Method and system for preventing abnormity caused by battery aging and medium

Abstract

The invention provides a method and system for preventing abnormity caused by battery aging and a medium. The method comprises the steps of monitoring a target battery, extracting battery performance parameters, processing to obtain a comprehensive health value of the battery, collecting real-time battery body temperature, real-time residual electric quantity and user behavior data of the target battery, processing in combination with the comprehensive health value of the battery, and obtaining electricity demand confidence coefficient; judging whether a user has an emergency power demand according to the power demand confidence, triggering a corresponding charging mode, monitoring the charging state of a target battery, extracting power supply characteristic parameters of a preset charging source, processing to obtain the charging type confidence, judging the type of the charging source according to the charging type confidence, and formulating a corresponding charging curve adaptation strategy; therefore, the technology for preventing abnormity caused by battery aging is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart home, in particular to a method, system and medium for preventing battery aging from causing abnormality. BACKGROUND

[0002] With the rapid development of mobile devices, new energy vehicles and other fields, the use safety and life of batteries as core energy components are of great concern. Battery aging can easily cause charging and discharging abnormalities, endurance decay and even safety hazards. How to prevent such problems through precise control has become an industry pain point.

[0003] Existing technologies mainly monitor single battery performance parameters, lack dynamic adaptation to user power demand and charging source type, and do not combine battery health status and user behavior data, which makes it difficult to judge emergency power consumption scenarios and easily accelerates aging due to improper charging mode. At the same time, the charging source characteristics are not identified, the optimal charging curve cannot be matched, and the battery loss is further aggravated.

[0004] Therefore, there is an urgent need for a solution that comprehensively considers battery health status, user demand and charging source characteristics, prevents battery aging from causing abnormality from the source through dynamic adjustment of charging strategy, and ensures battery safety and service life. SUMMARY

[0005] The present application aims to provide a method, system and medium for preventing battery aging from causing abnormality, which can monitor the target battery, extract battery performance parameters, process to obtain the battery comprehensive health value, collect the real-time battery body temperature, real-time remaining power and user behavior data of the target battery, process in combination with the battery comprehensive health value to obtain the power demand confidence, judge whether the user has emergency power demand according to the power demand confidence, trigger the corresponding charging mode, monitor the charging state of the target battery, extract the power supply characteristic parameters of the preset charging source, process to obtain the charging type confidence, determine the charging source type according to the charging type confidence, and develop the corresponding charging curve adaptation strategy, thereby realizing the technology of preventing battery aging from causing abnormality.

[0006] The present application also provides a method for preventing battery aging from causing abnormality, comprising the following steps: monitoring the target battery, extracting battery performance parameters, and processing to obtain the battery comprehensive health value; collecting the real-time battery body temperature, real-time remaining power and user behavior data of the target battery, processing in combination with the battery comprehensive health value to obtain the power demand confidence; judging whether the user has emergency power demand according to the power demand confidence, and triggering the corresponding charging mode; Monitor the charging status of the target battery, extract the power supply characteristic parameters of the preset charging source, and process them to obtain the charging type confidence level; The charging source type is determined based on the confidence level of the charging type, and a corresponding charging curve adaptation strategy is formulated.

[0007] Optionally, in the method for preventing abnormalities caused by battery aging described in this application, the step of monitoring the target battery, extracting battery performance parameters, and processing them to obtain a comprehensive battery health value includes: Monitor the status of the target battery within a first preset time period and extract battery performance parameters, including maximum capacity, battery internal resistance, load voltage fluctuation amplitude, high temperature usage time, and total usage time. Obtain the standard performance parameters of the target battery, including nominal capacity, initial internal resistance, and standard fluctuation range; The battery performance parameters are compared with standard performance parameters to obtain battery state characteristic parameters, including capacity decay coefficient, internal resistance growth coefficient, voltage stability coefficient, and temperature influence coefficient. The battery's overall health value is obtained by weighting the capacity decay coefficient, internal resistance growth coefficient, voltage stability coefficient, and temperature influence coefficient.

[0008] Optionally, in the method for preventing abnormalities caused by battery aging described in this application, the step of collecting real-time battery body temperature, real-time remaining power, and user behavior data of the target battery, and processing them in conjunction with the battery's comprehensive health value to obtain a confidence level of power demand includes: Collect real-time battery body temperature, real-time remaining power, and user behavior data of the target battery; The user behavior data includes historical charging time and charging duration preferences; Based on the real-time battery temperature, real-time remaining power, historical charging time, and charging duration preference, and combined with the battery's comprehensive health value, the data is processed through a preset power demand assessment model to obtain the power demand confidence level.

[0009] Optionally, in the method for preventing abnormalities caused by battery aging described in this application, the step of determining whether the user has an emergency power demand based on the power demand confidence level and triggering the corresponding charging mode includes: The first threshold comparison result is obtained by comparing the confidence level of the electricity demand with the preset confidence threshold of the emergency electricity demand. Based on the comparison results of the first threshold, determine whether the user has an emergency power demand; If the confidence level of the power demand is greater than the preset confidence level threshold for emergency power demand, then the user has an emergency power demand and the fast charging mode is triggered. If the confidence level of the power demand is less than or equal to the preset confidence level threshold for emergency power demand, then the user does not have an emergency power demand and the normal charging mode is triggered.

