A lithium battery charging chip simulation test method and system

Abstract

The present application relates to the technical field of chip simulation test, and discloses a lithium battery charging chip simulation test method and system, the method comprises the following steps: collecting a first signal set of a lithium battery charging chip in a first simulation test process, obtaining a first test data set through the first signal set; collecting original signal data of different models of lithium battery charging chips in a historical period, and obtaining an interference signal set in the original signal data. The present application can improve the authenticity of lithium battery charging chip simulation test, thereby improving test efficiency and accuracy. Through a multi-objective particle swarm optimization algorithm and setting a constraint condition, an optimal balance point can be found among multiple optimization targets, thereby determining an optimal optimization direction, which can ensure that the optimization direction not only brings performance improvement, but also controls cost and energy consumption within an acceptable range.

Description

Technical Field

[0001] This invention relates to the field of chip simulation testing technology, and more specifically, to a method and system for simulating testing lithium battery charging chips. Background Technology

[0002] With the rapid development of technology and the widespread use of electronic devices, lithium batteries, as the primary energy storage unit, are receiving increasing attention for their performance and safety. As a key component controlling the lithium battery charging process, the performance of the lithium battery charging chip directly affects the charging efficiency, charging speed, and safety of the lithium battery. Therefore, it is necessary to conduct simulation tests on the anti-interference capabilities of the lithium battery charging chip to ensure its stable and reliable performance.

[0003] However, traditional lithium battery charging chip simulation testing methods often have some problems. For example, the testing methods are usually relatively simple and fail to fully simulate the complex environment of actual use scenarios, so the realism needs to be improved. Furthermore, it is inconvenient to select the points to be optimized when obtaining simulation test results, and to choose the most suitable optimization direction for those points.

[0004] In view of this, the present invention proposes a lithium battery charging chip simulation test method and system to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a lithium battery charging chip simulation testing method and system, comprising the following steps:

[0006] S1: Collect the first signal set of the lithium battery charging chip in the first simulation test process, and obtain the first test dataset through the first signal set;

[0007] S2: Collect raw signal data of different models of lithium battery charging chips during historical periods, and obtain the set of interference signals in the raw signal data;

[0008] S3: Input the interference signal set in the second simulation test process, collect the second signal set of the lithium battery charging chip under the interference signal set, and obtain the second test dataset through the second signal set;

[0009] S4: Compare the first test dataset with the second test dataset to obtain the comparison sequence result. Set the comparison standard. If the comparison sequence result is within the comparison standard, the simulation test is completed. If the comparison sequence result is not within the comparison standard, the optimization points of the lithium battery charging chip are obtained.

[0010] S5: Optimize the points to be optimized in the lithium battery charging chip. After optimization, perform simulation tests on the lithium battery charging chip again until the obtained alignment sequence results are within the alignment standard.

[0011] Further, in S1, the process of acquiring the first signal set of the lithium battery charging chip in the first simulation test process and obtaining the first test dataset through the first signal set is as follows:

[0012] S10. Set up the simulation test environment for the first simulation test process and start the first simulation test process for the lithium battery charging chip.

[0013] S11. In the first simulation test process, the lithium battery charging chip acts as the transmitter, sending data to the receiver's main controller. Each communication data packet is collected. A first signal set during the transmission of a communication data packet, the first signal set including a first voltage signal, a first current signal, a first timing signal and a first noise signal;

[0014] S12, Obtain from the first signal set The first communication data packet loss, the first communication data packet transmission time, the first signal-to-noise ratio change value, and the first chip temperature change difference constitute the first test dataset.

[0015] Furthermore, in S2, the process of collecting raw signal data from different models of lithium battery charging chips during historical periods and obtaining the interference signal set from the raw signal data is as follows:

[0016] S20, Obtain different models of lithium battery charging chips. In a real-world environment Each raw signal data contains 1 raw signal data. One interference signal;

[0017] S21. Perform a Fourier transform on each original signal data to convert it to the frequency domain, obtaining the spectrum of the original signal data. Then, decompose the interference signals in the original signal data using the spectrum. One interference signal;

[0018] S22. Obtain the frequency and amplitude of each interference signal through the spectrum diagram of the original signal data, analyze the periodicity of each interference signal through the autocorrelation function, quantify the periodicity, and obtain the periodicity value.

[0019] S23. Deployment in different geographical locations in real-world environments Each receiving station records the time when it receives the interference signal. The time difference is calculated by comparing the times when different receiving stations receive the same interference signal. Based on the known location and time difference of each receiving station, the location information of the interference signal is determined. The location information corresponding to the determined interference signal is shielded by physical shielding equipment. Then, the location information of the remaining interference signals is obtained one by one.

[0020] S24, will Each interference signal is associated with its corresponding frequency, amplitude, periodicity, and location information to obtain a corresponding set of interference signals. The number of interference signal sets is... indivual;

[0021] In step S21, the process of decomposing the interference signal in the original signal data using the spectrum diagram of the original signal data is as follows:

[0022] The peak value of each interference signal is obtained by spectrogram, the frequency range of the interference signal is determined by filter, and the signal in the frequency domain is converted to the time domain by inverse Fourier transform, that is, the interference signal decomposed in the original data signal is obtained. For each interference signal decomposed, the peak value and frequency range of the decomposed interference signal in the original data signal are hidden in the spectrogram. The current step is repeated until all interference signals in the original signal data are decomposed.

[0023] Further, in S3, the process of inputting an interference signal set in the second simulation test procedure, acquiring a second signal set of the lithium battery charging chip under the interference signal set, and obtaining the second test dataset through the second signal set is as follows:

[0024] S30. Set up the simulation test environment for the second simulation test process, start the second simulation test process for the lithium battery charging chip, and input the interference signal set in the second simulation test process;

[0025] S31. In the second simulation test process, the lithium battery charging chip acts as the transmitter, sending data to the receiver's main controller. Each communication data packet is collected. A second signal set during the transmission of a communication data packet, the second signal set including a second voltage signal, a second current signal, a second timing signal and a second noise signal;

[0026] S32, Obtain from the second signal set The second test dataset is composed of the second communication data packet loss, the second communication data packet transmission time, the second signal-to-noise ratio change, and the second chip temperature difference under the interference signal set.

