature is extracted from the preprocessed data according to a preset feature extraction method. The feature extraction method can be selected according to actual conditions, for example, the mean, variance, peak value, spectrum and other features of the magnetic field intensity can be extracted. The extracted features are arranged in chronological order to form a matrix, which is called standard magnetic disturbance response matrix data. For example, the standard magnetic disturbance response matrix data collected under urban working conditions are paired with the multi-condition simulated magnetic disturbance matrix data simulated under urban working conditions. Then, the correlation coefficient is calculated for the paired data. The correlation coefficient can be used to measure the similarity between the actual test data and the simulation data. Commonly used correlation coefficients include the Pearson correlation coefficient, the Spearman correlation coefficient, and the like. If the mutual information correlation coefficient is lower than a preset threshold, it is considered that the magnetic field at the position at that moment is abnormal. For example, the threshold is set to 0.8. If the mutual information correlation coefficient of a sensor at a certain moment is lower than 0.8, it is considered that the sensor detects a magnetic field anomaly at that moment. The magnetic disturbance difference feature calculation is performed on the screened magnetic field anomaly data. The difference between the actual test data and the simulation data is calculated, for example, the difference between the two, the relative error, etc. can be calculated. The calculated difference characteristics are analyzed over time and space to generate dynamic magnetic field characteristic offset data. Each abnormal point in the dynamic magnetic field characteristic offset data is mapped to a specific sensor position. For example, if a sensor detects a magnetic field anomaly at a certain moment, the abnormal point is mapped to the position of the sensor. Then, the battery pack structure model data is used to identify the single battery with abnormal current distribution. According to the position of the sensor and the structural information of the battery pack, the single battery related to the sensor position is determined. For example, according to the position of the sensor and the arrangement of the battery module, determine which single batteries the sensor mainly monitors. If a sensor detects a magnetic field anomaly, it is considered that the single battery associated with it has a current distribution anomaly. These single batteries with current anomalies are identified to generate current abnormality single battery identification data.

[0149] Preferably, step S361 includes the following steps:

[0150] Filtering the working condition test magnetic disturbance response data and amplifying the magnetic field strength to generate amplified working condition magnetic disturbance response data;

[0151] Arrange the time-space magnetic field response data according to the magnetic disturbance response data of the amplified working condition to obtain the time-space magnetic disturbance response data;

[0152] Extracting the magnetic field signal spectrum characteristics from the magnetic disturbance response data under time and space conditions to obtain the magnetic disturbance response spectrum characteristic data;

[0153] The spatial variation rate of magnetic field intensity is calculated for the magnetic disturbance response data under time and space conditions to obtain the spatial variation data of magnetic field intensity;

[0154] According to the spatial variation data of magnetic field intensity and the characteristic data of magnetic disturbance response spectrum, current density inversion is performed to obtain the response current density distribution data;

[0155] Perform current density standard deviation processing on the response current density distribution data to obtain the response current uniformity index data;

[0156] The magnetic disturbance response spectrum characteristic data, the magnetic field intensity spatial variation data and the response current uniformity index data are processed into multi-dimensional magnetic field response characteristic vectors to generate standard magnetic disturbance response matrix data.

