Earphone charging box charging and discharging management method and earphone charging box

Abstract

The invention relates to the technical field of wearable equipment, in particular to an earphone charging box charging and discharging management method and an earphone charging box, and the method comprises the steps: obtaining the state information of a first battery, a second battery and a standby battery; through a preset habit prediction model, according to the historical behavior data and the real-time context data of the user, determining a predicted energy demand of the user for the first battery in the target time period; based on the state information and the predicted energy demand, obtaining a dynamic energy gap between the current total available energy of the first battery, the second battery and the standby battery and the predicted energy demand; and generating and executing a charging and discharging management strategy according to the dynamic energy gap. The invention mainly aims to provide a charging and discharging management method for an earphone charging box, and aims to relieve the problems of endurance anxiety and sudden power failure risk.

Description

Technical Field

[0001] This invention relates to the field of wearable device technology, and in particular to a charging and discharging management method for an earphone charging case and an earphone charging case. Background Technology

[0002] With the increasing popularity of wireless headphones, users are demanding higher and higher battery life from their headphones.

[0003] To improve battery life, the main improvement strategies in related technologies focus on increasing battery capacity, optimizing charging speed, and integrating backup batteries in the charging case. In terms of charging management strategies, current solutions are mostly based on real-time battery status, and passively respond to the charging control of the built-in batteries of the earphones and charging case, such as prioritizing charging devices with low battery or setting a fixed charging sequence.

[0004] However, there is a risk that the energy storage distribution of a composite energy system consisting of earphones, charging case batteries, and detachable backup batteries may be mismatched with the user's dynamically changing future electricity demand. Summary of the Invention

[0005] The main objective of this invention is to provide a charging and discharging management method for an earphone charging case, which aims to alleviate battery anxiety and the risk of sudden power outages.

[0006] To achieve the above objectives, the present invention proposes a charging and discharging management method for an earphone charging case, wherein the earphone charging case is used to charge and replenish the earphones, the earphones are equipped with a first battery, and the charging case contains a second battery and a removable spare battery. The method includes: Obtain the status information of the first battery, the second battery, and the backup battery; By using a preset habit prediction model, based on the user's historical behavior data and real-time context data, the predicted energy demand of the user for the first battery during the target time period is determined. Based on the state information and the predicted energy demand, obtain the dynamic energy gap between the current total available energy of the first battery, the second battery and the backup battery and the predicted energy demand; Based on the dynamic energy gap, a charge / discharge management strategy is generated and executed.

[0007] In one embodiment of the present invention, the step of obtaining the status information of the first battery, the second battery, and the backup battery includes: The battery management unit of the earphone charging case obtains the first status information based on the real-time power and health indicators of the second battery. When the earphones are placed in the charging case, the real-time power level of the earphones' first battery is received to obtain second status information; Based on the coupling event between the backup battery and the earphone, the nominal capacity and current charge of the backup battery are obtained to obtain the third state information; The status information is obtained based on the first status information, the second status information, and the third status information.

[0008] In one embodiment of the present invention, the step of determining the predicted energy demand of the user for the first battery within a target time period by using a preset habit prediction model based on the user's historical behavior data and real-time context data includes: Obtain device usage time periods, single usage durations, and usage scenario types from users' historical behavior data to construct temporal features of user behavior. The real-time context data is obtained, which includes the current time, calendar events, and location information; By using a preset habit prediction model, based on the user behavior time sequence characteristics and the real-time context data, the predicted usage scenario and expected usage duration of the headphones within the target time period are obtained; Based on the predicted usage scenario and the expected usage duration, the predicted energy demand of the user on the first battery during the target time period is determined.

[0009] In one embodiment of the present invention, before the step of determining the user's predicted energy demand for the first battery within a target time period based on the user's historical behavior data and real-time context data using a preset habit prediction model, the method further includes: Obtain historical behavior sequences of multiple users over a long period and their corresponding contextual labels; Based on the historical behavior sequence, periodic pattern characteristics, event correlation characteristics, and trend change characteristics are obtained; The preset habit prediction model is obtained by training the periodic pattern features, event association features, and trend change features through a time series prediction network.

[0010] In one embodiment of the present invention, the step of obtaining the dynamic energy gap between the current total available energy of the first battery, the second battery, and the backup battery and the predicted energy demand based on the state information and the predicted energy demand includes: Based on the real-time power levels of the first battery, the second battery, and the backup battery in the status information, the current total available energy is obtained; Based on the predicted energy demand and the current total available energy, the basic energy deficit value is obtained; The dynamic energy gap is obtained based on the health index of each battery in the status information and the basic energy gap value.

[0011] In one embodiment of the present invention, the step of generating and executing a charge / discharge management strategy based on the dynamic energy gap includes: When the dynamic energy gap is negative and its absolute value is greater than the first preset threshold, the first management mode is activated. In the first management mode, the charge / discharge management strategy is to control the second battery to charge the backup battery.

[0012] In one embodiment of the present invention, the step of generating and executing a charge / discharge management strategy based on the dynamic energy gap further includes: When the dynamic energy gap is positive and less than the second preset threshold, the second management mode is activated. In the second management mode, the charge / discharge management strategy is to control the second battery to charge the first battery.

[0013] In one embodiment of the present invention, the step of generating and executing a charge / discharge management strategy based on the dynamic energy gap further includes: When the dynamic energy gap is positive and greater than or equal to the second preset threshold, the third management mode is activated. In the third management mode, the charge / discharge management strategy outputs information suggesting the removal and use of the backup battery.

[0014] In one embodiment of the present invention, the method further includes: Obtain the actual energy consumption data of the first battery during the target time period; Based on the actual energy consumption data and the predicted energy demand, the actual deviation data is obtained; Based on the actual deviation data, the preset habit prediction model is incrementally updated.

[0015] The present invention also proposes an earphone charging case for implementing the earphone charging case charging and discharging management method described in any one of the above-mentioned methods. The earphone charging case is used to charge and replenish the earphones. The earphones are provided with a first battery. The earphone charging case includes a second battery, a removable backup battery, and a control motherboard. The control motherboard is electrically connected to the second battery, the backup battery, and the first battery. The control motherboard is configured to execute the charging and discharging management method.

