Battery management method and device, electronic equipment and storage medium

Abstract

The invention relates to a battery management method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring historical use information corresponding to the number of cycles of a battery; determining a current endurance attenuation parameter of the battery based on the historical use information; and on the basis of the current endurance attenuation parameter, predicting the usable duration of the battery before reaching a preset aging state. According to the method, the current endurance attenuation parameter of the battery is determined based on historical user battery use habits, so that the result of predicting the service life of the battery is more accurate, and the actual use conditions of different users are better met.

Description

Technical Field

[0001] The present disclosure relates to the field of batteries, and in particular, to a battery management method, apparatus, electronic device, and storage medium. Background Art

[0002] A lithium-ion battery is an electrochemical system that gradually ages with the degree of use. Its aging behavior macroscopically manifests as the deterioration of battery performance, such as: reduction of battery capacity, increase of battery internal resistance, etc.; microscopically, it manifests as the growth or destruction of the negative electrode SEI film, lithium deposition on the negative electrode surface, side reactions of the electrolyte, etc. The aging of the battery of an electronic device will greatly affect the user experience of the electronic device. Therefore, accurately predicting the remaining service life of a lithium-ion battery can not only ensure the safe and reliable operation of the electronic device, but also timely monitor the remaining service life and after-sales tendency of the batteries of various electronic devices in the market.

[0003] Currently, commonly used battery life prediction methods often adopt a mechanism model, that is, using a battery pack fuel gauge to directly simulate the reaction mechanism inside the battery based on the open-circuit voltage method to obtain the remaining service life of the battery. On the one hand, this method requires more hardware and complex algorithms to support, resulting in higher costs. On the other hand, due to the large influence of user usage habits on battery aging, it is difficult for the mechanism model to simulate the dynamically changing user usage habits during the actual use of the battery. Therefore, there is a certain gap between the simulation results and the actual situation. Summary of the Invention

[0004] The present disclosure provides a battery management method, apparatus, electronic device, and storage medium to overcome the problems of high cost, complex method, and inconsistent prediction results with actual results in predicting the remaining service life of the battery.

[0005] According to the first aspect of the embodiments of the present disclosure, a battery management method is provided, including:

[0006] Obtaining historical usage information corresponding to each cycle number of the battery;

[0007] Based on the historical usage information, determining the current battery life attenuation parameter of the battery;

[0008] Based on the current battery life attenuation parameter, predicting the available duration of the battery before reaching a preset aging state.

[0009] In some embodiments, the historical usage information includes: the consumption duration corresponding to each cycle number of the battery and the consumption time corresponding to each cycle number of the battery;

[0010] The determining the current battery life attenuation parameter of the battery based on the historical usage information includes:

[0011] Determine at least two target consumption times before the current moment, and target consumption durations corresponding to the at least two target consumption times;

[0012] Based on each of the target consumption times and the corresponding target consumption durations, determine the current battery life decay parameter.

[0013] In some embodiments, the determining the current battery life decay parameter based on each of the target consumption times and the corresponding target consumption durations includes:

[0014] Determine a first target consumption time closest to the current moment, a second target consumption time among all the target consumption times other than the first target consumption time, a first target consumption duration corresponding to the first target consumption time, and a second consumption duration corresponding to the second target consumption time;

[0015] Based on the first target consumption time, the first target consumption duration, the second target consumption time, and the second target consumption duration, determine the current battery life decay parameter.

[0016] In some embodiments, the battery management method further includes:

[0017] The determining the current battery life decay parameter based on the first target consumption time, the first target consumption duration, the second target depletion time, and the second target consumption duration includes:

[0018] Determine a first duration difference between the first target consumption duration and each of the second target consumption durations, and a first interval duration between the first target consumption time and each of the second target consumption times;

[0019] Based on each of the first duration differences and each of the first interval durations, determine the current battery life decay parameter.

[0020] In some embodiments, the determining the current battery life decay parameter based on each of the first duration differences and each of the first interval durations includes:

[0021] Respectively determine the ratios between each of the first duration differences and the corresponding first interval durations;

[0022] Based on a preset weight coefficient, perform weighted processing on each of the ratios to obtain the current battery life decay parameter.

[0023] In some embodiments, predicting the available duration of the battery before reaching a preset aging state based on the current battery life decay parameter includes:

[0024] Obtain the available number of consumption times corresponding to the preset aging state;

[0025] Based on the current battery life attenuation parameter, the available number of consumption times, and the expected battery life attenuation parameter, determine the available usage duration.

[0026] In some embodiments, the battery management method further includes:

[0027] Obtain the used duration of the battery up to the current moment;

[0028] In the case where the difference between the available usage duration and the used duration is less than a preset duration, output a warning message.

