Pulse simulation test method and test control equipment for battery of electric vehicle

Abstract

The invention discloses a pulse simulation test method and test control equipment for an electric vehicle battery, and relates to the technical field of electric vehicle battery test. In the method, a test control device firstly identifies voltage extreme points caused by inherent ripples of a charging pile, and generates an event sequence based on the extreme points. And then, the test control equipment determines the type of a synchronous trigger point from a preset trigger strategy table according to the state of charge of the battery, and executes a pulse pull flow operation when the type of the extreme point is consistent with the type of the trigger point. By collecting battery voltage and current response data, the test control equipment calculates characteristic parameters and generates a performance evaluation report. According to the method, the coupling degree of the pulse frequency and the ripple frequency of the charging pile can be reduced without adding a physical isolation module, and meanwhile, the specific voltage ripple of the charging pile is reserved as an evaluation basis, so that the microcosmic electrochemical damage test accuracy of the electric vehicle battery is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicle battery testing, and particularly relates to a pulse simulation testing method and testing control device for electric vehicle batteries. BACKGROUND

[0002] When electric vehicle batteries are subjected to pulse simulation testing, the direct current output by the charging pile is not an ideal smooth straight line, but contains voltage ripple unique to the charging pile generated during its operation. During the testing process, the electronic load simulating the behavior of the electric vehicle battery will draw current at a set pulse frequency. When the pulse frequency and the frequency of the voltage ripple unique to the charging pile are coupled, resonance may be triggered, resulting in unexpected peaks in current or voltage. This not only affects the accuracy of the test data, but also poses a safety hazard to the electric vehicle battery and the testing control device.

[0003] In related technologies, an energy buffer decoupling scheme is usually used to solve the above problem. This scheme usually involves a module composed of a large capacitor and a filter circuit connected in series between the charging pile and the load. The principle is to use the energy storage characteristics of the capacitor to meet the instantaneous pulse current demand of the load, thereby buffering the current impact of the load on the charging pile. At the same time, the filter circuit can filter out the voltage ripple unique to the charging pile. Through this physical isolation, the probability of resonance can be reduced, and the stability of the testing process can be improved.

[0004] However, in actual applications, the composite stress formed by the interaction between the voltage ripple filtered out by the above related technology and the dynamic impedance of the electric vehicle battery under a certain state of charge is an important reference data for determining whether the electric vehicle battery has microscopic electrochemical damage. After filtering out the voltage ripple unique to the charging pile, it is difficult to detect whether the electric vehicle battery has microscopic electrochemical damage, thereby reducing the accuracy of electric vehicle battery testing. SUMMARY

[0005] The present application provides a pulse simulation testing method and testing control device for electric vehicle batteries to improve the accuracy of electric vehicle battery testing.

[0006] In a first aspect, a pulse simulation test method for an electric vehicle battery is provided, which is applied to a test control device. The method comprises: based on a direct current output voltage between a charging pile and an electronic load serving as a simulation load of the electric vehicle battery, the test control device identifies voltage extreme points and types of the voltage extreme points caused by inherent ripple of the charging pile; based on the types of the voltage extreme points and time instants of the voltage extreme points, the test control device generates an extreme point event sequence; based on a state of charge of the electric vehicle battery to be tested, the test control device determines a synchronous trigger point type from a preset trigger strategy table; in a case where the extreme point types of events in the extreme point event sequence are consistent with the synchronous trigger point type, the test control device controls the electronic load to perform a preset pulse current draw operation; the test control device calculates characteristic parameters of the electric vehicle battery to be tested based on battery voltage response data and battery current response data collected during the preset pulse current draw operation; and the test control device generates a battery performance evaluation report containing an electrochemical damage degree of the electric vehicle battery to be tested based on the characteristic parameters.

[0007] By adopting the above technical solution, the test control device first identifies voltage extreme points and types of the charging pile ripple, generates an event sequence, determines a synchronous trigger point in combination with a state of charge of the battery, and controls the electronic load to perform a pulse current draw only when the types match. This synchronous trigger mechanism based on ripple characteristics avoids resonance caused by coupling of the pulse frequency and the ripple frequency, ensuring test safety; at the same time, the interaction between the ripple and the dynamic impedance of the battery is retained, so that the response data collected can reflect the state of the battery under combined stress, improving the accuracy of calculation of the characteristic parameters, and further improving the accuracy of the electric vehicle battery test.

[0008] In some embodiments of the first aspect, the test control device identifies the voltage extreme points and the types of the voltage extreme points caused by the inherent ripple of the charging pile based on the direct current output voltage between the charging pile and the electronic load as the simulation load of the electric vehicle battery, specifically comprising: the test control device samples the direct current output voltage between the charging pile and the electronic load as the simulation load of the electric vehicle battery; the test control device stores the sampled voltage data in time sequence as voltage time sequence data; the test control device performs first-order difference operation on the voltage time sequence data; the test control device marks the data points in the voltage time sequence data where the first-order difference value changes from positive to negative as voltage peak candidate points; the test control device marks the data points in the voltage time sequence data where the first-order difference value changes from negative to positive as voltage valley candidate points; the test control device takes the maximum difference between the voltage value of each candidate point and the voltage values of the adjacent data points before and after each candidate point as the amplitude variation of each candidate point; the test control device determines the voltage peak candidate points and the voltage valley candidate points with the amplitude variation greater than the dynamic ripple amplitude threshold as the voltage extreme points, and the dynamic ripple amplitude threshold is the product of the standard deviation of the voltage time sequence data and a preset multiple; the test control device identifies the voltage peak type as the voltage extreme points with the voltage value higher than the voltage value of the adjacent data points, and identifies the voltage valley type as the voltage extreme points with the voltage value lower than the voltage value of the adjacent data points.

[0009] By adopting the above technical solution, after sampling the voltage, the test control device marks the peak and valley candidate points by first-order difference, filters the real extreme points by dynamic threshold, and classifies them. The difference operation improves the ability to capture voltage mutations caused by ripple, the dynamic threshold helps to filter noise, reduces the interference of false extreme points, and improves the reliability of identifying voltage extreme points and the types of voltage extreme points.

[0010] In some embodiments of the first aspect, before the step of the test control device sampling the direct current output voltage between the charging pile and the electronic load as the simulation load of the electric vehicle battery, the method further comprises: the test control device obtains the frequency domain distribution information of the direct current output voltage data between the charging pile and the electronic load as the simulation load of the electric vehicle battery within a preset time window by fast Fourier transform; the test control device identifies the frequency components with an amplitude exceeding a preset amplitude threshold from the frequency domain distribution information; the test control device determines the frequency component with the maximum amplitude in the frequency components as the dominant ripple frequency; the test control device constructs a band-pass filter based on the dominant ripple frequency, the center frequency of the band-pass filter is the dominant ripple frequency, and the passband range is a preset percentage range of the dominant ripple frequency; the test control device performs filtering processing on the direct current output voltage between the charging pile and the electronic load as the simulation load of the electric vehicle battery based on the band-pass filter.

