Method, system, product and medium for testing direct-current charging performance of electric vehicle through built-in battery

Abstract

The invention provides a method, a system, a product and a medium for testing the direct current charging performance of an electric vehicle through a built-in battery, and relates to the technical field of electric vehicle charging piles. The specification information of the battery is obtained, the specification information comprises basic parameters such as the battery type, the capacity and the charge-discharge cycle number, and the parameters reflect the inherent characteristics of the battery. And meanwhile, the historical use data of the vehicle, including the mileage, the average speed and the charging frequency, are obtained, and the data reflect the actual use condition of the battery. And the system inputs the two types of data into a prediction model to generate a performance feature vector capable of representing the current state of the battery. By performing similarity matching on the vector and a plurality of groups of preset feature vectors, the system can find the closest battery working state reference. The target charging curve is adjusted based on the matching result, and the adaptability of the charging strategy and the actual performance of the battery is improved.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicle charging piles, and in particular to a method, system, product and medium for testing the DC charging performance of an electric vehicle with a built-in battery. Background Art

[0002] With the rapid growth of the electric vehicle market, DC charging has become the mainstream charging method due to its advantages of high power and short time. Since the DC charging process involves high-power power transmission, its safety and stability are directly related to the safety of vehicles and users, which makes the performance testing of charging equipment a key link in the development of the industry.

[0003] At present, the test of electric vehicle charging equipment mainly adopts the resistive simulated load solution. This solution simulates the vehicle charging process by setting a predetermined resistance sequence. The test equipment gradually adjusts the load resistance according to the preset time interval, and records the current, voltage and other parameters at each time point, and finally generates a test report.

[0004] However, in actual applications, the battery charging process is accompanied by complex phenomena such as temperature rise and internal resistance changes, which will cause dynamic adjustment of charging characteristics. The resistive analog load uses a preset fixed resistance sequence and cannot respond to these dynamic changes in a timely manner, making the test process unable to truly reflect the actual working status of the vehicle during charging, reducing the practicality of the test results.

[0005] Therefore, the current charging equipment performance test results are poorly consistent with actual usage conditions. Summary of the invention

[0006] The present application provides a method, system, product and medium for testing the DC charging performance of an electric vehicle with its own battery, which is used to improve the conformity of the charging equipment performance test results with the actual usage conditions.

[0007] In a first aspect, the present application provides a method for testing the DC charging performance of an electric vehicle with a built-in battery, comprising: receiving a target charging curve input by a test terminal, the target charging curve being a curve with a preset change pattern over time; identifying the model of the built-in battery, determining a corresponding preset mode according to the model, and adjusting the target charging curve according to the preset mode; upon receiving a charging request sent by the built-in battery, controlling a charging station to charge the built-in battery according to the adjusted target charging curve; simultaneously collecting current intensity and voltage level at different time points; during the collection process, if data abnormality is detected, controlling the charging station to interrupt the charging of the built-in battery; when it is determined that the power of the built-in battery has reached a preset threshold, controlling the charging station to interrupt the charging of the built-in battery, and generating analysis report information based on the current intensity and the voltage level.

[0008] By adopting the above technical solution, the specification information of the battery is obtained, including basic parameters such as battery type, capacity and number of charge and discharge cycles, which reflect the inherent characteristics of the battery. At the same time, the vehicle's usage history data is obtained, including mileage, average speed and charging frequency, which reflect the actual usage status of the battery. The system inputs these two types of data into the prediction model to generate a performance feature vector that can characterize the current state of the battery. By matching the vector with multiple sets of preset feature vectors for similarity, the system can find the closest reference to the battery's working state. Based on this matching result, the target charging curve is adjusted to improve the adaptability of the charging strategy to the actual performance of the battery.

[0009] In combination with some embodiments of the first aspect, in some embodiments, the steps of identifying the model of the built-in battery, determining the corresponding preset mode according to the model, and adjusting the target charging curve according to the preset mode specifically include: obtaining specification information of the built-in battery, the specification information including battery type, battery capacity, and number of charge and discharge cycles of the battery; obtaining usage history data of the vehicle corresponding to the built-in battery, the usage history data including past mileage, average driving speed, and charging frequency; using the specification information of the built-in battery and the usage history data of the vehicle through a prediction model to generate a battery performance feature vector; performing similarity matching on the battery performance feature vector with multiple sets of preset battery performance feature vectors; and adjusting the target charging curve based on the matching results.

[0010] By adopting the above technical solution, the target vector with the highest matching degree is firstly selected from the preset multiple groups of feature vectors, and the corresponding standard charging curve is obtained as the optimization benchmark. On this basis, the battery is graded according to the number of charge and discharge cycles: for batteries whose cycle times exceed the threshold, the risk is prevented by reducing the starting current to the protection threshold; for batteries whose cycle times are below the threshold, the starting current is adjusted to the standard curve level. By calculating the capacity ratio coefficient, the parameter conversion between batteries of different types and capacities is realized. Finally, according to the adjusted current extreme value and battery capacity, the time of each charging stage is optimized to form a complete adaptive charging curve.

[0011] In combination with some embodiments of the first aspect, in some embodiments, the step of adjusting the target charging curve according to the matching result specifically includes: selecting a target battery performance characteristic vector having the highest matching degree with the battery performance characteristic vector from multiple groups of battery performance characteristic vectors; obtaining a standard charging curve corresponding to the target battery performance characteristic vector; when the number of charge and discharge cycles of the built-in battery is greater than a state threshold, reducing the starting current in the target charging curve to a protection current threshold; when the number of charge and discharge cycles of the built-in battery is less than or equal to the state threshold, adjusting the starting current in the target charging curve to the standard charging curve. The starting current in the curve; the first capacity ratio is divided by the second capacity ratio to obtain a capacity ratio coefficient, the first capacity ratio is the ratio of the battery capacity of the built-in battery to its corresponding first nominal capacity, the first nominal capacity is determined by the battery type of the built-in battery, the second capacity ratio is the ratio of the battery capacity of the battery corresponding to the standard charging curve to its corresponding second nominal capacity, the second nominal capacity is determined by the battery type of the battery corresponding to the standard charging curve; the current extreme value of each stage in the target charging curve is adjusted according to the capacity ratio coefficient; the time of the stage in the target charging curve is adjusted according to the current extreme value and the battery capacity.