[0016] In this technical solution, the headphone charging case charging and discharging management method provided by the present invention, by adopting a prediction-based dynamic energy scheduling and collaborative management mechanism, can solve the technical problem that traditional charging cases can only passively respond to the current power level and cannot match the user's future dynamic power demand, resulting in low efficiency of the composite energy system and persistent user range anxiety. Specifically, the method provides an energy storage data foundation by acquiring the status information of the first battery, the second battery, and the backup battery; then, by using a habit prediction model based on historical and contextual data, it actively calculates the user's predicted energy demand in the target time period; based on this, by calculating the dynamic energy gap between the current total available energy of the system and future demand, the energy risk level can be quantitatively assessed; finally, based on different gap results, a collaborative management strategy is executed, either by scheduling the charging between the second battery and the backup battery to optimize energy storage, or by generating a prompt to activate the backup battery in a timely manner to guide user intervention. Thus, the method proposed in this invention makes the charging case an intelligent energy management center, systematically improving the reliability and efficiency of the composite energy system composed of multiple batteries; secondly, by providing early warnings and clear guidance, it solves users' battery anxiety and the risk of sudden power outages, significantly improving the user experience of headphones and headphone charging cases. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the first embodiment of the headphone charging case charging and discharging management method provided by the present invention; Figure 2 A flowchart illustrating a second embodiment of the headphone charging case charging and discharging management method provided by the present invention; Figure 3 A flowchart illustrating the third embodiment of the headphone charging case charging and discharging management method provided by the present invention; Figure 4 A flowchart illustrating the fourth embodiment of the headphone charging case charging and discharging management method provided by the present invention; Figure 5 A flowchart illustrating the fifth embodiment of the headphone charging case charging and discharging management method provided by the present invention; Figure 6 A flowchart illustrating the sixth embodiment of the headphone charging case charging and discharging management method provided by the present invention; Figure 7A flowchart illustrating the seventh embodiment of the headphone charging case charging and discharging management method provided by the present invention; Figure 8 A flowchart illustrating the eighth embodiment of the headphone charging case charging and discharging management method provided by the present invention; Figure 9 A flowchart illustrating the ninth embodiment of the headphone charging case charging and discharging management method provided by the present invention; Figure 10 A schematic diagram of the structure of an embodiment of the earphone charging case provided by the present invention; Figure 11 A schematic diagram of another embodiment of the earphone charging case provided by the present invention; Figure 12 A schematic diagram of the structure of an embodiment of the backup battery provided by the present invention; Figure 13 This is a schematic diagram of the structure of an embodiment of the wireless earphone provided by the present invention.

[0019] Explanation of icon numbers: 100. Earphone charging case; 10a, Backup battery module; 10a1, Backup battery outlet; 10a2, Backup battery compartment; 11. First charging case; 12. Second charging case; 121. Backup battery; 1211. Second magnetic structure; 1212. Second charging contact; 200. Wireless earphones; 20. Third magnetic attraction structure; 21. Third charging contact; 1000, Headphone Kit.

[0020] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0022] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0023] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0024] The main objective of this invention is to provide a charging and discharging management method for an earphone charging case, which aims to alleviate battery anxiety and the risk of sudden power outages.

[0025] To achieve the above objectives, the headphone charging case charging and discharging management method proposed in this invention is applied to the headphone charging case 100. The headphone charging case 100 is used to charge and replenish the power of wireless headphones. The wireless headphones are equipped with a first battery, and the headphone charging case 100 contains a second battery and a removable spare battery 121. Please refer to [link to relevant documentation]. Figure 1 , Figure 10 , Figure 11 , Figure 12 as well as Figure 13 The methods include: S10: Obtain the status information of the first battery, the second battery, and the backup battery 121; S20: By using a preset habit prediction model, based on the user's historical behavior data and real-time context data, determine the user's predicted energy demand for the first battery during the target period; S30: Based on the status information and predicted energy demand, obtain the dynamic energy gap between the current total available energy of the first battery, the second battery and the backup battery 121 and the predicted energy demand; S40: Generate and execute charge / discharge management strategies based on the dynamic energy gap.

[0026] First, in all specific embodiments of this invention, any user or device-related data, such as historical user behavior data, real-time context data (including current time, location information, calendar events, etc.), and battery status information (including real-time battery level, health indicators, etc.), is involved. When these embodiments are applied to specific products or technologies, user permission or consent is required, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. In other words, if any user or device-related data is involved in the embodiments of this invention, this data must be obtained with the user's authorization and consent, and in accordance with the relevant laws, regulations, and standards of the country and region.

[0027] Secondly, the first battery refers to the power supply battery built into the earphone, the second battery refers to the fixed battery built into the earphone charging case 100, and the spare battery 121 refers to the removable battery that can be manually removed from the charging case. The spare battery 121 is used to charge the earphones when they are in use, so as to avoid the problem of interruption of use of the wireless earphones 200 due to insufficient power when in use.

[0028] Specifically, in step 10, the status information of the first battery, the second battery, and the backup battery 121 is obtained. This status information includes the real-time power percentage, health indicators (such as the degree of battery aging), and coupling status (such as whether the backup battery 121 is in the case) of each battery to obtain the current energy storage level. For example, when the user puts the earphones into the earphone charging case 100 for overnight charging, the earphone charging case 100 automatically reads that the earphone battery has a remaining power of 20%, the charging case battery has a power of 80%, and the backup battery 121 has a power of 50% and is in good health. This step provides an accurate data basis for subsequent planning and avoids misjudgment due to missing information. If the status information is not obtained, the system may not be able to identify the idle state of the backup battery 121, resulting in energy waste.

