A test system for automobile air-conditioning fan controller

Abstract

The present application relates to the technical field of fan controller testing, and specifically to a test system for a car air-conditioning fan controller. The system includes: obtaining the output voltage data, PWM signal and fan speed data of the fan controller under different temperature states; dividing the test time period under each temperature state into several time periods, respectively obtaining the voltage steep drop coefficient, the distortion disorder degree and the speed cycle significance value of each time period; obtaining the tracking difference degree of each time period; obtaining the autocorrelation coefficient under each temperature state; obtaining the state abnormality coefficient of the fan controller; obtaining the test results of the fan controller by comparing the state abnormality coefficient with the preset abnormality threshold, and displaying it through the display unit. The present application improves the accuracy and reliability of the test results by analyzing the influence of power supply voltage fluctuation factors and temperature factors on the test results of the fan controller.

Description

Technical Field

[0001] The present application relates to the technical field of fan controller testing, and in particular to a testing system for a fan controller of an automobile air conditioner. Background Art

[0002] As one of the core components of the air conditioning system, the performance and reliability of the automotive air conditioning fan controller directly affect the operation effect of the air conditioning system and the user experience. Therefore, it is very important to effectively test the automotive air conditioning fan controller.

[0003] At present, the testing methods and technologies of automobile air-conditioning fan controllers have made certain progress, but there are still some shortcomings. As automobile air-conditioning systems become more and more complex, automobile air-conditioning fans may be affected by various working conditions during operation, such as electrical system abnormalities, mechanical system abnormalities, and ambient temperature changes, which may cause the air-conditioning fan controller to fail to work properly. The testing methods of conventional air-conditioning fan controllers have a single monitoring parameter and fail to comprehensively consider the impact of complex working conditions such as temperature changes and power supply voltage fluctuations, resulting in inaccurate test results.

[0004] Publication No. CN116125944A discloses a performance test circuit, system and method for a speed control module of a car air conditioner fan. The method mainly tests the performance of the speed control module, controls the blower voltage by converting PWM to linear voltage output, and collects the voltage to determine whether it is qualified. The test dimension is single, and the complex environment in the actual vehicle operation is not considered, such as the influence of temperature changes and power supply voltage fluctuations, resulting in inaccurate test results. Summary of the invention

[0005] In order to solve the above technical problems, the purpose of this application is to provide a test system for an automobile air-conditioning fan controller, and the technical solutions adopted are as follows: The present application proposes a test system for an automobile air-conditioning fan controller, the system comprising: Data acquisition module: obtains the output voltage data, PWM signal and fan speed data of the fan controller under different temperature conditions; Data analysis module: Divide the test time period under each temperature state into several time periods, and obtain the voltage sag coefficient of each time period according to the amplitude characteristics of the mutation point in the output voltage data in each time period; obtain the distortion disorder degree of each time period according to the data change characteristics before and after the mutation point in the PWM signal in each time period, the signal-to-noise ratio of the PWM signal, and the duty cycle fluctuation characteristics; obtain the speed cycle significance value of each time period according to the periodic fluctuation characteristics of the fan speed data in each time period; obtain the tracking difference degree of each time period according to the average level of the voltage sag coefficient and the distortion disorder degree in each time period, and the difference with the speed cycle significance value in the same time period; obtain the autocorrelation coefficient under each temperature state according to the tracking difference degree of all time periods under each temperature state; obtain the state abnormality coefficient of the fan controller according to the size and difference degree of the autocorrelation coefficient of the fan controller under all temperature states; obtain the test result of the fan controller by comparing the state abnormality coefficient with the preset abnormal threshold; Test result display module: displays the test results of the fan controller through the display unit.

[0006] Preferably, the method for determining the voltage sag coefficient is: respectively obtaining the mutation points of the output voltage data in each time period; respectively calculating the absolute difference between the amplitude of each mutation point and the mean of the amplitudes of all non-mutation points in a single time period, and taking the mean of the absolute differences of all mutation points in a single time period as the voltage sag coefficient of the single time period.

[0007] Preferably, the calculation method of the distortion disorder is: The rise and fall significance of each period is obtained according to the data change characteristics of the PWM signal before and after the mutation point in each period; Obtaining the duty cycle fluctuation value of each time period according to the duty cycle fluctuation characteristics of the PWM signal in each time period; The calculation formula of distortion disorder is: , where is the distortion disorder of the PWM signal in the i-th period, is the signal-to-noise ratio of the PWM signal in the i-th period, is the duty cycle fluctuation value of the PWM signal in the i-th period, is the significance of the rise and fall of the PWM signal in the i-th period.

