Startup control method, battery management system, electronic equipment and storage medium

Abstract

The invention relates to the technical field of battery management, and discloses a power-on control method, a battery management system, electronic equipment and a storage medium, and the method comprises the steps: obtaining the voltage, electric quantity, temperature, aging degree and other state information of a battery, correcting the electric quantity based on the aging degree to obtain the real electric quantity, and dynamically adjusting the power-on condition based on the temperature. And comparing the real electric quantity and the voltage with the adjusted startup condition to judge whether a startup requirement is met or not, and if yes, predicting a user use scene and providing startup and shutdown suggestions based on the real electric quantity. According to the method and the device, the problems of startup failure and abnormal power failure of a traditional fixed startup threshold value in a low-temperature environment are solved, the reliability of equipment in a severe environment is improved, hardware damage is avoided, user experience and the intelligent level of the equipment are improved, and the method and the device are particularly suitable for portable electronic equipment such as AR glasses and VR equipment.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and in particular to a power-on control method, a battery management system, an electronic device, and a storage medium. Background Technology

[0002] Existing electronic devices typically use a fixed power-on voltage as the power-on condition. When the battery voltage reaches a preset power-on voltage threshold, the device will perform a power-on operation; otherwise, it will refuse to power on or will automatically shut down.

[0003] This fixed-threshold power-on control method has significant technical flaws. First, it fails to consider the instantaneous load demands of the system during power-on. When the battery voltage is only slightly higher than the power-on voltage threshold, the high current load during power-on may cause the battery voltage to drop momentarily, resulting in insufficient power supply and abnormal power outages, which could potentially damage the hardware circuitry.

[0004] Secondly, existing technology overlooks the significant impact of ambient temperature on battery performance. In low-temperature environments, the battery's internal resistance increases substantially, and the impedance of the flexible connectors on the circuit board also rises, leading to a significant decrease in the battery's actual power supply capacity. If a fixed start-up voltage threshold determined at room temperature is still used in such conditions, insufficient voltage can easily occur during startup.

[0005] Furthermore, existing technologies do not fully consider the impact of battery aging on its capacity and performance. As the number of charge-discharge cycles increases, the actual capacity of the battery gradually decreases, but the existing power-on judgment mechanism is still based on the battery's nominal capacity for evaluation, leading to a decrease in the accuracy of power-on condition judgment.

[0006] Furthermore, existing devices lack the ability to intelligently identify user scenarios. Different usage scenarios correspond to different system power consumption levels, but current technology cannot predict the actual usage needs of users after powering on, and therefore cannot provide users with reasonable power-on / off suggestions. In this situation, users may launch high-power applications when the battery is low, causing the device to shut down quickly due to depleted power, affecting the user experience.

[0007] The aforementioned technical issues are particularly prominent in portable electronic devices, especially AR glasses and VR devices, which have high requirements for battery life and stability, affecting the reliability of the devices and the user experience. Summary of the Invention

[0008] The purpose of this invention is to provide a power-on control method, a battery management system, an electronic device, and a storage medium to solve the technical problem in the prior art that the use of a fixed power-on voltage threshold does not take into account the impact of battery aging and low temperature environment on battery performance, which easily leads to abnormal power loss or failure to power on normally during the power-on process.

[0009] To solve the above-mentioned technical problems, the present invention provides a power-on control method, comprising:

[0010] Obtain battery status information, including voltage, charge level, temperature, and degree of aging;

[0011] The actual power level is obtained by correcting the power consumption based on the aging degree, and the power-on conditions are dynamically adjusted based on the temperature.

[0012] The actual power consumption and voltage are compared with the adjusted power-on conditions to determine whether the power-on requirements are met.

[0013] When the power-on requirements are met, predict the user's usage scenario and provide power-on / off suggestions based on the actual power consumption.

[0014] Optionally, the degree of aging is characterized by the number of charge-discharge cycles, and the step of correcting the battery capacity based on the degree of aging includes:

[0015] Based on battery charge-discharge cycle test data, a curve showing the relationship between the number of charge-discharge cycles and the percentage of remaining battery capacity was established.

[0016] The relationship curve is piecewise fitted;

[0017] The actual capacity of the battery is calculated based on the current number of charge-discharge cycles.

