A voltage regulation function simulation test system and method

Abstract

This application relates to the technical field of voltage regulation function testing for power banks. It discloses a voltage regulation function simulation testing system and method. This application constructs a voltage regulation function simulation testing system including a simulation clock and a simulation cycle counter. At the software level, it directly simulates the passage of time and the accumulation of charge-discharge cycles during long-term battery use based on user-configured test parameters. The core algorithm layer uses simulation time and / or the number of simulated full-charge cycles to simulate the charging voltage of the power bank over time and the accumulation of charge-discharge cycles, thus completing the voltage regulation function simulation test of the power bank. This method of completing the voltage regulation function simulation test of a power bank through software simulation algorithms greatly reduces the physical testing time required without changing the physical testing environment or replacing the tested object. This significantly shortens the testing cycle while ensuring the test results accurately reflect real-world usage scenarios and have high reliability.

Description

Technical Field

[0001] This application relates to the field of voltage regulation function testing technology for mobile power supplies, and in particular to a voltage regulation function simulation testing system and method. Background Technology

[0002] With the widespread use of portable power banks, their safety has become an increasing concern. Portable power banks should have the function of regulating the maximum charging voltage of the battery or battery pack. Therefore, it is necessary to test the voltage regulation function of portable power banks to enhance their safety.

[0003] However, testing the voltage regulation function of power banks currently faces the problem of long testing cycles. For example, verifying the voltage regulation function in terms of time dimension requires an actual wait of 18-42 months (1.5-3.5 years); verifying the voltage regulation function in terms of the number of full charge cycles requires 70-210 complete charge-discharge cycles. Assuming each cycle lasts 4-6 hours, 210 cycles require continuous testing for 35-52 days. This long testing cycle seriously hinders product development and iteration.

[0004] To address the issue of long testing cycles, existing power bank voltage regulation function testing solutions primarily propose two methods to shorten the testing cycle: First, high-temperature accelerated aging: This involves increasing the temperature to accelerate battery aging and reduce the testing cycle for voltage regulation function across different testing time dimensions. However, this alters the battery's chemical reaction mechanism, resulting in significant differences from actual usage conditions and failing to meet the standard requirement of "testing through inspection and testing tools provided by the power bank manufacturer." Second, small-capacity battery substitution: This uses smaller-capacity batteries to shorten charging and discharging times, thereby reducing the testing cycle for voltage regulation function across different full-charge cycles. However, the battery characteristics differ significantly from the target product, leading to low reliability of the test results. Summary of the Invention

[0005] This application provides a voltage regulation function simulation test system and method to solve the problem that existing voltage regulation function test schemes shorten the test cycle by accelerating aging at high temperature or replacing with small-capacity batteries, but both of these will change the battery characteristics, which are very different from the actual use conditions, do not meet the standard requirements, and have low reliability of test results.

[0006] To address the aforementioned issues, the first aspect of this application proposes a voltage regulation function simulation test system. This system includes: a user interaction layer for allowing users to configure test parameters of the power bank; a business logic layer including a simulation clock and a simulation cycle counter, wherein the simulation clock responds to the test parameters and records the simulation time based on the test parameters; the simulation cycle counter responds to the test parameters and records the number of simulated full-charge cycles based on the test parameters; and a core algorithm layer for calculating the charging voltage of the power bank based on the simulation time, and / or the number of simulated full-charge cycles and the test parameters, calculating the standard charging voltage of the power bank based on the test parameters, and calculating the voltage regulation function simulation test result of the power bank based on the charging voltage and the standard charging voltage.

[0007] In some embodiments of this application, the test parameters include: battery charging limit voltage, number of battery series stages, and time dimension testing. The charging voltage includes a first charging voltage, and the standard charging voltage includes a first standard charging voltage corresponding to the power bank at the simulation time. The core algorithm layer is configured to: respond to the time dimension test; calculate the first charging voltage and the first standard charging voltage based on the simulation time, battery charging limit voltage, and number of battery series stages; and calculate the simulation test results of the power bank's voltage regulation function based on the first charging voltage and the first standard charging voltage.

[0008] In some embodiments of this application, the core algorithm layer further includes a test analyzer, which is configured to calculate the simulation test result of the voltage regulation function in the following manner: comparing the magnitude of the first charging voltage and the first standard charging voltage; when the first charging voltage is less than or equal to the first standard voltage, the simulation test result of the voltage regulation function is "pass"; or, when the first charging voltage is greater than the first standard voltage, the simulation test result of the voltage regulation function is "fail".

[0009] In some embodiments of this application, the test parameters include: battery charging limit voltage, battery series number, and cycle count tests. The charging voltage includes a second charging voltage, and the standard charging voltage includes a second standard charging voltage corresponding to the number of simulated full-charge cycles of the power bank. The core algorithm layer is configured to: respond to the cycle count test; calculate the second charging voltage and the second standard charging voltage based on the simulated full-charge cycle count, battery charging limit voltage, and battery series number; and calculate the voltage regulation function simulation test results of the power bank based on the second charging voltage and the second standard charging voltage.

[0010] In some embodiments of this application, the core algorithm layer further includes a test analyzer, which is configured to calculate the simulation test result of the voltage regulation function in the following manner: comparing the magnitude of the second charging voltage and the second standard charging voltage; when the second charging voltage is less than or equal to the second standard voltage, the simulation test result of the voltage regulation function is "pass"; or, when the second charging voltage is greater than the second standard voltage, the simulation test result of the voltage regulation function is "fail".

[0011] In some embodiments of this application, the test parameters include: battery charging limit voltage, number of battery series stages, time-dimensional test, and cycle count-dimensional test. The charging voltage includes a third charging voltage, and the standard charging voltage includes a first standard charging voltage corresponding to the power bank at the simulation time and a second standard charging voltage corresponding to the power bank at the simulated full-charge cycle count. The core algorithm layer is configured to: respond to the time-dimensional test and cycle count-dimensional test; calculate the third charging voltage based on the simulation time, simulated full-charge cycle count, battery charging limit voltage, and number of battery series stages; calculate the first standard charging voltage based on the simulation time, battery charging limit voltage, and number of battery series stages; calculate the second standard charging voltage based on the simulated full-charge cycle count, battery charging limit voltage, and number of battery series stages; and calculate the voltage regulation function simulation test result of the power bank based on the third charging voltage, the first standard charging voltage, and the second standard charging voltage.

[0012] In some embodiments of this application, the core algorithm layer further includes a test analyzer, which is configured to calculate the simulation test result of the voltage regulation function in the following manner: comparing the magnitude of the third charging voltage with the first standard charging voltage and the second standard charging voltage respectively; when the third charging voltage is less than or equal to the first standard charging voltage and less than or equal to the second standard charging voltage, the simulation test result of the voltage regulation function is "pass"; or, when the third charging voltage is greater than the first standard charging voltage and / or greater than the second standard charging voltage, the simulation test result of the voltage regulation function is "fail".

[0013] In some embodiments of this application, the core algorithm layer further includes: a first trend prediction analysis model, which is configured to: when the simulation time reaches a preset simulation time, calculate the first predicted charging voltage corresponding to the time step based on the preset simulation time, the preset time step, and the voltage decay rate corresponding to the preset simulation time, wherein the sum of the preset simulation time and the time step is the target time checkpoint; calculate the simulation test result of the first predicted voltage regulation function based on the first predicted charging voltage and the first standard charging voltage corresponding to the target time checkpoint; and determine the working state of the simulation clock, the first battery aging simulation model, and the first voltage regulation simulation model based on the simulation test result of the first predicted voltage regulation function.

[0014] In some embodiments of this application, the first trend prediction analysis model is further configured to: compare the magnitude of the first predicted charging voltage with the magnitude of the first standard charging voltage corresponding to the target time checkpoint; when the first predicted charging voltage is less than or equal to the first standard charging voltage corresponding to the target time checkpoint, the simulation test result of the first predicted voltage adjustment function is "passed"; or, when the first predicted charging voltage is greater than the first standard charging voltage corresponding to the target time checkpoint, the simulation test result of the first predicted voltage adjustment function is "failed".

[0015] In some embodiments of this application, the core algorithm layer further includes: a second trend prediction analysis model, which is configured to: when the number of simulated full-charge cycles reaches a preset number of simulated full-charge cycles, calculate the second predicted charging voltage corresponding to the cycle count step based on the preset number of simulated full-charge cycles, the preset cycle count step size, and the voltage decay rate corresponding to the number of simulated full-charge cycles, wherein the sum of the preset number of simulated full-charge cycles and the cycle count step size is the target cycle count checkpoint; calculate the simulation test result of the second predicted voltage regulation function based on the second predicted charging voltage and the second standard charging voltage corresponding to the target cycle count checkpoint; and determine the working state of the simulation clock, the simulation cycle counter, the second battery aging simulation model, and the second voltage regulation simulation model based on the simulation test result of the second predicted voltage regulation function.

