Battery rapid charging management method and device and storage medium

Abstract

The invention discloses a rapid battery charging management method and device and a storage medium, and relates to the field of charging management, and the method comprises the steps: collecting the initial voltage, environment temperature and transient response parameters of a battery before charging, and building an initial state reference model of the battery; a micro-amplitude pulse disturbance current is superposed in a constant current charging stage, dynamic equivalent internal resistance is calculated in real time, and a battery behavior feature vector is constructed in combination with a temperature change rate, a voltage change trend and voltage rebound time. And inputting the feature vector into a safety evaluation model, generating a charging safety coefficient, dynamically adjusting the charging rate or the charging power according to the charging safety coefficient, and automatically triggering current limiting, power reduction or power-off protection when abnormal deviation of the internal resistance occurs or the temperature rise rate exceeds a threshold value. According to the method, fine sensing and predictive safety control of the internal state of the battery are achieved, charging efficiency and safety are both considered, and the method is suitable for portable energy storage equipment such as a power bank and a hand warmer.

Description

Technical Field

[0001] This specification relates to the field of charging management, and more specifically, this application relates to a battery fast charging management method, apparatus, and storage medium. Background Technology

[0002] Existing battery charging management methods mostly employ fixed charging curves or threshold control strategies based on simple parameters, such as adjusting the charging process solely based on battery terminal voltage, battery temperature, or charging time. While these methods can achieve basic charging safety to a certain extent, they generally suffer from insufficient perception of the battery's true internal state.

[0003] On the one hand, traditional solutions often fail to accurately reflect the dynamic changes in battery internal resistance during charging, while internal resistance is a crucial indicator of battery aging, polarization state, and potential thermal runaway risk. On the other hand, existing technologies often rely on a single or limited number of parameters for judgment, neglecting the coupling relationship between temperature changes, voltage dynamic behavior, and transient response characteristics. This leads to a delayed system response when the battery exhibits latent anomalies or early signs of failure, resulting in misjudgments or missed diagnoses.

[0004] Furthermore, in small portable devices such as power banks and hand warmers, batteries often face higher safety risks due to limited size, poor heat dissipation, and complex usage scenarios. Existing charging management solutions are difficult to differentiate and adjust for different application scenarios, have insufficient safety redundancy, and cannot meet the demand for both high safety and high efficiency.

[0005] Therefore, there is an urgent need for a battery fast charging management method, device, and storage medium to at least solve some of the above-mentioned problems. Summary of the Invention

[0006] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. This summary section is not intended to limit the key and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0007] In a first aspect, this application proposes a battery fast charging management method, comprising: Before the battery starts charging, the initial voltage, ambient temperature and transient response parameters of the battery are collected, and a baseline model of the battery initial state is established based on the above initial voltage, ambient temperature and transient response parameters. During the constant current charging phase, the control unit periodically superimposes a micro-amplitude pulse disturbance current onto the charging current and simultaneously collects the battery voltage change before and after the disturbance in order to calculate the battery's dynamic equivalent internal resistance. The dynamic equivalent internal resistance is compared and analyzed with the battery initial state benchmark model. The battery behavior feature vector is constructed by combining the temperature change rate, voltage change trend and voltage rebound time. The battery behavior feature vectors mentioned above are input into the safety assessment model to generate a charging safety coefficient, and the charging rate or charging power is dynamically adjusted based on the charging safety coefficient. When an abnormal deviation is detected in the dynamic equivalent internal resistance relative to the battery initial state reference model, or when the temperature rise rate exceeds a preset threshold, at least one safety control strategy among current limiting, power reduction, pulse trickle charging, or power outage protection is automatically triggered.

[0008] In one feasible implementation, the above-mentioned acquisition of battery initial voltage, ambient temperature, and transient response parameters, and the establishment of a battery initial state baseline model based on the above-mentioned initial voltage, ambient temperature, and transient response parameters, includes: When the battery enters the charging standby state, the initial voltage value of the battery port and the ambient temperature value of the space where the battery is located are acquired, and the acquired initial voltage value and ambient temperature value are time-synchronized and calibrated to form initial measurement reference data. A micro-step excitation current of a preset amplitude is applied to the battery while the battery is in a static state, and the transient voltage change curve generated during the micro-step excitation current loading process is collected to obtain the transient response parameters. The transient response parameters mentioned above are analyzed to extract the transient rise slope, steady-state recovery time and voltage overshoot amplitude, and a set of battery transient response feature parameters is generated. The initial measurement baseline data and the transient response characteristic parameter set mentioned above are fused together to construct a battery initial state baseline model for characterizing the electrochemical stability and thermal response characteristics of the battery during the initial charging stage.

[0009] In one feasible implementation, during the constant current charging phase, the control unit periodically superimposes a micro-amplitude pulse disturbance current onto the charging current and simultaneously collects the battery voltage change before and after the disturbance to calculate the battery's dynamic equivalent internal resistance, including: During the constant current charging phase, the control unit superimposes a micro-pulse disturbance current with a preset amplitude and a preset duration onto the current charging current at preset time intervals, and records the first steady-state voltage value of the battery before the micro-pulse disturbance current is applied. During the loading of the micro-pulse disturbance current, the instantaneous voltage change data of the battery is collected in real time, and the second steady-state voltage value of the battery is recorded after the micro-pulse disturbance current is unloaded, so as to form a disturbance response voltage sequence. The dynamic voltage response difference of the battery is calculated based on the first steady-state voltage value, the second steady-state voltage value, and the change in current of the micro-pulse disturbance current. The dynamic equivalent internal resistance of the battery is obtained based on the dynamic voltage response difference and the change in current of the micro-pulse disturbance current.

[0010] In one feasible implementation, the dynamic equivalent internal resistance is compared and analyzed with the battery initial state benchmark model, and a battery behavior feature vector is constructed by combining the temperature change rate, voltage change trend, and voltage rebound time, including: Based on the real-time acquisition of the above dynamic equivalent internal resistance, the above dynamic equivalent internal resistance is compared with the corresponding reference internal resistance parameter in the above battery initial state reference model to calculate the battery internal resistance offset and internal resistance change rate, so as to obtain the internal resistance offset characteristic parameter that characterizes the degree of change in conductivity. The data sequence of battery temperature change over time is collected synchronously, and the differential operation is performed on the data sequence to obtain the rate of temperature change per unit time, thereby forming a temperature change characteristic parameter characterizing the intensity of thermal response. Slope analysis and fitting were performed on the time series data of battery terminal voltage to extract the voltage change trend parameters. The voltage rebound time parameters were obtained by measuring the voltage stabilization process after pulse disturbance unloading. The aforementioned internal resistance offset characteristic parameters, temperature change characteristic parameters, voltage change trend parameters, and voltage rebound time parameters are fused in a multi-dimensional manner to construct a battery behavior feature vector that characterizes the degree of deviation of the battery's real-time operating state from the aforementioned battery initial state benchmark model.