[0012] By adopting the above technical solution, the target vector with the highest matching degree is firstly selected from the preset multiple groups of feature vectors, and the corresponding standard charging curve is obtained as the optimization benchmark. On this basis, the battery is graded according to the number of charge and discharge cycles: for batteries whose cycle times exceed the threshold, the risk is prevented by reducing the starting current to the protection threshold; for batteries whose cycle times are below the threshold, the starting current is adjusted to the standard curve level. The system also realizes the parameter conversion between batteries of different types and capacities by calculating the capacity ratio coefficient. Finally, according to the adjusted current extreme value and battery capacity, the time of each charging stage is optimized to form a complete adaptive charging curve.

[0013] In combination with some embodiments of the first aspect, in some embodiments, after the step of controlling the charging station to interrupt the charging of the built-in battery when data abnormality is detected during the collection process, the method also includes: determining the current intensity and voltage level at different time points as the main test trajectory, and determining the corresponding ideal current intensity and voltage level as the backup test trajectory; obtaining the main test trajectory data and the backup test trajectory data at the moment of interruption, and calculating the deviation value; determining the transition level according to the deviation value; selecting the corresponding transition curve type according to the transition level; connecting the main test trajectory data at the moment of interruption with the backup test trajectory data at the final moment using the transition curve type to generate a transition charging curve.

[0014] By adopting the above technical solution, when a charging interruption occurs, the actual collected current intensity and voltage level data are determined as the main test track, and the ideal parameters are determined as the backup test track. By comparing the two sets of trajectory data at the moment of interruption, the specific deviation value is calculated, and the transition level is determined accordingly. According to the determined transition level, the corresponding type of transition curve is selected to achieve a smooth transition from the main test track to the backup test track. This dynamic transition mechanism based on actual deviations and this fusion method not only retains the actual performance in the actual test, but also fully presents the charging process through a reliable data completion solution.

[0015] In combination with some embodiments of the first aspect, in some embodiments, the step of selecting the corresponding transition curve type according to the transition level specifically includes: when the transition level is a small deviation level, selecting a linear gradient curve; when the transition level is a medium deviation level, selecting a quadratic curve; when the transition level is a large deviation level, selecting an S-shaped curve.

[0016] By adopting the above technical solution, different transition curves are selected according to the degree of deviation: a linear gradient curve is used for small deviation levels to ensure smooth parameter transition; a quadratic curve is used for medium deviation levels to provide more flexible adjustment space; an S-shaped curve is selected for large deviation levels to achieve gradual changes in parameters. This differentiated curve selection mechanism matches the deviation value, and the most appropriate transition method can be selected according to the actual situation at the time of interruption to ensure the reliability of the test.

[0017] In combination with some embodiments of the first aspect, in some embodiments, after the step of connecting the main test trajectory data at the interruption moment with the backup test trajectory data at the final moment using a transition curve type to generate a transition charging curve, the method also includes: connecting the main test trajectory data from the start moment to the interruption moment with the transition charging curve to obtain result data; and the step of generating analysis report information based on the current intensity and the voltage level specifically includes: generating analysis report information based on the result data.

[0018] By adopting the above technical solution, the main test trajectory data collected before the interruption is connected and processed with the generated transition charging curve to form a complete test data sequence. This data processing mechanism can retain the valid test data before the interruption and achieve a natural connection with the subsequent data through the transition curve. Finally, an analysis report is generated based on the complete result data, providing continuous and reliable data support for charging performance evaluation.

[0019] In combination with some embodiments of the first aspect, in some embodiments, during the collection process, if data abnormality is detected, after the step of controlling the charging station to interrupt the charging of the built-in battery, the method also includes: determining the current intensity and voltage level at the moment of interruption as the upper limit threshold; adjusting the target charging curve according to the upper limit threshold; adjusting the time of the stage in the target charging curve according to the adjusted target charging curve.

[0020] By adopting the above technical solution, the current intensity and voltage level at the time of interruption are used as the upper threshold to establish the safety boundary of the charging parameters. Based on this boundary condition, the target charging curve is readjusted and the time parameters of each charging stage are updated accordingly. This adaptive adjustment mechanism based on actual interruption data not only ensures the safety of the subsequent charging process, but also ensures the integrity of the test through dynamic adjustment of the charging time.

[0021] In a second aspect, the present application provides a system for testing the DC charging performance of an electric vehicle with a built-in battery, the system comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, the one or more processors call the computer instructions to enable the system for testing the DC charging performance of an electric vehicle with a built-in battery to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a third aspect, the present application provides a computer program product comprising instructions. When the computer program product is run on a system for testing the DC charging performance of an electric vehicle with its own battery, the system for testing the DC charging performance of an electric vehicle with its own battery executes the method described in the first aspect and any possible implementation method of the first aspect.

[0023] In a fourth aspect, the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on a system for testing the DC charging performance of an electric vehicle with its own battery, the system for testing the DC charging performance of an electric vehicle with its own battery executes the method described in the first aspect and any possible implementation of the first aspect.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Obtain the battery's specification information, including basic parameters such as battery type, capacity, and number of charge and discharge cycles, which reflect the inherent characteristics of the battery. At the same time, obtain the vehicle's usage history data, including mileage, average speed, and charging frequency, which reflect the actual usage of the battery. The system inputs these two types of data into the prediction model to generate a performance feature vector that can characterize the current state of the battery. By matching the vector with multiple sets of preset feature vectors for similarity, the system can find the closest reference to the battery's working state. Based on this matching result, the target charging curve is adjusted to improve the adaptability of the charging strategy to the actual performance of the battery.

[0025] 2. First, select the target vector with the highest matching degree from the preset multiple sets of feature vectors, and obtain its corresponding standard charging curve as the optimization benchmark. On this basis, the battery is graded according to the number of charge and discharge cycles: for batteries whose cycle times exceed the threshold, the risk is prevented by reducing the starting current to the protection threshold; for batteries whose cycle times are below the threshold, the starting current is adjusted to the standard curve level. By calculating the capacity ratio coefficient, parameter conversion between batteries of different types and capacities is realized. Finally, according to the adjusted current extreme value and battery capacity, the time of each charging stage is optimized to form a complete adaptive charging curve.

