Power supply IC self-detection method and system based on data analysis

Abstract

The invention relates to the technical field of power supply fault detection, in particular to a power supply IC self-detection method and system based on data analysis, and the method comprises the steps: collecting the charging power, electric quantity, temperature, voltage and current at each moment in the charging process of a power supply IC; based on the difference between the electric quantity at each moment and the electric quantity at the adjacent moment, determining the electric energy consumption degree at each moment in combination with the charging power at each moment; analyzing the change trend of all the electric energy consumption degrees in the local time window of each moment, and determining a first temperature influence coefficient of each moment; based on the electric quantity at each moment, determining a second temperature influence coefficient at each moment; and combining the first temperature influence coefficient and the second temperature influence coefficient to obtain a comprehensive temperature influence coefficient of each moment, and combining the temperature, the voltage, the current and the time sequence prediction model to detect the power supply IC. Therefore, the detection accuracy of the power supply IC is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power supply fault detection, and in particular to a power supply IC self-detection method and system based on data analysis. Background Art

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

[0003] However, the triggering of the protection mechanism in the existing power IC function usually depends on a preset threshold. The protection mechanism in the power IC will only be triggered when the voltage, current or temperature monitored at the current moment is greater than the preset voltage, current or temperature threshold. This may cause the power IC to be unable to respond to actual abnormal conditions during battery charging in a timely manner, resulting in delayed response or false 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. The technical solutions adopted are as follows:

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

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

[0007] Based on the difference between the power at each moment and the power at adjacent moments, combined with the charging power at each moment, the power consumption level at each moment is determined;

[0008] Analyze the change trend of all the electric energy consumption levels in the local time window at each moment to determine the first temperature influence coefficient at each moment; determine the second temperature influence coefficient at each moment based on the electric quantity at each moment;

[0009] The first temperature influence coefficient and the second temperature influence coefficient are combined to obtain the comprehensive temperature influence coefficient at each moment, and the power IC is detected by combining the temperature, charging voltage, charging current, and time series prediction model.

[0010] In one embodiment, the determination of the power consumption level includes:

[0011] The normalized result of the difference between the power supply power at each moment and the power supply power at the previous moment is calculated, and the power consumption degree is obtained by calculating the normalized result and the charging power at each moment.

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

[0013] In one embodiment, the variation trend is the slope of a fitted straight line of all the power consumption levels in a 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))]; wherein 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 power consumption level at the i-th moment, and Norm is the normalization function.

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

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

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

[0019] The temperature data of the current moment and the preset number of moments before are combined into the temperature data sequence of the current moment as the input of the time series prediction model. The normalized results of the charging voltage, charging current, and comprehensive temperature influence coefficient of the current moment and the preset number of moments before are respectively combined into the voltage sequence, current sequence, and comprehensive temperature influence coefficient sequence of the current moment as the exogenous variables of the time series prediction model. The output of the time series prediction model is the temperature prediction value of the next moment.

[0020] The power supply IC is detected based on the temperature prediction value.

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

[0022] In the second aspect, an 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, and the processor implements the steps of any one of the above methods when executing the computer program.

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

[0024] The present application collects the charging power, power supply charge, power supply temperature, charging voltage, and charging current at each moment during the charging process of the power supply IC; determines the degree of power consumption at each moment based on the difference between the charge at each moment and the charge at the adjacent moment, combined with the charging power at each moment; the degree of power consumption reflects the degree to which power is consumed by other factors during the charging process of the power supply IC, reflects the possibility of abnormalities in the charging process of the power supply IC, and improves the reliability of power supply IC detection; further, analyzes the changing trend of all the power consumption levels within the local time window at each moment, and determines the first temperature influence coefficient at each moment; determines the second temperature influence coefficient at each moment based on the charge at each moment; its beneficial effect is to accurately evaluate the degree of power consumption at each moment, and improve the generation of abnormalities during the charging process The method can distinguish the degree of influence of heat on the power supply temperature at different moments; combine the first temperature influence coefficient and the second temperature influence coefficient to obtain the comprehensive temperature influence coefficient at each moment, and detect the power supply IC by combining the temperature, voltage, current, and time series prediction model; the beneficial effect is that the influence of heat generated by the electric energy consumed by other factors instead of being converted into battery power during charging and the influence of battery power during battery charging on battery temperature are taken into account, an exogenous variable that more accurately reflects the change of battery temperature is obtained, the accuracy of power supply temperature prediction by the time series prediction model during the charging process of the power supply IC is improved, the power supply IC can respond to the over-temperature situation during the charging process in a timely manner, and the detection accuracy of the power supply IC is improved. BRIEF 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 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.