[0029] In some embodiments, obtaining the historical usage information corresponding to each cycle of the battery includes:

[0030] In the case of reaching the prediction time node corresponding to the prediction period, obtain the historical usage information within the prediction period before the prediction time node from the cloud;

[0031] Wherein, the historical usage information includes: the information collected by the electronic device of the battery for each cycle.

[0032] In some embodiments, the battery management method further includes:

[0033] According to the predicted available usage duration at the prediction time node corresponding to the current prediction period, adjust the prediction period to determine the prediction time node corresponding to the next prediction period.

[0034] According to a second aspect of the embodiments of the present disclosure, there is provided a battery management device, including:

[0035] An acquisition module configured to acquire historical usage information corresponding to each cycle of the battery;

[0036] A determination module configured to determine the current battery life attenuation parameter based on the historical usage information;

[0037] A prediction module configured to predict the available usage duration of the battery before reaching the preset aging state based on the current battery life attenuation parameter.

[0038] In some embodiments, the determination module is further configured to:

[0039] Determine at least two target consumption times before the current moment, and the target consumption durations corresponding to the at least two target consumption times;

[0040] Based on each of the target consumption times and the corresponding target consumption durations, determine the current battery life attenuation parameter.

[0041] In some embodiments, the determining module is further configured to:

[0042] Determine a first target consumption time closest to the current moment, a second target consumption time other than the first target consumption time among the respective target consumption times, a first target consumption duration corresponding to the first target consumption time, and a second consumption duration corresponding to the second target consumption time;

[0043] Based on the first target consumption time, the first target consumption duration, the second target consumption time, and the second target consumption duration, determine the current battery life decay parameter.

[0044] In some embodiments, the determining module is further configured to:

[0045] Determine a first duration difference between the first target consumption duration and the respective second target consumption durations, and a first interval duration between the first target consumption time and the respective second target consumption times;

[0046] Based on the respective first duration differences and the respective first interval durations, determine the current battery life decay parameter.

[0047] In some embodiments, the determining module is further configured to:

[0048] Respectively determine the ratios between the respective first duration differences and the corresponding first interval durations;

[0049] Based on a preset weight coefficient, perform a weighted process on the respective ratios to obtain the current battery life decay parameter.

[0050] In some embodiments, the prediction module is further configured to:

[0051] Obtain the available number of consumption times corresponding to the preset aging state;

[0052] Based on the current battery life decay parameter, the available number of consumption times, and the expected battery life decay parameter, determine the available usage duration.

[0053] In some embodiments, the prediction module is further configured to:

[0054] Obtain the used duration of the battery up to the current moment;

[0055] In a case where the difference between the available usage duration and the used duration is less than a preset duration, output a warning message.

[0056] In some embodiments, the obtaining module is further configured to:

[0057] When reaching the prediction time node corresponding to the prediction period, obtain the historical usage information within the prediction period before the prediction time node from the cloud;

[0058] Among them, the historical usage information includes: the information collected for each cycle of the electronic device of the battery.

[0059] In some embodiments, the prediction module is further configured to:

[0060] Adjust the prediction period according to the predicted available duration corresponding to the prediction time node of the current prediction period, and determine the prediction time node corresponding to the next prediction period.

[0061] According to the third aspect of the embodiments of the present disclosure, an electronic device is provided, including:

[0062] A processor;

[0063] A memory configured to store executable instructions of the processor;

[0064] Among them, the processor is configured to be able to execute the battery management method described in the first aspect above when calling the executable instructions in the memory.

[0065] According to the fourth aspect of the embodiments of the present disclosure, a non-transitory computer-readable storage medium is provided. When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device can execute the battery management method described in the first aspect above.

[0066] The technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects:

[0067] In the battery management method of the present disclosure, historical usage information corresponding to each cycle of the battery is obtained; based on the historical usage information, the current battery life attenuation parameter of the battery is determined; based on the current battery life attenuation parameter, the available duration of the battery before reaching the preset aging state is predicted. Since the historical usage information obtained in the present disclosure is the historical usage information corresponding to each cycle, and the historical usage information covers the dynamically changing user battery usage habits, the current battery life attenuation parameter of the battery is determined based on the historical user battery usage habits, and the predicted result of the battery life is more accurate and more in line with the actual usage conditions of different users. At the same time, this method does not require additional hardware for measurement and simulation, has low cost, and the method is simple.

[0068] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure.

[0070] Figure 1 is a schematic flowchart of a battery management method shown according to an exemplary embodiment Figure 1 .

[0071] Figure 2 is a schematic flowchart of a battery management method shown according to an exemplary embodiment Figure 2 .

[0072] Figure 3 is a schematic flowchart of a battery management method shown according to an exemplary embodiment Figure 3 .

[0073] Figure 4 is a schematic flowchart of a battery management method shown according to an exemplary embodiment Figure 4 .

