Battery charging management method and power accurate display method based on electronic equipment

Abstract

The present invention relates to the technical field of electronic device battery management, and discloses a battery charging management method and a method for accurately displaying battery charge based on electronic devices. Regarding charging management, real-time battery parameters and environmental data are first acquired. A dynamic adjustment module generates a multi-dimensional strategy through a staged charging control unit (including fast, balanced, and trickle charging subunits), a parameter transition processing layer, and a global charging state pooling layer. An environmental adaptation module adjusts charging power and temperature thresholds based on environmental data, including environmental feature extraction, adaptive compensation, and anti-interference switching functions. A strategy fusion module weights and outputs control instructions, and also includes exception handling and wireless charging optimization mechanisms. When displaying battery charge, collected data undergoes dynamic calibration to compensate for polarization errors, and capacity fusion matches historical curves to generate a high-precision battery charge percentage, which is then dynamically rendered and displayed. This solution improves charging safety, efficiency, and battery charge display accuracy.

Description

Technical Field

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

[0002] With the popularization of electronic devices, batteries serve as their core energy source. The accuracy and reliability of their charging management and power display have become key issues that need to be addressed urgently. Traditional battery charging management methods usually adopt fixed charging strategies and cannot be dynamically adjusted according to the real-time status of the battery and the usage environment, resulting in low charging efficiency, shortened battery life, and even safety hazards. For example, under different temperature environments, the battery charging mechanism will change significantly. High temperature environments may cause the battery to overheat, leading to safety accidents such as explosions, while low temperature environments will reduce the battery's charging acceptance and extend the charging time. At the same time, traditional power display methods are mostly based on simple voltage detection, ignoring the influence of factors such as battery polarization effects and charge and discharge history. This leads to large errors in power display and fails to provide users with accurate remaining power information, affecting the user experience.

[0003] In existing technologies, although some charging management methods have introduced the concept of staged charging, such as fast charging, equalizing charging, and trickle charging, these methods often lack a comprehensive analysis of the battery's health status and the dynamic generation of multi-dimensional charging strategies. Furthermore, environmental factors are often considered in a limited way, typically only implementing simple temperature protection measures without fully considering the combined impact of multiple environmental factors such as temperature, humidity, and electromagnetic interference on the charging process. Regarding battery level display, traditional methods lack an effective calibration mechanism to compensate for display errors caused by battery polarization effects, nor do they fully utilize historical charge and discharge data to improve the accuracy of battery level estimation.

[0004] With the continuous development of smart electronic devices, users are increasingly demanding higher battery performance. There is an urgent need for a method that can dynamically adjust charging strategies based on the battery's real-time status and usage environment, while also accurately displaying the battery level. Therefore, research on a battery charging management method and accurate battery level display method based on electronic devices has important practical significance and application value. This method must be able to acquire battery parameters and usage environment data in real time, generate a multi-dimensional charging strategy, and dynamically adjust charging power and temperature control thresholds to achieve intelligent management of the charging process. Furthermore, an effective battery level calibration and fusion mechanism must be established to improve the accuracy and reliability of the battery level display, providing 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 a method for accurately displaying the power level based on an electronic device to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a battery charging management method based on an electronic device, the method comprising:

[0007] Obtain real-time battery parameters and usage environment data of the device to be charged;

[0008] Based on the battery parameters, the dynamic adjustment module is used to analyze the battery health status and generate a multi-dimensional charging strategy;

[0009] Input the usage environment data into the environment adaptation module to dynamically adjust the charging power and temperature control threshold;

[0010] The strategy fusion module performs weighted decision making on the multi-dimensional charging strategy and the adjusted charging parameters to output a final charging control instruction.

[0011] Preferably, the dynamic adjustment module includes:

[0012] A staged charging control unit, comprising a fast charging subunit, a balancing charging subunit, and a trickle charging subunit. The fast charging subunit is used to identify a low battery state and trigger a high current input, and the balancing charging subunit is used to balance the internal voltage differences of the battery;

[0013] a parameter transition processing layer, configured to compress the feature dimensions output by the staged charging control unit;

[0014] The global charge state pooling layer is used to uniformly map the processed charging parameters to the preset control range.

[0015] Preferably, three stage-by-stage charging control units are deployed in sequence in the dynamic adjustment module, the first stage-by-stage charging control unit is connected to the second stage-by-stage charging control unit through the parameter transition processing layer, the second stage-by-stage charging control unit is connected to the third stage-by-stage charging control unit through the parameter transition processing layer, and the output of the third stage-by-stage charging control unit is integrated by the global charging state pooling layer and transmitted to the strategy fusion module.

[0016] Preferably, the staged charging control unit processes the input battery parameters through the following steps:

[0017] Extracting features of the battery parameters according to the first processing link to generate a first feature group including current demand and voltage fluctuation range;

[0018] Performing a time series analysis on the battery parameters according to the second processing link to generate a second feature group including a charging cycle and a decay trend;

[0019] The first feature group and the second feature group are integrated, and an output instruction of the staged charging control unit is generated through a nonlinear activation function.

[0020] Preferably, the fast charging subunit performs control through the following steps:

[0021] Real-time monitoring of battery surface temperature and input current change rate;

[0022] When it is detected that the temperature exceeds the dynamic threshold, the current attenuation coefficient is activated and a current reduction control signal is generated;

[0023] The current reduction control signal is superimposed on the original charging instruction to output the adjusted charging current parameter.