[0027] Furthermore, in step S4, the process of comparing the first test dataset with the second test dataset to obtain the alignment sequence result is as follows:

[0028] S40. The loss of the first communication data packet, the transmission time of the first communication data packet, the change value of the first signal-to-noise ratio, and the temperature difference of the first chip in the first test dataset are compared one by one with the loss of the second communication data packet, the transmission time of the second communication data packet, the change value of the second signal-to-noise ratio, and the temperature difference of the second chip in the second test dataset to obtain the comparison sequence result.

[0029] S41. By adjusting the upper and lower thresholds for each data point in the first test dataset, a preset comparison standard is obtained. The comparison standard includes the standard range corresponding to each data point in the first test dataset.

[0030] S42. If the alignment sequence results are within the alignment criteria, the simulation test is complete.

[0031] S43. If the alignment sequence result is not within the alignment standard, then obtain the optimization points of the lithium battery charging chip.

[0032] Furthermore, the process of obtaining the optimization points of the lithium battery charging chip is as follows:

[0033] S430. For each set of interference signals, reduce the interference signals in the input set one by one, and test the lithium battery charging chip to obtain... One current test dataset and one unmasked test dataset;

[0034] S431, through calculation The result is obtained by taking the mean difference between each current test dataset and the unmasked test dataset, and then comparing each mean difference to the mean of the unmasked dataset as a percentage. One influence coefficient;

[0035] S432. If the current alignment sequence result is within the preset influence range, the shielded interference signal is a non-major interference signal; if the current alignment sequence result is not within the preset influence range, it is a major interference signal.

[0036] S433. Obtain the occurrence count of the main interference signal in the set of all interference signals, multiply the influence coefficient corresponding to the main interference signal by its occurrence count, and obtain the influence value of the main interference signal.

[0037] S434. Traverse the influence values ​​of all major interference signals, sort the influence values ​​in descending order of magnitude to obtain the major interference signal sequence list, and then select the top... The main interference signals are the points to be optimized for lithium battery charging chips.

[0038] Furthermore, in step S5, the optimization points of the lithium battery charging chip are optimized, and the lithium battery charging chip is simulated and tested again after optimization until the obtained alignment sequence result is within the alignment standard.

[0039] S50, Acquisition The optimization directions for the points to be optimized include shielding design, filtering design, and fault detection design.

[0040] S51, Obtain The number of times each optimization direction is selected in the points to be optimized is used to sort each optimization direction in descending order according to the number of times it is selected, thus obtaining the first optimization direction sort;

[0041] S52. Obtain the comprehensive cost corresponding to each optimization direction. The comprehensive cost includes time cost, labor cost, material cost and testing cost. Preset the first weight, second weight, third weight and fourth weight for time cost, labor cost, material cost and testing cost respectively. Add the weighted time cost, labor cost, material cost and testing cost to obtain the comprehensive cost value of each optimization direction. Sort the comprehensive cost of each optimization direction in ascending order according to the size to obtain the second optimization direction ranking.

[0042] S53. Obtain the optimization rate corresponding to each optimization direction and sort them in descending order to obtain the third optimization direction sort. Obtain the energy loss value corresponding to each optimization direction and sort them in ascending order to obtain the fourth optimization direction sort.

[0043] S54. Obtain the best optimization direction from the first optimization direction sort, the second optimization direction sort, the third optimization direction sort, and the fourth optimization direction sort using a multi-objective particle swarm optimization algorithm.

[0044] S55. The obtained optimal direction is applied to the lithium battery charging chip.

[0045] Furthermore, the process of obtaining the optimal optimization direction from the first optimization direction sort, the second optimization direction sort, the third optimization direction sort, and the fourth optimization direction sort using the multi-objective particle swarm optimization algorithm is as follows:

[0046] S540. Set constraints and randomly initialize a group of particles, with each particle representing a possible optimization direction.

[0047] S541. Set fitness functions for the first, second, third, and fourth optimization direction sorting respectively, evaluate each particle, and calculate its fitness in the first, second, third, and fourth optimization direction sorting.

[0048] S542. For each particle, update its individual optimal solution, and select the global optimal solution from the individual optimal solutions of all particles.

[0049] S543. Update the particle's position and velocity based on its current position, velocity, individual optimal solution, and global optimal solution;

[0050] S544. Repeat S541 to S543 until the number of iterations is reached or the stopping condition is met. Select the optimization direction with the highest fitness as the best optimization direction.

[0051] Furthermore, the constraints are: a preset comprehensive cost threshold for sorting in the second optimization direction, and a penalty point is applied to particles that reach the comprehensive cost threshold; a preset energy loss threshold for sorting in the fourth optimization direction, and a penalty point is applied to particles that reach the energy loss threshold.

[0052] The process of penalizing particles that reach the overall cost threshold is as follows:

[0053] When evaluating the fitness of each particle, the ratio of the overall cost corresponding to each particle to the overall cost threshold is used. The deviation ratio is obtained based on the ratio. According to the deviation ratio decrease The fitness of;

[0054] The process of penalizing particles that reach the energy loss threshold is as follows:

[0055] When evaluating the fitness of each particle, the ratio of the energy loss value corresponding to each particle to the energy loss threshold is used. The deviation ratio is obtained based on the ratio. According to the deviation ratio, it decreases The degree of adaptability.

[0056] A lithium battery charging chip simulation test system, comprising:

[0057] The first acquisition module acquires the first signal set of the lithium battery charging chip in the first simulation test process, and obtains the first test dataset through the first signal set.

[0058] The second acquisition module collects raw signal data of different models of lithium battery charging chips during historical periods and obtains the set of interference signals in the raw signal data.