[0074] Figure 5 is a schematic flowchart of a battery management method shown according to an exemplary embodiment Figure 5 .

[0075] Figure 6 is a schematic structural diagram of a battery management device shown according to an exemplary embodiment.

[0076] Figure 7 is a schematic structural diagram of an electronic device shown according to an exemplary embodiment. Detailed Description of the Embodiments

[0077] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0078] In the specification, unless otherwise clearly stated, the terms "first" and "second" are only used for description to distinguish constituent elements and should not be construed as indicating an order. Unless otherwise clearly stated, terms such as "connected" and "fixed" should be understood in a broad sense, including but not limited to "connected" and "fixed" directly, indirectly, detachably, etc.

[0079] Figure 1 is the flowchart of a battery management method shown according to an exemplary embodiment Figure 1 , as Figure 1As shown in the figure, the method mainly includes the following steps:

[0080] In step 101, historical usage information corresponding to each cycle number of the battery is obtained.

[0081] In this embodiment, the cycle number is recorded as one cycle number each time the battery accumulatively consumes a preset capacity. The preset capacity can be the rated capacity (the nominal capacity of the battery), or the typical capacity (the actual capacity of the battery), or a capacity value set by itself according to the environment in which the electronic device using the battery is used, the population using it, the type of the battery, the process of the battery, etc. After the preset capacity is determined, historical usage information corresponding to each cycle number of the battery is obtained. The historical usage information may include: the consumption duration corresponding to each cycle number, the consumption time corresponding to each cycle number, the number of charging times corresponding to each consumption duration, the charging time corresponding to each charge, and so on. These information can be obtained and stored in real time or periodically from the time when the electronic device is activated to the current time.

[0082] Taking the preset capacity of 1000 mAh as an example, the electronic device is activated at time T0, and the battery starts to be used from the full capacity. At time T1, the battery has consumed 200 mAh of capacity. At time T2, the battery has consumed 500 mAh of capacity. At time T3, the user charges the battery for 20 minutes and then it reaches the full capacity. At time T4, the battery has consumed 300 mAh of capacity. Then at this time, the battery has accumulatively consumed a preset capacity of 1000 mAh, that is, it has reached one cycle number. At this time, historical usage information is obtained and stored. For example: the consumption duration corresponding to each cycle number is (T4 - T0), the consumption time corresponding to each cycle number is time T4, the number of charging times during the consumption duration of (T4 - T0) is 1 time, and the charging time is 20 minutes.

[0083] It can be understood that different users have different usage habits for the battery, and the usage habits of the same user in different time periods are not fixed either. Some users may use the battery from 100% power (full capacity) to 0% power and then charge and use it again. Some users may use the battery from 100% power (full capacity) to 20% power, then charge it to 90% power and continue to use it. Therefore, the battery consumption reaching the preset capacity, that is, the battery reaching one cycle number is not achieved in one discharge process, but may be achieved through multiple discharge processes accumulatively. What is obtained in this disclosure is the historical usage information during the period when the preset capacity is accumulatively reached.

[0084] In step 102, based on the historical usage information, the current battery endurance attenuation parameter is determined.

[0085] In this embodiment, the historical usage information implies the user's battery usage habits, and the current battery life attenuation parameter is determined based on the user's battery usage habits. Since the user's battery usage habits are dynamically changing, that is, the specific parameter values of the historical usage information are also dynamically changing, the current battery life attenuation parameter is also constantly changing. It represents the current battery life ability when using the electronic device under the user's battery usage habit state represented by the historical usage information.

[0086] It can be understood that the battery life of a fresh battery (a battery that has not been used) is the strongest. As the battery is continuously used, the battery life will continuously attenuate, and the attenuation speed and degree are determined according to the user's battery usage habits. Therefore, the battery life attenuation parameter represents the change in the battery life during the use process.

[0087] In step 103, based on the current battery life attenuation parameter, predict the available usage duration of the battery before reaching the preset aging state.

[0088] In this embodiment, the current battery life attenuation parameter represents the current battery life ability of the battery. Under the same battery usage habit, if the battery life is strong, the number of battery charges is less, the charging time for each time is short, the power consumption speed is slow, and the battery is healthy; if the battery life is weak, the number of battery charges is more, the charging time for each time is long, the power consumption speed is fast, and the battery is aging. Therefore, based on the current battery life attenuation parameter, it is possible to predict the available usage duration of the battery before reaching the preset aging state based on the current battery usage habit.

[0089] That is to say, the present disclosure takes the user's battery usage habits, that is, the current battery life attenuation parameter, as the aging factor of the battery. As the user uses the battery, the aging factor also changes accordingly. In this way, a dynamic mapping relationship between the battery aging factor and the available usage duration is established to achieve accurate prediction of the battery life.