[0011] By adopting the technical scheme, the test control device takes the frequency component with the largest amplitude as the dominant ripple frequency, and constructs a band-pass filter based on the dominant ripple frequency, so that the electromagnetic interference signals of adjacent charging piles can be filtered out in a multi-charging pile parallel working environment, the inherent ripple characteristics of the charging pile connected with the electronic load are identified, and the accuracy of the electric vehicle battery test is improved in the multi-charging pile parallel working environment.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the test control device identifies the frequency component with an amplitude exceeding a preset amplitude threshold from the frequency domain distribution information, specifically comprising: the test control device calculates the power spectral density of each frequency point in the frequency domain distribution information; the test control device takes the maximum value of the power spectral density multiplied by a preset proportion coefficient as the preset amplitude threshold; the test control device identifies the frequency point with the power spectral density exceeding the preset amplitude threshold as a candidate frequency point; the test control device searches for a local peak point in the candidate frequency point; and the test control device determines the frequency corresponding to the local peak point as the frequency component.

[0013] By adopting the technical scheme, the test control device dynamically sets the preset amplitude threshold based on the maximum value of the power spectral density, and identifies the frequency component by searching for the local peak point, so that the real frequency characteristics can be identified adaptively following the change of signal intensity, and the reliability of the dominant ripple frequency identification is improved.

[0014] In combination with some embodiments of the first aspect, in some embodiments, the test control device generates the extreme point event sequence based on the type of the voltage extreme point and the occurrence time of the voltage extreme point, specifically comprising: the test control device assigns a unique event identifier to each voltage extreme point, and the event identifier contains type information and timestamp information of the voltage extreme point; the test control device generates an initial extreme point event sequence based on the chronological order of the timestamp information of the voltage extreme point; the test control device calculates the time interval of adjacent events in the initial extreme point event sequence; the test control device calculates the median and interquartile range of the time interval; the test control device takes the preset multiple of the median minus the interquartile range as the lower limit of the time interval, and takes the preset multiple of the median plus the interquartile range as the upper limit of the time interval; the test control device marks the events with the time interval greater than the upper limit of the time interval or less than the lower limit of the time interval in the initial extreme point event sequence as abnormal events; and the test control device eliminates the abnormal events from the initial extreme point event sequence to generate the extreme point event sequence.

[0015] By adopting the technical scheme, the test control device constructs a dynamic time interval threshold based on the median and the interquartile range, and eliminates the events with abnormal time intervals, so that the false extreme points caused by random interference can be identified and excluded, the event sequence with time consistency is generated, and the accuracy of the synchronous triggering is improved.

[0016] In some embodiments in combination with the first aspect, in the case where the test control device determines that the type of the extreme point event in the sequence of extreme point events matches the type of the synchronous trigger point, the test control device controls the electronic load to perform the preset pulse current draw operation, specifically including: in the case where the test control device determines that the type of the extreme point event in the sequence of extreme point events matches the type of the synchronous trigger point, the test control device determines the pulse current amplitude, the pulse duration and the pulse interval time according to the state of charge of the battery under test and the preset pulse parameter table; after detecting the occurrence of the extreme point event, the test control device sends a pulse current draw start instruction to the electronic load after a preset synchronization time; in the case where the electronic load receives the pulse current draw start instruction, the test control device controls the electronic load to perform the pulse current draw operation according to the pulse current amplitude for the pulse duration, and stops the pulse current draw operation for the pulse interval time.

[0017] By adopting the above technical solutions, the test control device dynamically adjusts the pulse parameters according to the state of charge of the battery, and performs the pulse current draw operation after a preset time after detecting a suitable extreme point event, so as to avoid harmful coupling with the charging pile ripple, thereby improving the controllability of the pulse current draw operation. At the same time, the pulse current draw operation is performed at a specific stage of the ripple, the interaction between the inherent ripple of the charging pile and the battery is preserved, and the response data can better reflect the battery characteristics under combined stress.

[0018] In some embodiments in combination with the first aspect, after the step in which, in the case where the electronic load receives the pulse current draw start instruction, the test control device controls the electronic load to perform the pulse current draw operation according to the pulse current amplitude for the pulse duration, and stops the pulse current draw operation for the pulse interval time, the method further includes: in the case where the electronic load receives the pulse current draw start instruction, the test control device controls the electronic load to collect the actual current draw current value of the electronic load during the pulse current draw operation according to the pulse current amplitude for the pulse duration; the test control device calculates the deviation percentage of the actual current draw current value and the pulse current amplitude; the test control device determines whether the deviation percentage is greater than or equal to a preset current deviation threshold; in the case where the deviation percentage is greater than or equal to the preset current deviation threshold, the test control device determines that there is external interference; in the case where it is determined that there is external interference, the test control device stops the current pulse current draw operation and marks the battery voltage response data and the battery current response data collected during the current pulse current draw operation as invalid data; in the case where the deviation percentage is less than the preset current deviation threshold, the test control device marks the battery voltage response data and the battery current response data collected during the current pulse current draw operation as valid data.

[0019] By adopting the technical solution, the test control device monitors the deviation of the actual current draw value from the set value, and marks the battery voltage response data and the battery current response data as invalid data when the deviation is greater than the preset current deviation threshold, so as to identify external interference and take protective measures, thereby improving the reliability of the battery voltage response data and the battery current response data.

[0020] In a second aspect, the embodiments of the present application provide a test control device, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is configured to store computer program codes, the computer program codes comprising computer instructions, and the one or more processors invoke the computer instructions to enable the test control device to perform the method described in the first aspect and any possible implementation manner of the first aspect.

[0021] In a third aspect, the embodiments of the present application provide a computer program product comprising instructions, which, when executed on a test control device, enable the test control device to perform the method described in the first aspect and any possible implementation manner of the first aspect.

[0022] In a fourth aspect, the embodiments of the present application provide a computer-readable storage medium comprising instructions, which, when executed on a test control device, enable the test control device to perform the method described in the first aspect and any possible implementation manner of the first aspect.

[0023] It can be understood that the test control device provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved are referred to the beneficial effects in the corresponding method, which will not be described here.