[0059] The simulation test module inputs an interference signal set in the second simulation test process, collects a second signal set of the lithium battery charging chip under the interference signal set, and obtains a second test dataset through the second signal set.

[0060] The comparison module compares the first test dataset with the second test dataset to obtain the comparison sequence result. It presets the comparison standard. If the comparison sequence result is within the comparison standard, the simulation test is completed; if the comparison sequence result is not within the comparison standard, the optimization points of the lithium battery charging chip are obtained.

[0061] The optimization module optimizes the points to be optimized in the lithium battery charging chip. After optimization, the lithium battery charging chip is simulated and tested again until the obtained comparison sequence results are within the comparison standard.

[0062] The technical effects and advantages of the lithium battery charging chip simulation testing method and system of the present invention are as follows:

[0063] 1. This invention improves the realism of lithium battery charging chip simulation testing by collecting interference signal sets from the actual environment, thereby improving testing efficiency and accuracy.

[0064] 2. By acquiring and comprehensively considering multiple optimization directions, the anti-interference performance of lithium battery charging chips can be comprehensively and systematically improved. By statistically analyzing and sorting the number of times each optimization direction is selected, more attention-grabbing and more promising optimization directions can be identified, making the optimization process more objective and scientific, and improving optimization efficiency. By comprehensively considering multiple factors such as time cost, labor cost, material cost, and testing cost, and by weighting these costs, effective cost control can be ensured during the optimization process. By adopting a multi-objective particle swarm optimization algorithm, the optimal balance point can be found among multiple optimization objectives, thereby determining the optimal optimization direction, which can significantly improve the efficiency and accuracy of the optimization process. By setting constraints in the multi-objective particle swarm optimization algorithm, it can be ensured that the optimization direction not only brings performance improvement, but also controls costs and energy consumption within an acceptable range, so that the algorithm does not ignore the importance of cost and energy consumption while pursuing performance optimization. Attached Figure Description

[0065] Figure 1 This is a flowchart illustrating a lithium battery charging chip simulation test method according to the present invention.

[0066] Figure 2 This is a flowchart of a lithium battery charging chip simulation test method according to the present invention;

[0067] Figure 3 This is a schematic diagram of the structure of a lithium battery charging chip simulation test system according to the present invention. Detailed Implementation

[0068] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] Example 1

[0070] Please see Figure 1 and Figure 2 As shown in the figure, this embodiment of a lithium battery charging chip simulation test method includes the following steps:

[0071] S1: The process of acquiring the first signal set of the lithium battery charging chip in the first simulation test process and obtaining the first test dataset from the first signal set is as follows:

[0072] S10. Set up the simulation test environment for the first simulation test process, install the simulation test equipment corresponding to the first simulation test process, and the simulation test equipment may include oscilloscope, spectrum analyzer, voltage measuring instrument, current measuring instrument, mixed signal generator, electromagnetic interference generator and signal analyzer, etc., and start the first simulation test process of lithium battery charging chip;

[0073] S11. In the first simulation test process, the lithium battery charging chip acts as the transmitter, sending data to the receiver's main controller. Each communication data packet is collected. A first signal set during the transmission of a communication data packet, the first signal set including a first voltage signal, a first current signal, a first timing signal and a first noise signal;

[0074] S12, Obtain from the first signal set The first communication data packet loss, the first communication data packet transmission time, the first signal-to-noise ratio change value, and the first chip temperature change difference constitute the first test dataset.

[0075] It should be noted that the first communication data packet loss is the number of communication data packets lost during transmission. The ratio of individual communication data packets;

[0076] The transmission time of the first communication data packet is the total time required from the start of transmission at the sending end to the successful reception at the receiving end. It can be determined by analyzing the first timing signal. Specifically, the calculation can be based on the difference between the timestamp of the first data packet sent by the sending end and the timestamp of the last data packet received by the receiving end.

[0077] The first signal-to-noise ratio (SNR) change value is the fluctuation value of the first SNR during the test. The signal power is obtained through the first voltage signal and the first current signal, and the noise power is calculated based on the first noise signal. The SNR formula is: ,in, It is the power of the signal. It is the power of the noise, calculated at each measurement point. Then obtain different measurement points Calculate different measurement points The first signal-to-noise ratio change value can then be obtained.

[0078] The chip temperature difference refers to the range of chip temperature changes during the test, which can be obtained through temperature measurement equipment, such as temperature sensors.

[0079] The first communication data packet loss, first communication data packet transmission time, first signal-to-noise ratio change, and first chip temperature difference in the first test dataset can be obtained by conducting multiple tests and taking the average value of the data in the first test dataset.

[0080] Specifically, by calculating the amount of communication data packet loss, the loss of data packets during communication can be quantitatively assessed, thus reflecting the reliability and stability of the communication link between the sender and receiver. Transmission duration can provide the efficiency of the communication process, which is helpful in evaluating the communication speed and response time performance indicators of the charging chip. The calculation of the signal-to-noise ratio (SNR) change value can reflect the fluctuation of signal quality during communication. A stable SNR indicates good communication signal quality, while a large SNR change may indicate interference or signal attenuation during communication. Chip temperature variation helps to obtain the thermal performance of the lithium battery charging chip during operation, including its heat dissipation capacity and temperature stability. This is beneficial for evaluating the reliability and lifespan of the lithium battery charging chip during long-term operation. By collecting and analyzing the first signal set, a comprehensive evaluation of the communication performance of the lithium battery charging chip can be carried out, thus providing strong data support for subsequent optimization points.

[0081] S2: The process of collecting raw signal data from different models of lithium battery charging chips during historical periods and obtaining the interference signal set from the raw signal data is as follows:

[0082] S20, Obtain different models of lithium battery charging chips. In a real-world environment Each raw signal data contains 1 raw signal data. One interference signal;

[0083] S21. Perform a Fourier transform on each original signal data to convert it to the frequency domain, obtaining the spectrum of the original signal data. Then, decompose the interference signals in the original signal data using the spectrum. One interference signal;

[0084] S22. Obtain the frequency and amplitude of each interference signal through the spectrum diagram of the original signal data, analyze the periodicity of each interference signal through the autocorrelation function, quantify the periodicity, and obtain the periodicity value.

