CN115912561A - Battery charging management method, system, device and storage medium - Google Patents
Battery charging management method, system, device and storage medium Download PDFInfo
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Abstract
The application discloses a battery charging management method, a system, equipment and a storage medium, wherein the method comprises the following steps: acquiring battery use data of a battery at regular time according to a preset frequency; taking time corresponding to preset days before the current date as a sample date, and extracting data in the sample date from the battery use data to obtain charging sample data; taking preset prediction time after the current moment as sample time, and extracting data corresponding to the sample time in the sample date from the charging sample data to obtain prediction statistical data; and analyzing the predicted statistical data according to a preset analysis strategy to obtain a predicted charging mode, switching the current working mode into the predicted charging mode, and setting the charging parameters of the battery according to the predicted charging mode. According to the application method and the application system, the prediction statistical data used for the associated time are obtained by extracting the historical battery use data, the historical battery use data corresponding to different time periods can reflect the application scenes corresponding to the battery, and the management control effect is good and the adaptability is strong.
Description
Technical Field
The present disclosure relates to the field of battery management, and in particular, to a battery charging management method, system, device, and storage medium.
Background
Battery management, typically implemented by a Battery Management System (BMS), is used to manage the rechargeable batteries of electronic systems. In the related technology, the battery management system acquires voltage, temperature and current information of the battery by adopting an electricity meter, AD signal acquisition and other modes, calculates the current electric quantity through a related algorithm, and controls a charging mode according to the current electric quantity detected by the electricity meter to realize charging management in the later use process of the battery, but the mode cannot adapt to different requirements on the electric quantity in different scenes, and has large use limitation.
Disclosure of Invention
The present application is directed to solving at least one of the problems in the prior art. Therefore, the application provides a battery charging management method, a system, equipment and a storage medium, which can extract the historical use data of the battery to perform predictive analysis, obtain the predictive charging mode at the corresponding time and effectively improve the accuracy of the battery charging management.
An embodiment of a first aspect of the present application provides a battery charging management method, including:
acquiring battery use data of a battery at regular time according to a preset frequency;
taking time corresponding to preset days before the current date as a sample date, and extracting data in the sample date from the battery use data to obtain charging sample data;
taking the preset prediction time after the current moment as sample time, and extracting data corresponding to the sample time in the sample date from the charging sample data to obtain prediction statistical data;
and analyzing the predicted statistical data according to a preset analysis strategy to obtain a predicted charging mode, switching the current working mode into the predicted charging mode, and setting the charging parameters of the battery according to the predicted charging mode.
According to the above embodiment of the present application, at least the following advantages are provided: the method comprises the steps of obtaining battery use data of a battery at regular time through preset frequency, extracting historical battery use data according to current date and current time, obtaining predicted statistical data used for correlating time to carry out predictive analysis, analyzing the predicted statistical data according to a preset analysis strategy to obtain a predicted charging mode at corresponding time, and obtaining historical battery use data corresponding to different time periods to indirectly reflect application scenes corresponding to the battery at the time.
According to some embodiments of the first aspect of the present application, the periodically acquiring battery usage data of a battery according to a preset frequency includes:
acquiring charging counting data corresponding to the battery at regular time according to a preset frequency, wherein the charging counting data are used for representing the counting of the battery connected with an external power supply and the counting of the battery not connected with the external power supply;
and storing all charging counting data as battery use data every time a preset sampling time passes.
According to some embodiments of the first aspect of the present application, saving all charge count data as battery usage data every predetermined sampling time includes:
and storing all the charging counting data every preset sampling time until the days corresponding to the stored charging counting data are more than or equal to the preset days, and obtaining the battery use data consisting of all the charging counting data.
According to some embodiments of the first aspect of the present application, analyzing the predicted statistical data according to a preset analysis strategy to obtain a predicted charging mode includes:
extracting the sum of times for representing connection with an external power supply in the predicted statistical data to obtain data of the total amount of external power;
extracting the sum of times for representing the unconnected external power supply in the predicted statistical data to obtain total power-free data;
calculating the difference value between the data of the total amount of the external electricity and the data of the total amount of the non-external electricity;
and when the difference value is a natural number, obtaining that the predicted charging mode is an external electric mode.