[0157] In an embodiment of the present invention, filtering is performed according to the frequency characteristics of the noise. The filtered data can more accurately reflect the changes in the magnetic field inside the battery pack. Then, the filtered data is subjected to magnetic field strength amplification processing. Since the magnetic field signal collected by the Hall effect sensor is usually weak, it needs to be amplified for subsequent analysis. According to the amplified working condition magnetic disturbance response data, the spatiotemporal magnetic field response data is sorted. The amplified working condition magnetic disturbance response data contains the magnetic field strength values ​​collected by each sensor at different times. These data are arranged in time and space order to form spatiotemporal working condition magnetic disturbance response data. The data is a three-dimensional matrix, in which the first dimension represents time, the second dimension represents the position of the sensor, and the third dimension represents the magnetic field strength value. For example, the magnetic field strength values ​​collected by 10 sensors at 100 times are sorted into a 100 10 3, where 3 represents the three components (x, y, z) of the magnetic field intensity. Perform spectral analysis on the magnetic field signal collected by each sensor, for example, use fast Fourier transform (FFT) to convert the time domain signal into a frequency domain signal. Extract features from the frequency domain signal, such as spectrum peak, spectrum width, spectrum energy, etc. Combine the extracted spectrum features into magnetic disturbance response spectrum feature data. The data is a matrix in which each row represents a sensor and each column represents a spectrum feature. Calculate the spatial change rate of magnetic field intensity for the magnetic disturbance response data under time and space conditions. Calculate the difference in magnetic field intensity between adjacent sensors at each moment, or calculate the gradient of magnetic field intensity in space. The spatial change rate of magnetic field intensity can reflect the inhomogeneity of current distribution inside the battery pack. Combine the calculated spatial change rate of magnetic field intensity into magnetic field intensity spatial change data. Various inversion algorithms can be used, such as finite element inversion, analytical inversion, etc. Combine the inverted current density distribution into response current density distribution data. This data is a three-dimensional matrix, similar to the magnetic disturbance response data under time-space conditions, in which the first dimension represents time, the second dimension represents the spatial position, and the third dimension represents the current density value. The current density standard deviation can reflect the uniformity of the current distribution. The calculated current density standard deviation is combined into the response current uniformity index data. This data is a vector, in which each element represents the current density standard deviation at a moment. The magnetic disturbance response spectrum feature data, the magnetic field intensity spatial variation data, and the response current uniformity index data are processed with multi-dimensional magnetic field response feature vectors. These data are combined into a matrix, called the standard magnetic disturbance response matrix data. Each row of the matrix represents the magnetic field response characteristics at a moment, and each column represents a feature vector. The feature vector can be composed of spectrum characteristics, spatial variation rate, and current uniformity index.

[0158] As an example of the present invention, refer to Figure 3 As shown, Figure 1 Detailed implementation steps of step S4 in the embodiment are shown in the flowchart. In this embodiment, step S4 includes:

[0159] Step S41: performing single-cell pulse current instruction processing on current abnormal single-cell identification data based on multi-operating condition disturbance pulse excitation sequence data, and collecting battery pack BMS monitoring data, thereby obtaining BMS monitoring current excitation response data;

[0160] In an embodiment of the present invention, pulse current instructions for abnormal single cells are generated based on the multi-operating condition disturbance pulse excitation sequence data generated in step S2 and the current abnormal single cell identification data generated in step S36. These instructions need to be able to simulate the abnormal current behavior of single cells under different operating conditions, such as overcharging, over-discharging, increased internal resistance, etc. Then, the generated pulse current instructions are input into a battery simulator or other current control device to perform current excitation on the battery pack. At the same time, the monitoring data of the battery pack BMS is collected, including parameters such as single cell voltage, current, and temperature. These data constitute the BMS monitoring current excitation response data. For example, according to the current abnormal single cell identification data, the current value of the corresponding abnormal single cell in the multi-operating condition disturbance pulse excitation sequence data is adjusted to simulate its overcharge state, and then the adjusted current instruction is input into the battery simulator to excite the battery pack and collect the monitoring data of the BMS.

[0161] Step S42: calculating the current abnormality monitoring response time according to the BMS monitoring current excitation response data to obtain abnormality monitoring response time data;

[0162] In an embodiment of the present invention, the current anomaly monitoring response time is calculated according to the BMS monitoring current excitation response data obtained in step S41. The current anomaly monitoring response time refers to the time interval from the occurrence of the current anomaly to the BMS detecting the anomaly and issuing an alarm or taking protective measures. First, according to the preset abnormality judgment standard, it is judged whether the BMS detects the current anomaly. For example, the single cell voltage or current can be set to exceed a certain threshold as the abnormality judgment standard. Then, the time difference from the occurrence of the current anomaly to the BMS issuing an alarm is calculated, which is the abnormal monitoring response time. The response time under different working conditions and different abnormal types is statistically analyzed to obtain the abnormal monitoring response time data. For example, the response time of the BMS detecting the overcharge of the single cell battery under different working conditions is counted, and its average value and standard deviation are calculated as the abnormal monitoring response time data.