[0085] Specifically, by analyzing the spectrum, the frequency distribution and corresponding amplitude of each interfering signal can be obtained. Then, using autocorrelation function analysis, the periodicity of each interfering signal can be assessed, and the period length can be estimated by observing the peak distribution in the autocorrelation function. Finally, by specifically calculating the peak ratio or periodicity index of each interfering signal, the periodicity is converted into a numerical form for subsequent analysis and processing.

[0086] S23. Deployment in different geographical locations in real-world environments Each receiving station records the time when it receives the interference signal. The time difference is calculated by comparing the times when different receiving stations receive the same interference signal. Based on the known location and time difference of each receiving station, the location information of the interference signal is determined. The location information corresponding to the determined interference signal is shielded by physical shielding equipment. Then, the location information of the remaining interference signals is obtained one by one.

[0087] It should be noted that, based on the principle of triangulation, the location of the interfering signal source is estimated by knowing the location of the receiving stations and the time difference between their received signals. Multiple circles or spheres are drawn with the receiving stations as the focal points and the possible distance from the receiving stations to the interfering signal source (calculated based on the time difference) as the radius. The intersection of these circles or spheres represents the possible location of the interfering signal source. By solving the equations of these circles, the location information of the interfering signal can be obtained, including latitude and longitude coordinates and altitude. Alternatively, an interfering signal location distribution map can be generated based on the location information of each interfering signal within the interfering signal set and the chip under test (i.e., different models of lithium battery charging chips).

[0088] S24, will Each interference signal is associated with its corresponding frequency, amplitude, periodicity, and location information to obtain a corresponding set of interference signals. The number of interference signal sets is... indivual;

[0089] In S21, the process of decomposing the interference signal in the original signal data using the spectrum diagram of the original signal data is as follows:

[0090] The peak value of each interference signal is obtained by spectrogram, the frequency range of the interference signal is determined by filter, and the signal in the frequency domain is converted to the time domain by inverse Fourier transform, that is, the interference signal decomposed in the original data signal is obtained. For each interference signal decomposed, the peak value and frequency range of the decomposed interference signal in the original data signal are hidden in the spectrogram. The current step is repeated until all interference signals in the original signal data are decomposed.

[0091] It should be noted that by performing a Fourier transform on the time-domain signal data of each original signal data and converting it to the frequency domain, the spectrum of the original signal data can be obtained.

[0092] S3: Input the interference signal set in the second simulation test process, collect the second signal set of the lithium battery charging chip under the interference signal set, and obtain the second test dataset through the second signal set;

[0093] In S3, the process of inputting an interference signal set in the second simulation test procedure, acquiring a second signal set from the lithium battery charging chip under the interference signal set, and obtaining the second test dataset through the second signal set is as follows:

[0094] S30. Set up the simulation test environment for the second simulation test process. The simulation test environment sets the interference signal according to the location information (or interference signal location distribution map) corresponding to the input interference signal set, starts the second simulation test process of the lithium battery charging chip, and inputs the interference signal set in the second simulation test process.

[0095] S31. In the second simulation test process, the lithium battery charging chip acts as the transmitter, sending data to the receiver's main controller. Each communication data packet is collected. A second signal set during the transmission of a communication data packet, the second signal set including a second voltage signal, a second current signal, a second timing signal and a second noise signal;

[0096] S32, Obtain from the second signal set The second test dataset is composed of the second communication data packet loss, the second communication data packet transmission time, the second signal-to-noise ratio change, and the second chip temperature difference under the interference signal set.

[0097] It should be noted that the first and second simulation test procedures are the same except for the input interference signal set; the difference between the second test dataset and the first test dataset is that the second test dataset is an interference signal set, which makes it easier to highlight the changes in the data when comparing the first test dataset with the second test dataset later.

[0098] S4: Compare the first test dataset with the second test dataset to obtain the comparison sequence result. Set the comparison standard. If the comparison sequence result is within the comparison standard, the simulation test is completed. If the comparison sequence result is not within the comparison standard, the optimization points of the lithium battery charging chip are obtained.

[0099] In S4, the process of comparing the first test dataset with the second test dataset to obtain the alignment sequence results is as follows:

[0100] S40. The loss of the first communication data packet, the transmission time of the first communication data packet, the change value of the first signal-to-noise ratio, and the temperature difference of the first chip in the first test dataset are compared one by one with the loss of the second communication data packet, the transmission time of the second communication data packet, the change value of the second signal-to-noise ratio, and the temperature difference of the second chip in the second test dataset to obtain the comparison sequence result.

[0101] Specifically, the method for comparing sequence results is as follows:

[0102] For example, the loss of the first communication data packet is The loss of the second communication data packet is The comparison result between the loss of the first communication data packet and the loss of the second communication data packet is: ;

[0103] The first communication data packet transmission time is The first communication data packet transmission time is The comparison result between the transmission time of the first communication data packet and the transmission time of the second communication data packet is: ;

[0104] The first communication noise ratio change value is The second signal-to-noise ratio change value is The comparison result between the first signal-to-noise ratio change value and the second signal-to-noise ratio change value is: ;

[0105] The temperature difference of the first chip is... The temperature difference of the second chip is The comparison result between the temperature difference changes of the first chip and the temperature difference changes of the second chip is as follows: ;

[0106] Therefore, the alignment sequence results between the first test dataset and the second test dataset are as follows: ;

[0107] S41. By adjusting the upper and lower thresholds for each data point in the first test dataset, a preset comparison standard is obtained. The comparison standard includes the standard range corresponding to each data point in the first test dataset.

[0108] Specifically, the upper and lower thresholds can be set by relevant personnel according to the actual situation. For example, the data in the first test dataset could be... Therefore, the upper and lower threshold fluctuation ranges for each data point are respectively... , , , ,in, It is a percentage.