[0010] Optionally, in the method for preventing abnormalities caused by battery aging described in this application, the step of monitoring the charging state of the target battery, extracting the power supply characteristic parameters of a preset charging source, and processing to obtain the charging type confidence level includes: Monitor the charging status of the target battery during charging and extract the power supply characteristic parameters of the preset charging source; The power supply characteristic parameters include power supply voltage fluctuation amplitude, power supply voltage ripple RMS value, load sudden change response time, and continuous power supply capability. The charging type confidence level is obtained by processing the power supply voltage fluctuation amplitude, power supply voltage ripple RMS value, load sudden change response time, and continuous power supply capability through a preset charging source type determination model.

[0011] Optionally, in the method for preventing abnormalities caused by battery aging described in this application, the step of determining the charging source type based on the charging type confidence level and formulating a corresponding charging curve adaptation strategy includes: A second threshold comparison result is obtained by comparing the confidence level of the charging type with the preset confidence threshold of the charging type. The charging source type is determined based on the comparison result of the second threshold, and a corresponding charging curve adaptation strategy is formulated. If the confidence level of the charging type is less than or equal to the preset confidence level threshold of the charging type, the charging source type is determined to be an unstable power supply, and an unstable power supply adaptation strategy is formulated. If the confidence level of the charging type is greater than the preset confidence level threshold for the charging type, the charging source type is determined to be a stable power supply, and a stable power supply adaptation strategy is formulated.

[0012] Secondly, this application provides a system for preventing abnormalities caused by battery aging. The system includes a memory and a processor. The memory includes a program for a method of preventing abnormalities caused by battery aging. When the program for the method of preventing abnormalities caused by battery aging is executed by the processor, it performs the following steps: Monitor the target battery, extract battery performance parameters, and process them to obtain the battery's overall health value; The real-time battery body temperature, real-time remaining power, and user behavior data of the target battery are collected and processed in combination with the comprehensive health value of the battery to obtain the confidence level of power demand. Based on the confidence level of the electricity demand, determine whether the user has an emergency electricity demand and trigger the corresponding charging mode; Monitor the charging status of the target battery, extract the power supply characteristic parameters of the preset charging source, and process them to obtain the charging type confidence level; The charging source type is determined based on the confidence level of the charging type, and a corresponding charging curve adaptation strategy is formulated.

[0013] Optionally, in the system for preventing abnormalities caused by battery aging as described in this application, the monitoring of the target battery, extraction of battery performance parameters, and processing to obtain a comprehensive battery health value includes: Monitor the status of the target battery within a first preset time period and extract battery performance parameters, including maximum capacity, battery internal resistance, load voltage fluctuation amplitude, high temperature usage time, and total usage time. Obtain the standard performance parameters of the target battery, including nominal capacity, initial internal resistance, and standard fluctuation range; The battery performance parameters are compared with standard performance parameters to obtain battery state characteristic parameters, including capacity decay coefficient, internal resistance growth coefficient, voltage stability coefficient, and temperature influence coefficient. The battery's overall health value is obtained by weighting the capacity decay coefficient, internal resistance growth coefficient, voltage stability coefficient, and temperature influence coefficient.

[0014] Optionally, in the system for preventing abnormalities caused by battery aging as described in this application, the step of collecting real-time battery body temperature, real-time remaining power, and user behavior data of the target battery, and processing them in conjunction with the battery's comprehensive health value to obtain a confidence level of power demand includes: Collect real-time battery body temperature, real-time remaining power, and user behavior data of the target battery; The user behavior data includes historical charging time and charging duration preferences; Based on the real-time battery temperature, real-time remaining power, historical charging time, and charging duration preference, and combined with the battery's comprehensive health value, the data is processed through a preset power demand assessment model to obtain the power demand confidence level.

[0015] Thirdly, this application also provides a computer-readable storage medium storing a method program for preventing abnormalities caused by battery aging, wherein when the method program for preventing abnormalities caused by battery aging is executed by a processor, it implements the steps of the method for preventing abnormalities caused by battery aging as described in any of the preceding claims.

[0016] As can be seen from the above, the method, system, and medium disclosed in this invention for preventing abnormalities caused by battery aging monitor the target battery, extract battery performance parameters, process them to obtain a comprehensive battery health value, collect real-time battery body temperature, real-time remaining power, and user behavior data of the target battery, process them in conjunction with the comprehensive battery health value to obtain a power demand confidence level, determine whether the user has an emergency power demand based on the power demand confidence level, and trigger the corresponding charging mode, monitor the charging status of the target battery, extract the power supply characteristic parameters of the preset charging source, process them to obtain a charging type confidence level, determine the charging source type based on the charging type confidence level, and formulate a corresponding charging curve adaptation strategy, thereby realizing the technology for preventing abnormalities caused by battery aging.