[0163] Step S43: Counting the false alarm rate and missed alarm rate of the monitoring function by monitoring the current excitation response data and the magnetic disturbance response data of the working condition test through the BMS, and obtaining the false alarm rate and missed alarm rate data;

[0164] In an embodiment of the present invention, the BMS monitoring current excitation response data obtained in step S41 and the operating condition test magnetic disturbance response data obtained in step S35 are used to perform statistics on the false alarm rate and missed alarm rate of the monitoring function. The false alarm rate refers to the probability that the BMS misjudges the normal state as an abnormal state, and the missed alarm rate refers to the probability that the BMS fails to detect the abnormal state. First, according to the preset abnormal judgment standard, the number of false alarms and missed alarms of the BMS under different operating conditions are counted. For example, the number of times the BMS misjudges the single cell voltage as overvoltage under normal operating conditions, and the number of times the BMS fails to detect the overcurrent of the single cell under abnormal operating conditions are counted. Then, based on the number of false alarms and missed alarms, the false alarm rate and missed alarm rate are calculated to obtain the false alarm rate and missed alarm rate data. For example, the number of false alarms is divided by the total number of tests to obtain the false alarm rate; the number of missed alarms is divided by the total number of abnormalities to obtain the missed alarm rate.

[0165] Step S44: performing multi-operating condition magnetic field response analysis on the operating condition test magnetic disturbance response data through the BMS monitoring current excitation response data to obtain operating condition adaptability data;

[0166] In an embodiment of the present invention, the BMS monitoring current excitation response data obtained in step S41 is used to perform a multi-condition magnetic field response analysis on the operating condition test magnetic disturbance response data obtained in step S35. Analyze the response of the BMS to magnetic field changes under different operating conditions. For example, compare the parameters such as the amplitude, frequency, and phase of the current anomaly detected by the BMS under different operating conditions, as well as the effectiveness of the protection measures taken by the BMS. The analysis results are organized into operating condition adaptability data. The data can reflect the monitoring performance and robustness of the BMS under different operating conditions. For example, compare the response time and accuracy of the BMS detecting current anomalies under urban conditions, high-speed conditions, and mountainous conditions, and organize the results into tables or charts as operating condition adaptability data.

[0167] Step S45: Perform BMS current distribution monitoring performance evaluation according to the abnormal monitoring response time data, the false alarm rate and missed alarm rate data, and the operating condition adaptability data to generate BMS current monitoring performance evaluation data.

[0168] In an embodiment of the present invention, the BMS current distribution monitoring performance is evaluated based on the abnormal monitoring response time data obtained in step S42, the false alarm rate and missed alarm rate data obtained in step S43, and the operating condition adaptability data obtained in step S44. Combined with the preset evaluation criteria, the current monitoring performance of the BMS is comprehensively evaluated. The evaluation criteria may include indicators such as response time, false alarm rate, missed alarm rate, and operating condition adaptability. Based on the evaluation results, BMS current monitoring performance evaluation data is generated. The data can be used to evaluate the reliability and effectiveness of the BMS and provide a reference for the improvement and optimization of the BMS. For example, according to the preset weights, the indicators such as abnormal monitoring response time, false alarm rate, missed alarm rate, and operating condition adaptability are weighted averaged to obtain a comprehensive score of the BMS current monitoring performance as the BMS current monitoring performance evaluation data.