[0109] S42. If the alignment sequence results are within the alignment criteria, the simulation test is complete.

[0110] S43. If the alignment sequence result is not within the alignment standard, then obtain the optimization points of the lithium battery charging chip.

[0111] The process of obtaining the optimization points of the lithium battery charging chip is as follows;

[0112] S430. For each set of interference signals, reduce the interference signals in the input set one by one, and test the lithium battery charging chip to obtain... There are two test datasets: a current test dataset and an unshielded test dataset. The unshielded test dataset can reflect the performance of the lithium battery charging chip when there is no specific interference signal.

[0113] Specifically, the process of gradually reducing the interference signals within the input interference signal set is as follows:

[0114] If the input interference signal set includes the numbered If there are 5 interference signals, then the input will contain [the following] during the next input. The four interference signals will be input again on the next input. The three interference signals, until the input If there is one interference signal, then no further input will be made.

[0115] Through calculation The difference between the mean of each current test dataset and the unmasked test dataset is calculated, and each difference is given as a percentage compared to the mean of the unmasked dataset. The influence coefficient of the degree of change in quantification performance.

[0116] S431, through calculation The result is obtained by taking the mean difference between each current test dataset and the unmasked test dataset, and then comparing each mean difference to the mean of the unmasked dataset as a percentage. The influence coefficient, specifically, indicates the degree of change in test data / performance after shielding a specific interference signal. For example, if the current test dataset is 2, 4, 6, 4 with a mean of 4, and the unshielded test dataset is 4, 6, 6, 8 with a mean of 6, then, taking two decimal places, its influence coefficient is 1 - 4 / 6 = 0.33.

[0117] S432. If the current alignment sequence result is within the preset influence range, the shielded interference signal is a non-major interference signal. If the current alignment sequence result is not within the preset influence range, it is a major interference signal. It is possible to identify major and non-major interference signals. The influence range is set by relevant personnel according to the actual situation.

[0118] S433. Obtain the occurrence count of the main interference signal in the set of all interference signals, multiply the influence coefficient of the main interference signal by its occurrence count to obtain the influence value of the main interference signal; be able to quantitatively assess the overall influence of each main interference signal.

[0119] S434. Traverse the influence values ​​of all major interference signals, sort the influence values ​​in descending order of magnitude to obtain the major interference signal sequence list, and then select the top... The main interference signals are the points to be optimized for lithium battery charging chips.

[0120] In this embodiment, by calculating the amount of communication data packet loss, the reliability and stability of the communication link can be quantitatively evaluated; transmission duration reflects the efficiency of the communication process, helping to assess the communication speed and response time of the charging chip; the signal-to-noise ratio (SNR) variation reveals the fluctuation of signal quality; a stable SNR indicates good signal quality, while large variations may indicate interference or signal attenuation; the chip temperature variation reflects the thermal performance of the charging chip during operation, helping to assess its reliability and lifespan during long-term operation. In summary, collecting and analyzing these signals can comprehensively evaluate the anti-interference performance of the charging chip during communication, providing strong data support for subsequent optimization.

[0121] Example 2

[0122] Please see Figure 1 and Figure 2 As shown in this embodiment, a lithium battery charging chip simulation test method includes:

[0123] S5: Optimize the points to be optimized in the lithium battery charging chip. After optimization, perform simulation tests on the lithium battery charging chip again until the obtained alignment sequence results are within the alignment standard.

[0124] In S5, the optimization points of the lithium battery charging chip are optimized, and the lithium battery charging chip is simulated and tested again after optimization until the obtained alignment sequence result is within the alignment standard. The process is as follows:

[0125] S50, Acquisition The optimization directions for the points to be optimized include shielding design, filtering design, and fault detection design.

[0126] Among them, the optimization direction of each point to be optimized is the optimization direction with the most usage times corresponding to the same category of points to be optimized in the historical period;

[0127] S51, Obtain The number of times each optimization direction is selected in the points to be optimized is used to sort each optimization direction in descending order according to the number of times it is selected, thus obtaining the first optimization direction sort;

[0128] S52. Obtain the comprehensive cost corresponding to each optimization direction. The comprehensive cost includes time cost, labor cost, material cost and testing cost. Preset the first weight, second weight, third weight and fourth weight for time cost, labor cost, material cost and testing cost respectively. Add the weighted time cost, labor cost, material cost and testing cost to obtain the comprehensive cost value of each optimization direction. Sort the comprehensive cost of each optimization direction in ascending order according to the size to obtain the second optimization direction ranking.

[0129] S53. Obtain the optimization rate corresponding to each optimization direction and sort them in descending order to obtain the third optimization direction sort. Obtain the energy loss value corresponding to each optimization direction and sort them in ascending order to obtain the fourth optimization direction sort.

[0130] Specifically, the optimization rate is obtained as follows:

[0131] Obtaining historical data For each optimization approach applied to the same type of lithium battery charging chip, the improvement in the anti-interference performance of the lithium battery charging chip is as follows: (excluding...) By finding the maximum and minimum values ​​among the improvement values, and then calculating the average of the remaining improvement values, the optimization rate corresponding to each optimization direction can be obtained.

[0132] The energy loss value is obtained as follows:

[0133] Energy loss value within the historical period For two identical lithium battery charging chips, after applying each optimization direction, the heat loss / heat change value of the lithium battery charging chip is removed. By finding the maximum and minimum values ​​among the heat loss values, and then calculating the average of the remaining heat loss values, the energy loss value corresponding to each optimization direction can be obtained.

[0134] S54. Obtain the best optimization direction from the first optimization direction sort, the second optimization direction sort, the third optimization direction sort, and the fourth optimization direction sort using a multi-objective particle swarm optimization algorithm.

[0135] S55. The obtained optimal direction is applied to the lithium battery charging chip.