[0090] In some embodiments, the historical usage information includes: the consumption duration corresponding to each cycle of the battery and the consumption moment corresponding to each cycle of the battery. As Figure 2 shown, step 102 includes:

[0091] In step 1021, determine at least two target consumption moments before the current moment, and the target consumption durations corresponding to the at least two target consumption moments.

[0092] In step 1022, based on each target consumption moment and the corresponding target consumption duration, determine the current battery life attenuation parameter.

[0093] In this embodiment, the historical usage information includes all the consumption durations and consumption times from the activation of the electronic device to the current moment. The consumption time and the consumption duration have a one-to-one correspondence relationship. When predicting the remaining usage duration of the battery, first determine the current battery life attenuation parameter at the current moment. It should be noted that the current moment may be the target consumption time (i.e., at the current moment, the battery has just accumulated consumption reaching the preset capacity), or it may not be the target consumption time. Then, if the current moment is the target consumption time, when determining at least two target consumption times before the current moment, the current moment is taken into account; if the current moment is not the target consumption time, when determining at least two target consumption times before the current moment, the current moment is not considered.

[0094] In addition, when determining the current battery life attenuation parameter, it can be determined based on the two target consumption times and target consumption durations closest to the current moment; it can also be determined based on more than two target consumption times and more than two target consumption durations within a period of time closest to the current moment, or it can be determined based on all the target consumption times and all the target consumption durations before the current moment.

[0095] Specifically, when determining the current battery life attenuation parameter based on more than two target consumption times and more than two target consumption durations, it is possible to calculate the multiple battery life attenuation parameters corresponding to each time period according to the divided time periods and then obtain the final current attenuation parameter by taking the average, or it is possible to determine the weight coefficient of each time period based on the user's frequency and duration of using the electronic device in each time period, and perform weighted averaging on the battery life attenuation parameters corresponding to each time period based on the weight coefficient.

[0096] It can be understood that if the user's battery usage habit is relatively stable within a period of time, an accurate current battery life attenuation parameter can be determined by using a smaller number of parameters (target consumption time and target consumption duration). If the user's battery usage habit changes frequently and varies greatly in a short period of time, a larger number of parameters (target consumption time and target consumption duration) are required to accurately express the current battery life of the battery.

[0097] In some embodiments, step 1022 includes: determining the first target consumption time closest to the current moment and the second target consumption time other than the first target consumption time, as well as the first target consumption duration corresponding to the first target consumption time and the second consumption duration corresponding to the second target consumption time; determining the current battery life attenuation parameter based on the first target consumption time, the first target consumption duration, the second target consumption time, and the second target consumption duration.

[0098] In this embodiment, all target consumption times are divided into two categories: the first target consumption time and the second target consumption time. The first target consumption time is the target consumption time closest to the current time (which may also be the current time), and the second target consumption time is the target consumption time other than the first target consumption time. Correspondingly, the target consumption durations are also divided into two categories: the first target consumption duration and the second target consumption duration. Based on the first target consumption time, the second target consumption time, the first target consumption duration, and the second target consumption duration, the current endurance decay parameter can be determined.

[0099] Specifically, in the process of calculating the current endurance decay parameter, determine the first duration difference between the first target consumption duration and each second target consumption duration, and the first interval duration between the first target consumption time and each second target consumption time; based on each first duration difference and each first interval duration, determine the current endurance decay parameter.

[0100] In this embodiment, based on the ratio of each first duration difference and the corresponding first interval duration, the current endurance decay parameter can be determined. It can be understood that based on the target consumption duration and the target consumption time, a decay curve graph of the target consumption duration with respect to the target consumption time can be established, and the current endurance decay parameter is the slope of this curve graph. The slope is different in different time periods, and the endurance decay parameter is different.

[0101] Specifically, determining the current endurance decay parameter based on each first duration difference and each of the first interval durations includes: respectively determining the ratio between each first duration difference and the corresponding first interval duration; based on a preset weight coefficient, performing a weighted process on each ratio to obtain the current endurance decay parameter.

[0102] In this embodiment, if the number of second target consumption times is one, then the number of second target consumption durations is also one. At this time, one first duration difference and one first interval duration are calculated, and the weight coefficient of the ratio of the first duration difference and the first interval duration is 1, and this ratio is the current endurance decay parameter. Then the calculation formula of the endurance decay parameter is expressed as: k_0 = (DOU_1 - DOU_2) / (Month_1 - Month_2). Wherein, k_0 is the current endurance decay parameter, DOU_1 is the first consumption duration, DOU_2 is the second consumption duration, Month_1 is the first consumption time, and Month_2 is the second consumption time. Specifically, DOU_1 and DOU_1 can be in the unit of "1 day" or in the unit of "1 hour"; Month_1 and Month_2 can be in the unit of "1 month", or in the unit of "1 day" or "1 hour". The specific values of these parameters can be decimals.