[0016] In some embodiments of this application, the second trend prediction analysis model is further configured to: compare the second predicted charging voltage with the second standard charging voltage corresponding to the target cycle count checkpoint; when the second predicted charging voltage is less than or equal to the second standard charging voltage corresponding to the target cycle count checkpoint, the simulation test result of the first predicted voltage regulation function is "passed"; or, when the second predicted charging voltage is greater than the second standard charging voltage corresponding to the target cycle count checkpoint, the simulation test result of the second predicted voltage regulation function is "failed".

[0017] In some embodiments of this application, when the simulation test result of the first predicted voltage regulation function is "passed", the working state of the simulation clock, the first battery aging simulation model, and the first voltage regulation simulation model is "continued to work"; or, when the simulation test result of the first predicted voltage regulation function is "failed", the working state of the simulation clock, the first battery aging simulation model, and the first voltage regulation simulation model is "stopped working"; or, when the simulation test result of the second predicted voltage regulation function is "passed", the working state of the simulation clock, the simulation cycle counter, the battery aging simulation model, and the voltage regulation simulation model is "continued to work"; or, when the simulation test result of the second predicted voltage regulation function is "failed", the working state of the simulation cycle counter, the second battery aging simulation model, and the second voltage regulation simulation model is "stopped working".

[0018] In some embodiments of this application, the test parameters include a time acceleration factor, and the simulation clock is configured to calculate the simulation time based on the physical time corresponding to the start of the test, the physical time corresponding to the current test, and the time acceleration factor.

[0019] In some embodiments of this application, the test parameters include: a cycle count acceleration factor, and the simulation cycle counter is configured to calculate the simulated full-charge cycle count based on the full-charge cycle count corresponding to the start of the test, the physical time corresponding to the start of the test, the physical time corresponding to the current test, and the cycle count acceleration factor.

[0020] Based on the same inventive concept, this application proposes a voltage regulation function simulation test method, which includes: configuring test parameters of a power bank; recording the simulation time and / or the number of simulated full-charge cycles in response to the test parameters; calculating the charging voltage of the power bank based on the simulation time, and / or the number of simulated full-charge cycles and the test parameters; calculating the standard charging voltage of the power bank based on the test parameters; and calculating the voltage regulation function simulation test result of the power bank based on the charging voltage and the standard charging voltage.

[0021] The beneficial effects of this application are as follows: This application constructs a voltage regulation function simulation test system that includes a simulated clock and a simulated cycle counter. At the software level, it directly simulates the passage of time and the accumulation of charge-discharge cycles during long-term battery use based on user-configured test parameters. The core algorithm layer uses simulation time and / or the number of simulated full-charge cycles to simulate the charging voltage of the power bank as time passes and charge-discharge cycles accumulate, thus completing the voltage regulation function simulation test of the power bank. This method of completing the voltage regulation function simulation test of the power bank through software simulation algorithms greatly reduces the physical testing time required without changing the physical test environment or replacing the test object. This significantly shortens the test cycle while ensuring that the test results accurately reflect real-world usage scenarios and have high reliability. Attached Figure Description

[0022] Figure 1 This is a first flowchart illustrating the simulation test results of the voltage regulation function provided in this application. Figure 2 This is a second flowchart illustrating the simulation test results of the voltage regulation function provided in this application. Figure 3 This is a third flowchart illustrating the simulation test results of the voltage regulation function provided in this application. Figure 4 This is a fourth flowchart illustrating the simulation test results of the voltage regulation function provided in this application. Figure 5 This is a first flowchart illustrating the simulation test results of the voltage regulation function provided in this application. Figure 6 This is a second flowchart illustrating the second embodiment of the simulation test results of the voltage regulation function provided in this application; Figure 7 This is a third flowchart illustrating the simulation test results of the voltage regulation function provided in this application, representing the second embodiment. Figure 8 This is a fourth flowchart of the second embodiment of the simulation test results of the voltage regulation function provided in this application; Figure 9 This is a first flowchart illustrating the simulation test results of the voltage regulation function provided in this application. Figure 10 This is a second flowchart illustrating the simulation test results of the voltage regulation function provided in this application, representing the third embodiment. Figure 11 This is a flowchart illustrating an embodiment of the voltage regulation function simulation test method provided in this application. Detailed Implementation

[0023] To facilitate understanding of this application, a more detailed description is provided below with reference to the accompanying drawings and specific embodiments. Preferred embodiments of this application are shown in the drawings. However, this application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of this application.

[0024] It should be noted that, unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0025] In the voltage regulation function test of power banks, the voltage regulation function of power banks is usually tested from the time dimension and the full charge cycle number dimension. Among them, when testing the voltage regulation function corresponding to the time dimension, it is usually necessary to calculate the maximum charging voltage of the power bank corresponding to the following time ranges: (1) ≤18 months after the production date, maximum charging voltage ≤n×(U1-0.1)V; ≤30 months after the production date, maximum charging voltage ≤n×(U1-0.15)V; ≤42 months after the production date, maximum charging voltage ≤n×(U1-0.2)V. When testing the voltage regulation function corresponding to the full charge cycle number dimension, it is usually necessary to calculate the maximum charging voltage of the power bank corresponding to the following full charge cycle number ranges: (1) ≤70 full charge cycles, maximum charging voltage ≤n×(U1-0.1)V; ≤140 full charge cycles, maximum charging voltage ≤n×(U1-0.15)V; ≤210 full charge cycles, maximum charging voltage ≤n×(U1-0.2)V. Where U1 is the battery charging limit voltage (not the battery pack charging limit voltage), and n is the number of battery series stages.

[0026] Therefore, according to traditional testing methods, verifying the voltage regulation function corresponding to the time dimension requires an actual wait of 18-42 months (1.5-3.5 years), and verifying the voltage regulation function corresponding to the number of full charge cycles requires 70-210 complete charge-discharge cycles. Assuming each cycle lasts 4-6 hours, 210 cycles require continuous testing for 35-52 days. This testing cycle severely hinders product development and iteration. Therefore, as mentioned in the background section, to quickly test the voltage regulation function of power banks, existing voltage regulation function testing schemes shorten the testing cycle by using high-temperature accelerated aging or replacing batteries with smaller capacities. However, both methods alter battery characteristics, resulting in significant differences from actual usage conditions, failure to meet standard requirements, and low reliability of test results. Therefore, there is an urgent need for a new testing scheme that can significantly shorten the testing cycle while ensuring compliance with testing standards and accuracy of results.

[0027] To address the aforementioned issues, this application proposes a voltage regulation function simulation test system. The system includes: a user interaction layer for allowing users to configure test parameters for the power bank; a business logic layer including a simulation clock and a simulation cycle counter, wherein the simulation clock responds to the test parameters and records the simulation time based on the test parameters; the simulation cycle counter responds to the test parameters and records the number of simulated full-charge cycles based on the test parameters; and a core algorithm layer for calculating the charging voltage of the power bank based on the simulation time, and / or the number of simulated full-charge cycles and the test parameters, calculating the standard charging voltage of the power bank based on the test parameters, and calculating the voltage regulation function simulation test results of the power bank based on the charging voltage and the standard charging voltage.

[0028] As described above, the embodiments of this application construct a voltage regulation function simulation test system that includes a simulation clock and a simulation cycle counter. At the software level, based on user-configured test parameters, it directly simulates the passage of time (i.e., the corresponding simulation time) and the accumulation of charge-discharge cycles (i.e., the corresponding number of simulated full-charge cycles) of a battery during long-term use. Furthermore, the core algorithm layer uses the simulation time and / or the number of simulated full-charge cycles to simulate the charging voltage of the power bank as time passes and the number of charge-discharge cycles accumulates, thus completing the voltage regulation function simulation test of the power bank. This method of completing the voltage regulation function simulation test of a power bank through software simulation algorithms significantly reduces the physical time required for testing without changing the physical test environment (e.g., high temperature) or replacing the test object (e.g., a small-capacity battery). It not only significantly shortens the test cycle but also strictly adheres to standard test conditions, ensuring the test results accurately reflect real-world usage scenarios and have high reliability.