[0026] 3. When a charging interruption occurs, the actual collected current intensity and voltage level data are determined as the main test track, and the ideal parameters are determined as the backup test track. By comparing the two sets of track data at the time of interruption, the specific deviation value is calculated, and the transition level is determined accordingly. According to the determined transition level, the corresponding type of transition curve is selected to achieve a smooth transition from the main test track to the backup test track. This dynamic transition mechanism based on actual deviations and this fusion method not only retains the actual performance in the actual test, but also fully presents the charging process through a reliable data completion solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of a method for testing the DC charging performance of an electric vehicle with a self-contained battery according to an embodiment of the present application; Figure 2 This is another flow chart of a method for testing the DC charging performance of an electric vehicle with a self-contained battery according to an embodiment of the present application; Figure 3 yes Figure 2 A schematic diagram of a specific process of step S205 in the embodiment; Figure 4 This is another flow chart of a method for testing the DC charging performance of an electric vehicle with a self-contained battery according to an embodiment of the present application; Figure 5It is a schematic diagram of an exemplary hardware structure of a system for testing the DC charging performance of an electric vehicle with its own battery in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] See also Figure 1 , Figure 1 It is a flow chart of a method for testing the DC charging performance of an electric vehicle with a self-contained battery according to an embodiment of the present application; A method for testing the DC charging performance of an electric vehicle using a self-contained battery, comprising: S101, receiving a target charging curve input by a test terminal, where the target charging curve is a curve with a predetermined change rule over time; The test terminal refers to the operating equipment used to perform the electric vehicle charging test, which can be a dedicated test device or a computer terminal with test functions. The target charging curve refers to the regular curve of the pre-planned charging parameters changing over time, which is used to guide the charging process. The changing pattern refers to the preset change mode of charging parameters such as current and voltage in different charging stages, including constant current charging stage, constant voltage charging stage, etc.

[0031] In some embodiments, the target charging curve input or selected by the operator is first received through the human-computer interaction interface of the test terminal. The curve includes a complete charging process parameter plan, defines the charging current and voltage values ​​that should be used at different time points, and the duration and conversion conditions of each charging stage.

[0032] In some preferred embodiments, the top five mainstream electric vehicle models in terms of market share are selected as test objects, which are highly representative. The test samples must meet the following characteristic requirements: the battery capacity is distributed in the range of 30-100kWh, covering different capacity levels; the depth of discharge (DOD) should be controlled in the range of 20%-80% to avoid the impact of deep discharge on the test; the battery management system (BMS) must support standard communication protocols to ensure the reliability of data collection.

[0033] Charging piles should strictly follow the GB / T27930-2015 standard specifications. Specific requirements include: output voltage range 200-750V, maximum output power not less than 120kW; the communication interface must support CAN bus and Ethernet dual modes; have complete safety protection functions, including overcurrent, overvoltage, short circuit and other multiple protections; be equipped with a standard charging gun connector to ensure reliable docking with the test vehicle.

[0034] Measuring instruments should be professional-grade products, and the core indicators include: voltage measurement accuracy better than ±0.5%, current measurement accuracy better than ±0.5%, sampling frequency not less than 10kHz; real-time data storage function, storage capacity not less than 32GB; support multi-channel synchronous acquisition, and can simultaneously monitor parameters such as voltage, current, and temperature; equipped with a high-precision clock source to ensure the accuracy of data timestamps.

[0035] The software system needs to have the following functional modules: Parameter configuration interface: supports charging strategy setting, protection threshold adjustment, sampling parameter configuration, etc.

[0036] Test control module: provides basic operation functions such as test start, pause, and termination.

[0037] Data monitoring panel: real-time display of charging curve, key parameters, alarm information, etc.

[0038] Report generation system: automatically organizes test data and generates analysis reports in a standard format.

[0039] S102, identifying the model of the built-in battery, determining a corresponding preset mode according to the model, and adjusting the target charging curve according to the preset mode; The battery model refers to the coding information that can uniquely identify the battery specifications and characteristics. The preset mode refers to the charging parameter adjustment strategy pre-configured according to the battery model.

[0040] After completing the input of the target charging curve, it is necessary to adjust the parameters according to the characteristics of the actual test battery. In some embodiments, the system first reads the battery identification information through a communication interface or a scanning device to obtain its model information. Based on the model information, the system matches the corresponding adjustment strategy from the preset pattern library, which contains the parameter correction rules for this type of battery. Finally, the system optimizes the various parameters of the target charging curve based on the selected adjustment strategy to make it more consistent with the actual characteristics of the battery to be tested.

[0041] In some specific embodiments, the system first obtains key battery information through BMS communication, including battery manufacturer code, battery pack model identification, chemical system type (such as lithium iron phosphate, ternary lithium, etc.), nominal capacity and rated voltage. Based on the identified battery model, the system matches the corresponding charging parameter template from the preset mode library, including dedicated charging curves of different manufacturers, standard charging strategies of different chemical systems, charging parameter configurations of different capacity levels, and the optimal charging mode recommended by the manufacturer. The preset mode specifically includes key parameters such as charging current value setting for each stage, stage transition point voltage definition, charging cutoff condition setting, and protection threshold parameter configuration.

[0042] According to the selected preset mode, the system makes targeted adjustments to the charging curve: in the constant current charging stage, the charging current is set according to the standard value of the battery model, the maximum charging rate is determined according to the manufacturer's recommendations, and the current climbing rate is set to meet the characteristics of the model; in the constant voltage charging stage, the charging cut-off voltage specified by the model is adopted, the conversion point voltage is set according to the manufacturer's requirements, and the current drop curve that meets the model is configured; at the same time, the protection parameters are adjusted, including using the overvoltage protection threshold of the model, configuring the corresponding overcurrent protection limit, and setting the temperature protection range of the model. This precise adjustment method based on the specific battery model can ensure that the charging parameters meet the manufacturer's specifications, give full play to the optimal performance of batteries of different models, ensure charging safety and reliability, extend the service life of specific battery models, and avoid the incompatibility problems that may be caused by general charging strategies.

