A Power Supply IC Self-Testing Method and System Based on Data Analysis

Abstract

This application relates to the technical field of power failure detection, and particularly relates to a self-detection method and system for a power supply IC based on data analysis. The method includes: collecting the charging power, battery level, temperature, voltage, and current at each moment during the charging process of the power supply IC; determining the degree of power consumption at each moment based on the difference between the battery levels at each moment and the battery levels at adjacent moments, in combination with the charging power at each moment; analyzing the change trend of all the degrees of power consumption within the local time window at each moment to determine the first temperature influence coefficient at each moment; determining the second temperature influence coefficient at each moment based on the battery level at each moment; combining the first temperature influence coefficient and the second temperature influence coefficient to obtain the comprehensive temperature influence coefficient at each moment, and combining the temperature, voltage, current, and a time series prediction model to detect the power supply IC. Thereby improving the detection accuracy of the power supply IC.

Description

Technical Field

[0001] This application relates to the technical field of power failure detection, and specifically relates to a power IC self-detection method and system based on data analysis. Background Art

[0002] A power IC, that is, a power chip, also known as a power management integrated circuit, is an integrated circuit specifically used to control power management, current, and voltage conversion, and is commonly used in mobile phones and various mobile terminal devices. The power IC can monitor the voltage, current, and temperature during the battery charging process, and trigger corresponding protection mechanisms when abnormal voltage, current, or temperature of the battery is detected. Through the self-detection of the power IC, the stability and reliability of the battery during the charging process can be ensured.

[0003] However, the triggering of the protection mechanism in the existing power IC functions usually depends on preset thresholds. Only when the voltage, current, or temperature monitored at the current moment is greater than the preset voltage, current, or temperature threshold, the protection mechanism in the power IC will be triggered. This may cause the power IC to fail to respond to the actual abnormal situations during the battery charging process in a timely manner, resulting in a delayed response or mis-triggering of the protection mechanism of the power IC. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a power IC self-detection method and system based on data analysis, and the specific technical solutions adopted are as follows:

[0005] In the first aspect, an embodiment of this application provides a power IC self-detection method based on data analysis, and this method includes the following steps:

[0006] Collect the charging power, power supply power, power supply temperature, charging voltage, and charging current at each moment during the charging process of the power IC;

[0007] Based on the difference between the power at each moment and the power at the adjacent moment, and in combination with the charging power at each moment, determine the degree of power consumption at each moment;

[0008] Analyze the change trend of all the degrees of power consumption within the local time window at each moment, and determine the first temperature influence coefficient at each moment; based on the power at each moment, determine the second temperature influence coefficient at each moment;

[0009] Combine the first temperature influence coefficient and the second temperature influence coefficient to obtain the comprehensive temperature influence coefficient at each moment, and combine the temperature, charging voltage, charging current, and time series prediction model to detect the power IC.

[0010] In one of the embodiments, the determination of the degree of power consumption includes:

[0011] Calculate the normalized result of the difference between the power of the power supply at each moment and the power of the power supply at the previous moment, and calculate the degree of power consumption based on the normalized result and the charging power at each moment.

[0012] In one embodiment, the degree of power consumption is the ratio of the normalized value of the charging power at each moment to the normalized result.

[0013] In one embodiment, the change trend is the slope of the fitting line of all the degrees of power consumption within the local time window at each moment.

[0014] In one embodiment, the calculation formula of the first temperature influence coefficient is:

[0015] pl(i) = Norm[f(i) × exp(k(i))]; where pl(i) is the first temperature influence coefficient at the i-th moment, k(i) is the slope at the i-th moment, f(i) is the degree of power consumption at the i-th moment, and Norm is the normalization function.

[0016] In one embodiment, the second temperature influence coefficient is the reciprocal of the normalized result of the power of the power supply at each moment.

[0017] In one embodiment, the comprehensive temperature influence coefficient is the average value of the first temperature influence coefficient and the second temperature influence coefficient at each moment.

[0018] In one embodiment, the detection of the power supply IC includes:

[0019] Form the temperature data sequence at the current moment by using the temperature data at the current moment and a preset number of moments before, as the input of the time series prediction model. Correspondingly, form the voltage sequence, current sequence, and comprehensive temperature influence coefficient sequence at the current moment by using the normalized results of the charging voltage, charging current, and comprehensive temperature influence coefficient at the current moment and a preset number of moments before, as the exogenous variables of the time series prediction model. The output of the time series prediction model is the temperature prediction value at the next moment;

[0020] Detect the power supply IC based on the temperature prediction value.

