A charging control method and system for a power module

Abstract

The present invention relates to the field of intelligent charging technology, and discloses a charging control method and system for a power module. Compared with the current traditional charging control method, the method significantly improves the intelligence level through systematic multi-source data collection, processing, calculation, analysis and dynamic adjustment of charging status. The method has real-time data processing and feedback capabilities, making the charging process more efficient and accurate. The improvements include greatly improving the analysis capability of multi-source data, optimizing charging safety and efficiency, and extending the service life of the battery. By taking graded response measures for different abnormal levels, the method can take the most appropriate processing method according to the severity of the abnormality, thereby improving the flexibility and pertinence of abnormality processing. Whether it is voltage, current or temperature control, the method can adapt to battery and environmental changes in real time, optimize the charging process, and improve charging efficiency and equipment service life.

Description

Technical Field

[0001] The present invention relates to the field of intelligent charging technology, and in particular to a charging control method and system for a power module. Background Art

[0002] In the use of modern electronic devices, charging control of power modules plays a vital role, especially when providing energy for mobile devices such as smartphones. Traditional charging control strategies mainly determine the charging mode of the charger based on the remaining power of the charged device, such as constant current charging or constant voltage charging.

[0003] During the charging process, traditional charging control methods often have shortcomings such as insufficient battery status monitoring, low data processing capabilities, and delayed response, resulting in low charging efficiency, poor charging safety, and untimely handling of abnormal situations. These problems directly affect the overall performance of the equipment, especially in complex environments. The charging module fails to flexibly adjust according to real-time data, which may cause problems such as battery overcharging, over-discharging or overheating, thereby accelerating battery aging and shortening battery life.

[0004] Therefore, we propose a charging control method for a power module to solve the above-mentioned problems. Summary of the invention

[0005] The purpose of the present invention is to provide a charging control method for a power module to solve the problem proposed in the above background technology that the charging module fails to be flexibly adjusted according to real-time data, which may cause problems such as battery overcharging, over-discharging or overheating, thereby accelerating battery aging and shortening service life.

[0006] To achieve the above object, the present invention provides the following technical solution: a charging control method for a power module, the specific steps are as follows;

[0007] Step S1, data collection, by setting a data collection module, collecting battery performance data, charging status data, environmental adjustment data and battery status data during the charging process of the power module, and integrating them into multi-source data;

[0008] Step S2, data processing, preprocessing and dimensionless processing the multi-source data collected by the data collection module through the data processing module, and reorganizing the processed multi-source data into a first data set, a second data set, and a third data set;

[0009] Step S3, data calculation, integrating and calculating the first data set, the second data set and the third data set sorted by the data processing module through the data calculation module, so as to generate an adjustment reference coefficient TZX;

[0010] Step S4, data analysis, performing data analysis on the adjustment reference coefficient TZX calculated by the data calculation module through the data analysis module, thereby generating a first comparison result, and judging whether it is necessary to switch the battery charging state according to the first comparison result;

[0011] If the first comparison result shows that the battery charging state does not need to be switched, a second analysis is performed to generate a second comparison result, and the battery charging state is adjusted according to the second comparison result;

[0012] Step S5, adjustment and feedback, the charging state of the battery is adjusted through the execution module, and various data are fed back to the terminal through the communication module.

[0013] Preferably, in step S1, the data acquisition module includes a first acquisition unit, a second acquisition unit, a third acquisition unit and a fourth acquisition unit;

[0014] The first acquisition unit is used to collect battery performance data, the second acquisition unit is used to collect charging status data, the third acquisition unit is used to collect environmental adjustment data, and the fourth acquisition unit is used to collect battery status data.

[0015] Preferably, in step S2, the data processing module includes a data preprocessing unit and a data sorting unit, the data preprocessing unit is used to preprocess and dimensionless the multi-source data, and the data sorting unit is used to re-sort the processed multi-source data, thereby generating the first data set, the second data set and the third data set;

[0016] The first data set includes battery voltage, battery current, battery temperature, and battery internal resistance;

[0017] The battery voltages are recorded as DDY1, DDY2, DDY3, ..., DDY according to the timestamps. n ;

[0018] The battery current is recorded as DDL1, DDL2, DDL3, ..., DDL according to the timestamp. n ;

[0019] The battery temperatures are recorded as DWD1, DWD2, DWD3, ..., DWD according to the timestamps. n ;

[0020] The battery internal resistance is recorded as DNZ1, DNZ2, DNZ3, ..., DNZ according to the timestamp. n , n represents the total number of timestamps;

[0021] The second data set includes charging voltage, charging current, charging time, and number of charging cycles;

[0022] The charging voltages are recorded as CDY1, CDY2, CDY3, ..., CDY according to the timestamps. n ;

[0023] The charging currents are recorded as CDL1, CDL2, CDL3, ..., CDL according to the timestamps. n ;

[0024] The number of charging cycles is recorded as CXH1, CXH2, CXH3, ..., CXH according to the timestamp. n ;

[0025] The third data set includes ambient temperature, ambient humidity, and workload;

[0026] The ambient temperatures are recorded as HWD1, HWD2, HWD3, ..., HWD n ;

[0027] The ambient humidity is recorded as HSD1, HSD2, HSD3, ..., HSD n ;

[0028] The workloads are recorded as GFH1, GFH2, GFH3, ..., GFH according to the timestamp. n .