[0018] Optionally, the step of dynamically adjusting the power-on conditions based on temperature includes:

[0019] Obtain power-on parameter data at different temperatures;

[0020] Establish a model relating temperature to startup parameters;

[0021] The power-on conditions are determined based on the current temperature and the aforementioned relationship model.

[0022] Optionally, the power-on parameters include a power-on voltage threshold and a power-on energy threshold.

[0023] Optionally, the step of determining whether the power-on requirements are met includes: determining whether the battery voltage meets the power-on voltage requirements and whether the actual power capacity meets the power-on power requirements.

[0024] Optionally, the process of predicting user usage scenarios includes: using a multimodal time-series fusion scenario prediction algorithm, trained based on historical user usage data.

[0025] The present invention also provides a battery management system for implementing the above method, comprising:

[0026] Status acquisition module, used to acquire battery status information;

[0027] A capacity calibration module is used to calibrate battery capacity based on the degree of battery aging.

[0028] The condition adjustment module is used to dynamically adjust the power-on conditions based on temperature.

[0029] The power-on judgment module is used to determine whether to execute the power-on process based on the adjusted power-on conditions;

[0030] The scenario prediction module is used to predict user scenarios and provide power on / off suggestions.

[0031] Optionally, the status acquisition module includes a fuel meter.

[0032] The present invention also provides an electronic device including the battery management system described above.

[0033] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the power-on control method as described above.

[0034] Compared with the prior art, the present invention has at least the following technical effects:

[0035] By comprehensively considering the impact of battery aging and ambient temperature on battery performance, the system dynamically adjusts power-on conditions, avoiding power-on failures or abnormal power outages that may occur with traditional fixed threshold methods. Simultaneously, a dual judgment mechanism based on actual battery level and voltage improves the accuracy and reliability of power-on condition determination, effectively protecting electronic device hardware from damage and enhancing device stability and user experience under various environmental conditions.

[0036] This invention also introduces a multimodal time-series fusion scenario prediction algorithm to predict user usage scenarios and provides users with personalized power-on / off suggestions based on the current battery status, thereby improving the intelligence level of the device and helping users to rationally arrange device usage, avoiding rapid shutdown due to insufficient power in high-power scenarios, thus improving the user experience and extending the effective usage time of the device. Attached Figure Description

[0037] Figure 1 This is a schematic flowchart of a power-on control method according to an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of the steps for correcting battery capacity in an embodiment of the present invention;

[0039] Figure 3 This is a flowchart illustrating the steps for adjusting the power-on conditions in an embodiment of the present invention;

[0040] Figure 4This is a schematic diagram of the steps for providing power-on / off suggestions in an embodiment of the present invention;

[0041] Figure 5 This is a schematic diagram of a scene prediction algorithm for multimodal temporal fusion in an embodiment of the present invention;

[0042] Figure 6 This is a schematic diagram of a battery management system module in an embodiment of the present invention. Detailed Implementation

[0043] The following description, with reference to schematic diagrams, illustrates a power-on control method, a battery management system, an electronic device, and a storage medium according to the present invention. Preferred embodiments of the invention are shown. It should be understood that those skilled in the art can modify the invention described herein while still achieving its advantageous effects. Therefore, the following description should be understood as being of general knowledge to those skilled in the art and is not intended to limit the invention.

[0044] Based on the teachings of this specification, those skilled in the art can form new technical solutions by combining different implementation methods without creating technical contradictions. Such variations should be considered to fall within the protection scope of this application.

[0045] The invention is described more specifically by way of example in the following paragraphs with reference to the accompanying drawings. The advantages and features of the invention will become clearer from the following description. It should be noted that the drawings are in a very simplified form and use non-precise proportions, and are only used to facilitate and clarify the illustration of the embodiments of the invention.

[0046] Example 1

[0047] This embodiment provides a power-on control method, such as... Figure 1 As shown, it includes the following steps:

[0048] S1. Obtain battery status information, including voltage, charge, temperature, and degree of aging;

[0049] S2. Correct the power consumption based on the aging degree to obtain the actual power consumption, and dynamically adjust the power-on conditions based on the temperature;

[0050] S3. Compare the actual power consumption and the voltage with the adjusted power-on conditions to determine whether the power-on requirements are met.

[0051] S4. When the power-on requirements are met, predict the user's usage scenario and provide power-on / off suggestions based on the actual power consumption.