[0029] This application will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0030] I. First Embodiment According to one embodiment of this application, the test parameters include: battery charging limit voltage, number of battery series stages, and time dimension testing. The charging voltage includes a first charging voltage, and the standard charging voltage includes the first standard charging voltage of the power bank corresponding to the simulation time. Figure 1 As shown, the core algorithm layer is configured to obtain the simulation test results of the voltage regulation function of the power bank by executing the following steps S1-S3: S1, responding to the time dimension test; S2, calculating the first charging voltage and the first standard charging voltage based on the simulation time, the battery charging limit voltage and the number of battery series stages; S3, calculating the simulation test results of the voltage regulation function of the power bank based on the first charging voltage and the first standard charging voltage.

[0031] As can be seen from the above description, the system of the above embodiments of this application can dynamically calculate and compare the actual charging voltage with the standard charging voltage based on the simulation time, battery charging limit voltage and series stage, thereby realizing accurate and efficient simulation evaluation of the performance degradation of the power bank voltage regulation function during long-term use, significantly shortening the long test cycle that traditionally relies on physical time cycles.

[0032] In this embodiment, the core algorithm layer includes a first battery aging simulation model, and the first battery aging simulation model is configured to calculate the first charging voltage in the following manner:

[0033]

[0034] in, Indicates simulation time The corresponding time decay coefficient, Limit the charging voltage for the battery. For the number of battery series cascades, Indicates simulation time The corresponding time-dimensional voltage decay. The time decay coefficient is a key parameter used to quantify the linear rate of voltage decay over time during battery aging. Its unit is typically voltage / time (e.g., mV / hour or V / year), describing the magnitude of the battery's allowable charging voltage decrease per unit time as the simulation progresses.

[0035] As described above, the embodiments of this application construct a first battery aging simulation model that includes a time decay coefficient. By using the number of series stages, the charging limit voltage, and the voltage decay over time as parameters to calculate the charging voltage, the dynamic changes in voltage over time during battery aging can be accurately simulated. This provides reliable data support for the study of mobile power bank aging characteristics and performance optimization, and improves the accuracy and practicality of the simulation results.

[0036] In this embodiment, the test parameters also include: time checkpoints and voltage reduction thresholds. The core algorithm layer also includes a first voltage regulation simulation model, wherein the first voltage regulation simulation model is configured to calculate the first standard charging voltage in the following manner:

[0037] in, Indicates simulation time The corresponding first standard charging voltage, , and These are the preset first, second, and third time checkpoints. For example... , , It should be noted that time checkpoints can be dynamically added based on user needs or battery properties, and the specific setting of time checkpoints is not limited by this embodiment.

[0038] As can be seen from the above description, the above embodiments of this application check at preset time points (e.g., , , 2) Set standard charging voltages that decrease in a stepped manner (based on the number of battery series stages n and the charging limit voltage). The fixed offset) is used to compare the charging voltage at the corresponding time calculated by the first battery aging simulation model with the first standard charging voltage, thereby realizing a phased quantitative assessment of the battery aging process; this "checkpoint + voltage reduction threshold" mechanism not only makes it easy to intuitively judge whether the battery performance has degraded to the critical level, but also accurately locates the aging acceleration range, providing a structured and operable decision basis for battery health status classification and early warning, maintenance strategy formulation and life prediction, significantly improving the engineering practicality of test results and system response efficiency.

[0039] In this embodiment, considering the accuracy limitations of the actual circuit implementation, an adjustment error term is introduced to obtain the actual first standard charging voltage:

[0040] in, The first standard charging voltage, The preset sampling error of the analog-to-digital converter (ADC) is used. The preset digital-to-analog converter (DAC) output error, This is a preset temperature drift error. Temperature drift error refers to the error caused by changes in ambient temperature leading to deviations in circuit component parameters (such as resistance, capacitance, and semiconductor characteristics), resulting in the output voltage or signal deviating from its ideal value. In this embodiment, it is introduced as an adjustment error term to more accurately simulate the performance deviation of the actual circuit under different temperature conditions, ensuring that the calculation of the first standard charging voltage is closer to real physical behavior.

[0041] As can be seen from the above description, the embodiments of this application introduce non-ideal factors of actual circuits such as ADC sampling error, DAC output error and temperature drift error on the basis of the first standard charging voltage, and construct a first voltage regulation simulation model that is closer to the real hardware environment, so that the simulation results have engineering feasibility and robustness, and effectively improve the control accuracy and reliability of the battery management system under complex working conditions.

[0042] In this embodiment, the core algorithm layer also includes a test analyzer, such as... Figure 2 As shown, the test analyzer is configured to obtain the simulation test result of the voltage regulation function by performing the following steps S31-S32: S31, comparing the magnitude of the first charging voltage and the first standard charging voltage; S32, when the first charging voltage is less than or equal to the first standard voltage, the simulation test result of the voltage regulation function is "pass"; or, when the first charging voltage is greater than the first standard voltage, the simulation test result of the voltage regulation function is "fail".

[0043] As can be seen from the above description, the embodiments of this application establish an automated simulation test mechanism based on voltage threshold determination by introducing a test analyzer into the system. This mechanism can quickly and objectively verify the compliance of the voltage regulation function, thereby significantly improving the efficiency and accuracy of power bank performance testing.

[0044] Furthermore, the inventors' research also found that existing testing methods can only obtain results after the test is completed, and cannot monitor subtle changes in the voltage regulation curve in real time, making it difficult to provide early warning and cause analysis before failure. Therefore, this application introduces a machine learning trend prediction algorithm to predict the final test result based on prior data, achieving intelligent optimization of test resources. Therefore, according to one embodiment of this application, the core algorithm layer also includes: a first trend prediction analysis model, and as... Figure 3 As shown, the first trend prediction analysis model is configured to execute the following steps S4-S6 to achieve real-time prediction of the predicted charging voltage corresponding to the future simulation time, thereby determining the working state of the simulation clock, the first battery aging simulation model, and the first voltage regulation simulation model: S4: When the simulation time reaches the preset simulation time, calculate the first predicted charging voltage corresponding to the time step based on the preset simulation time, the preset time step, and the voltage decay rate corresponding to the preset simulation time, wherein the sum of the preset simulation time and the time step is the target time checkpoint; S5: Calculate the simulation test result of the first predicted voltage regulation function based on the first predicted charging voltage and the first standard charging voltage corresponding to the target time checkpoint; S6: Determine the working state of the simulation clock, the first battery aging simulation model, and the first voltage regulation simulation model based on the simulation test result of the first predicted voltage regulation function.

[0045] In this embodiment, the first trend prediction analysis model is configured to calculate the first predicted charging voltage corresponding to the time step in the following manner:

[0046]

[0047] in, For the preset time step, Indicates simulation time The corresponding first predicted charging voltage, The preset smoothing coefficient is a key control parameter in the exponential smoothing prediction algorithm, used to adjust the weight balance between "smoothing and suppressing noise" and "responding to changing trends" during the prediction process. The value range is 0.3 to 0.7. Specifically, when the value of 'a' is between 0.3 and 0.7, the exponent term... When the value of 'a' falls between 0.5 and 0.75, the corresponding smooth weight distribution matches the changing pattern of battery charging voltage. Since the change in charging voltage is slow and trend-based, but is superimposed with sampling noise and short-term fluctuations, a value of 'a' between 0.3 and 0.7 allows the first trend prediction analysis model to be neither excessively disturbed by noise nor excessively lagging behind the real trend, thus better adapting to the engineering requirements of battery voltage prediction.

[0048] As can be seen from the above description, the embodiments of this application introduce a preset smoothing coefficient to adjust the rate of change of the charging voltage. Exponentially weighted smoothing is applied to ensure that the predicted charging voltage for future time steps smoothly tracks the current voltage trend. This method effectively suppresses noise interference from voltage fluctuations while also taking into account the dynamic response characteristics of voltage changes, thus improving the accuracy of trend capture while maintaining prediction stability.

[0049] In this embodiment, as Figure 4 As shown, step S5 includes: S51, comparing the magnitude of the first predicted charging voltage with the magnitude of the first standard charging voltage corresponding to the target time check point; S52, when the first predicted charging voltage is less than or equal to the first standard charging voltage corresponding to the target time check point, the simulation test result of the first predicted voltage adjustment function is "passed"; or, when the first predicted charging voltage is greater than the first standard charging voltage corresponding to the target time check point, the simulation test result of the first predicted voltage adjustment function is "failed".

[0050] In this embodiment, step S6 includes: when the simulation test result of the first predicted voltage regulation function is "passed", the working state of the simulation clock, the first battery aging simulation model, and the first voltage regulation simulation model is "continue to work"; or, when the simulation test result of the first predicted voltage regulation function is "failed", the working state of the simulation clock, the first battery aging simulation model, and the first voltage regulation simulation model is "stop working".