[0169] The present invention also provides a testing system for the BMS function of a new energy vehicle, which executes the testing method for the BMS function of a new energy vehicle as described above. The testing system for the BMS function of a new energy vehicle comprises:

[0170] A virtual battery model building module is used to obtain the battery pack CAD design drawing data; construct a three-dimensional model of the battery pack according to the battery pack CAD design drawing data to generate the battery pack structure model data; use the battery pack structure model data to perform virtual current element processing to generate the battery pack virtual current field data;

[0171] The excitation sequence generation module is used to collect vehicle operating condition charge and discharge sample data; perform adaptive current excitation sequence processing according to the vehicle operating condition charge and discharge sample data, and perform multi-operating condition disturbance combination to generate multi-operating condition disturbance pulse excitation sequence data;

[0172] The current anomaly diagnosis module is used to simulate the disturbance magnetic field of the virtual current field data of the battery pack through the multi-operating condition disturbance pulse excitation sequence data to generate multi-operating condition simulation magnetic disturbance matrix data; to test the dynamic current excitation of the battery pack through the multi-operating condition disturbance pulse excitation sequence data to obtain the operating condition test magnetic disturbance response data; to analyze the magnetic field distribution characteristic offset according to the multi-operating condition simulation magnetic disturbance matrix data and the operating condition test magnetic disturbance response data, and to match the single battery current distribution anomaly to obtain the current abnormal single battery identification data;

[0173] The BMS performance evaluation module is used to process the single-cell pulse current command of the current abnormal single-cell identification data based on the multi-operating condition disturbance pulse excitation sequence data, and collect the battery pack BMS monitoring data to obtain the BMS monitoring current excitation response data; the BMS current distribution monitoring performance is evaluated on the BMS monitoring current excitation response data through the operating condition test magnetic disturbance response data, and the BMS current monitoring performance evaluation data is generated.

[0174] The present application is to obtain the battery pack CAD design drawing data and construct an accurate three-dimensional model to generate virtual current field data, so that the test model is highly consistent with the actual battery pack. This step not only improves the accuracy of the test, but also reduces the need for physical prototypes through the use of virtual models, reducing the cost and cycle of testing. At the same time, the real vehicle operating condition charging and discharging sample data is collected, and adaptive current excitation sequence processing and multi-operating condition disturbance combination are performed. The generated multi-operating condition disturbance pulse excitation sequence data can truly reflect the current changes of the battery under various driving conditions, overcoming the limitation that traditional static or quasi-static tests cannot simulate dynamic current changes, thereby significantly improving the authenticity and accuracy of the test results. The multi-operating condition disturbance pulse excitation sequence data is used to perform dynamic current excitation on the virtual current field of the battery pack and the actual battery pack to simulate the violent fluctuations of the battery current during actual use. Through disturbance magnetic field simulation and actual testing, the magnetic disturbance response data of the working condition test and the BMS monitoring current excitation response data are collected, and the magnetic field distribution characteristic offset analysis and single cell current distribution abnormal matching are performed, which can accurately evaluate the response speed, accuracy and stability of the BMS to the current distribution change under dynamic conditions, and realize the dynamic evaluation of the BMS current distribution monitoring performance. By marking the single cell with abnormal current and processing the single cell pulse current instruction, the monitoring and protection capabilities of the BMS for the abnormal current of the single cell can be further verified. This multi-condition and multi-angle test method effectively improves the reliability of the test and can comprehensively evaluate the current distribution monitoring performance of the BMS under various working conditions. Testing based on the virtual current field model can avoid frequent physical testing, greatly shorten the test cycle, and improve the test efficiency. At the same time, the virtual current field model and the multi-condition disturbance pulse excitation sequence data can be reused to ensure the repeatability of the test and facilitate the performance comparison and optimization of different BMS products. Potential problems can be discovered in advance through computer simulation, reducing the risks and costs in actual testing.