[0029] In step 20, the preset habit prediction model refers to an algorithm model trained based on machine learning. It combines user historical behavior data, such as the headphone usage time records of the past week, the duration of a single use and the type of scenario, as well as real-time context data, such as the current time, meeting schedules in the phone calendar and GPS location information, to determine the predicted energy demand (the expected power consumption in milliampere-hours) of the first battery for the target user during the target time period (referring to a specific future time period, such as 2 hours after work). The model dynamically outputs the demand by analyzing historical patterns (such as the user usually uses the noise cancellation function for 30 minutes of exercise in the evening) and the current environment (such as detecting that the user is in a gym). For example, when the user leaves the office at 5 pm, the model predicts that the user will consume 40% of the headphone battery power when exercising at home from 7 pm to 9 pm. This step is to shift the management perspective from passive response to proactive planning. If the demand is not predicted, the system cannot cope with the scenario of the user working overtime and extending the usage time in advance, resulting in power outage during the journey.

[0030] In step 30, based on the status information obtained in step 10 and the predicted energy demand output in step 20, the dynamic energy gap between the current total available energy of the system and the demand during the target period is obtained. The dynamic energy gap quantifies the energy risk level. For example, if the total available energy is 1200 mAh and the predicted demand is 1500 mAh, the dynamic energy gap is -300 mAh (a negative value indicates a shortage). This step can assess future risks. For example, calculating the gap before a long trip can avoid underestimating the actual shortage due to ignoring battery health degradation, thus ensuring the reliability of the strategy.

[0031] In step 40, a collaborative management strategy is generated and executed based on the dynamic energy gap. This strategy includes charging scheduling operations between the second battery and the backup battery 121. For example, when the gap is negative, the second battery is controlled to replenish the backup battery 121 with power. When an energy shortage is predicted, a prompt message about activating the backup battery 121 is generated, such as a warning message suggesting that the user take out the backup battery 121 pops up via the charging box LED or a mobile APP, to achieve active collaboration among multiple battery units. For example, when the dynamic energy gap is -250 mAh, the system automatically schedules the charging box battery to charge the backup battery 121 to store energy, or pushes a notification "It is recommended to carry the backup battery 121" when the gap is +400 mAh. In outdoor hiking scenarios, advance charging can prevent the backup battery 121 from self-discharge loss, while timely prompts can eliminate the user's anxiety about sudden power outages and significantly improve battery life.

[0032] Taking an important weekday meeting as an example, suppose at 9:50 AM on a weekday, a user puts the earphones into the earphone charging case 100. The control board in the earphone charging case 100 obtains the following status: the first battery has a charge of 40% (the earphones were not fully charged overnight), the second battery has a charge of 90%, and the backup battery 121 has a charge of 100%. The preset habit prediction model, combined with historical data, determines that the user often has meetings between 10 AM and 12 PM on weekdays and the real-time context (currently 9:50 AM, with a meeting scheduled for 10 AM in the calendar). It predicts that during this target period of 10 AM to 12 PM, there will be "continuous meeting calls," requiring 50% of the first battery's capacity. The control board in the earphone charging case 100 calculates that the current total available energy ratio is 230%, compared to the predicted demand of 50%, resulting in a basic deficit of 180%. However, considering the importance of the meeting, the control board determines this to be a critical scenario. Although there is a total energy surplus, to ensure that the earphones themselves have sufficient power during the meeting, the control board chooses to execute the following strategy: control the second battery to charge the first battery at maximum power, striving to charge the first battery to the highest possible level before the meeting begins.

[0033] In this technical solution, the headphone charging case charging and discharging management method provided by the present invention, by adopting a prediction-based dynamic energy scheduling and collaborative management mechanism, can solve the technical problem that traditional charging cases can only passively respond to the current power level and cannot match the user's future dynamic power demand, resulting in low efficiency of the composite energy system and persistent user range anxiety. Specifically, the method provides an energy storage data foundation by acquiring the status information of the first battery, the second battery, and the backup battery 121; then, through a habit prediction model based on historical and contextual data, it actively calculates the user's predicted energy demand in the target time period; based on this, by calculating the dynamic energy gap between the current total available energy of the system and future demand, the energy risk level can be quantitatively assessed; finally, based on different gap results, a collaborative management strategy is executed, either by scheduling the charging between the second battery and the backup battery 121 to optimize energy storage, or by generating a prompt to activate the backup battery 121 in a timely manner to guide user intervention. Thus, the method proposed in this invention makes the charging case an intelligent energy management center, systematically improving the reliability and efficiency of the composite energy system composed of multiple batteries; secondly, by providing early warnings and clear guidance, it solves users' battery anxiety and the risk of sudden power outages, significantly improving the user experience of the headphones and the headphone charging case 100.

[0034] In one embodiment of the present invention, please refer to Figure 2 Step 10 includes: S110: The first status information is obtained by the battery management unit of the earphone charging case 100 based on the real-time power and health indicators of the second battery; S120: When the earbuds are placed in the charging case, the real-time power level of the earbuds' first battery is received to obtain the second status information; S130: Based on the coupling event between the backup battery 121 and the earphone, obtain the nominal capacity and current power of the backup battery 121 to obtain the third state information; S140: Obtain state information based on the first state information, the second state information, and the third state information.

[0035] In step S11, the control motherboard obtains the first status information by combining the real-time power level and health index of the second battery through the battery management unit of the earphone charging case 100. The battery management unit is the core module within the charging case responsible for monitoring and managing the battery status. It has the functions of collecting battery power and assessing health. The first status information is specific data reflecting the current status of the second battery, including the real-time power level and health percentage. The health index is a parameter reflecting the degree of battery degradation; a higher value indicates better battery performance. In this step, the control motherboard specifically collects status information for the second battery, ensuring the accuracy and real-time nature of the data through a dedicated management module, providing reliable built-in battery data for subsequent overall status information aggregation.

[0036] In step S12, when the earphones are placed in the charging case, the control motherboard receives the real-time battery level of the earphones' first battery and obtains the second status information. The second status information reflects the specific data of the current real-time battery level of the first battery. When the earphones are placed in the charging case, the charging case and earphones can transmit battery data via electrical connection or wireless communication, and the control motherboard obtains the data accordingly. This step clarifies the conditions and methods for the control motherboard to obtain the first battery status information. The control motherboard utilizes the compatibility between the earphones and the charging case to accurately collect earphone battery data, avoiding data collection failures caused by the earphones being removed from the charging case.