[0024] Preferably, the environment adaptation module includes:

[0025] Environmental feature extraction unit, used to identify the temperature, humidity and electromagnetic interference intensity of the environment in which the device is located;

[0026] Adaptive compensation unit, used to dynamically correct the charging voltage fluctuation range according to environmental characteristics;

[0027] Interference shielding decision layer, used to switch to anti-interference charging mode when electromagnetic interference exceeds the limit.

[0028] Preferably, the adaptive compensation unit operates through the following steps:

[0029] Construct a mapping table between environmental data and battery internal resistance;

[0030] Query the mapping relationship table according to the real-time environmental characteristics and output the corresponding voltage compensation amount;

[0031] The voltage compensation amount is embedded in the charging control instruction to offset the influence of environmental fluctuations on the charging process.

[0032] Preferably, an exception handling mechanism is also included:

[0033] Continuously collect battery expansion data and internal impedance change rate during charging;

[0034] When the expansion rate is detected to exceed the safety threshold, the charging circuit is immediately cut off and an early warning signal is triggered;

[0035] The battery aging degree is reversely calculated based on the impedance change trend, and the maximum current limit of the subsequent charging strategy is updated.

[0036] Preferably, the strategy fusion module further includes a wireless charging optimization function:

[0037] The coupling efficiency of the wireless charging coil is obtained through an electromagnetic field strength sensor;

[0038] Dynamically adjust the transmitter frequency to match the receiver resonance characteristics;

[0039] When multiple devices are charging simultaneously, differentiated energy transfer priorities are assigned based on spatial location data.

[0040] Preferably, a method for accurately displaying the battery charge level of an electronic device is also included, which is applied to the above-mentioned method for managing the battery charge of an electronic device, and includes the following steps:

[0041] S1: Real-time acquisition of battery terminal voltage, load current and remaining capacity estimation;

[0042] S2: Inputting the terminal voltage and load current into a dynamic calibration module to compensate for display errors caused by battery polarization effects;

[0043] S3: The capacity fusion unit matches the calibrated power data with the historical charge and discharge curve to generate a high-precision remaining power percentage;

[0044] S4: Dynamically render the battery icon in the display interface and switch the display accuracy mode according to the usage scenario.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] In terms of charging management, by acquiring real-time battery parameters and usage environment data from the device being charged, a comprehensive understanding of the battery's current state and environment is achieved. The dynamic adjustment module analyzes the battery's health status based on these parameters and generates a multi-dimensional charging strategy. The staged charging control unit, consisting of a fast charging subunit, a balancing charging subunit, and a trickle charging subunit, can adopt appropriate charging methods for different charge states. The fast charging subunit triggers high current input when the battery is low, improving charging efficiency; the balancing charging subunit balances internal battery voltage differences, preventing local overcharging or undercharging, and extending battery life; and the trickle charging subunit uses a low current when the battery is nearly fully charged, preventing overcharging. The parameter transition processing layer and the global charge state pooling layer process and integrate charging parameters, making them more stable and reliable.

[0047] The environmental adaptation module dynamically adjusts charging power and temperature control thresholds based on environmental data. The environmental feature extraction unit identifies the temperature, humidity, and electromagnetic interference intensity of the device's environment. The adaptive compensation unit dynamically corrects the charging voltage fluctuation range based on environmental characteristics. The interference shielding decision layer switches to anti-interference charging mode when electromagnetic interference exceeds the limit. This effectively addresses the impact of different environmental factors on the charging process and improves charging stability and safety. The strategy fusion module makes weighted decisions based on multi-dimensional charging strategies and adjusted charging parameters, outputting the final charging control instructions and achieving the optimal combination of charging strategies.

[0048] The exception handling mechanism continuously collects battery expansion data and internal impedance change rate during the charging process. When an anomaly is detected, it promptly disconnects the charging circuit and triggers a warning signal. It also reversely infers the battery's aging based on the impedance change trend and updates the maximum current limit for subsequent charging strategies, further ensuring charging safety and extending battery life. The wireless charging optimization function uses an electromagnetic field strength sensor to measure the coupling efficiency of the wireless charging coil, dynamically adjusting the transmitter frequency to match the receiver's resonant characteristics, improving wireless charging efficiency. When multiple devices are charging simultaneously, differentiated energy transmission priorities are assigned based on spatial location data, enabling optimal scheduling of multi-device charging.

[0049] In terms of accurate display of power, the battery terminal voltage, load current and remaining capacity estimation are collected in real time, and 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, dynamically render the power icon in the display interface, and switch the display accuracy mode according to the difference in usage scenarios, so that users can accurately understand the remaining battery power, improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a working principle diagram of the battery charging management method based on an electronic device according to the present invention;

[0051] Figure 2 A flow chart showing the connection relationship of the staged charging control units in the dynamic adjustment module;

[0052] Figure 3 Flowchart of battery parameter processing for staged charging control unit;

[0053] Figure 4 is a flow chart of a method for controlling a fast charging subunit;

[0054] Figure 5 Flowchart for the adaptive compensation unit operation. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] See also Figure 1-5 The present invention provides a technical solution: a battery charging management method based on an electronic device, the method comprising:

[0057] Acquire real-time battery parameters and usage environment data for the device being charged. Real-time battery parameters include at least physical quantities reflecting the battery's status, such as battery terminal voltage, charging current, estimated remaining capacity, battery surface temperature, internal impedance, and expansion data. Environmental data includes environmental characteristics such as the device's surroundings, including temperature and humidity, and electromagnetic interference intensity. These data can be collected in real time using the device's built-in sensors or external sensor modules.