[0017] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a method for preventing abnormalities caused by battery aging, as provided in an embodiment of this application; Figure 2 A flowchart illustrating the method for obtaining a comprehensive battery health value in an embodiment of this application to prevent abnormalities caused by battery aging. Figure 3 , Figure 4 This is a high-level flowchart of methods for various embodiments of this application, which can be used to prevent abnormalities caused by battery aging. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] Please refer to Figure 1 , Figure 1 This is a flowchart of a method for preventing battery aging-induced abnormalities according to some embodiments of this application. This method for preventing battery aging-induced abnormalities is used in terminal devices, such as computers and mobile phones. The method for preventing battery aging-induced abnormalities includes the following steps: S11. Monitor the target battery, extract battery performance parameters, and process them to obtain the battery's overall health value; S12. Collect the real-time battery body temperature, real-time remaining power, and user behavior data of the target battery, and process them in conjunction with the comprehensive health value of the battery to obtain the confidence level of power demand. S13. Determine whether the user has an emergency power demand based on the power demand confidence level, and trigger the corresponding charging mode; S14. Monitor the charging status of the target battery, extract the power supply characteristic parameters of the preset charging source, and process them to obtain the charging type confidence level. S15. Determine the charging source type based on the confidence level of the charging type, and formulate a corresponding charging curve adaptation strategy.

[0023] It is important to note that as handheld smart devices become more widespread and are used for longer periods, the demands on their battery capacity are increasing, with developers continuously expanding battery capacity. However, larger battery capacities also pose greater potential risks, leading to more severe accidents caused by battery issues. To address the problems associated with abnormal battery aging, the first step is to monitor the target battery and extract its performance parameters, including maximum capacity, internal resistance, load voltage fluctuation, high-temperature usage time, and total usage time. This data is then processed to obtain a comprehensive battery health value. Additionally, real-time battery temperature, remaining charge, and user behavior data, including historical charging time and charging duration preferences, are collected. By combining the battery's overall health value, a power demand confidence level is obtained. Based on this confidence level, it is determined whether the user has an urgent power demand and the corresponding charging mode is triggered. If an urgent power demand exists, a fast charging mode is triggered. The charging status of the target battery is monitored, and the power supply characteristic parameters of the preset charging source are extracted, including the power supply voltage fluctuation amplitude, the power supply voltage ripple RMS value, the load change response time, and the continuous power supply capability. This is processed to obtain a charging type confidence level. Based on the charging type confidence level, the charging source type is determined, and corresponding charging curve adaptation strategies are formulated, including unstable power supply adaptation strategies and stable power supply adaptation strategies, thereby achieving a technology to prevent abnormalities caused by battery aging.

[0024] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating the method for obtaining a comprehensive battery health value in some embodiments of this application for preventing abnormalities caused by battery aging. According to embodiments of the present invention, monitoring the target battery, extracting battery performance parameters, and processing them to obtain a comprehensive battery health value includes: S21. Monitor the status of the target battery within a first preset time period and extract battery performance parameters, including maximum capacity, battery internal resistance, load voltage fluctuation amplitude, high temperature usage time, and total usage time. S22. Obtain the standard performance parameters of the target battery, including nominal capacity, initial internal resistance, and standard fluctuation range; S23. Compare the battery performance parameters with the standard performance parameters to obtain battery state characteristic parameters, including capacity decay coefficient, internal resistance growth coefficient, voltage stability coefficient and temperature influence coefficient. S24. The battery's overall health value is obtained by weighting the capacity decay coefficient, internal resistance growth coefficient, voltage stability coefficient, and temperature influence coefficient.

[0025] It is important to note that to accurately assess battery health, a multi-dimensional parameter monitoring and analysis system needs to be constructed. First, dynamic monitoring of the target battery is crucial. Its operating status is continuously tracked over a pre-defined time period, and core battery performance parameters are extracted simultaneously. These parameters include the maximum capacity reflecting energy storage capability, internal resistance characterizing internal losses, load voltage fluctuation amplitude reflecting power supply stability, high-temperature usage time related to aging acceleration factors, and total usage time with accumulated losses, forming a comprehensive performance data foundation. Simultaneously, standard performance parameters for this battery model are acquired, including the factory nominal capacity, initial internal resistance, and design standard fluctuation amplitude. Next, by quantitatively comparing the real-time extracted battery performance parameters with the standard performance parameters, battery state characteristic parameters are generated: the capacity decay coefficient directly reflects the degree of energy storage capacity decline, the internal resistance growth coefficient reflects the intensification of internal losses, the voltage stability coefficient characterizes changes in power supply reliability, and the temperature influence coefficient quantifies the cumulative damage of high temperatures to the battery. Finally, combining the influence weights of each characteristic parameter on battery health, a weighted algorithm is used for comprehensive calculation, ultimately obtaining a comprehensive and accurate battery health value that reflects the current health status of the battery.

[0026] According to an embodiment of the present invention, the step of collecting real-time battery body temperature, real-time remaining power, and user behavior data of the target battery, and processing them in conjunction with the battery's comprehensive health value to obtain a confidence level of electricity demand includes: Collect real-time battery body temperature, real-time remaining power, and user behavior data of the target battery; The user behavior data includes historical charging time and charging duration preferences; Based on the real-time battery temperature, real-time remaining power, historical charging time, and charging duration preference, and combined with the battery's comprehensive health value, the data is processed through a preset power demand assessment model to obtain the power demand confidence level.