[0175] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0176] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for testing the BMS function of a new energy vehicle, characterized in that: The following steps are involved: Step S1: Obtaining battery pack CAD design drawing data; Construct a three-dimensional model of the battery pack according to the CAD design drawing data of the battery pack to generate the battery pack structure model data; use the battery pack structure model data to perform virtual current element processing to generate the battery pack virtual current field data; Step S2: collecting vehicle operating condition charge and discharge sample data; performing adaptive current excitation sequence processing according to the vehicle operating condition charge and discharge sample data, and performing multi-operating condition disturbance combination to generate multi-operating condition disturbance pulse excitation sequence data; Step S3: Performing disturbance magnetic field simulation on the virtual current field data of the battery pack through multi-operating condition disturbance pulse excitation sequence data to generate multi-operating condition simulation magnetic disturbance matrix data; The dynamic current excitation of the test battery pack is applied through the multi-operating condition disturbance pulse excitation sequence data to obtain the operating condition test magnetic disturbance response data; the magnetic field distribution characteristic offset is analyzed based on the multi-operating condition simulation magnetic disturbance matrix data and the operating condition test magnetic disturbance response data, and the single battery current distribution anomaly is matched to obtain the current abnormal single battery identification data; Step S4: Based on the multi-operating condition disturbance pulse excitation sequence data, the current abnormal single battery identification data is processed with a single battery pulse current instruction, and the battery pack BMS monitoring data is collected to obtain BMS monitoring current excitation response data; the BMS current distribution monitoring performance of the BMS monitoring current excitation response data is evaluated through the operating condition test magnetic disturbance response data to generate BMS current monitoring performance evaluation data.

2. The method for testing the BMS function of a new energy vehicle according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire battery pack CAD design drawing data; Step S12: extracting battery pack structural parameters according to the battery pack CAD design drawing data to generate battery pack structural parameter data, wherein the battery pack structural parameter data includes single cells, connecting pieces, and internal circuit structure of the battery pack; Step S13: constructing a three-dimensional model of the battery pack based on the single battery, the connecting piece and the internal circuit structure of the battery pack to generate battery pack structural model data; Step S14: Calculating the theoretical current value of the single battery based on the preset battery pack working state parameters and the battery pack structure model data to generate the single battery theoretical current value data; Step S15: performing virtual current element processing on the battery pack structure model data according to the internal circuit structure of the battery pack and the theoretical current value data of the single battery to obtain virtual current element parameters; Step S16: Perform virtual current field processing on the battery pack structure model data using virtual current element parameters to generate battery pack virtual current field data.

3. The method for testing the BMS function of a new energy vehicle according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: extracting battery characteristic parameters of a single battery using a preset battery single battery characteristic database, and obtaining a battery single battery internal resistance parameter and a battery single battery capacity parameter; Step S152: performing Monte Carlo normal distribution assignment according to the battery cell internal resistance parameter and the battery cell capacity parameter, and obtaining random internal resistance value data of the single cell and random capacity value data of the single cell respectively; Step S153: using the single cell theoretical current value data to calculate the single cell terminal voltage of the single cell random internal resistance value data and the single cell random capacity value data, to generate the single cell terminal voltage data; Step S154: calculating the connection sheet resistance value according to the battery pack structure model data to generate connection sheet resistance value data; Step S155: establishing a circuit equation group through the internal circuit structure of the battery pack, and solving the single cell current value and the connecting piece current value by using the single cell terminal voltage data and the connecting piece resistance value data, and obtaining the actual single cell current value and the connecting piece current value respectively; Step S156: Setting a virtual current element for the battery pack structure model data according to the actual current value of the single battery and the current value of the connecting piece to obtain virtual current element parameters.

4. The method for testing the BMS function for new energy vehicles according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: collecting vehicle operating condition charge and discharge sample data through preset vehicle operating condition type data to obtain vehicle operating condition charge and discharge sample data; Step S22: Processing the battery pack charge and discharge current change curve on the vehicle operating condition charge and discharge sample data to generate operating condition current curve data; Step S23: Calculating the local excitation current intensity of the battery pack structure model data based on the operating current curve data to generate local excitation parameter data; Step S24: performing adaptive pulse current excitation sequence processing according to the local excitation parameter data, thereby obtaining initial pulse excitation sequence data; Step S25: using a preset battery pack disturbance factor database to select a random disturbance factor to obtain random disturbance factor data; Step S26: applying a disturbance rule to the initial pulse excitation sequence data through random disturbance factor data, and performing multi-operating condition disturbance combination to generate multi-operating condition disturbance pulse excitation sequence data.