[0008] Preferably, the method for determining the rise and fall significance is: respectively obtaining the mutation points of the PWM signal in each time period, and taking a section of data before and after each mutation point to form the rise and fall zone of the PWM signal; calculating the cumulative sum of the absolute differences between all two adjacent data in each rise and fall zone, and taking the average of the cumulative sums of all rise and fall zones in a single time period as the rise and fall significance of the corresponding single time period.

[0009] Preferably, the method for calculating the duty cycle fluctuation value is: respectively obtain the rising edge in the PWM signal in each time period, and take the time length between the starting times of two consecutive rising edges as a cycle; calculate the ratio of the time length corresponding to the high level in each cycle in a single time period to the time length of the cycle in which it is located, and record the standard deviation of all the ratios as the duty cycle fluctuation value of a single time period.

[0010] Preferably, the method for determining the speed cycle significance value is: obtaining the periodic item data in the fan speed data of each time period, and obtaining the trigonometric function fitting curve of the periodic item data of each time period; calculating the shortest distance between each fan speed data in a single time period and the fitting curve, and taking the inverse of the cumulative sum of all the distances as the speed cycle significance value of the single time period.

[0011] Preferably, the method for calculating the tracking difference is: calculating the mean of the voltage sag coefficient and the distortion disorder in a single time period, and recording the difference between the mean and the speed cycle significant value in the same time period as the tracking difference of the time period.

[0012] Preferably, the process of determining the autocorrelation coefficient is: arranging the tracking differences of all time periods under a single temperature state in ascending time order to obtain a tracking difference sequence, and calculating the autocorrelation coefficient of the obtained tracking difference sequence, which is recorded as the autocorrelation coefficient corresponding to the single temperature state.

[0013] Preferably, the method for determining the state abnormality coefficient is: taking the product of the standard deviation and the mean of the autocorrelation coefficient corresponding to the fan controller under all temperature states as the state abnormality coefficient of the fan controller.

[0014] Preferably, the specific process of obtaining the test result of the fan controller is: if the normalized result of the state abnormality coefficient of the fan controller is greater than or equal to the preset abnormality threshold, the quality of the fan controller is defective; otherwise, the quality of the fan controller is good.

[0015] This application has the following beneficial effects: The present application proposes a test system for an automobile air-conditioning fan controller, which consists of a data acquisition module, a data analysis module, and a test result display module. The present application performs simulation tests under different temperature environments, and its advantage is that it can analyze the stability of the fan controller under different temperatures, thereby increasing the reliability of the test results. The application also deeply analyzes the influence characteristics of the fan controller output voltage, PWM signal, and fan speed under the power supply voltage fluctuation state, combines the characteristics of the PWM signal and output voltage affected by the power supply voltage fluctuation, and the consistency relationship between the characteristics of the fan speed affected by the power supply voltage fluctuation, further analyzes the difference in operating status under different temperature conditions, obtains the state abnormality coefficient of the fan controller, and then determines whether the quality of the fan controller has defects, thereby improving the accuracy of the test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A block diagram of a test system for an automobile air-conditioning fan controller provided by an embodiment of the present application; Figure 2 A flowchart for constructing a state abnormality coefficient provided in one embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a test system for a car air-conditioning fan controller proposed in the present application in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0020] The specific scheme of a test system for an automobile air-conditioning fan controller provided by the present application is described in detail below with reference to the accompanying drawings.

[0021] See also Figure 1, which shows a block diagram of a test system for an automobile air-conditioning fan controller provided by an embodiment of the present application. The system includes: a data acquisition module 101, a data analysis module 102, and a test result display module 103.

[0022] Data Acquisition Module 101: In order to simulate the working state of the automobile air-conditioning fan controller in a real environment, this embodiment places the fan controller in different temperature states for testing during the test process. The temperature test range is -20°C to 90°C. A temperature state is obtained every 10°C within the temperature test range. Tests are performed separately in each temperature state. The test duration in each temperature state is set to P seconds. In this embodiment, P is 10, and the power supply voltage drop state that may exist in actual work is simulated during the test process. The power supply system of the car may be affected by various factors such as engine start-up, causing the power supply voltage to fluctuate, thereby affecting the output voltage of the fan controller and the stability of the PWM signal. Therefore, by detecting the output voltage and PWM signal of the fan controller, the adaptability of the fan controller to the supply voltage fluctuation can be evaluated. The fan speed directly reflects the adjustment state of the fan controller. The greater the fluctuation of the fan speed, the more unstable the adjustment of the fan controller. Therefore, this embodiment uses the lower computer test tool to obtain the output voltage data, PWM signal and fan speed data of the fan controller in the same time period under various temperature states. The acquisition frequency of each data is 200Hz.