[0027] It is important to note that, to accurately predict the power demand of the target battery, real-time battery temperature, real-time remaining power, and user behavior data are first collected. Battery status data, including real-time battery temperature, is a key indicator for judging the battery's current operational safety and activity; excessively high or low temperatures directly affect power supply stability. Real-time remaining power directly reflects the battery's current available energy, serving as the basis for meeting immediate power needs. User behavior data includes historical charging time and charging duration preferences. Historical charging time can outline users' power consumption habits, such as whether they frequently charge at night or replenish power during the day. Charging duration preferences reflect the user's minimum battery power requirement, providing a reference for predicting subsequent charging opportunities. Next, after acquiring the above data, it is integrated with the previously calculated comprehensive battery health value and input into a preset power demand assessment model. This model quantitatively analyzes the impact of temperature on the user experience, the matching degree between remaining power and user habits, and the constraints of battery health status on range, ultimately outputting a power demand confidence level.

[0028] According to an embodiment of the present invention, determining whether a user has an emergency power demand based on the power demand confidence level and triggering the corresponding charging mode includes: The first threshold comparison result is obtained by comparing the confidence level of the electricity demand with the preset confidence threshold of the emergency electricity demand. Based on the comparison results of the first threshold, determine whether the user has an emergency power demand; If the confidence level of the power demand is greater than the preset confidence level threshold for emergency power demand, then the user has an emergency power demand and the fast charging mode is triggered. If the confidence level of the power demand is less than or equal to the preset confidence level threshold for emergency power demand, then the user does not have an emergency power demand and the normal charging mode is triggered.

[0029] It's important to note that to achieve intelligent adaptation of battery charging modes, a core principle of power demand confidence is needed, combined with preset thresholds for accurate judgment. First, the power demand confidence level is quantitatively compared with a preset emergency power demand confidence threshold to generate a first threshold comparison result. This result directly determines the urgency of the user's power need. If the power demand confidence level exceeds the preset threshold, it indicates that the user has a clear emergency power need, such as being about to go out and having low battery. In this case, fast charging mode should be triggered immediately to increase charging power, shorten charging time, and quickly replenish power to meet the emergency need. If the power demand confidence level does not exceed the threshold, it indicates that the user's power demand is moderate and there is no immediate pressure to replenish power. The system then triggers normal charging mode to replenish power smoothly while ensuring battery health, avoiding excessive battery wear from fast charging.

[0030] According to an embodiment of the present invention, the step of monitoring the charging state of the target battery, extracting the power supply characteristic parameters of a preset charging source, and processing to obtain the charging type confidence level includes: Monitor the charging status of the target battery during charging and extract the power supply characteristic parameters of the preset charging source; The power supply characteristic parameters include power supply voltage fluctuation amplitude, power supply voltage ripple RMS value, load sudden change response time, and continuous power supply capability. The charging type confidence level is obtained by processing the power supply voltage fluctuation amplitude, power supply voltage ripple RMS value, load sudden change response time, and continuous power supply capability through a preset charging source type determination model.

[0031] It is important to note that, to achieve accurate identification and adaptation of charging sources, the battery charging status is first tracked in real time, and core power supply characteristic parameters of the preset charging source are extracted simultaneously. These parameters include the power supply voltage fluctuation amplitude, the effective value of the power supply voltage ripple, the load change response time, and the continuous power supply capability. Among these, the power supply voltage fluctuation amplitude reflects the stability of the power supply output; excessive fluctuation will affect charging efficiency. The effective value of the power supply voltage ripple is related to the purity of the power supply; excessive ripple can easily damage the battery. The load change response time reflects the power supply's ability to adapt to changes in battery load; a slow response may cause power interruption. The continuous power supply capability determines the reliability of the power supply's long-term stable power replenishment. Next, the above four parameters are input into a preset charging source type determination model. The model quantitatively analyzes the matching degree of each parameter with the characteristics of different types of charging sources (such as original fast charging, third-party slow charging, etc.), performs multi-dimensional weighted calculations, and finally outputs the charging type confidence score.

[0032] According to an embodiment of the present invention, the step of determining the charging source type based on the charging type confidence level and formulating a corresponding charging curve adaptation strategy includes: A second threshold comparison result is obtained by comparing the confidence level of the charging type with the preset confidence threshold of the charging type. The charging source type is determined based on the comparison result of the second threshold, and a corresponding charging curve adaptation strategy is formulated. If the confidence level of the charging type is less than or equal to the preset confidence level threshold of the charging type, the charging source type is determined to be an unstable power supply, and an unstable power supply adaptation strategy is formulated. If the confidence level of the charging type is greater than the preset confidence level threshold for the charging type, the charging source type is determined to be a stable power supply, and a stable power supply adaptation strategy is formulated.

[0033] It is important to note that to ensure the safety and efficiency of battery charging, a dynamic adaptation mechanism needs to be built based on the confidence level of the charging source type. First, the confidence level of the charging type is accurately compared with a preset confidence threshold, generating a second threshold comparison result. Based on this second threshold comparison result, the charging source type is determined, and a corresponding charging curve adaptation strategy is matched. If the confidence level of the charging type is less than or equal to the preset threshold, it indicates that the charging source's output characteristics fluctuate greatly and its reliability is insufficient, classifying it as an unstable power source (such as a power bank). For such power sources, an unstable power source adaptation strategy needs to be developed, dynamically adjusting the charging current and strengthening the voltage fluctuation compensation mechanism to avoid the impact of unstable power supply on the battery cells. If the confidence level of the charging type is greater than the preset threshold, it indicates that the charging source's power supply is stable and its characteristic parameters meet the standards, classifying it as a stable power source. In this case, a stable power source adaptation strategy will be activated, combining the battery's current health status and power demand to adopt the optimal charging curve, improving charging efficiency while minimizing battery loss, achieving a balance between safety and efficiency.

