Battery charging management method and accurate electric quantity display method based on electronic equipment

Abstract

The invention relates to the technical field of electronic equipment battery management, and discloses a battery charging management method and an electric quantity accurate display method based on electronic equipment. The dynamic adjusting module generates a multi-dimensional strategy through a staged charging control unit (including a fast, balanced and trickle charging sub-unit), a parameter transition processing layer and a global charging state pooling layer; the environment adaptation module adjusts the charging power and the temperature threshold according to the environment data, and has the functions of environment feature extraction, self-adaptive compensation and anti-interference switching; and the strategy fusion module outputs a control instruction in a weighted manner, and is additionally provided with an exception handling and wireless charging optimization mechanism. When the electric quantity is displayed, the collected data is dynamically calibrated to compensate polarization errors, and the capacity is fused with a matching historical curve to generate a high-precision electric quantity percentage which is dynamically rendered and displayed. According to the scheme, the charging safety, efficiency and electric quantity display precision are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management for electronic devices, and particularly to a battery charging management method and an accurate power display method based on an electronic device. Background Art

[0002] With the popularization of electronic devices, the battery, as its core energy source, the accuracy and reliability of its charging management and power display have become key issues to be solved urgently. Traditional battery charging management methods usually adopt fixed charging strategies and cannot be dynamically adjusted according to the real-time state of the battery and the usage environment, resulting in low charging efficiency, shortened battery life, and even potential safety hazards. For example, in different temperature environments, the charging mechanism of the battery will change significantly. High-temperature environments may cause the battery to overheat and trigger safety accidents such as explosions, while low-temperature environments will reduce the battery's charging acceptance ability and prolong the charging time. At the same time, most traditional power display methods are based on simple voltage detection, ignoring the influence of factors such as battery polarization effect and charge-discharge history, resulting in large power display errors and being unable to provide users with accurate remaining power information, affecting the user experience.

[0003] In the prior art, although some charging management methods introduce the concept of staged charging, such as fast charging, balanced charging, and trickle charging, these methods often lack a comprehensive analysis of the battery health state and the dynamic generation of multi-dimensional charging strategies. In addition, the consideration of environmental factors is relatively single, usually only taking simple protection measures for temperature, without fully considering the comprehensive influence of multi-environmental factors such as temperature and humidity, and electromagnetic interference on the charging process. In terms of power display, traditional methods do not establish an effective calibration mechanism to compensate for the display error caused by the battery polarization effect, nor do they make full use of historical charge-discharge data to improve the accuracy of power estimation.

[0004] With the continuous development of intelligent electronic devices, users have higher and higher requirements for battery performance, and there is an urgent need for a method that can dynamically adjust the charging strategy according to the real-time state of the battery and the usage environment and achieve accurate power display. Therefore, researching a battery charging management method and an accurate power display method based on an electronic device has important practical significance and application value. This method needs to be able to obtain battery parameters and usage environment data in real time, generate multi-dimensional charging strategies, dynamically adjust the charging power and temperature control thresholds, and realize the intelligent management of the charging process; at the same time, it is necessary to establish an effective power calibration and fusion mechanism to improve the accuracy and reliability of power display and provide users with a better user experience. Summary of the Invention

[0005] The purpose of the present invention is to provide a battery charging management method and an accurate power display method based on an electronic device to solve the problems raised in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solutions: A battery charging management method based on an electronic device, the method comprising: Obtain the real-time battery parameters and usage environment data of the device to be charged; Input the battery parameters into a dynamic adjustment module for analyzing the battery health status and generating a multi-dimensional charging strategy; Input the usage environment data into an environment adaptation module for dynamically adjusting the charging power and temperature control threshold; Perform a weighted decision on the multi-dimensional charging strategy and the adjusted charging parameters through a strategy fusion module to output a final charging control instruction.

[0007] Preferably, the dynamic adjustment module includes: A stage-based charging control unit, the stage-based charging control unit includes a fast charging sub-unit, an equalizing charging sub-unit, and a trickle charging sub-unit. The fast charging sub-unit is used to identify the low battery state and trigger a large current input, and the equalizing charging sub-unit is used to balance the voltage difference inside the battery; A parameter transition processing layer for compressing the feature dimension output by the stage-based charging control unit; A global charging status pooling layer for uniformly mapping the processed charging parameters to a preset regulation range.

[0008] Preferably, three of the stage-based charging control units are sequentially deployed in the dynamic adjustment module. The first stage-based charging control unit is connected to the second stage-based charging control unit through the parameter transition processing layer, and the second stage-based charging control unit is connected to the third stage-based charging control unit through the parameter transition processing layer. The output of the third stage-based charging control unit is integrated by the global charging status pooling layer and then transmitted to the strategy fusion module.

[0009] Preferably, the stage-based charging control unit processes the input battery parameters through the following steps: Extract features from the battery parameters according to the first processing link to generate a first feature group including current demand and voltage fluctuation range; Perform time series analysis on the battery parameters according to the second processing link to generate a second feature group including charging cycle and decay trend; Fuse the first feature group and the second feature group, and generate an output instruction of the stage-based charging control unit through a non-linear activation function.

[0010] Preferably, the fast charging sub-unit executes control through the following steps: Real-time monitor the surface temperature of the battery and the change rate of the input current; When the detected temperature exceeds the dynamic threshold, activate the current attenuation coefficient and generate a current reduction control signal; Superimpose the current reduction control signal on the original charging instruction and output the adjusted charging current parameter.