[0103] If the number of second target consumption moments is multiple, the number of second target consumption durations is also multiple. Correspondingly, there are multiple ratios of the first duration difference and the first interval duration calculated. Then, the weight coefficient can be determined based on the distance from the current moment. The closer the second target consumption moment is to the current moment, the higher the weight coefficient of the corresponding ratio. The farther the second target consumption moment is from the current moment, the lower the weight coefficient of the corresponding ratio. The sum of all weight coefficients is 1.

[0104] Taking the number of second target consumption moments as 3 (Month_2, Month_3, Month_4) as an example, the calculation formula of the battery life attenuation parameter is expressed as: k_0 = [β1(DOU_1 - DOU_2) / (Month_1 - Month_2)] + [β2(DOU_1 - DOU_3) / (Month_1 - Month_3)] + [β3(DOU_1 - DOU_4) / (Month_1 - Month_4)], where β1, β2, and β3 are the corresponding weight coefficients.

[0105] In one embodiment, as Figure 3 shown, in step 103, based on the current battery life attenuation parameter, predicting the available usage duration of the battery before reaching the preset aging state includes:

[0106] In step 1031, obtain the available consumption times corresponding to the preset aging state.

[0107] In step 1032, determine the available usage duration based on the current battery life attenuation parameter, the available consumption times, and the expected battery life attenuation parameter.

[0108] In this embodiment, different batteries (such as different battery types and different battery manufacturing processes) correspond to different preset aging states, and the preset aging state can be represented by the available consumption times. That is to say, for each cycle of the battery, the consumption times increase by one. When the consumption times increase to the available consumption times, the battery enters the preset aging state. Therefore, based on the current battery life attenuation parameter (k_0), the available consumption times (Cyclecount_alert), and the expected battery life attenuation parameter (DOU_0), the available usage duration (Month_alert) can be determined. The specific calculation formula is:

[0109] Month_alert = (DOU_0 × Cyclecount) / (30 - Cyclecount × k_0)

[0110] Among them, when calculating the available usage duration, the consumption times (Cyclecount) takes the value of the available consumption times (Cyclecount_alert).

[0111] It should be noted that the above formula represents the attenuation model of the usable time with the number of consumption times, which can be obtained based on the definition of consumption time and the attenuation model of consumption time with the battery life attenuation parameter.

[0112] The definition formula of the consumption time is expressed as:

[0113] DOU=30*Month / Cyclecount

[0114] Month is the usable time (in this case, "1 month" is the unit), and Cyclecount is the number of times it is consumed.

[0115] The attenuation model formula of the consumption time and the endurance attenuation parameter is expressed as:

[0116] DOU=k_0*Month+DOU0

[0117] Among them, DOU is the consumption time, k_0 is the current endurance attenuation parameter, Month is the usable time, and DOU_0 is the expected endurance attenuation parameter, that is, the endurance attenuation parameter of a fresh battery.

[0118] Combining the above two formulas, we can get the decay model of the usable time with the number of consumption times.

[0119] In some embodiments, the battery management method further includes: obtaining the battery usage time up to the current moment; and outputting a warning message when the difference between the usable time and the used time is less than a preset time.

[0120] In this embodiment, when the difference between the usable time and the used time is less than the preset time, it is determined that the battery is about to enter the aging state, and an early warning message is output to prompt the user to replace the battery, and the early warning message is sent to the after-sales system for reference by after-sales personnel. When the difference between the usable time and the used time is greater than the preset time, the next prediction is continued until the early warning state appears.

[0121] In some embodiments, in step 101, obtaining historical usage information corresponding to each cycle of the battery includes: when the prediction time node corresponding to the prediction cycle is reached, obtaining historical usage information within the prediction cycle before the prediction time node from the cloud; wherein the historical usage information includes: information collected by the electronic device of the battery for each cycle.

[0122] In this embodiment, every time the battery reaches a cycle count, the corresponding historical usage information is collected and stored in the cloud to save the storage space of the electronic device. When the predicted time node corresponding to the prediction period is reached, the historical usage information within the prediction period before the predicted time node is obtained from the cloud, and the remaining usable duration of the battery is predicted based on the historical usage information within this prediction period. For example, if the prediction period is 30 days, then on the 30th day, the historical usage information between the 1st day and the 30th day is obtained and predicted; on the 60th day, the historical usage information between the 31st day and the 60th day is obtained and predicted.

[0123] In one embodiment, the battery management method further includes: adjusting the prediction period according to the remaining usable duration predicted at the predicted time node corresponding to the current prediction period, and determining the predicted time node corresponding to the next prediction period.