[0029] Preferably, in step S3, the data calculation module includes a first calculation unit and a second calculation unit, the first calculation unit is used to integrate the first data set, the second data set and the third data set, so as to calculate and obtain the first reference coefficient S1, the second reference coefficient S2 and the third reference coefficient S3, and the second calculation unit (32) is used to integrate the first reference coefficient S1, the second reference coefficient S2 and the third reference coefficient S3, so as to calculate and obtain the adjustment reference coefficient TZX.

[0030] Preferably, in step S3, the first reference coefficient S1, the second reference coefficient S2 and the third reference coefficient S3 are respectively calculated and obtained by the following formulas:

[0031] S1=(DDY n -DDY n-1 )×(DDL n -DDL n-1 )+(DWD n -DWD n-1 )×

[0032] (DNZ n -DNZ n-1 );

[0033] S2=[(CDY n -CDY n-1)×(CDL n -CDL n-1 )+(CXH n -CXH n-1 )]×CSJ;

[0034] S3=(HWD n -HWD n-1 )×(HSD n -HSD n-1 )+(GFH n -GFH n-1 );

[0035] Where: DDY n 、DDL n 、DWD n DNZ n 、CDY n 、CDL n ,CXH n 、HWD n , HSD n and GFH n They are the battery voltage, battery current, battery temperature, battery internal resistance, charging voltage, charging current, number of charging cycles, ambient temperature, ambient humidity and workload at timestamp n respectively;

[0036] DDY n-1 、DDL n-1 、DWD n-1 DNZ n-1 、CDY n-1 、CDL n-1 ,CXH n-1 、HWD n-1 , HSD n-1 and GFH n-1 They are the battery voltage, battery current, battery temperature, battery internal resistance, charging voltage, charging current, number of charging cycles, ambient temperature, ambient humidity and workload at the n-1 timestamp;

[0037] CSJ is the charging time.

[0038] Preferably, in step S3, the adjustment reference coefficient TZX is obtained by calculating through the following formula:

[0039]

[0040] Wherein: S1 is the first reference coefficient, S2 is the second reference coefficient, and S3 is the third reference coefficient.

[0041] Preferably, in step S4, the data analysis module includes a first analysis unit and a second analysis unit;

[0042] The first analysis unit is preset with a first threshold Y. The first analysis unit (41) generates a first comparison result by comparing the first threshold Y with the calculated adjustment reference coefficient TZX.

[0043] The second analysis unit (42) is set with a second threshold R. The second analysis unit (42) performs an integrated calculation of the adjustment reference coefficient TZX and the first threshold Y to generate a magnitude division coefficient LJZ, and compares the magnitude division coefficient LJZ with the preset second threshold R to generate a second comparison result.

[0044] Preferably, the first comparison result is as follows;

[0045] When TZX ≤ Y, it indicates that the charging module is normal;

[0046] When TZX > Y, it indicates that the charging module has an abnormality;

[0047] The second comparison result is as follows;

[0048] When LJZ < R, it indicates that a first-level abnormality occurs in the current charging module;

[0049] When LJZ = R, it indicates that a second-level abnormality occurs in the current charging module;

[0050] When LJZ > R, it indicates that a third-level abnormality occurs in the current charging module;

[0051] The magnitude division coefficient LJZ is calculated by the following formula;

[0052]

[0053] In the formula: TZX is the adjustment reference coefficient, and Y is the first threshold.