[0024] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The test control device generates an event sequence based on the voltage extreme point caused by the inherent ripple of the charging pile, determines the synchronous trigger point type from the preset trigger strategy table according to the state of charge of the battery, and performs the pulse current draw operation when the extreme point type is consistent with the trigger point type, thereby reducing the coupling degree of the pulse frequency and the charging pile ripple frequency without the need to increase the physical isolation module, while retaining the voltage ripple unique to the charging pile, so that the voltage ripple interacts with the dynamic impedance of the battery at a certain state of charge, and the feature parameters are calculated by collecting the voltage and current response data of the battery, thereby improving the accuracy of the microscopic electrochemical damage test of the electric vehicle battery.

[0025] 2. Since the test control equipment uses the frequency component with the largest amplitude as the dominant ripple frequency and constructs a bandpass filter based on the dominant ripple frequency, it can filter out the electromagnetic interference signals of adjacent charging piles in the parallel operation environment of multiple charging piles, identify the inherent ripple characteristics of the charging piles connected to the electronic load, and thus improve the accuracy of electric vehicle battery testing in the parallel operation environment of multiple charging piles.

[0026] 3. Because the test control equipment monitors the deviation between the actual current draw value and the set value, and marks the battery voltage response data and battery current response data as invalid data when the deviation exceeds the preset current deviation threshold, it can identify external interference and take protective measures, thereby improving the reliability of battery voltage response data and battery current response data. Attached Figure Description

[0027] Figure 1 This is a schematic flowchart of a pulse simulation test method for an electric vehicle battery according to an embodiment of this application.

[0028] Figure 2 This is another schematic flowchart of a pulse simulation test method for an electric vehicle battery according to an embodiment of this application.

[0029] Figure 3 This is a schematic diagram of the physical device structure of a test control device in the embodiments of this application. Detailed Implementation

[0030] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0031] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0032] This application provides a pulse simulation test method and test control device for electric vehicle batteries to improve the accuracy of electric vehicle battery testing.

[0033] Please see Figure 1A flowchart of a pulse simulation test method for an electric vehicle battery in the embodiments of the present application.

[0034] In S101, the test control device identifies the voltage extreme points and the type of the voltage extreme points caused by the inherent ripple of the charging pile based on the DC output voltage between the charging pile and the electronic load as the simulation load of the electric vehicle battery.

[0035] The charging pile refers to an external power supply device that provides charging power for the electric vehicle battery. The power electronic converter inside the charging pile will generate periodic voltage fluctuations when working. The electronic load of the electric vehicle battery simulation load refers to a programmable electronic load that is not a real battery but is used to accurately simulate the electrical characteristics of a real electric vehicle battery in different states in the laboratory through software and can perform accurate current pulling operations. The DC output voltage is the original composite voltage signal measured between the output end of the charging pile and the input end of the electronic load without any additional filtering treatment, which contains a high-amplitude DC component and a low-amplitude AC ripple component superimposed on it. The test control device refers to an industrial computer or an embedded controller running test control software. The inherent ripple refers to the periodic AC component superimposed on the DC voltage caused by the switching devices (such as IGBT, MOSFET) inside the charging pile during high-frequency switching operations and subsequent incomplete rectification filtering, which is the key signal that the related art tries to filter out and the embodiments of the present application choose to analyze and utilize directly. The voltage extreme point is used to represent the local maximum point (i.e. the peak) and the local minimum point (i.e. the valley) of the inherent ripple in a period. The type of the voltage extreme point is a classification identifier that clearly indicates whether an identified voltage extreme point belongs to the voltage peak type or the voltage valley type.

[0036] Specifically, the test control device first performs high-frequency sampling on the DC output voltage between the charging pile and the electronic load, and stores the sampled voltage data points in time sequence as voltage time series data. Next, to locate the turning points of voltage change, the test control device performs a first-order difference operation on the voltage time series data, thereby generating a sequence of first-order difference values. By traversing the sequence of first-order difference values, the test control device marks the data point where the difference value changes from positive to negative as a voltage peak candidate point, and marks the data point where the difference value changes from negative to positive as a voltage valley candidate point. Subsequently, the test control device calculates the amplitude variation of each candidate point, which is the maximum difference between the voltage value of each candidate point and the voltage values of the adjacent data points before and after each candidate point. Then, the test control device compares the amplitude variation with a dynamic ripple amplitude threshold value. The dynamic threshold value can adapt to the fluctuation characteristics of the signal, and is obtained by multiplying the standard deviation of the current voltage time series data by a preset multiple. Only when the amplitude variation of a candidate point is greater than the dynamic threshold value, the candidate point is confirmed as a valid voltage extreme point. Finally, the test control device identifies the valid voltage extreme points as voltage peaks or valleys according to whether the voltage value of each valid voltage extreme point is higher or lower than the voltage values of the adjacent data points, thereby completing the identification and classification of voltage extreme points.

[0037] In some embodiments, the voltage extreme points can be identified and classified in various ways. Optionally, a method based on sliding window extreme value search can be used. First, the test control device defines a sliding window with a width approximately equal to the estimated ripple period, and moves the window point by point on the voltage time series data. Second, at each window position, the test control device searches for and records the maximum and minimum voltage values within the window and their corresponding time stamps as candidate extreme points. Finally, the test control device merges and removes duplicate extreme points found by consecutive windows, and filters out false extreme points caused by minor noise through a fixed amplitude variation threshold value, to finally determine the extreme points and their types. Optionally, a method based on local curve fitting can also be used. First, for each point in the voltage time series data, the test control device takes itself and the previous and subsequent data points to form a local data point set. Second, the test control device uses a quadratic polynomial (i.e. a parabola) to perform least squares fitting on the local data point set. Finally, the test control device determines the exact time and voltage value of the extreme point by solving the vertex coordinates of the fitted parabola, and determines its type according to the opening direction of the parabola (opening downward for peaks, and opening upward for valleys).

[0038] It can be understood that other ways can also be used to identify and classify the voltage extreme points, for example, by comparing a data point with multiple points before and after it to determine whether it is a local extreme value, instead of only two adjacent points before and after it, so as to enhance the noise suppression capability, which is not limited here.

[0039] In S102, the test control device generates an extreme point event sequence based on the type of the voltage extreme point and the occurrence time of the voltage extreme point.

[0040] The type of the voltage extreme point refers to the classification identifier of the voltage peak type or the voltage trough type assigned to each voltage extreme point in step S101. The occurrence time of the voltage extreme point represents the position of each extreme point on the time axis identified in step S101, which is recorded in the form of high-precision hardware timestamp in the embodiments of the present application. The extreme point event sequence refers to a cleaned and verified, structured, and strictly arranged in time sequence data set. Each element (i.e. "event") in the sequence encapsulates the complete information of a voltage extreme point that has been confirmed to truly reflect the stable ripple characteristics of the charging pile, for subsequent logical judgment and synchronous control.