[0051] As described above, the embodiments of this application introduce a machine learning trend prediction algorithm to calculate the charging voltage for future simulation times in real time using simulation time, time step, and voltage decay rate, and compare it with standard values, thus realizing the transformation from "post-event detection" to "pre-event warning". This not only enables real-time monitoring of subtle changes in the voltage regulation curve and timely termination of the test before failure to protect the model, but also intelligently and dynamically controls the working status of the simulation clock, counter, and each simulation model based on the prediction results, thereby significantly optimizing the configuration of test resources and improving test efficiency and safety.

[0052] In addition, to verify the effectiveness of the above embodiments, the inventors conducted the following experiments: (1) Test scenario: Verify a portable power bank containing two batteries connected in series (n=2, Does the voltage regulation function corresponding to the time dimension comply with the GB standard?

[0053] (2) Test parameter configuration: number of battery series stages n=2; single cell charging limit voltage Initial charging voltage of battery pack = 8.4V (2×4.2V); Test type: Time dimension test (TYPE_TIME); Time acceleration factor =100 times; Target simulation time: 42 months; Estimated physical testing time: 42 × 30 × 24 × 3600 / 100 = 1088640 seconds = 302.4 hours (3) Test execution process: Initialization phase (t=0); establish the first battery aging simulation model and set the time decay coefficient. =0.015 (based on historical data of similar batteries); the simulation clock is reset to zero, and the current virtual month M=0; calculate the standard charging voltage limit for each checkpoint: time checkpoint 1 (≤18 months): limit = 2×(4.2-0.1)=8.2V; time checkpoint 2 (≤30 months): limit = 2×(4.2-0.15)=8.1V; time checkpoint 3 (≤42 months): limit = 2×(4.2-0.2)=8.0V.

[0054] (4) Simulation Operation Phase: The simulation clock advances at 100 times the normal speed, advancing 100 seconds of simulation time every physical second; the state of the first battery aging simulation model is updated once every additional virtual month. =8.4 2 × 0.015 × M, when M crosses 18, 30, or 42 months, a time checkpoint determination is triggered; for example, a time checkpoint determination example (assuming the actual product's adjustment function is normal): M = 18 months: First battery aging simulation model calculation =7.86V, limit 8.2V, judged PASS, that is, "pass" (voltage is below the limit, meets the requirements); M=30 months: first battery aging simulation model calculation =7.5V, limit 8.1V, pass; M=42 months: first battery aging simulation model calculation =7.14V, limit 8.0V, pass.

[0055] (5) Trend prediction optimization: When M=12 months, the decay curve is fitted based on the data of the previous 12 months to predict the trend when M=42 months. ≈8.21V, where the predicted value of 8.21V is greater than the limit of 8.0V, but the difference is within the allowable range, so testing continues; when M=35 months, the forecast is re-predicted when M=42 months. ≈8.08V, still higher than the limit but close, continue testing until the end.

[0056] (6) Test completion: After 302.4 hours of physical time, the 42-month virtual time test is completed.

[0057] (7) Generate report: All time checkpoints PASS, product meets GB standard requirements.

[0058] It should be noted that the relevant values ​​in this experiment are only designed for testing the voltage regulation function of a specified power bank during the experiment. The values ​​of the relevant test parameters in this application can be dynamically set according to the actual product performance of the power bank and are not limited by the specific implementation method.

[0059] As described above, the experiment constructed a battery aging simulation model of two mobile power banks and used a 100x time acceleration factor to compress the physical test, which originally required 42 months, into 302.4 hours. At three key time checkpoints of 18, 30, and 42 months, the continuous compliance of the voltage regulation function during the aging process was verified by comparing the charging voltage with the standard limit. This significantly improved testing efficiency, reduced R&D costs, and ensured that the product complies with GB standards throughout its entire life cycle.

[0060] II. Second Embodiment According to one embodiment of this application, the test parameters include: battery charging limit voltage, number of battery series stages, and cycle count testing. The charging voltage includes a second charging voltage, and the standard charging voltage includes a second standard charging voltage corresponding to the number of simulated full-charge cycles of the power bank. Figure 5As shown, the core algorithm layer is configured to obtain the simulation test results of the power bank's voltage regulation function by executing the following steps T1-T3: T1, responding to the cycle count dimension test; T2, calculating the second charging voltage and the second standard charging voltage based on the simulated full charge cycle count, battery charging limit voltage, and battery series stage number; T3, calculating the simulation test results of the power bank's voltage regulation function based on the second charging voltage and the second standard charging voltage.

[0061] As described above, the system of the above embodiments of this application can quickly simulate and evaluate the performance degradation of the voltage regulation function of a power bank after long-term charge-discharge cycles based on the number of simulated full-charge cycles, battery charging limit voltage, and number of series stages. Thus, it can efficiently verify the voltage testing function of the product without the need for time-consuming and laborious physical cycle testing, so as to achieve accurate and efficient simulation evaluation of the performance degradation of the power bank's voltage regulation function after long-term charge-discharge cycles, significantly shortening the long test cycle that traditionally relies on physical time cycles.

[0062] In this embodiment, the core algorithm layer includes a second battery aging simulation model, and the second battery aging simulation model is configured to calculate the second charging voltage in the following manner:

[0063]

[0064] in, Limit the charging voltage for the battery. For the number of battery series cascades, Indicates the number of full-charge cycles in the simulation. The corresponding cyclic decay coefficient, Indicates the number of full-charge cycles in the simulation. The corresponding voltage decay over cycles. The cycle decay coefficient is a parameter used to quantify the degree of battery aging. Specifically, it represents the battery's performance (expressed as voltage decay) after a certain number of charge-discharge cycles. This indicates the linear degradation relationship between the battery voltage and the number of cycles. The larger the coefficient, the faster the voltage decays and the more severe the aging of the battery at the same number of cycles.

[0065] As described above, the embodiments of this application construct a second battery aging simulation model that includes a cycle decay coefficient. By using the number of series stages, the charging limit voltage, and the voltage decay amount in the cycle dimension as parameters to calculate the charging voltage, the dynamic change of voltage with the number of full-charge cycles during battery aging can be accurately simulated. This provides reliable data support for the study of mobile power bank aging characteristics and performance optimization, and improves the accuracy and practicality of simulation results.

[0066] In this embodiment, the test parameters also include: cycle count checkpoint and voltage reduction threshold. The core algorithm layer also includes a second voltage regulation simulation model, which is further configured to calculate the second standard charging voltage in the following manner:

[0067] in, Indicates the number of full-charge cycles in the simulation. The corresponding second standard charging voltage, , and These are the preset checkpoints for the first, second, and third iterations, respectively.

[0068] As can be seen from the above description, the above embodiments of this application check at a preset number of loop counts (such as...). , , Set a standard charging voltage that decreases in a stepped manner (based on the number of battery series stages n and the charging limit voltage). The fixed offset) and the charging voltage calculated by the second battery aging simulation model at the corresponding number of cycles are compared with the second standard charging voltage to achieve a phased quantitative assessment of the battery aging process; this "checkpoint + voltage reduction threshold" mechanism not only makes it easy to intuitively judge whether the battery performance has degraded to the critical level, but also accurately locates the aging acceleration range, providing a structured and operable decision basis for battery health status classification and early warning, maintenance strategy formulation and life prediction, significantly improving the engineering practicality of test results and system response efficiency.

[0069] In this embodiment, considering the accuracy limitations of the actual circuit implementation, an adjustment error term is introduced to obtain the actual second standard charging voltage:

[0070] in, The second standard charging voltage, This is the preset ADC sampling error. This is the preset DAC output error. This is the preset temperature drift error.

[0071] As can be seen from the above description, the embodiments of this application introduce non-ideal factors of actual circuits such as ADC sampling error, DAC output error and temperature drift error on the basis of the second standard charging voltage, and construct a second voltage regulation simulation model that is closer to the real hardware environment, so that the simulation results have engineering feasibility and robustness, and effectively improve the control accuracy and reliability of the battery management system under complex working conditions.

[0072] In this embodiment, the core algorithm layer also includes a test analyzer, such as... Figure 6 As shown, the test analyzer is configured to obtain the voltage regulation function simulation test result by performing the following steps T31-T32: T31, compare the magnitude of the second charging voltage and the second standard charging voltage; T32, when the second charging voltage is less than or equal to the second standard voltage, the voltage regulation function simulation test result is "pass"; or, when the second charging voltage is greater than the second standard voltage, the voltage regulation function simulation test result is "fail".

[0073] As can be seen from the above description, the embodiments of this application establish an automated simulation test mechanism based on voltage threshold determination by introducing a test analyzer into the system. This mechanism can quickly and objectively verify the compliance of the voltage regulation function, thereby significantly improving the efficiency and accuracy of power bank performance testing.