[0054] Preferably, in step S5, the specific adjustment method of the execution module is as follows;

[0055] When a first-level abnormality occurs in the charging module, the charging voltage deviates within ±5% of the set value. In this method, the output of the charger is adjusted by PWM technology to ensure that the charging voltage returns to the normal range. When the charging current deviates within ±5% of the set value, this method will activate the current limiting protection, adjust the current to an appropriate range and decrease by 5% - 10% to reduce the pressure on the battery. After the current returns to normal, it gradually increases back to the default value, increases the temperature monitoring frequency, collects temperature data every 1 second to ensure that the temperature is maintained within the normal range. If the temperature rises beyond expectation but does not reach the dangerous temperature, this method will reduce the current to reduce heat generation;

[0056] When the charging module has a secondary abnormality, when the current deviation is large and exceeds the set value by 10%-20%, this method will immediately start the current limiting mode, adjust the current to a safe range, and gradually reduce the current by 20% according to the situation to reduce the battery load. When this method detects that the temperature rises significantly and is 20% higher than the normal temperature, this method will adopt an intermittent charging strategy, that is, suspend charging for a period of time, and resume charging after the temperature drops to reduce overheating. The length of the intermittent period is dynamically adjusted according to temperature changes;

[0057] When the charging module has a level 3 abnormality, this method will immediately cut off the charging power supply and stop all charging actions when it detects the level 3 abnormality to avoid damage to the device or battery due to overload or overheating. After power failure, this method will lock the current abnormal state to prevent automatic restart or restoration of the charging function.

[0058] The present application also provides a charging control system for a power module, including a data acquisition module, a data processing module, a data calculation module, a data analysis module and an execution module;

[0059] The data acquisition module is used to collect multi-source data during battery operation;

[0060] The data processing module is used to pre-process the multi-source data and organize the processed multi-source data into a first data set, a second data set and a third data set;

[0061] The data calculation module is used to integrate and calculate the first data set, the second data set and the third data set, so as to generate an adjustment reference coefficient TZX;

[0062] The data analysis module performs data analysis on the adjustment reference coefficient TZX calculated by the data calculation module to generate a first comparison result, and determines whether the battery charging state needs to be switched according to the first comparison result;

[0063] If the first comparison result shows that the battery charging state does not need to be switched, a second analysis is performed to generate a second comparison result, and the battery charging state is adjusted according to the second comparison result;

[0064] The execution module is used for adjusting the charging state of the battery;

[0065] The communication module is used to feed back various data to the terminal.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] 1. This method significantly improves the level of intelligence compared with the current traditional charging control method through systematic multi-source data collection, processing, calculation, analysis and dynamic adjustment of charging status. This method has real-time data processing and feedback capabilities, making the charging process more efficient and accurate. The improvements include greatly improving the ability to analyze multi-source data, optimizing charging safety and efficiency, and extending the battery life.

[0068] 2. By taking graded response measures for different abnormal levels, this method can take the most appropriate treatment method according to the severity of the abnormality, thereby improving the flexibility and pertinence of abnormality handling. Whether it is voltage, current or temperature control, this method maximizes the safety of the charging process through precise adjustment and monitoring, and avoids the risks caused by overvoltage, overcurrent or overheating. By adjusting the charging strategy, this method can adapt to battery and environmental changes in real time, optimize the charging process, and improve charging efficiency and equipment life. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a diagram of the method steps of the present invention.

[0070] Figure 2 It is a system block diagram of the present invention.

[0071] In the figure: 1. Data acquisition module; 11. First acquisition unit; 12. Second acquisition unit; 13. Third acquisition unit; 14. Fourth acquisition unit; 2. Data processing module; 21. Data preprocessing unit; 22. Data sorting unit; 3. Data calculation module; 31. First calculation unit; 32. Second calculation unit; 4. Data analysis module; 41. First analysis unit; 42. Second analysis unit; 5. Execution module; 6. Communication module. DETAILED DESCRIPTION

[0072] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0073] Embodiment 1

[0074] See also Figure 1 , a charging control method for a power module, the specific steps are as follows;

[0075] S1, data acquisition, by setting up a data acquisition module 1, collecting battery performance data, charging status data, environmental adjustment data and battery status data during the charging process of the power module, and integrating them into multi-source data;

[0076] S2, data processing, preprocessing and dimensionless processing the multi-source data collected by the data collection module 1 through the data processing module 2, and reorganizing the processed multi-source data into a first data set, a second data set, and a third data set;

[0077] S3, data calculation, the data calculation module 3 integrates and calculates the first data set, the second data set and the third data set sorted by the data processing module 2, so as to generate an adjustment reference coefficient TZX;

[0078] S4, data analysis, performing data analysis on the adjustment reference coefficient TZX calculated by the data calculation module 3 through the data analysis module 4, thereby generating a first comparison result, and judging whether it is necessary to switch the battery charging state according to the first comparison result;

[0079] If the first comparison result shows that the battery charging state does not need to be switched, a second analysis is performed to generate a second comparison result, and the battery charging state is adjusted according to the second comparison result;

[0080] S5, adjustment and feedback, adjusting the charging state of the battery through the execution module 5, and feeding back various data to the terminal through the communication module 6.