[0041] Specifically, the test control device first assigns a unique event identifier to each voltage extreme point verified through step S101, which contains the type information and timestamp information of the voltage extreme point. Then, based on the timestamp sequence of the events, an initial extreme point event sequence is generated. To eliminate abnormalities, the test control device then calculates the time intervals between all adjacent events in the initial sequence, and performs statistics on these time interval data to calculate the median and interquartile range (IQR). Based on this, the test control device defines a dynamic time interval valid range: the lower limit of the time interval is the median minus a preset multiple of the interquartile range, and the upper limit of the time interval is the median plus the preset multiple of the interquartile range. Subsequently, the test control device traverses the initial sequence and marks any event with a time interval greater than the upper limit of the time interval or less than the lower limit of the time interval as an abnormal event. Finally, the test control device removes all marked abnormal events from the initial sequence, and the remaining events that are evenly distributed in time and meet the periodicity rule constitute the final extreme point event sequence.

[0042] In some embodiments, the generation and cleaning of the extreme point event sequence can be accomplished in several ways: Optionally, a verification method based on state machine forced alternation can be used. First, the test control device maintains a state variable in memory, which records the type of the last event successfully added to the final sequence (e.g., a voltage peak). Second, when a new event is processed from the initial extreme point event sequence, the test control device checks whether its type matches the type expected by the state variable (in this case, a voltage trough). Finally, only if the type of the new event matches the alternation expectation is it accepted into the final event sequence, and the state variable is updated to the type of the current event. Otherwise, the event is considered to be physically incompatible and is discarded. Optionally, a refining method based on a software phase-locked loop can be used. First, the test control device uses the timestamp of the initial extreme point event sequence as an input signal and sends it to a software-implemented phase-locked loop module. Second, the phase-locked loop dynamically learns and locks onto the average frequency and phase of the event sequence through an internal phase detector, loop filter, and voltage-controlled oscillator. Finally, the phase-locked loop outputs a smoothed, low-jitter idealized event time series. The test control equipment can compare the events in the initial sequence with this ideal sequence, retaining only those events that deviate from the ideal time point within a very small range, thus generating a final event sequence that is extremely accurate and stable in time.

[0043] It is understandable that other methods can be used to generate and clean up the extreme point event sequence, such as using a Kalman filter to predict and correct the timestamps of the events to obtain better sequence stability, which is not limited here.

[0044] S103. Based on the state of charge of the battery of the electric vehicle under test, the test control equipment determines the type of synchronous trigger point from the preset trigger strategy table.

[0045] The electric vehicle battery under test refers to the power battery pack or battery module used in this test. State of Charge (SOC) is a key parameter representing the percentage of a battery's current remaining charge relative to its total capacity, ranging from 0% to 300%, and is an important indicator of the battery's current electrochemical state. The preset trigger strategy table is a data structure, such as a lookup table or rule set, pre-stored in the test control equipment. This table establishes a mapping relationship between different SOC ranges of the battery and the optimal synchronization trigger point type. The synchronization trigger point type is the decision output of this step, clarifying which type of voltage extreme point (i.e., voltage peak or voltage trough) the subsequent pulse current pulling operation should be synchronized with.

[0046] Specifically, the test control device determines the synchronization trigger point type closely around the electrochemical characteristics of the battery: first, the test control device obtains the state of charge value of the battery to be tested from the battery management test control device (BMS) through the communication interface, which is a key basis for subsequent decision-making, because the internal electrochemical environment and potential damage mode of the battery are significantly different at different states of charge. Then, the state of charge value is input as a query to find the corresponding state of charge interval in the preset trigger strategy table, and the synchronization trigger point type corresponding to the interval is read. The design of the preset trigger strategy table is based on the inherent ripple superimposed on the direct current voltage output of the charging pile. The inherent ripple of the charging pile can be regarded as a periodic change in the electric stress applied to the battery. When the test control device applies a pulse current operation at a specific phase point (peak or valley) of the ripple, the two stresses will act on the battery at the same time to form a composite stress. By combining the pulse current stress with the specific ripple phase point, the electrical signal characteristics caused by the potential damage most closely related to the corresponding state of charge interval can be more effectively exposed or amplified.

[0047] For example: in the high state of charge region (for example, 80%-300%): at this time, the negative electrode potential of the battery is low, close to the deposition potential of lithium. If a pulse current operation is applied at the trough of the charging voltage, it will cause the electrode potential to change rapidly in the opposite direction. This dramatic disturbance in potential is particularly sensitive to detecting abnormal voltage responses related to lithium deposition. Therefore, the preset trigger strategy table will specify that when the state of charge is detected in this high region, the synchronization trigger point type should be determined as the voltage trough.

[0048] In the low state of charge region (for example, 0%-20%): at this time, the positive electrode is in a deep delithiation state, and its material structure stability is relatively poor. If a pulse current operation is applied at the peak of the charging voltage, it will further raise the positive electrode potential, thereby exerting greater pressure on the structural stability of the positive electrode material. If the positive electrode has microscopic structural damage, its voltage response characteristics under this composite stress will be more significantly different from that in a healthy state. Therefore, the strategy table will specify that when the state of charge is detected in this low region, the synchronization trigger point type should be determined as the voltage peak.

[0049] In some embodiments, the synchronization trigger point type can be determined in several ways. Optionally, a multi-dimensional parameter table based decision method can be adopted. Firstly, the test control device internally stores a strategy table, the index dimensions of which include not only SOC, but also real-time temperature and known state of health (SOH) of the battery, etc. These parameters also affect the electrochemical response of the battery. Secondly, the test control device obtains the SOC, temperature and SOH values of the battery before making a decision. Finally, the test control device uses these parameters to index the multi-dimensional table and finds a more accurate synchronization trigger point type that takes into account various state factors. Optionally, a model simulation based decision method can also be adopted. Firstly, the test control device internally integrates an equivalent circuit model or a simplified electrochemical model of the battery. Secondly, before determining the trigger type, the test control device uses the current battery state parameters (such as SOC, temperature) as model inputs to simulate the expected voltage response when the voltage peak and voltage trough trigger pulse current draw, respectively. Finally, the test control device analyzes the results of the two simulations and selects the trigger type that produces a more sensitive response to the target damage characteristics (such as internal resistance change) as the synchronization trigger point type for this test.