[0037] In step S13, the control motherboard obtains the nominal capacity and current charge level of the backup battery 121 based on the coupling event between the backup battery 121 and the earphone, thus obtaining the third state information. The coupling event is an electrical connection or signal interaction event between the backup battery 121 and the earphone, such as the backup battery 121 being inserted into the charging case or establishing a charging connection with the earphone. The nominal capacity is the standard energy storage capacity marked at the factory when the backup battery 121 leaves the factory. The third state information is specific data reflecting the capacity and current charge level of the backup battery 121. In this step, the control motherboard triggers data acquisition through the coupling event for the removable backup battery 121, ensuring timely understanding of the actual energy storage capacity of the backup battery 121 and compensating for the difficulty in state monitoring caused by the removability of the backup battery 121.

[0038] In step S14, the control motherboard summarizes the status information based on the first status information, the second status information, and the third status information. In this step, the control motherboard summarizes and integrates the individual status information of each battery collected in the first three steps to form a complete set of status information covering the first, second, and backup batteries 121. This provides a comprehensive data foundation for subsequent prediction of energy demand and calculation of energy gap, avoiding analytical biases caused by missing status information.

[0039] In one application scenario, the user frequently takes out the earphones for use. The user works in an office, taking out the earphones for 10 minutes every hour before putting them back in the charging case. The control board monitors the status of the second battery in real time via the battery management unit in step S11, updating the first status information every 30 minutes; in step S12, it immediately receives the real-time battery level of the first battery each time the user puts the earphones back in the charging case and updates the second status information; in step S13, when the user inserts the spare battery 121 into the charging case in the morning, a coupling event is triggered, collecting the nominal capacity of the spare battery 121 (1000mAh) and its current charge level (95%) to generate third status information; in step S14, the three types of information are summarized to form complete status information.

[0040] In another application scenario, the backup battery 121 is frequently used. Outdoor enthusiasts carry a charging case and the backup battery 121 with them, and take out the backup battery 121 multiple times during the day to temporarily charge the earphones. The control board continuously monitors the status of the second battery and records its power changes through step S11; collects the power of the first battery through step S12 when the earphones are put back into the charging case; triggers a coupling event each time the backup battery 121 is inserted into the charging case or connected to the earphones for charging through step S13, updates the current power of the backup battery 121, and generates third status information; and summarizes the information through step S14.

[0041] In one embodiment of the present invention, please refer to Figure 3 Step 20 includes: S210: Obtain device usage time records, single usage duration, and usage scenario type from the user's historical behavior data to construct user behavior time-series features; S220: Obtain real-time context data, which includes the current time, calendar events, and location information; S230: By using a preset habit prediction model, based on user behavior time sequence characteristics and real-time context data, the predicted usage scenario and expected usage duration of the headphones within the target time period are obtained; S240: Based on the predicted usage scenario and expected usage duration, determine the predicted energy demand of the user on the first battery during the target period.

[0042] In step S21, the control board acquires device usage time records, single usage duration, and usage scenario types from the user's historical behavior data to construct user behavior temporal features. Device usage time records are the specific time periods during which the user has used the headphones in the past, such as 7:00-8:00 AM daily. Single usage duration is the duration of each headphone use. Usage scenario type refers to the environment and purpose in which the user uses the headphones, such as commuting, exercising, or working. User behavior temporal features are feature data formed by integrating user usage patterns based on the time dimension, reflecting the user's usage habits at different times. In this step, the control board extracts key information from historical data to refine the temporal patterns of user behavior, providing data support for subsequent predictions and enabling the preset habit prediction model to capture the temporal correlation of user usage.

[0043] In step S22, the control motherboard acquires real-time context data, which includes the current time, calendar events, and location information. The current time is the specific point in time when the data is collected; calendar events are to-do items recorded in the user's calendar, such as meetings, exercise, and business trips; and location information is the user's current geographical location, such as at home, at work, or outdoors. This step allows the control motherboard to acquire real-time environmental and schedule information, compensating for the inability of historical data to reflect real-time changes. This enables the preset habit prediction model to adjust its prediction results based on the current situation, improving prediction accuracy.

[0044] In step S23, the control board uses a preset habit prediction model, combined with user behavior time-series characteristics and real-time context data, to obtain the predicted usage scenario and estimated usage duration of the headphones within the target time period. The predicted usage scenario anticipates the environment and purpose in which the user will use the headphones within the target time period, while the estimated usage duration predicts the total duration of the user's headphone use within the target time period. This step is an intermediate step in predicting energy demand. The control board integrates historical patterns and real-time information through the preset habit prediction model to first clarify the details of the user's usage behavior during the target time period, providing a direct basis for subsequent energy demand calculations.

[0045] In step S24, the control board determines the user's predicted energy demand for the first battery during the target period based on the predicted usage scenario and expected usage duration. In this step, the control board combines the predicted usage scenario and expected usage duration to accurately calculate the energy required by the first battery. The power consumption of the headphones may vary in different scenarios. For example, when noise cancellation is turned on during exercise, the power consumption is higher. The control board uses this to convert behavior prediction into energy demand prediction, providing a clear target guidance for subsequent energy scheduling.

[0046] In one application scenario, business professionals are engaged in daily office work. User history data shows that weekday office hours are 9:00-12:00 and 14:00-18:00. A single use of headphones lasts 30 minutes, with noise cancellation enabled, consuming 10% of battery every 30 minutes. Real-time context data shows Wednesday at 13:30, with a calendar event from 14:00-15:00, and the user is at the office. The control board extracts the device usage periods (9:00-12:00, 14:00-18:00), single use duration (30 minutes), and office scenario (in step S21) to construct temporal features. Step S22 obtains the current time (13:30), the calendar event (14:00-15:00 meeting), and the location (office). Step S23 uses a model to predict the target usage period (14:00-15:00) as office work, with an estimated usage duration of 60 minutes. Step S24, combined with office scenario power consumption, determines a predicted energy demand of 20%.