[0136] Specifically, by statistically analyzing and ranking the number of times each optimization direction is selected, more attention-grabbing and potentially more promising optimization directions can be identified, making the optimization process more objective and scientific. This avoids subjective assumptions and blind attempts, improving optimization efficiency. By comprehensively considering multiple factors such as time cost, labor cost, material cost, and testing cost, and by weighting these costs, effective cost control can be ensured during the optimization process. By employing a multi-objective particle swarm optimization algorithm, the optimal balance point can be found among multiple optimization objectives, thereby determining the best optimization direction and significantly improving the efficiency and accuracy of the optimization process.

[0137] The process of obtaining the optimal optimization direction from the first, second, third, and fourth optimization direction rankings using the multi-objective particle swarm optimization algorithm is as follows:

[0138] S540. Set constraints and randomly initialize a group of particles, with each particle representing a possible optimization direction.

[0139] S541. Set fitness functions for the first, second, third, and fourth optimization direction sorting respectively, evaluate each particle, and calculate its fitness in the first, second, third, and fourth optimization direction sorting.

[0140] Specifically, the fitness function for the first optimization direction ranking is: ,in, Indicates the first The fitness function value of each particle sorted in the first optimization direction. This represents the total number of points to be optimized. Indicates the first The particle targets the first The number of times each point to be optimized was selected;

[0141] The fitness function for the second optimization direction ranking is: ,in, Indicates the first The fitness function value of each particle sorted in the second optimization direction. These represent the weights of time cost, labor cost, material cost, and testing cost, respectively. Indicates the first The time cost per particle Indicates the first The labor cost per particle Indicates the first The material cost per particle Indicates the first The cost of testing each particle;

[0142] The fitness function for the third optimization direction ranking is: ,in, Indicates the first The fitness function value of each particle sorted in the third optimization direction. Indicates the first The optimization rate of each particle;

[0143] The fitness function for the fourth optimization direction ranking is: ,in, Indicates the first The fitness function value of each particle sorted in the fourth optimization direction. Indicates the first The energy loss value of each particle.

[0144] S542. For each particle, update its individual optimal solution, and select the global optimal solution from the individual optimal solutions of all particles.

[0145] S543. Update the particle's position and velocity based on its current position, velocity, individual optimal solution, and global optimal solution;

[0146] S544. Repeat S541 to S543 until the number of iterations is reached or the stopping condition is met. Select the optimization direction with the highest fitness as the best optimization direction.

[0147] Specifically, the number of iterations can be: Second-rate, The values ​​are set by relevant personnel according to the actual situation;

[0148] The stopping condition is: when the improvement of the solution is less than a preset threshold in multiple consecutive iterations, it is considered to have converged, and the iteration can be stopped.

[0149] The constraints are as follows: a comprehensive cost threshold is preset for the second optimization direction sorting, and a penalty is imposed on particles that reach the comprehensive cost threshold; an energy loss threshold is preset for the fourth optimization direction sorting, and a penalty is imposed on particles that reach the energy loss threshold; the comprehensive cost threshold and the energy loss threshold are set by relevant personnel according to the actual situation.

[0150] The process of penalizing particles that reach the overall cost threshold is as follows:

[0151] When evaluating the fitness of each particle, the ratio of the overall cost corresponding to each particle to the overall cost threshold is used. The deviation ratio is obtained based on the ratio. According to the deviation ratio, it decreases The fitness of;

[0152] For example, if If the deviation is 90%, then the deviation ratio is 1-90%.

[0153] The process of penalizing particles that reach the energy loss threshold is as follows:

[0154] When evaluating the fitness of each particle, the ratio of the energy loss value corresponding to each particle to the energy loss threshold is used. The deviation ratio is obtained based on the ratio. According to the deviation ratio, it decreases The fitness of;

[0155] For example, if If the deviation ratio is 80%, then the deviation ratio is either 1-80% or 20%, and the deviation ratio is taken as a positive value.

[0156] Specifically, by setting constraints in the multi-objective particle swarm optimization algorithm, it can be ensured that the optimization direction not only brings performance improvement, but also controls the cost and energy consumption within an acceptable range, so that the algorithm does not ignore the importance of cost and energy consumption while pursuing performance optimization.

[0157] In this embodiment, by acquiring and comprehensively considering multiple optimization directions, the anti-interference performance of the lithium battery charging chip can be comprehensively and systematically improved. By statistically analyzing and sorting the number of times each optimization direction is selected, more attention-grabbing and more promising optimization directions can be identified, making the optimization process more objective and scientific, and improving optimization efficiency. By comprehensively considering multiple factors such as time cost, labor cost, material cost, and testing cost, and by weighting these costs, effective cost control can be ensured during the optimization process. By adopting a multi-objective particle swarm optimization algorithm, the optimal balance point can be found among multiple optimization objectives, thereby determining the optimal optimization direction. This algorithm has advantages such as strong global search capability and fast convergence speed, which can significantly improve the efficiency and accuracy of the optimization process. By setting constraints in the multi-objective particle swarm optimization algorithm, it can be ensured that the optimization direction not only brings performance improvement, but also controls cost and energy consumption within an acceptable range, so that the algorithm does not ignore the importance of cost and energy consumption while pursuing performance optimization.

[0158] Example 3

[0159] Please see Figure 3 As shown in this embodiment, a lithium battery charging chip simulation test system includes:

[0160] The first acquisition module acquires the first signal set of the lithium battery charging chip in the first simulation test process, and obtains the first test dataset through the first signal set.

[0161] The second acquisition module collects raw signal data of different models of lithium battery charging chips during historical periods and obtains the set of interference signals in the raw signal data.

[0162] The simulation test module inputs an interference signal set in the second simulation test process, collects a second signal set of the lithium battery charging chip under the interference signal set, and obtains a second test dataset through the second signal set.

[0163] The comparison module compares the first test dataset with the second test dataset to obtain the comparison sequence result. It presets the comparison standard. If the comparison sequence result is within the comparison standard, the simulation test is completed; if the comparison sequence result is not within the comparison standard, the optimization points of the lithium battery charging chip are obtained.