[0124] In this embodiment, the prediction period is not fixed and can be adjusted according to the prediction result of each time. Specifically, when the difference between the remaining usable duration and the used duration is much greater than the preset duration, that is, the battery is currently relatively healthy and far from entering the aging state, the prediction period can be increased, the prediction frequency can be reduced, and the consumption of computing resources can be reduced. When the difference between the remaining usable duration and the used duration is less than the preset duration, that is, the battery is currently about to enter the aging state, the prediction period can be shortened, the prediction frequency can be increased, and the prediction accuracy can be improved.

[0125] In addition, when it is determined that the difference between the remaining usable duration and the used duration is less than the preset duration, a more stringent battery management strategy can be adopted for the battery. For example, the charging speed and discharging speed are controlled according to the current battery power. When the battery is in the charging process, the use of applications with power consumption greater than the preset power consumption threshold is restricted, and the discharge cut-off voltage and full charge voltage are controlled, etc. When it is determined that the difference between the remaining usable duration and the used duration is greater than the preset duration, a more relaxed battery management strategy can be adopted. For example, the maximum capacity of the battery is increased, and the fast charging mode is preferentially used during charging, etc.

[0126] As Figure 4 shown, it is a schematic flowchart of performing the battery management method on the battery in the mobile phone Figure 4 and its specific process includes:

[0127] In step 401, the mobile phone device is activated and the used duration Month _ 0 is started to be recorded;

[0128] In step 402, the cloud stores the consumed duration DOU and the consumption time Month _ i corresponding to each cycle count, where i = 1, 2, 3... n;

[0129] In step 403, when the used duration Month _ 0 is an integer, and for each +1, retrieve cloud data;

[0130] In step 404, calculate the battery life attenuation parameter k _ at node Month _ 0;

[0131] In step 405, substitute k_0 into the life prediction model (i.e., the attenuation model of available usage duration with the number of consumption times);

[0132] In step 406, calculate the available usage duration Month_alert of the aging state when the number of consumption times cyclecount is the available number of consumption times Cyclecount _ alert;

[0133] In step 407, calculate the difference Month between the available usage duration Month_alert and the used duration _ 0, and determine whether the difference is less than or equal to 1 (1 month);

[0134] If it is less than or equal to 1, execute steps 408 and 409. If it is greater than 1, execute step 403;

[0135] In step 408, report to the after-sales system to prompt a warning;

[0136] In step 409, push a user suggestion to go to the service point to replace the battery.

[0137] As Figure 5 shown, it is a flowchart showing the process of executing the battery management method for the battery in the mobile phone Figure 5 , and its process specifically includes:

[0138] In step 501, the mobile phone is activated, and the usage time Month _ 0, the consumption duration DOU, and the consumption time Month _ i are started to be recorded;

[0139] In step 502, when Month _ 0 is an integer, retrieve the two nearest consumption durations (DOU _ 1 and DOU _ 2) and consumption times (Month _ 1 and Month _ 2) in the cloud from this node;

[0140] In step 503, calculate the battery life attenuation parameter k _0 = (DOU _ 1 - DOU _ 2) / (Month _ 1 - Month _ 2);

[0141] In step 504, substituting k = 0 into the DOU decay model gives: DOU = k _ 0 * Month + DOU _ 0;

[0142] In step 505, substituting the DOU decay model with the growth of Month into the DOU definition formula (DOU = Month * 30 / Cyclecount) gives the life prediction model Month_alert = (DOU_0 × Cyclecount) / (30 - Cyclecount × k_0);

[0143] In step 506, when cyclecount is Cyclecount - alert, calculate the available usage duration Month_alert of the aging state;

[0144] In step 507, calculate the difference Month _ 0 between the available usage duration Month_alert and the used duration, and determine whether the difference is less than or equal to 1 (1 month);

[0145] If it is less than or equal to 1, execute steps 508 and 509; if it is greater than 1, execute step 502;

[0146] In step 508, report to the after - sales system to prompt an early warning;

[0147] In step 509, push a user suggestion to go to the service point to replace the battery.

[0148] Figure 6 It is a battery management device shown according to an exemplary embodiment, as Figure 6 shown, the device includes:

[0149] An acquisition module 601, configured to acquire the historical usage information corresponding to each cycle of the battery;

[0150] A determination module 602, configured to determine the current endurance decay parameter of the battery based on the historical usage information;

[0151] A prediction module 603, configured to predict the available usage duration of the battery before reaching the preset aging state based on the current endurance decay parameter.

[0152] In some embodiments, the determination module 602 is further configured to:

[0153] Determine at least two target consumption times before the current moment, and target consumption durations corresponding to the at least two target consumption times;

[0154] Based on each of the target consumption times and the corresponding target consumption durations, determine the current battery life attenuation parameter.