[0023] Data Analysis Module 102: Under the influence of voltage fluctuation and temperature change, the parameters of the fan controller of the automobile air conditioner have different change characteristics. Specifically, if the power supply is unstable, especially the voltage drops, the input voltage is not enough to maintain the required output, which will cause the output voltage of the fan controller to drop instantly. Unstable power supply can also cause PWM signal distortion, such as large upgrade slope, noise, and unstable duty cycle of PWM signal. For fan speed data, voltage drop will cause insufficient motor torque, causing the fan speed to drop. During the process of restoring stable voltage, if there is a delay in adjusting the PWM duty cycle, the fan speed will fluctuate periodically. At the same time, temperature changes may affect the performance of electronic components. For example, high temperature will increase the on-resistance of some components of the controller, thereby reducing the output voltage of the fan controller. Low temperature may reduce the capacity of the capacitor, affecting the power supply filtering effect and causing output voltage fluctuations. Under high temperature, the reference voltage inside the fan controller will drift, which may misjudge the duty cycle setting value, making the actual output duty cycle lower than the set duty cycle.

[0024] As can be seen from the above, under the influence of power supply voltage fluctuations and temperature changes, there is a certain coupling between the change characteristics of various parameters. Since the occurrence time of these change characteristics is relatively short, this embodiment divides the test time of P seconds under each temperature state into 0.5 seconds, and obtains several time periods, and then obtains the data change characteristics in each time period. The following analysis takes the i-th time period as an example.

[0025] For the instantaneous drop in the output voltage of the fan controller caused by the supply voltage fluctuation, there will be a relatively obvious mutation point at the drop position. This embodiment uses the Bernaola Galvan (BG) mutation point detection algorithm to obtain the mutation point position of the output voltage and output all the mutation points of the output voltage in this period. The BG mutation point detection algorithm is a well-known technology, and the specific process is not repeated here. The absolute difference between the amplitude of each mutation point and the mean of the amplitudes of all non-mutation points is calculated respectively, and the mean of all the absolute differences is used as the voltage steep drop coefficient. Represents the voltage steep drop coefficient of the output voltage data in the i-th period, The larger it is, the more obvious the instantaneous drop of the output voltage in the i-th time period is.

[0026] Unstable power supply will cause distortion of the PWM signal output by the fan controller and unstable duty cycle. To obtain this feature, each cycle in the PWM signal is first extracted. This embodiment uses a timer capture method to respectively obtain the rising edge and falling edge in the PWM signal in each time period. The timer capture method is a well-known technology and will not be described in detail. The time length between the starting moments of two consecutive rising edges is taken as a cycle, and each cycle contains only one rising edge and one falling edge. The standard PWM signal waveform should be a rectangular wave with clear rising and falling edges, and the rising and falling positions of the waveform will be very steep. When the controller outputs a distorted PWM signal, a slope will appear in the rising and falling positions of the waveform, and as the degree of distortion increases, the corresponding slope will be flatter.

[0027] Therefore, taking the i-th time period as an example, the BG mutation point detection algorithm is used to obtain the mutation point of the PWM signal in the i-th time period, and the local range with the mutation point as the center and the total number of data as M is used as the rise and fall zone of the PWM signal. M can be any odd number in [9,13]. In this embodiment, M is 11. It should be noted that when the mutation point is located at the top or end of the PWM signal, resulting in the total number of data in the local range being less than M, the calculation is performed according to the actual number of data. The cumulative sum of the absolute differences between all two adjacent data in each rise and fall zone is calculated, and the average of the cumulative sums of all rise and fall zones in the i-th time period is used as the rise and fall significance of the i-th time period. is the significance of the rise and fall of the PWM signal in the i-th period, The smaller it is, the smoother the rising and falling edges in the i-th time period are. The distortion feature is also manifested as increased noise, so the signal-to-noise ratio of the PWM signal in the i-th time period can be obtained. , the calculation of the signal-to-noise ratio is a well-known technology, and the specific process is not repeated here. In addition, for the unstable characteristic of the duty cycle, the ratio of the time length corresponding to the high level in each cycle in the i-th time period to the time length of the cycle in which it is located is calculated respectively, and the standard deviation of all the ratios is recorded as the duty cycle fluctuation value, is the duty cycle fluctuation value of the PWM signal in the i-th period, The larger the value, the more unstable the duty cycle of the PWM signal in each cycle in the i-th period. The formula for calculating the distortion disorder of the PWM signal in the i-th period is: , where is the distortion disorder of the PWM signal in the i-th period, is the signal-to-noise ratio of the PWM signal in the i-th period, is the duty cycle fluctuation value of the PWM signal in the i-th period, is the significance of the rise and fall of the PWM signal in the i-th period. The larger the value is, the higher the distortion of the PWM signal in the i-th period is and the more unstable the duty cycle is.

