A charging control method and system for electronic equipment

Abstract

The present invention relates to the field of battery charging control technology, and in particular to a charging control method and system for electronic devices. The method collects statistics on each historical charging process under a constant current charging process in a historical database, divides it into historical time periods by time period division, and analyzes the comprehensive evaluation coefficient of each historical time period under the current constant current value. For a real-time charging process, the time period similarity weight of each historical time period to the real-time time period can be determined by time period matching, and the preference degree of the real-time time period for each constant current value can be determined by weighted fusion of the comprehensive evaluation coefficients, thereby determining the charging current under the current real-time time period. The present invention performs data comparison through big data analysis and data collaborative filtering to achieve dynamic adjustment of the constant current size during the charging process of an electronic device, thereby improving charging safety and extending the battery cycle life while ensuring charging efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of battery charging control, and in particular to a charging control method and system for an electronic device. Background Art

[0002] With the rapid development of mobile communications, smart terminals, wearable devices, and electric vehicles, lithium-ion batteries have become the mainstream power supply solution. To meet users' growing demand for charging speed and battery life, electronic devices generally adopt the constant current-constant voltage (CC-CV) mode for battery charging management. This charging strategy is widely used in charging systems of various electronic products due to its simple structure, reliable control, and high efficiency. However, this mode generally uses a fixed maximum charging current during the constant current phase, failing to dynamically adjust to changes in the battery's internal impedance and temperature rise during the charging process. Especially in the later stages of the constant current phase, as the battery voltage gradually increases and the internal resistance increases, battery heating becomes significantly more severe. If the maximum current is still maintained, the battery temperature can easily become too high, which not only affects the safety of the charging process but also accelerates battery aging and reduces its cycle life. Summary of the Invention

[0003] In order to solve the technical problem that the battery is damaged by long-term constant current charging during the existing battery charging process, the purpose of the present invention is to provide a charging control method and system for electronic devices. The technical solutions adopted are as follows:

[0004] The present invention provides a charging control method for an electronic device, the method comprising:

[0005] Collecting charging information of each historical charging process of the electronic device battery under the constant current charging process in the statistical history database, wherein the charging information includes at least temperature, internal resistance and state of charge;

[0006] Each historical charging process is divided into multiple historical time periods based on the fluctuations and changes in charging information. For each historical time period, a comprehensive evaluation coefficient is obtained based on the changes in battery temperature and state of charge during the historical time period.

[0007] Determine the real-time time period corresponding to the real-time moment in the real-time charging process; obtain a time period similarity weight based on the difference in charging information between the real-time time period and each historical time period; for each constant current value in the constant current charging process, perform weighted fusion on the comprehensive evaluation coefficients of all historical time periods corresponding to the constant current value based on the time period similarity weight to obtain the preference degree of each constant current value for the real-time time period; determine the charging current corresponding to the real-time time period based on the preference degree.

[0008] Furthermore, each historical charging process is divided into multiple historical time periods according to the fluctuations and changes of the charging information, including:

[0009] The length of the time period is preset according to the range of the state of charge. The length of the time period is negatively correlated with the state of charge. Based on the time period length, each historical charging process is divided into initial time periods to obtain multiple initial historical time periods for each historical charging process.

[0010] In each historical charging process, the battery fluctuation degree at each moment is obtained according to the changes in temperature and internal resistance at each moment, segmentation points are screened out based on the battery fluctuation degree, and the initial historical time period is segmented based on the segmentation points to obtain all historical time periods of each historical charging process.

[0011] Furthermore, the method for obtaining the battery fluctuation degree includes:

[0012] For each moment, search forward and backward for each moment according to the preset search length to obtain the forward analysis range and backward analysis range of each moment;

[0013] For any dimension between temperature and internal resistance, the first-order difference mean of the dimension in each analysis range is used as the change characteristic of the dimension in each analysis range; the difference between the change characteristics of the backward analysis range and the forward analysis range is normalized to obtain the initial fluctuation degree of the dimension;

[0014] Between the two dimensions of temperature and internal resistance, the maximum initial fluctuation degree is selected as the battery fluctuation degree at each moment.