[0155] In some embodiments, the determining module 602 is further configured to:

[0156] Determine a first target consumption time closest to the current moment, a second target consumption time among all the target consumption times other than the first target consumption time, a first target consumption duration corresponding to the first target consumption time, and a second consumption duration corresponding to the second target consumption time;

[0157] Based on the first target consumption time, the first target consumption duration, the second target consumption time, and the second target consumption duration, determine the current battery life attenuation parameter.

[0158] In some embodiments, the determining module 602 is further configured to:

[0159] Determine a first duration difference between the first target consumption duration and each of the second target consumption durations, and a first interval duration between the first target consumption time and each of the second target consumption times;

[0160] Based on each of the first duration differences and each of the first interval durations, determine the current battery life attenuation parameter.

[0161] In some embodiments, the determining module 602 is further configured to:

[0162] Respectively determine ratios between each of the first duration differences and the corresponding first interval durations;

[0163] Based on a preset weight coefficient, perform weighted processing on each of the ratios to obtain the current battery life attenuation parameter.

[0164] In some embodiments, the prediction module 603 is further configured to:

[0165] Obtain the available number of consumption times corresponding to the preset aging state;

[0166] Based on the current battery life attenuation parameter, the available number of consumption times, and the expected battery life attenuation parameter, determine the available usage duration.

[0167] In some embodiments, the prediction module 603 is further configured to:

[0168] Obtain the elapsed time of the battery up to the current moment;

[0169] When the difference between the available duration and the elapsed time is less than a preset duration, output a warning message.

[0170] In some embodiments, the obtaining module 601 is further configured to:

[0171] When reaching the prediction time node corresponding to the prediction period, obtain the historical usage information within the prediction period before the prediction time node from the cloud;

[0172] Wherein, the historical usage information includes: the information collected for each cycle of the electronic device of the battery.

[0173] In some embodiments, the prediction module 603 is further configured to:

[0174] According to the predicted available duration at the prediction time node corresponding to the current prediction period, adjust the prediction period to determine the prediction time node corresponding to the next prediction period.

[0175] Regarding the device in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0176] As Figure 7 shown, the embodiments of the present disclosure further provide an electronic device 700, including:

[0177] A memory 704 for storing processor-executable instructions;

[0178] A processor 720, connected to the memory 704;

[0179] Wherein, the processor 720 is configured to execute the battery management method provided by any of the foregoing technical solutions.

[0180] A block diagram of an electronic device 700 shown according to an exemplary embodiment. For example, the electronic device 700 may be a smart phone, a tablet computer, a laptop computer, a portable learning machine, etc.

[0181] Reference Figure 7 , the electronic device 700 may include one or more of the following components: a processing component 702, a memory 704, a power supply component 706, a multimedia component 708, an audio component 710, an input / output (I / O) interface 712, a sensor component 714, and a communication component 718.

[0182] The processing component 702 generally controls the overall operation of the electronic device 700, such as operations associated with display, telephone calls, data communication, camera operations, and recording operations. The processing component 702 may include one or more processors 720 to execute instructions to complete all or part of the steps of the above-described methods. In addition, the processing component 702 may include one or more modules to facilitate the interaction between the processing component 702 and other components. For example, the processing component 702 may include a multimedia module to facilitate the interaction between the multimedia component 708 and the processing component 702.

[0183] The memory 704 is configured to store various types of data to support the operation of the electronic device 700. Examples of such data include instructions for any application or method operating on the electronic device 700, contact data, phone book data, messages, pictures, videos, etc. The memory 704 may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0184] The power component 706 provides power to various components of the electronic device 700. The power component 706 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 700.

[0185] The multimedia component 708 includes a screen that provides an output interface between the electronic device 700 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe actions but also detect the duration and pressure associated with the touch or swipe operations. In some embodiments, the multimedia component 708 includes a front camera and / or a rear camera. When the electronic device 700 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each of the front camera and the rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0186] The audio component 710 is configured to output and / or input audio signals. For example, the audio component 710 includes a microphone (MIC) that is configured to receive external audio signals when the electronic device 700 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory 704 or transmitted via the communication component 718. In some embodiments, the audio component 710 further includes a speaker for outputting audio signals.

[0187] The I / O interface 712 provides an interface between the processing component 702 and peripheral interface modules, and the above peripheral interface modules can be a keyboard, a click wheel, buttons, etc. These buttons can include but are not limited to: a home button, a volume button, a power button, and a lock button.