[0043] S103, when receiving a charging request sent by the built-in battery, controlling the charging station to charge the built-in battery according to the adjusted target charging curve; and collecting current intensity and voltage level at different time points; After the charging curve adjustment is completed, the actual charging test phase begins. In some embodiments, when the battery terminal sends a charging request, the system responds to the charging request and controls the charging station to perform charging operations according to the adjusted target charging curve through control instructions, and at the same time starts the data acquisition system to monitor various parameters in the charging process in real time. The system will record the specific values ​​of current intensity and voltage level within a preset time interval to form a complete test data sequence. These data will be used for subsequent analysis and evaluation.

[0044] S104, in the process of collecting data, if abnormal data is detected, control the charging station to interrupt the charging of the built-in battery; The data anomaly refers to a warning signal issued when an abnormal condition is detected. The charging interruption refers to a control action of immediately stopping the charging operation.

[0045] During the charging test, the safety status needs to be continuously monitored. In some embodiments, if the system finds that 1. the voltage of the single cell is too high; 2. the voltage of the single cell is too low; 3. the battery data is abnormal and the SOC data is abnormally too low; 4. the battery data is abnormal and the SOC data is abnormally too high; 5. the battery is overcharged; 6. the battery temperature is too high; 7. the insulation state is abnormal; 8. the connector state is abnormal; 9. the BMS data abnormality is actively terminated; 10. the BMS data abnormality is abnormally terminated; 11. the high-voltage relay is abnormal; 12. the detection point voltage is abnormal; 13. other faults are terminated, the system will immediately send an interrupt command to the charging station to stop the charging operation and ensure the safety of the test. At the same time, the system will record the values ​​of various parameters at the time of interruption to provide a basis for subsequent analysis.

[0046] S105: When it is determined that the power level of the built-in battery reaches a preset threshold, the charging station is controlled to interrupt the charging of the built-in battery, and analysis report information is generated according to the current intensity and the voltage level.

[0047] The preset threshold value refers to the upper limit of the battery charge amount set in advance. The analysis report information refers to the evaluation document generated based on the test data. The power refers to the state of charge of the battery.

[0048] In the final stage of the charging test, it is necessary to ensure the charge quantity control and data analysis. In some embodiments, the system continuously monitors the battery charge state, and when it detects that the charge reaches a preset threshold, the charging station is automatically controlled to stop the charging operation.

[0049] It should be noted that the technology related to generating reports based on data has been recorded and will not be repeated here.

[0050] Therefore, in some embodiments, after step S105, the following steps are further included: S106, determining the current intensity and voltage level at the interruption time as the upper limit threshold; The upper threshold value represents the maximum allowable value of the charging parameter adjustment. The current intensity and voltage level at the time of interruption reflect the actual working state of the battery before the interruption. The determination process refers to the operation of setting the parameter value at the time of interruption as a limiting condition.

[0051] In order to ensure the safety of the charging process, it is necessary to set parameter limits based on the state at the time of interruption. In some embodiments, the system uses the current intensity and voltage level measured at the time of interruption as the upper limit threshold of the subsequent charging process. These thresholds will be used to constrain the adjustment range of the charging parameters to avoid damage to the battery caused by sudden changes in parameters.

[0052] S107, adjusting the target charging curve according to the upper limit threshold; After determining the upper threshold, the charging curve needs to be adjusted accordingly. In some embodiments, the system compresses the parameters in the target charging curve that exceed the upper threshold to ensure that all charging parameters do not exceed the state value at the time of interruption, while maintaining the overall shape of the charging curve and the characteristics of each stage.

[0053] S108. Adjust the time of the stages in the target charging curve according to the adjusted target charging curve.

[0054] After the parameter adjustment is completed, the time allocation needs to be adjusted accordingly. In some embodiments, the system recalculates the time required for each charging stage based on the adjusted charging curve parameters. This adjustment ensures that the battery can still be fully charged under the new parameter constraints while maintaining a reasonable allocation of the charging amount in each stage.

[0055] It can be seen that the current intensity and voltage level at the time of interruption are used as the upper threshold to establish the safety boundary of the charging parameters. Based on this boundary condition, the target charging curve is readjusted and the time parameters of each charging stage are updated accordingly. This adaptive adjustment mechanism based on actual interruption data not only ensures the safety of the subsequent charging process, but also ensures the integrity of the test through dynamic adjustment of the charging time.

[0056] It can be seen that the battery specification information is obtained, including basic parameters such as battery type, capacity, and number of charge and discharge cycles, which reflect the inherent characteristics of the battery. At the same time, the vehicle usage history data is obtained, including mileage, average speed, and charging frequency, which reflect the actual usage status of the battery. The system inputs these two types of data into the prediction model to generate a performance feature vector that can characterize the current state of the battery. By matching the vector with multiple sets of preset feature vectors for similarity, the system can find the closest reference to the battery's working state. Based on this matching result, the target charging curve is adjusted to improve the adaptability of the charging strategy to the actual performance of the battery.

[0057] During the electric vehicle charging test, since the target charging curve is pre-set based on the ideal specification parameters, the actual battery used often has performance parameters that deviate from the ideal specifications due to various factors. This deviation makes the unadjusted target charging curve mismatched with the actual battery capacity: for example, if the charging power is too large, the battery temperature will rise rapidly, which will not only easily trigger the protection mechanism and cause the test to be interrupted, but may also accelerate the battery performance degradation and even bring safety hazards; if the charging power is too small, the test cycle will be extended, the test efficiency will be reduced, and the battery charging performance and safety margin cannot be truly reflected.

[0058] Therefore, in some embodiments, step S102 specifically includes: See also Figure 2 , Figure 2 This is another flow chart of a method for testing the DC charging performance of an electric vehicle with a self-contained battery according to an embodiment of the present application; S201, obtaining specification information of a built-in battery, the specification information including battery type, battery capacity, and number of charge and discharge cycles of the battery; The specification information refers to a set of parameters that describe the basic characteristics of the battery. The battery type refers to the chemical system of the battery, such as lithium-ion battery, lithium iron phosphate battery, etc. The battery capacity refers to the amount of electricity that the battery can store. The number of charge and discharge cycles refers to the cumulative number of complete charge and discharge cycles of the battery, which is used to characterize the degree of battery usage.