[0047] In another application scenario, students' weekend life is analyzed. Historical user data shows that from 10:00 AM to 12:00 PM, using headphones for studying without noise cancellation consumes 8% of the battery per hour; from 3:00 PM to 5:00 PM, using headphones for exercise with noise cancellation consumes 15% of the battery per hour. Real-time context data shows Sunday at 9:30 AM, with a calendar event of a fitness class from 3:00 PM to 4:30 PM, and the user is at home. The control board extracts the usage time period, duration, and scenario in step S21 to construct temporal features; obtains real-time data in step S22; predicts the target usage time period (10:00 AM to 12:00 PM for 2 hours of studying) and (3:00 PM to 4:30 PM for 90 minutes of exercise) in step S23; and calculates the predicted energy demand as 43.5% in step S24.

[0048] In one embodiment of the present invention, please refer to Figure 4 Before step 20, the following are also included: S201: Obtain the historical behavior sequence of multiple users over a long period and the corresponding contextual labels; S202: Based on historical behavior sequences, periodic pattern characteristics, event correlation characteristics, and trend change characteristics are obtained; S203: A preset habit prediction model is obtained by training a time series prediction network on periodic pattern features, event correlation features, and trend change features.

[0049] In step S201, the development end of the control motherboard acquires the historical behavior sequences of multiple users over a long period and their corresponding contextual labels. "Multiple users" refers to multiple headphone users with different usage habits, and "long period" refers to a sufficiently long time span, such as 3 months, to ensure the capture of periodic patterns in user behavior. The historical behavior sequence is a collection of records of user headphone use arranged chronologically, and the contextual labels are categorized annotations of the contextual information of each user's headphone use, such as "weekdays - commuting - indoor" or "weekends - sports - outdoor." This step provides sufficient sample data for training the preset habit prediction model. Multi-user data improves the model's versatility, long-period data captures periodic patterns, and contextual labels enrich the model's input features, ensuring the model has a foundation for accurate prediction.

[0050] In step S202, the development end of the control motherboard extracts periodic pattern features, event association features, and trend change features based on historical behavior sequences. Periodic pattern features are recurring patterns in user behavior over time, such as usage at fixed times each day or high-frequency usage on certain days of the week. Event association features are the correlation between user behavior and specific events, such as turning on headphones 10 minutes before a meeting. Trend change features are the gradual changes in user behavior over time, such as a gradual increase in usage time during recent exercise. This step extracts features from historical behavior sequences, transforming raw data into effective features that the model can recognize, providing core input for model training, and ensuring that the model can learn the core patterns of user behavior.

[0051] In step S203, the R&D team of the control motherboard trains a pre-defined habit prediction model using a time series prediction network to analyze periodic pattern features, event correlation features, and trend change features. Time series prediction networks are algorithms adept at processing time-series data and uncovering temporal correlation patterns, such as LSTM and GRU. They can perform deep learning on time-series features and output a model with predictive capabilities. This step trains the feature data using a professional time series prediction network, enabling the model to understand the correlation between user behavior and time and environment, and to accurately predict future user behavior and energy needs. This provides a core tool for the control motherboard to execute subsequent prediction steps.

[0052] In one application scenario, headphone product model optimization is performed before mass production. Before mass production, the manufacturer collects three months of historical behavior sequences and contextual labels from 1000 different groups (students, office workers, and outdoor enthusiasts). The R&D team of the control board obtains long-term data and labels from multiple users in step S01; extracts the periodic patterns of each user in step S02, such as usage during commuting hours for office workers and usage during weekend study hours for students; extracts event-related features, such as usage during meetings for office workers and usage before class for students; and extracts trend change features, such as increased usage time for some users in winter. In step S03, these features are trained using a time series prediction network to obtain a preset habit prediction model adapted to different groups.

[0053] In another application scenario, model iterative updates involve data collection and training. The manufacturer found that the existing model had low prediction accuracy for newly hired employees, so they collected two months' worth of historical behavioral sequences and contextual labels from 500 newly hired employees. The development team of the control board acquires targeted, multi-user, long-term data in step S01; extracts characteristics of this group in step S02, such as increased commuting time and increased meeting frequency in the early stages of employment; and trains the features using a time-series prediction network in step S03 to update the preset habit prediction model.

[0054] In one embodiment of the present invention, please refer to Figure 5 Step 30 includes: S310: Based on the real-time power levels of the first battery, the second battery, and the backup battery 121 in the status information, obtain the current total available energy; S320: Based on the predicted energy demand and the current total available energy, the basic energy deficit value is obtained; S330: Based on the health indicators and basic energy gap values ​​of each battery in the status information, the dynamic energy gap is obtained.

[0055] In step S310, the control motherboard calculates the current total available energy based on the real-time charge levels of the first battery, the second battery, and the backup battery 121 in the status information. The current total available energy is the sum of the real-time charge levels of the three batteries after conversion according to a unified capacity standard. For example, if the first battery has 40% charge and a capacity of 500mAh, the second battery has 60% charge and a capacity of 1500mAh, and the backup battery has 80% charge and a capacity of 1000mAh, the current total available energy is 40%×500 + 60%×1500 + 80%×1000 = 200 + 900 + 800 = 1900mAh. In this step, the control motherboard summarizes the real-time charge levels of the three batteries to determine the total energy reserves of the multiple batteries, providing basic energy data for subsequent energy gap calculations and ensuring the comprehensiveness of the gap calculation.

[0056] In step S320, the control board calculates a basic energy gap value based on the predicted energy demand and the current total available energy. The basic energy gap value is the difference between the current total available energy and the predicted energy demand. If the difference is positive, it means the total available energy can meet the predicted demand; if it is negative, it means the total available energy is insufficient. For example, if the current total available energy is 1900mAh and the predicted energy demand is 500mAh, the basic energy gap value is 1900-500=1400mAh; if the predicted energy demand is 2000mAh, the basic energy gap value is 1900-2000=-100mAh. In this step, the control board initially quantifies the energy supply and demand difference, providing a basic value for subsequent dynamic adjustment of the gap, making the energy supply and demand relationship more intuitive.

[0057] In step S330, the control motherboard corrects the dynamic energy gap based on the health indicators and basic energy gap values ​​of each battery in the status information. In this step, the control motherboard corrects the basic energy gap value by combining the health indicators of each battery. Batteries with lower health may have less usable energy than their nominal capacity. For example, a battery with a health of 80% and a nominal capacity of 1000mAh may only have 800mAh usable. The corrected dynamic energy gap is closer to the actual energy supply and demand situation, avoiding gap judgment errors caused by ignoring battery health, and ensuring the accuracy of subsequent strategy formulation.