[0034] Please refer to Figure 3 and Figure 4 , Figure 3 , Figure 4 This is a high-level flowchart of various embodiments of the methods described in this application, which can be used to prevent abnormalities caused by battery aging. According to embodiments of the present invention, for example, a comparison is made between a power demand confidence level and a preset emergency power demand confidence threshold. When the power demand confidence level exceeds the preset threshold, it indicates that the user currently has a clear emergency power demand, such as being about to go out and having insufficient power. In this case, a fast charging mode should be triggered immediately to shorten charging time by increasing charging power and quickly replenishing power to meet the emergency demand. During the charging process, a threshold comparison is made between the obtained charging type confidence level and a preset charging type confidence threshold. When the charging type confidence level is less than or equal to the preset threshold, it indicates that the output characteristics of the charging source fluctuate greatly and its reliability is insufficient, and it is determined to be an unstable power source (such as a power bank). For such power sources, an unstable power source adaptation strategy needs to be developed, which dynamically adjusts the charging current and strengthens the voltage fluctuation compensation mechanism to avoid the impact of unstable power supply on the battery cells.

[0035] Secondly, the present invention also discloses a system for preventing abnormalities caused by battery aging, comprising a memory and a processor. The memory includes a method program for preventing abnormalities caused by battery aging, which, when executed by the processor, performs the following steps: Monitor the target battery, extract battery performance parameters, and process them to obtain the battery's overall health value; The real-time battery body temperature, real-time remaining power, and user behavior data of the target battery are collected and processed in combination with the comprehensive health value of the battery to obtain the confidence level of power demand. Based on the confidence level of the electricity demand, determine whether the user has an emergency electricity demand and trigger the corresponding charging mode; Monitor the charging status of the target battery, extract the power supply characteristic parameters of the preset charging source, and process them to obtain the charging type confidence level; The charging source type is determined based on the confidence level of the charging type, and a corresponding charging curve adaptation strategy is formulated.

[0036] It is important to note that as handheld smart devices become more widespread and are used for longer periods, the demands on their battery capacity are increasing, with developers continuously expanding battery capacity. However, larger battery capacities also pose greater potential risks, leading to more severe accidents caused by battery issues. To address the problems associated with abnormal battery aging, the first step is to monitor the target battery and extract its performance parameters, including maximum capacity, internal resistance, load voltage fluctuation, high-temperature usage time, and total usage time. This data is then processed to obtain a comprehensive battery health value. Additionally, real-time battery temperature, remaining charge, and user behavior data, including historical charging time and charging duration preferences, are collected. By combining the battery's overall health value, a power demand confidence level is obtained. Based on this confidence level, it is determined whether the user has an urgent power demand and the corresponding charging mode is triggered. If an urgent power demand exists, a fast charging mode is triggered. The charging status of the target battery is monitored, and the power supply characteristic parameters of the preset charging source are extracted, including the power supply voltage fluctuation amplitude, the power supply voltage ripple RMS value, the load change response time, and the continuous power supply capability. This is processed to obtain a charging type confidence level. Based on the charging type confidence level, the charging source type is determined, and corresponding charging curve adaptation strategies are formulated, including unstable power supply adaptation strategies and stable power supply adaptation strategies, thereby achieving a technology to prevent abnormalities caused by battery aging.

[0037] According to an embodiment of the present invention, the monitoring of the target battery, extraction of battery performance parameters, and processing to obtain a comprehensive battery health value include: Monitor the status of the target battery within a first preset time period and extract battery performance parameters, including maximum capacity, battery internal resistance, load voltage fluctuation amplitude, high temperature usage time, and total usage time. Obtain the standard performance parameters of the target battery, including nominal capacity, initial internal resistance, and standard fluctuation range; The battery performance parameters are compared with standard performance parameters to obtain battery state characteristic parameters, including capacity decay coefficient, internal resistance growth coefficient, voltage stability coefficient, and temperature influence coefficient. The battery's overall health value is obtained by weighting the capacity decay coefficient, internal resistance growth coefficient, voltage stability coefficient, and temperature influence coefficient.

[0038] It is important to note that to accurately assess battery health, a multi-dimensional parameter monitoring and analysis system needs to be constructed. First, dynamic monitoring of the target battery is crucial. Its operating status is continuously tracked over a pre-defined time period, and core battery performance parameters are extracted simultaneously. These parameters include the maximum capacity reflecting energy storage capability, internal resistance characterizing internal losses, load voltage fluctuation amplitude reflecting power supply stability, high-temperature usage time related to aging acceleration factors, and total usage time with accumulated losses, forming a comprehensive performance data foundation. Simultaneously, standard performance parameters for this battery model are acquired, including the factory nominal capacity, initial internal resistance, and design standard fluctuation amplitude. Next, by quantitatively comparing the real-time extracted battery performance parameters with the standard performance parameters, battery state characteristic parameters are generated: the capacity decay coefficient directly reflects the degree of energy storage capacity decline, the internal resistance growth coefficient reflects the intensification of internal losses, the voltage stability coefficient characterizes changes in power supply reliability, and the temperature influence coefficient quantifies the cumulative damage of high temperatures to the battery. Finally, combining the influence weights of each characteristic parameter on battery health, a weighted algorithm is used for comprehensive calculation, ultimately obtaining a comprehensive and accurate battery health value that reflects the current health status of the battery.