[0059] In some specific embodiments, the BMS data is read through the CAN communication protocol, the battery parameters are analyzed, the specification information is stored, and the parameter file is established; this is not limited here.

[0060] In some other specific embodiments, the vehicle connected to the vehicle and corresponding to the built-in battery is connected to the network and determined by the system of the vehicle.

[0061] S202, obtaining the usage history data of the vehicle corresponding to the self-contained battery, the usage history data including the past driving mileage, average driving speed, and charging frequency; Among them, historical data is used to represent a data set that records the actual operating status of the vehicle. Mileage refers to the total distance traveled by the vehicle. Average driving speed refers to the average operating speed of the vehicle over a period of time. Charging frequency refers to the number of times the vehicle is charged per unit time.

[0062] On the basis of obtaining the battery specification information, it is necessary to understand the working status of the battery in actual use. In some embodiments, the system obtains the historical operation data of the vehicle through the vehicle terminal or cloud platform, including the accumulated mileage data, speed statistics, and charging record data. These data reflect the vehicle's usage intensity and charging habits, which helps to evaluate the actual working status of the battery.

[0063] S203, using the specification information of the battery and the usage history data of the vehicle through a prediction model to generate a battery performance feature vector; In some embodiments, the system inputs the collected battery specification information and vehicle usage history data into a pre-trained prediction model. The model converts the multi-dimensional input information into a standardized battery performance feature vector through complex data analysis and processing, which is used to characterize the comprehensive performance status of the battery.

[0064] In some embodiments, during the preprocessing of battery specification information, the rated capacity is converted into standard units (Ah) and normalized, the rated voltage is converted into standard units (V) and normalized, the internal resistance value is standardized, the chemical type is converted into a one-hot encoding, and the manufacturer is converted into a numerical encoding; historical data preprocessing includes segmenting the charge and discharge data by time window (such as 24 hours), resampling the SOC curve to a fixed time interval, standardizing the temperature data, removing outliers, and smoothing the data.

[0065] Specification feature calculations include energy density (rated capacity × rated voltage / volume), power density (rated voltage² / (4 × internal resistance × volume)), chemical property coefficient (preset parameter table query based on chemical type); historical feature calculations include charge and discharge times (counting the number of complete charge and discharge cycles), average charge depth (Σ (single charge capacity) / rated capacity), temperature distribution characteristics (calculating the mean, variance, and quantile of temperature data), SOC usage range (counting the usage time proportion of each SOC range); decay index calculations include capacity decay rate ((initial capacity - current capacity) / initial capacity), internal resistance growth rate ((current internal resistance - initial internal resistance) / initial internal resistance), and charge acceptance rate (actual charged capacity / theoretical chargeable capacity).

[0066] Current status characteristics include actual capacity ratio (current capacity / rated capacity), internal resistance change rate (current internal resistance / nominal internal resistance), and temperature coefficient (performance influencing factor calculated based on temperature distribution); usage mode characteristics include charging regularity (calculated based on the variance of charging time intervals), usage intensity (average daily charge and discharge times), and temperature exposure index (cumulative exposure time to extreme temperatures); health status characteristics include remaining life coefficient (life prediction based on attenuation trend), performance degradation rate (speed of change of capacity attenuation rate), and abnormal risk index (calculated based on the frequency of abnormal events).

[0067] The input matrix construction includes the specification feature matrix "batch size, specification feature number" and the historical feature sequence "batch size, time step number, historical feature number"; the label data construction includes the actual value of the performance indicator (capacity, internal resistance and other measured data) and the state classification label (based on performance level division). The focus of this implementation process is to convert the original data into physically meaningful features, and obtain the feature vector that can reflect the performance of all aspects of the battery through reasonable calculation methods. The calculation of each feature has its specific physical or statistical basis to ensure that the generated feature vector can accurately express the performance status of the battery.

[0068] Batch size, number of specification features represents the specification feature matrix: Batch size: The number of battery samples contained in each batch of data during training, such as 64 or 128 batteries.

[0069] Number of specification features: The number of specification parameters contained in each battery, such as static features such as energy density, power density, and chemical characteristic coefficient. Assuming there are 15 specification features, the matrix shape is [64, 15].

[0070] Batch size, number of time steps, and number of historical features represent the historical feature sequence: Batch size: Same as above, number of battery samples per batch.

[0071] Time step number: The time length of historical data. For example, if you use data from the past 30 days, it would be 30.

[0072] Number of historical features: The number of features recorded at each time point, such as temperature, state of charge, charge and discharge current, and other dynamic features. Assuming there are 10 historical features, the matrix shape is [64, 30, 10].

[0073] These two matrices are input into the specification feature extraction layer and LSTM layer of the model to process static specification parameters and dynamic time series data respectively.

[0074] S204, performing similarity matching between the battery performance feature vector and a plurality of preset groups of battery performance feature vectors; The similarity matching refers to the process of calculating the similarity between feature vectors. The preset multiple sets of battery performance feature vectors refer to the standard feature vector library pre-stored by the system. The matching process refers to the operation of calculating and comparing the distance between feature vectors.

[0075] In some embodiments, the system compares the generated battery performance feature vector with a pre-established feature vector database, and evaluates the closeness of the current battery state to each preset state by calculating indicators such as the Euclidean distance or cosine similarity between the vectors, thereby finding the most matching reference state.

[0076] In some specific embodiments, the vector distance is calculated, the similarity values ​​are sorted, the best match is selected, and the matching result is output; in other specific embodiments, the category is determined by clustering analysis method, the center distance is calculated, and the nearest sample is selected. This is not limited here.

[0077] S205: Adjust the target charging curve according to the matching result.

[0078] In some specific embodiments, the adjustment coefficient is calculated through parameter mapping, the curve parameters are corrected, and a new curve is generated; in other specific embodiments, the interpolation algorithm is used to optimize the key nodes, adjust the curve shape, and update the charging strategy, which is not limited here.