[0058] In one embodiment of the present invention, please refer to Figure 6 Step 40 includes: S410: When the dynamic energy gap is negative and its absolute value is greater than the first preset threshold, the first management mode is activated; S420: In the first management mode, the charge and discharge management strategy is to control the second battery to charge the backup battery 121.

[0059] In step S410, the control board of the charging battery box judges the calculated dynamic energy gap. A negative dynamic energy gap indicates that the current total available energy of the system is greater than the predicted energy demand, i.e., there is an energy surplus. The first preset threshold is an empirical value set by the system, for example, equivalent to 50% of the headphone battery capacity, used to distinguish between "slight surplus" and "significant surplus". When the absolute value of the surplus is greater than this threshold, the control board determines that the system is in a state of abundant energy and idleness, thereby activating the first management mode. The core task of this mode is to utilize idle energy to optimize the internal energy storage structure of the system, rather than rushing to fully charge all batteries.

[0060] In step S420, the control motherboard executes a specific charge / discharge management strategy under the first management mode. The strategy's decision is to instruct the second battery to charge the backup battery 121. Here, "charging" specifically refers to energy transfer when the backup battery 121 is located inside or connected to the charging box. This step aims to actively manage energy distribution, transferring excess electrical energy from the charging box to the backup battery 121. Its purpose is not only to replenish power but, more importantly, to ensure that the backup battery 121, a critical mobile energy source, is in a high state of readiness, while simultaneously optimizing the energy storage location of the entire system.

[0061] In one application scenario, a user uses the headphones for only 1 hour over the weekend, with a predicted energy demand of 100mAh. The current total available energy is 1500mAh, resulting in a dynamic energy gap of -1400mAh. The first preset threshold is 300mAh, and the absolute value of 1400mAh is greater than 300mAh. The control board activates the first management mode via step S41; and via step S42, it controls the second battery (70% charge) to charge the backup battery 121 (50% charge), transferring the redundant energy from the second battery to the backup battery 121.

[0062] In another application scenario, a user temporarily cancels their usage plan. The user originally planned to use the headphones during a business trip, predicting a power demand of 500mAh. After canceling the trip, the predicted power demand is adjusted to 100mAh, resulting in a current total available power of 800mAh and a dynamic power deficit of -700mAh, which is greater than the first preset threshold of 250mAh. The control board activates the first management mode via step S41 and controls the second battery to charge the backup battery 121 via step S42, storing redundant energy. If either step is missing, the redundant energy cannot be utilized effectively, the backup battery 121 will be underpowered, and if there is a sudden need for additional use, the control board will be unable to allocate sufficient energy, leading to user battery life anxiety.

[0063] In one embodiment of the present invention, please refer to Figure 7 Step 40 also includes: S430: When the dynamic energy gap is positive and less than the second preset threshold, activate the second management mode; S440: In the second management mode, the charge / discharge management strategy is to control the second battery to charge the first battery.

[0064] In step S430, the control board makes a judgment based on the dynamic energy gap. A positive dynamic energy gap indicates that the predicted energy demand exceeds the current total available energy, meaning the system faces a power shortage. The second preset threshold is a further parameter used to determine the energy shortage. For example, the second preset threshold is equivalent to 15% of the headphone battery capacity, used to define the boundary between "mild shortage" and "severe shortage". When a shortage exists but does not exceed this threshold, the control board determines that the risk is controllable and has not yet reached the point where the backup battery 121 must be used or a strong warning must be issued, thereby activating the second management mode. The core task of this mode is to prioritize and reliably ensure the power of the most basic and direct power-consuming unit—the headphone itself.

[0065] In step S440, the control motherboard executes the specific charge / discharge management strategy under the second management mode. The charge / discharge management strategy under the second management mode is as follows: the second battery is instructed to charge the first battery. At this time, the control motherboard will not handle the backup battery 121; it neither charges it nor recommends removing it. Instead, it focuses all available charging power on increasing the battery capacity of the earphone itself. This aims to eliminate potential risks by filling gaps before the target period begins in the most direct and efficient way, ensuring that the earphones have the highest possible base battery capacity when used outside the charging case.

[0066] In one application scenario, a user returns their earphones, with 40% battery remaining, to the charging case after a lunch break. The control board predicts light usage in the afternoon based on historical data. However, suddenly, the user's phone calendar syncs a 30-minute conference call starting in 30 minutes. The control board reassesses; specifically, the new prediction results in a positive dynamic energy deficit (requiring an additional 20% battery, with a 5% deficit), but less than the second preset threshold (15%). The control board activates a second management mode; it immediately controls the second battery (80% battery) to charge the first battery at a faster rate, aiming to raise it to over 50% before the meeting begins.

[0067] In another application scenario, during a user's commute home, 10 minutes before arriving home, the user places the earphones with 15% battery in the charging case. The system predicts the user will have one hour of leisure listening time that evening. Specifically, if the dynamic energy gap is positive (40% battery demand, 25% gap), but still less than the second preset threshold (assumed to be 30%), the control board activates the second management mode. During the short time the user is preparing dinner, the control board controls the second battery to quickly replenish the first battery's charge, restoring it to over 35% before the user removes the earphones again.

[0068] In one embodiment of the present invention, please refer to Figure 8 Step 40 also includes: S450: When the dynamic energy gap is positive and greater than or equal to the second preset threshold, the third management mode is activated; S460: In the third management mode, the charge / discharge management strategy outputs information suggesting the removal and use of the backup battery 121.

[0069] In step S450, the control motherboard performs the highest level of risk assessment. When the dynamic energy gap (i.e., the predicted shortage) is not only positive but also reaches or exceeds the second preset threshold (such as 15% or 30% in the previous example), meaning the difference between the total available energy and the predicted demand is large, it indicates that the first battery may be insufficient in power and the backup battery 121 needs to be activated to supplement it. For example, if the second preset threshold is 300mAh and the dynamic energy gap is 400mAh, it is greater than 300mAh. The third management mode is for situations where the energy gap is large and the user needs to be guided to activate the backup battery 121. The control motherboard determines the size of the energy gap through the threshold. When the gap is large, the corresponding mode is activated to promptly remind the user to intervene and avoid the inability to meet usage needs due to insufficient power of the first battery.