[0164] The optimization module optimizes the points to be optimized in the lithium battery charging chip. After optimization, the lithium battery charging chip is simulated and tested again until the obtained comparison sequence results are within the comparison standard.

[0165] In this embodiment, the first and second acquisition modules acquire signal data of the lithium battery charging chip in simulated testing and actual working environments, respectively. This allows for rapid and accurate evaluation of the charging chip's performance. The simulated testing module simulates interference signal sets in a real environment, further enhancing the comprehensiveness and realism of the test, thereby improving testing efficiency and accuracy. The comparison module compares the simulated test dataset with actual historical data, accurately identifying interference signals that cause the lithium battery charging chip's performance to fail, i.e., points to be optimized. This provides a clear direction and target for subsequent optimization work. The optimization module performs precise optimization on the identified points to be optimized, significantly improving the communication performance, stability, and thermal performance of the lithium battery charging chip. The optimized charging chip can better adapt to the actual working environment, reducing the failure rate and extending its service life. Through simulated testing and comparative analysis, problems can be identified and resolved in the early stages of R&D, avoiding large-scale modifications and redesigns that may occur later, thus reducing R&D costs and time costs. The optimized lithium battery charging chip has superior performance and can meet higher anti-interference requirements.

[0166] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

Claims

1. A method for simulating testing lithium battery charging chips, characterized in that, Includes the following steps: S1: Collect the first signal set of the lithium battery charging chip in the first simulation test process, and obtain the first test dataset through the first signal set; S2: Collect raw signal data of different models of lithium battery charging chips during historical periods, and obtain the set of interference signals in the raw signal data; S3: Input the interference signal set in the second simulation test process, collect the second signal set of the lithium battery charging chip under the interference signal set, and obtain the second test dataset through the second signal set; S4: Compare the first test dataset with the second test dataset to obtain the comparison sequence result. Set the comparison standard. If the comparison sequence result is within the comparison standard, the simulation test is completed. If the comparison sequence result is not within the comparison standard, the optimization points of the lithium battery charging chip are obtained. S5: Optimize the points to be optimized in the lithium battery charging chip. After optimization, perform simulation tests on the lithium battery charging chip again until the obtained alignment sequence results are within the alignment standard.

2. The lithium battery charging chip simulation test method according to claim 1, characterized in that, In S1, the process of acquiring the first signal set of the lithium battery charging chip in the first simulation test process and obtaining the first test dataset from the first signal set is as follows: S10. Set up the simulation test environment for the first simulation test process and start the first simulation test process for the lithium battery charging chip. S11. In the first simulation test process, the lithium battery charging chip acts as the transmitter, sending data to the receiver's main controller. Each communication data packet is collected. A first signal set during the transmission of a communication data packet, the first signal set including a first voltage signal, a first current signal, a first timing signal and a first noise signal; S12, Obtain from the first signal set The first communication data packet loss, the first communication data packet transmission time, the first signal-to-noise ratio change value, and the first chip temperature change difference constitute the first test dataset.

3. The lithium battery charging chip simulation test method according to claim 1, characterized in that, In step S2, the process of collecting raw signal data from different models of lithium battery charging chips over a historical period and obtaining the set of interference signals from the raw signal data is as follows: S20, Obtain different models of lithium battery charging chips. In a real-world environment Each raw signal data contains 1 raw signal data. One interference signal; S21. Perform a Fourier transform on each original signal data to convert it to the frequency domain, obtaining the spectrum of the original signal data. Then, decompose the interference signals in the original signal data using the spectrum. One interference signal; S22. Obtain the frequency and amplitude of each interference signal through the spectrum diagram of the original signal data, analyze the periodicity of each interference signal through the autocorrelation function, quantify the periodicity, and obtain the periodicity value. S23. Deployment in different geographical locations in real-world environments Each receiving station records the time when it receives the interference signal. The time difference is calculated by comparing the times when different receiving stations receive the same interference signal. Based on the known location and time difference of each receiving station, the location information of the interference signal is determined. The location information corresponding to the determined interference signal is shielded by physical shielding equipment. Then, the location information of the remaining interference signals is obtained one by one. S24, will Each interference signal is associated with its corresponding frequency, amplitude, periodicity, and location information to obtain a corresponding set of interference signals. The number of interference signal sets is... indivual; In step S21, the process of decomposing the interference signal in the original signal data using the spectrum diagram of the original signal data is as follows: The peak value of each interference signal is obtained by spectrogram, the frequency range of the interference signal is determined by filter, and the signal in the frequency domain is converted to the time domain by inverse Fourier transform, that is, the interference signal decomposed in the original data signal is obtained. For each interference signal decomposed, the peak value and frequency range of the decomposed interference signal in the original data signal are hidden in the spectrogram. The current step is repeated until all interference signals in the original signal data are decomposed.

4. The lithium battery charging chip simulation test method according to claim 1, characterized in that, In S3, the process of inputting an interference signal set in the second simulation test procedure, acquiring a second signal set from the lithium battery charging chip under the interference signal set, and obtaining the second test dataset through the second signal set is as follows: S30. Set up the simulation test environment for the second simulation test process, start the second simulation test process for the lithium battery charging chip, and input the interference signal set in the second simulation test process; S31. In the second simulation test process, the lithium battery charging chip acts as the transmitter, sending data to the receiver's main controller. Each communication data packet is collected. A second signal set during the transmission of a communication data packet, the second signal set including a second voltage signal, a second current signal, a second timing signal and a second noise signal; S32, Obtain from the second signal set The second test dataset is composed of the second communication data packet loss, the second communication data packet transmission time, the second signal-to-noise ratio change, and the second chip temperature difference under the interference signal set.