[0052] The power-on control method provided in this embodiment optimizes the power-on conditions of electronic devices. It considers the impact of low temperatures on battery voltage and charge, dynamically adjusts the power-on conditions, and avoids abnormal power outages during power-on that could damage hardware. This optimizes the user experience and system stability during power-on. The method predicts user scenarios and provides power-on / off suggestions based on the current battery charge and the predicted usage scenario, improving the device's intelligence and user choice.

[0053] In step S1, after the electronic device is powered on, the battery status information is obtained through the fuel gauge. The status information includes voltage, charge, temperature and aging degree.

[0054] The degree of aging is characterized by the number of charge-discharge cycles, which can accurately reflect the actual capacity decay of the battery.

[0055] Specifically, the fuel gauge connects to the system chip via an FPC (flexible printed circuit board) to monitor various battery parameters in real time. In one specific example, the fuel gauge uses coulomb calculation or voltage integration to obtain the battery capacity, uses a thermistor or built-in temperature sensor to obtain the battery temperature, and records the cumulative number of charge-discharge cycles.

[0056] In step S2, firstly, the power is corrected based on the degree of aging to obtain the true power.

[0057] like Figure 2 As shown, this step includes:

[0058] S211: Based on battery charge-discharge cycle test data, establish a curve showing the relationship between the number of charge-discharge cycles and the percentage of remaining battery capacity.

[0059] For example, by collecting capacity decay data of batteries within the range of 0-1000 charge-discharge cycles, the pattern of battery capacity change with the number of cycles can be obtained.

[0060] S212: Perform piecewise fitting on the relationship curve.

[0061] In a specific example, a polynomial fitting or exponential function fitting method can be used to express the relationship between the number of iterations and the capacity percentage as a mathematical formula.

[0062] In a specific example, test data of the remaining battery capacity corresponding to 0-1000 battery charge-discharge cycles are obtained. The average value of the test data of multiple battery cells is taken, and the remaining battery capacity value is converted into the percentage of remaining battery capacity. Piecewise curve fitting is performed on the discrete data of battery charge-discharge cycle number - battery remaining capacity percentage, and the calculation formula of battery remaining capacity percentage corresponding to different battery charge-discharge cycles is obtained.

[0063] S213: Calculate the actual battery capacity based on the current number of charge-discharge cycles.

[0064] In step S2, the power-on conditions are further dynamically adjusted based on the temperature. For example... Figure 3 As shown, this step includes:

[0065] S221: Obtain power-on parameter data at different temperatures.

[0066] For example, through experimental testing, within a temperature range of -20℃ to 40℃, at 5℃ intervals, the minimum voltage and power requirements for the equipment to start normally at each temperature point were measured.

[0067] S222: Establish a model relating temperature to startup parameters.

[0068] The power-on parameters include the power-on voltage threshold and the power-on power threshold.

[0069] By fitting and analyzing the experimental data, the temperature-start-up voltage relationship and the temperature-start-up power relationship were obtained.

[0070] S223: Determine the power-on conditions based on the current temperature and the relationship model.

[0071] Specifically, the chamber temperature is set to -30℃. A device with 1% battery charge is placed in the chamber, powered on, and the battery voltage, charge / discharge cycle count, and battery capacity are acquired. If the device fails to power on, it is charged until the charge increases by 1% and it can then power on. After powering on, the remaining battery capacity percentage is calculated based on the current charge / discharge cycle count. The actual battery capacity is obtained by multiplying the remaining capacity percentage by the actual battery capacity. The power-on battery voltage and capacity at this temperature are recorded. The chamber temperature is checked to ensure it is above 25℃. The power-on voltage and capacity data at different temperatures are compiled. Segmented curves are fitted to the discrete data of temperature versus power-on charge and the discrete data of temperature versus battery charge, respectively, and the formulas for calculating power-on voltage and battery charge for different temperature ranges are obtained. If the chamber temperature is below 25℃, the chamber temperature is increased by 1℃, the device is discharged to 1% charge, and the data is retrieved again to acquire the battery voltage, charge / discharge cycle count, and battery capacity.

[0072] In this embodiment, by comprehensively considering the effects of battery aging and temperature, this step solves the problem that the fixed power-on threshold in the prior art cannot adapt to the actual use environment, significantly improving the power-on success rate and avoiding abnormal power outages.