[0011] Preferably, the environment adaptation module includes: An environmental feature extraction unit for identifying the temperature, humidity and electromagnetic interference intensity of the environment where the device is located; An adaptive compensation unit for dynamically correcting the charging voltage fluctuation range according to the environmental features; An interference shielding decision layer for switching to the anti-interference charging mode when the electromagnetic interference exceeds the limit.

[0012] Preferably, the adaptive compensation unit operates through the following steps: Construct a mapping relationship table between environmental data and battery internal resistance; Query the mapping relationship table according to the real-time environmental features and output the corresponding voltage compensation amount; Embed the voltage compensation amount into the charging control instruction to offset the influence of environmental fluctuations on the charging process.

[0013] Preferably, it further includes an exception handling mechanism: Continuously collect battery expansion data and the change rate of internal impedance during the charging process; When the detected expansion rate exceeds the safety threshold, immediately cut off the charging circuit and trigger a warning signal; Reverse calculate the battery aging degree according to the impedance change trend and update the maximum current limit of the subsequent charging strategy.

[0014] Preferably, the strategy fusion module further includes a wireless charging optimization function: Obtain the coupling efficiency of the wireless charging coil through an electromagnetic field intensity sensor; Dynamically adjust the transmitting end frequency to match the resonance characteristics of the receiving end; When multiple devices are charging simultaneously, allocate different energy transmission priorities based on the spatial position data.

[0015] Preferably, it further includes a method for accurately displaying the charging power of the battery of an electronic device, which is applied to the battery charging management method of an electronic device as described above, and includes the following steps: S1: Real-time collect the battery terminal voltage, load current and the estimated remaining capacity; S2: Input the terminal voltage and load current into the dynamic calibration module to compensate for the display error caused by the battery polarization effect; S3: Match the calibrated power data with the historical charge and discharge curves through the capacity fusion unit to generate a high-precision remaining power percentage; S4: Dynamically render the power icon on the display interface and switch the display precision mode according to the differences in usage scenarios.

[0016] Compared with the prior art, the beneficial effects of the present invention are: In terms of charging management, by obtaining the real-time battery parameters and usage environment data of the device to be charged, the current state and the environment of the battery can be comprehensively understood. The dynamic adjustment module analyzes the battery health status based on the battery parameters and generates a multi-dimensional charging strategy. The stage charging control unit includes a fast charging sub-unit, a balancing charging sub-unit, and a trickle charging sub-unit, which can adopt appropriate charging methods for different power states. The fast charging sub-unit can trigger a large current input at low power to improve the charging efficiency; the balancing charging sub-unit can balance the internal voltage difference of the battery to avoid local overcharging or undercharging of the battery and extend the battery life; the trickle charging sub-unit uses a small current to charge the battery when it is close to full to prevent overcharging. The parameter transition processing layer and the global charging status pooling layer process and integrate the charging parameters to make the charging parameters more stable and reliable.

[0017] The environment adaptation module dynamically adjusts the charging power and temperature control threshold according to the usage environment data. The environment feature extraction unit identifies the temperature, humidity, and electromagnetic interference intensity of the environment where the device is located. The adaptive compensation unit dynamically corrects the charging voltage fluctuation range according to the environment features. The interference shielding decision layer switches to the anti-interference charging mode when the electromagnetic interference exceeds the limit, effectively coping with the influence of different environmental factors on the charging process and improving the stability and safety of charging. The strategy fusion module makes a weighted decision on the multi-dimensional charging strategy and the adjusted charging parameters and outputs the final charging control instruction, realizing the optimal combination of charging strategies.

[0018] The abnormal handling mechanism continuously collects the battery expansion data and the internal impedance change rate during the charging process. When an abnormality is detected, the charging circuit is cut off in time and a warning signal is triggered. At the same time, the battery aging degree is inversely calculated according to the impedance change trend, and the maximum current limit of the subsequent charging strategy is updated, further ensuring charging safety and extending the battery life. The wireless charging optimization function obtains the coupling efficiency of the wireless charging coil through the electromagnetic field intensity sensor, dynamically adjusts the transmitting end frequency to match the resonance characteristics of the receiving end, and improves the wireless charging efficiency. When multiple devices are charged simultaneously, different energy transmission priorities are allocated based on the spatial position data, realizing the reasonable scheduling of multi-device charging.

[0019] In terms of accurate power display, the battery terminal voltage, load current, and estimated remaining capacity are collected in real time. The terminal voltage and load current are input into the dynamic calibration module to compensate for the display error caused by the battery polarization effect. The calibrated power data is matched with the historical charge and discharge curve through the capacity fusion unit to generate a high-precision remaining power percentage. The power icon is dynamically rendered on the display interface, and the display precision mode is switched according to the usage scenario difference, enabling users to accurately understand the remaining power of the battery and improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is the working schematic diagram of the battery charging management method based on the electronic device of the present invention; Figure 2 is the flowchart of the connection relationship of the stage charging control unit in the dynamic adjustment module; Figure 3 is the flowchart of the battery parameter processing by the stage charging control unit; Figure 4 is the flowchart of the control method of the fast charging sub-unit; Figure 5 is the flowchart of the operation of the adaptive compensation unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] Please refer to Figures 1-5 , the present invention provides a technical solution: a battery charging management method based on an electronic device, the method includes: Obtain the real-time battery parameters and usage environment data of the device to be charged. Among them, the real-time battery parameters at least include physical quantities reflecting the battery state such as battery terminal voltage, charging current, estimated remaining capacity, battery surface temperature, internal impedance, expansion data, etc.; the usage environment data includes environmental characteristic parameters such as temperature, humidity, and electromagnetic interference intensity of the environment where the device is located, and can be obtained by real-time collection through sensors built in the device or external sensor modules.