According to some embodiments of the first aspect of the present application, after calculating the difference between the total amount of power available and the total amount of power unavailable, further comprising:
when the difference is negative, the predicted charging mode is the no-external-power mode.
According to some embodiments of the first aspect of the present application, setting a charging parameter of the battery according to the predicted charging mode comprises:
setting a first full charge voltage and a first re-charge voltage when the predicted charge mode is the external power mode;
setting a second full charge voltage and a second re-charge voltage when the predicted charging mode is the no-external-power mode;
the first full-charge voltage is less than the second full-charge voltage, and the first recharging voltage is less than the second recharging voltage.
According to some embodiments of the first aspect of the present application, after analyzing the predicted statistical data according to a preset analysis policy to obtain a predicted charging mode and switching the current operating mode to the predicted charging mode, and setting the charging parameter of the battery according to the predicted charging mode, the method further includes:
and re-extracting the charging sample data every time the preset trigger time is reached to update the predicted charging mode, switching the updated predicted charging mode into the current working mode, and setting the charging parameters of the battery according to the updated predicted charging mode.
An embodiment of a second aspect of the present application provides a battery charging management system, including:
the sampling module is used for acquiring the battery use data of the battery at regular time according to the preset frequency;
the extraction module is used for extracting data in a sample date from the battery use data by taking the time corresponding to the preset number of days before the current date as the sample date to obtain charging sample data;
the statistical module is used for extracting data corresponding to the sample time in the sample date from the charging sample data by taking the preset predicted time after the current moment as the sample time to obtain predicted statistical data;
and the prediction module is used for analyzing the prediction statistical data according to a preset analysis strategy to obtain a prediction charging mode, switching the current working mode into the prediction charging mode, and setting the charging parameters of the battery according to the prediction charging mode.
According to the above embodiments of the present application, at least the following advantages are provided: the method comprises the steps of obtaining battery use data of a battery at regular time through preset frequency, extracting historical battery use data according to current date and current time, obtaining predicted statistical data used for correlating time to carry out predictive analysis, analyzing the predicted statistical data according to a preset analysis strategy to obtain a predicted charging mode at corresponding time, and obtaining historical battery use data corresponding to different time periods to indirectly reflect application scenes corresponding to the battery at the time.
An embodiment of a third aspect of the present application provides an electronic device, including:
at least one memory;
at least one processor;
at least one computer program;
the computer program is stored in the memory, and the processor executes the at least one computer program to implement: the battery charge management method of any one of the first aspects of the present application.
The computer storage medium according to an embodiment of the third aspect may perform the battery charge management method according to any one of the first aspect, thereby having all the advantages of the first aspect of the present application.
According to a fourth aspect of the present application, a computer storage medium is provided, which stores computer-executable instructions for performing the battery charging management method of any one of the first aspect.
The computer storage medium of the embodiment of the fourth aspect can execute the battery charge management method of any one of the first aspect, so that all the advantages of the first aspect of the present application are achieved.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The above and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic diagram illustrating main steps of a battery charge management method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of steps before step S100 in FIG. 1;
FIG. 3 is a schematic diagram illustrating the detailed steps of step S100 in FIG. 1;
FIG. 4 is a schematic diagram illustrating a specific step of step S120 in FIG. 2;
FIG. 5 is a schematic diagram of a portion of step S400 in FIG. 1;
fig. 6 is a schematic diagram of an actual work flow of a battery charging management method according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a battery charge management system according to an embodiment of the present application.
Detailed Description
In the description of the present application, unless otherwise expressly limited, terms such as set, mounted, connected and the like should be construed broadly, and those skilled in the art can reasonably determine the specific meaning of the terms in the present application by combining the detailed contents of the technical solutions. In the description of the present application, the meaning of a plurality is one or more, the meaning of a plurality is two or more, and larger, smaller, larger, etc. are understood as excluding the present number, and larger, smaller, inner, etc. are understood as including the present number. Furthermore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless otherwise specified.
Battery management is generally implemented by a battery management system, which is also called BMS, and the BMS is used to intelligently manage and maintain the rechargeable battery of an electronic system, prevent the battery from being overcharged and overdischarged, and achieve the effects of prolonging the service life of the battery and monitoring the state of the battery.