[0058] The battery parameters are input into the dynamic adjustment module, which analyzes the battery's health status and generates a multi-dimensional charging strategy. The dynamic adjustment module processes and analyzes the battery parameters to determine the battery's current charging stage (e.g., low, medium, high), and health status (e.g., aging level, internal resistance trend), thereby generating charging strategies tailored to different dimensions, such as charging current and voltage control ranges at different stages.

[0059] The environmental data is input into the environmental adaptation module to dynamically adjust the charging power and temperature control thresholds. The environmental adaptation module analyzes the impact of environmental factors on the charging process based on data such as ambient temperature, humidity, and electromagnetic interference. For example, in high-temperature environments, the charging power may need to be reduced to prevent battery overheating. In high-humidity environments, the temperature control threshold may need to be adjusted to ensure charging safety. In cases of strong electromagnetic interference, appropriate anti-interference measures may need to be taken. These factors dynamically adjust the power output and temperature control standards during the charging process.

[0060] The strategy fusion module performs a weighted decision on the multi-dimensional charging strategy and the adjusted charging parameters to output the final charging control instructions. The strategy fusion module comprehensively considers the multi-dimensional charging strategy generated by the dynamic adjustment module and the charging parameters adjusted by the environmental adaptation module, making decisions based on preset weighting rules to form the final charging control instructions. These instructions are used to control the charging process of the charging device, such as adjusting parameters such as charging current, voltage, and frequency, to achieve safe, efficient, and intelligent charging management.

[0061] The present invention will be further described below in conjunction with Examples 1 to 5:

[0062] Example 1:

[0063] The dynamic adjustment module's structure comprises a hierarchical processing architecture comprised of a staged charging control unit, a parameter transition processing layer, and a global charge state pooling layer. The staged charging control unit, serving as the core execution unit, comprises three functional submodules: a fast charging submodule, a balanced charging submodule, and a trickle charging submodule. These submodules work together through pre-defined logic to cover the full battery lifecycle charging requirements.

[0064] The triggering mechanism for the fast charging subunit is based on a charge threshold. When the real-time estimated remaining battery capacity falls below a preset low charge threshold (e.g., 20%), the unit triggers high-current input mode via a current control circuit. The input current is dynamically adjusted based on the battery's specifications and health status. For example, for a lithium-ion battery with a nominal capacity of 3000mAh, the initial high current can be set to 1.5A (0.5C). During fast charging, the unit continuously monitors the battery surface temperature and charging time. When the temperature exceeds a staged safety threshold (e.g., 40°C) or the charge level reaches a medium charge threshold (e.g., 80%), it automatically exits fast charging mode.

[0065] The balancing charging subunit's mechanism addresses the need for internal battery voltage balancing. During charging, the built-in voltage detection circuit collects the terminal voltage of each battery cell in real time. If the voltage difference between any two cells exceeds the balancing start threshold (e.g., 50mV), the unit uses pulse current regulation technology to apply a lower charging current or a brief bypass discharge to the cell with the higher voltage, while maintaining a normal charging current for the cell with the lower voltage until the voltage difference between the cells narrows to the balancing end threshold (e.g., 20mV). This process effectively prevents overcharging or undercharging of some cells due to uneven cell voltages, improving the overall performance consistency of the battery pack.

[0066] The trickle charge subunit starts when the battery is close to being fully charged. The specific trigger condition is that the estimated remaining capacity reaches 95% of the full charge threshold (such as 95% of the nominal capacity). The unit supplements the battery by using a constant voltage and low current mode. The charging voltage is set to the battery's nominal full charge voltage (such as 4.2V for lithium-ion batteries), and the charging current is usually less than 0.1C (such as 300mA). During this stage, the unit continuously monitors the battery's internal impedance change rate. When it detects that the impedance change rate tends to stabilize and the voltage remains constant for a preset period of time (such as 30 minutes), it determines that the battery is fully charged and triggers the charging end instruction.

[0067] The parameter transition processing layer is located after the staged charging control unit. Its core function is to perform dimensionality reduction processing on the multi-dimensional feature data output by the staged charging control unit. During operation, the staged charging control unit outputs feature data containing multiple dimensions such as current, voltage, time, and temperature. For example, during the fast charging phase, it may output more than 10 feature dimensions, including the current current value, temperature change rate, and remaining power change curve. The parameter transition processing layer uses algorithms such as principal component analysis (PCA) to filter and compress these features, retaining the 3-5 key features that have the greatest impact on subsequent charging strategy decisions (such as the current charging stage, real-time current, and battery temperature). This removes redundant information to reduce data processing complexity and improve system response speed.