[0188] The sensor component 714 includes one or more sensors for providing status assessments of various aspects of the electronic device 700. For example, the sensor component 714 can detect the on / off state of the electronic device 800, the relative positioning of components, such as the display and keypad of the electronic device 700. The sensor component 714 can also detect a change in the position of the electronic device 700 or a component of the electronic device 700, the presence or absence of user contact with the electronic device 700, the orientation or acceleration / deceleration of the electronic device 700, and the temperature change of the electronic device 700. The sensor component 714 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor component 714 can also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor component 714 can further include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0189] The communication component 718 is configured to facilitate communication between the electronic device 700 and other devices in a wired or wireless manner. The electronic device 700 can access a wireless network based on communication standards, such as WiFi, 2G, or 3G, or a combination thereof. In an exemplary embodiment, the communication component 718 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 718 further includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0190] In an exemplary embodiment, the electronic device 700 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above method.

[0191] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 704 including instructions, and the above instructions can be executed by a processor 720 of the electronic device 700 to complete the above method. For example, the non-transitory computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0192] An embodiment of the present application provides a non-transitory computer-readable storage medium. When the instructions in the storage medium are executed by a processor of a computer, the computer can execute the battery management method described in the foregoing one or more technical solutions.

[0193] Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not disclosed by the present disclosure. The specification and examples are only to be considered as exemplary, and the true scope and spirit of the present disclosure are pointed out by the following claims.

[0194] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A battery management method, characterized in that: include: Get the historical usage information corresponding to each battery cycle; Determining a current battery life attenuation parameter of the battery based on the historical usage information; Based on the current endurance decay parameter, predict the usable time of the battery before reaching a preset aging state.

2. The battery management method according to claim 1, characterized in that: The historical usage information includes: the consumption time corresponding to each cycle of the battery and the consumption time corresponding to each cycle of the battery; The determining, based on the historical usage information, a current battery life attenuation parameter of the battery includes: Determine at least two target consumption moments before the current moment, and target consumption durations corresponding to at least two of the target consumption moments; Based on each of the target consumption moments and the corresponding target consumption durations, the current endurance attenuation parameter is determined.

3. The battery management method according to claim 2, characterized in that: The determining the current endurance attenuation parameter based on each of the target consumption moments and the corresponding target consumption durations includes: Determine a first target consumption time closest to the current time, and a second target consumption time among the target consumption times except the first target consumption time, and a first target consumption duration corresponding to the first target consumption time and a second consumption duration corresponding to the second target consumption time; The current endurance attenuation parameter is determined based on the first target consumption time, the first target consumption duration, the second target consumption time, and the second target consumption duration.

4. The battery management method according to claim 3, characterized in that: The determining the current endurance attenuation parameter based on the first target consumption time, the first target consumption duration, the second target consumption time, and the second target consumption duration includes: Determine a first duration difference between the first target consumption duration and each of the second target consumption durations, and a first interval duration between the first target consumption moment and each of the second target consumption moments; The current endurance attenuation parameter is determined based on each of the first duration differences and each of the first interval durations.

5. The battery management method according to claim 4, characterized in that: The determining the current endurance attenuation parameter based on each of the first duration differences and each of the first interval durations includes: respectively determining a ratio between each of the first duration differences and the corresponding first interval durations; Based on the preset weight coefficient, each ratio is weighted to obtain the current endurance attenuation parameter.

6. The battery management method according to claim 1, characterized in that: The predicting, based on the current battery life attenuation parameter, the usable time of the battery before reaching a preset aging state includes: Obtaining the available consumption times corresponding to the preset aging state; The usable duration is determined based on the current endurance decay parameter, the available consumption times and the expected endurance decay parameter.

7. The battery management method according to any one of claims 1 to 6, characterized in that: The method further comprises: Obtaining the battery usage time up to the current moment; When the difference between the usable time and the used time is less than a preset time, a warning message is output.

8. The battery management method according to any one of claims 1 to 6, characterized in that: The obtaining of the historical usage information corresponding to each cycle of the battery includes: When a prediction time node corresponding to a prediction period is reached, the historical usage information in the prediction period before the prediction time node is obtained from the cloud; The historical usage information includes: information collected during each cycle of the electronic device of the battery.

9. The battery management method according to claim 8, characterized in that: The method further comprises: According to the usable time predicted at the prediction time node corresponding to the current prediction cycle, the prediction cycle is adjusted to determine the prediction time node corresponding to the next prediction cycle.

10. A battery management device, characterized in that: include: An acquisition module configured to acquire historical usage information corresponding to each cycle of the battery; A determination module, configured to determine a current battery life attenuation parameter of the battery based on the historical usage information; The prediction module is configured to predict the usable time of the battery before reaching a preset aging state based on the current endurance attenuation parameter.

11. An electronic device, characterized in that: include: processor; a memory configured to store processor-executable instructions; The processor is configured to execute the battery management method according to any one of claims 1 to 9 when calling the executable instructions in the memory. 12 . A non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the battery management method according to any one of claims 1 to 9.

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