In the related technology, a battery management system acquires voltage, temperature and current information of a battery by adopting methods such as an electricity meter and AD signal acquisition, calculates the current electric quantity through a related algorithm, and controls a charging mode to realize charging management according to the current electric quantity detected by the electricity meter in the subsequent battery using process.
Based on the above, in order to enable the working mode of the battery to be adapted to the corresponding application scene, the battery charging management is designed and improved based on the BMS, the battery is managed by a set of relatively perfect charging management method, and the battery can be adapted to the corresponding application scene as much as possible.
The following describes a battery charging management method, system, device and storage medium with reference to fig. 1 to 7, which can extract historical usage data of a battery for predictive analysis, obtain a predictive charging mode at a corresponding time, and effectively improve the accuracy of battery charging management.
Referring to fig. 1, an embodiment of the first aspect of the present application provides a battery charge management method, including but not limited to the following steps:
s100: acquiring battery use data of a battery at regular time according to a preset frequency;
s200: taking time corresponding to preset days before the current date as a sample date, and extracting data in the sample date from the battery use data to obtain charging sample data;
s300: taking the preset prediction time after the current moment as sample time, and extracting data corresponding to the sample time in the sample date from the charging sample data to obtain prediction statistical data;
s400: and analyzing the predicted statistical data according to a preset analysis strategy to obtain a predicted charging mode, switching the current working mode into the predicted charging mode, and setting the charging parameters of the battery according to the predicted charging mode.
The method comprises the steps of acquiring battery use data of a battery at regular time through preset frequency, extracting historical battery use data according to current date and current moment, obtaining forecast statistical data used for correlating time to carry out forecast analysis, analyzing the forecast statistical data according to a preset analysis strategy to obtain a forecast charging mode under corresponding time, and obtaining historical battery use data corresponding to different time periods to indirectly reflect an application scene corresponding to the battery at the time.
According to the battery charging management method provided by the embodiment of the application, the predicted charging mode is determined according to the battery use data in the same time period in a period of time, the current working mode of the battery is switched according to the predicted charging mode, the battery charging management method can be matched with the use habits of users, the matching degree between the working mode of the battery and an application scene can be improved, different predicted charging modes can be adapted through different scenes, and the accuracy and the reliability of battery charging management can be effectively improved.
It will be appreciated that the predictive charging mode is used to control the state of charge of the battery by adjusting the charging parameters of the battery.
It is understood that, referring to fig. 2, step S100, before the battery usage data of the battery is obtained according to the preset frequency timing, further includes, but is not limited to, the following steps:
s010: abstracting out an interface for acquiring battery usage data.
It should be noted that, since the BMS on the market lacks a unified charging management channel, management and control of the battery are inconvenient. The battery use data of the battery can be directly acquired by abstracting a corresponding interface mode and matching a corresponding chip or device to write a corresponding program. The preset analysis strategy can realize corresponding analysis learning and carry out charging and discharging control through the abstracted interface, and can be adaptive to a BMS (battery management system) for charging management.
It is understood that, in step S010, the interface for acquiring the battery usage data is abstracted, including but not limited to the following steps: a battery status interface for obtaining battery status data, which is one of the battery usage data, is abstracted.
The battery state data comprises the voltage state, the charging state, the battery error state and the like of the current battery, the abstracted battery state interface is used for assisting in judging the charging state of the battery, the control parameters for predicting the charging mode are set according to the battery state data, and whether the battery in the state needs to be charged or not can be judged, so that reliable charging control is realized, the charging probability of the battery in a high-energy state can be improved, and the charging and discharging times of the battery can be reduced on the premise of ensuring the whole endurance.
It can be understood that, in step S010, abstracting the interface for acquiring the battery usage data, the following steps are included: an external power insertion state interface for acquiring external power connection data, which is one of battery use data, is abstracted.
The external power supply connection data of the battery are obtained through the abstracted external power plug-in state interface, the external power supply connection data are used for indicating whether the battery is connected with an external power supply for charging, the external power supply connection data are used for analyzing to obtain a prediction charging mode, and whether the battery is charged or not is controlled according to the prediction charging mode by combining the current battery state data and the battery charging and discharging data.
It can be understood that, in step S010, abstracting the interface for acquiring the battery usage data, the following steps are included: abstracting a charge-discharge interface for setting battery charge-discharge data, wherein the battery charge-discharge data is one of battery use data.