[0028] In order to solve the periodic fluctuation of fan speed due to voltage drop, this embodiment adopts the singular spectrum analysis (SSA) algorithm to obtain the periodic item data of fan speed data in the ith time period. When there is a delay in the duty cycle adjustment of PWM, its periodic fluctuation has a sine wave characteristic, and the least squares method is used to obtain the trigonometric function fitting curve of the periodic item data. The singular spectrum analysis algorithm and the least squares method are both well-known technologies, and the specific process will not be repeated. Then, the shortest distance between each fan speed data and the fitting curve is calculated respectively, and the inverse of the cumulative sum of all distances is taken as the significant value of the speed period, which is recorded as . The larger the value, the greater the periodic fluctuation of the fan speed.

[0029] Furthermore, when the operating stability of the fan controller is poor, the characteristics of the PWM signal and the output voltage affected by the power supply voltage fluctuation will show a high degree of consistency with the characteristics of the fan speed affected by the power supply voltage fluctuation. That is, in the same time period, when the instantaneous drop characteristics of the output voltage are more obvious or the distortion degree of the PWM signal is higher, the corresponding fan speed period fluctuation degree will be greater. Therefore, first calculate the mean of the voltage sag coefficient and the distortion disorder in the same time period, and record the difference between the mean and the significant value of the speed cycle in the same time period as the tracking difference degree of the time period. is the following difference of the fan controller in the i-th period, The smaller the value, the closer the characteristics of the PWM signal and the output voltage in the i-th time period affected by the power supply voltage are to the characteristics of the fan speed affected by the power supply voltage. Then, the tracking difference of all time periods under a single temperature state is arranged in ascending time order to obtain a tracking difference sequence, and the autocorrelation coefficient of the obtained tracking difference sequence is calculated when the lag interval is H. In this embodiment, the value of H is an integer in [4,6], and H is 5 in this embodiment. The calculation of the autocorrelation coefficient is a well-known technology, and the specific process will not be repeated. The larger the autocorrelation coefficient under a single temperature state, the stronger the consistency between the output voltage, PWM signal and fan speed of the fan controller under this temperature state, and the worse the operating state of the air conditioner fan controller.

[0030] This embodiment simulates the operating state of an automobile air-conditioning fan controller under complex temperature change conditions, and is tested under different temperature conditions. The test temperature range is -20°C to 90°C. However, under extremely low or high temperature conditions, the performance of some electronic components in the fan controller will be significantly reduced, which will cause the various types of data it outputs to be more easily disturbed by power supply voltage fluctuations, and the operating state of the air-conditioning fan controller will deviate. Under intermediate temperature conditions, the temperature interference is small, and its operating state is relatively good. Therefore, if there is a defect in the automobile air-conditioning fan controller, the difference between the autocorrelation coefficients under different temperature conditions will be more significant. Therefore, the product of the standard deviation and the mean of the corresponding autocorrelation coefficients under all temperature conditions is used as the state abnormality coefficient of the fan controller. The flow chart for constructing the state abnormality coefficient is shown in the figure. Figure 2 The higher the abnormal state coefficient is, the worse the quality of the fan controller is.

[0031] Finally, the corresponding test evaluation results are obtained according to the state abnormality coefficient of the fan controller. First, the state abnormality coefficient is normalized using the tanh function. In this embodiment, the preset abnormality threshold is set to 0.75. If the normalized result is greater than or equal to the preset abnormality threshold, the quality of the fan controller is defective. Otherwise, the quality of the fan controller is good.

[0032] Test result display module 103: After the test result of the fan controller is obtained, the test result is displayed through a display unit, wherein the display unit may be, but is not limited to, a display screen, an indicator light or a buzzer.