[0074] In this embodiment, the core algorithm layer further includes: a second trend prediction and analysis model, and as follows: Figure 7 As shown, the second trend prediction analysis model is configured to execute the following steps T4-T6 to achieve real-time prediction of the predicted charging voltage corresponding to the future simulated full-charge cycle count, thereby determining the working status of the simulation clock, the second battery aging simulation model, and the second voltage regulation simulation model: T4: When the simulated full-charge cycle count reaches the preset simulated full-charge cycle count, calculate the second predicted charging voltage corresponding to the cycle count step size based on the preset simulated full-charge cycle count, the preset cycle count step size, and the voltage decay rate corresponding to the simulated full-charge cycle count. The sum of the preset simulated full-charge cycle count and the cycle count step size is the target cycle count checkpoint; T5: Calculate the simulation test result of the second predicted voltage regulation function based on the second predicted charging voltage and the second standard charging voltage corresponding to the target cycle count checkpoint; T6: Based on the simulation test result of the second predicted voltage regulation function, determine the working status of the simulation clock, the simulation cycle counter, the second battery aging simulation model, and the second voltage regulation simulation model.

[0075] In this embodiment, the second trend prediction analysis model is configured to calculate the second predicted charging voltage corresponding to the cycle number step size in the following manner:

[0076]

[0077] in, The preset iteration step size, Indicates the number of full-charge cycles in the simulation. The corresponding second predicted charging voltage, This is the preset smoothing coefficient. The value ranges from 0.3 to 0.7. The step size for the number of iterations is [not specified]. instruct After the second full charge cycle One full charge cycle.

[0078] As can be seen from the above description, the embodiments of this application introduce a preset smoothing coefficient to adjust the rate of change of the charging voltage. Exponentially weighted smoothing is applied to ensure that the charging voltage for the predicted future cycle steps smoothly tracks the current voltage trend. This method effectively suppresses noise interference from voltage fluctuations while also taking into account the dynamic response characteristics of voltage changes, thus improving the accuracy of trend capture while ensuring prediction stability.

[0079] In this embodiment, as Figure 8 As shown, step T5 includes: T51, comparing the second predicted charging voltage with the second standard charging voltage corresponding to the target cycle count checkpoint; T52, when the second predicted charging voltage is less than or equal to the second standard charging voltage corresponding to the target cycle count checkpoint, the simulation test result of the first predicted voltage regulation function is "passed"; or, when the second predicted charging voltage is greater than the second standard charging voltage corresponding to the target cycle count checkpoint, the simulation test result of the second predicted voltage regulation function is "failed".

[0080] In this embodiment, when the simulation test result of the second predictive voltage regulation function is "passed", the working state of the simulation cycle counter, the second battery aging simulation model, and the second voltage regulation simulation model is "continue to work"; or when the simulation test result of the second predictive voltage regulation function is "failed", the working state of the simulation cycle counter, the second battery aging simulation model, and the second voltage regulation simulation model is "stopped working".

[0081] As described above, the embodiments of this application introduce a machine learning trend prediction algorithm to calculate the charging voltage for future cycles in real time using the number of simulated full-charge cycles, cycle step size, and voltage decay rate, and compare it with the standard value, thus realizing the transformation from "post-event detection" to "pre-event warning". This not only enables real-time monitoring of subtle changes in the voltage regulation curve and timely termination of the test before failure to protect the model, but also intelligently and dynamically controls the working status of the simulation clock, counter, and each simulation model based on the prediction results, thereby significantly optimizing the configuration of test resources and improving test efficiency and safety.

[0082] In addition, to verify the effectiveness of the above embodiments, the inventors conducted the following experiments: (1) Test scenario: Verify a portable power bank containing 3 batteries connected in series (n=3, Does the voltage regulation function corresponding to the cycle count dimension comply with the GB standard?

[0083] (2) Test parameter configuration: number of battery series stages n=3; single cell charging limit voltage

[0084] (High-voltage lithium battery); Initial charging voltage of battery pack = 13.05V; Test type: Cycle count test (TYPE_CYCLE); Cycle count acceleration factor β = 10 times / second; Target cycle count: 210 cycles; Estimated physical test time: 210 / 10 = 21 seconds.

[0085] (3) Test execution results: Physical test time: 21 seconds to complete 210 virtual cycles; Number of cycles and checkpoints: 70: Measured voltage 12.75V, limit 12.75V (=3×(4.35-0.1)), PASS is determined; Cycle count checkpoint 140: Measured voltage 12.60V, limit 12.60V (=3×(4.35-0.15)), pass; Cycle count checkpoint 210: Measured voltage 12.45V, limit 12.45V (=3×(4.35-0.2)), pass.

[0086] It should be noted that the relevant values ​​in this experiment are only designed for testing the voltage regulation function of a specified power bank during the experiment. The values ​​of the relevant test parameters in this application can be dynamically set according to the actual product performance of the power bank and are not limited by the specific implementation method.

[0087] As described above, the experiments verified the effectiveness of the cycle count testing scheme of this application. Within a short physical test time of 21 seconds, the system, by introducing an acceleration factor, efficiently simulated the performance degradation process of the power bank after 210 complete charge-discharge cycles. It successfully compared the measured voltage with the standard limit calculated based on the degradation model at multiple preset cycle count checkpoints in real time, accurately determining that the product complies with the GB standard. This fully demonstrates that this scheme can quickly and reliably evaluate the long-term cycle durability of the power bank without conducting actual long-term cycle tests, greatly improving testing efficiency.

[0088] III. Third Embodiment According to one embodiment of this application, the test parameters include: battery charging limit voltage, number of battery series stages, time-dimensional test and cycle count-dimensional test. The charging voltage includes a third charging voltage, and the standard charging voltage includes a first standard charging voltage corresponding to the simulated time and a second standard charging voltage corresponding to the simulated full-charge cycle count. Figure 9As shown, the core algorithm layer is configured to obtain the simulation test results of the power bank's voltage regulation function by executing the following steps P1-P5: P1, responding to time-dimensional testing and cycle count-dimensional testing; P2, calculating the third charging voltage based on simulation time, simulated full-charge cycle count, battery charging limit voltage, and battery series stage number; P3, calculating the first standard charging voltage based on simulation time, battery charging limit voltage, and battery series stage number; P4, calculating the second standard charging voltage based on simulated full-charge cycle count, battery charging limit voltage, and battery series stage number; P5, calculating the simulation test results of the power bank's voltage regulation function based on the third charging voltage, the first standard charging voltage, and the second standard charging voltage.

[0089] In this embodiment, the core algorithm layer also includes a test analyzer, such as... Figure 10 As shown, the test analyzer is configured to obtain the simulation test result of the voltage regulation function by performing the following steps P51-P52: P51, compare the magnitude of the third charging voltage with the first standard charging voltage and the second standard charging voltage respectively; P52, when the third charging voltage is less than or equal to the first standard charging voltage and less than or equal to the second standard charging voltage, the simulation test result of the voltage regulation function is "pass"; or, when the third charging voltage is greater than the first standard charging voltage and / or greater than the second standard charging voltage, the simulation test result of the voltage regulation function is "fail".

[0090] As described above, the system of the above embodiments of this application simultaneously tests the response time dimension and the cycle count dimension. It can comprehensively calculate an actual charging voltage that integrates the effects of time aging and cycle decay based on simulation time, cycle count, battery parameters, etc., and compare it with the standard voltage that only considers time aging and the standard voltage that only considers cycle decay. This enables a more comprehensive and realistic simulation evaluation of the performance degradation of the power bank's voltage regulation function under combined stress (long-term storage and frequent use), significantly improving the coverage and accuracy of the test.

[0091] In this embodiment, the core algorithm layer includes a third battery aging simulation model, and the third battery aging simulation model is configured to calculate the third charging voltage in the following manner:

[0092]

[0093]

[0094] in, Simulation time, Indicates simulation time The corresponding time decay coefficient, Limit the charging voltage for the battery. For the number of battery series cascades, Indicates simulation time The corresponding voltage decay over time. Indicates the number of full-charge cycles in the simulation. The corresponding cyclic decay coefficient, Indicates the number of full-charge cycles in the simulation. The corresponding voltage decay in the cyclic dimension.

[0095] It should be noted that the calculation methods for the first standard charging voltage and the second standard charging voltage have been given in the first embodiment and the second embodiment, and will not be repeated here.