[0039] According to an embodiment of the present invention, the step of collecting real-time battery body temperature, real-time remaining power, and user behavior data of the target battery, and processing them in conjunction with the battery's comprehensive health value to obtain a confidence level of electricity demand includes: Collect real-time battery body temperature, real-time remaining power, and user behavior data of the target battery; The user behavior data includes historical charging time and charging duration preferences; Based on the real-time battery temperature, real-time remaining power, historical charging time, and charging duration preference, and combined with the battery's comprehensive health value, the data is processed through a preset power demand assessment model to obtain the power demand confidence level.

[0040] It is important to note that, to accurately predict the power demand of the target battery, real-time battery temperature, real-time remaining power, and user behavior data are first collected. Battery status data, including real-time battery temperature, is a key indicator for judging the battery's current operational safety and activity; excessively high or low temperatures directly affect power supply stability. Real-time remaining power directly reflects the battery's current available energy, serving as the basis for meeting immediate power needs. User behavior data includes historical charging time and charging duration preferences. Historical charging time can outline users' power consumption habits, such as whether they frequently charge at night or replenish power during the day. Charging duration preferences reflect the user's minimum battery power requirement, providing a reference for predicting subsequent charging opportunities. Next, after acquiring the above data, it is integrated with the previously calculated comprehensive battery health value and input into a preset power demand assessment model. This model quantitatively analyzes the impact of temperature on the user experience, the matching degree between remaining power and user habits, and the constraints of battery health status on range, ultimately outputting a power demand confidence level.

[0041] According to an embodiment of the present invention, determining whether a user has an emergency power demand based on the power demand confidence level and triggering the corresponding charging mode includes: The first threshold comparison result is obtained by comparing the confidence level of the electricity demand with the preset confidence threshold of the emergency electricity demand. Based on the comparison results of the first threshold, determine whether the user has an emergency power demand; If the confidence level of the power demand is greater than the preset confidence level threshold for emergency power demand, then the user has an emergency power demand and the fast charging mode is triggered. If the confidence level of the power demand is less than or equal to the preset confidence level threshold for emergency power demand, then the user does not have an emergency power demand and the normal charging mode is triggered.

[0042] It's important to note that to achieve intelligent adaptation of battery charging modes, a core principle of power demand confidence is needed, combined with preset thresholds for accurate judgment. First, the power demand confidence level is quantitatively compared with a preset emergency power demand confidence threshold to generate a first threshold comparison result. This result directly determines the urgency of the user's power need. If the power demand confidence level exceeds the preset threshold, it indicates that the user has a clear emergency power need, such as being about to go out and having low battery. In this case, fast charging mode should be triggered immediately to increase charging power, shorten charging time, and quickly replenish power to meet the emergency need. If the power demand confidence level does not exceed the threshold, it indicates that the user's power demand is moderate and there is no immediate pressure to replenish power. The system then triggers normal charging mode to replenish power smoothly while ensuring battery health, avoiding excessive battery wear from fast charging.

[0043] According to an embodiment of the present invention, the step of monitoring the charging state of the target battery, extracting the power supply characteristic parameters of a preset charging source, and processing to obtain the charging type confidence level includes: Monitor the charging status of the target battery during charging and extract the power supply characteristic parameters of the preset charging source; The power supply characteristic parameters include power supply voltage fluctuation amplitude, power supply voltage ripple RMS value, load sudden change response time, and continuous power supply capability. The charging type confidence level is obtained by processing the power supply voltage fluctuation amplitude, power supply voltage ripple RMS value, load sudden change response time, and continuous power supply capability through a preset charging source type determination model.

[0044] It is important to note that, to achieve accurate identification and adaptation of charging sources, the battery charging status is first tracked in real time, and core power supply characteristic parameters of the preset charging source are extracted simultaneously. These parameters include the power supply voltage fluctuation amplitude, the effective value of the power supply voltage ripple, the load change response time, and the continuous power supply capability. Among these, the power supply voltage fluctuation amplitude reflects the stability of the power supply output; excessive fluctuation will affect charging efficiency. The effective value of the power supply voltage ripple is related to the purity of the power supply; excessive ripple can easily damage the battery. The load change response time reflects the power supply's ability to adapt to changes in battery load; a slow response may cause power interruption. The continuous power supply capability determines the reliability of the power supply's long-term stable power replenishment. Next, the above four parameters are input into a preset charging source type determination model. The model quantitatively analyzes the matching degree of each parameter with the characteristics of different types of charging sources (such as original fast charging, third-party slow charging, etc.), performs multi-dimensional weighted calculations, and finally outputs the charging type confidence score.