[0050] It can be understood that other ways of determining the synchronization trigger point type can also be adopted, such as introducing a fuzzy logic controller, fuzzifying the input quantities such as SOC and temperature, and making inferences through a series of pre-set fuzzy rules to finally obtain an optimal trigger decision, which is not limited here.

[0051] S104, in the case where the extreme point type of the event in the extreme point event sequence is consistent with the synchronization trigger point type, the test control device controls the electronic load to perform a pre-set pulse current draw operation.

[0052] Among them, the extreme point event sequence refers to the ordered event stream generated in S102, which represents the stable ripple characteristics of the charging pile. The synchronization trigger point type is the target event type (voltage peak or voltage trough) determined in S103 according to the current state of the battery. The test control device is responsible for real-time monitoring and accurate triggering in this step. The electronic load is a physical device that performs pulse current draw operation. The pre-set pulse current draw operation refers to a current draw action that has been standardized in terms of current amplitude and duration.

[0053] This step artificially creates a composite stress test condition that is conducive to observing the internal state changes of the battery by applying a synchronized, standardized pulsed current draw operation at the moment when the electronic load is subjected to a specific background stress caused by the charging post ripple. By analyzing the response of the electronic load to this composite stress, its health condition can be determined. It is difficult to efficiently find early microscopic damage inside the battery by passively monitoring the voltage and current during the charging process. The embodiments of the present application actively apply a pulsed current draw at a specific moment to stimulate the dynamic response of the battery. The voltage extreme point represents the moment when the charging stress reaches the periodic maximum or minimum. Superimposing this discharging stress of pulsed current draw on the extreme point of the charging stress during the charging process can maximize the amplification of changes in specific electrochemical parameters (such as polarization resistance), so that the originally imperceptible damage characteristics become significant.

[0054] Specifically, first, the test control device will query and load the specific parameters required for this operation from a preset pulse parameter table according to the current state of charge (SOC) of the battery to be tested, mainly including the pulse current amplitude and pulse duration. Then, the test control device enters a continuous monitoring and matching cycle to receive and analyze the extreme point event sequence generated by S102. For each new event in the sequence, the test control device compares the extreme point type of the event with the synchronization trigger point type determined by S103. The synchronization condition is met only when the two are consistent (for example, S103 requires peak trigger, and the current event is a voltage peak type). At this time, in order to compensate for the inherent delay from identification to execution inside the test control device, the test control device will wait for a very short, pre-calibrated synchronization time. After the synchronization time ends, the test control device immediately issues an instruction to the electronic load to control it to perform a current draw operation according to the preset pulse current amplitude and duration. After the operation is completed, the electronic load returns to standby state and waits for the arrival of the next eligible extreme point event to perform the next synchronized pulsed current draw.

[0055] In some embodiments, the preset pulse current draw operation can be controlled by the electronic load in several ways. Optionally, a precise synchronization method based on hardware triggering can be used. First, the comparator output signal identifying the extreme point in S101 is directly connected to the external trigger port of the electronic load through the hardware I / O port. Second, the test control device pre-configures the pulse parameters into the electronic load and makes it in the mode of waiting for external hardware triggering. Finally, when the extreme point type meeting the decision in S103 generates a valid level jump at the hardware level, the signal directly triggers the electronic load to perform the current draw operation with extremely low delay, thereby achieving high-precision synchronization. Optionally, an instruction synchronization method based on high-speed real-time control bus can also be used. First, the test control device is connected with the electronic load through a real-time industrial Ethernet protocol (such as EtherCAT) with high determinacy and low delay. Second, after detecting the qualified extreme point event, the test control device sends instructions containing pulse parameters and precise execution timestamp to the electronic load. The electronic load starts the pulse current draw operation at the specified timestamp based on the internal clock synchronized with the master control.

[0056] It can be understood that other ways of controlling the electronic load to perform the pulse current draw operation can also be used, such as controlling the delay and pulse width through a software timer, which is a cost-effective implementation in scenarios where the synchronization accuracy requirement is not extremely strict, and is not limited herein.

[0057] S105, the test control device calculates the characteristic parameters of the electric vehicle battery to be tested based on the battery voltage response data and the battery current response data collected during the preset pulse current draw operation.

[0058] The preset pulse current draw operation period refers to the time period from the start time to the end time of the pulse current application in S104, for example, if the pulse current starts at 10 seconds and ends at 15 seconds, then the time from 10 seconds to 15 seconds is the preset pulse current draw operation period. The battery voltage response data refers to the sequence of values of the battery terminal voltage changing with time recorded by the test control device during the pulse current draw process, which directly reflects the changes of the battery internal state under current disturbance. The battery current response data refers to the record of the current actually applied to or extracted from the battery changing with time during the test. The characteristic parameters are used to represent key indicators that can reflect the battery internal state, performance level and health status, such as the ohmic resistance, polarization resistance, open circuit voltage (OCV), state of charge (SOC) and state of health (SOH) of the battery, etc.

[0059] Specifically, when the test control device applies a short, large current discharge pulse to the battery, the terminal voltage of the battery drops rapidly. This voltage drop is not a simple linear change, but a complex response involving multiple electrochemical processes. The test control device records the complete voltage and current curves before the start of the pulse, during the pulse, and after the end of the pulse. By analyzing these data, different internal characteristics of the battery can be decoupled. For example, at the instant when the current pulse is applied (usually on the order of microseconds to milliseconds), the sudden drop in voltage is mainly caused by the ohmic internal resistance of the battery. Subsequently, during the duration of the pulse, the voltage continues to slowly drop, which reflects processes such as electrochemical polarization and concentration polarization, related to polarization resistance and diffusion processes. When the pulse ends, the current disappears, and the voltage begins to rise, the rate and shape of its recovery also contain rich information about the internal relaxation processes of the battery.