[0011] In one feasible implementation, the above-mentioned input of the battery behavior feature vector into the safety assessment model to generate a charging safety coefficient, and the dynamic adjustment of the charging rate or charging power based on the charging safety coefficient, includes: The battery behavior feature vector is input into a pre-built safety assessment model, and a weighted calculation is performed based on the internal resistance offset feature parameter, temperature change feature parameter, voltage change trend parameter and voltage rebound time parameter contained in the battery behavior feature vector to generate a charging safety coefficient that characterizes the current battery safety state. Based on the above charging safety factor and the preset safety threshold range, range mapping is performed to determine the corresponding charging adjustment level, and a target charging rate parameter or target charging power parameter that matches the above charging adjustment level is generated. The charging control signal output by the charging control unit is corrected in real time according to the above target charging rate parameter or the above target charging power parameter, so as to dynamically adjust the output amplitude of charging current or the output level of charging voltage, so that the charging process is always in a safe charging range that is compatible with the real-time operating state of the battery.

[0012] In one feasible implementation, the above security assessment model includes a feature normalization processing unit, a weight mapping unit, a multi-dimensional security scoring unit, a threshold decision unit, and a security coefficient output unit. The aforementioned feature normalization processing unit is used to standardize the dynamic equivalent internal resistance offset, temperature change rate, voltage change trend, and voltage rebound time in the aforementioned battery behavior feature vector. The aforementioned weight mapping unit is used to automatically determine the type of application scenario based on the identification results of the current operating mode and thermal response behavior of the device, and to construct the scenario feature mapping relationship by combining the internal resistance offset feature parameter and temperature change feature parameter in the battery behavior feature vector to generate the scenario adaptive weight matrix. The aforementioned multidimensional security scoring unit is used to perform weighted fusion calculations on the normalized feature parameters to form a comprehensive security score; The aforementioned threshold decision unit is used to generate corresponding security level ranges based on the comprehensive security score; The aforementioned safety factor output unit is used to output the charging safety factor and serve as the basis for adjusting the charging control strategy.

[0013] In one feasible implementation, when the above-mentioned fast charging management method is applied to a power bank, based on the detection results of the external load output state, the above-mentioned dynamic equivalent internal resistance offset characteristic parameter and the above-mentioned voltage change trend parameter are preferentially used as the dominant evaluation factors to construct a battery behavior feature vector, and a first charging safety control strategy focusing on voltage stability is generated through the above-mentioned safety evaluation model to ensure efficient and safe fast charging under external load fluctuation conditions; or, When the above-mentioned fast charging management method for batteries is applied to hand warmers, based on the detection results of the working status and surface temperature distribution of the heating components, the above-mentioned temperature change characteristic parameters and the above-mentioned voltage rebound time parameters are preferentially used as the dominant evaluation factors to construct a battery behavior characteristic vector. The above-mentioned safety evaluation model is used to generate a second charging safety control strategy that focuses on thermal safety, so as to achieve thermal risk suppression and energy synergistic regulation under the parallel operation of charging and heating.

[0014] Secondly, the present invention also proposes a battery fast charging management system, comprising: The data acquisition unit is used to acquire the battery's initial voltage, ambient temperature, and transient response parameters before the battery starts charging, and to establish a battery initial state reference model based on the aforementioned initial voltage, ambient temperature, and transient response parameters. The calculation unit is used to periodically superimpose a micro-amplitude pulse disturbance current onto the charging current during the constant current charging stage, and simultaneously collect the battery voltage change before and after the disturbance, so as to calculate the dynamic equivalent internal resistance of the battery. The construction unit is used to compare and analyze the above dynamic equivalent internal resistance with the above battery initial state benchmark model, and construct the battery behavior feature vector by combining the temperature change rate, voltage change trend and voltage rebound time. The generation unit is used to input the above-mentioned battery behavior feature vector into the safety assessment model, generate a charging safety coefficient, and dynamically adjust the charging rate or charging power based on the above-mentioned charging safety coefficient. An abnormal handling unit is used to automatically trigger at least one safety control strategy among current limiting, power reduction, pulse trickle charging, or power failure protection when an abnormal deviation of the above-mentioned dynamic equivalent internal resistance relative to the above-mentioned battery initial state reference model is detected or the temperature rise rate exceeds a preset threshold.

[0015] Thirdly, the present invention also proposes an electronic device comprising: a memory and a processor, characterized in that the processor is used to execute a computer program stored in the memory to implement the steps of any of the battery fast charging management methods described in the first aspect.

[0016] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the battery fast charging management method as described in any one of the first aspects.

[0017] In summary, the battery fast charging management method proposed in this invention establishes a comprehensive, closed-loop, refined safety control mechanism by constructing a baseline model of the battery's initial state before charging, introducing micro-pulse disturbances during charging and calculating the dynamic equivalent internal resistance in real time, constructing a battery behavior feature vector by combining temperature change rate, voltage change trend, and voltage rebound time, and further generating a charging safety factor through a safety assessment model. This invention achieves high sensitivity to changes in the battery's internal state through comparative analysis of the dynamic equivalent internal resistance and the initial baseline model, enabling early identification of potential risks before anomalies escalate into serious faults, thereby significantly improving the safety of the charging process. The invention constructs a battery behavior feature vector through multi-dimensional feature fusion, avoiding errors caused by single-parameter judgments and effectively improving the accuracy of safety assessment results. Based on the charging safety factor, this invention dynamically adjusts the charging rate or charging power, allowing the charging process to adaptively optimize within a safe range. Compared to traditional static control strategies, this ensures charging efficiency while reducing the risks of overheating, overload, and thermal runaway. When an abnormal deviation in internal resistance or an excessive rate of temperature rise is detected, the system can automatically trigger graded protection measures such as current limiting, power reduction, pulse trickle charging, or power cutoff, achieving multi-level safety protection from early warning to forced protection, and significantly improving the system's reliability. This invention overcomes the limitations of traditional charging management, which relies solely on static parameters and coarse-grained judgment. By introducing dynamic internal resistance analysis, multi-dimensional behavioral feature fusion, and a safety factor-driven adaptive control strategy, the battery charging process is upgraded from passive response management to proactive predictive safety management. This not only effectively reduces safety hazards such as battery bulging, overheating, and fire, but also improves charging efficiency while ensuring safety. It is particularly suitable for portable energy storage devices such as power banks and hand warmers, which have higher requirements for safety and stability.