[0023] Input the battery parameters into the dynamic adjustment module, which is used to analyze the battery health status and generate a multi-dimensional charging strategy. The dynamic adjustment module processes and analyzes the battery parameters to determine the current charging stage of the battery (such as low battery state, medium battery state, high battery state, etc.) and health status (such as aging degree, internal resistance change trend, etc.), so as to generate charging strategies for different dimensions, such as the charging current magnitude and voltage control range at different stages.

[0024] Input the usage environment data into the environment adaptation module, which is used to dynamically adjust the charging power and temperature control threshold. The environment adaptation module analyzes the impact of environmental factors on the charging process based on data such as environmental temperature and humidity, electromagnetic interference, etc. For example, in a high-temperature environment, it is necessary to reduce the charging power to avoid overheating of the battery. In a high-humidity environment, it may be necessary to adjust the temperature control threshold to ensure charging safety. When the electromagnetic interference is strong, corresponding anti-interference measures need to be taken, etc., and then dynamically adjust the power output and temperature control standard during the charging process.

[0025] The strategy fusion module makes a weighted decision on the multi-dimensional charging strategy and the adjusted charging parameters to output the final charging control instruction. The strategy fusion module comprehensively considers the multi-dimensional charging strategy generated by the dynamic adjustment module and the charging parameters adjusted by the environment adaptation module, makes a decision according to the preset weight rules, and forms the final charging control instruction, which is used to control the charging process of the charging device, such as adjusting parameters such as charging current, voltage, and frequency, so as to achieve safe, efficient, and intelligent charging management.

[0026] The following further illustrates the present invention in conjunction with Embodiments 1 to 5: Embodiment 1: In the specific structure of the dynamic adjustment module, the stage-based charging control unit, the parameter transition processing layer, and the global charging status pooling layer form a hierarchical processing architecture. The stage-based charging control unit, as the core execution unit, includes three functional sub-modules: the fast charging sub-unit, the balancing charging sub-unit, and the trickle charging sub-unit. Each sub-module works together through preset logic to cover the charging requirements of the entire battery life cycle.

[0027] The triggering mechanism of the fast charging sub-unit is based on the determination of the power threshold. When the estimated value of the remaining battery capacity collected in real time is lower than the preset low power threshold (such as 20%), this unit triggers the large current input mode through the current control circuit, and the input current magnitude is dynamically adjusted according to the battery specifications and health status. For example, for a lithium-ion battery with a nominal capacity of 3000 mAh, the initial large current can be set to 1.5 A (0.5 C). During the fast charging process, this unit continuously monitors the battery surface temperature and charging time. When the temperature exceeds the stage safety threshold (such as 40 °C) or the power reaches the medium power threshold (such as 80%), it automatically exits the fast charging mode.

[0028] The operating mechanism of the equalization charging sub-unit targets the internal voltage equalization requirements of the battery. During the charging process, the terminal voltages of each single cell in the battery are collected in real time through a built-in voltage detection circuit. When the voltage difference between any two cells is detected to exceed the equalization start threshold (such as 50 mV), this unit applies a smaller charging current or a short bypass discharge to the cell with a higher voltage through pulse current regulation technology, and maintains the normal charging current for the cell with a lower voltage until the voltage difference between the cells is reduced to the equalization end threshold (such as 20 mV). This process can effectively avoid the problems of overcharging or undercharging of some cells caused by uneven cell voltages, and improve the overall performance consistency of the battery pack.

[0029] The trickle charging sub-unit starts when the battery charge is close to the full charge state. The specific trigger condition is that the estimated remaining capacity reaches 95% of the full charge threshold (such as 95% of the nominal capacity). This unit performs supplementary charging on the battery in a constant voltage and small current mode. The charging voltage is set to the nominal full charge voltage of the battery (such as 4.2 V for a lithium-ion battery), and the charging current is usually below 0.1C (such as 300 mA). At this stage, the unit continuously monitors the change rate of the internal impedance of the battery. When it is detected that the change rate of the impedance tends to be stable and the voltage remains constant for a preset duration (such as 30 minutes), it is determined that the battery is fully charged, and a charging end command is triggered.

[0030] The parameter transition processing layer is located after the stage-based charging control unit. Its core function is to perform dimensionality reduction processing on the multi-dimensional feature data output by the stage-based charging control unit. The stage-based charging control unit outputs feature data containing multiple dimensions such as current, voltage, time, and temperature during operation. For example, during the fast charging stage, it may output more than 10 feature dimensions such as the current value at present, the temperature change rate, and the remaining charge change curve. The parameter transition processing layer screens and compresses these features through algorithms such as principal component analysis (PCA), retains 3 - 5 key features (such as the current charging stage, the real-time current, and the battery temperature) that have the greatest impact on the subsequent charging strategy decision-making, removes redundant information to reduce the data processing complexity, and improves the system response speed.

[0031] The role of the global charging status pooling layer is to achieve the standardized mapping of charging parameters. The feature data compressed by the parameter transition processing layer may be distributed in different numerical ranges (such as the current value range of 0 - 2A and the temperature value range of 25 - 45°C). The global charging status pooling layer maps this data to a preset regulation range through a linear transformation algorithm (such as mapping the current to the 0 - 100% range and the temperature to the 0 - 100 range). This standardization process enables charging parameters of different stages and types to be compared and fused on the same dimension, providing a unified data basis for the weighted decision-making of the subsequent policy fusion module. For example, mapping the real-time charging current of 2A to 100% represents full-load charging, and mapping the temperature of 45°C to 100 represents the high-temperature warning threshold, facilitating the policy fusion module to quickly identify the urgency of the current charging status and adjust the priority.