[0096] As can be seen from the above description, the embodiments of this application construct a system that includes a time decay coefficient. and cyclic decay coefficient The third battery aging simulation model realizes the simulation of battery charging voltage as a function of simulation time. and number of full charge cycles Precise quantitative simulations help to more accurately assess the aging characteristics and lifespan of batteries under different usage scenarios.

[0097] Similarly, this embodiment can also use the first trend prediction analysis model and the second trend prediction analysis model of the first embodiment and the second embodiment to predict the charging voltage of the future time step and the future cycle number step, which will not be repeated here.

[0098] The inventors' research also revealed the following problems with existing testing methods: High testing costs: Long-term testing requires significant investment in testing equipment, manpower, and site resources. For companies requiring multi-batch, multi-model verification, traditional methods necessitate establishing a massive testing matrix, resulting in substantial equipment investment and power consumption. Poor testing consistency: Long-term manual monitoring is prone to oversights, and differences in operation by different testers lead to poor comparability of results. Environmental factors (temperature, humidity) fluctuate over testing processes lasting several months, affecting test accuracy. The simulation testing system proposed in this application replaces physical testing with software simulation. It rapidly calculates the aging process in a virtual environment using battery aging simulation models and voltage regulation simulation models, fundamentally eliminating the long-term occupation of substantial physical equipment, manpower, and site resources, significantly reducing testing costs. Simultaneously, the system ensures complete consistency in conditions and procedures for each test through parameterized configuration and automated algorithm execution, eliminating the impact of differences in human operation and long-term environmental fluctuations, thereby greatly improving the consistency and accuracy of test results.

[0099] To gain a clearer and more comprehensive understanding of this application, the functions performed by some modules of the voltage regulation function simulation test system of this application will be described in detail below.

[0100] I. User Interaction Layer In this embodiment, the user interaction layer is a UI interface, which includes a parameter configuration module, a test monitoring panel, and a report generation and export module. The parameter configuration module allows the user to configure the power bank's test parameters. The test monitoring panel displays at least one of the following: simulation time, number of full-charge cycles, and voltage regulation function simulation test results. The report generation and export module, in response to a user-inputted report generation command, generates a test report based on the voltage regulation function simulation test results, and, in response to a report export command, exports the test report in a predetermined format to a specified save path. It should be noted that the test monitoring panel can also be used to display other content that the user wants to display, and is not specifically limited to this embodiment.

[0101] As described above, the embodiments of this application provide users with a highly automated and visualized simulation testing experience through a UI interface that integrates parameter configuration, real-time monitoring, and report generation functions. This not only simplifies the testing process and improves testing efficiency, but also ensures the transparency of the testing process and the traceability of the results through intuitive data display and convenient report export functions.

[0102] II. Business Logic Layer The inventors discovered that, in this embodiment, the business logic layer also includes a test scheduling engine and a parallel test coordinator. The test scheduling engine dynamically allocates and launches test tasks based on preset strategies (such as priority, resource availability, and dependencies), ensuring that test cases run efficiently at appropriate times and execution nodes, thereby maximizing the utilization of test environment resources and shortening the overall test cycle. The parallel test coordinator focuses on managing multiple concurrently executing test threads, resolving issues such as resource sharing conflicts, synchronization control, result aggregation, and anomaly isolation, ensuring the stability and data consistency of parallel testing, and enabling large-scale automated testing to be carried out safely and reliably simultaneously. The two work together to build a high-throughput, low-latency intelligent test execution system.

[0103] In this embodiment, the parallel test coordinator is configured to implement parallel testing of voltage regulation functionality simulation for multiple power banks using a master-slave architecture: the master thread is responsible for task scheduling, resource allocation, and test result aggregation, while each worker thread is responsible for the independent testing of one power bank, thereby maintaining independent simulation clocks, simulation cycle counters, and the working state of the battery aging simulation model. Data exchange between threads is achieved through a thread-safe queue, avoiding performance bottlenecks caused by lock contention.

[0104] As described above, the embodiments of this application achieve parallel testing of multiple power banks through a master-slave architecture: the master thread is responsible for task scheduling, resource allocation, and final result aggregation, while an independent worker thread is started for each power bank under test, enabling each worker thread to maintain its own simulation clock, simulation cycle counter, and battery aging simulation model state to perform independent voltage regulation simulation calculations; during this process, the master thread and each worker thread exchange data efficiently without lock contention through a thread-safe queue, thereby significantly improving the overall efficiency of multi-device concurrent testing while ensuring the isolation and accuracy of test data for each device.

[0105] In this embodiment, the test parameters include a time acceleration factor, and the simulation clock is configured to calculate the simulation time based on the physical time corresponding to the start of the test, the physical time corresponding to the current test, and the time acceleration factor.

[0106] In this embodiment, the simulation clock is configured to record simulation time in the following manner:

[0107] in, To begin testing the corresponding physical time, As a time acceleration factor, This is the physical time corresponding to the current test. This is the simulation time. In this embodiment, The value range is between 100 and 1000. It should be noted that... The specific value can be dynamically adjusted based on the battery's properties and test results; no specific limit is set here.

[0108] As can be seen from the above description, the embodiments of this application introduce a time acceleration factor, enabling the simulated clock to accelerate the actual physical time proportionally, thereby simulating a long aging process within a very short physical test time, greatly shortening the test cycle, significantly improving test efficiency, and effectively reducing the consumption of time-related test resources.

[0109] In this embodiment, the test parameters also include: a cycle count acceleration factor, and the simulation cycle counter is configured to calculate the simulated full-charge cycle count based on the full-charge cycle count corresponding to the start of the test, the physical time corresponding to the start of the test, the physical time corresponding to the current test, and the cycle count acceleration factor.

[0110] In this embodiment, the simulation cycle counter is configured to record the number of simulation full-charge cycles in the following manner:

[0111] in, To begin testing the corresponding number of full-charge cycles, Physical time increment, To simulate the number of full-charge cycles, This is the acceleration factor for the number of iterations. Wherein, The value is 10. It should be noted that... The specific value can be dynamically adjusted based on the battery's properties and test results; no specific limit is set here.

[0112] As described above, the embodiments of this application introduce a cycle acceleration factor and combine the mapping relationship between physical time and full-charge cycle count to dynamically calculate the simulated full-charge cycle count. Thus, in the simulation process where physical time is greatly compressed, the aging state of the battery under long-term charge-discharge cycles is accurately and synchronously simulated, achieving efficient and quantitative evaluation of the battery life decay process.

[0113] In this embodiment, a single full-charge cycle includes a charging phase and a discharging phase. The test parameters also include battery capacity, and the time required for a single full-charge cycle is calculated as follows: The charging time for the charging phase (corresponding to the simulated constant current-constant voltage charging process) is:

[0114] in, The time required for a single charging phase. For battery capacity, This is the preset charging current.

[0115] The discharge time during the discharge phase (corresponding to the constant current discharge process) is:

[0116] in, This refers to the time required for a single discharge phase. This is the preset discharge current.

[0117]

[0118] in, This represents the total number of full-charge cycles. The time required for a single full-charge cycle, and in the virtual loop according to 1 / β. Compression was performed to obtain the simulation time required for a single full-charge cycle.

[0119] As described above, the embodiments of this application establish a theoretical time model based on battery capacity and charge / discharge current, and introduce a virtual acceleration coefficient to compress the actual long charge / discharge cycle into an extremely short simulation step, which greatly shortens the simulation time for battery life cycle aging tests. This method can quickly simulate the impact of tens of thousands of cycles on battery performance with limited computing resources, thereby efficiently predicting battery life, assessing aging trends, and optimizing battery management strategies, significantly reducing the time cost of R&D verification and the risk of hardware testing.

[0120] To verify the parallel testing of the voltage regulation function simulation test system of this application, the inventors conducted the following experiments: (1) Test scenario: A manufacturer needs to screen the voltage regulation function of 10 mobile power banks with different design schemes and adopts the parallel testing and trend prediction function of this application.

[0121] (2) System configuration: Server: 16-core CPU, 32GB memory; Number of parallel threads: 10 (1 thread per sample); Configuration per thread: Samples 1-5: Time dimension test, =200 times (estimated to be completed in 5 hours); Samples 6-10: Cyclic dimension test, β=20 times / second (estimated to be completed in 10.5 seconds).

[0122] (3) The results of the trend prediction experiment are shown in Table 1. Table 2 shows the sample number, test type, prediction trigger time, and meaning of the prediction result corresponding to Table 1.

[0123] Table 1

[0124] Table 2

[0125] Taking "termination 3.5 hours early" of Sample 1 as an example, the logic for breaking down the data is as follows: 1. Baseline settings: System configuration instructions, time acceleration factor =200 times, it is estimated that it will take 5 hours of physical time for a single sample to complete the simulation cycle (e.g., the target is 42 months).