[0060] In some embodiments, the characteristic parameters of the battery under test can be calculated in various ways. Optionally, an equivalent circuit model parameter identification method based on time domain analysis can be used. First, the test control device selects an appropriate equivalent circuit model to describe the dynamic characteristics of the battery, such as the commonly used second-order RC model (Thevenin model), which includes an ohmic resistance (R0), two parallel RC networks (R1C1 and R2C2) to simulate electrochemical polarization and concentration polarization, respectively. Second, the test control device extracts the voltage and current values before the start of the pulse, at the current mutation moment, at the end of the pulse, and a period of time after the end of the pulse from the collected voltage and current data sequences. Finally, the test control device uses least squares method, recursive least squares method or Kalman filter algorithm to fit the collected voltage response curve with the mathematical expression of the equivalent circuit model. Through iterative optimization, a set of model parameter values (i.e. R0, R1, C1, R2, C2) that minimize the error between the model output voltage curve and the actual measured curve are calculated. These parameters are the characteristic parameters of the battery. Optionally, a data transformation method based on frequency domain analysis can also be used. First, the test control device converts the collected time-domain pulse current data I(t) and voltage response data V(t) from time domain to frequency domain by using the Fast Fourier Transform (FFT) algorithm to obtain the current frequency spectrum I(j) and the voltage frequency spectrum V(j). Then, in the frequency domain, the test control device calculates V(j) / I(j) to obtain the complex impedance Z(j) of the battery at different frequency points according to the complex form of Ohm's law. Since a current pulse contains a wealth of frequency components, a single pulse test can obtain impedance spectrum data in a wide frequency range. Finally, the test control device plots the calculated electrochemical impedance spectrum data on the complex plane (i.e. Nyquist plot), and uses professional impedance spectrum fitting software to select an appropriate equivalent circuit to fit the impedance spectrum. Through fitting, the ohmic resistance, charge transfer resistance (related to polarization), and Weber impedance (related to diffusion) and other more detailed characteristic parameters of the battery can be accurately separated and calculated.

[0061] It can be understood that other ways of calculating the characteristic parameters of the battery under test can also be used, such as data-driven methods based on machine learning or deep learning models, which directly establish the mapping relationship from voltage and current response to characteristic parameters by training a large amount of test data, which is not limited here.

[0062] S106、the test control device generates a battery performance evaluation report containing the electrochemical damage degree of the battery under test based on the characteristic parameters.

[0063] wherein the characteristic parameters refer to a set of quantitative indicators calculated by S105. The test control device performs data comparison, logical inference and report generation in this step. The electrochemical damage degree refers to the evaluation of specific aging modes inside the battery, such as the qualitative or semi-quantitative rating of SEI film thickening as slight, moderate or severe. The battery performance evaluation report refers to a final generated, structured document containing test summary, key parameter trend chart, diagnostic conclusion and recommendations, etc.

[0064] Specifically, the test control device first compares the current measured characteristic parameters (such as internal resistance) with the initial factory value of the battery (i.e. the state of the new battery), thereby calculating the overall aging degree and performance degradation of the battery. Secondly, the test control device compares it with the historical test data of the battery longitudinally, analyzes the rate and trend of its performance degradation. Then, the test control device compares the parameters with the preset end-of-life threshold to determine whether the battery has approached or reached the scrap standard. Next, the test control device maps the changes of different characteristic parameters to specific electrochemical damage mechanisms. For example, a significant increase in ohmic resistance is usually related to the degradation of physical connection inside the battery (such as corrosion of solder joints, loosening of busbars) or depletion of electrolyte. While the increase in polarization resistance is more indicative of the aging of the electrode material surface, deactivation of active materials or blockage of porous structure. By comprehensively analyzing the change patterns of multiple parameters, the test control device can infer the damage type that dominates the current battery aging and evaluate its severity. Finally, the test control device automatically integrates and formats all these analysis results into a clear and structured evaluation report, which includes data comparison, trend chart, damage type judgment, comprehensive health score (SOH) and maintenance recommendations generated based on the diagnostic results.

[0065] In some embodiments, the performance evaluation report can be generated in various ways: optionally, the test control device adopts a semi-quantitative evaluation method based on a scorecard. The diagnostic knowledge base associates a damage score with each rule. When multiple rules are triggered, the test control device sums the damage scores of these rules with weights to obtain the final score for different damage modes. The report displays the scores of each damage dimension in the form of radar chart, etc., providing users with an intuitive multi-dimensional battery health portrait. Optionally, an anomaly detection method based on data clustering can also be used. The test control device divides a large number of battery characteristic parameter data into clusters representing different health states in its historical database through unsupervised learning algorithms. When the characteristic parameters of a new battery are calculated, the test control device classifies it into the nearest cluster and takes the health state represented by the cluster as the diagnostic conclusion. If the data point is far away from all known clusters, it is reported as an abnormal sample that needs special attention.

[0066] It can be understood that other ways can also be used to generate the performance evaluation report, which is not limited here.

[0067] In the above embodiment, the test control device generates an event sequence based on the voltage extreme points caused by the inherent ripple of the charging pile, determines the synchronous trigger point type from the preset trigger strategy table according to the state of charge of the battery, and performs the pulse current drawing operation when the extreme point type is consistent with the trigger point type, thereby reducing the coupling degree of the pulse frequency and the charging pile ripple frequency without the need to increase the physical isolation module. At the same time, the voltage ripple specific to the charging pile is retained, which interacts with the dynamic impedance of the battery at a specific state of charge, and the characteristic parameters are calculated by collecting the voltage and current response data of the battery, thereby improving the accuracy of the microscopic electrochemical damage test of the electric vehicle battery. However, in the actual environment of multiple charging piles working in parallel, the electromagnetic interference signals of adjacent charging piles will be superimposed on the output voltage of the charging pile connected to the battery under test. These interference signals not only affect the identification accuracy of the voltage extreme points, but also may cause the test control device to trigger the pulse current drawing operation at an inappropriate time. In addition, external electromagnetic interference may cause a large deviation between the actual current value and the set value of the electronic load, reducing the reliability of the test data. The existence of these problems limits the application effect of the above embodiment in the actual industrial environment.

[0068] Please refer to Figure 2 for another flowchart of the pulse simulation test method for an electric vehicle battery in the embodiment of the present application.

[0069] S201, based on the direct current output voltage between the charging pile after filtering and the electronic load as the simulation load of the electric vehicle battery, the test control device identifies the voltage extreme points caused by the inherent ripple of the charging pile and the type of the voltage extreme points.

[0070] Specifically, before starting to identify the extreme value points, the test control device first performs a frequency domain analysis on the collected DC output voltage data based on a fast Fourier transform, to obtain frequency domain distribution information of the voltage signal, i.e. a distribution diagram of signal energy at different frequencies. Then, the test control device calculates the power spectral density of each frequency point in the frequency domain distribution information, and multiplies the maximum value of the power spectral density by a preset proportion coefficient (e.g. 0.1) as an amplitude threshold value. All frequency points whose power spectral density exceeds the threshold value are identified as significant frequency components, and the frequency component with the maximum power spectral density is determined as the dominant ripple frequency. Then, the test control device constructs a digital band-pass filter based on the dominant ripple frequency, with the center frequency being the dominant ripple frequency and the passband width being set as a preset percentage range (e.g. ±10%) of the frequency. Finally, the original DC output voltage data is filtered by the band-pass filter to filter out noise and interference signals outside the dominant ripple frequency range, and then sent to the subsequent extreme value point identification module. The subsequent identification of voltage extreme value points and the type of voltage extreme value points is similar to S101, which will not be described here.