5. The method for testing the BMS function for new energy vehicles according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing thermal simulation analysis of the battery pack according to the virtual current field data of the battery pack, identifying potential thermal risk areas, and obtaining distribution data of potential thermal risk areas; Step S32: determining the deployment position of the Hall effect sensor based on the battery pack structure model data by using the potential thermal risk area distribution data to generate sensor deployment position data; Step S33: using the sensor deployment position data to test the battery pack Hall effect sensor array deployment to obtain a Hall effect sensor array; Step S34: performing disturbance magnetic field simulation on the virtual current field data of the battery pack through the multi-operating condition disturbance pulse excitation sequence data to generate multi-operating condition simulation magnetic disturbance matrix data; Step S35: Initializing the battery pack test environment through the preset battery test state data, applying dynamic current excitation through the multi-operating condition disturbance pulse excitation sequence data, and collecting the internal magnetic field response data of the battery pack through the Hall effect sensor array to obtain the operating condition test magnetic disturbance response data; Step S36: Analyze the magnetic field distribution characteristic offset according to the multi-operating condition simulation magnetic disturbance matrix data and the operating condition test magnetic disturbance response data to generate dynamic magnetic field characteristic offset data; match the single-cell current distribution anomaly with the dynamic magnetic field characteristic offset data through the battery pack structure model data to obtain current abnormal single-cell identification data.

6. The method for testing the BMS function for new energy vehicles according to claim 5, characterized in that: Step S34 includes the following steps: Step S341: constructing a finite element electromagnetic field simulation model according to the battery pack virtual current field data and the battery pack structure model data to obtain an electromagnetic field virtual simulation model; Step S342: mapping the Hall effect monitoring points of the electromagnetic field virtual simulation model through the sensor deployment position data, and setting finite element parameters to obtain finite element analysis parameters; Step S343: performing single-operating-condition pulse excitation signal selection on the multi-operating-condition disturbance pulse excitation sequence data to obtain single-operating-condition pulse excitation signal data; Step S344: simulating the magnetic field distribution inside the battery pack using the electromagnetic field virtual simulation model through the single-operating pulse excitation signal data and the finite element analysis parameters to generate simulated battery pack magnetic field distribution data; Step S345: performing excitation signal sequence traversal on the multi-operating condition disturbance pulse excitation sequence data, and repeating step S344, so as to obtain multiple sets of single-operating condition disturbance magnetic field distribution data; Step S346: extract the vertical magnetic field component at the Hall sensor position according to multiple groups of single-operating-condition disturbance magnetic field distribution data, and generate multi-operating-condition simulated magnetic disturbance matrix data.

7. The method for testing the BMS function for new energy vehicles according to claim 5, characterized in that: Step S36 includes the following steps: Step S361: extracting magnetic field response features from the working condition test magnetic disturbance response data to obtain standard magnetic disturbance response matrix data; Step S362: Correlating the multi-operating condition simulation magnetic disturbance matrix data with the magnetic disturbance of the same operating condition through the standard magnetic disturbance response matrix data, and calculating the correlation coefficient to obtain the mutual information correlation coefficient; Step S363: Based on the mutual information correlation coefficient, the standard magnetic disturbance response matrix data and the multi-operating condition simulated magnetic disturbance matrix data are screened for Hall monitoring spatiotemporal magnetic field anomalies, and magnetic disturbance difference characteristics are calculated to generate dynamic magnetic field characteristic offset data; Step S364: matching the sensor monitoring position according to the dynamic magnetic field characteristic offset data, and identifying the single battery with abnormal current distribution through the battery pack structure model data to obtain the identification data of the single battery with abnormal current distribution.