[0081] In this embodiment: by setting up a data acquisition module 1, this method collects battery performance data, charging status data, environmental adjustment data and battery status data in real time during the charging process of the power module, and integrates them into multi-source data. The main task of this step is to comprehensively and accurately collect key parameters that affect the charging process, ensure that all necessary information can be monitored and recorded in real time, improve the breadth and comprehensiveness of data collection, provide a rich data basis for subsequent analysis and calculation, and ensure that the entire charging process is controllable and the information is transparent.

[0082] Through the data processing module 2, the method preprocesses and dimensionlessly processes the collected multi-source data to eliminate outliers and unit differences in the data. The processed data will be organized into a first data set, a second data set, and a third data set, which correspond to data classifications of different dimensions, respectively, to ensure the cleanliness and consistency of the data and eliminate noise data. At the same time, dimensionlessly processing is used to make different data sets comparable, thereby improving the efficiency and reliability of data processing, providing high-quality input for subsequent calculations, and ensuring the accuracy of calculation results.

[0083] Through the data calculation module 3, the method integrates and calculates the processed data set to generate the adjustment reference coefficient TZX. The core task of this step is to combine the parameters in different data sets such as battery performance, charging status and environmental factors, and use the algorithm to generate key coefficients to guide the adjustment of the charging process. It can dynamically calculate the optimal charging strategy according to the actual situation, ensure that the battery charging process is both efficient and safe, reduce unnecessary energy consumption and optimize the charging speed.

[0084] Through the data analysis module 4, the method conducts an in-depth analysis on the generated adjustment reference coefficient TZX to generate a first comparison result. If the first comparison result shows that there is no need to switch the battery charging state, the method will conduct a second analysis to generate a second comparison result, and further adjust the battery charging state according to the result. This ensures that the battery charging state is accurate through a double-level analysis, prevents unnecessary interruptions during the charging process, increases the accuracy and reliability of the analysis of the method, and ensures that the charging process continues to be in the best state.

[0085] By executing module 5, the method adjusts the battery charging state according to the data analysis results to ensure real-time optimization of charging parameters. At the same time, all adjustment data will be fed back to the terminal through the communication module 6 to achieve real-time monitoring and adjustment of the charging process. According to the analysis results, the battery charging process is dynamically optimized, and the feedback mechanism is used to ensure that the method has all-round control. Through real-time feedback and adjustment, the method improves its ability to control the charging process, prevents abnormalities during the charging process, and ensures the long-term safety and stable performance of the battery.

[0086] Compared with the current traditional charging control method, this method has significantly improved the level of intelligence through systematic multi-source data collection, processing, calculation, analysis and dynamic adjustment of charging status. This method has real-time data processing and feedback capabilities, making the charging process more efficient and accurate. Improvements include greatly improving the ability to analyze multi-source data, optimizing charging safety and efficiency, and extending the battery life.

[0087] Embodiment 2

[0088] See also Figure 1 , in step S1, the data acquisition module 1 includes a first acquisition unit 11, a second acquisition unit 12, a third acquisition unit 13 and a fourth acquisition unit 14;

[0089] The first acquisition unit 11 is used to acquire battery performance data, the second acquisition unit 12 is used to acquire charging status data, the third acquisition unit 13 is used to acquire environmental adjustment data, and the fourth acquisition unit 14 is used to acquire battery status data.

[0090] In this embodiment: In step S1, the design of the data acquisition module 1 realizes comprehensive coverage and independent acquisition of multi-dimensional data in the charging process by setting a first acquisition unit 11, a second acquisition unit 12, a third acquisition unit 13 and a fourth acquisition unit 14. The first acquisition unit 11 is specifically used to collect battery performance data, ensuring accurate monitoring of battery core performance indicators such as voltage, current, temperature, etc.; the second acquisition unit 12 is for charging status data, and can track the changes of key parameters in the charging process in real time; the third acquisition unit 13 is responsible for the collection of environmental adjustment data, which enhances the perception ability of the present method to external environmental factors such as temperature and humidity, and ensures that the charging process can dynamically adapt to environmental changes; the fourth acquisition unit 14 is used to collect battery status data to ensure that the present method can grasp the battery health status in real time and avoid the risk of overcharging or over-discharging.

[0091] Embodiment 3

[0092] See also Figure 1 In step S2, the data processing module 2 includes a data preprocessing unit 21 and a data sorting unit 22, the data preprocessing unit 21 is used to preprocess and dimensionless the multi-source data, and the data sorting unit 22 is used to re-sort the processed multi-source data to generate a first data set, a second data set, and a third data set;

[0093] The first data set includes battery voltage, battery current, battery temperature, and battery internal resistance;

[0094] The battery voltages are recorded as DDY1, DDY2, DDY3, ..., DDY according to the timestamps. n ;

[0095] The battery current is recorded as DDL1, DDL2, DDL3, ..., DDL according to the timestamp. n ;