[0071] In some embodiments, a wavelet transform-based denoising method can also be used to filter the DC output voltage data. First, the test control device performs multi-scale wavelet decomposition on the original DC output voltage data to obtain wavelet coefficients at different scales. Second, the test control device performs threshold processing on the coefficients at scales representing high-frequency noise, and sets smaller coefficients to zero. Finally, a smooth signal that removes most random noise but retains the main characteristics of the ripple is obtained by reconstructing the signal using the processed wavelet coefficients, and then the extreme value points of the signal are identified.

[0072] It can be understood that other methods can also be used to filter the DC output voltage data, which are not limited here.

[0073] S202, based on the type of voltage extreme value points and the occurrence time of voltage extreme value points, the test control device generates an extreme value point event sequence.

[0074] S203, based on the state of charge of the battery of the electric vehicle under test, the test control device determines the type of synchronous trigger point from the preset trigger strategy table.

[0075] S204, in the case where the type of extreme value point in the extreme value point event sequence and the type of synchronous trigger point are consistent, the test control device controls the electronic load to perform a preset pulse current draw operation.

[0076] Step S202 is similar to step S102, step S203 is similar to step S103, and step S204 is similar to step S104, which will not be described here.

[0077] S205. The test control equipment collects the actual current value of the electronic load during the pulse current pulling operation.

[0078] Specifically, after issuing a command to the electronic load to execute a pulse current pull, the test control equipment initiates a high-priority data acquisition task strictly synchronized with the system master clock. This task continuously samples the output signal of the electronic load's current measurement unit at an extremely high sampling frequency (e.g., 1MHz) through a dedicated data acquisition channel to capture the dynamic characteristics of the current pulse, including rapid rising edges, potential overshoot or oscillations, the flatness of the pulse, and the shape of its falling edge. Each acquired current data point is timestamped and stored in an array or buffer in memory in chronological order, forming a current timing data sequence.

[0079] S206. The test control equipment calculates the percentage deviation between the actual current value and the pulse current amplitude.

[0080] Specifically, the test control equipment first retrieves the timestamped current timing data sequence cached in S205. To avoid interference from the transient processes of rising and falling edges on the calculation results, the test control equipment calculates based on the settling period of the current pulse, which is the time window from when the current rises to 90% of the target value until the current begins to decline. Next, the test control equipment calculates the arithmetic mean of the data points within this settling period to obtain an actual average current value that reflects the steady-state current pulling level of this operation. Then, the test control equipment reads the pulse current amplitude (i.e., the target value) of this operation from the configuration parameters of the test procedure. Finally, the test control equipment uses the percentage of the absolute value of the difference between the actual average current and the pulse current amplitude divided by the pulse current amplitude as the deviation percentage.

[0081] S207. Test the control equipment to determine whether the deviation percentage is greater than or equal to the preset current deviation threshold.

[0082] After the test control device determines in step S207 that the deviation percentage is greater than or equal to the preset current deviation threshold, steps S209-S210 are executed. After the test control device determines in step S207 that the deviation percentage is less than the preset current deviation threshold, step S208 is executed.

[0083] S208. The test control equipment marks the battery voltage response data and battery current response data collected during the currently executed pulse pull-current operation as valid data.

[0084] S209. Test control equipment has determined that there is external interference.

[0085] S210, in the case of determining that there is external interference, the test control device stops the current pulse current drawing operation and marks the battery voltage response data and battery current response data collected during the pulse current drawing operation currently being executed as invalid data.

[0086] After step S208 and step S210 are executed, step S211 is executed.

[0087] S211, the test control device calculates the characteristic parameters of the electric vehicle battery to be tested based on the valid battery voltage response data and battery current response data collected during the preset pulse current drawing operation.

[0088] S212, the test control device generates a battery performance evaluation report containing the degree of electrochemical damage of the electric vehicle battery to be tested based on the characteristic parameters.

[0089] Step S211 is similar to step S105, and step S212 is similar to step S106, which will not be described here.

[0090] The embodiment of the application reduces the electromagnetic interference problem in the multi-charging pile parallel working environment by increasing the frequency domain analysis and band pass filtering processing before identifying the voltage extreme point, and introduces a real-time current deviation monitoring mechanism to improve the reliability of the test data, thereby improving the accuracy of the test of the electric vehicle battery in the complex working environment.

[0091] The above describes an electric vehicle battery pulse simulation test method in the embodiment of the application, and the following introduces an exemplary test control device 300 provided by the embodiment of the application.

[0092] Figure 3 is an exemplary hardware structure schematic diagram of the test control device 300 provided by the embodiment of the application. In some embodiments, the test control device 300 is a computer device, which includes a processor, a memory and a network interface connected through a test control device bus. 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 test control device, a computer program and a database. The internal memory provides an environment for the operation of the operating test control device 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 terminals or servers outside through 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. The computer program is executed by the processor to implement the electric vehicle battery pulse simulation test method in the embodiment of the application.

[0093] Those skilled in the art can understand that Figure 3 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0094] In some embodiments of the present application, a computer readable storage medium is also provided, including instructions which, when executed on the test control device 300, can cause the test control device 300 to perform the pulse simulation test method of the electric vehicle battery in the embodiments of the present application.

[0095] The above, the above embodiments are only to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

[0096] In the above embodiments, according to the context, the term "when" can be interpreted as meaning "if" or "after" or "in response to determining" or "in response to detecting". Similarly, according to the context, the phrase "upon determining" or "if detecting (the stated condition or event)" can be interpreted as meaning "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)".

[0097] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented 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, all or part of the processes or functions according to the embodiments of the present application are generated. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions can 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 can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (for example, coaxial cable, optical fiber, digital subscriber line) or wireless (for example, infrared, wireless, microwave, etc.) mode. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The available media can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk), etc.

[0098] Those of ordinary skill in the art understand that all or part of the processes in the above embodiments can be implemented by a computer program to instruct the relevant hardware, which can be stored in a computer readable storage medium. The program can include the processes of the above embodiments when executed. The storage medium includes ROM or random access memory (RAM), magnetic disk or optical disk, and other media that can store program codes.