[0018] Other advantages, objectives and features of this application will be apparent in part from the description which follows, and in part from what those skilled in the art will understand through study and practice of this application. Attached Figure Description

[0019] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a schematic flowchart of a battery fast charging management method provided in an embodiment of this application; Figure 2 This is a structural schematic diagram of a battery fast charging management system provided in an embodiment of this application; Figure 3This is a schematic diagram of an electronic device structure provided in an embodiment of this application. Detailed Implementation

[0020] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0021] Please see Figure 1 This is a flowchart illustrating a battery fast charging management method provided in an embodiment of this application, which may specifically include: S110. Before the battery starts charging, collect the battery's initial voltage, ambient temperature, and transient response parameters, and establish a battery initial state benchmark model based on the aforementioned initial voltage, ambient temperature, and transient response parameters. S120. During the constant current charging stage, the control unit periodically superimposes a micro-amplitude pulse disturbance current onto the charging current and simultaneously collects the battery voltage change before and after the disturbance in order to calculate the dynamic equivalent internal resistance of the battery. S130. Compare and analyze the above dynamic equivalent internal resistance with the above battery initial state benchmark model, and construct a battery behavior feature vector by combining the temperature change rate, voltage change trend and voltage rebound time. S140. Input the above battery behavior feature vector into the safety assessment model to generate a charging safety coefficient, and dynamically adjust the charging rate or charging power based on the above charging safety coefficient. S150. When an abnormal deviation is detected in the above-mentioned dynamic equivalent internal resistance relative to the above-mentioned battery initial state reference model or the temperature rise rate exceeds a preset threshold, at least one safety control strategy among current limiting, power reduction, pulse trickle charging or power failure protection is automatically triggered.

[0022] For example, in step S110, before the battery actually begins charging, the system first comprehensively senses the initial state of the battery. By collecting the initial voltage of the battery terminals, the temperature of the environment in which the battery is located, and the transient response parameters generated when a small excitation is applied, the system can obtain the electrochemical and thermal response characteristics of the battery in a static state. Based on the aforementioned initial voltage, ambient temperature, and transient response parameters, a baseline model of the battery's initial state is constructed, so that the state at any time during subsequent charging can be compared with this baseline, thereby quantitatively characterizing the degree of deviation in the battery state and providing a reference coordinate system for subsequent safety assessment and strategy adjustment.

[0023] In step S120, after the battery enters the constant current charging stage, it does not simply maintain a fixed charging current. Instead, the control unit periodically superimposes a micro-amplitude pulsed disturbance current on top of the predetermined constant current. This slight, controllable current disturbance avoids significant interference with the charging process or causes perceptible fluctuations for the user. Furthermore, it allows for the simultaneous acquisition of minute changes in battery voltage before and after the disturbance. Based on the relationship between the voltage change and the disturbance current change before and after the disturbance, the dynamic equivalent internal resistance of the battery under the current operating conditions can be calculated in real time. Compared to traditional methods that rely solely on open-circuit voltage or simple temperature monitoring, this method of online inversion of dynamic internal resistance during charging can more sensitively reflect internal battery polarization, aging, and potential abnormal states.

[0024] In step S130, this embodiment does not solely rely on the dynamic equivalent internal resistance as a single criterion. Instead, it compares and analyzes the dynamic equivalent internal resistance with the reference internal resistance parameter in the battery initial state baseline model to obtain the deviation of the battery internal resistance relative to the initial state. Simultaneously, it combines the temperature change rate, voltage change trend, and voltage rebound time required for the voltage to recover to a stable state after the pulse disturbance is removed during charging to construct a battery behavior feature vector from these different dimensions. This multi-feature fusion approach allows for a comprehensive characterization of the battery's current operating state from three levels: conductivity, thermal response characteristics, and voltage dynamic behavior. This results in a more comprehensive and robust evaluation, avoiding the risk of misjudgment based on a single parameter.

[0025] In step S140, the aforementioned battery behavior feature vector is input into a pre-constructed safety assessment model. This model can be a multi-dimensional scoring and rating model combining internal resistance shift, temperature rise slope, voltage change, and rebound behavior. By performing weighted calculations or trajectory analysis on the battery behavior feature vector, the model outputs a charging safety factor characterizing the safety margin of the current charging process. Based on this charging safety factor, the system dynamically adjusts the current charging rate or charging power: when the safety factor is high, it allows maintaining or moderately increasing the charging rate to achieve fast charging; when the safety factor decreases, it automatically reduces the charging rate or limits the charging power, thereby achieving proactive risk control without completely sacrificing charging efficiency.

[0026] In step S150, when an abnormal deviation is detected in the dynamic equivalent internal resistance relative to the battery initial state reference model, such as an abnormal increase or aggravated fluctuation in internal resistance, or when the temperature rise rate exceeds a preset threshold, indicating potential internal damage, thermal runaway risk, or abnormal operating conditions, the system will no longer simply respond by finely adjusting the charging rate. Instead, it will automatically trigger at least one safety control strategy among current limiting, power reduction, pulse trickle charging, or direct power-off protection. Through a graded, multi-strategy protection approach, this embodiment can take differentiated measures for risks of varying severity. For mild anomalies, current limiting or power reduction controls the risk; for moderate risks, pulse trickle charging reduces heat and stress accumulation; and when obvious danger signs appear, rapid power-off protection prevents serious safety accidents such as battery bulging, smoke, or even fire.