[0021] In one embodiment, if the temperature prediction value is greater than or equal to the preset temperature threshold, start the over-temperature protection mechanism of the power supply IC; otherwise, continue charging.

[0022] In a second aspect, an embodiment of the present application further provides a power supply IC self-detection system based on data analysis, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0023] The present application has at least the following beneficial effects:

[0024] In the present application, the charging power, power supply power, power supply temperature, charging voltage, and charging current at each moment during the charging process of the power supply IC are collected; based on the difference in the power at each moment and the power at the adjacent moment, combined with the charging power at each moment, the degree of power consumption at each moment is determined; the degree of power consumption reflects the degree to which electrical energy is consumed by other factors during the charging process of the power supply IC, reflects the possibility of abnormalities during the charging process of the power supply IC, and improves the reliability of power supply IC detection; further, by analyzing the change trend of all the degrees of power consumption within the local time window at each moment, the first temperature influence coefficient at each moment is determined; based on the power at each moment, the second temperature influence coefficient at each moment is determined; the beneficial effect is that it accurately evaluates the degree of power consumption of electrical energy at each moment and improves the discrimination degree of the influence of the heat generated during the charging process on the power supply temperature at different moments; by combining the first temperature influence coefficient and the second temperature influence coefficient, the comprehensive temperature influence coefficient at each moment is obtained, and by combining the temperature, voltage, current, and time series prediction model, the power supply IC is detected; the beneficial effect is that it considers the influence of the heat generated by the electrical energy consumed by other factors rather than being converted into battery power during the charging process on the battery temperature, and the influence of the battery power during battery charging on the battery temperature, obtains exogenous variables that more accurately reflect the change of the battery temperature, improves the accuracy of the time series prediction model in predicting the power supply temperature during the charging process of the power supply IC, enables the power supply IC to respond in a timely manner to the over-temperature situation during the charging process, and improves the detection accuracy of the power supply IC. Description of the Drawings

[0025] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following described drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0026] Figure 1 It is a flowchart of the steps of a method for self-detecting a power supply IC based on data analysis provided by an embodiment of the present application;

[0027] Figure 2 It is a flowchart for constructing temperature prediction indexes during the charging process of a mobile phone battery. Detailed implementation manners

[0028] In order to further elaborate on the technical means and effects adopted by this application to achieve the intended invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, elaborate in detail on a power supply IC self-detection method and system based on data analysis proposed according to this application, including its specific implementation manners, structures, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0030] The following will specifically describe the specific solutions of a power supply IC self-detection method and system based on data analysis provided by this application in conjunction with the accompanying drawings.

[0031] Please refer to Figure 1 , which shows a flowchart of the steps of a power supply IC self-detection method based on data analysis provided by an embodiment of this application. The method includes the following steps:

[0032] S1. Collect the charging power, power supply power, power supply temperature, charging voltage, and charging current at each moment during the charging process of the power supply IC, and perform preprocessing.

[0033] In this embodiment, taking the charging process of a mobile phone battery as an example, synchronously collect the charging power, battery power, battery temperature, charging voltage, and charging current of the mobile phone battery at each moment in the battery management system of the power supply IC. Abbreviate the battery power, battery temperature, charging voltage, and charging current as power, temperature, voltage, and current respectively. The sampling time intervals of the charging power, power, temperature, voltage, and current are all set to 1 s. Implementers can set them according to actual situations, and this embodiment does not limit them here.

[0034] Form a temperature data sequence at each moment by arranging all the temperature data collected at each moment and within the previous 2 minutes in chronological order. Among them, the length of the temperature data sequence can be set by the implementer according to actual situations, and this embodiment does not limit it here. The temperature data sequence reflects the data change of the mobile phone battery temperature over time.

[0035] After normalizing all the voltage data collected at each moment and within the previous 2 minutes using the Min-Max normalization method, form a voltage sequence at each moment in chronological order; for the current data, use the same acquisition method as the voltage sequence to obtain a current sequence at each moment.

[0036] It should be noted that the implementer can set the lengths of the voltage sequence and the current sequence according to the actual situation; Min-Max normalization is a well-known existing technology, and the implementer can choose other existing normalization methods according to the actual situation.

[0037] S2. Based on the difference between the power at each moment and the power at the adjacent moment, and combining the charging power at each moment, determine the degree of power consumption at each moment.