[0079] It can be seen that first, the target vector with the highest matching degree is screened out from the preset multiple groups of feature vectors, and its corresponding standard charging curve is obtained as the optimization benchmark. On this basis, the battery is graded according to the number of charge and discharge cycles: for batteries whose cycle times exceed the threshold, the risk is prevented by reducing the starting current to the protection threshold; for batteries whose cycle times are below the threshold, the starting current is adjusted to the standard curve level. By calculating the capacity ratio coefficient, the parameter conversion between batteries of different types and capacities is realized. Finally, according to the adjusted current extreme value and battery capacity, the time of each charging stage is optimized to form a complete adaptive charging curve.

[0080] In some specific embodiments, step S205 specifically includes: See also Figure 3 , Figure 3 yes Figure 2 A schematic diagram of a specific process of step S205 in the embodiment; S2051, selecting a target battery performance feature vector having the highest matching degree with the battery performance feature vector from multiple groups of battery performance feature vectors; After obtaining the similarity calculation results, it is necessary to determine the best match. In some embodiments, the system sorts the matching degrees of all preset feature vectors and selects the vector with the highest matching degree as the target feature vector. This vector represents the standard working state that is closest to the current battery state and will serve as a reference for subsequent adjustments.

[0081] S2052, obtaining a standard charging curve corresponding to the target battery performance characteristic vector; In some embodiments, the system extracts the corresponding standard charging curve from the charging curve database according to the index of the target feature vector. The curve contains complete charging parameter settings and will serve as a basic template for subsequent adjustments.

[0082] S2053, when the number of charge and discharge cycles of the built-in battery is greater than the state threshold, reducing the starting current in the target charging curve to the protection current threshold; For batteries with a long service life, a protective charging strategy is required. In some embodiments, the system first compares the actual number of cycles of the battery with a preset state threshold. When the number of cycles exceeds the threshold, the system reduces the starting current of the charging curve to a safe level to protect the battery and extend its service life.

[0083] S2054: When the number of charge and discharge cycles of the built-in battery is less than or equal to the state threshold, adjust the starting current in the target charging curve to the starting current in the standard charging curve; For batteries with a shorter service life, a standard charging strategy can be used. In some embodiments, when the battery cycle count does not exceed the state threshold, the system will use the starting current value in the standard charging curve to achieve the best charging efficiency. This adjustment ensures that the battery obtains the best charging performance within a safe range.

[0084] S2055. Divide the first capacity ratio by the second capacity ratio to obtain a capacity ratio coefficient, wherein the first capacity ratio is a ratio of the battery capacity of the built-in battery to its corresponding first nominal capacity, the first nominal capacity being determined by the battery type of the built-in battery, and the second capacity ratio is a ratio of the battery capacity of the battery corresponding to the standard charging curve to its corresponding second nominal capacity, the second nominal capacity being determined by the battery type of the battery corresponding to the standard charging curve; The capacity ratio coefficient represents the conversion coefficient of the capacity characteristics of different types of batteries. The first capacity ratio represents the ratio of the actual capacity of the battery to be tested to the nominal capacity. The second capacity ratio represents the ratio of the actual capacity of the reference battery to the nominal capacity.

[0085] In order to accurately convert parameters between different types of batteries, it is necessary to establish a unified capacity comparison benchmark. In some embodiments, the system first determines the corresponding nominal capacity value according to the battery type, then calculates the capacity ratio of the battery to be tested and the reference battery respectively, and finally obtains the capacity ratio coefficient by dividing the two ratios. This coefficient will be used for the subsequent proportional adjustment of charging parameters.

[0086] S2056, adjusting the current extreme value of each stage in the target charging curve according to the capacity ratio coefficient; After completing the capacity conversion, the charging parameters need to be adjusted accordingly. In some embodiments, the system uses the calculated capacity ratio coefficient to proportionally adjust the current extremes at each charging stage in the charging curve. This adjustment ensures that the charging current matches the actual capacity of the battery, avoiding overcharging or undercharging.

[0087] S2057: Adjust the time of the stage in the target charging curve according to the current extreme value and the battery capacity.

[0088] To ensure the integrity of the charging process, the time of each stage needs to be adjusted. In some embodiments, the system recalculates the time required for each charging stage based on the adjusted current extreme value and the actual capacity of the battery. This adjustment ensures that the battery can be fully charged under the new charging parameters while maintaining a reasonable distribution of the charging amount in each stage.

[0089] In some specific embodiments, the charging time is estimated, the stage time is allocated, and the time parameters are optimized through a capacity calculation formula, which is not limited here.

[0090] It can be seen that first, the target vector with the highest matching degree is screened out from the preset multiple sets of feature vectors, and its corresponding standard charging curve is obtained as the optimization benchmark. On this basis, the battery is graded according to the number of charge and discharge cycles: for batteries whose cycle times exceed the threshold, the risk is prevented by reducing the starting current to the protection threshold; for batteries whose cycle times are below the threshold, the starting current is adjusted to the standard curve level. The system also realizes parameter conversion between batteries of different types and capacities by calculating the capacity ratio coefficient. Finally, according to the adjusted current extreme value and battery capacity, the time of each charging stage is optimized to form a complete adaptive charging curve.

[0091] When an abnormality is detected and the protection mechanism is triggered, there is a lack of effective recovery strategies and continuity guarantee mechanisms. Since complete test data is required to evaluate charging performance, test interruptions will result in discontinuous data, affecting the accuracy and reliability of the analysis results. The retesting method not only wastes time and resources, but also aggravates battery performance degradation due to repeated charging and discharging, resulting in distortion of subsequent test results and failure to accurately reflect the actual charging characteristics of the battery.

[0092] Therefore, in some embodiments, step S104 further includes: See also Figure 4 , Figure 4 This is another flow chart of a method for testing the DC charging performance of an electric vehicle with a self-contained battery according to an embodiment of the present application; S301, determining the current intensity and voltage level at different time points as the main test track, and determining the corresponding ideal current intensity and voltage level as the backup test track; The main test track represents the current and voltage data sequence collected during the actual test process. The backup test track represents the standard current and voltage data sequence during the ideal charging process. The ideal current intensity and voltage level refer to the expected standard charging parameter values.

[0093] S302, obtaining the main test track data and the backup test track data at the interruption time, and calculating the deviation value; The interruption time indicates the time point when the charging process is forced to stop. The main test track data refers to the actual parameter value at the interruption time. The backup test track data refers to the ideal parameter value at the same time. The deviation value indicates the degree of difference between the actual value and the ideal value.