[0032] The collaborative workflow of the above components is as follows: The battery parameters are first input into the staged charging control unit. The fast charging sub-unit, balanced charging sub-unit, and trickle charging sub-unit sequentially or in parallel execute the charging stage control according to the battery status and output charging parameters containing multi-dimensional features. After being compressed by the parameter transition processing layer, these parameters are uniformly mapped to the standard range by the global charging status pooling layer and finally output to the policy fusion module to participate in the weighted decision-making. Through hierarchical processing and data standardization, the entire process realizes the refined analysis of the battery charging status and the generation of multi-dimensional strategies, laying a foundation for subsequent dynamic charging control in combination with environmental data.

[0033] Embodiment 2: The dynamic adjustment module adopts a cascaded architecture of a three-stage staged charging control unit, and realizes data connection between adjacent units through the parameter transition processing layer, forming a progressive analysis and policy generation process for battery parameters. The functional positioning of the three-stage unit shows a hierarchical evolution from macroscopic to microscopic and from basic feature extraction to in-depth policy generation. The specific implementation method is as follows: The first-stage staged charging control unit, as the initial processing layer, is mainly responsible for the coarse-grained identification of the battery state and the generation of the basic charging strategy. After receiving the real-time battery parameters (such as terminal voltage, charging current, remaining capacity, surface temperature), this unit extracts immediate features such as current demand and voltage fluctuation range through the first processing link. For example, when it is detected that the remaining capacity is lower than the preset threshold and the terminal voltage is lower than a specific value (taking a lithium-ion battery with a nominal voltage of 3.7V as an example, if the remaining capacity is lower than 30% and the terminal voltage is lower than 3.0V), it is determined to be in a deep low-power state, triggering the fast charging sub-unit to start charging with a large current, and the specific value of the large current is dynamically adjusted according to the battery specifications and health status. At the same time, through the second processing link, the time-series analysis of the data of several recent charging cycles is carried out. If it is found that the charging cycle is extended compared with the initial stage, a preliminary warning signal of battery aging can be generated, and the current upper limit of the subsequent stage can be preliminarily adjusted accordingly. The characteristic data output by this unit contains multiple dimensions, such as the current stage type, initial current value, voltage fluctuation threshold, aging warning level, etc.

[0034] The parameter transition processing layer (the first stage), located between the first stage and the second stage units, is used to compress and abstract the multi-dimensional features output by the first stage unit. The key features with higher influence weights on the subsequent strategies are selected through the feature selection algorithm, such as the current charging stage, real-time current, voltage fluctuation amplitude, aging warning level, temperature change rate, etc. At the same time, the numerical features are normalized, and the feature values with different dimensions are mapped to a unified interval to facilitate the unified calculation of the second stage unit. The processed feature data is transmitted to the second-stage staged charging control unit in the form of a vector.

[0035] Based on the processing results of the first stage unit, the second-stage staged charging control unit conducts meso-level charging strategy optimization and dynamic adjustment. This unit first conducts a secondary analysis of the normalized current and voltage features through the first processing link, such as calculating the product of the current charging current and the battery internal resistance (i.e., the internal voltage drop), and combining the real-time temperature data to determine whether the battery enters the polarization state (such as determining polarization when the internal voltage drop exceeds the preset value). If polarization is detected, the balanced charging sub-unit is triggered to start pulse current regulation to eliminate concentration polarization and electrochemical polarization. At the same time, through the second processing link, the capacity attenuation trend of past multiple charges is analyzed, the capacity retention rate of the current charging cycle is predicted, and a fine-tuning instruction for the charging termination voltage is generated accordingly. The characteristic data output by this unit is further focused on specific dimensions, such as polarization state identification, balance adjustment parameters, termination voltage adjustment amount, capacity attenuation prediction value, etc.

[0036] The parameter transition processing layer (second level) deeply abstracts the features output by the second-level units, and uses matrix compression algorithms to reduce the dimensionality of high-dimensional feature vectors to the principal components. For example, features such as polarization state, equalization adjustment parameters, and termination voltage adjustment amounts are fused into a comprehensive adjustment factor, and the capacity attenuation prediction value and aging warning level are fused into a health status index. The features after dimensionality reduction are transmitted to the third-level stage charging control unit in the form of scalars or short vectors.

[0037] The third-level stage charging control unit, as the final processing layer, is responsible for generating refined charging control instructions and connecting to the global strategy. This unit integrates the feature data of the first two levels of units through the first processing link. For example, by combining the health status index and the comprehensive adjustment factor, it calculates the upper limit of the safe current for the current charging. At the same time, through the second processing link, it analyzes the battery swelling data and the internal impedance change rate. If the relevant parameters exceed the safety threshold, it generates an emergency current reduction instruction. Finally, this unit outputs control parameters including current instructions, voltage thresholds, and stage switching conditions, such as specifying the current charging stage, current value, voltage value, and duration conditions.

[0038] After receiving the output of the third-level unit, the global charging status pooling layer first converts parameters such as current and voltage into digital signals recognizable by the device's main control chip, and then unifies all parameters into the regulation range through a preset mapping table. The pooled parameters are transmitted to the policy fusion module in the form of data packets, including information such as the current charging stage code, real-time control parameters, and safety warning flags, providing standardized inputs for subsequent weighted decision-making in combination with environmental data.