[0026] Figure 1 A flowchart of a power IC self-detection method based on data analysis provided in one embodiment of the present application;

[0027] Figure 2 Construct a flow chart for the temperature prediction indicator of the mobile phone battery charging process. DETAILED DESCRIPTION

[0028] 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, structure, features and effects of a power IC self-detection method and system based on data analysis 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.

[0029] 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.

[0030] The following is a detailed description of a specific scheme of a power IC self-detection method and system based on data analysis provided by the present application in conjunction with the accompanying drawings.

[0031] See also Figure 1 , which shows a flow chart of a power IC self-detection method based on data analysis provided by an embodiment of the present application, the method comprising the following steps:

[0032] S1, collects the charging power, power supply capacity, power supply temperature, charging voltage, and charging current at each moment during the charging process of the power supply IC, and performs pre-processing.

[0033] This embodiment takes the charging process of a mobile phone battery as an example, and synchronously collects the charging power, battery power, battery temperature, charging voltage, and charging current of the mobile phone battery at each moment in the charging process in the battery management system of the power IC. The battery power, battery temperature, charging voltage, and charging current are referred to as power, temperature, voltage, and current. The sampling time intervals of the charging power, power, temperature, voltage, and current are all set to 1s, and the implementer can set them according to the actual situation, and this embodiment does not limit them here.

[0034] All the temperature data collected at each moment and within the previous 2 minutes are combined into a temperature data sequence at each moment in chronological order, wherein the length of the temperature data sequence can be set by the implementer according to the actual situation, and this implementation does not limit it here. The temperature data sequence reflects the data change of the mobile phone battery temperature over time.

[0035] All voltage data collected at each moment and within the previous 2 minutes are normalized using the Min-Max normalization method and then formed into a voltage sequence at each moment in chronological order; for current data, the same acquisition method as the voltage sequence is used to obtain the current sequence at each moment.

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

[0037] S2, based on the difference between the power at each moment and the power at adjacent moments, combined with the charging power at each moment, determine the power consumption level at each moment.

[0038] Specifically, under normal circumstances, 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 growth rate of the mobile phone battery power should be. However, when the mobile phone battery is charging, the mobile phone circuit board and the battery resistor will consume part of the electrical energy, and part of the consumed electrical energy will be released in the form of heat energy, which will cause the temperature of the mobile phone battery to rise. The more electrical energy the mobile phone battery consumes when charging, the less electrical energy the charging current is converted into the mobile phone battery power, the slower the growth rate of the mobile phone battery power will be, and the faster the mobile phone battery temperature will rise. Therefore, if the charging power of the mobile phone battery is greater, but the mobile phone battery power grows slower, this may mean that the mobile phone battery consumes more electrical energy when charging, causing the mobile phone circuit board and the battery resistor to generate more heat, resulting in more electrical energy being converted into heat energy instead of being stored in the battery, causing the mobile phone battery temperature to rise faster.

[0039] Based on the above analysis, the difference between the power at each moment during the charging process of the mobile phone battery and the power at the previous moment is calculated, and the difference is normalized to the interval of (0,1) using the Sigmoid function to obtain a normalized result; the ratio of the normalized value of the charging power at each moment to the normalized result is used as the power consumption level at each moment.

[0040] It should be noted that, at the start of the charging process, the present embodiment sets the power consumption level to 0, and the normalized 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 growth rate of the mobile phone power, that is, the smaller the normalized result, the less electrical energy the charging current is converted into the mobile phone battery power, and the greater the degree of electrical energy consumed by the mobile phone circuit board and battery resistors instead of the charging current being converted into the mobile phone battery power, that is, the greater the degree of electrical energy consumption, which means that the mobile phone circuit board and battery resistors generate more heat.

[0042] S3, analyzing the change trend of all the electric energy consumption levels in the local time window at each moment, and determining the first temperature influence coefficient at each moment; and determining the second temperature influence coefficient at each moment based on the electric quantity at each moment.

[0043] With each moment as the center, a time window of length N is set. In this embodiment, N=15. The implementer can set it according to the actual situation. This embodiment does not limit it. With time as the horizontal axis and the power consumption level as the vertical axis, the power consumption levels of all moments in the time window of each moment are linearly fitted to obtain the fitting straight line at each moment.