[0094] When the charging process is interrupted, it is necessary to evaluate the difference between the actual state and the expected state. In some embodiments, the system extracts the main test track data at the time of interruption and the backup test track data at the corresponding time, and calculates the difference between the two sets of data to obtain the deviation value of current and voltage for the subsequent transition strategy selection.

[0095] S303, determining a transition level according to the deviation value; Based on the deviation calculation results, it is necessary to determine an appropriate transition processing strategy. In some embodiments, the system compares the calculated deviation value with a preset level threshold and determines the corresponding transition level according to the size of the deviation value. Different transition levels correspond to different transition processing methods to ensure a smooth transition of the charging process.

[0096] In some specific embodiments, a threshold comparison algorithm is used to divide the level intervals, determine the level, and determine the transition level, which is not limited here.

[0097] S304, selecting a corresponding transition curve type according to the transition level; After determining the transition level, it is necessary to select a suitable transition processing method. In some embodiments, the system selects a corresponding processing method from a preset transition curve type library according to the determined transition level. Different transition curve types have different smoothing characteristics and are suitable for different degrees of parameter transition requirements.

[0098] S305 , connecting the main test track data at the interruption time and the backup test track data at the final time using a transition curve type to generate a transition charging curve.

[0099] After completing the transition mode selection, a specific transition curve needs to be generated. In some embodiments, the system uses the selected transition curve type to smoothly connect the actual parameter value at the time of interruption with the target parameter value to generate a continuous transition charging curve. The transition charging curve is constructed by fusing two types of data: one is the real main test trajectory data up to the time when the protection action is triggered, and the other is the ideal backup trajectory data based on historical data fitting. This fusion method not only retains the actual performance in the actual test, but also fully presents the charging process through a reliable data completion solution. Although the main test data is incomplete due to the triggering of the protection mechanism, the complete charging characteristics can be reasonably inferred in combination with the verified backup trajectory data.

[0100] In some specific embodiments, step S305 specifically includes: S3051. When the transition level is a small deviation level, a linear gradient curve is selected; Among them, the small deviation level indicates that the actual parameter is slightly different from the ideal parameter. The linear gradient curve refers to the transition method in which the parameter changes at a fixed rate. Gradient represents the linear transition process of the parameter.

[0101] When the parameter deviation is small, a simple linear transition method can be used. In some embodiments, when the system determines that the transition level is a small deviation level, a linear gradient curve is selected as the transition method. This transition method achieves a uniform change of parameters by establishing a linear relationship between the starting point and the target point, and is suitable for a smooth transition with a small deviation.

[0102] In some specific embodiments, a uniform segmentation method is used to determine the change step size, perform linear progression, and form a transition sequence, which is not limited here.

[0103] S3052, when the transition level is a medium deviation level, select a quadratic curve; Among them, the medium deviation level indicates that there is a medium degree of difference between the actual parameters and the ideal parameters. The quadratic curve refers to the transition mode in which the parameters change according to the parabolic law. The quadratic relationship represents the nonlinear characteristics of parameter changes.

[0104] When the parameter deviation is at a medium level, a softer transition method is required. In some embodiments, when the system determines that the transition level is a medium deviation level, the coefficient parameters are determined by parabola fitting, the transition trajectory is calculated, and the curve shape is optimized, which is not limited here.

[0105] S3053. When the transition level is a large deviation level, select the S-curve.

[0106] Among them, the large deviation level indicates that the actual parameters are significantly different from the ideal parameters. The S-curve refers to the transition mode in which the parameter change presents the sigmoid characteristic. The S-shaped feature indicates the gradualness and smoothness of the parameter change.

[0107] When the parameter deviation is large, a more complex transition strategy needs to be adopted. In some embodiments, when the system determines that the transition level is a large deviation level, an S-curve is selected as the transition method. This transition method uses the sigmoid function characteristics to achieve a smooth process in which the parameters change slowly in the initial stage, transition quickly in the middle stage, and slow down again in the final stage. It is suitable for safe transition in large deviation situations.

[0108] In some specific embodiments, a sigmoid function is used to model, adjust curve parameters, calculate transition sequences, and optimize smoothing effects, which is not limited here.

[0109] In other specific embodiments, piecewise cubic splines are used to construct an S-shaped trajectory, ensure continuity, and generate a transition curve, which is not limited here.

[0110] It can be seen that different transition curves are selected according to the degree of deviation: a linear gradient curve is used for small deviation levels to ensure a smooth transition of parameters; a quadratic curve is used for medium deviation levels to provide more flexible adjustment space; an S-shaped curve is selected for large deviation levels to achieve a gradual change of parameters. This differentiated curve selection mechanism matches the deviation value, and the most appropriate transition method can be selected according to the actual situation at the time of interruption to ensure the reliability of the test.

[0111] It can be seen that when a charging interruption occurs, the actual collected current intensity and voltage level data are determined as the main test track, and the ideal parameters are determined as the backup test track. By comparing the two sets of trajectory data at the moment of interruption, the specific deviation value is calculated, and the transition level is determined accordingly. According to the determined transition level, the corresponding type of transition curve is selected to achieve a smooth transition from the main test track to the backup test track. This dynamic transition mechanism based on actual deviations and this fusion method not only retains the actual performance in the actual test, but also fully presents the charging process through a reliable data completion solution.

[0112] In some embodiments, after step S305, the method further includes: S306, connecting the main test trajectory data from the start time to the interruption time with the transition charging curve to obtain result data; In order to obtain complete charging process data, the data of each stage needs to be effectively connected. In some embodiments, the system sequentially connects the actual test data from the start of charging to the interruption with the generated transition charging curve data to form a complete charging process data sequence. This connection ensures the continuity and integrity of the charging process data.

[0113] Step S105 may be replaced by S307, generating analysis report information according to the result data.

[0114] It should be noted that the principle and process of this step are similar to those of step S105. The relevant principles and processes can be referred to step S105 and will not be repeated here.