[0039] The cascaded processing of the three-level units forms a progressive logic of "state recognition - policy optimization - fine control": the first-level unit completes the basic stage division and preliminary aging warning, the second-level unit realizes the polarization state response and capacity attenuation compensation, and the third-level unit generates the final control instructions including safety boundaries. The parameter transition processing layer avoids data redundancy between multiple-level units through feature dimensionality reduction and abstraction, improving processing efficiency; the global pooling layer ensures that policy parameters at different levels can participate in the final decision-making under a unified framework through standardized mapping, realizing the full-cycle and multi-dimensional management of the battery charging process.

[0040] Embodiment 3: The processing of the input battery parameters by the stage charging control unit is realized through a dual-link feature analysis and fusion mechanism, specifically including three links: instant feature extraction of the first processing link, sequential feature analysis of the second processing link, and feature fusion and instruction generation. Each link works in coordination through preset algorithms and logics to form a multi-dimensional analysis of the battery state.

[0041] The first processing link: instant feature extraction.

[0042] ① This link focuses on the analysis of the immediate physical characteristics of the current state of the battery, and extracts the characteristics reflecting the immediate charging demand from the real-time collected battery parameters through signal processing and statistical algorithms. The specific steps are as follows: ② Analysis of current demand: Based on the estimated battery terminal voltage and remaining capacity, calculate the target current required for the current charge through the ampere-hour integration method or the equivalent circuit model. For example, when the remaining capacity is below 20% and the terminal voltage is below 3.2V, it is determined as the fast charging stage, and the target current is set to the maximum continuous charging current allowed by the battery (such as 1.5C, where C is the battery capacity multiple); when the remaining capacity exceeds 80%, it switches to the trickle charging stage, and the target current drops below 0.1C.

[0043] ③ Determination of voltage fluctuation range: Dynamically adjust the allowable voltage fluctuation range by real-time monitoring of the charging current change rate and the battery surface temperature. For example, when the temperature exceeds 35°C, the voltage upper limit is reduced by 50mV to avoid overcharging risk; when the current change rate exceeds 0.2 A / s, the voltage fluctuation lower limit is increased by 30mV to suppress the charging circuit oscillation.

[0044] Generate the first feature group: Integrate parameters such as the current demand value, the upper limit of voltage fluctuation, and the lower limit of voltage fluctuation into the first feature group, which is represented in vector form as where is the target current, , are respectively the upper and lower limit values of the voltage fluctuation range.

[0045] The second processing link: Temporal feature analysis. This link extracts the temporal features reflecting the long-term state of the battery through historical data mining and trend prediction algorithms. The specific steps are as follows: ① Charging cycle statistics: Record and analyze the time required for the battery to charge from low battery (such as 20%) to full charge (such as 95%), and establish a charging cycle database. By comparing the current cycle with the historical average cycle, judge the change trend of charging efficiency. For example, if the current cycle is 15% longer than the historical average cycle, a charging efficiency decline flag is generated.

[0046] ② Calculation of capacity decay trend: Based on the capacity data of multiple charge-discharge cycles, use linear regression or exponential smoothing algorithms to predict the decay rate of the battery's remaining capacity. For example, fit the capacity decay curve through the data of the first 50 cycles to calculate the capacity retention rate of the current cycle (such as the expected remaining capacity is 92% of the initial capacity).

[0047] ③ Generate the second feature group: Integrate parameters such as the charging cycle change amount ( ), the capacity decay rate ( ), and the capacity retention rate ( ) into the second feature group, which is represented in vector form as , where is the difference between the current cycle and the historical average cycle, is the capacity attenuation per unit cycle count, is the ratio of the current predicted capacity to the initial capacity.

[0048] Feature fusion and instruction generation include: ① Feature dimension alignment: Align the instant features of the first feature group and the time-series features of the second feature group to the same time scale through a data interpolation algorithm. For example, convert the minute-level instant features and the cycle-level time-series features into unified dimension data with the charging stage as the unit.

[0049] ② Nonlinear fusion operation: Use addition or multiplication operators to fuse the two groups of features to highlight the synergistic effects of different features. For example, multiply the current demand value ( ) by the capacity retention rate ( ) to obtain the corrected current instruction base value ( ) to reflect the limitation of the charging current due to battery aging; add the upper limit of voltage fluctuation ( ) to the change in the charging cycle ( ) to obtain the dynamic voltage upper limit ( , is a preset weight coefficient) to reflect the impact of charging efficiency changes on voltage control.

[0050] ③ Activation function processing: Perform threshold conversion on the fused feature vector through a nonlinear activation function (such as the Sigmoid function or the ReLU function) to generate binary or continuous control instructions. For example, when the upper limit of the fused voltage fluctuation exceeds the nominal full charge voltage of the battery (such as 4.2 V), map it to a value between 0 and 1 through the Sigmoid function to trigger the voltage clamping instruction for the trickle charging stage; when the capacity attenuation rate ( ) exceeds a preset threshold (such as 0.3% / cycle), generate a current attenuation coefficient ( ) through the ReLU function to reduce the current upper limit in subsequent charging stages.

[0051] The output instructions are composed of. The output instructions of the stage charging control unit include the following core elements: Stage control signal: Indicates the current charging stage (such as fast charging, equalization charging, trickle charging) and the switching conditions (such as the battery level threshold, voltage threshold); Current control parameters: Include the target current value, the current adjustment step (such as 50 mA per adjustment), and the current change rate limit (such as not exceeding 0.1 A / s); Voltage control parameters: Include the voltage fluctuation range, the overvoltage protection threshold (such as 4.3 V), and the undervoltage protection threshold (such as 2.5 V); Safety warning signal: When the battery parameters exceed the safety boundaries (such as temperature > 45°C, expansion rate > 0.1 mm / h), a warning identifier is generated and transmitted to the exception handling module.