[0096] The battery temperatures are recorded as DWD1, DWD2, DWD3, ..., DWD according to the timestamps. n ;

[0097] The battery internal resistance is recorded as DNZ1, DNZ2, DNZ3, ..., DNZ according to the timestamp. n , n represents the total number of timestamps;

[0098] The second data set includes charging voltage, charging current, charging time, and number of charging cycles;

[0099] The charging voltages are recorded as CDY1, CDY2, CDY3, ..., CDY according to the timestamps. n ;

[0100] The charging currents are recorded as CDL1, CDL2, CDL3, ..., CDL according to the timestamps. n ;

[0101] The number of charging cycles is recorded as CXH1, CXH2, CXH3, ..., CXH according to the timestamp. n ;

[0102] The third data set includes ambient temperature, ambient humidity, and workload;

[0103] The ambient temperatures are recorded as HWD1, HWD2, HWD3, ..., HWD n ;

[0104] The ambient humidity is recorded as HSD1, HSD2, HSD3, ..., HSD n ;

[0105] The workloads are recorded as GFH1, GFH2, GFH3, ..., GFH according to the timestamp. n .

[0106] In this embodiment: In step S2, the design of the data processing module 2 includes a data preprocessing unit 21 and a data sorting unit 22, which perform systematic processing on multi-source data. The data preprocessing unit 21 eliminates noise and unit differences in the data through preprocessing and dimensionless operations, so that data of different dimensions have higher comparability and uniformity; the data sorting unit 22 reorganizes the processed data into three data sets according to timestamps, namely, a first data set of battery performance related data, a second data set of charging status data, and a third data set of environment and workload data.

[0107] Embodiment 4

[0108] See also Figure 1 In step S3, the data calculation module 3 includes a first calculation unit 31 and a second calculation unit 32. The first calculation unit 31 is used to integrate the first data set, the second data set and the third data set, so as to calculate and obtain the first reference coefficient S1, the second reference coefficient S2 and the third reference coefficient S3. The second calculation unit (32) is used to integrate the first reference coefficient S1, the second reference coefficient S2 and the third reference coefficient S3 to calculate and obtain the adjustment reference coefficient TZX.

[0109] In step S3, the first reference coefficient S1, the second reference coefficient S2 and the third reference coefficient S3 are calculated and obtained by the following formulas respectively;

[0110] S1=(DDY n -DDY n-1 )×(DDL n-DDL n-1 )+(DWD n -DWD n-1 )×

[0111] (DNZ n -DNZ n-1 );

[0112] S2=[(CDY n -CDY n-1 )×(CDL n -CDL n-1 )+(CXH n -CXH n-1 )]×CSJ;

[0113] S3=(HWD n -HWD n-1 )×(HSD n -HSD n-1 )+(GFH n -GFH n-1 );

[0114] Where: DDY n 、DDL n 、DWD n DNZ n 、CDY n 、CDL n ,CXH n 、HWD n , HSD n and GFH n They are the battery voltage, battery current, battery temperature, battery internal resistance, charging voltage, charging current, number of charging cycles, ambient temperature, ambient humidity and workload at timestamp n respectively;

[0115] DDY n-1 、DDL n-1 、DWD n-1 DNZ n-1 、CDY n-1 、CDL n-1 ,CXH n-1 、HWD n-1 , HSD n-1 and GFH n-1 They are the battery voltage, battery current, battery temperature, battery internal resistance, charging voltage, charging current, number of charging cycles, ambient temperature, ambient humidity and workload at the n-1 timestamp;

[0116] CSJ is the charging time.

[0117] In step S3, the adjustment reference coefficient TZX is calculated through the following formula;

[0118]

[0119] In the formula: S1 is the first reference coefficient, S2 is the second reference coefficient, and S3 is the third reference coefficient.

[0120] In this embodiment: In step S3, the data calculation module 3 effectively integrates multi-dimensional data and reference coefficients by setting the first calculation unit 31 and the second calculation unit 32, realizing the dynamic adjustment and optimization of the charging process. The first calculation unit 31 calculates the first reference coefficient S1, the second reference coefficient S2, and the third reference coefficient S3 respectively by integrating the first data set, the second data set, and the third data set, and calculates S1, S2, and S3 using the formula, so as to be able to evaluate the influence of battery performance, charging status, and external environment respectively. The second calculation unit 32 further integrates these three reference coefficients and calculates the adjustment reference coefficient TZX through the formula to guide the optimization and adjustment of the charging process of this method.