[0044] It should be noted that when the number of power consumption levels in a time window at a certain moment is less than N, the mean filling method is used to fill it to ensure that the number of power consumption levels at all moments in the time window of each moment is N. The implementer can use other existing methods to fill it, and this embodiment does not limit it here; this embodiment uses the least squares method for straight line fitting, wherein the least squares method is an existing well-known technology, and the implementer can use other existing straight line fitting algorithms.

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

[0046] pl(i)=Norm[f(i)×exp(k(i))]; wherein 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 power consumption level at the i-th moment, and Norm is the normalization function.

[0047] At the i-th moment, the greater the degree of consumption of electric energy consumed by the mobile phone circuit board and the battery resistor instead of being converted into the mobile phone battery power by the charging current, that is, the greater f(i), and the more obvious the growth trend of the electric energy consumption in the time window of the i-th moment, that is, the greater exp(k(i)), the greater the heat generated by the electric energy consumed by the mobile phone circuit board and the battery resistor, the greater the influence on the mobile phone battery temperature in the subsequent time period, the more likely it is to cause the mobile phone battery temperature to rise, and the greater the first temperature influence coefficient pl(i); and at the i-th moment, the smaller the degree of consumption of electric energy consumed by the mobile phone circuit board and the battery resistor when charging the mobile phone battery, that is, the smaller f(i), and the less the growth trend of the electric energy consumption in the time window of the i-th moment, that is, the smaller exp(k(i)), the less heat generated by the electric energy consumed by the mobile phone circuit board and the battery resistor, the smaller the influence on the mobile phone battery temperature in the subsequent time period, the less likely it is to cause the mobile phone battery temperature to rise, and the smaller the first temperature influence coefficient pl(i).

[0048] Secondly, the power level of the mobile phone battery during charging will also have a certain impact on the temperature of the mobile phone battery. When the mobile phone is charged at a high power level, the electrochemical reaction rate inside the mobile phone battery will slow down, reducing the heat generated when the battery is discharged. When the mobile phone battery is charged at a low power level, the electrochemical reaction rate inside the mobile phone battery will increase, causing the temperature of the mobile phone battery to rise rapidly in a short period of time.

[0049] Based on the above analysis, the power at each moment in the mobile phone battery charging process is normalized to the (0,1) interval using the Sigmoid function, and the inverse of the normalized result of the power 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 power of the mobile phone battery when charging on the temperature of the mobile phone battery in the subsequent time period at each moment. The lower the power of the mobile phone battery at each moment, the faster the temperature of the mobile phone battery can rise in a short time, and the greater the influence of the power of the mobile phone battery at that moment on the temperature of the mobile phone battery in the subsequent time period, the greater the second temperature influence coefficient.

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

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

[0053] The normalized results of the comprehensive temperature influence coefficient at the current moment and all moments before 2 minutes are combined into a comprehensive temperature influence coefficient sequence at the current moment in chronological order. For the normalized results of the comprehensive temperature influence coefficient, the Min-Max normalization processing method is adopted. The implementer can adopt other existing normalization processing methods according to the actual situation. The implementer can set the length of the comprehensive temperature influence coefficient sequence according to the actual situation, and this embodiment does not limit it here.

[0054] The comprehensive temperature influence coefficient sequence can characterize the temporal distribution of the comprehensive influence of the heat generated by the electric energy consumed by the mobile phone circuit board and the battery resistor and the power of the mobile phone battery on the mobile phone battery temperature during the data collection period of the mobile phone battery.

[0055] The temperature data sequence at the current moment is used as the prediction sequence of the ARIMAX model, and the voltage sequence, current sequence and comprehensive temperature influence coefficient sequence at the current moment are used as the exogenous variables of the ARIMAX model. The ARIMAX model is used to predict the temperature prediction value of the mobile phone battery at the next moment after the current moment. The ARIMAX model is a well-known technology, and the specific process will not be repeated; the implementer can choose other existing time series prediction models according to the actual situation, and this embodiment does not limit it here. The flowchart of the construction of the temperature prediction index of the mobile phone battery charging process is as follows Figure 2 shown.

[0056] Set the temperature threshold y1 in °C. If the predicted temperature value at the next moment is greater than or equal to y1, the over-temperature protection mechanism in the power IC function is activated, such as stopping the charging of the mobile phone battery, and completing the over-temperature protection of the mobile phone battery during the charging process. Otherwise, charging can continue. In this embodiment, y1=45, the implementer can set it according to the actual situation, and this embodiment is not limited here.