[0068] The global state-of-charge pooling layer standardizes the mapping of charging parameters. After being compressed by the parameter transition processing layer, the characteristic data may be distributed across different numerical ranges (e.g., current range 0-2A, temperature range 25-45°C). The global state-of-charge pooling layer uses a linear transformation algorithm to uniformly map this data to pre-set control ranges (e.g., current mapping to the 0-100% range, temperature mapping to the 0-100 range). This standardization allows charging parameters from different stages and types to be compared and integrated on the same dimension, providing a unified data foundation for the weighted decisions of the subsequent strategy fusion module. For example, mapping a real-time charging current of 2A to 100% represents full-load charging, and mapping a temperature of 45°C to 100 represents the high-temperature warning threshold. This allows the strategy fusion module to quickly identify the urgency of the current charging state and adjust its priority.

[0069] The collaborative workflow of these components is as follows: battery parameters are first input into the staged charging control unit. The fast charging subunit, balanced charging subunit, and trickle charging subunit execute charging stage control sequentially or in parallel based on the battery status, outputting charging parameters containing multi-dimensional features. These parameters are compressed by the parameter transition processing layer, uniformly mapped to standard intervals by the global charge state pooling layer, and ultimately output to the strategy fusion module for weighted decision-making. Through hierarchical processing and data standardization, this entire process enables refined analysis of the battery charging state and generation of multi-dimensional strategies, laying the foundation for subsequent dynamic charging control based on environmental data.

[0070] Example 2:

[0071] The dynamic adjustment module adopts a cascaded architecture of three-level staged charging control units. The parameter transition processing layer realizes data connection between adjacent units, forming a progressive analysis and strategy generation process for battery parameters. The functional positioning of the three-level units presents a hierarchical evolution from macro to micro, from basic feature extraction to deep strategy generation. The specific implementation method is as follows:

[0072] The first-level staged charging control unit serves as the initial processing layer, primarily responsible for coarse-grained battery status identification and basic charging strategy generation. This unit receives real-time battery parameters (such as terminal voltage, charging current, remaining capacity, and surface temperature) and extracts immediate features such as current demand and voltage fluctuation range through the first processing chain. For example, if the remaining capacity falls below a preset threshold and the terminal voltage falls below a specific value (for a lithium-ion battery with a nominal voltage of 3.7V, for example, if the remaining capacity falls below 30% and the terminal voltage falls below 3.0V), it determines a deep low-battery state and triggers the fast-charging subunit to initiate charging at a high current. The specific high current value is dynamically adjusted based on the battery specifications and health status. Simultaneously, the second processing chain performs time series analysis on recent charging cycle data. If the charging cycle is found to be extended compared to the initial stage, a preliminary warning signal for battery aging is generated, and the current limit for subsequent stages is adjusted accordingly. The feature data output by this unit includes multiple dimensions, such as the current stage type, initial current value, voltage fluctuation threshold, and aging warning level.

[0073] The parameter transition processing layer (first level) lies between the first and second level units. Its function is to compress and abstract the multidimensional features output by the first level units. A feature selection algorithm is used to select key features that have a significant impact on subsequent strategies, such as the current charging stage, real-time current, voltage fluctuation amplitude, aging warning level, and temperature change rate. Numerical features are also normalized, mapping feature values ​​of different dimensions to a unified interval to facilitate unified calculations by the second level units. The processed feature data is transmitted as vectors to the second level staged charging control unit.

[0074] The second-level staged charging control unit optimizes and dynamically adjusts the charging strategy at the meso-level based on the processing results of the first-level unit. This unit first performs a secondary analysis of the normalized current and voltage characteristics through the first processing chain. For example, it calculates the product of the current charging current and the battery's internal resistance (i.e., the internal voltage drop). Combined with real-time temperature data, it determines whether the battery has entered a polarization state (e.g., if the internal voltage drop exceeds a preset value). If polarization is detected, the equalization charging subunit initiates pulse current adjustment to eliminate concentration polarization and electrochemical polarization. Simultaneously, the second processing chain analyzes capacity decay trends from past charge cycles to predict the capacity retention rate for the current charge cycle and, based on this information, generates fine-tuning instructions for the final charge voltage. The characteristic data output by this unit is further focused on specific dimensions, such as polarization state indicator, equalization adjustment parameters, final charge voltage adjustment amount, and capacity decay prediction value.

[0075] The parameter transition processing layer (second level) deeply abstracts the features output by the second-level units, using a matrix compression algorithm to reduce the high-dimensional feature vectors to principal components. For example, features such as polarization state, equalization adjustment parameters, and termination voltage adjustment are combined into a comprehensive adjustment factor, and the capacity decay prediction value and aging warning level are combined into a health status index. The reduced features are transmitted as scalars or short vectors to the third-level staged charging control unit.

[0076] The third-level staged charging control unit serves as the final processing layer, responsible for generating refined charging control instructions and connecting the global strategy. This unit integrates the characteristic data of the first two levels of units through the first processing link, for example, combining the health status index and the comprehensive adjustment factor to calculate the upper limit of the current safe charging current. At the same time, the battery expansion data and the internal impedance change rate are analyzed through the second processing link. If the relevant parameters exceed the safety threshold, an emergency current reduction instruction is generated. Finally, the unit outputs control parameters including current instructions, voltage thresholds, and stage switching conditions, such as clarifying the current charging stage, current value, voltage value, and duration conditions.