[0015] Furthermore, the method for obtaining the comprehensive evaluation coefficient includes:

[0016] The temperature evaluation coefficient is obtained based on the relationship between the average battery temperature in the historical time period and the preset safety temperature range;

[0017] Obtaining a state of charge growth rate within a historical time period; and taking a product of the state of charge growth rate and the temperature evaluation coefficient as the comprehensive evaluation coefficient.

[0018] Furthermore, the method for obtaining the temperature evaluation coefficient includes:

[0019] If the battery temperature average is lower than the lower limit of the safe temperature range, a negative correlation mapping is performed on the difference between the lower limit of the safe temperature range and the battery temperature average to obtain a temperature evaluation coefficient;

[0020] If the battery temperature average is within the safe temperature range, the temperature evaluation coefficient is assigned a positive integer of 1;

[0021] If the battery temperature average is greater than the upper limit of the safe temperature range, a negative correlation mapping is performed on the difference between the battery temperature average and the upper limit of the safe temperature range to obtain a temperature evaluation coefficient.

[0022] Furthermore, the method for obtaining the similarity weight of the time period includes:

[0023] The four dimensions of temperature, internal resistance, state of charge, and battery health status are used as the dimensions to be analyzed. The DTW distance of the data series of the dimensions to be analyzed between the real-time time period and the historical time period is obtained. The ratio of the DTW distance to the number of matching data pairs in the DTW matching process is used as the dimension difference under the dimensions to be analyzed;

[0024] The dimensional differences of all dimensions to be analyzed are accumulated and then negatively correlated to obtain the similarity weight of the time period.

[0025] Furthermore, the weighted fusion method is weighted averaging.

[0026] Furthermore, determining the charging current corresponding to the real-time time period according to the degree of preference includes:

[0027] The constant current value with the highest degree of preference is selected as the corresponding charging current.

[0028] Furthermore, the method of determining the charging current corresponding to the real-time time period according to the degree of preference further includes:

[0029] The ratio of the preset maximum battery voltage to the battery internal resistance at the real time is used as the maximum charging current; between the maximum charging current and the most preferred constant current value, the minimum current value is selected as the charging current.

[0030] The present invention also proposes a charging control system for an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor implements any one of the steps of the charging control method for an electronic device.

[0031] The present invention has the following beneficial effects:

[0032] The present invention utilizes a big data analysis method to statistically analyze each historical charging process under the constant current charging process in a historical database. The present invention aims to dynamically adjust the constant current as the battery progresses based on the constant current charging process. Therefore, the historical charging process can be divided into historical time periods by time period division, and then the comprehensive evaluation coefficient of each historical time period under the current constant current value is analyzed. Then, for the real-time charging process, the time period similarity weight of each historical time period to the real-time time period can be determined by time period matching. By weighted fusion of the comprehensive evaluation coefficients, the preferred degree of the real-time time period for each constant current value can be determined, and then the charging current under the current real-time time period can be determined. The present invention uses big data analysis and data collaborative filtering to perform data comparison, and realizes dynamic adjustment of the constant current size during the charging process of the electronic device. It can effectively reduce the temperature rise rate during the charging process, suppress heat accumulation, and avoid the risk of battery overheating and life degradation caused by current value mismatch. On the basis of ensuring charging efficiency, it improves charging safety and extends the battery cycle life. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0034] Figure 1 A flow chart of a charging control method for an electronic device provided by one embodiment of the present invention;

[0035] Figure 2 A schematic diagram showing the relationship between the length of a time period and the state of charge provided by one embodiment of the present invention;

[0036] Figure 3 A schematic diagram of a comprehensive rating coefficient provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0037] To further illustrate the technical means and effectiveness of the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a charging control method and system for an electronic device proposed in accordance with the present invention. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0038] 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 invention belongs.

[0039] The following describes in detail a method and system for controlling charging of an electronic device provided by the present invention with reference to the accompanying drawings.

[0040] See also Figure 1 , which shows a flow chart of a charging control method for an electronic device provided by one embodiment of the present invention, the method comprising:

[0041] Step S1: Collecting charging information of each historical charging process of the electronic device battery under the constant current charging process in a historical database.