[0027] In summary, the battery fast charging management method proposed in this invention establishes a comprehensive, closed-loop, refined safety control mechanism by constructing a baseline model of the battery's initial state before charging, introducing micro-pulse disturbances during charging and calculating the dynamic equivalent internal resistance in real time, constructing a battery behavior feature vector by combining temperature change rate, voltage change trend, and voltage rebound time, and further generating a charging safety factor through a safety assessment model. This invention achieves high sensitivity to changes in the battery's internal state through comparative analysis of the dynamic equivalent internal resistance and the initial baseline model, enabling early identification of potential risks before anomalies escalate into serious faults, thereby significantly improving the safety of the charging process. The invention constructs a battery behavior feature vector through multi-dimensional feature fusion, avoiding errors caused by single-parameter judgments and effectively improving the accuracy of safety assessment results. Based on the charging safety factor, this invention dynamically adjusts the charging rate or charging power, allowing the charging process to adaptively optimize within a safe range. Compared to traditional static control strategies, this ensures charging efficiency while reducing the risks of overheating, overload, and thermal runaway. When an abnormal deviation in internal resistance or an excessive rate of temperature rise is detected, the system can automatically trigger graded protection measures such as current limiting, power reduction, pulse trickle charging, or power cutoff, achieving multi-level safety protection from early warning to forced protection, and significantly improving the system's reliability. This invention overcomes the limitations of traditional charging management, which relies solely on static parameters and coarse-grained judgment. By introducing dynamic internal resistance analysis, multi-dimensional behavioral feature fusion, and a safety factor-driven adaptive control strategy, the battery charging process is upgraded from passive response management to proactive predictive safety management. This not only effectively reduces safety hazards such as battery bulging, overheating, and fire, but also improves charging efficiency while ensuring safety. It is particularly suitable for portable energy storage devices such as power banks and hand warmers, which have higher requirements for safety and stability.

[0028] In one feasible implementation, the above-mentioned acquisition of battery initial voltage, ambient temperature, and transient response parameters, and the establishment of a battery initial state baseline model based on the above-mentioned initial voltage, ambient temperature, and transient response parameters, includes: When the battery enters the charging standby state, the initial voltage value of the battery port and the ambient temperature value of the space where the battery is located are acquired, and the acquired initial voltage value and ambient temperature value are time-synchronized and calibrated to form initial measurement reference data. A micro-step excitation current of a preset amplitude is applied to the battery while the battery is in a static state, and the transient voltage change curve generated during the micro-step excitation current loading process is collected to obtain the transient response parameters. The transient response parameters mentioned above are analyzed to extract the transient rise slope, steady-state recovery time and voltage overshoot amplitude, and a set of battery transient response feature parameters is generated. The initial measurement baseline data and the transient response characteristic parameter set mentioned above are fused together to construct a battery initial state baseline model for characterizing the electrochemical stability and thermal response characteristics of the battery during the initial charging stage.

[0029] For example, the process of collecting the battery's initial voltage, ambient temperature, and transient response parameters and establishing a baseline model of the battery's initial state can be understood as first taking the battery at the instant before it is charged as a standard reference state, and then quantitatively describing this reference state through three dimensions: voltage, temperature, and step response, and finally condensing it into a quasi-fingerprint that can be called by subsequent algorithms.

[0030] Specifically, when the battery enters charging standby mode, the system first reads the initial voltage value of the battery port and the ambient temperature value of the space where the battery is located, and records them as follows: and Since there may be a slight time difference in the acquisition of these two quantities, time synchronization calibration is required to map them to the same reference time. In one feasible form, the initial measurement reference data can be considered to consist of binary sets. Composition, in which It characterizes the terminal voltage level of a battery in a resting state and usually reflects the initial point of the battery's state of charge. Characterizing the initial temperature conditions of the environment and the battery itself provides a benchmark for subsequent thermal response analysis.

[0031] With the battery in a static state, the system applies a micro-step excitation current of a preset amplitude to the battery, denoted as . Its meaning is at a certain moment Afterwards, the current abruptly changes from 0 (or a very small charging current) to a small constant value. During the step excitation loading process, the transient response curve of the battery terminal voltage changing with time was acquired in real time. For many battery equivalent models, this transient response can be approximated in a first-order form: in, The equivalent resistance component obtained under this small-signal excitation reflects the comprehensive conductivity of the battery polarization, electrolyte, and electrode interface. The equivalent time constant reflects the response speed of the internal electrochemical process of the battery from disturbance to stability; This term reflects the dynamic characteristics of a transient process gradually approaching a steady state from rapid change. This is achieved by fitting the measured transient voltage curve. This allows us to deduce the values ​​that match the current battery state. and The estimated value.

[0032] To extract more intuitive features from the transient response described above, this embodiment performs feature analysis on the transient response parameters. For example, the transient rise slope of the voltage can be estimated using differential or fitting methods. Its approximate form can be written as: in, For a short time point after a step excitation, For the corresponding voltage value, It reflects the rate of voltage rise, and indirectly reflects the sensitivity of internal impedance and polarization process. Steady-state recovery time It can be defined as the voltage response entering and remaining within a certain error band of the final steady-state value (e.g., The time required within ) is to satisfy: The smallest ,in This is the final steady-state value of the voltage. This is the preset tolerance coefficient. A larger value generally indicates a slower electrochemical process inside the battery, which may be related to aging or increased polarization. Voltage overshoot amplitude This can be defined as the difference between the peak value of the voltage response curve before steady state and the steady-state value, i.e. in, This represents the maximum voltage value that occurs during the response process. A large overshoot often indicates that the system's RC combination or dynamic process exhibits significant oscillation or overresponse characteristics.

[0033] Through the above analysis process, this embodiment will incorporate the transient rise slope, steady-state recovery time, voltage overshoot amplitude, and the data obtained from the fitting process. and Equal summation into a set of characteristic parameters of the battery transient response can be denoted as: Each parameter in the set corresponds to a specific characteristic of the battery under initial excitation: Characterizes the speed of response. The time required for characterization to stabilize Characterize whether the response is too aggressive. Characterizing equivalent conductivity, Characterizing the inertia of electrochemical processes.

[0034] Subsequently, this embodiment will use the initial measurement reference data. With the above set of transient response characteristic parameters Perform fusion calculations. Mathematically, the battery initial state baseline model can be represented as a parameter vector or function mapping, for example: in, This characterizes the battery's electrochemical stability and thermal response characteristics during the initial charging phase, and can be understood as a standard profile of the battery under current environmental and health conditions. At the application level, It can be stored as a set of structured parameters for comparison with the real-time measured dynamic equivalent internal resistance, real-time temperature rise behavior, and voltage response characteristics during subsequent charging. When the measured internal resistance, temperature rise slope, or voltage response deviates significantly at a certain moment... When the battery reaches the normal state it represents, it can be determined that the current state of the battery has deviated from the initial health baseline, thereby triggering a more stringent safety assessment and control strategy.

[0035] Through the above process, this embodiment establishes a multi-dimensional, multi-parameter initial state benchmark model for the battery before charging even begins, utilizing small-signal excitation and transient response analysis. This model considers both static voltage and ambient temperature, and integrates transient electrochemical and thermal response behaviors under dynamic excitation. It provides a quantitative and comparable initial reference for subsequent dynamic internal resistance calculation, behavioral feature vector construction, and safety assessment models during fast charging, thereby improving the sensitivity and reliability of the entire fast charging management method in anomaly identification and risk prediction.