[0126] 2. Testing process for sample 1: (1) When the simulation progresses to "virtual 8 months", trend prediction is triggered; (2) Algorithm prediction: The voltage of the sample will continue to be lower than the standard limit and will not exceed the standard during the subsequent 42-month aging cycle. Therefore, it is determined to pass ahead of schedule.

[0127] (3) The test ends at this point, and the actual physical time is 5-3.5=1.5 hours.

[0128] Therefore, "terminating 3.5 hours early" means that the original 5-hour full test only took 1.5 hours to get the predicted result, saving 3.5 hours.

[0129] 3. Experimental data is used to demonstrate the algorithm's two main values: (1) Time dimension test: The test cycle was significantly shortened, that is, samples 1, 3 and 5 were terminated early, saving physical time ranging from 2.25 to 4.2 hours. This shows that the system of this application can avoid running the complete aging test on some samples by predicting trends in advance, thus greatly improving the screening efficiency; samples 2 and 4 showed "no savings" because the system could not give a clear conclusion at the set prediction trigger time point and could only continue to run the entire cycle. This is also consistent with the real scenario - not all samples can be predicted in advance.

[0130] (2) Cycle count dimension test: This is extremely time-consuming, specifically the test of 6-10 cycles for samples, with a cycle count acceleration factor of [missing value]. =20 times / second, originally expected to complete in only 10.5 seconds. This type of test is inherently very short, so terminating it early is not very meaningful, as described in the table as "the test time is inherently very short".

[0131] The above experiments demonstrate that the voltage regulation function simulation test system designed in this application can effectively save testing costs in some scenarios. For long-term tests that take several hours, the system can shorten the testing time of some samples by more than 45% by terminating early. This proves the engineering practicality of this application in the screening of mobile power bank voltage regulation functions. For example, when any of the following conditions are met, the test is terminated early and the prediction result is output: Early pass prediction: If the current charging voltage decay trend shows that even under the most conservative estimate, the charging voltage at all future checkpoints will be lower than the limit, then the prediction is "pass"; Early failure warning: If the current charging voltage has exceeded the current checkpoint limit, or the trend prediction shows that the next checkpoint will definitely exceed the limit, then it is immediately marked as "fail" and terminated. Furthermore, the optimization effect statistics are as follows: Total estimated test time: 5 hours × 5 + 10.5 seconds × 5 ≈ 25 hours; Actual test time: Approximately 17.5 hours; Overall time saving: 30%; Early identification of 1 unqualified sample (sample 3), avoiding ineffective resource investment.

[0132] III. Data Management Layer In this embodiment, the voltage regulation function simulation test system of this application further includes a data management layer, which includes a real-time database, a historical data warehouse, and a standard rule base. The functions performed by the real-time database, the historical data warehouse, and the standard rule base will be described in detail below.

[0133] (1) The real-time database serves as the core of dynamic data storage and interaction during the testing process. It is mainly responsible for storing real-time dynamic data generated during test execution, providing data support for test monitoring, real-time judgment, and dynamic scheduling of the business logic layer. Core functions include: real-time collection and storage of dynamic running data of each test thread / sample, such as current simulation time / simulation full-charge cycle count, real-time charging voltage, SOC status, adjustment error value, checkpoint trigger status, etc.; providing real-time data interface for the test monitoring panel of the user interaction layer, realizing visual monitoring of the test process, and allowing operators to grasp the test progress and status in real time; providing low-latency data reading / writing services for the test scheduling engine and simulation clock of the business logic layer, as well as the battery aging simulation model and trend prediction analysis model of the core algorithm layer, ensuring the efficient execution of operations such as real-time judgment and dynamic model updates; and recording temporary status data during the test process (such as pause / resume markers, real-time adjustment values ​​of acceleration factors), ensuring the continuity of the test process and the consistency of the status.

[0134] (2) The historical data warehouse serves as the static data persistence storage center for the test system, responsible for storing all historical data and results data throughout the entire test lifecycle in a long-term manner, providing a data foundation for subsequent traceability, review, and model optimization. Core functions include: persistently storing all data of completed test tasks, including test parameter configurations, judgment results of each checkpoint, original data of voltage regulation curves, trend prediction process data, core information of test reports, etc.; storing historical training data, attenuation coefficient iteration data, and test benchmark data of different models of mobile power supplies for the battery aging simulation model, providing data support for the optimization of the battery aging simulation model of the core algorithm layer; providing data sources for the generation of test reports, query and audit of historical test results, and supporting multi-condition retrieval by sample number, test type, time, and other dimensions; accumulating historical case data of batch tests, providing samples for the algorithm optimization of the trend prediction analysis model (such as smoothing coefficient adjustment and prediction feature iteration), and improving prediction accuracy.

[0135] (3) The standard rule base serves as the unified management center for rules and thresholds of the testing system. It is the core source of the entire test judgment and ensures that the testing process strictly complies with national standards and custom test specifications. Core functionalities include: establishing fixed storage voltage regulation standard rules, including checkpoint thresholds for time / cycle dimensions (18 / 30 / 42 months, 70 / 140 / 210 cycles), corresponding voltage reductions (0.1 / 0.15 / 0.2V), and calculation logic for series stages and voltage limits; storing general rules for test judgment, such as checkpoint judgment logic, mathematical conditions for early test termination, standard compliance judgment criteria, and baseline values ​​and fluctuation ranges for error terms; supporting the configuration and storage of custom rules, such as enterprise-level additional test thresholds, exclusive degradation coefficient baselines for different battery types (e.g., high-voltage lithium batteries), and batch test screening rules; providing a unified rule calling interface for the test scheduling engine of the business logic layer, the battery aging simulation model of the core algorithm layer, and the voltage regulation simulation model, ensuring consistent judgment criteria for all test threads / samples and solving the problem of poor consistency in traditional testing; and supporting version management of standard rules to adapt to standard updates or enterprise test specification adjustments, ensuring system scalability.

[0136] Furthermore, based on the voltage regulation function simulation test system of the above embodiments, this application proposes a voltage regulation function simulation test method, such as... Figure 11 As shown, the method of this application completes the voltage regulation function simulation test of the power bank by performing the following steps Q1-Q5: Q1, configure the test parameters of the power bank; Q2, in response to the test parameters, record the simulation time and / or the number of simulated full-charge cycles; Q3, calculate the charging voltage of the power bank based on the simulation time, and / or the number of simulated full-charge cycles and the test parameters; Q4, calculate the standard charging voltage of the power bank based on the test parameters; Q5, calculate the voltage regulation function simulation test result of the power bank based on the charging voltage and the standard charging voltage.

[0137] To verify the effectiveness of the above embodiments, the inventors conducted a series of experiments and obtained the comparative experimental results shown in Table 3.

[0138] Table 3

[0139] In summary, compared to traditional hardware-accelerated aging or sample replacement methods, this application establishes a precise mathematical model of battery charging voltage regulation (i.e., the voltage regulation function simulation test system of this application) and uses software scripts to virtualize the test process on a standard PC / server. This method does not alter the battery's physical characteristics; it only simulates the effects of time and cyclic accumulation through algorithms, ensuring a high degree of equivalence between test results and real long-term tests. A unified test framework is designed, supporting flexible switching and parallel testing of the two adjustment methods (time dimension and cycle count dimension) specified in the GB standard. An abstract checkpoint mechanism and judgment engine ensure that the same software system can adapt to different test requirements. A precise mapping mechanism between the simulated clock and the physical clock achieves a non-linear mapping from physical test time to virtual usage time. Multi-threaded independent clock domains support parallel testing of different samples at different speeds. Predictive analysis capabilities are introduced; by monitoring early behavioral characteristics of voltage regulation, long-term test results are predicted. This algorithm can identify obviously qualified or unqualified samples before test completion, saving an average of 30-50% of test time, making it particularly suitable for batch screening scenarios. A complete test data recording and report generation mechanism is designed to ensure that the data generated by the software simulation test meets the standard requirement of "passing inspection and testing with the testing tools provided by the power bank manufacturer." All simulation parameters, intermediate calculation processes, and judgment criteria are recorded, forming a traceable digital evidence chain.

Claims

1. A voltage regulation function emulation test system, characterized by, The system includes: The user interaction layer is used for users to configure the test parameters of the power bank; The business logic layer includes a simulation clock and a simulation cycle counter. The simulation clock is used to respond to the test parameters and record the simulation time based on the test parameters. The simulation cycle counter is used to respond to the test parameters and record the number of full-charge simulation cycles based on the test parameters. The core algorithm layer is used to calculate the charging voltage of the power bank based on the simulation time, and / or the number of full-charge cycles and the test parameters, and to calculate the standard charging voltage of the power bank based on the test parameters, and to calculate the simulation test results of the voltage regulation function of the power bank based on the charging voltage and the standard charging voltage.