[0121] Embodiment Five

[0122] Please refer to Figure 1 , in step S4, the data analysis module 4 includes a first analysis unit 41 and a second analysis unit 42;

[0123] The first analysis unit 41 presets a first threshold Y. The first analysis unit 41 generates a first comparison result by comparing the first threshold Y with the calculated adjustment reference coefficient TZX;

[0124] The second analysis unit 42 sets a second threshold R inside. The second analysis unit 42 integrates and calculates the adjustment reference coefficient TZX with the first threshold Y to generate a magnitude division coefficient LJZ, and compares the magnitude division coefficient LJZ with the preset second threshold R to generate a second comparison result.

[0125] The first comparison result is as follows;

[0126] When TZX ≤ Y, it means that the charging module is normal;

[0127] When TZX > Y, it means that the charging module has an abnormality;

[0128] The second comparison result is as follows;

[0129] When LJZ < R, it means that the current charging module has a first-level abnormality;

[0130] When LJZ = R, it means that the current charging module has a second-level abnormality;

[0131] When LJZ>R, it means that the current charging module has a level 3 abnormality;

[0132] The magnitude division coefficient LJZ is calculated by the following formula:

[0133]

[0134] Where: TZX is the adjustment reference coefficient, and Y is the first threshold.

[0135] In this embodiment: In step S4, the data analysis module 4 introduces a two-level threshold comparison and magnitude division mechanism by setting a first analysis unit 41 and a second analysis unit 42, thereby realizing a detailed analysis of the adjustment reference coefficient TZX and an accurate judgment of the abnormal state. The first analysis unit 41 uses a preset first threshold value Y to compare it with the calculated adjustment reference coefficient TZX to generate a first comparison result; the second analysis unit 42 integrates the adjustment reference coefficient TZX with the first threshold value Y to generate a magnitude division coefficient LJZ, and compares it with the second threshold value R to generate a second comparison result, thereby judging the abnormal level of the charging module.

[0136] Embodiment 6

[0137] See also Figure 1 , in step S5, the specific adjustment method of executing module 5 is as follows;

[0138] When the charging module has a level 1 abnormality, the charging voltage deviates from the set value within ±5%. This method adjusts the output of the charger through PWM technology to ensure that the charging voltage returns to the normal range. If the charging current deviates from the set value within ±5%, this method will start the current limiting protection and adjust the current to a suitable range of 5%-10% to reduce the pressure on the battery. After the current returns to normal, it will gradually increase back to the default value, increase the temperature monitoring frequency, collect temperature data every 1 second, and ensure that the temperature is maintained within the normal range. If the temperature rises beyond expectations but does not reach a dangerous temperature, this method will reduce the current to reduce heat;

[0139] When the charging module has a secondary abnormality, when the current deviation is large and exceeds the set value by 10%-20%, this method will immediately start the current limiting mode, adjust the current to a safe range, and gradually reduce the current by 20% according to the situation to reduce the battery load. When this method detects that the temperature rises significantly and is 20% higher than the normal temperature, this method will adopt an intermittent charging strategy, that is, suspend charging for a period of time, and resume charging after the temperature drops to reduce overheating. The length of the intermittent period is dynamically adjusted according to temperature changes;

[0140] When the charging module has a level 3 abnormality, this method will immediately cut off the charging power supply and stop all charging actions when it detects the level 3 abnormality to avoid damage to the device or battery due to overload or overheating. After power failure, this method will lock the current abnormal state to prevent automatic restart or restoration of the charging function.

[0141] In this embodiment: by taking graded response measures for different abnormality levels, this method can take the most appropriate processing method according to the severity of the abnormality, thereby improving the flexibility and pertinence of abnormality processing. Whether it is voltage, current or temperature control, this method maximizes the safety of the charging process through precise adjustment and monitoring, and avoids the risks caused by overvoltage, overcurrent or overheating. By adjusting the charging strategy, this method can adapt to battery and environmental changes in real time, optimize the charging process, and improve charging efficiency and equipment life.

[0142] See also Figure 2 , the present application also provides a charging control system for a power module, including a data acquisition module 1, a data processing module 2, a data calculation module 3, a data analysis module 4 and an execution module 5;

[0143] The data acquisition module 1 is used to collect multi-source data during battery operation;

[0144] The data processing module 2 is used to pre-process the multi-source data and organize the processed multi-source data into a first data set, a second data set and a third data set;

[0145] The data calculation module 3 is used to perform integrated calculation on the first data set, the second data set and the third data set, so as to generate an adjustment reference coefficient TZX;

[0146] The data analysis module 4 performs data analysis on the adjustment reference coefficient TZX calculated by the data calculation module 3, thereby generating a first comparison result, and judging whether it is necessary to switch the battery charging state according to the first comparison result;

[0147] If the first comparison result shows that the battery charging state does not need to be switched, a second analysis is performed to generate a second comparison result, and the battery charging state is adjusted according to the second comparison result;

[0148] The execution module 5 is used to adjust the charging state of the battery;

[0149] The communication module 6 is used to feed back various data to the terminal.