The charging and discharging data comprise a full-charging voltage, a recharging voltage, a highest and lowest temperature voltage threshold value supported by the battery, a highest and lowest voltage value supported by the battery and the like, an abstracted charging and discharging interface is used for assisting in judging the charging performance of the battery, a charging and discharging structure for predicting a charging mode is assisted to be set according to the charging and discharging data, the abstracted charging and discharging structure can control different devices and adjust corresponding charging and discharging parameters, the probability of overcharging of the battery can be effectively reduced, and the charging safety can be improved.
It can be understood that, referring to fig. 3, the step S100 of acquiring the battery usage data of the battery periodically according to the preset frequency includes, but is not limited to, the following steps:
s110: the method comprises the steps that charging counting data corresponding to a battery are obtained regularly according to a preset frequency, wherein the charging counting data are used for representing the counting of the battery connected with an external power supply and the counting of the battery not connected with the external power supply;
s120: and storing all charging counting data as battery use data every time a preset sampling time passes.
And obtaining and storing the corresponding counts of the battery when the battery is connected with an external power supply and when the battery is not connected with the external power supply according to the preset frequency, wherein the battery use data is used for subsequent analysis to obtain a predicted charging mode, and the battery use data is analyzed according to whether the battery is connected with the external power supply at the corresponding moment for charging.
It is understood that, referring to fig. 4, step S120, saving all the charge count data as the battery usage data every time a preset sampling time passes, includes but is not limited to the following steps:
step S121: and storing all the charging counting data every preset sampling time until the days corresponding to the stored charging counting data are more than or equal to the preset days, and obtaining the battery use data consisting of all the charging counting data.
That is, all the charging counting data are stored every preset sampling time until the number of days corresponding to the date span of the stored charging counting data is greater than or equal to the number of days corresponding to the preset number of days, and the battery use data composed of all the charging counting data are obtained. The method can ensure that the sample size of the battery use data meets the preset requirement, thereby effectively improving the reliability of the predicted charging mode obtained by final analysis based on the battery use data.
It can be understood that, in step S200, the time corresponding to the preset number of days before the current date is taken as the sample date, and the battery usage data in the sample date is extracted to obtain the charging sample data, specifically: and calculating the date within the preset number of days before the current date to obtain a sample date based on the current date and the preset number of days, and extracting data corresponding to the sample date from the battery use data to obtain charging sample data associated with the sample date.
It can be understood that, in step S300, the preset predicted time after the current time is used as a sample time, and the charging sample data corresponding to the sample time in the sample date is extracted to obtain the predicted statistical data, specifically: and calculating a time period within the preset prediction time after the current time to obtain sample time based on the current time and the preset prediction time, and extracting data corresponding to the sample time from the charging sample data to obtain prediction statistical data associated with the sample time.
It is understood that, referring to fig. 5, in step S400, the predicted statistical data is analyzed according to a preset analysis strategy to obtain a predicted charging mode, which includes but is not limited to the following steps:
s410: extracting the sum of times for representing the connection of an external power supply in the predicted statistical data to obtain data of the total amount of the power with external power;
s420: extracting the sum of times for representing the unconnected external power supply in the predicted statistical data to obtain total power-free data;
s430: calculating the difference value of the data of the total amount of external electricity and the data of the total amount of non-external electricity;
s440: and when the difference value is a natural number, obtaining that the predicted charging mode is an external electric mode.
It is understood that after calculating the difference between the total amount of power available and the total amount of power unavailable, the method further comprises:
s450: when the difference is negative, the predicted charging mode is an external power mode, wherein the external power mode can be also called a handheld mode and is used in a situation that the user is not required to charge the user in a handheld mode.
The following further describes steps S410 to S450: extracting the times for representing the connection with the external power supply in the predicted statistical data and summing to obtain the total number of the external power supplies, wherein the total number of the external power supplies represents the total charging times of the external power supply in the time period corresponding to the sample time in each day in the sample date; extracting the times for representing the unconnected external power supplies in the predicted statistical data and summing to obtain the total number of the external power supplies, wherein the total number of the external power supplies indicates the total charging times of the unconnected external power supplies in the time period corresponding to the sample time in each day in the sample date; subtracting the total power data without external power from the total power data with external power to obtain judgment data; when the judgment data is larger than or equal to zero, predicting that the charging mode is an external power mode, and controlling the battery to switch the working mode to the external power mode; and when the judgment data is less than zero, predicting that the charging mode is the power-off mode, and controlling the battery to switch the working mode to the power-off mode. The external power mode and the non-external power mode can control specific charging parameters according to the charging and discharging data and the battery state data of the battery so as to realize more accurate charging control, effectively enable the battery to realize charging in a high-energy state and effectively prolong the service life of the battery.