[0038] Specifically, generally, during the charging process of a mobile phone battery, the greater the charging power, the more electrical energy is transmitted to the mobile phone battery per unit time, and the faster the power of the mobile phone battery should increase. However, when the mobile phone battery is charging, both the mobile phone circuit board and the battery resistance will consume part of the electrical energy, and the consumed part of the electrical energy will be released in the form of heat, which will cause the temperature of the mobile phone battery to rise. Moreover, the more electrical energy is consumed when the mobile phone battery is charging, the less electrical energy the charging current is converted into the power of the mobile phone battery, and the slower the power increase rate of the mobile phone battery will be. Then the faster the temperature of the mobile phone battery will rise. Therefore, if the charging power of the mobile phone battery is greater, but the power increase rate of the mobile phone battery is slower, this may mean that more electrical energy is consumed when the mobile phone battery is charging, resulting in more heat generated by the mobile phone circuit board and the battery resistance, causing more electrical energy to be converted into heat rather than stored in the battery, leading to a faster increase in the temperature of the mobile phone battery.

[0039] Based on the above analysis, calculate the difference between the power at each moment and the power at the previous moment during the charging process of the mobile phone battery, normalize the difference to the interval (0, 1) using the Sigmoid function to obtain the normalization result; take the ratio of the normalization value of the charging power at each moment to the normalization result as the degree of power consumption at each moment.

[0040] It should be noted that for the starting moment of the charging process, in this embodiment, the degree of power consumption is set to 0, and the normalization value of the charging power is also obtained using the Sigmoid function.

[0041] At each moment, the greater the charging power of the mobile phone battery, and at the same time, the slower the power increase rate of the mobile phone battery, that is, the smaller the normalization result, the less electrical energy the charging current is converted into the power of the mobile phone battery, and the greater the degree of power consumption of the electrical energy that the charging current is not converted into the power of the mobile phone battery but consumed by the mobile phone circuit board and the battery resistance, that is, the greater the degree of power consumption, which means that more heat is generated by the mobile phone circuit board and the battery resistance.

[0042] S3. Analyze the change trend of all the degrees of power consumption within the local time window at each moment to determine the first temperature influence coefficient at each moment; based on the power at each moment, determine the second temperature influence coefficient at each moment.

[0043] Centered on each moment, a time window with a length of N is set. In this embodiment, N = 15, and the implementer can set it according to the actual situation, which is not limited in this embodiment. Taking time as the horizontal axis and the degree of power consumption as the vertical axis, linear fitting is performed on the degrees of power consumption at all moments within the time window of each moment to obtain the fitting line for each moment.

[0044] It should be noted that when the number of degrees of power consumption within the time window of a certain moment is less than N, the mean filling method is used for filling to ensure that the number of degrees of power consumption at all moments within the time window of each moment is N. The implementer can use other existing methods for filling, which is not limited in this embodiment; in this embodiment, the least squares method is used for linear fitting. Among them, the least squares method is a well-known existing technology, and the implementer can use other existing linear fitting algorithms.

[0045] Based on the slope of the fitting line at each moment, combined with the degree of power consumption at each moment, the first temperature influence coefficient at each moment is determined, which is used to characterize the influence degree of the heat generated by the power consumed by the mobile phone circuit board and battery resistance on the mobile phone battery temperature in the subsequent time period. In this embodiment, the specific calculation method is as follows:

[0046] pl(i) = Norm[f(i) × exp(k(i))]; where pl(i) is the first temperature influence coefficient at the i-th moment, k(i) is the slope at the i-th moment, f(i) is the degree of power consumption at the i-th moment, and Norm is the normalization function.

[0047] At the i-th moment, the greater the degree of power consumption of the power that is not converted into the mobile phone battery power but consumed by the mobile phone circuit board and battery resistance, that is, the greater f(i), and the more obvious the growth trend of the degree of power consumption within the time window at the i-th moment, that is, the greater exp(k(i)), the more heat is generated by the power consumed by the mobile phone circuit board and battery resistance, the greater the influence degree on the mobile phone battery temperature in the subsequent time period, and the more likely it is to cause the increase of the mobile phone battery temperature, and the greater the first temperature influence coefficient pl(i); while at the i-th moment, the smaller the degree of power consumption of the power consumed by the mobile phone circuit board and battery resistance during the charging of the mobile phone battery, that is, the smaller f(i), and the less the degree of power consumption has a growth trend within the time window at the i-th moment, that is, the smaller exp(k(i)), the less heat is generated by the power consumed by the mobile phone circuit board and battery resistance, the smaller the influence degree on the mobile phone battery temperature in the subsequent time period, and the less likely it is to cause the increase of the mobile phone battery temperature, and the smaller the first temperature influence coefficient pl(i).