[0150] The contents not described in detail in this specification belong to the prior art known to professionals in the field.

[0151] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A charging control method for a power module, characterized in that: The specific steps are as follows; Step S1, data collection, by setting a data collection module (1), collecting battery performance data, charging status data, environmental adjustment data and battery status data during the charging process of the power module, and integrating them into multi-source data; Step S2, data processing, preprocessing and dimensionless processing the multi-source data collected by the data collection module (1) by the data processing module (2), and reorganizing the processed multi-source data into a first data set, a second data set and a third data set; Step S3, data calculation, the data calculation module (3) performs integrated calculation on the first data set, the second data set and the third data set sorted by the data processing module (2), so as to generate an adjustment reference coefficient TZX; Step S4, data analysis, performing data analysis on the adjustment reference coefficient TZX calculated and obtained by the data calculation module (3) through the data analysis module (4), thereby generating a first comparison result, and judging whether it is necessary to switch the battery charging state according to the first comparison result; If the first comparison result shows that the battery charging state does not need to be switched, a second analysis is performed to generate a second comparison result, and the battery charging state is adjusted according to the second comparison result; Step S5, adjustment and feedback, adjusting the charging state of the battery through the execution module (5), and feeding back various data to the terminal through the communication module (6); In the step S3, the data calculation module (3) comprises a first calculation unit (31) and a second calculation unit (32), the first calculation unit (31) being used to integrate the first data set, the second data set and the third data set, thereby calculating and obtaining the first reference coefficient S1, the second reference coefficient S2 and the third reference coefficient S3, and the second calculation unit (32) being used to integrate the first reference coefficient S1, the second reference coefficient S2 and the third reference coefficient S3, thereby calculating and obtaining the adjustment reference coefficient TZX; In the step S3, the first reference coefficient S1, the second reference coefficient S2 and the third reference coefficient S3 are respectively calculated and obtained by the following formulas: S1=(DDY n -DDY n-1 )×(DDL n -DDL n-1 )+(DWD n -DWD n-1 )× (DNZ n -DNZ n-1 ); S2=[(CDY n -CDY n-1 )×(CDL n -CDL n-1 )+(CXH n -CXH n-1 )]×CSJ; <h2 style=";text-align:left;direction:ltr">S3=(HWD<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> -HWD<h2 style=";text-align:left;direction:ltr"> n-1 <h2 style=";text-align:left;direction:ltr"> )×(HSD<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> -HSD<h2 style=";text-align:left;direction:ltr"> n-1 <h2 style=";text-align:left;direction:ltr"> )+(GFH<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> -GFH<h2 style=";text-align:left;direction:ltr"> n-1 <h2 style=";text-align:left;direction:ltr"> ); Where: DDY n 、DDL n 、DWD n DNZ n 、CDY n 、CDL n ,CXH n 、HWD n , HSD n and GFH n They are the battery voltage, battery current, battery temperature, battery internal resistance, charging voltage, charging current, number of charging cycles, ambient temperature, ambient humidity and workload at timestamp n respectively; DDY n-1 、DDL n-1 、DWD n-1 DNZ n-1 、CDY n-1 、CDL n-1 ,CXH n-1 、HWD n-1 , HSD n-1 and GFH n-1 They are the battery voltage, battery current, battery temperature, battery internal resistance, charging voltage, charging current, number of charging cycles, ambient temperature, ambient humidity and workload at the n-1 timestamp; CSJ is the charging time; In step S3, the adjustment reference coefficient TZX is calculated and obtained by the following formula: Where: S1 is the first reference coefficient, S2 is the second reference coefficient, and S3 is the third reference coefficient; In the step S4, the data analysis module (4) comprises a first analysis unit (41) and a second analysis unit (42); The first analysis unit (41) is preset with a first threshold value Y, and the first analysis unit (41) generates a first comparison result by comparing the first threshold value Y with the calculated adjustment reference coefficient TZX; The second analysis unit (42) is provided with a second threshold R, and the second analysis unit (42) integrates and calculates the adjustment reference coefficient TZX and the first threshold Y to generate a magnitude division coefficient LJZ, and compares the magnitude division coefficient LJZ with a preset second threshold R to generate a second comparison result; The first comparison result is as follows: When TZX≤Y, it means that there is no abnormality in the charging module; When TZX > Y, it indicates that there is an abnormality in the charging module; The second comparison result is as follows; When LJZ < R, it indicates that a first-level abnormality occurs in the current charging module; When LJZ = R, it indicates that a second-level abnormality occurs in the current charging module; When LJZ > R, it indicates that a third-level abnormality occurs in the current charging module; The magnitude division coefficient LJZ is obtained by calculating through the following formula; In the formula: TZX is the adjustment reference coefficient, and Y is the first threshold; In the step S5, the specific adjustment method of the execution module (5) is as follows; When a first-level abnormality occurs in the charging module, the charging voltage deviates within ±5% of the set value. In this method, the output of the charger is adjusted through PWM technology to ensure that the charging voltage returns to the normal range. When the charging current deviates within ±5% of the set value, this method will activate the current limiting protection, adjust the current to an appropriate range and decrease by 5% - 10% to reduce the pressure on the battery. After the current returns to normal, it gradually increases back to the default value, increases the temperature monitoring frequency, collects temperature data every 1 second to ensure that the temperature is maintained within the normal range. If the temperature rises beyond expectation but does not reach the dangerous temperature, this method will reduce the current to reduce heat generation; When a second-level abnormality occurs in the charging module, when the current deviation is relatively large and exceeds 10% - 20% of the set value, this method will immediately activate the current limiting mode, adjust the current to a safe range, and gradually reduce the current by 20% according to the situation to slow down the battery load. When this method detects that the temperature rises significantly by 20% higher than the normal temperature, this method will adopt an intermittent charging strategy, that is, pause charging for a period of time and then resume charging after the temperature drops to reduce overheating. The length of the intermittent period is dynamically adjusted according to the temperature change; When a third-level abnormality occurs in the charging module, when this method detects a third-level abnormality, it will immediately cut off the charging power supply and stop all charging behaviors to avoid damage to the device or battery caused by overload or overheating. After power-off, this method will lock the current abnormal state to prevent automatic restart or recovery of the charging function.