[0033] It should be noted that the sequence of the above embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0034] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0035] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A test system for an automobile air-conditioning fan controller, characterized in that: The system comprises: Data acquisition module (101): obtains output voltage data, PWM signal and fan speed data of the fan controller under different temperature conditions; Data analysis module (102): divide the test time period under each temperature state into a plurality of time periods, obtain the voltage steep drop coefficient of each time period according to the amplitude characteristics of the mutation point in the output voltage data in each time period; obtain the distortion disorder degree of each time period according to the data change characteristics before and after the mutation point in the PWM signal in each time period, the signal-to-noise ratio of the PWM signal, and the duty cycle fluctuation characteristics; obtain the speed cycle significance value of each time period according to the periodic fluctuation characteristics of the fan speed data in each time period; obtain the tracking difference degree of each time period according to the average level of the voltage steep drop coefficient and the distortion disorder degree in each time period and the difference with the speed cycle significance value in the same time period; obtain the autocorrelation coefficient in each temperature state according to the tracking difference degree of all time periods under each temperature state; obtain the state abnormality coefficient of the fan controller according to the size and difference degree of the autocorrelation coefficient of the fan controller under all temperature states; obtain the test result of the fan controller by comparing the state abnormality coefficient with a preset abnormality threshold; Test result display module (103): displays the test result of the fan controller via a display unit.

2. A test system for an automobile air-conditioning fan controller as claimed in claim 1, characterized in that: The method for determining the voltage sag coefficient is as follows: obtaining the mutation point of the output voltage data in each time period; calculating the absolute difference between the amplitude of each mutation point and the average of the amplitudes of all non-mutation points in a single time period, and taking the average of the absolute differences of all mutation points in a single time period as the voltage sag coefficient of the single time period.

3. A test system for an automobile air-conditioning fan controller as claimed in claim 1, characterized in that: The calculation method of the distortion disorder is: The significance of the rise and fall in each period is obtained according to the data change characteristics of the PWM signal before and after the mutation point in each period; Obtaining the duty cycle fluctuation value of each time period according to the duty cycle fluctuation characteristics of the PWM signal in each time period; The calculation formula of distortion disorder is: , where is the distortion disorder of the PWM signal in the i-th period, is the signal-to-noise ratio of the PWM signal in the i-th period, is the duty cycle fluctuation value of the PWM signal in the i-th period, is the significance of the rise and fall of the PWM signal in the i-th period.

4. A test system for an automobile air-conditioning fan controller as claimed in claim 3, characterized in that: The method for determining the rise and fall significance is: respectively obtaining the mutation points of the PWM signal in each time period, and taking a section of data before and after each mutation point to form the rise and fall area of ​​the PWM signal; The cumulative sum of the absolute differences between all two adjacent data in each lifting zone is calculated, and the average of the cumulative sums of all lifting zones in a single period is used as the lifting significance of the corresponding single period.

5. A test system for an automobile air-conditioning fan controller as claimed in claim 3, characterized in that: The calculation method of the duty cycle fluctuation value is as follows: respectively obtain the rising edge in the PWM signal in each time period, and take the time length between the starting moments of two consecutive rising edges as a cycle; calculate the ratio of the time length corresponding to the high level in each cycle in a single time period to the time length of the cycle in which it is located, and record the standard deviation of all the ratios as the duty cycle fluctuation value of a single time period.

6. A test system for an automobile air-conditioning fan controller as claimed in claim 1, characterized in that: The method for determining the significant value of the speed cycle is: obtaining the periodic item data in the fan speed data of each time period, and obtaining the trigonometric function fitting curve of the periodic item data of each time period; The shortest distance between each fan speed data in a single period and the fitting curve is calculated, and the reciprocal of the cumulative sum of all the distances is used as the speed cycle significance value of the single period.

7. A test system for an automobile air-conditioning fan controller as claimed in claim 1, characterized in that: The method for calculating the tracking difference is: calculating the mean of the voltage sag coefficient and the distortion disorder in a single period, and recording the difference between the mean and the significant value of the speed cycle in the same period as the tracking difference of the period.

8. A test system for an automobile air-conditioning fan controller as claimed in claim 1, characterized in that: The process of determining the autocorrelation coefficient is as follows: arranging the following difference degrees of all time periods under a single temperature state in ascending time order to obtain a following difference sequence, and calculating the autocorrelation coefficient of the obtained following difference sequence, which is recorded as the autocorrelation coefficient corresponding to the single temperature state.

9. A test system for an automobile air-conditioning fan controller as claimed in claim 1, characterized in that: The method for determining the state abnormality coefficient is: taking the product of the standard deviation and the mean of the autocorrelation coefficient corresponding to the fan controller under all temperature states as the state abnormality coefficient of the fan controller.

10. A test system for a car air conditioner fan controller as claimed in claim 1, characterized in that: The specific process of obtaining the test result of the fan controller is: if the normalized result of the state abnormality coefficient of the fan controller is greater than or equal to the preset abnormality threshold, the quality of the fan controller is defective; otherwise, the quality of the fan controller is good.

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