[0036] In one feasible implementation, during the constant current charging phase, the control unit periodically superimposes a micro-amplitude pulse disturbance current onto the charging current and simultaneously collects the battery voltage change before and after the disturbance to calculate the battery's dynamic equivalent internal resistance, including: During the constant current charging phase, the control unit superimposes a micro-pulse disturbance current with a preset amplitude and a preset duration onto the current charging current at preset time intervals, and records the first steady-state voltage value of the battery before the micro-pulse disturbance current is applied. During the loading of the micro-pulse disturbance current, the instantaneous voltage change data of the battery is collected in real time, and the second steady-state voltage value of the battery is recorded after the micro-pulse disturbance current is unloaded, so as to form a disturbance response voltage sequence. The dynamic voltage response difference of the battery is calculated based on the first steady-state voltage value, the second steady-state voltage value, and the change in current of the micro-pulse disturbance current. The dynamic equivalent internal resistance of the battery is obtained based on the dynamic voltage response difference and the change in current of the micro-pulse disturbance current.

[0037] For example, during the constant current charging phase, the control unit periodically superimposes a micro-amplitude pulse disturbance current and calculates the dynamic equivalent internal resistance of the battery. Without interrupting the constant current charging, a very small step-like fluctuation is applied to the charging current. By observing the changes in voltage before and after, the equivalent internal resistance of the battery at this moment is inverted online, thereby obtaining state information that is more sensitive than simply looking at voltage or temperature.

[0038] Specifically, during the constant current charging phase, assuming the base constant current charging current is... The control unit operates according to a preset time interval. A small-amplitude pulse disturbance current is periodically superimposed on the constant current. In other words, at a certain preset disturbance time... Nearby, the actual charging current that the battery experiences changes from the original... It rose to: in, The preset duration of the micro-pulse disturbance is usually selected from tens to hundreds of milliseconds, which can obtain a clear and measurable voltage change without causing macroscopic interference to the entire charging process.

[0039] Before superimposing the micro-pulse disturbance, the control unit first records the first steady-state voltage value of the battery port during the stable period before the disturbance begins. We can approximate the current at this time as follows: The corresponding voltage is: Subsequently, during the application of a micro-pulse disturbance current, the control unit acquires the instantaneous voltage change data of the battery in real time at a high sampling frequency, thus obtaining a disturbance response voltage curve. When the disturbance lasts After completion, the control unit is deactivated. This restores the charging current to The second steady-state voltage value at the battery port is recorded during the steady-state phase after the disturbance ends. By using these three data segments—the steady-state voltage before the disturbance, the transient response during the disturbance, and the steady-state voltage after the disturbance—a complete disturbance response voltage sequence can be formed.

[0040] In a simple and commonly used approach, the calculation of the dynamic equivalent internal resistance can be based on the steady-state voltage change before and after the disturbance. Specifically, the voltage change is defined as: in, For the current before or after the disturbance, The steady-state voltage value at the time of disturbance (in practical implementation, the voltage before disturbance can be used) After the disturbance The average value reduces noise, i.e. ), For the current is The voltage value after it stabilizes. Due to the superimposed micro-pulse disturbance current being... The dynamic equivalent internal resistance of the battery under this tiny current disturbance is... It can be approximated as: in, This reflects the response amplitude of the battery terminal voltage under a small current step. The ratio of these two values ​​represents the corresponding minute current change and is equivalent to the differential internal resistance near this operating condition. This differs from traditional coarse-grained internal resistances obtained based on open-circuit voltage or large-amplitude operating condition switching. It more closely reflects the instantaneous equivalent impedance under the current charging state.

[0041] In some implementations that require consideration of transient processes, voltage time series acquired during the disturbance loading can also be utilized. This allows for further fitting of the voltage response over time. For example, an exponential approximation can be used: in, The start time of the disturbance. Let be the dynamic time constant of the disturbance response. By fitting the measured voltage curve, we can simultaneously obtain... and The former and Together they are used to calculate the dynamic equivalent internal resistance: and This reflects the battery's response speed from disturbance to stability under this small signal excitation, and indirectly reflects the speed and polarization characteristics of the electrochemical process.

[0042] In the above formula, The basic constant current charging current represents the charging current applied to the battery by the system under undisturbed conditions. Let be the amplitude of the micro-pulse disturbance current, and be in A small current step is superimposed on the basis. The duration of the small-amplitude pulse disturbance current determines whether the voltage has enough time to approach a new steady state. For the current before and after the disturbance, The steady-state voltage value measured at the time can be used to estimate the reference voltage level before and after the disturbance. For the current is The corresponding steady-state voltage value or the steady-state voltage level obtained by fitting. It represents the change in voltage before and after the disturbance, reflecting the voltage response amplitude of the battery under a small current disturbance. The dynamic equivalent internal resistance is the instantaneous equivalent internal resistance of the battery estimated online through small-signal excitation during constant current charging in this embodiment. It is the dynamic response time constant, used to characterize the voltage's response speed and buffering characteristics to current disturbances.

[0043] By periodically repeating the above superimposed micro-pulses, measuring the voltage, and calculating... The process allows obtaining a dynamic equivalent internal resistance curve that varies with time throughout the constant current charging phase. In this embodiment, the curve is considered a sensitive indicator of battery health and internal electrochemical processes: when When the current charging state is stable and close to the initial state baseline model, it can be determined that the current charging state is safe, and a higher charging rate can be appropriately increased or maintained. If it is found that... If an abnormal rise, violent fluctuation, or significant deviation from the initial benchmark occurs, it can be considered that there are potential risks such as accelerated aging, severe polarization, or abnormal heating inside the battery. The system can then combine other characteristics such as temperature changes to trigger more stringent safety strategies such as current limiting, power reduction, pulse trickle charging, or even power-off protection.

[0044] This embodiment obtains the dynamic equivalent internal resistance of the battery without significantly interfering with normal charging by superimposing a micro-amplitude pulsed disturbance current during the constant current charging stage and simultaneously collecting the voltage changes before and after the disturbance. It also provides a highly sensitive electrical characteristic basis for subsequent construction of battery behavior feature vectors and execution of safety assessment.