It is understood that in step S400, the charging parameters of the battery are set according to the predicted charging mode, including but not limited to the following steps:
setting a first full-charge voltage and a first re-charge voltage when the predicted charging mode is an external power mode;
setting a second full charge voltage and a second re-charge voltage when the predicted charging mode is the no-external-power mode;
the first full-charge voltage is less than the second full-charge voltage, and the first recharging voltage is less than the second recharging voltage.
It can be understood that, after the step S400 analyzes the predicted statistical data according to the preset analysis strategy to obtain the predicted charging mode, and switches the current operating mode to the predicted charging mode, and sets the charging parameters of the battery according to the predicted charging mode, the method further includes, but is not limited to, the following steps:
and re-extracting the charging sample data every time the preset trigger time is reached to update the predicted charging mode, switching the updated predicted charging mode into the current working mode of the battery, and setting the charging parameters of the battery according to the updated predicted charging mode.
Specifically, every time the integral point or the half integral point is reached, the fact that the charging sample data is extracted again is judged, a new prediction charging mode is obtained through further analysis, if the prediction charging mode is not changed, the original working mode is kept to set the charging parameters of the battery, otherwise, the charging parameters of the battery are set according to the new prediction charging mode, the timeliness of battery charging management can be effectively improved, and the probability of overcharge of the battery can be effectively reduced.
The following describes a battery charging management method according to an embodiment of the first aspect of the present application with reference to specific data:
abstracting a universal battery state interface and an external power plug-in state interface, and acquiring corresponding battery data for analysis and prediction;
acquiring charging counting data corresponding to the battery at regular time through an abstracted interface in a mode of recording once every 10 seconds, storing the data once every time the integral point or half the integral point is reached, if the number of days corresponding to the stored charging counting data is less than 7 days, continuously acquiring and storing the charging counting data until the number of days corresponding to the stored charging counting data is more than or equal to 7 days, and acquiring battery use data consisting of the charging counting data for at least 7 days;
calculating a corresponding sample date within 7 days before the current date, wherein the sample date corresponds to dates from day 1 to day 7 before the current date, extracting data corresponding to a date section where the sample date is located in the battery use data, and obtaining charging sample data which corresponds to battery use data from day 1 to day 7 before the current date, wherein the battery use data is the corresponding times of charging when the battery is connected with an external power supply;
calculating the corresponding sample time within 3 hours after the current time, wherein the sample time corresponds to the time within 3 hours from the current time, extracting data corresponding to the time period of the sample time of each day in the charging sample data, and obtaining the battery use data of which the predicted statistical data is the corresponding battery use data of each day within 7 days before and within 3 hours after the current time;
extracting the times for representing the connection of the external power supply in the predicted statistical data and summing to obtain the total number of external power supplies, wherein the total number of external power supplies represents the total charging times of the external power supply in the time period corresponding to the sample time in each day of the sample date, the times for representing the disconnection of the external power supply in the predicted statistical data and summing to obtain the total number of no-external-power-supply, the total number of no-external-power-supply represents the total charging times of the external power supply in the time period corresponding to the sample time in each day of the sample date, subtracting the total number of no-external-power-supply data from the total number of external power supplies to obtain judgment data, and when the judgment data is greater than or equal to zero, namely the sum of the times for connecting the external power supplies is greater than or equal to the sum of the times for disconnecting the external power supplies, predicting the charging mode is the external-power-supply mode for controlling the battery to switch to the external-power-supply mode, and when the judgment data is less than zero, namely the external-supply count is less than the external-supply count and the external-supply count is the external-supply count for controlling the external-supply to switch operation mode for controlling the external-supply to switch to the external-supply mode;
the charging parameter structure is abstracted, the charging parameter structure comprises full charging voltage, repeated charging voltage, highest temperature and lowest temperature voltage thresholds supported by the battery, highest and lowest voltage values supported by the battery and the like, different working modes correspond to different charging parameter structures, charging and discharging are controlled by setting corresponding charging parameters to an external power mode and an external power-free mode, charging actions under different devices can be supported, and charging and discharging can be reduced in a high-power state.