[0077] After receiving the output of the third-level unit, the global charge state pooling layer first converts parameters such as current and voltage into digital signals recognizable by the device's main control chip. Then, using a preset mapping table, all parameters are unified into control ranges. The pooled parameters are transmitted to the strategy fusion module in the form of data packets containing information such as the current charging stage code, real-time control parameters, and safety warning indicators. This provides standardized input for subsequent weighted decision-making based on environmental data.

[0078] The cascaded processing of three levels of cells forms a progressive logic of "state identification - strategy optimization - fine-grained control": the first-level unit completes basic stage division and preliminary aging warning, the second-level unit implements polarization state response and capacity decay compensation, and the third-level unit generates the final control instructions with safety margins. The parameter transition processing layer uses feature dimensionality reduction and abstraction to avoid data redundancy between multi-level cells and improve processing efficiency. The global pooling layer uses standardized mapping to ensure that policy parameters at different levels can participate in the final decision-making within a unified framework, achieving full-cycle, multi-dimensional management of the battery charging process.

[0079] Example 3:

[0080] The staged charging control unit processes the input battery parameters through a dual-link feature analysis and fusion mechanism, which specifically includes three links: real-time feature extraction in the first processing link, timing feature analysis in the second processing link, and feature fusion and instruction generation. Each link works together through preset algorithms and logic to form a multi-dimensional analysis of the battery status.

[0081] First processing link: real-time feature extraction.

[0082] ① This link focuses on the real-time physical property analysis of the battery's current state, extracting features reflecting the immediate charging needs from the real-time collected battery parameters through signal processing and statistical algorithms. The specific steps are as follows:

[0083] ② Current demand analysis: Based on the battery terminal voltage and estimated remaining capacity, the target current required for the current charge is calculated using the ampere-hour integration method or an equivalent circuit model. For example, when the remaining capacity is less than 20% and the terminal voltage is less than 3.2V, it is determined to be in the fast charging phase, 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 rate). When the remaining capacity exceeds 80%, it switches to the trickle charging phase, and the target current is reduced to less than 0.1C.

[0084] ③ Determining the voltage fluctuation range: By real-time monitoring of the charging current change rate and battery surface temperature, the voltage fluctuation range is dynamically adjusted. For example, when the temperature exceeds 35°C, the upper voltage limit is reduced by 50mV to avoid overcharging risks; when the current change rate exceeds 0.2A / s, the lower voltage fluctuation limit is increased by 30mV to suppress charging circuit oscillations.

[0085] Generate the first feature group: Integrate the current demand value, voltage fluctuation upper limit, voltage fluctuation lower limit and other parameters into the first feature group, which is expressed in vector form as ,in is the target current, 、 are the upper and lower limits of the voltage fluctuation range respectively.

[0086] The second processing link: Time series feature analysis. This link extracts time series features that reflect the long-term status of the battery through historical data mining and trend prediction algorithms. The specific steps are as follows:

[0087] ① Charging cycle statistics: Record and analyze the time required for the battery to charge from a low charge (e.g., 20%) to a full charge (e.g., 95%), and build a charging cycle database. By comparing the current cycle with the historical average cycle, we can determine the charging efficiency trend. For example, if the current cycle is 15% longer than the historical average, a charging efficiency degradation indicator is generated.

[0088] ②Capacity decay trend calculation: Based on the capacity data of multiple charge and discharge cycles, linear regression or exponential smoothing algorithms are used to predict the decay rate of the battery's remaining capacity. For example, a capacity decay curve can be fitted using the data from the first 50 cycles to estimate the capacity retention rate of the current cycle (e.g., the remaining capacity is expected to be 92% of the initial capacity).

[0089] ③ Generate the second feature group: the charging cycle change ( ), capacity decay rate ( ), capacity retention rate ( ) and other parameters are integrated into the second feature group, which is expressed in vector form as ,in is the difference between the current cycle and the historical average cycle, is the capacity attenuation per unit cycle number, It is the ratio of the current expected capacity to the initial capacity.

[0090] Feature fusion and instruction generation include:

[0091] ① Feature dimension alignment: Use a data interpolation algorithm to align the instantaneous features of the first feature group and the time series features of the second feature group to the same time scale. For example, the minute-level instantaneous features and the cycle-level time series features are converted into unified dimensional data based on the charging stage.

[0092] ② Nonlinear fusion operation: Use addition or multiplication operators to fuse two sets of features to highlight the synergistic effects of different features. For example, the current demand value ( ) and capacity retention rate ( ) to obtain the corrected current command base ( ), to reflect the limitation of battery aging on charging current; the voltage fluctuation upper limit ( ) and the change in charging cycle ( ) are added to obtain the dynamic voltage upper limit ( , is the preset weight coefficient) to reflect the impact of charging efficiency changes on voltage control.

[0093] ③Activation function processing: The fused feature vector is threshold-converted through a nonlinear activation function (such as Sigmoid function or 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 4.2V), it is mapped to a value between 0 and 1 through the Sigmoid function, triggering the voltage clamping instruction in the trickle charge stage; when the capacity decay rate ( ) exceeds a preset threshold (such as 0.3% / cycle), the current attenuation coefficient is generated by the ReLU function ( ), which is used to reduce the current upper limit in the subsequent charging stage.