[0045] In one feasible implementation, the dynamic equivalent internal resistance is compared and analyzed with the battery initial state benchmark model, and a battery behavior feature vector is constructed by combining the temperature change rate, voltage change trend, and voltage rebound time, including: Based on the real-time acquisition of the above dynamic equivalent internal resistance, the above dynamic equivalent internal resistance is compared with the corresponding reference internal resistance parameter in the above battery initial state reference model to calculate the battery internal resistance offset and internal resistance change rate, so as to obtain the internal resistance offset characteristic parameter that characterizes the degree of change in conductivity. The data sequence of battery temperature change over time is collected synchronously, and the differential operation is performed on the data sequence to obtain the rate of temperature change per unit time, thereby forming a temperature change characteristic parameter characterizing the intensity of thermal response. Slope analysis and fitting were performed on the time series data of battery terminal voltage to extract the voltage change trend parameters. The voltage rebound time parameters were obtained by measuring the voltage stabilization process after pulse disturbance unloading. The aforementioned internal resistance offset characteristic parameters, temperature change characteristic parameters, voltage change trend parameters, and voltage rebound time parameters are fused in a multi-dimensional manner to construct a battery behavior feature vector that characterizes the degree of deviation of the battery's real-time operating state from the aforementioned battery initial state benchmark model.

[0046] For example, firstly, based on the aforementioned dynamic equivalent internal resistance collected in real time, the current moment is recorded. The dynamic equivalent internal resistance calculated by the micro-amplitude pulse perturbation is: The reference internal resistance parameter stored in the aforementioned battery initial state reference model is denoted as... The offset of the battery's internal resistance can be calculated using a simple difference operation: in, This characterizes the degree of deviation of the battery's conductivity under current operating conditions from the initial baseline. A consistently positive and gradually increasing value usually indicates intensified polarization, accelerated aging, or increased internal damage. To more sensitively characterize the change in internal resistance over time, this embodiment can also be based on data from multiple consecutive time points. Calculate the rate of change of internal resistance, for example, using the discrete difference form: in, The time interval between two consecutive dynamic internal resistance measurements. This reflects the rate at which internal resistance rises or falls. When Smaller but A significantly positive value indicates that the internal resistance is rapidly deteriorating; even if the absolute value has not yet exceeded the limit, it should still be a cause for concern. Through the above calculations, a set of characteristic parameters for internal resistance deviation can be obtained, for example... It is used to characterize the degree of change in the battery's conductivity.

[0047] Regarding thermal response, this embodiment synchronously collects a data sequence of battery temperature changes over time, denoted as... To obtain the rate of temperature change per unit time, the temperature sequence can be differentially analyzed or locally fitted; its discrete form can be written as: The rate of temperature rise This will be used as a temperature change characteristic parameter to characterize the rate of heat accumulation in the battery and its surrounding structure under the current charging conditions. When When the temperature remains at a low level for an extended period, it usually indicates good heat dissipation and controllable internal losses; when A significantly higher temperature rise rate than the initial stage or historical average at a similar charging rate may indicate increased internal resistance, abnormal localized heating, or obstructed heat dissipation. This embodiment can also store a set of corresponding reference temperature rise curves or reference temperature rise rate ranges in the battery initial state benchmark model for further comparison of the deviation of the current temperature behavior from the benchmark.

[0048] Regarding voltage behavior, this embodiment performs slope analysis and fitting processing on the time series data of battery terminal voltage. Let the continuously acquired voltage series be denoted as... It can be linearly fitted within a relatively short time window: Among them, the fitting coefficient This is the voltage change trend parameter, reflecting whether the voltage is rising, falling, or basically stable within the current time window; This indicates an overall increase in voltage. This indicates a downward trend. An abnormal negative slope appearing in the middle to late stages of constant current charging may indicate an abnormal internal battery condition or a measurement system malfunction. In addition to the overall trend, this embodiment also obtains the aforementioned voltage rebound time parameter by measuring the voltage stabilization process after pulse disturbance removal. Specifically, when the micro-pulse disturbance current is removed, the current changes from... Restore to The battery terminal voltage will gradually return to a new steady-state value from the disturbed state. A steady-state tolerance band can be set, for example: in, This represents the desired steady-state voltage after the disturbance ends. For the preset ratio threshold (e.g.) or The voltage rebound time is... It can be defined as the time interval from the moment the disturbance is removed to the moment the above steady-state condition is first met, reflecting the voltage recovery speed after the disturbance. If The value is significantly longer than the reference value in the initial baseline model, indicating that the dynamic response of the battery under the current operating conditions has become sluggish, which may be related to enhanced polarization, increased internal resistance, or interface degradation.

[0049] After obtaining the aforementioned characteristic parameters, this embodiment performs a multi-dimensional fusion operation to jointly construct a battery behavior feature vector by combining the aforementioned internal resistance offset characteristic parameters, temperature change characteristic parameters, voltage change trend parameters, and voltage rebound time parameters. To facilitate the joint analysis of features with different dimensions, each feature can first be normalized or standardized, for example: in, These represent the reference temperature rise rate, voltage trend, and rebound time recorded in the initial state baseline model, respectively. The normalized results more intuitively reflect the deviation ratio of the current state relative to the initial baseline. Finally, the battery behavior feature vector can be expressed as: Alternatively, several key components can be selected to form a feature vector based on specific implementation needs. This feature vector comprehensively encodes the degree of change in the battery's conductivity, thermal response intensity, voltage evolution trend, and disturbance recovery capability at the current moment. Each of these features is referenced to the initial state baseline model, which helps the safety assessment model quickly determine whether the battery is currently on a normal evolution trajectory or has already deviated towards a risky state in subsequent steps.

[0050] In this embodiment, instead of simply checking whether a certain instantaneous value exceeds a threshold, a battery behavior feature vector is constructed from multiple dimensions such as internal resistance shift, temperature change, voltage trend, and rebound dynamics. The real-time operating state is compared with the initial baseline state in a multi-dimensional and multi-scale manner, which significantly improves the ability to identify abnormal operating conditions, potential failures, and early risks. This provides sufficient and detailed input information for subsequent calculation of charging safety factor and adaptive adjustment of charging strategy.

[0051] In one feasible implementation, the above-mentioned input of the battery behavior feature vector into the safety assessment model to generate a charging safety coefficient, and the dynamic adjustment of the charging rate or charging power based on the charging safety coefficient, includes: The battery behavior feature vector is input into a pre-built safety assessment model, and a weighted calculation is performed based on the internal resistance offset feature parameter, temperature change feature parameter, voltage change trend parameter and voltage rebound time parameter contained in the battery behavior feature vector to generate a charging safety coefficient that characterizes the current battery safety state. Based on the above charging safety factor and the preset safety threshold range, range mapping is performed to determine the corresponding charging adjustment level, and a target charging rate parameter or target charging power parameter that matches the above charging adjustment level is generated. The charging control signal output by the charging control unit is corrected in real time according to the above target charging rate parameter or the above target charging power parameter, so as to dynamically adjust the output amplitude of charging current or the output level of charging voltage, so that the charging process is always in a safe charging range that is compatible with the real-time operating state of the battery.