[0052] Through the dual-link feature analysis and non-linear fusion mechanism, the stage charging control unit can comprehensively consider the immediate state and long-term health trend of the battery, generating charging control instructions with both real-time responsiveness and long-term strategic nature. The first processing link ensures the rapid adaptation of the charging process to the current state, while the second processing link realizes the forward-looking management of battery aging through historical data accumulation. The organic combination of the two provides a multi-dimensional strategy generation basis for the dynamic adjustment module, thus supporting the intelligent and refined control of the entire charging management system.

[0053] Example 4: The control process of the fast charging sub-unit is based on real-time temperature monitoring and current dynamic adjustment mechanism, achieving safe regulation of the charging current through three logical links, specifically including real-time parameter acquisition, temperature threshold determination, current instruction generation and superposition. Each link is executed collaboratively by hardware circuits and software algorithms.

[0054] In the real-time parameter acquisition link, data is obtained through the sensor module integrated in the Battery Management System (BMS): ① Temperature monitoring: NTC thermistors or thermocouple sensors are used, deployed on the surface of the battery case or between battery cells, to collect the surface temperature data of the battery in real-time at a cycle of 0.5 seconds (accuracy ±0.5°C). After the sensor signals are amplified and analog-to-digital converted (ADC), they are converted into digital signals and input into the microcontroller (MCU) of the fast charging sub-unit.

[0055] ② Calculation of current change rate: The charging current value is monitored in real-time through sampling resistors or Hall current sensors. The MCU performs differential operations on multiple consecutive sampling points (such as 10 sampling points within every 1 second), calculating the current change rate ( , unit A / s). For example, if the current is 1.2 A and the current 1 second ago was 1.0 A, then the current change rate is 0.2 A / s.

[0056] In the temperature threshold determination link, the microcontroller has a built-in dynamic temperature threshold model, which includes a basic threshold and an environmental compensation coefficient: ① Setting of basic threshold: The initial temperature threshold is preset according to the battery type and specifications. For example, the basic threshold for the fast charging stage of lithium-ion batteries is set to 40°C.

[0057] ②Environmental compensation mechanism: Dynamically adjust the base threshold based on the environmental temperature data transmitted by the environmental adaptation module. For example, when the environmental temperature is 25°C, the base threshold remains at 40°C; when the environmental temperature rises to 30°C, the threshold is correspondingly reduced to 38°C to reserve more heat dissipation margin. ③Threshold comparison logic: When the battery surface temperature collected in real time exceeds the dynamic threshold, trigger the current decay process; if the temperature remains below the threshold and the current change rate is stable (such as A / s), then maintain the current charging current.

[0058] Current command generation and superposition section, which dynamically adjusts the current through a closed-loop control algorithm: ①Calculation of current decay coefficient: When the temperature exceeds the threshold, the microcontroller calculates the current decay coefficient according to the over-temperature amplitude ( ), and the calculation formula is , where is the real-time temperature, is the dynamic threshold, is the preset maximum allowable over-temperature value (such as 10°C). For example, if , , , then , corresponding to a 30% current decay.

[0059] ②Generation of down-current control signal: Calculate the target current adjustment amount according to the current decay coefficient ( ), and generate a down-current control signal. This signal is output in the form of a PWM (pulse width modulation) wave, and the pulse width corresponds to the target current value. For example, after the original current of 1.5A decays by 30%, the duty cycle of the PWM wave is adjusted to the output corresponding to 1.05A.

[0060] ③Command superposition and output: The down-current control signal and the original charging command (the basic current command generated by the dynamic adjustment module) are superimposed through an adder circuit to form the final charging current control command. For example, the original command is 1.5A, and the down-current signal is -0.45A. After superposition, an adjusted current parameter of 1.05A is output. This command is transmitted to the charging power device (such as MOSFET) through a drive circuit to adjust the charging current in real time.

[0061] Safety boundary control, the fast charging sub-unit has a built-in dual safety protection mechanism: Hard threshold limit: Regardless of whether the temperature exceeds the limit, the charging current does not exceed the maximum allowable continuous current specified in the battery specification (such as 2C) to avoid hardware overload.

[0062] Soft start buffer: During the current adjustment process, a ramp-up / ramp-down method is adopted. For example, the adjustment step each time does not exceed 0.2 A, and the adjustment rate does not exceed 0.5 A / s to reduce the impact on the charging circuit and prevent voltage oscillation.

[0063] Collaborative workflow: The fast charging sub-unit and other components of the dynamic adjustment module achieve information interaction through the data bus: The global charging status pooling layer of the dynamic adjustment module provides the fast charging sub-unit with the current charging stage identifier (such as "fast charging stage") and battery health status parameters (such as internal resistance, aging level); The fast charging sub-unit activates the temperature monitoring and current adjustment logic according to the stage identifier, and dynamically adjusts the threshold and attenuation coefficient in combination with the health status parameters (such as the temperature threshold of the aging battery is reduced by 2°C); After being compressed by the parameter transition processing layer, the adjusted current parameters are fed back to the strategy generation link of the dynamic adjustment module to participate in the strategy optimization in the subsequent stage.