In the process of practical application, referring to fig. 6, fig. 6 is a flowchart of a battery charging management method in the process of practical application, and after the system is started, a previous learning mode is obtained from a database to set a corresponding charging parameter. If the external power mode is obtained, a charging parameter of 85% of the current full-charge battery capacity is set, the battery can be kept charged for a long time under the state that an external power supply is connected, so that the battery can be damaged due to overcharging, and the service life of the battery can be effectively prolonged on the premise of keeping a certain cruising ability by setting the full-charge voltage to be 85%, namely the full-charge capacity of the battery to be 85% of the rated capacity; if the database does not have the record of the working mode (namely the automatic mode) or the no-external-power mode (the handheld mode) is obtained, the charging parameter with the full charge voltage of 100 percent is set, namely the full charge capacity of the battery is 100 percent of the rated capacity, and charging and discharging are carried out according to the current battery state. In the automatic mode, battery use data of the battery are acquired regularly according to a preset frequency, data corresponding to relevant dates in the battery use data are extracted according to the current date to obtain charging sample data, data corresponding to relevant times in the charging sample data are extracted according to the current moment to obtain predicted statistical data, the predicted statistical data are analyzed according to a preset analysis strategy to obtain a predicted charging mode, the current working mode is switched to the predicted charging mode, and different charging parameters are set. Considering that the service life of the current battery is mainly determined by the times of complete charging and discharging, in order to prolong the service life of the battery, in order to avoid repeated charging under the condition of not pulling out an external power supply, a recharging voltage is set to control the charging times of the battery, the battery is charged only when the voltage is lower than the recharging voltage, the charging times can be effectively reduced under the high-power state, the full-charge voltage of an external power mode is 85 percent and 75 percent, the full-charge voltage of an external power mode is 100 percent and the recharging voltage is 90 percent.
According to the battery charging management method, the corresponding interfaces are abstracted, the corresponding interfaces can be adapted to different chips and devices, the corresponding predicted charging modes are obtained by analyzing and predicting historical use data of the battery, the current charging mode of the battery can be controlled to be adapted to the application scenes obtained by prediction, the adaptability is high, corresponding full charge and recharging voltages are set for different control modes, the cruising ability of the device can be protected within a certain range, and the service life of the battery is prolonged.
Referring to fig. 7, an embodiment of the second aspect of the present application provides a battery charging management system, including:
the sampling module 510 is configured to periodically obtain battery usage data of a battery according to a preset frequency;
the extracting module 520 is configured to extract data within a sample date from the battery usage data by using time corresponding to a preset number of days before the current date as the sample date to obtain charging sample data;
the statistical module 530 is configured to extract data corresponding to the sample time in the sample date from the charging sample data by using a preset predicted time after the current time as the sample time, so as to obtain predicted statistical data;
and the prediction module 540 analyzes the predicted statistical data according to a preset analysis strategy to obtain a predicted charging mode, switches the current working mode into the predicted charging mode, and sets the charging parameters of the battery according to the predicted charging mode.
The method comprises the steps of acquiring battery use data of a battery at regular time through preset frequency, extracting historical battery use data according to current date and current moment, obtaining forecast statistical data used for correlating time to carry out forecast analysis, analyzing the forecast statistical data according to a preset analysis strategy to obtain a forecast charging mode under corresponding time, and obtaining historical battery use data corresponding to different time periods to indirectly reflect an application scene corresponding to the battery at the time.
An embodiment of the third aspect of the present application further provides an electronic device, including:
at least one memory;
at least one processor;
at least one program;
programs are stored in the memory and the processor executes at least one of the programs to implement the present disclosure to implement the battery charge management methods described above. The electronic device may be any intelligent terminal including a mobile phone, a tablet computer, a Personal Digital Assistant (PDA), a vehicle-mounted computer, and the like.
Furthermore, a fourth aspect of the present application provides a computer-readable storage medium storing computer-executable instructions, which are executed by a processor or a controller, for example, by a processor in the above-mentioned device embodiment, and can make the above-mentioned processor execute the battery charging management method in the above-mentioned embodiment, for example, execute the above-mentioned method steps S100 to S400, S010, S110 to S120, S121, and S410 to S450.