[0094] The output command structure of the staged charging control unit includes the following core elements:

[0095] Stage control signal: indicates the current charging stage (such as fast charging, equalizing charging, trickle charging) and switching conditions (such as power threshold, voltage threshold);

[0096] Current control parameters: including target current value, current adjustment step size (e.g., 50 mA each time), and current change rate limit (e.g., no more than 0.1 A / s);

[0097] Voltage control parameters: including voltage fluctuation range, overvoltage protection threshold (such as 4.3V), and undervoltage protection threshold (such as 2.5V);

[0098] Safety warning signal: When battery parameters exceed safety limits (such as temperature > 45°C, expansion rate > 0.1 mm / h), a warning sign is generated and passed to the abnormality handling module.

[0099] Through dual-link feature analysis and a nonlinear fusion mechanism, the staged charging control unit integrates the battery's immediate state and long-term health trends to generate charging control instructions that are both responsive and strategic. The first processing link ensures rapid adaptation of the charging process to the current state, while the second processing link enables proactive management of battery aging by accumulating historical data. The combination of these two provides a multi-dimensional strategy generation foundation for the dynamic adjustment module, thereby supporting intelligent and refined control of the entire charging management system.

[0100] Example 4:

[0101] The control process of the fast charging subunit is based on real-time temperature monitoring and dynamic current adjustment mechanisms, and safe regulation of the charging current is achieved through triple logic links. Specifically, it includes real-time parameter acquisition, temperature threshold determination, and current command generation and superposition. Each link is executed in coordination through hardware circuits and software algorithms.

[0102] Real-time parameter collection: This part uses the sensor module integrated into the battery management system (BMS) to obtain data:

[0103] ① Temperature Monitoring: An NTC thermistor or thermocouple sensor, deployed on the battery casing surface or between cells, collects real-time battery surface temperature data (accuracy ±0.5°C) with a 0.5-second cycle. The sensor signal is amplified and converted to a digital signal by an analog-to-digital converter (ADC) before being input into the fast-charging subunit's microcontroller (MCU).

[0104] ② Current change rate calculation: The charging current value is monitored in real time through a sampling resistor or a Hall current sensor. The MCU performs differential operations on multiple consecutive sampling points (such as 10 sampling points per second) to calculate the current change rate ( , unit A / s). For example, if the current is 1.2 A and the current was 1.0 A 1 second ago, the current change rate is 0.2 A / s.

[0105] In the temperature threshold determination phase, the microcontroller has a built-in dynamic temperature threshold model, which includes a basic threshold and an environmental compensation coefficient:

[0106] ① Basic threshold setting: preset the initial temperature threshold according to the battery type and specifications. For example, the basic threshold of the fast charging stage of lithium-ion batteries is set to 40°C.

[0107] ②Environmental compensation mechanism: The basic threshold is dynamically adjusted through the ambient temperature data transmitted by the environmental adaptation module. For example, when the ambient temperature is 25°C, the basic threshold remains at 40°C; when the ambient temperature rises to 30°C, the threshold is correspondingly reduced to 38°C to reserve more heat dissipation margin. ③Threshold comparison logic: When the real-time collected battery surface temperature exceeds the dynamic threshold, the current attenuation process is triggered; if the temperature continues to be lower than the threshold and the current change rate is stable (such as A / s), then maintain the current charging current.

[0108] Current command generation and superposition link, which realizes dynamic current adjustment through closed-loop control algorithm:

[0109] ① Current attenuation coefficient calculation: When the temperature exceeds the threshold, the microcontroller calculates the current attenuation coefficient according to the over-temperature amplitude ( ), the calculation formula is ,in is the real-time temperature, is the dynamic threshold, is the preset maximum over-temperature value (such as 10°C). , , ,but , corresponding to a current attenuation of 30%.

[0110] ② Current reduction control signal generation: Calculate the target current adjustment amount according to the current attenuation coefficient ( ), generating a current reduction control signal. This signal is output as a PWM (pulse width modulation) wave, with the pulse width corresponding to the target current value. For example, after the original current of 1.5A is attenuated by 30%, the PWM wave duty cycle is adjusted to correspond to an output of 1.05A.

[0111] ③ Command Superposition and Output: The current reduction control signal and the original charging command (the basic current command generated by the dynamic regulation module) are superimposed through an adder circuit to form the final charging current control command. For example, if the original command is 1.5A and the current reduction signal is -0.45A, the output current parameter is 1.05A. This command is transmitted to the charging power device (such as MOSFET) through the driver circuit, adjusting the charging current in real time.

[0112] Safety boundary control, fast charging subunit built-in double safety protection mechanism:

[0113] 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.

[0114] Soft start buffer: The current adjustment process adopts a ramp up / down method. For example, the adjustment step size does not exceed 0.2A each time, and the adjustment rate does not exceed 0.5A / s to reduce the impact on the charging circuit and prevent voltage oscillation.

[0115] Collaborative workflow: The fast charging subunit and other components of the dynamic regulation module exchange information via the data bus:

[0116] The global state-of-charge pooling layer of the dynamic regulation module provides the fast charging subunit with the current charging stage identifier (such as "fast charging stage") and battery health status parameters (such as internal resistance and aging level);

[0117] The fast charging subunit activates the temperature monitoring and current adjustment logic according to the stage identifier, and dynamically adjusts the threshold and attenuation coefficient based on the health status parameters (for example, the temperature threshold of an aging battery is reduced by 2°C);

[0118] The adjusted current parameters are compressed by the parameter transition processing layer and fed back to the strategy generation link of the dynamic adjustment module to participate in the strategy optimization in the subsequent stage.