[0064] Through the above mechanism, the fast charging sub-unit realizes the temperature-sensitive dynamic control of the charging current, avoiding the risk of battery overheating while ensuring the charging efficiency. This process forms a closed-loop feedback control link through real-time data acquisition, dynamic threshold calculation, and instruction superposition technology, ensuring that the charging process operates efficiently within a safe and controllable range, especially suitable for the thermal management requirements in high-power charging scenarios.

[0065] Embodiment 5: The environment adaptation module realizes the dynamic response to the charging environment through multi-unit cooperation, specifically including the linkage operation of the environment feature extraction unit, the adaptive compensation unit, and the interference shielding decision layer. At the same time, an exception handling mechanism and a wireless charging optimization function are integrated to form a complete environment perception and control system.

[0066] The environment feature extraction unit collects environmental data in real time through sensors deployed inside or outside the electronic device. Among them, the temperature and humidity sensors are used to obtain the temperature and humidity values of the environment where the device is located, and the electromagnetic interference detection sensors (such as Hall effect sensors or radio frequency detection modules) are used to monitor the spatial electromagnetic signal intensity and convert it into an interference level. These sensors output analog signals at a fixed frequency (such as once per second), and after passing through the signal conditioning circuit (including filtering and amplification) and the analog-to-digital converter, they are converted into digital signals and input to the adaptive compensation unit and the interference shielding decision layer.

[0067] The adaptive compensation unit performs compensation operations based on a pre-constructed mapping relationship table between environmental data and battery internal resistance. This mapping relationship table is obtained through experimental tests and records the typical values of battery internal resistance under different temperature and humidity combinations. For example, in an environment of 25°C and 50% humidity, the internal resistance of a certain type of lithium-ion battery is 150 mΩ; in an environment of 35°C and 80% humidity, the internal resistance may drop to 130 mΩ. When receiving real-time environmental characteristic data, the adaptive compensation unit matches the corresponding internal resistance value through a look-up table method and calculates the voltage compensation amount based on the change in internal resistance. For example, if the current environment causes the internal resistance to decrease by 10% compared to the reference value, to maintain a constant charging power, the charging voltage needs to be reduced proportionally (such as reducing 0.1 V when the reference voltage is 4.0 V). After the voltage compensation amount is generated, it is embedded into the charging control instruction output by the dynamic adjustment module through the data bus to directly adjust the charging voltage setting value to offset the influence of environmental changes on the battery internal resistance and keep the charging current stable.

[0068] The interference shielding decision layer continuously monitors the electromagnetic interference intensity data. When it detects that the interference level exceeds a preset threshold (such as exceeding 50 dBμV / m), it triggers the anti-interference charging mode. This mode is achieved by adjusting the operating frequency of the charging circuit. Specifically, in the wireless charging scenario, the frequency synthesizer dynamically adjusts the frequency of the transmitting end oscillation circuit to scan the natural frequency of the receiving end resonant coil. When a frequency matching point (i.e., the peak point of the coupling efficiency) is detected, the transmitting frequency is locked; in the wired charging scenario, the built-in LC filter circuit or digital filtering algorithm is activated to filter out the modulation influence of high-frequency interference signals on the charging current. During the activation of the anti-interference mode, the interference shielding decision layer continuously monitors the change in the interference intensity. If the interference level drops to a safe range (such as below 40 dBμV / m), the mode is automatically exited and the normal charging control logic is restored.

[0069] The abnormal handling mechanism continuously monitors the battery swelling data and the internal impedance change rate through an independent data acquisition channel. The battery swelling data is obtained through a microelectromechanical system (MEMS) displacement sensor deployed on the battery case to measure the deformation displacement of the case in real time; the internal impedance change rate is calculated by the alternating current impedance spectroscopy method, injecting a small alternating current signal regularly and measuring the response voltage to calculate the impedance value and its change rate. When the change rate of the displacement amount exceeds the safety threshold (such as 0.05 mm / h) or the impedance change rate exceeds the preset value, the abnormal handling module immediately sends a cut-off signal to the charging control circuit to disconnect the charging circuit relay and triggers a warning signal through the sound and light alarm module. At the same time, according to the impedance change trend and historical data, the interpolation algorithm is used to inversely calculate the battery aging degree. For example, the aging curve is fitted through 5 consecutive impedance measurement values to determine the remaining capacity attenuation ratio of the current battery, and the maximum current limit in the subsequent charging strategy is updated accordingly (such as reducing the original maximum current of 1.5 A to 1.2 A).

[0070] The wireless charging optimization function of the strategy integration module obtains the coupling efficiency data of the wireless charging coil in real time through an electromagnetic field intensity sensor. This data reflects the spatial alignment degree and energy transfer efficiency between the transmitting and receiving coils. The system dynamically adjusts the transmitting frequency according to the coupling efficiency value. The specific implementation method is as follows: A frequency-varying excitation signal is sent through a frequency-sweeping signal generator, and the amplitude of the induced voltage at the receiving end is monitored. When the induced voltage reaches the peak value, the current transmitting frequency is locked to achieve resonance frequency matching. When multiple devices are wirelessly charged simultaneously, based on the spatial position data of each device (obtained through infrared sensors or Bluetooth positioning), the energy transfer weight is allocated according to the preset priority rules. For example, devices with a battery level lower than 20% are preferentially allocated a higher energy transfer power, or different priority levels are set according to the device type (such as mobile phones, headphones). Devices with a higher priority obtain a larger proportion of the energy transfer, ensuring the rationality of energy distribution and charging efficiency in the multi-device charging scenario.