[0048] Secondly, the battery level of the mobile phone during charging also has a certain impact on the temperature of the mobile phone battery. When the mobile phone is charged at a high battery level, the rate of the electrochemical reaction inside the mobile phone battery will slow down, resulting in less heat generation during battery discharge. When the mobile phone battery is charged at a low battery level, the rate of the electrochemical reaction inside the mobile phone battery will increase, causing the temperature of the mobile phone battery to rise rapidly within a short period of time.

[0049] Based on the above analysis, the battery levels at each moment during the charging process of the mobile phone battery are normalized to the interval (0, 1) using the Sigmoid function, and the reciprocal of the normalization result of the battery level at each moment is used as the second temperature influence coefficient at each moment.

[0050] The second temperature influence coefficient is used to characterize the degree of influence of the battery level of the mobile phone battery during charging on the temperature of the mobile phone battery in the subsequent time period. The lower the battery level of the mobile phone battery at each moment, the faster the temperature of the mobile phone battery can rise within a short period of time, and the greater the degree of influence of the battery level of the mobile phone battery at this moment on the temperature of the mobile phone battery in the subsequent time period, and the greater the second temperature influence coefficient.

[0051] S4. Combine the first temperature influence coefficient and the second temperature influence coefficient to obtain the comprehensive temperature influence coefficient at each moment, and combine the temperature, voltage, current, and time series prediction model to detect the power supply IC.

[0052] Furthermore, the average value of the first temperature influence coefficient and the second temperature influence coefficient at each moment is used as the comprehensive temperature influence coefficient at each moment. The comprehensive temperature influence coefficient is used to characterize the comprehensive degree of influence of the heat generated by the electrical energy consumed by the mobile phone circuit board and the battery resistance and the battery level of the mobile phone battery on the temperature of the mobile phone battery in the subsequent time period. At each moment, the greater the degree of influence of the heat generated by the electrical energy consumed by the mobile phone circuit board and the battery resistance on the temperature of the mobile phone battery in the subsequent time period, that is, the greater the first temperature influence coefficient, and at the same time, the greater the degree of influence of the battery level of the mobile phone battery at this moment on the temperature of the mobile phone battery in the subsequent time period, that is, the greater the second temperature influence coefficient, the greater the comprehensive degree of influence, that is, the greater the comprehensive temperature influence coefficient.

[0053] The normalization results of the comprehensive temperature influence coefficients at the current moment and all moments within the previous 2 minutes are arranged in chronological order to form the comprehensive temperature influence coefficient sequence at the current moment. For the normalization results of the comprehensive temperature influence coefficients, the Min - Max normalization processing method is adopted. Implementers can use other existing normalization processing methods according to the actual situation. The length of the comprehensive temperature influence coefficient sequence can be set by the implementer according to the actual situation, and this embodiment does not limit it here.

[0054] The comprehensive temperature influence coefficient sequence can characterize the distribution over time of the comprehensive influence degree of the heat generated by the electric energy consumed by the mobile phone circuit board and battery resistance during the data acquisition of the mobile phone battery and the battery power of the mobile phone battery on the temperature of the mobile phone battery.

[0055] Take the temperature data sequence at the current moment as the prediction sequence of the ARIMAX model, and take the voltage sequence, current sequence and comprehensive temperature influence coefficient sequence at the current moment as the exogenous variables of the ARIMAX model. Use the ARIMAX model to predict the temperature prediction value of the mobile phone battery at the next moment of the current moment. The ARIMAX model is a well-known existing technology, and the specific process will not be elaborated here; the implementer can choose other existing time series prediction models according to the actual situation, and this embodiment does not make any restrictions here. The flow chart for constructing the temperature prediction index during the charging process of the mobile phone battery is as Figure 2 shown.

[0056] Set the temperature threshold y1, with the unit of °C. If the temperature prediction value at the next moment is greater than or equal to y1, then activate the over-temperature protection mechanism in the power supply IC function, such as stopping charging the mobile phone battery, to complete the over-temperature protection of the mobile phone battery during the charging process. Otherwise, charging can continue. In this embodiment, y1 = 45, and the implementer can set it by himself according to the actual situation, and this embodiment does not make any restrictions here.

[0057] Based on the same inventive concept as the above method, the embodiment of the present application also provides a power supply IC self-detection system based on data analysis, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above methods of a power supply IC self-detection method based on data analysis.