[0073] In step S3, the actual power consumption and the voltage are compared with the adjusted power-on conditions to determine whether the power-on requirements are met.

[0074] Specifically, it is determined whether the battery voltage meets the power-on voltage requirement and whether the actual power capacity meets the power-on power requirement.

[0075] The judgment condition is: current voltage ≥ power-on voltage threshold AND actual power level ≥ power-on power level threshold.

[0076] This dual-judgment mechanism ensures that there is sufficient voltage and power to support the system startup during the power-on process, effectively avoiding abnormal power outages during startup.

[0077] In step S4, when the power-on requirements are met, the user's usage scenario is predicted and a power-on / off suggestion is provided based on the actual power consumption.

[0078] The process of predicting user usage scenarios includes: using a multimodal temporal fusion scenario prediction algorithm, trained based on historical user usage data.

[0079] like Figure 4 As shown, the process includes:

[0080] S41: Record user usage scenario data, including but not limited to application type, usage duration, system power consumption, etc.

[0081] S42: Upload the usage scenario data to the cloud server to train the multimodal temporal fusion scenario prediction model.

[0082] S43: Based on a pre-trained multimodal temporal fusion scene prediction model, predict the possible usage scenarios of the user after this power-on.

[0083] S44: Combines predicted power consumption in the usage scenario with the current actual battery level to calculate the estimated usage time and provide users with personalized power on / off suggestions.

[0084] Usage scenarios can be categorized into low-power scenarios (reading, browsing), medium-power scenarios (video playback, simple games), and high-power scenarios (3D games, AR applications). Different scenarios correspond to different power consumption coefficients. The system calculates the usable time based on the predicted scenario and the current battery level. When the usable time is lower than a preset threshold, it recommends that the user charge the device before use.

[0085] In a specific example, the predicted usage scenario is taking photos and videos, and the system checks if the battery level is greater than 20%; the predicted usage scenario is translation, and the system checks if the battery level is greater than 16%; the predicted usage scenario is AI image and object recognition, and the system checks if the battery level is greater than 13%; the predicted usage scenario is music playback, and the system checks if the battery level is greater than 10%. If the prediction is correct, the device is powered on; otherwise, a pop-up window reminds the user that the battery is low for this scenario and recommends charging. The user then chooses whether to power on the device.

[0086] Specifically, the multimodal temporal fusion scene prediction algorithm is an intelligent scene prediction technology based on neural networks, which achieves accurate scene recognition and prediction by fusing multiple data sources. For details, please refer to... Figure 5 This includes the following steps:

[0087] 1. Multimodal data acquisition:

[0088] The cloud collects multimodal data such as sensor data, user behavior data, and spatiotemporal information in real time to build a comprehensive data input foundation.

[0089] 2. Data preprocessing and feature extraction:

[0090] The sliding window technique is used to preprocess the raw data, and the feature sequences of each mode are generated through the modality coding layer to extract key time-related feature information.

[0091] 3. Cross-modal feature fusion:

[0092] By employing an attention mechanism, cross-modal feature fusion is achieved, automatically identifying the correlation and importance between different modalities and generating a comprehensive fusion feature representation.

[0093] 4. Scene prediction output:

[0094] By combining historical scene sequence data to perform time series modeling, specific scene category prediction results are output, and prediction confidence assessment is also provided.

[0095] 5. Continuous model optimization:

[0096] The weight allocation of each modality feature is dynamically adjusted, and new data is continuously received in the cloud for model fine-tuning, thereby continuously improving the generalization ability and prediction accuracy of the algorithm.

[0097] In this embodiment, the algorithm is combined with power information to provide intelligent usage suggestions for terminal devices, achieving a balance between energy consumption optimization and user experience improvement.

[0098] The dynamic power-on control method of this embodiment has the following beneficial effects:

[0099] By comprehensively considering battery aging and ambient temperature, the system dynamically adjusts power-on conditions, resolving the issue of power-on failure in low-temperature environments caused by traditional fixed threshold methods, thus improving the power-on success rate in low-temperature environments by approximately 30%. A dual judgment mechanism (voltage + actual battery level) is employed to avoid abnormal power outages during power-on, reducing the risk of hardware damage and improving device reliability. The introduction of intelligent scene prediction functionality provides users with personalized usage suggestions, enhancing the user experience and the device's intelligence level.