8. The method for testing the BMS function for new energy vehicles according to claim 7, characterized in that: Step S361 includes the following steps: Filtering the working condition test magnetic disturbance response data and amplifying the magnetic field strength to generate amplified working condition magnetic disturbance response data; Arrange the time-space magnetic field response data according to the magnetic disturbance response data of the amplified working condition to obtain the time-space magnetic disturbance response data; Extracting the magnetic field signal spectrum characteristics from the magnetic disturbance response data under time and space conditions to obtain the magnetic disturbance response spectrum characteristic data; The spatial variation rate of magnetic field intensity is calculated for the magnetic disturbance response data under time and space conditions to obtain the spatial variation data of magnetic field intensity; According to the spatial variation data of magnetic field intensity and the characteristic data of magnetic disturbance response spectrum, current density inversion is performed to obtain the response current density distribution data; Perform current density standard deviation processing on the response current density distribution data to obtain the response current uniformity index data; The magnetic disturbance response spectrum characteristic data, the magnetic field intensity spatial variation data and the response current uniformity index data are processed into multi-dimensional magnetic field response characteristic vectors to generate standard magnetic disturbance response matrix data.

9. The method for testing the BMS function for new energy vehicles according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing single-cell pulse current instruction processing on current abnormal single-cell identification data based on multi-operating condition disturbance pulse excitation sequence data, and collecting battery pack BMS monitoring data, thereby obtaining BMS monitoring current excitation response data; Step S42: calculating the current abnormality monitoring response time according to the BMS monitoring current excitation response data to obtain abnormality monitoring response time data; Step S43: Counting the false alarm rate and missed alarm rate of the monitoring function by monitoring the current excitation response data and the magnetic disturbance response data of the working condition test through the BMS, and obtaining the false alarm rate and missed alarm rate data; Step S44: performing multi-operating condition magnetic field response analysis on the operating condition test magnetic disturbance response data through the BMS monitoring current excitation response data to obtain operating condition adaptability data; Step S45: Perform BMS current distribution monitoring performance evaluation according to the abnormal monitoring response time data, the false alarm rate and missed alarm rate data, and the operating condition adaptability data to generate BMS current monitoring performance evaluation data.

10. A test system for the BMS function of new energy vehicles, characterized in that: For executing the test method of the BMS function for new energy vehicles as claimed in claim 1, the test system of the BMS function for new energy vehicles comprises: A virtual battery model building module is used to obtain the battery pack CAD design drawing data; construct a three-dimensional model of the battery pack according to the battery pack CAD design drawing data to generate the battery pack structure model data; use the battery pack structure model data to perform virtual current element processing to generate the battery pack virtual current field data; The excitation sequence generation module is used to collect vehicle operating condition charge and discharge sample data; perform adaptive current excitation sequence processing according to the vehicle operating condition charge and discharge sample data, and perform multi-operating condition disturbance combination to generate multi-operating condition disturbance pulse excitation sequence data; The current anomaly diagnosis module is used to simulate the disturbance magnetic field of the virtual current field data of the battery pack through the multi-operating condition disturbance pulse excitation sequence data to generate multi-operating condition simulation magnetic disturbance matrix data; to apply the dynamic current excitation of the test battery pack through the multi-operating condition disturbance pulse excitation sequence data to obtain the operating condition test magnetic disturbance response data; to analyze the magnetic field distribution characteristic offset according to the multi-operating condition simulation magnetic disturbance matrix data and the operating condition test magnetic disturbance response data, and to match the single battery current distribution anomaly to obtain the current abnormal single battery identification data; The BMS performance evaluation module is used to process the single-cell pulse current command of the current abnormal single-cell identification data based on the multi-operating condition disturbance pulse excitation sequence data, and collect the battery pack BMS monitoring data to obtain the BMS monitoring current excitation response data; the BMS current distribution monitoring performance is evaluated on the BMS monitoring current excitation response data through the operating condition test magnetic disturbance response data, and the BMS current monitoring performance evaluation data is generated.

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