5. The lithium battery charging chip simulation test method according to claim 2, characterized in that, In step S4, the process of comparing the first test dataset with the second test dataset to obtain the alignment sequence result is as follows: S40. The loss of the first communication data packet, the transmission time of the first communication data packet, the change value of the first signal-to-noise ratio, and the temperature difference of the first chip in the first test dataset are compared one by one with the loss of the second communication data packet, the transmission time of the second communication data packet, the change value of the second signal-to-noise ratio, and the temperature difference of the second chip in the second test dataset to obtain the comparison sequence result. S41. By adjusting the upper and lower thresholds for each data point in the first test dataset, a preset comparison standard is obtained. The comparison standard includes the standard range corresponding to each data point in the first test dataset. S42. If the alignment sequence results are within the alignment criteria, the simulation test is complete. S43. If the alignment sequence result is not within the alignment standard, then obtain the optimization points of the lithium battery charging chip.

6. The lithium battery charging chip simulation test method according to claim 5, characterized in that, The process of obtaining the optimization points of the lithium battery charging chip is as follows: S430. For each set of interference signals, reduce the interference signals in the input set one by one, and test the lithium battery charging chip to obtain... One current test dataset and one unmasked test dataset; S431, through calculation The result is obtained by taking the mean difference between each current test dataset and the unmasked test dataset, and then comparing each mean difference to the mean of the unmasked dataset as a percentage. One influence coefficient; S432. If the current alignment sequence result is within the preset influence range, the shielded interference signal is a non-major interference signal; if the current alignment sequence result is not within the preset influence range, it is a major interference signal. S433. Obtain the occurrence count of the main interference signal in the set of all interference signals, multiply the influence coefficient corresponding to the main interference signal by its occurrence count, and obtain the influence value of the main interference signal. S434. Traverse the influence values ​​of all major interference signals, sort the influence values ​​in descending order of magnitude to obtain the major interference signal sequence list, and then select the top... The main interference signals are the points to be optimized for lithium battery charging chips.

7. The lithium battery charging chip simulation test method according to claim 6, characterized in that, In step S5, the optimization points of the lithium battery charging chip are optimized, and the lithium battery charging chip is simulated and tested again after optimization until the obtained alignment sequence result is within the alignment standard. S50, Acquisition The optimization directions for the points to be optimized include shielding design, filtering design, and fault detection design. S51, Obtain The number of times each optimization direction is selected in the points to be optimized is used to sort each optimization direction in descending order according to the number of times it is selected, thus obtaining the first optimization direction sort; S52. Obtain the comprehensive cost corresponding to each optimization direction. The comprehensive cost includes time cost, labor cost, material cost and testing cost. Preset the first weight, second weight, third weight and fourth weight for time cost, labor cost, material cost and testing cost respectively. Add the weighted time cost, labor cost, material cost and testing cost to obtain the comprehensive cost value of each optimization direction. Sort the comprehensive cost of each optimization direction in ascending order according to the size to obtain the second optimization direction ranking. S53. Obtain the optimization rate corresponding to each optimization direction and sort them in descending order to obtain the third optimization direction sort. Obtain the energy loss value corresponding to each optimization direction and sort them in ascending order to obtain the fourth optimization direction sort. S54. Obtain the best optimization direction from the first optimization direction sort, the second optimization direction sort, the third optimization direction sort, and the fourth optimization direction sort using a multi-objective particle swarm optimization algorithm. S55. The obtained optimal direction is applied to the lithium battery charging chip.

8. The lithium battery charging chip simulation test method according to claim 7, characterized in that, The process of obtaining the optimal optimization direction from the first, second, third, and fourth optimization direction rankings using the multi-objective particle swarm optimization algorithm is as follows: S540. Set constraints and randomly initialize a group of particles, with each particle representing a possible optimization direction. S541. Set fitness functions for the first, second, third, and fourth optimization direction sorting respectively, evaluate each particle, and calculate its fitness in the first, second, third, and fourth optimization direction sorting. S542. For each particle, update its individual optimal solution, and select the global optimal solution from the individual optimal solutions of all particles. S543. Update the particle's position and velocity based on its current position, velocity, individual optimal solution, and global optimal solution; S544. Repeat S541 to S543 until the number of iterations is reached or the stopping condition is met. Select the optimization direction with the highest fitness as the best optimization direction.

9. The lithium battery charging chip simulation test method according to claim 8, characterized in that, The constraints are: a preset comprehensive cost threshold for sorting in the second optimization direction, and a penalty point is applied to particles that reach the comprehensive cost threshold; a preset energy loss threshold for sorting in the fourth optimization direction, and a penalty point is applied to particles that reach the energy loss threshold. The process of penalizing particles that reach the overall cost threshold is as follows: When evaluating the fitness of each particle, the ratio of the overall cost corresponding to each particle to the overall cost threshold is used. The deviation ratio is obtained based on the ratio. According to the deviation ratio, it decreases The fitness of; The process of penalizing particles that reach the energy loss threshold is as follows: When evaluating the fitness of each particle, the ratio of the energy loss value corresponding to each particle to the energy loss threshold is used. The deviation ratio is obtained based on the ratio. According to the deviation ratio, it decreases The degree of adaptability.

10. A lithium battery charging chip simulation testing system, used to implement the lithium battery charging chip simulation testing method according to any one of claims 1 to 9, characterized in that, include: The first acquisition module acquires the first signal set of the lithium battery charging chip in the first simulation test process, and obtains the first test dataset through the first signal set. The second acquisition module collects raw signal data of different models of lithium battery charging chips during historical periods and obtains the set of interference signals in the raw signal data. The simulation test module inputs an interference signal set in the second simulation test process, collects a second signal set of the lithium battery charging chip under the interference signal set, and obtains a second test dataset through the second signal set. The comparison module compares the first test dataset with the second test dataset to obtain the comparison sequence result. It presets the comparison standard. If the comparison sequence result is within the comparison standard, the simulation test is completed; if the comparison sequence result is not within the comparison standard, the optimization points of the lithium battery charging chip are obtained. The optimization module optimizes the points to be optimized in the lithium battery charging chip. After optimization, the lithium battery charging chip is simulated and tested again until the obtained comparison sequence results are within the comparison standard.

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