[0045] According to an embodiment of the present invention, the step of determining the charging source type based on the charging type confidence level and formulating a corresponding charging curve adaptation strategy includes: A second threshold comparison result is obtained by comparing the confidence level of the charging type with the preset confidence threshold of the charging type. The charging source type is determined based on the comparison result of the second threshold, and a corresponding charging curve adaptation strategy is formulated. If the confidence level of the charging type is less than or equal to the preset confidence level threshold of the charging type, the charging source type is determined to be an unstable power supply, and an unstable power supply adaptation strategy is formulated. If the confidence level of the charging type is greater than the preset confidence level threshold for the charging type, the charging source type is determined to be a stable power supply, and a stable power supply adaptation strategy is formulated.

[0046] It is important to note that to ensure the safety and efficiency of battery charging, a dynamic adaptation mechanism needs to be built based on the confidence level of the charging source type. First, the confidence level of the charging type is accurately compared with a preset confidence threshold, generating a second threshold comparison result. Based on this second threshold comparison result, the charging source type is determined, and a corresponding charging curve adaptation strategy is matched. If the confidence level of the charging type is less than or equal to the preset threshold, it indicates that the charging source's output characteristics fluctuate greatly and its reliability is insufficient, classifying it as an unstable power source (such as a power bank). For such power sources, an unstable power source adaptation strategy needs to be developed, dynamically adjusting the charging current and strengthening the voltage fluctuation compensation mechanism to avoid the impact of unstable power supply on the battery cells. If the confidence level of the charging type is greater than the preset threshold, it indicates that the charging source's power supply is stable and its characteristic parameters meet the standards, classifying it as a stable power source. In this case, a stable power source adaptation strategy will be activated, combining the battery's current health status and power demand to adopt the optimal charging curve, improving charging efficiency while minimizing battery loss, achieving a balance between safety and efficiency.

[0047] According to embodiments of the present invention, for example, the confidence level of power demand is compared with a preset emergency power demand confidence threshold. When the confidence level of power demand exceeds the preset threshold, it indicates that the user currently has a clear emergency power demand, such as being about to go out and having insufficient power. In this case, fast charging mode should be triggered immediately to shorten charging time by increasing charging power and quickly replenishing power to meet the emergency demand. During the charging process, the confidence level of the obtained charging type is compared with a preset charging type confidence threshold. When the confidence level of the charging type is less than or equal to the preset threshold, it indicates that the output characteristics of the charging source fluctuate greatly and the reliability is insufficient. It is judged as an unstable power source (such as a power bank). For such power sources, an unstable power source adaptation strategy needs to be formulated. By dynamically adjusting the charging current and strengthening the voltage fluctuation compensation mechanism, the unstable power supply can be prevented from impacting the battery cells.

[0048] A third aspect of the present invention provides a readable storage medium storing a method program for preventing abnormalities caused by battery aging, wherein when the method program for preventing abnormalities caused by battery aging is executed by a processor, it implements the steps of the method for preventing abnormalities caused by battery aging as described in any of the preceding claims.

[0049] This invention discloses a method, system, and medium for preventing abnormalities caused by battery aging. By monitoring the target battery, extracting battery performance parameters, processing them to obtain a comprehensive battery health value, collecting real-time battery temperature, real-time remaining power, and user behavior data, and combining this with the comprehensive battery health value to obtain a power demand confidence level, determining whether the user has an emergency power demand based on the power demand confidence level, triggering the corresponding charging mode, monitoring the charging status of the target battery, extracting the power supply characteristic parameters of a preset charging source, processing them to obtain a charging type confidence level, determining the charging source type based on the charging type confidence level, and formulating a corresponding charging curve adaptation strategy, thereby achieving the technology to prevent abnormalities caused by battery aging.

[0050] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0051] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0052] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0053] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0054] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method of preventing an abnormality caused by battery aging, characterized by, The method comprises the following steps: monitoring the target battery, extracting battery performance parameters, and processing to obtain a battery comprehensive health value; collecting real-time battery body temperature, real-time remaining power and user behavior data of the target battery, processing the battery comprehensive health value to obtain a power demand confidence level; judging whether there is an emergency power demand of the user according to the power demand confidence level, and triggering a corresponding charging mode; monitoring the charging state of the target battery, extracting power supply characteristic parameters of a preset charging source, and processing to obtain a charging type confidence level; determining the charging source type according to the charging type confidence level, and formulating a corresponding charging curve adaptation strategy.

2. The method of claim 1, wherein the battery is a lithium-ion battery. The monitoring of the target battery, the extraction of the battery performance parameters, and the processing to obtain the battery comprehensive health value comprise: monitoring the state of the target battery within a first preset time period, and extracting battery performance parameters including maximum capacity, battery internal resistance, load voltage fluctuation amplitude, high-temperature use time length, and total use time length; obtaining standard performance parameters of the target battery, including nominal power, initial internal resistance, and standard fluctuation amplitude; comparing and processing the battery performance parameters and the standard performance parameters to obtain battery state characteristic parameters, including capacity attenuation coefficient, internal resistance growth coefficient, voltage stability coefficient, and temperature influence coefficient; weighting the capacity attenuation coefficient, the internal resistance growth coefficient, the voltage stability coefficient, and the temperature influence coefficient to obtain the battery comprehensive health value.