[0058] In summary, the present application collects the charging power, power, temperature, voltage, and current at each moment during the charging process of the power supply IC; based on the difference in power at each moment and the power at adjacent moments, combined with the charging power at each moment, the degree of power consumption at each moment is determined; the degree of power consumption reflects the degree to which electrical energy is consumed by other factors during the charging process of the power supply IC, reflects the possibility of abnormalities during the charging process of the power supply IC, and improves the reliability of the detection of the power supply IC; further, by analyzing the change trend of all the degrees of power consumption within the local time window at each moment, the first temperature influence coefficient at each moment is determined; based on the power at each moment, the second temperature influence coefficient at each moment is determined; the beneficial effect is that the degree of power consumption of electrical energy at each moment is accurately evaluated, and the discrimination degree of the influence of the heat generated during the charging process on the power supply temperature at different moments is improved; by combining the first temperature influence coefficient and the second temperature influence coefficient, the comprehensive temperature influence coefficient at each moment is obtained, and combined with the temperature, voltage, current, and time series prediction model, the power supply IC is detected; the beneficial effect is that it considers the influence of the heat generated by the electrical energy consumed by other factors rather than being converted into battery power during the charging process on the battery temperature, as well as the influence of the battery power during battery charging on the battery temperature, obtains exogenous variables that more accurately reflect the change of the battery temperature, improves the accuracy of the time series prediction model in predicting the power supply temperature during the charging process of the power supply IC, enables the power supply IC to respond in a timely manner to the over-temperature situation during the charging process, and improves the detection accuracy of the power supply IC.

[0059] It should be noted that: the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0060] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

[0061] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A power supply IC self-detection method based on data analysis, characterized in that The method includes the following steps: Collect the charging power, power supply power, power supply temperature, charging voltage, and charging current at each moment during the charging process of the power supply IC; Based on the difference in the power at each moment and the power at the adjacent moment, and in combination with the charging power at each moment, determine the degree of power consumption at each moment; Analyze the change trend of all the degrees of power consumption within the local time window at each moment to determine the first temperature influence coefficient at each moment; based on the power at each moment, determine the second temperature influence coefficient at each moment; Combine the first temperature influence coefficient and the second temperature influence coefficient to obtain the comprehensive temperature influence coefficient at each moment, and in combination with the temperature, charging voltage, charging current, and time series prediction model, detect the power supply IC; The detection of the power supply IC includes: Form a temperature data sequence at the current moment from the temperature data at the current moment and a preset number of moments before, as the input of the time series prediction model. Respectively, form a voltage sequence, a current sequence, and a comprehensive temperature influence coefficient sequence at the current moment from the normalized results of the charging voltage, charging current, and comprehensive temperature influence coefficient at the current moment and a preset number of moments before, as the exogenous variables of the time series prediction model. The output of the time series prediction model is the temperature prediction value at the next moment; Detect the power supply IC based on the temperature prediction value; If the temperature prediction value is greater than or equal to the preset temperature threshold, activate the over-temperature protection mechanism of the power supply IC, otherwise, continue charging.

2. The self-detection method of a power supply IC based on data analysis according to claim 1, characterized in that, The determination of the degree of power consumption includes: Calculate the normalized result of the difference between the power supply power at each moment and the power supply power at the previous moment, and calculate based on the normalized result and the charging power at each moment to obtain the degree of power consumption.

3. The self-detection method of a power supply IC based on data analysis according to claim 2, characterized in that The degree of power consumption is the ratio of the normalized value of the charging power at each moment to the normalized result.

4. The self-detection method of a power supply IC based on data analysis according to claim 1, characterized in that, The change trend is the slope of the fitting line of all the degrees of power consumption within the local time window at each moment.

5. The self-detection method of a power supply IC based on data analysis according to claim 4, characterized in that The calculation formula for the first temperature influence coefficient is: ; where pl(i) is the first temperature influence coefficient at the i-th moment, k(i) is the slope at the i-th moment, f(i) is the degree of power consumption at the i-th moment, and Norm is the normalization function.

6. The self-detection method of a power supply IC based on data analysis according to claim 1, characterized in that The second temperature influence coefficient is the reciprocal of the normalized result of the power supply power at each moment.

7. The self-detection method of a power supply IC based on data analysis according to claim 1, characterized in that, The comprehensive temperature influence coefficient is the mean of the first temperature influence coefficient and the second temperature influence coefficient at each moment.

8. A power supply IC self-detection system based on data analysis, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-7.

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