[0070] In step S460, the control motherboard executes the charge / discharge management strategy under the third management mode. This strategy is no longer internal current control, but generates a clear suggestion message and outputs it to the user through the charging case's prompt unit (such as a flashing LED indicator, a buzzer sound, or a push notification via Bluetooth to the mobile app). This message prompts the user to take action, such as "Battery is critically low, please remove and install the spare battery 121." The charge / discharge management strategy here manifests as a communication and interaction strategy, aiming to guide the user to manually complete the physical coupling of the spare battery 121 and the earphones, thereby directly integrating the energy of the spare battery 121 into the system. This is the most direct and effective way to solve the severe shortage.

[0071] In one application scenario, a user discovers early in the morning that a video conference scheduled to begin in 30 minutes and last for 2 hours has been added to their calendar. Their headset battery is only 10%, the charging case battery is 40%, and the backup battery 121 inside the case is 100%. The control board predicts a high energy demand in the coming days. Based on the status information of each battery and the predicted energy demand, it determines that the dynamic energy deficit is as high as +130%, far exceeding the second preset threshold. At this point, the control board immediately activates the third management mode; the indicator light on the headset charging case 100 flashes rapidly red, and a notification pops up on the user's phone: "Meeting battery low, please remove the backup battery 121 and insert it into the headset." In one embodiment of the present invention, please refer to Figure 9 The methods also include: S50: Obtain the actual energy consumption data of the first battery during the target time period; S60: Based on actual energy consumption data and predicted energy demand, actual deviation data is obtained; S70: Based on actual deviation data, incrementally update the preset habit prediction model.

[0072] In step S50, after the target time period (such as a predicted meeting period or commuting period) ends, the control motherboard obtains detailed consumption records of the first battery from the earphone during that time period, i.e., actual energy consumption data, through its communication link with the earphone. This data is in milliampere-hours (mAh) and reflects the actual energy consumption of the earphone during that time period.

[0073] In step S60, the control board compares the actual energy consumption data (e.g., actual power consumption of 450mAh during the meeting) with the predicted energy demand made in step 20 (e.g., predicted demand of 500mAh). The actual deviation data is obtained by calculating the difference or ratio between the two. This actual deviation data quantifies the magnitude and direction of the error in the preset habit prediction model after each use of the headphones.

[0074] In step S52, the control motherboard uses the actual deviation data as a feedback signal to incrementally update the preset habit prediction model in the firmware. It is understood that using incremental updates does not mean retraining the model, but rather fine-tuning some parameters within the model with a very small learning rate (e.g., adjusting the association weights between the "video conferencing" scenario and energy consumption), so that when encountering similar contexts in the future, the predicted usage time or scenario energy consumption mapping is closer to the actual situation.

[0075] In one application scenario, a user's commuting mode changes from driving to taking public transportation, such as buses or subways. When driving, the user only needs to connect the headphones to the car's Bluetooth using low-power Bluetooth. However, when taking the subway, the user needs to use the headphones' high-power active noise cancellation function. Therefore, the predictions for the first few days will have a significant deviation. Based on this, the control board first obtains the actual energy consumption data of the first battery during subway commuting, which is 40% higher than the prediction. Then, the control board calculates continuous positive actual deviation data. Finally, based on these deviation data, the control board gradually fine-tunes the preset habit prediction model, increasing its expected energy consumption value associated with the context of "location: subway station".

[0076] The present invention also proposes an earphone charging case 100 for implementing the earphone charging case charging and discharging management method of any of the above-mentioned embodiments. The earphone charging case 100 is used to charge and replenish the earphones. The earphones are provided with a first battery. The earphone charging case 100 includes a second battery, a removable backup battery 121, and a control motherboard. The control motherboard is electrically connected to the second battery, the backup battery 121, and the first battery. The control motherboard is configured to execute the charging and discharging management method. The removability of the backup battery 121 is explained in one embodiment; please refer to [reference needed]. Figure 10The earphone charging case 100 has a corner with a backup battery module 10a that exposes the earphone charging case 100. The backup battery module 10a includes a backup battery outlet 10a1 and a backup battery compartment 10a2. The battery outlet is located on the outer side of the corner of the charging case body, and the backup battery compartment 10a2 is located on the inner side of the battery outlet and away from the outer side of the charging case body. The backup battery 121 is located in the backup battery compartment 10a2. When the backup battery 121 is in the backup battery compartment 10a2, the first battery in the earphone charging case 100 charges the backup battery 121 through a PIN. When the backup battery 121 needs to be removed, the user can remove the backup battery 121 from the backup battery compartment 10a2 through the backup battery outlet 10a1 and connect it to the wireless earphone 200 magnetically to charge the first battery of the wireless earphone 200. For example, please refer to [link to relevant documentation]. Figure 12 and Figure 13 The backup battery 121 has a second magnetic structure 1211 and a second charging contact 1212. The bottom of the wireless earphone 200 has a third magnetic structure 20 and a third charging contact 21. The second magnetic structure 1211 and the third magnetic structure 20 are magnetically fixed together so that the second charging contact 1212 contacts the third charging contact 21 to achieve point conduction. In another embodiment, please refer to... Figure 11 The earphone charging case 100 includes a first charging case 11 and a second charging case 12. The first charging case 11 has an earphone charging compartment for accommodating and charging the wireless earphones 200. The second charging case 12 has a spare battery compartment 10a2 for accommodating and charging the spare battery 121. The second charging case 12 and the first charging case 11 can be separate or integrated. When the second charging case 12 and the first charging case 11 are separate, the second charging case 12 can be fixed to the first charging case 11 by means of clips, magnets, etc., and the user can remove the spare battery 121 by removing the second charging case 12. When the second charging case 12 and the first charging case 11 are integrated, the second charging case 12 has an openable flip cover, which the user can open to remove the spare battery 121. The spare battery 121 located in the second charging case 12 can then be removed. The method of removing the spare battery 121 is not limited. Based on the above embodiments, the headphone charging case 100 or the first battery case is equipped with a control motherboard, circuit system, etc. The control motherboard can be electrically connected to the spare battery 121 through wires, contacts, etc., thereby obtaining the status information of the second battery. Because the headphone charging case 100 proposed in this invention uses the control motherboard to implement any of the above-mentioned headphone charging case charging and discharging management methods, the specific method of the headphone charging case charging and discharging management method refers to the above embodiments. Since the headphone charging case 100 proposed in this invention adopts all the technical solutions of the above embodiments, it at least has all the beneficial effects brought by the technical solutions of the above embodiments, which will not be elaborated here.