2. The charging control method of the power module according to claim 1, characterized in that: In the step S1, the data acquisition module (1) includes a first acquisition unit (11), a second acquisition unit (12), a third acquisition unit (13), and a fourth acquisition unit (14); Among them, the first acquisition unit (11) is used to acquire battery performance data, the second acquisition unit (12) is used to acquire charging status data, the third acquisition unit (13) is used to acquire environmental adjustment data, and the fourth acquisition unit (14) is used to acquire battery status data.

3. The charging control method of the power module according to claim 1, characterized in that: In the step S2, the data processing module (2) includes a data preprocessing unit (21) and a data sorting unit (22). The data preprocessing unit (21) is used to preprocess and dimensionlessize multi-source data, and the data sorting unit (22) is used to reorganize the processed multi-source data to generate a first data set, a second data set, and a third data set; The first data set includes battery voltage, battery current, battery temperature, and battery internal resistance; Among them The battery voltages are recorded as DDY1, DDY2, DDY3, ..., DDY according to the timestamps. n ; The battery current is recorded as DDL1, DDL2, DDL3, ..., DDL according to the timestamp. n ; The battery temperatures are recorded as DWD1, DWD2, DWD3, ..., DWD according to the timestamps. n ; The battery internal resistance is recorded as DNZ1, DNZ2, DNZ3, ..., DNZ according to the timestamp. n , n represents the total number of timestamps; The second data set includes charging voltage, charging current, charging time, and charging cycle times; The charging voltages are recorded as CDY1, CDY2, CDY3, ..., CDY according to the timestamps. n ; The charging currents are recorded as CDL1, CDL2, CDL3, ..., CDL according to the timestamps. n ; The number of charging cycles is recorded as CXH1, CXH2, CXH3, ..., CXH according to the timestamp. n ; The third data set includes environmental temperature, environmental humidity, and working load; The ambient temperatures are recorded as HWD1, HWD2, HWD3, ..., HWD n ; The ambient humidity is recorded as HSD1, HSD2, HSD3, ..., HSD n ; The workloads are recorded as GFH1, GFH2, GFH3, ..., GFH according to the timestamp. n .

4. A charging control system for a power module, comprising the charging control method for a power module according to any one of claims 1 to 3, characterized in that: It comprises a data acquisition module (1), a data processing module (2), a data calculation module (3), a data analysis module (4) and an execution module (5); The data acquisition module (1) is used to collect multi-source data during battery operation; The data processing module (2) is used to pre-process the multi-source data and organize the processed multi-source data into a first data set, a second data set and a third data set; The data calculation module (3) is used to perform integrated calculation on the first data set, the second data set and the third data set, so as to generate an adjustment reference coefficient TZX; The data analysis module (4) performs data analysis on the adjustment reference coefficient TZX calculated and obtained by the data calculation module (3), thereby generating a first comparison result, and judging whether it is necessary to switch the battery charging state according to the first comparison result; If the first comparison result shows that the battery charging state does not need to be switched, a second analysis is performed to generate a second comparison result, and the battery charging state is adjusted according to the second comparison result; The execution module (5) is used to adjust the charging state of the battery; The communication module (6) is used to feed back various data to the terminal.

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