[0052] In one feasible implementation, the above security assessment model includes a feature normalization processing unit, a weight mapping unit, a multi-dimensional security scoring unit, a threshold decision unit, and a security coefficient output unit. The aforementioned feature normalization processing unit is used to standardize the dynamic equivalent internal resistance offset, temperature change rate, voltage change trend, and voltage rebound time in the aforementioned battery behavior feature vector. The aforementioned weight mapping unit is used to automatically determine the type of application scenario based on the identification results of the current operating mode and thermal response behavior of the device, and to construct the scenario feature mapping relationship by combining the internal resistance offset feature parameter and temperature change feature parameter in the battery behavior feature vector to generate the scenario adaptive weight matrix. The aforementioned multidimensional security scoring unit is used to perform weighted fusion calculations on the normalized feature parameters to form a comprehensive security score; The aforementioned threshold decision unit is used to generate corresponding security level ranges based on the comprehensive security score; The aforementioned safety factor output unit is used to output the charging safety factor and serve as the basis for adjusting the charging control strategy.

[0053] For example, the aforementioned battery behavior feature vector is fed as input into a pre-built safety assessment model. Let the feature vector at a certain moment be... The form is: in, This represents the offset or normalized offset of the dynamic equivalent internal resistance relative to the initial reference internal resistance. This represents the current rate of temperature change (temperature rise slope). This refers to parameters related to the voltage variation trend within the spatial window (e.g., the slope of the linear fit). , where is the voltage rebound time parameter. The aforementioned feature normalization unit first standardizes these quantities with different dimensions, mapping them to a unified dimensionless interval for easier subsequent weighted fusion. For example, linear normalization can be used: in, Indicates a certain primitive feature (such as or ), and These are the lower and upper limits preset based on historical data, experimental calibration, or standard ranges. These are the normalized eigenvalues, which typically fall within... or Within the interval. The normalized eigenvector can be denoted as: in, Corresponding to the internal resistance offset related characteristics, Corresponding characteristics related to temperature changes Corresponding characteristics of voltage change trends Corresponding characteristics related to voltage rebound time.

[0054] Next, the weight mapping unit automatically determines the application scenario type based on the device's current operating mode and thermal response behavior. For example, if the device is detected to be in power bank mode and the external load experiences significant power fluctuations, it can be classified as a scenario prioritizing output stability. If the device is detected to be in hand warmer mode and the heating module is working simultaneously, with the outer casing or internal temperature rising rapidly, it can be classified as a scenario prioritizing thermal safety.

[0055] For different scenarios, the weight mapping unit constructs a scenario feature mapping relationship based on the internal resistance offset feature parameter and temperature change feature parameter in the battery behavior feature vector mentioned above, and generates a scenario-adaptive weight matrix. For example, a weight vector can be defined as follows: For power bank applications that prioritize conductivity and stable output, it can make and Larger, and and Relatively small; for hand warmer scenarios where heat safety is a priority, it can make and Increase the weights. The weight mapping unit can also fine-tune the above weights based on the safety performance over multiple consecutive charging cycles, for example, by introducing a correction factor that evolves over time. ,form: in, The weighting matrix can be adaptively adjusted based on the correlation between the feature and risk events over a period of time, so that the weight of the feature dimension with frequent problems gradually increases, thereby realizing the evolution of the scene adaptive weighting matrix.

[0056] After feature normalization and weight generation, the multidimensional security scoring unit performs a weighted fusion operation on the normalized feature parameters to form a comprehensive security score. A typical implementation is a linear weighted sum: in, For comprehensive safety scoring, higher values ​​generally indicate greater risk or a smaller safety margin. Nonlinear functions can also be introduced as needed, such as adding threshold amplification or square / exponential forms to certain features, to enhance sensitivity to extreme cases. In this embodiment, for ease of explanation, [the following can be used] It is considered a risk score.

[0057] The threshold decision unit is based on a comprehensive security score. Generate corresponding security level ranges. For example, multiple level thresholds can be preset: in, These are grading thresholds set based on experimental data and safety standards. This interval mapping simplifies complex, multi-dimensional feature analysis results into intuitive safety level labels.

[0058] After obtaining the comprehensive safety score and safety level, the safety factor output unit converts them into a charging safety factor. This coefficient is usually designed to be one. The closer the value is to 1, the safer the charging and the higher the available charging rate. A simple form could be: in, This serves as a theoretical or conventional upper limit for the overall security score, used for normalization. Alternatively, it can be directly designed as a piecewise function, such as under high security levels. Approaching 1, at the danger level A significant decrease. Ultimately, the charging control logic can be designed with the target charging rate parameter as follows: Alternatively, the target charging power parameters can be designed as follows: in, The maximum charging rate allowed by the system. This represents the maximum charging power allowed by the system. Thus, when the safety factor is high, the target charging rate or power approaches the upper limit; when the safety factor decreases due to increased risk, the target charging rate or power decreases accordingly.

[0059] At the execution level, the charging control unit corrects the output control signal in real time based on the target charging rate parameter or target charging power parameter. This can be achieved by adjusting the PWM duty cycle of the DC-DC converter, adjusting the constant current source setpoint, etc. For example, the target charging current can be calculated as: Alternatively, the current setpoint can be calculated from the target power: in, For the battery's rated capacity, This is the current battery terminal voltage. In the control loop, the system will... As a set value, the output is adjusted in real time to adapt the charging current output amplitude or charging voltage output level to changes in the battery operating state, so that the entire charging process is always within the preset safe charging range.

[0060] In summary, this embodiment uses a feature normalization processing unit to unify internal resistance offsets, temperature change rates, voltage change trends, and voltage rebound times of different dimensions into a calculable standardized space. A weight mapping unit generates a scenario-adaptive weight matrix based on the identification of the current device operating mode and thermal response behavior, automatically adjusting the safety assessment focus for different application scenarios such as power banks and hand warmers. A multi-dimensional safety scoring unit quantifies the comprehensive risk of multi-dimensional features. A threshold decision unit maps the comprehensive score into clear safety level ranges. Finally, a safety coefficient output unit generates a charging safety coefficient, driving the charging control unit to dynamically adjust the current or power.