[0057] Based on the same inventive concept as the above method, an 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, and when the processor executes the computer program, the steps of any one of the above-mentioned power supply IC self-detection methods based on data analysis are implemented.

[0058] In summary, the present application collects the charging power, power, temperature, voltage, and current at each moment in the charging process of the power IC; determines the degree of power consumption at each moment based on the difference between the power at each moment and the power at the adjacent moment, combined with the charging power at each moment; the power consumption degree reflects the degree to which power is consumed by other factors during the charging process of the power IC, reflects the possibility of abnormalities in the charging process of the power IC, and improves the reliability of power IC detection; further, analyzes the changing trends of all the power consumption degrees within the local time window at each moment, and determines the first temperature influence coefficient at each moment; based on the power at each moment, determines the second temperature influence coefficient at each moment; its beneficial effect is to accurately evaluate the power consumption degree at each moment, and improve the generation of abnormalities during the charging process. The degree of differentiation of the influence of heat on the power supply temperature at different moments; combining the first temperature influence coefficient and the second temperature influence coefficient to obtain the comprehensive temperature influence coefficient at each moment, combining the temperature, voltage, current, and time series prediction model to detect the power supply IC; its beneficial effect is that it takes into account the influence of heat generated by the electric energy consumed by other factors instead of being converted into battery power during the charging process, and the influence of the battery power during charging on the battery temperature, obtains an exogenous variable that more accurately reflects the change in 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 to the over-temperature situation that occurs during the charging process in a timely manner, and improves the detection accuracy of the power supply IC.

[0059] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, 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.

[0060] 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.

[0061] 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 power IC self-detection method based on data analysis, characterized in that: The method comprises the following steps: Collect the charging power, power supply capacity, power supply temperature, charging voltage, and charging current at each moment during the charging process of the power supply IC; Based on the difference between the power at each moment and the power at adjacent moments, combined with the charging power at each moment, the power consumption level at each moment is determined; Analyze the change trend of all the electric energy consumption levels in the local time window at each moment to determine the first temperature influence coefficient at each moment; determine the second temperature influence coefficient at each moment based on the electric quantity at each moment; The first temperature influence coefficient and the second temperature influence coefficient are combined to obtain the comprehensive temperature influence coefficient at each moment, and the power IC is detected by combining the temperature, charging voltage, charging current, and time series prediction model.

2. A power IC self-detection method based on data analysis as claimed in claim 1, characterized in that: The determination of the degree of power consumption includes: The normalized result of the difference between the power supply power at each moment and the power supply power at the previous moment is calculated, and the power consumption degree is obtained by calculating the normalized result and the charging power at each moment.

3. A power IC self-detection method based on data analysis as claimed in claim 2, characterized in that: The electric energy consumption degree is the ratio of the normalized value of the charging power at each moment to the normalized result.

4. A power IC self-detection method based on data analysis as claimed in claim 1, characterized in that: The variation trend is the slope of the fitted straight line of all the electric energy consumption levels in the local time window at each moment.

5. A power IC self-detection method based on data analysis as claimed in claim 4, characterized in that: The calculation formula of the first temperature influence coefficient is: pl(i)=Norm[f(i)×exp(k(i))]; wherein 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 power consumption level at the i-th moment, and Norm is the normalization function.

6. A power IC self-detection method based on data analysis as claimed in claim 1, characterized in that: The second temperature influence coefficient is the reciprocal of the normalized result of the power supply electric quantity at each moment.

7. A power IC self-detection method based on data analysis as claimed in claim 1, characterized in that: The comprehensive temperature influence coefficient is the average of the first temperature influence coefficient and the second temperature influence coefficient at each moment.

8. A power IC self-detection method based on data analysis as claimed in claim 1, characterized in that: The detecting of the power supply IC comprises: The temperature data of the current moment and the preset number of moments before are combined into the temperature data sequence of the current moment as the input of the time series prediction model. The normalized results of the charging voltage, charging current, and comprehensive temperature influence coefficient of the current moment and the preset number of moments before are respectively combined into the voltage sequence, current sequence, and comprehensive temperature influence coefficient sequence of the current moment as the exogenous variables of the time series prediction model. The output of the time series prediction model is the temperature prediction value of the next moment. The power supply IC is detected based on the temperature prediction value.

9. A power IC self-detection method based on data analysis as claimed in claim 8, characterized in that: If the temperature prediction value is greater than or equal to the preset temperature threshold, the over-temperature protection mechanism of the power IC is activated, otherwise, charging continues.

10. A power IC self-detection system based on data analysis, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 9 when executing the computer program.

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