2. The voltage regulation function emulation test system of claim 1, wherein, The test parameters include: battery charging limit voltage, number of battery series stages, and time dimension testing. The charging voltage includes a first charging voltage, and the standard charging voltage includes the first standard charging voltage of the power supply corresponding to the simulation time. The core algorithm layer is configured as follows: In response to the aforementioned time-dimensional test; Based on the simulation time, the battery charging limit voltage, and the number of battery series stages, the first charging voltage and the first standard charging voltage are calculated respectively. Based on the first charging voltage and the first standard charging voltage, the simulation test results of the voltage regulation function of the mobile power supply are calculated.

3. The voltage regulation function emulation test system of claim 2, wherein, The core algorithm layer also includes a test analyzer, which is configured to calculate the simulation test results of the voltage regulation function in the following manner: Compare the magnitudes of the first charging voltage and the first standard charging voltage; When the first charging voltage is less than or equal to the first standard voltage, the simulation test result of the voltage regulation function is "pass"; or, when the first charging voltage is greater than the first standard voltage, the simulation test result of the voltage regulation function is "fail".

4. The voltage regulation function emulation test system of claim 1, wherein, The test parameters include: battery charging limit voltage, number of battery series stages, and cycle count. The charging voltage includes a second charging voltage, and the standard charging voltage includes the second standard charging voltage corresponding to the number of simulated full-charge cycles of the power supply. The core algorithm layer is configured as follows: In response to the loop count dimension test; Based on the simulated full-charge cycle count, battery charging limit voltage, and number of battery series stages, the second charging voltage and the second standard charging voltage are calculated respectively. The simulation test results of the voltage regulation function of the mobile power supply are calculated based on the second charging voltage and the second standard charging voltage.

5. The voltage regulation function emulation test system of claim 4, wherein, The core algorithm layer also includes a test analyzer, which is configured to calculate the simulation test results of the voltage regulation function in the following manner: Compare the magnitudes of the second charging voltage and the second standard charging voltage; When the second charging voltage is less than or equal to the second standard voltage, the simulation test result of the voltage regulation function is "pass"; or, when the second charging voltage is greater than the second standard voltage, the simulation test result of the voltage regulation function is "fail".

6. The voltage regulation function simulation test system according to claim 1, characterized in that, The test parameters include: battery charging limit voltage, number of battery series stages, time-dimensional test, and cycle count-dimensional test. The charging voltage includes a third charging voltage, and the standard charging voltage includes a first standard charging voltage corresponding to the simulation time and a second standard charging voltage corresponding to the simulation full-charge cycle count. The core algorithm layer is configured as follows: In response to the time dimension test and the loop count dimension test; The third charging voltage is calculated based on the simulation time, the number of full-charge cycles, the battery charging limit voltage, and the number of battery series stages. Calculate the first standard charging voltage based on the simulation time, the battery charging limit voltage, and the number of battery series stages; The second standard charging voltage is calculated based on the number of simulated full-charge cycles, the battery charging limit voltage, and the number of battery series stages. Based on the third charging voltage, the first standard charging voltage, and the second standard charging voltage, the simulation test results of the voltage regulation function of the power bank are calculated.

7. The voltage regulation function simulation test system according to claim 6, characterized in that, The core algorithm layer also includes a test analyzer, which is configured to calculate the simulation test results of the voltage regulation function in the following manner: Compare the magnitudes of the third charging voltage with the first standard charging voltage and the second standard charging voltage, respectively. When the third charging voltage is less than or equal to the first standard charging voltage and less than or equal to the second standard charging voltage, the simulation test result of the voltage regulation function is "pass"; or, when the third charging voltage is greater than the first standard charging voltage and / or greater than the second standard charging voltage, the simulation test result of the voltage regulation function is "fail".

8. The voltage regulation function simulation test system according to claim 2, characterized in that, The core algorithm layer further includes: a first trend prediction analysis model, and the first trend prediction analysis model is configured as follows: When the simulation time reaches the preset simulation time, the first predicted charging voltage corresponding to the time step is calculated based on the preset simulation time, the preset time step, and the voltage decay rate corresponding to the preset simulation time. The sum of the preset simulation time and the time step is the target time check point. The simulation test results of the first predicted voltage regulation function are calculated based on the first predicted charging voltage and the first standard charging voltage corresponding to the target time check point. Based on the simulation test results of the first predicted voltage regulation function, the working status of the simulation clock, the first battery aging simulation model, and the first voltage regulation simulation model is determined.

9. The voltage regulation function simulation test system according to claim 8, characterized in that, The first trend prediction analysis model is also configured as follows: Compare the magnitude of the first predicted charging voltage with the magnitude of the first standard charging voltage corresponding to the target time checkpoint; When the first predicted charging voltage is less than or equal to the first standard charging voltage corresponding to the target time checkpoint, the simulation test result of the first predicted voltage adjustment function is "passed"; or, when the first predicted charging voltage is greater than the first standard charging voltage corresponding to the target time checkpoint, the simulation test result of the first predicted voltage adjustment function is "failed".

10. The voltage regulation function simulation test system according to claim 4, characterized in that, The core algorithm layer also includes a second trend prediction analysis model, and the second trend prediction analysis model is configured as follows: When the number of simulated full-charge cycles reaches the preset number of simulated full-charge cycles, the second predicted charging voltage corresponding to the number of cycles is calculated based on the preset number of simulated full-charge cycles, the preset cycle number step size, and the voltage decay rate corresponding to the number of simulated full-charge cycles. The sum of the preset number of simulated full-charge cycles and the cycle number step size is the target cycle number check point. The simulation test results of the second predicted voltage regulation function are calculated based on the second predicted charging voltage and the second standard charging voltage corresponding to the target cycle number check point. Based on the simulation test results of the second predictive voltage regulation function, the working status of the simulation clock, the simulation cycle counter, the second battery aging simulation model, and the second voltage regulation simulation model is determined.

11. The voltage regulation function simulation test system according to claim 10, characterized in that, The second trend prediction analysis model is also configured as follows: Compare the second predicted charging voltage with the second standard charging voltage corresponding to the target cycle count checkpoint; When the second predicted charging voltage is less than or equal to the second standard charging voltage corresponding to the target cycle count checkpoint, the simulation test result of the first predicted voltage regulation function is "passed". Alternatively, if the second predicted charging voltage is greater than the second standard charging voltage corresponding to the target cycle count checkpoint, the simulation test result of the second predicted voltage regulation function is "failed".

12. The voltage regulation function simulation test system according to claim 9 or 11, characterized in that, When the simulation test result of the first predictive voltage regulation function is "passed", the working status of the simulation clock, the first battery aging simulation model, and the first voltage regulation simulation model is "continue to work". Alternatively, if the simulation test result of the first predicted voltage regulation function is "failed", the working state of the simulation clock, the first battery aging simulation model and the first voltage regulation simulation model is determined to be stopped. Alternatively, if the simulation test result of the second predictive voltage regulation function is "passed", the working state of the simulation clock, the simulation cycle counter, the battery aging simulation model, and the voltage regulation simulation model is "continued to work"; or if the simulation test result of the second predictive voltage regulation function is "failed", the working state of the simulation cycle counter, the second battery aging simulation model, and the second voltage regulation simulation model is "stopped working".

13. The voltage regulation function simulation test system according to claim 1, characterized in that, The test parameters include a time acceleration factor, and the simulation clock is configured to calculate the simulation time based on the physical time corresponding to the start of the test, the physical time corresponding to the current test, and the time acceleration factor.

14. The voltage regulation function simulation test system according to claim 1, characterized in that, The test parameters include a cycle count acceleration factor, and the simulation cycle counter is configured to calculate the simulated full-charge cycle count based on the full-charge cycle count corresponding to the start of the test, the physical time corresponding to the start of the test, the physical time corresponding to the current test, and the cycle count acceleration factor.

15. A voltage regulation function simulation test method based on the voltage regulation function simulation test system as described in any one of claims 1 to 14, characterized in that, The method includes: Configure the test parameters for the power bank; In response to the test parameters, record the simulation time and / or the number of full-charge simulation cycles; The charging voltage of the power bank is calculated based on the simulation time, and / or the number of simulated full-charge cycles and the test parameters. The standard charging voltage of the power bank is calculated based on the test parameters. Based on the charging voltage and the standard charging voltage, the simulation test results of the voltage regulation function of the power bank are calculated.

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