[0071] The accurate power display method based on the above battery charging management method realizes high-precision power display through four steps. After the battery terminal voltage, load current, and estimated remaining capacity are collected in real time, the terminal voltage and load current are input into the dynamic calibration module. This module calculates the polarization voltage component and subtracts it from the terminal voltage by establishing a battery polarization effect model to compensate for the display error caused by the polarization effect. The calibrated power data is matched with the historical charge and discharge curve through the capacity fusion unit. The historical curve stores the voltage-capacity correspondence relationship at different temperatures and charge and discharge rates. The most similar historical curve segment is found through a pattern matching algorithm (such as the dynamic time warping algorithm) to generate a high-precision remaining power percentage. Finally, the display accuracy mode is switched according to the current usage scenario (such as standby, call, video playback) on the display interface. For example, the power is displayed in 5% steps in the standby scenario and in 1% steps in the video playback scenario. At the same time, the power icon is dynamically rendered (such as changing the icon color, filling the progress) to provide users with intuitive and accurate power information.

[0072] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0073] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A battery charging management method based on an electronic device, characterized in that, It includes the following steps: Obtain the real-time battery parameters and usage environment data of the device to be charged; Input the battery parameters into the dynamic adjustment module, which is used to analyze the battery health status and generate a multi-dimensional charging strategy; Input the usage environment data into the environment adaptation module, which is used to dynamically adjust the charging power and temperature control threshold; The strategy fusion module makes a weighted decision on the multi-dimensional charging strategy and the adjusted charging parameters to output the final charging control instruction.

2. The battery charging management method based on an electronic device according to claim 1, wherein The dynamic adjustment module includes: A stage-based charging control unit, which includes a fast charging sub-unit, an equalizing charging sub-unit, and a trickle charging sub-unit. The fast charging sub-unit is used to identify the low battery state and trigger a large current input, and the equalizing charging sub-unit is used to balance the internal voltage difference of the battery; A parameter transition processing layer, which is used to compress the feature dimension output by the stage-based charging control unit; A global charging status pooling layer, which is used to uniformly map the processed charging parameters to a preset regulation interval.

3. The battery charging management method based on an electronic device according to claim 2, characterized in that, Three of the stage-based charging control units are sequentially deployed in the dynamic adjustment module. The first stage-based charging control unit is connected to the second stage-based charging control unit through the parameter transition processing layer, the second stage-based charging control unit is connected to the third stage-based charging control unit through the parameter transition processing layer, and the output of the third stage-based charging control unit is integrated by the global charging status pooling layer and then transmitted to the strategy fusion module.

4. The battery charging management method based on an electronic device according to claim 2, wherein The stage-based charging control unit processes the input battery parameters through the following steps: Extract features from the battery parameters according to the first processing link to generate a first feature group including current demand and voltage fluctuation range; Perform time series analysis on the battery parameters according to the second processing link to generate a second feature group including charging cycle and decay trend; Fuse the first feature group and the second feature group, and generate the output instruction of the stage-based charging control unit through a non-linear activation function.

5. The battery charging management method based on an electronic device according to claim 4, wherein The fast charging sub-unit executes control through the following steps: Real-time monitor the battery surface temperature and the input current change rate; When it is detected that the temperature exceeds the dynamic threshold, activate the current decay coefficient and generate a current reduction control signal; Superimpose the current reduction control signal on the original charging instruction and output the adjusted charging current parameters.

6. The battery charging management method based on an electronic device according to claim 1, characterized in that, The environment adaptation module includes: An environment feature extraction unit, which is used to identify the temperature, humidity, and electromagnetic interference intensity of the environment where the device is located; An adaptive compensation unit, which is used to dynamically correct the charging voltage fluctuation range according to the environment features; An interference shielding decision layer, which is used to switch to the anti-interference charging mode when the electromagnetic interference exceeds the limit.

7. The battery charging management method based on an electronic device according to claim 6, wherein The adaptive compensation unit operates through the following steps: Construct a mapping relationship table between the environment data and the battery internal resistance; Query the mapping relationship table according to the real-time environment features and output the corresponding voltage compensation amount; Embed the voltage compensation amount into the charging control instruction to offset the influence of the environment fluctuation on the charging process.

8. The battery charging management method based on an electronic device according to claim 1, characterized in that, It also includes an exception handling mechanism: Continuously collect battery swelling data and internal impedance change rate during the charging process; When the detected inflation rate exceeds the safety threshold, immediately cut off the charging circuit and trigger a warning signal; Reverse calculate the battery aging degree according to the impedance change trend, and update the maximum current limit of the subsequent charging strategy.

9. The battery charging management method based on an electronic device according to claim 1, wherein The strategy fusion module further includes a wireless charging optimization function: Obtain the coupling efficiency of the wireless charging coil through an electromagnetic field intensity sensor; Dynamically adjust the transmitter frequency to match the resonant characteristics of the receiver; When multiple devices are charging simultaneously, allocate different energy transfer priorities based on spatial position data.

10. A method for accurately displaying the battery charge level of an electronic device, which is applied to the battery charging management method of an electronic device according to any one of claims 1 to 9, characterized in that, It includes the following steps: S1: Real-time collect the battery terminal voltage, load current, and estimated remaining capacity; S2: Input the terminal voltage and load current into the dynamic calibration module to compensate for the display error caused by the battery polarization effect; S3: Match the calibrated power data with the historical charge and discharge curves through the capacity fusion unit to generate a high-precision remaining power percentage; S4: Dynamically render the power icon on the display interface and switch the display precision mode according to the usage scenario differences.

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