[0042] During the charging process, existing electronic devices can use a battery management unit to collect real-time charging information of the battery during each charging process. In the embodiment of the present invention, the charging information includes at least temperature, internal resistance, and state of charge, and may also include voltage, current, charging time node, ambient temperature, charge amount, etc. All of this charging information can be recorded in chronological order to the local storage device of the device or a cloud database, thereby forming a historical database. In the embodiment of the present invention, in a new charging process, the charging information reflected in the historical charging process under the constant current charging process is used to determine the charging current under the real-time charging process using a statistical analysis method.

[0043] In the embodiment of the present invention, the charging information collection frequency may be set to once per minute.

[0044] Step S2: Divide each historical charging process into multiple historical time periods according to the fluctuations and changes in charging information; for each historical time period, obtain a comprehensive evaluation coefficient for each historical time period according to the changes in battery temperature and state of charge during the historical time period.

[0045] In the traditional constant current-constant voltage (CC-CV) charging mode, the initial stage is typically set to the maximum allowable charging current to improve charging efficiency. However, as the charging process progresses, the battery's internal impedance gradually increases, leading to increased heat accumulation per unit time. Excessive temperature rise not only affects charging safety but can also accelerate battery aging and reduce cycle life. Therefore, embodiments of the present invention propose an improved strategy based on the traditional CC-CV mode. In the later stages of the constant current phase, when a significant increase in battery heating is detected, the constant current is dynamically reduced to suppress heat generation. This effectively reduces temperature rise while maintaining charging efficiency as much as possible, thereby improving charging safety and extending battery life. Considering that during the constant current charging phase, the battery terminal voltage gradually increases with charging, and the internal temperature and internal resistance of the battery do not change linearly, but rather exhibit distinct stage-by-stage characteristics. In particular, in the middle and late stages, the internal resistance increases rapidly, and the rate of heat generation increases significantly. Analyzing only the entire constant current phase as a single unit can easily obscure abnormal trends at key time points, affecting the accuracy of the control strategy. Therefore, when performing statistical analysis on historical charging processes, embodiments of the present invention first divide each historical charging process into multiple historical time periods based on the fluctuations and changes in charging information. Each historical time period can be considered a charging stage. By using the fluctuations and changes in multi-dimensional charging information, the charging state of the historical charging process can be effectively divided, ensuring a relatively uniform battery state within a historical time period, which can then be used for subsequent collaborative comparative analysis.

[0046] Preferably, in one embodiment of the present invention, considering the close relationship between the battery's state of charge and SoC (State of Charge), the State of Charge can be considered the battery's charge level. Temperature rise is more sensitive and dramatic at medium to high SoC levels. Therefore, this embodiment of the present invention performs an initial segmentation based on the State of Charge (SoC). Using this initial segmentation as a foundation, further analysis is performed on fluctuations and changes in other charging information, specifically including:

[0047] The length of the time period is preset according to the range of the state of charge. The length of the time period is negatively correlated with the state of charge. That is, the higher the state of charge range, the shorter the corresponding time period. Figure 2 , which shows a schematic diagram of the relationship between time segment length and state of charge, provided by one embodiment of the present invention. In the 0%-50% SOC range, the time segment length is 5 minutes; in the 50%-80% range, the time segment length is 3 minutes; in the 80%-95% range, the time segment length is 1 minute; and in the 95%-100% range, the time segment length is 30 seconds. This means that when the SOC is low, heat generation is low and SOC changes slowly, making it suitable for coarse-grained monitoring. As the SOC increases, the length of the divided time segments needs to be gradually reduced.

[0048] Each historical charging process is divided into initial time periods based on the size of the time periods, and multiple initial historical time periods in each historical charging process are obtained.

[0049] Further considering changes in charging information from other dimensions, temperature and internal resistance are relatively intuitive indicators of the charging status. If the battery temperature or internal resistance fluctuates significantly at a certain point in time, it indicates that this point in time is a critical heating point, after which the battery charging phase enters the next phase. Therefore, during each historical charging process, the degree of battery fluctuation at each moment is determined based on the changes in temperature and internal resistance at each moment. Segmentation points are selected based on the degree of battery fluctuation, and the initial historical time period is segmented based on the segmentation points to obtain all historical time periods for each historical charging process.