It will be understood by those of ordinary skill in the art that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, or suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
In the description herein, reference to the description of the terms "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the application, the scope of which is defined by the claims and their equivalents.
Claims (10)
1. A battery charge management method, comprising:
acquiring battery use data of a battery at regular time according to a preset frequency;
taking time corresponding to preset days before the current date as a sample date, and extracting data in the sample date from the battery use data to obtain charging sample data;
taking preset prediction time after the current moment as sample time, and extracting data corresponding to the sample time in the sample date from the charging sample data to obtain prediction statistical data;
and analyzing the predicted statistical data according to a preset analysis strategy to obtain a predicted charging mode, switching the current working mode into the predicted charging mode, and setting the charging parameters of the battery according to the predicted charging mode.
2. The battery charge management method according to claim 1, wherein the periodically acquiring battery usage data of the battery according to the preset frequency comprises:
the method comprises the steps that charging counting data corresponding to a battery are obtained regularly according to a preset frequency, wherein the charging counting data are used for representing the counting of the battery connected with an external power supply and the counting of the battery not connected with the external power supply;
and storing all the charging counting data as the battery use data every time a preset sampling time passes.
3. The method for battery charge management according to claim 2, wherein said saving all of said charge count data as said battery usage data every time a preset sampling time elapses comprises:
and storing all the charging counting data every preset sampling time until the number of days corresponding to the stored charging counting data is greater than or equal to the preset number of days, and obtaining the battery use data consisting of all the charging counting data.
4. The method of claim 1, wherein the analyzing the predicted statistical data according to a predetermined analysis strategy to obtain a predicted charging mode comprises:
extracting the sum of times for representing the connection of an external power supply in the predicted statistical data to obtain data of the total amount of the power with external power;
extracting the sum of times for representing the unconnected external power supply in the predicted statistical data to obtain total power-free data;
calculating the difference value of the total electric quantity data with the external power and the total electric quantity data without the external power;
and when the difference is a natural number, obtaining that the predicted charging mode is an external power mode.
5. The battery charge management method according to claim 4, further comprising, after said calculating the difference between said total power-on data and said total power-off data:
and when the difference is negative, obtaining that the predicted charging mode is the no-external-power mode.
6. The battery charge management method of claim 5, wherein said setting the charge parameters of the battery according to the predicted charge mode comprises:
setting a first full charge voltage and a first re-charge voltage when the predicted charging mode is the powered mode;
setting a second full charge voltage and a second recharging voltage when the predicted charging mode is the no-power mode;
wherein the first full charge voltage is less than the second full charge voltage, and the first re-charge voltage is less than the second re-charge voltage.
7. The battery charging management method according to any one of claims 1 to 6, wherein after analyzing the predicted statistical data according to a preset analysis strategy to obtain a predicted charging mode, switching a current operating mode to the predicted charging mode, and setting charging parameters of the battery according to the predicted charging mode, the method further comprises:
and re-extracting the charging sample data every time a preset trigger time is reached so as to update the predicted charging mode, switching the updated predicted charging mode into the current working mode, and setting the charging parameters of the battery according to the updated predicted charging mode.
8. A battery charge management system, comprising:
the sampling module is used for acquiring the battery use data of the battery regularly according to a preset frequency;
the extraction module is used for extracting data in a sample date from the battery use data by taking time corresponding to preset days before the current date as the sample date to obtain charging sample data;
the statistical module is used for taking preset predicted time after the current moment as sample time, and extracting data corresponding to the sample time in the sample date from the charging sample data to obtain predicted statistical data;
and the prediction module is used for analyzing the prediction statistical data according to a preset analysis strategy to obtain a prediction charging mode, switching the current working mode into the prediction charging mode, and setting the charging parameters of the battery according to the prediction charging mode.
9. An electronic device, comprising:
at least one memory;
at least one processor;
at least one computer program;
the computer programs are stored in the memory, and the processor executes the at least one computer program to implement:
the battery charge management method according to any one of claims 1 to 7.
10. A computer storage medium having stored thereon computer-executable instructions for performing the battery charge management method of any of claims 1 to 7.
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