Claims

1. A pulse simulation test method for electric vehicle batteries, characterized in that, Applied to test control equipment, the method includes: Based on the DC output voltage between the charging pile and the electronic load that serves as a simulated load for the electric vehicle battery, the test control device identifies the voltage extreme points caused by the inherent ripple of the charging pile and the type of the voltage extreme points. Based on the type of the voltage extreme point and the time when the voltage extreme point occurs, the test control device generates an extreme point event sequence; Based on the state of charge of the electric vehicle battery under test, the test control device determines the type of synchronization trigger point from a preset trigger strategy table; If the extreme point type of the event in the extreme point event sequence matches the synchronous trigger point type, the test control device controls the electronic load to perform a preset pulse current pulling operation; The test control equipment calculates the characteristic parameters of the electric vehicle battery under test based on the battery voltage response data and battery current response data collected during the preset pulse pull-current operation. The test control equipment generates a battery performance evaluation report based on the characteristic parameters, which includes the degree of electrochemical damage to the electric vehicle battery under test.

2. The method according to claim 1, characterized in that, The test control equipment identifies voltage extreme points caused by the inherent ripple of the charging pile and the type of such extreme points, based on the DC output voltage between the charging pile and the electronic load simulating the electric vehicle battery. Specifically, this includes: The test control equipment samples the DC output voltage between the charging pile and the electronic load, which serves as a simulated load for the electric vehicle battery. The test control device stores the sampled voltage data in chronological order as voltage time series data. The test control device performs a first-order difference operation on the voltage timing data; The test control device marks the data points in the voltage timing data whose first-order difference value changes from positive to negative as candidate voltage peak points; The test control device marks the data points in the voltage time series data whose first-order difference value changes from negative to positive as candidate points for voltage troughs. The test control device uses the maximum difference between the voltage value of each candidate point and the voltage value of the adjacent data points before and after each candidate point as the amplitude change of each candidate point. The test control device determines the voltage peak candidate points and voltage trough candidate points whose amplitude changes are greater than the dynamic ripple amplitude threshold as voltage extreme points. The dynamic ripple amplitude threshold is the product of the standard deviation of the voltage time series data and a preset multiple. The test control device identifies voltage peaks where the voltage value is higher than that of adjacent data points as voltage peaks and voltage troughs where the voltage value is lower than that of adjacent data points.

3. The method according to claim 2, characterized in that, Before the step of the test control equipment sampling the DC output voltage between the charging pile and the electronic load that serves as a simulated load for the electric vehicle battery, the method further includes: The test control equipment obtains the frequency domain distribution information of the DC output voltage data between the charging pile and the electronic load, which serves as a simulated load for the electric vehicle battery, within a preset time window through a fast Fourier transform. The test control device identifies frequency components whose amplitude exceeds a preset amplitude threshold from the frequency domain distribution information; The test control device determines the frequency component with the largest amplitude among the frequency components as the dominant ripple frequency. The test control device constructs a bandpass filter based on the dominant ripple frequency, wherein the center frequency of the bandpass filter is the dominant ripple frequency, and the passband range is a preset percentage range of the dominant ripple frequency. The test control equipment filters the DC output voltage between the charging pile and the electronic load that serves as a simulated load for the electric vehicle battery based on the bandpass filter.

4. The method according to claim 3, characterized in that, The test control device identifies frequency components whose amplitude exceeds a preset amplitude threshold from the frequency domain distribution information, specifically including: The test control equipment calculates the power spectral density at each frequency point in the frequency domain distribution information; The test control device uses the product of the maximum value of the power spectral density and a preset proportional coefficient as the preset amplitude threshold. The test control device identifies frequency points where the power spectral density exceeds the preset amplitude threshold as candidate frequency points; The test control equipment searches for local peak points among the candidate frequency points; The test control device determines the frequency corresponding to the local peak point as the frequency component.

5. The method according to claim 1, characterized in that, Based on the type and occurrence time of the voltage extreme point, the test control device generates an extreme point event sequence, specifically including: The test control device assigns a unique event identifier to each voltage extreme point, and the event identifier contains the type information and timestamp information of the voltage extreme point; Based on the chronological order of the timestamp information of the voltage extreme points, the test control device generates an initial extreme point event sequence; The test control device calculates the time interval between adjacent events in the initial extreme point event sequence; The test control device calculates the median and interquartile range of the time interval; The test control device uses a preset multiple of the median minus the interquartile range as the lower limit of the time interval, and the median plus a preset multiple of the interquartile range as the upper limit of the time interval. The test control device marks events in the initial extreme point event sequence whose time interval is greater than the upper limit of the time interval or less than the lower limit of the time interval as abnormal events; The test control device removes the abnormal events from the initial extreme point event sequence to generate an extreme point event sequence.

6. The method according to claim 1, characterized in that, When the extreme point type of the event in the extreme point event sequence matches the synchronous trigger point type, the test control device controls the electronic load to perform a preset pulse current pulling operation, specifically including: When it is determined that the extreme point type of the event in the extreme point event sequence matches the synchronous trigger point type, the test control device determines the pulse current amplitude, pulse duration and pulse interval time according to the state of charge of the electric vehicle battery under test and the preset pulse parameter table. When an extreme point event is detected, the test control device sends a pulse current pulling start command to the electronic load after a preset synchronization time delay; When the electronic load receives the pulse current pulling start command, the test control device controls the electronic load to perform pulse current pulling operation according to the pulse current amplitude during the pulse duration, and stops the pulse current pulling operation during the pulse interval.

7. The method according to claim 6, characterized in that, After the step of the test control device controlling the electronic load to perform pulse current pulling operation according to the pulse current amplitude during the pulse duration when the electronic load receives the pulse current pulling start command, and stopping the pulse current pulling operation during the pulse interval, the method further includes: When the electronic load receives the pulse current pulling start command, the test control device controls the electronic load to perform pulse current pulling operation according to the pulse current amplitude within the pulse duration and collects the actual current pulling value of the electronic load during this period. The test control equipment calculates the percentage deviation between the actual pull current value and the pulse current amplitude; The test control device determines whether the deviation percentage is greater than or equal to a preset current deviation threshold. When the deviation percentage is greater than or equal to the preset current deviation threshold, the test control device determines that there is external interference; If external interference is detected, the test control device stops the current pulse current pulling operation and marks the battery voltage response data and battery current response data collected during the currently executing pulse current pulling operation as invalid data; When the deviation percentage is less than the preset current deviation threshold, the test control device marks the battery voltage response data and the battery current response data collected during the currently executed pulse current pulling operation as valid data.

8. A test control device, characterized in that, The test control device 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 including computer instructions, and the one or more processors call the computer instructions to cause the test control device to perform the method as described in any one of claims 1-7.

9. A computer program product containing instructions, characterized in that, When the computer program product is run on the test control device, the test control device performs the method as described in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the test control device, the test control device performs the method as described in any one of claims 1-7.

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