[0119] Through this mechanism, the fast-charging subunit achieves temperature-sensitive dynamic control of the charging current, ensuring charging efficiency while avoiding the risk of battery overheating. This process, through real-time data acquisition, dynamic threshold calculation, and command superposition technology, forms a closed-loop feedback control chain, ensuring efficient charging within a safe and controllable range. This is particularly suitable for the thermal management requirements of high-power charging scenarios.

[0120] Example 5:

[0121] The environmental adaptation module achieves dynamic response to the environment during the charging process through multi-unit collaboration, specifically including the coordinated operation of the environmental feature extraction unit, the adaptive compensation unit and the interference shielding decision layer. At the same time, it integrates the exception handling mechanism and the wireless charging optimization function to form a complete environmental perception and control system.

[0122] The environmental feature extraction unit collects real-time environmental data from sensors deployed inside or outside the electronic device. Temperature and humidity sensors are used to obtain the temperature and humidity of the device's environment, while electromagnetic interference detection sensors (such as Hall-effect sensors or radio frequency detection modules) monitor electromagnetic signal strength in space and convert it into interference levels. These sensors output analog signals at a fixed frequency (e.g., once per second). These signals are converted to digital signals through signal conditioning circuits (including filtering and amplification) and analog-to-digital converters, and then fed into the adaptive compensation unit and the interference shielding decision layer.

[0123] The adaptive compensation unit performs compensation based on a pre-built mapping table of environmental data and battery internal resistance. This mapping table, derived through experimental testing, records typical values ​​of battery internal resistance under different temperature and humidity combinations. For example, at 25°C and 50% humidity, the internal resistance of a certain lithium-ion battery model is 150mΩ; at 35°C and 80% humidity, the internal resistance may drop to 130mΩ. Upon receiving real-time environmental characteristic data, the adaptive compensation unit uses a table lookup to match the corresponding internal resistance value and calculates the voltage compensation based on the change in internal resistance. For example, if the current environment causes the internal resistance to drop by 10% compared to the baseline value, the charging voltage must be reduced proportionally to maintain constant charging power (e.g., by 0.1V for a 4.0V baseline voltage). Once the voltage compensation is generated, it is embedded into the charging control command output by the dynamic regulation module via the data bus, directly adjusting the charging voltage setpoint to offset the impact of environmental changes on the battery internal resistance and maintain a stable charging current.

[0124] The interference shielding decision layer continuously monitors electromagnetic interference intensity data. When it detects that the interference level exceeds a preset threshold (such as exceeding 50dBμV / 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 transmitter's oscillation circuit, scans the natural frequency of the receiver's resonant coil, and locks the transmission frequency when the frequency matching point (i.e., the peak coupling efficiency point) is detected. In the wired charging scenario, the built-in LC filter circuit or digital filtering algorithm is activated to filter out the modulation effect of high-frequency interference signals on the charging current. While the anti-interference mode is activated, the interference shielding decision layer monitors the changes in interference intensity in real time. If the interference level drops to a safe range (such as below 40dBμV / m), it automatically exits the mode and resumes the normal charging control logic.

[0125] The exception handling mechanism continuously monitors battery expansion data and the rate of change of internal impedance through independent data acquisition channels. Battery expansion data is acquired using a microelectromechanical system (MEMS) displacement sensor deployed in the battery casing, measuring the deformation and displacement of the casing in real time. The rate of change of internal impedance is calculated using AC impedance spectroscopy, which periodically injects a small AC signal and measures the response voltage to calculate the impedance value and its rate of change. If the rate of change of displacement exceeds a safety threshold (e.g., 0.05 mm / h) or the rate of change of impedance exceeds a preset value, the exception handling module immediately sends a disconnect signal to the charging control circuit, disconnecting the charging circuit relay and triggering a warning signal through the audible and visual alarm module. Furthermore, based on impedance change trends and historical data, an interpolation algorithm is used to reversely infer the battery's aging status. For example, by fitting the aging curve with five consecutive impedance measurements, the current remaining capacity decay ratio of the battery is determined, and the maximum current limit in the subsequent charging strategy is updated accordingly (e.g., reducing the original maximum current of 1.5A to 1.2A).

[0126] The strategy fusion module's wireless charging optimization function uses electromagnetic field strength sensors to acquire real-time coupling efficiency data from wireless charging coils. This data reflects the degree of spatial alignment between the transmitter and receiver coils and the energy transfer efficiency. The system dynamically adjusts the transmitter frequency based on the coupling efficiency value. This is achieved by sending a gradually varying frequency excitation signal through a swept frequency generator, monitoring the amplitude of the induced voltage at the receiver, and locking the current transmitting frequency when the induced voltage reaches its peak, achieving resonant frequency matching. When multiple devices are wirelessly charging simultaneously, energy transfer weights are allocated according to preset priority rules based on each device's spatial location data (obtained through infrared sensors or Bluetooth positioning). For example, higher energy transfer power may be allocated to devices with a battery level below 20%. Alternatively, different priority levels may be set based on device type (such as mobile phones or headphones), with higher-priority devices receiving a larger share of energy transfer. This ensures optimal energy distribution and charging efficiency in multi-device charging scenarios.