[0050] Furthermore, in one embodiment of the present invention, a method for obtaining the battery fluctuation degree includes:

[0051] For each moment, a search is performed forward and backward at each moment according to a preset search length to obtain a forward analysis range and a backward analysis range for each moment. In this embodiment of the present invention, the search length is set to 5, i.e., with each moment as the center, the five time points forward constitute the forward analysis range, and the five time points backward constitute the backward analysis range.

[0052] For any dimension between temperature and internal resistance, the mean of the first-order differences within each analysis range for that dimension is used as the variation characteristic for that dimension within each analysis range. The difference between the variation characteristics between the backward analysis range and the forward analysis range is normalized to obtain the initial degree of fluctuation for that dimension. The greater the initial degree of fluctuation, the more significant the temperature or internal resistance change after that moment in the historical charging process, and the more important it is to use that moment as a segmentation point.

[0053] Between the two dimensions of temperature and internal resistance, the maximum initial fluctuation degree is selected as the battery fluctuation degree at each moment.

[0054] It should be noted that the normalization process in the embodiment of the present invention may be performed by range normalization. The battery fluctuation threshold is set to 0.85. If the battery fluctuation is greater than the battery fluctuation threshold, the moment is determined to be a segmentation point.

[0055] After obtaining each historical time period in the historical charging process, each time period can be regarded as a charging stage. Therefore, the charging information in each historical time period can be analyzed to determine the charging efficiency of the current constant current in the historical time period, and then the charging efficiency of the corresponding constant current value in the charging stage can be quantified. The battery charging temperature is an intuitive data that affects the battery life. Therefore, the embodiment of the present invention obtains a comprehensive evaluation coefficient for each historical time period based on the battery temperature and charge state changes in the historical time period. That is, the more normal the battery temperature is, the greater the charge state change is, indicating that the battery has charged more electricity in the historical time period, and the battery temperature is maintained in a safe temperature range. At this time, the charging efficiency is high and the safety is also high, and the greater the comprehensive evaluation coefficient is.

[0056] Preferably, in an embodiment of the present invention, the method for obtaining the comprehensive evaluation coefficient includes:

[0057] The temperature evaluation coefficient is calculated based on the relationship between the average battery temperature over a historical period and the preset safe temperature range. That is, the closer the average battery temperature is to the safe temperature range, the larger the temperature evaluation coefficient.

[0058] Obtain a state of charge growth rate within a historical time period; and multiply the state of charge growth rate by the temperature evaluation coefficient as the comprehensive evaluation coefficient. Specifically, a greater state of charge growth rate indicates greater charging efficiency, while a greater temperature evaluation coefficient indicates safer charging, and thus a greater comprehensive evaluation coefficient.

[0059] In the embodiment of the present invention, in order to quantify the comprehensive evaluation coefficient more intuitively, it is necessary to perform normalization processing on the comprehensive evaluation coefficient after it is calculated.

[0060] It should be noted that the state of charge growth rate is the ratio of the state of charge at the end time of the historical time period minus the state of charge at the start time to the length of the historical time period.

[0061] Furthermore, the method for obtaining the temperature evaluation coefficient includes:

[0062] If the battery temperature average is lower than the lower limit of the safe temperature range, a negative correlation mapping is performed on the difference between the lower limit of the safe temperature range and the battery temperature average to obtain a temperature evaluation coefficient;

[0063] If the battery temperature average is within the safe temperature range, the temperature evaluation coefficient is assigned a positive integer of 1;

[0064] If the battery temperature average is greater than the upper limit of the safe temperature range, a negative correlation mapping is performed on the difference between the battery temperature average and the upper limit of the safe temperature range to obtain a temperature evaluation coefficient.

[0065] It should be noted that, in the embodiment of the present invention, the negative correlation mapping adopts the inverse form.