[0115] It can be seen that the main test trajectory data collected before the interruption is connected and processed with the generated transition charging curve to form a complete test data sequence. This data processing mechanism can retain the valid test data before the interruption and achieve a natural connection with the subsequent data through the transition curve. Finally, an analysis report is generated based on the complete result data, providing continuous and reliable data support for charging performance evaluation.

[0116] It should be noted that once an abnormality occurs in the target charging curve, a chain reaction will be triggered: tests or repeated tests of other batteries will be matched based on this abnormal curve, thereby triggering frequent protection actions.

[0117] The following is an introduction to an exemplary system 500 for testing the DC charging performance of an electric vehicle with a built-in battery provided in an embodiment of the present application. Figure 5 It is a schematic diagram of an exemplary hardware structure of a system 500 for testing the DC charging performance of an electric vehicle with its own battery provided in an embodiment of the present application.

[0118] In some embodiments, the system 500 for testing the DC charging performance of an electric vehicle with a built-in battery is a computer device or the system 500 for testing the DC charging performance of an electric vehicle with a built-in battery includes a computer device. The computer device includes a processor, a memory, and a network interface connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with other external terminals or servers through a network connection. In some embodiments, the network interface can be a wired network interface, and in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, the method in the embodiment of the present application is implemented.

[0119] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0120] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0121] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0122] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0123] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A method for testing the DC charging performance of an electric vehicle using a self-contained battery, characterized in that: include: Receiving a target charging curve input by a test terminal, where the target charging curve is a curve with a predetermined change rule over time; Identify the model of the built-in battery, determine the corresponding preset mode according to the model, and adjust the target charging curve according to the preset mode; Upon receiving a charging request sent by the built-in battery, controlling the charging station to charge the built-in battery according to the adjusted target charging curve; and simultaneously collecting current intensity and voltage level at different time points; During the collection process, if abnormal data is detected, the charging station is controlled to interrupt the charging of the self-contained battery; When it is determined that the power level of the built-in battery reaches a preset threshold, the charging station is controlled to interrupt the charging of the built-in battery, and analysis report information is generated according to the current intensity and the voltage level.

2. The method according to claim 1, characterized in that The steps of identifying the model of the built-in battery, determining the corresponding preset mode according to the model, and adjusting the target charging curve according to the preset mode specifically include: Obtaining specification information of the built-in battery, wherein the specification information includes battery type, battery capacity, and number of charge and discharge cycles of the battery; Obtaining usage history data of the vehicle corresponding to the self-contained battery, the usage history data including past mileage, average driving speed, and charging frequency; Generate a battery performance feature vector by using the specification information of the self-contained battery and the usage history data of the vehicle through a prediction model; Performing similarity matching on the battery performance feature vector and multiple sets of preset battery performance feature vectors; According to the matching result, the target charging curve is adjusted.

3. The method according to claim 2, characterized in that The step of adjusting the target charging curve according to the matching result specifically includes: Filter out a target battery performance feature vector having the highest matching degree with the battery performance feature vector from multiple groups of battery performance feature vectors; Obtaining a standard charging curve corresponding to the target battery performance characteristic vector; When the number of charge and discharge cycles of the built-in battery is greater than the state threshold, reducing the starting current in the target charging curve to the protection current threshold; When the number of charge and discharge cycles of the built-in battery is less than or equal to the state threshold, adjusting the starting current in the target charging curve to the starting current in the standard charging curve; Dividing the first capacity ratio by the second capacity ratio to obtain a capacity ratio coefficient, wherein the first capacity ratio is a ratio of the battery capacity of the built-in battery to a first nominal capacity corresponding thereto, the first nominal capacity being determined by the battery type of the built-in battery, and the second capacity ratio is a ratio of the battery capacity of the battery corresponding to the standard charging curve to a second nominal capacity corresponding thereto, the second nominal capacity being determined by the battery type of the battery corresponding to the standard charging curve; Adjusting the current extreme value of each stage in the target charging curve according to the capacity ratio coefficient; The timing of the phases in the target charging curve is adjusted based on the current limit and the battery capacity.

4. The method according to claim 1, characterized in that: After the step of controlling the charging station to interrupt the charging of the self-contained battery when abnormal data is detected during the collection process, the method further includes: The current intensity and voltage level at different time points are determined as the main test track, and the corresponding ideal current intensity and voltage level are determined as the backup test track; Acquire the main test track data and the backup test track data at the interruption time, and calculate the deviation value; determining a transition level according to the deviation value; Selecting a corresponding transition curve type according to the transition level; The main test track data at the interruption time and the backup test track data at the final time are connected using a transition curve type to generate a transition charging curve.

5. The method according to claim 4, characterized in that The step of selecting a corresponding transition curve type according to the transition level specifically includes: When the transition level is a small deviation level, a linear gradient curve is selected; When the transition level is a medium deviation level, a quadratic curve is selected; When the transition level is a large deviation level, the S-curve is selected.

6. The method according to claim 4, characterized in that After the step of connecting the main test track data at the interruption time with the standby test track data at the final time using a transition curve type to generate a transition charging curve, the method further includes: Connecting the main test track data from the start time to the interruption time with the transition charging curve to obtain result data; The step of generating analysis report information according to the current intensity and the voltage level specifically includes: generating analysis report information according to the result data.

7. The method according to claim 1, characterized in that During the collection process, if data abnormality is detected, after the step of controlling the charging station to interrupt the charging of the self-contained battery, the method further includes: The current intensity and voltage level at the interruption moment are determined as the upper threshold value; adjusting the target charging curve according to the upper limit threshold; The time of the phases in the target charging curve is adjusted according to the adjusted target charging curve.

8. A system for testing the DC charging performance of electric vehicles with self-contained batteries, characterized in that: The system for testing the DC charging performance of an electric vehicle with a built-in battery includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the system for testing the DC charging performance of an electric vehicle with a built-in battery to execute the method described in any one of claims 1-7.

9. A computer program product comprising instructions, characterized in that When the computer program product runs on a system for testing the DC charging performance of an electric vehicle with a built-in battery, the system for testing the DC charging performance of an electric vehicle with a built-in battery executes the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a system for testing the DC charging performance of an electric vehicle with a built-in battery, the system for testing the DC charging performance of an electric vehicle with a built-in battery executes the method as described in any one of claims 1 to 7.

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