[0100] Example 2

[0101] This embodiment provides an electronic device battery management system that implements the dynamic power-on control method in Embodiment 1. Please refer to [link / reference]. Figure 6 The electronic device battery management system includes: a status acquisition module, a capacity correction module, a condition adjustment module, a power-on judgment module, and a scenario prediction module.

[0102] The status acquisition module is used to acquire battery status information.

[0103] The status acquisition module includes a power meter, which is connected to the system main control chip SOC via an FPC.

[0104] The power meter uses a high-precision ADC sampling circuit, which can monitor battery voltage, current, temperature and other parameters in real time, and count the number of charge and discharge cycles through the built-in coulomb counter.

[0105] The capacity calibration module is used to calibrate battery capacity based on the degree of battery aging.

[0106] In one specific example, the capacity correction module has a built-in battery aging algorithm that stores the correspondence between the number of charge-discharge cycles and the battery capacity decay.

[0107] The actual battery capacity percentage can be determined by looking up a table or using a formula, based on the current number of cycles, thus obtaining the true battery capacity.

[0108] The condition adjustment module is used to dynamically adjust the power-on conditions based on temperature.

[0109] The condition adjustment module stores a model of the relationship between temperature and power-on parameters, including the temperature-power-on voltage relationship and the temperature-power-on power relationship.

[0110] This module calculates the corresponding power-on voltage threshold and power-on power threshold in real time based on the current temperature.

[0111] The power-on determination module is used to determine whether to execute the power-on process based on the adjusted power-on conditions.

[0112] The power-on judgment module receives battery parameters from the status acquisition module and dynamic thresholds from the condition adjustment module, performs logical judgment operations, and outputs a power-on or power-off command.

[0113] The scenario prediction module is used to predict user scenarios and provide power on / off suggestions.

[0114] In a specific example, the scene prediction module connects to the cloud server via Wi-Fi or cellular network to obtain scene prediction results trained by a scene prediction algorithm based on multimodal time-series fusion, and generates usage suggestions by combining local power information.

[0115] Please continue to refer to this. Figure 6 The connections between the modules are as follows: The status acquisition module acts as the data source, connecting to the capacity correction module, condition adjustment module, and power-on judgment module. It transmits battery level and cycle count to the capacity correction module, temperature information to the condition adjustment module, and voltage information to the power-on judgment module. The capacity correction module connects to both the power-on judgment module and the scene prediction module, transmitting the calculated actual battery level to both. The condition adjustment module connects to the power-on judgment module, transmitting the dynamically adjusted power-on threshold to it. The power-on judgment module connects to the scene prediction module, triggering its operation when the power-on conditions are met. All modules communicate via an internal data bus.

[0116] The status acquisition module first acquires various status parameters of the battery; the capacity correction module calculates the actual charge level based on the degree of aging; the condition adjustment module determines the dynamic power-on threshold based on temperature; the power-on judgment module performs a comparison judgment; and the scenario prediction module provides intelligent suggestions when needed. Data transmission between the modules is conducted via an internal bus, a common practice among those skilled in the art and will not be elaborated further.

[0117] This embodiment employs a modular design, resulting in a clear system structure that facilitates integration and maintenance. Each functional module can be independently optimized and upgraded. It achieves accurate battery status monitoring and intelligent power-on control, improving system reliability and user experience. The system possesses machine learning capabilities, enabling continuous optimization of prediction accuracy and adaptation to different user habits.

[0118] Furthermore, taking AR glasses as an example, the AR glasses device includes the aforementioned battery management system. As a wearable device, AR glasses are frequently used in low-temperature outdoor environments, thus requiring high reliability in battery management.

[0119] When users attempt to activate AR glasses in low-temperature environments, traditional solutions may fail to start properly due to a fixed power-on voltage threshold. The dynamic power-on control method of this invention adjusts the power-on threshold according to the current temperature, improving the success rate of power-on.

[0120] At the same time, when the battery is low, the system can predict whether the user is going to use it lightly (such as viewing information) or heavily (such as playing AR games), and provide suggestions accordingly, such as whether it can be used for a short time or should be charged before use.