[0066] See also Figure 3 , which shows a schematic representation of the comprehensive rating coefficient provided by an embodiment of the present invention. Figure 3 The horizontal axis is the charging time period number, that is, the number of the historical time period. Because multiple charging processes with different constant current values ​​are integrated for display, the numbering result is the combined result of different historical charging processes, resulting in some historical charging processes having no scoring results under certain numbers, that is, 0.00 points shown in the table.

[0067] Step S3: Determine the real-time time period corresponding to the real-time moment in the real-time charging process; obtain the time period similarity weight based on the difference in charging information between the real-time time period and each historical time period; for each constant current value in the constant current charging process, weightedly fuse the comprehensive evaluation coefficients of all historical time periods corresponding to the constant current value based on the time period similarity weight to obtain the preference degree of each constant current value for the real-time time period; determine the charging current corresponding to the real-time time period based on the preference degree.

[0068] After completing data quantification of the historical charging process, the real-time time period under the real-time charging process can be collaboratively compared and analyzed with various historical time periods. It should be noted that the division method of the real-time time period is the same as the division method of the historical time period. The time range between the real-time moment and the start moment is regarded as the local charging process. The real-time time period corresponding to the real-time moment can be determined by using the division rules of the historical time period.

[0069] Based on the concept of collaborative filtering, this embodiment of the present invention analyzes the time period similarity weights between the real-time time period and historical time periods, and then performs a weighted fusion of the comprehensive evaluation coefficients of the historical time periods for each constant current value. This determines the degree of preference for each constant current value for the real-time time period. Based on this preference, the charging current for the corresponding real-time time period at the real-time moment can be determined.

[0070] Preferably, in an embodiment of the present invention, the method for obtaining the time period similarity weight includes:

[0071] The four dimensions of temperature, internal resistance, state of charge, and battery health status are taken as the dimensions to be analyzed. The DTW distance of the data series of the dimensions to be analyzed between the real-time time period and the historical time period is obtained. The ratio of the DTW distance to the number of matching data pairs in the DTW matching process is taken as the dimensional difference under the dimensions to be analyzed.

[0072] After accumulating the dimension differences of all dimensions to be analyzed, negative correlation mapping is performed to obtain the time period similarity weight. In the embodiment of the present invention, the negative correlation mapping of the dimension difference accumulation value can also be in the inverse form, which will not be described in detail here.

[0073] In the embodiment of the present invention, the weighted fusion method is weighted averaging, that is, for each historical time period, the time period similarity weight corresponding to the time period is multiplied by the comprehensive evaluation coefficient, and the average value of the multiplication results for all historical time periods is obtained.

[0074] In one embodiment of the present invention, the constant current value with the highest degree of preference may be directly used as the corresponding charging current.

[0075] Additionally, in other embodiments of the present invention, taking into account the different resistance values ​​at different times, in order to prevent the charging current from being too large, causing the voltage to exceed the maximum voltage, the ratio of the preset maximum battery voltage to the battery internal resistance at the real time is used as the maximum charging current; between the maximum charging current and the most preferred constant current value, the minimum current value is selected as the charging current.

[0076] In summary, the present invention utilizes a big data analysis method to statistically analyze each historical charging process under the constant current charging process in the historical database. The historical charging process can be divided into historical time periods by time period division, and then the comprehensive evaluation coefficient of each historical time period under the current constant current value is analyzed. For the real-time charging process, the time period similarity weight of each historical time period to the real-time time period can be determined by time period matching. The degree of preference of the real-time time period for each constant current value can be determined by weighted fusion of the comprehensive evaluation coefficients, and then the charging current under the current real-time time period can be determined. The present invention realizes the dynamic adjustment of the constant current size during the charging process of the electronic device through big data analysis and data collaborative filtering to compare data, which can effectively reduce the temperature rise rate during the charging process, suppress heat accumulation, and avoid the risk of battery overheating and life degradation caused by current value mismatch. On the basis of ensuring charging efficiency, it improves charging safety and extends the battery cycle life.

[0077] The present invention also proposes a charging control system for an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor implements any one of the steps of the charging control method for an electronic device.