3. The method of claim 2, wherein the battery is a lithium-ion battery. The collection of the real-time battery body temperature, the real-time remaining power, and the user behavior data of the target battery, and the processing of the battery comprehensive health value to obtain the power demand confidence level comprise: collecting the real-time battery body temperature, the real-time remaining power, and the user behavior data of the target battery; The user behavior data includes historical charging time and charging duration preference; processing the real-time battery body temperature, the real-time remaining power, the historical charging time, and the charging duration preference in combination with the battery comprehensive health value through a preset power demand evaluation model to obtain the power demand confidence level.

4. The method of claim 3, wherein the battery is a lithium-ion battery. The judgment of whether there is an emergency power demand of the user according to the power demand confidence level, and the triggering of a corresponding charging mode comprise: comparing the power demand confidence level with a preset emergency power demand confidence level threshold to obtain a first threshold comparison result; judging whether there is an emergency power demand of the user according to the first threshold comparison result; if the power demand confidence level is greater than the preset emergency power demand confidence level threshold, there is an emergency power demand of the user, and a fast charging mode is triggered; if the power demand confidence level is less than or equal to the preset emergency power demand confidence level threshold, there is no emergency power demand of the user, and a normal charging mode is triggered.

5. The method of claim 1, wherein the battery is a lithium-ion battery. The monitoring of the charging state of the target battery, the extraction of the power supply characteristic parameters of the preset charging source, and the processing to obtain the charging type confidence level comprise: monitoring the charging state of the target battery, and extracting power supply characteristic parameters of a preset charging source; The power supply characteristic parameters include power supply voltage fluctuation amplitude, power supply voltage ripple effective value, load mutation response time, and continuous power supply capacity; According to the power supply voltage fluctuation amplitude, the power supply voltage ripple effective value, the load mutation response time and the continuous power supply capability, a preset charging source type judgment model is processed to obtain a charging type confidence.

6. The method of claim 5, wherein the battery is a lithium-ion battery. The charging source type is determined according to the charging type confidence, and a corresponding charging curve adaptation strategy is formulated, which comprises: According to the charging type confidence and the preset charging type confidence threshold, a second threshold comparison result is obtained; According to the second threshold comparison result, the charging source type is determined, and a corresponding charging curve adaptation strategy is formulated; If the charging type confidence is less than or equal to the preset charging type confidence threshold, the charging source type is determined as a non-stable power supply, and a non-stable power supply adaptation strategy is formulated; If the charging type confidence is greater than the preset charging type confidence threshold, the charging source type is determined as a stable power supply, and a stable power supply adaptation strategy is formulated.

7. A system for preventing abnormalities caused by battery aging, characterized in that, The system comprises a memory and a processor, the memory comprising a program of a method for preventing battery aging from causing abnormalities, the program of the method for preventing battery aging from causing abnormalities being executed by the processor to implement the following steps: Monitoring the target battery, extracting battery performance parameters, and processing to obtain a battery comprehensive health value; Collecting real-time battery body temperature, real-time remaining power and user behavior data of the target battery, combining the battery comprehensive health value for processing to obtain a power demand confidence; According to the power demand confidence, it is judged whether there is an emergency power demand of the user, and a corresponding charging mode is triggered; Monitoring the charging state of the target battery, extracting the power supply characteristics parameters of the preset charging source, and processing to obtain a charging type confidence; According to the charging type confidence, the charging source type is determined, and a corresponding charging curve adaptation strategy is formulated.

8. The system for preventing battery aging-induced abnormality according to claim 7, characterized by, The monitoring of the target battery, the extraction of the battery performance parameters, and the processing to obtain the battery comprehensive health value comprise: Monitoring the state of the target battery in a first preset time period, extracting battery performance parameters, including maximum capacity, battery internal resistance, load voltage fluctuation amplitude, high temperature use time and total use time; Obtaining standard performance parameters of the target battery, including nominal capacity, initial internal resistance and standard fluctuation amplitude; According to the comparison processing of the battery performance parameters and the standard performance parameters, battery state characteristic parameters are obtained, including capacity attenuation coefficient, internal resistance growth coefficient, voltage stability coefficient and temperature influence coefficient; According to the capacity attenuation coefficient, the internal resistance growth coefficient, the voltage stability coefficient and the temperature influence coefficient, weighted processing is performed to obtain the battery comprehensive health value.

9. The system for preventing battery aging-induced abnormality according to claim 8, characterized by, The collection of real-time battery body temperature, real-time remaining power and user behavior data of the target battery, and the processing combining the battery comprehensive health value to obtain the power demand confidence comprise: Collecting real-time battery body temperature, real-time remaining power and user behavior data of the target battery; The user behavior data comprises historical charging time and charging time preference; According to the real-time battery body temperature, real-time residual power, historical charging time and charging time length preference, combined with the battery comprehensive health value, a preset power demand evaluation model is processed to obtain a power demand confidence.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a method program for preventing battery aging from causing abnormalities, and when the method program for preventing battery aging from causing abnormalities is executed by the processor, the steps of the method for preventing battery aging from causing abnormalities in any one of claims 1 to 6 are implemented.

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