[0121] For example, when the ambient temperature is -10℃, the battery displays 15% charge, and the actual charge level after correction is 12%, a traditional solution might refuse to power on. However, this solution will determine whether power-on is possible based on the temperature-adjusted threshold and will prompt the user that the device can be used for approximately 20 minutes based on the predicted light usage scenario.

[0122] In this embodiment, the usability of AR glasses in low-temperature environments is significantly improved, expanding the range of application scenarios for the device. Intelligent power management and usage suggestions extend the effective operating time of the device, improving the user experience. The risk of data loss and hardware damage due to abnormal power outages is reduced, enhancing product reliability and user satisfaction.

[0123] The method and battery management system provided in this embodiment are not only applicable to AR glasses, but can also be widely applied to other portable electronic devices, including but not limited to: VR headsets, smartwatches, smart bracelets, portable game consoles, drones, and smart camera devices.

[0124] Accordingly, other embodiments of this application may also provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the various method embodiments of this application. Computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information may be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0125] In summary, this invention acquires battery status information such as voltage, charge, temperature, and aging level. Based on the aging level, it corrects the charge level to obtain the true charge. It dynamically adjusts the power-on conditions based on temperature and compares the true charge and voltage with the adjusted power-on conditions to determine if the requirements are met. If the requirements are met, it predicts the user's usage scenario and provides power-on / off suggestions. This solution effectively solves the problems of power-on failure and abnormal power loss caused by fixed power-on thresholds in low-temperature environments in existing technologies. By comprehensively considering the effects of battery aging and ambient temperature, it achieves intelligent dynamic adjustment of power-on conditions, significantly improving the reliability of electronic devices and user experience in low-temperature environments. Simultaneously, through a multimodal time-series fusion scenario prediction algorithm, it predicts user usage scenarios and provides personalized usage suggestions, improving the device's intelligence level and market competitiveness. This invention is applicable to various portable electronic devices such as AR glasses, VR devices, and smart wearable devices, and has broad application prospects and significant technical value.

[0126] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A power-on control method, characterized in that, include: Obtain battery status information, including voltage, charge level, temperature, and degree of aging; The actual power level is obtained by correcting the power consumption based on the aging degree, and the power-on conditions are dynamically adjusted based on the temperature. The actual power consumption and voltage are compared with the adjusted power-on conditions to determine whether the power-on requirements are met. When the power-on requirements are met, predict the user's usage scenario and provide power-on / off suggestions based on the actual power consumption.

2. The power-on control method according to claim 1, characterized in that, The degree of aging is characterized by the number of charge-discharge cycles, and the step of correcting battery capacity based on the degree of aging includes: Based on battery charge-discharge cycle test data, a curve showing the relationship between the number of charge-discharge cycles and the percentage of remaining battery capacity was established. The relationship curve is piecewise fitted; The actual capacity of the battery is calculated based on the current number of charge-discharge cycles.

3. The power-on control method according to claim 1, characterized in that, The steps for dynamically adjusting the power-on conditions based on temperature include: Obtain power-on parameter data at different temperatures; Establish a model relating temperature to startup parameters; The power-on conditions are determined based on the current temperature and the aforementioned relationship model.

4. The power-on control method according to claim 3, characterized in that, The power-on parameters include the power-on voltage threshold and the power-on power threshold.

5. The power-on control method according to claim 1, characterized in that, The step of determining whether the power-on requirements are met includes: determining whether the battery voltage meets the power-on voltage requirements and whether the actual power capacity meets the power-on power requirements.

6. The power-on control method according to claim 1, characterized in that, The process of predicting user usage scenarios includes: using a multimodal temporal fusion scenario prediction algorithm, trained based on historical user usage data.

7. A battery management system for implementing the method of any one of claims 1 to 6, characterized in that, include: Status acquisition module, used to acquire battery status information; A capacity calibration module is used to calibrate battery capacity based on the degree of battery aging. The condition adjustment module is used to dynamically adjust the power-on conditions based on temperature. The power-on judgment module is used to determine whether to execute the power-on process based on the adjusted power-on conditions; The scenario prediction module is used to predict user scenarios and provide power on / off suggestions.

8. The battery management system according to claim 7, characterized in that, The status acquisition module includes a fuel meter.

9. An electronic device, characterized in that, The battery management system includes any one of claims 7 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the power-on control method as described in any one of claims 1 to 6.

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