[0077] The present invention also proposes an earphone kit 1000, which includes wireless earphones 200 and an earphone charging case 100. The earphone charging case 100 is used to charge the wireless earphones 200. Please refer to [link to relevant documentation]. Figure 12 The bottom of the wireless earphone 200 is provided with a third charging contact 21 and a third magnetic structure 20. The third magnetic structure 20 can be attracted and fixed with the magnetic structure corresponding to the spare earphone. The third charging contact 21 can be electrically connected with the contact structure corresponding to the spare earphone. The specific structure of the earphone charging case 100 and the wireless earphone 200 refers to the above embodiment. The earphone charging case 100 is used to implement the earphone charging case charging and discharging management method of any of the above embodiments. Since the earphone kit 1000 proposed in the invention adopts all the technical solutions of the above embodiments, it has at least all the beneficial effects brought by the technical solutions of the above embodiments, which will not be described in detail here.

[0078] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural transformations made using the contents of the specification and drawings of the present invention under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of the present invention.

Claims

1. A method for managing the charging and discharging of an earphone charging case, characterized in that, The charging and discharging management method for the earphone charging case is applied to the earphone charging case, which is used to charge and recharge the earphones. The earphones are equipped with a first battery, and the charging case contains a second battery and a removable spare battery. The method includes: Obtain the status information of the first battery, the second battery, and the backup battery; By using a preset habit prediction model, based on the user's historical behavior data and real-time context data, the predicted energy demand of the user for the first battery during the target time period is determined. Based on the state information and the predicted energy demand, obtain the dynamic energy gap between the current total available energy of the first battery, the second battery and the backup battery and the predicted energy demand; Based on the dynamic energy gap, a charge / discharge management strategy is generated and executed.

2. The method as described in claim 1, characterized in that, The step of obtaining the status information of the first battery, the second battery, and the backup battery includes: The battery management unit of the earphone charging case obtains the first status information based on the real-time power and health indicators of the second battery. When the earphones are placed in the charging case, the real-time power level of the earphones' first battery is received to obtain second status information; Based on the coupling event between the backup battery and the earphone, the nominal capacity and current charge of the backup battery are obtained to obtain the third state information; The status information is obtained based on the first status information, the second status information, and the third status information.

3. The method as described in claim 1, characterized in that, The step of determining the predicted energy demand of the user for the first battery within a target time period by using a preset habit prediction model based on the user's historical behavior data and real-time context data includes: Obtain device usage time periods, single usage durations, and usage scenario types from users' historical behavior data to construct temporal features of user behavior. The real-time context data is obtained, which includes the current time, calendar events, and location information; By using a preset habit prediction model, based on the user behavior time sequence characteristics and the real-time context data, the predicted usage scenario and expected usage duration of the headphones within the target time period are obtained; Based on the predicted usage scenario and the expected usage duration, the predicted energy demand of the user on the first battery during the target time period is determined.

4. The method as described in claim 1, characterized in that, Before the step of determining the user's predicted energy demand for the first battery within a target time period based on the user's historical behavior data and real-time context data using a preset habit prediction model, the method further includes: Obtain historical behavior sequences of multiple users over a long period and their corresponding contextual labels; Based on the historical behavior sequence, periodic pattern characteristics, event correlation characteristics, and trend change characteristics are obtained; The preset habit prediction model is obtained by training the periodic pattern features, event association features, and trend change features through a time series prediction network.

5. The method as described in claim 1, characterized in that, The step of obtaining the dynamic energy gap between the current total available energy of the first battery, the second battery, and the backup battery and the predicted energy demand based on the state information and the predicted energy demand includes: Based on the real-time power levels of the first battery, the second battery, and the backup battery in the status information, the current total available energy is obtained; Based on the predicted energy demand and the current total available energy, the basic energy deficit value is obtained; The dynamic energy gap is obtained based on the health index of each battery in the status information and the basic energy gap value.

6. The method according to any one of claims 1 to 5, characterized in that, The step of generating and executing a charge / discharge management strategy based on the dynamic energy gap includes: When the dynamic energy gap is negative and its absolute value is greater than the first preset threshold, the first management mode is activated. In the first management mode, the charge / discharge management strategy is to control the second battery to charge the backup battery.

7. The method as described in claim 6, characterized in that, The step of generating and executing a charge / discharge management strategy based on the dynamic energy gap further includes: When the dynamic energy gap is positive and less than the second preset threshold, the second management mode is activated. In the second management mode, the charge / discharge management strategy is to control the second battery to charge the first battery.

8. The method as described in claim 7, characterized in that, The method for generating and executing a charge / discharge management strategy based on the dynamic energy gap also includes: When the dynamic energy gap is positive and greater than or equal to the second preset threshold, the third management mode is activated. In the third management mode, the charge / discharge management strategy outputs information suggesting the removal and use of the backup battery.

9. The method as described in claim 1, characterized in that, The method further includes: Obtain the actual energy consumption data of the first battery during the target time period; Based on the actual energy consumption data and the predicted energy demand, the actual deviation data is obtained; Based on the actual deviation data, the preset habit prediction model is incrementally updated.

10. An earphone charging case for implementing the earphone charging case charging and discharging management method as described in any one of claims 1 to 9, wherein the earphone charging case is used to charge and replenish the earphones, and the earphones are provided with a first battery, characterized in that, The earphone charging case includes a second battery, a removable spare battery, and a control motherboard, which is electrically connected to the second battery, the spare battery, and the first battery. The control motherboard is configured to execute the charge / discharge management method.

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