[0127] The accurate battery charge display method based on the aforementioned battery charging management method achieves high-precision battery charge display through four steps. After real-time acquisition of the battery terminal voltage, load current, and estimated remaining capacity, the terminal voltage and load current are input into a dynamic calibration module. This module models the battery polarization effect, calculates the polarization voltage component, and deducts it from the terminal voltage to compensate for display errors caused by the polarization effect. The calibrated battery charge data is then matched to historical charge and discharge curves using a capacity fusion unit. The historical curves store voltage-capacity relationships at different temperatures and charge and discharge rates. A pattern matching algorithm (such as a dynamic time warping algorithm) is used to identify the most similar historical curve segments and generate a high-precision remaining charge percentage. Finally, the display interface switches the display accuracy mode based on the current usage scenario (e.g., standby, call, or video playback). For example, the battery level is displayed in 5% increments in standby mode and 1% increments in video playback mode. Dynamic rendering of the battery icon (e.g., changing color and fill progress) provides users with intuitive and accurate battery level information.

[0128] It should be noted that, in this document, relational terms such as first and second, etc., are used only 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 terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0129] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the 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: The steps include: Obtain real-time battery parameters and usage environment data of the device to be charged; Based on the battery parameters, the dynamic adjustment module 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 to dynamically adjust the charging power and temperature control threshold; A strategy fusion module performs weighted decision-making on the multi-dimensional charging strategy and the adjusted charging parameters to output a final charging control instruction; The dynamic adjustment module includes: A staged charging control unit, comprising a fast charging subunit, a balancing charging subunit, and a trickle charging subunit. The fast charging subunit is used to identify a low battery state and trigger a high current input, and the balancing charging subunit is used to balance the internal voltage differences of the battery; a parameter transition processing layer, configured to compress the feature dimensions output by the staged charging control unit; The global charge state pooling layer is used to uniformly map the processed charging parameters to the preset control range; Three staged charging control units are sequentially deployed in the dynamic adjustment module. The first staged charging control unit is connected to the second staged charging control unit via the parameter transition processing layer. The second staged charging control unit is connected to the third staged charging control unit via the parameter transition processing layer. The output of the third staged charging control unit is integrated by the global charging state pooling layer and then transmitted to the strategy fusion module. The staged charging control unit processes the input battery parameters through the following steps: Extracting features of the battery parameters according to the first processing link to generate a first feature group including current demand and voltage fluctuation range; Performing a time series analysis on the battery parameters according to the second processing link to generate a second feature group including a charging cycle and a decay trend; fusing the first feature group and the second feature group, and generating output instructions of the staged charging control unit through a nonlinear activation function; The environment adaptation module includes: Environmental feature extraction unit, used to identify the temperature, humidity and electromagnetic interference intensity of the environment in which the device is located; Adaptive compensation unit, used to dynamically correct the charging voltage fluctuation range according to environmental characteristics; Interference shielding decision layer, used to switch to anti-interference charging mode when electromagnetic interference exceeds the limit; The adaptive compensation unit operates through the following steps: Construct a mapping table between environmental data and battery internal resistance; Query the mapping relationship table according to the real-time environmental characteristics and output the corresponding voltage compensation amount; The voltage compensation amount is embedded in the charging control instruction to offset the influence of environmental fluctuations on the charging process.

2. The battery charging management method based on an electronic device according to claim 1, characterized in that: The fast charging subunit performs control through the following steps: Real-time monitoring of battery surface temperature and input current change rate; When it is detected that the temperature exceeds the dynamic threshold, the current attenuation coefficient is activated and a current reduction control signal is generated; The current reduction control signal is superimposed on the original charging instruction to output the adjusted charging current parameter.

3. The battery charging management method based on an electronic device according to claim 1, characterized in that: It also includes exception handling mechanisms: Continuously collect battery expansion data and internal impedance change rate during charging; When the expansion rate is detected to exceed the safety threshold, the charging circuit is immediately cut off and an early warning signal is triggered; The battery aging degree is reversely calculated based on the impedance change trend, and the maximum current limit of the subsequent charging strategy is updated.

4. The battery charging management method based on an electronic device according to claim 1, characterized in that: The strategy fusion module further includes wireless charging optimization functions: The coupling efficiency of the wireless charging coil is obtained through an electromagnetic field strength sensor; Dynamically adjust the transmitter frequency to match the receiver resonance characteristics; When multiple devices are charging simultaneously, differentiated energy transfer priorities are assigned based on spatial location data.

5. A method for accurately displaying the battery charge level of an electronic device, applied to the method for managing the battery charge of an electronic device according to any one of claims 1 to 4, characterized in that: The steps include: S1: Real-time acquisition of battery terminal voltage, load current and remaining capacity estimation; S2: Inputting the terminal voltage and load current into a dynamic calibration module to compensate for display errors caused by battery polarization effects; S3: The capacity fusion unit matches the calibrated power data with the historical charge and discharge curve to generate a high-precision remaining power percentage; S4: Dynamically render the battery icon in the display interface and switch the display accuracy mode according to the usage scenario.

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