[0078] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A charging control method for an electronic device, characterized in that: The method comprises: Collecting charging information of each historical charging process of the electronic device battery under the constant current charging process in the statistical history database, wherein the charging information includes at least temperature, internal resistance and state of charge; Each historical charging process is divided into multiple historical time periods based on the fluctuations and changes in charging information. For each historical time period, a comprehensive evaluation coefficient is obtained based on the changes in battery temperature and state of charge during the historical time period. Determine the real-time time period corresponding to the real-time moment in the real-time charging process; obtain a time period similarity weight based on the difference in charging information between the real-time time period and each historical time period; for each constant current value in the constant current charging process, perform a weighted fusion of the comprehensive evaluation coefficients of all historical time periods corresponding to the constant current value based on the time period similarity weight to obtain the preference degree of each constant current value for the real-time time period; and determine the charging current corresponding to the real-time time period based on the preference degree; The method divides each historical charging process into multiple historical time periods according to the fluctuations and changes of the charging information, including: The length of the time period is preset according to the range of the state of charge. The length of the time period is negatively correlated with the state of charge. Based on the time period length, each historical charging process is divided into initial time periods to obtain multiple initial historical time periods for each historical charging process. In each historical charging process, the battery fluctuation degree at each moment is obtained based on the changes in temperature and internal resistance at each moment, segmentation points are selected based on the battery fluctuation degree, and the initial historical time period is segmented based on the segmentation points to obtain all historical time periods of each historical charging process; The method for obtaining the battery fluctuation degree includes: For each moment, search forward and backward for each moment according to the preset search length to obtain the forward analysis range and backward analysis range of each moment; For any dimension between temperature and internal resistance, the first-order difference mean of the dimension in each analysis range is used as the change characteristic of the dimension in each analysis range; the difference between the change characteristics of the backward analysis range and the forward analysis range is normalized to obtain the initial fluctuation degree of the dimension; Between the two dimensions of temperature and internal resistance, the maximum initial fluctuation degree is selected as the battery fluctuation degree at each moment; The method for obtaining the comprehensive evaluation coefficient includes: The temperature evaluation coefficient is obtained based on the relationship between the average battery temperature in the historical time period and the preset safety temperature range; Obtaining a state of charge growth rate within a historical time period; and taking the product of the state of charge growth rate and the temperature evaluation coefficient as the comprehensive evaluation coefficient; The method for obtaining the temperature evaluation coefficient includes: If the battery temperature average is lower than the lower limit of the safe temperature range, a negative correlation mapping is performed on the difference between the lower limit of the safe temperature range and the battery temperature average to obtain a temperature evaluation coefficient; If the battery temperature average is within the safe temperature range, the temperature evaluation coefficient is assigned a positive integer of 1; If the battery temperature average is greater than the upper limit of the safe temperature range, a negative correlation mapping is performed on the difference between the battery temperature average and the upper limit of the safe temperature range to obtain a temperature evaluation coefficient.

2. The method for controlling charging of an electronic device according to claim 1, wherein: The method for obtaining the similarity weight of the time period includes: The four dimensions of temperature, internal resistance, state of charge, and battery health status are used as the dimensions to be analyzed. The DTW distance of the data series of the dimensions to be analyzed between the real-time time period and the historical time period is obtained. The ratio of the DTW distance to the number of matching data pairs in the DTW matching process is used as the dimension difference under the dimensions to be analyzed; The dimensional differences of all dimensions to be analyzed are accumulated and then negatively correlated to obtain the similarity weight of the time period.

3. The charging control method of an electronic device according to claim 1, characterized in that: The weighted fusion method is weighted averaging.

4. The method for controlling charging of an electronic device according to claim 1, wherein: The step of determining the charging current corresponding to the real-time time period according to the degree of preference includes: The constant current value with the highest degree of preference is selected as the corresponding charging current.

5. The method for controlling charging of an electronic device according to claim 1, wherein: The step of determining the charging current corresponding to the real-time time period according to the degree of preference further includes: The ratio of the preset maximum battery voltage to the battery internal resistance at the real time is used as the maximum charging current; between the maximum charging current and the most preferred constant current value, the minimum current value is selected as the charging current.

6. A charging control system for an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the charging control method of the electronic device as described in any one of claims 1 to 5 are implemented.

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