[0061] In one feasible implementation, when the above-mentioned fast charging management method is applied to a power bank, based on the detection results of the external load output state, the above-mentioned dynamic equivalent internal resistance offset characteristic parameter and the above-mentioned voltage change trend parameter are preferentially used as the dominant evaluation factors to construct a battery behavior feature vector, and a first charging safety control strategy focusing on voltage stability is generated through the above-mentioned safety evaluation model to ensure efficient and safe fast charging under external load fluctuation conditions; or, When the above-mentioned fast charging management method for batteries is applied to hand warmers, based on the detection results of the working status and surface temperature distribution of the heating components, the above-mentioned temperature change characteristic parameters and the above-mentioned voltage rebound time parameters are preferentially used as the dominant evaluation factors to construct a battery behavior characteristic vector. The above-mentioned safety evaluation model is used to generate a second charging safety control strategy that focuses on thermal safety, so as to achieve thermal risk suppression and energy synergistic regulation under the parallel operation of charging and heating.

[0062] For example, when the above-mentioned fast-charging battery management method is applied to different types of small portable devices, the system first automatically identifies whether it is in power bank mode or hand warmer mode based on device-side sensing and operating status information. Then, in the process of constructing the battery behavior feature vector, different physical quantities are given targeted reinforcement, and the feature most closely related to the safety risks of the scenario is used as the dominant evaluation factor, thereby forming a charging safety control strategy with scenario awareness capabilities.

[0063] Specifically, when the aforementioned fast-charging battery management method is applied to a power bank, the control unit obtains the current output voltage by detecting the output status of the external load. Output current And load power fluctuations over a time window, such as power sequences. And calculate the power fluctuation within this window. in, The average power within the window. It reflects the intensity of fluctuations in the external load. If detected... If the preset threshold is exceeded, the system determines that the current scenario involves a power bank with significant external load fluctuations and higher requirements for output voltage stability. In this scenario, the battery behavior feature vector preferentially selects the dynamic equivalent internal resistance offset feature parameter and the voltage change trend parameter as the dominant components. For example, a simplified dominant feature vector can be constructed as follows: in, It is the normalized offset of the battery's dynamic equivalent internal resistance relative to the initial reference internal resistance, characterizing the degree of degradation in conductivity. This is a normalized slope parameter representing the voltage change trend, reflecting whether there are abnormal fluctuations or a downward trend in voltage under the current operating conditions. In this scenario, the safety assessment model employs a weight configuration centered on voltage stability, for example, by setting a weight vector. And calculate the comprehensive risk score in the power bank scenario: according to Mapping yields the charging safety factor Based on this, a first charging safety control strategy is then generated, for example, through... The charging current setting is compressed in real time, so that when the voltage change trend deteriorates due to external load fluctuations, the system will prioritize reducing the charging current or limiting the charging power to ensure that the output voltage remains within a small fluctuation and safe range. This allows for efficient and stable fast charging even under conditions of frequent plugging and unplugging of external loads or large power changes.

[0064] When the aforementioned fast-charging battery management method is applied to hand warmers, the system conducts a focused assessment of thermal safety risks by collecting data on the operating status of the heating element and the temperature distribution on the outer shell surface. The control unit acquires the real-time power of the heating element. and surface temperature field And calculate the maximum gradient of surface temperature. and average temperature rise rate in, The spatial average of the surface temperature. Describes the extent to which local hotspots appear. When detected... or When the preset threshold is exceeded, the system determines that it is in a high heat load scenario where charging and heating occur simultaneously. In this scenario, the internal heating of the battery is superimposed with the external heating, and the risk of thermal runaway is significantly higher than that of ordinary power banks. Therefore, this embodiment prioritizes using temperature change characteristic parameters and voltage rebound time parameters as dominant factors to construct a battery behavior feature vector, for example, constructing: in, It is a normalized value of the battery or casing temperature rise rate relative to the initial reference temperature rise rate, reflecting the overall heat accumulation intensity; This is a normalized value of the voltage rebound time relative to the reference rebound time, indirectly reflecting the recovery capability and internal polarization degree after disturbance. The safety assessment model in the hand warmer scenario adopts a weight configuration prioritizing thermal safety, for example... Its comprehensive risk score can be written as: Then, the charging safety factor for hand warmers is obtained through nonlinear or linear mapping. In terms of control strategy, the second charging safety control strategy not only limits the charging current, but also applies a safety factor to the heating power simultaneously, for example: in, This is the rated maximum power of the heating element. When the temperature rises too quickly or the voltage rebound time is significantly prolonged, it causes... When the power is reduced, the system simultaneously reduces the charging current and heating power to achieve integrated power limiting control of charging and heating. This suppresses overheating of the casing and internal temperature runaway under parallel charging and heating conditions, ensuring user comfort while also protecting the safety of the battery and the user.

[0065] Through the aforementioned scenario-based design, this embodiment prioritizes dynamic equivalent internal resistance shift and voltage change trends in power bank mode, emphasizing stable output voltage and compatibility with external loads. In hand warmer mode, it prioritizes temperature changes and voltage rebound time, emphasizing thermal safety and coordinated energy regulation during charging and heating. The safety assessment model automatically switches between different feature weights and control strategies in the two scenarios, enabling the same battery fast charging management method to exhibit differentiated, adaptive safety control behavior across different product forms. This significantly improves the system's safety and practicality in multiple application scenarios.

[0066] Secondly, this invention also proposes a battery fast charging management system, such as... Figure 2 As shown, it includes: The acquisition unit 21 is used to acquire the battery initial voltage, ambient temperature and transient response parameters before the battery starts charging, and to establish a battery initial state reference model based on the initial voltage, ambient temperature and transient response parameters. The calculation unit 22 is used to periodically superimpose a micro-amplitude pulse disturbance current onto the charging current during the constant current charging stage, and simultaneously collect the battery voltage change before and after the disturbance, so as to calculate the dynamic equivalent internal resistance of the battery. Construction unit 23 is used to compare and analyze the above dynamic equivalent internal resistance with the above battery initial state benchmark model, and construct a battery behavior feature vector by combining the temperature change rate, voltage change trend and voltage rebound time. The generation unit 24 is used to input the above-mentioned battery behavior feature vector into the safety assessment model, generate a charging safety coefficient, and dynamically adjust the charging rate or charging power based on the above-mentioned charging safety coefficient. The abnormal handling unit 25 is used to automatically trigger at least one safety control strategy among current limiting, power reduction, pulse trickle charging or power failure protection when it detects that the above-mentioned dynamic equivalent internal resistance has an abnormal deviation relative to the above-mentioned battery initial state reference model or the temperature rise rate exceeds a preset threshold.

[0067] In one feasible implementation, a battery fast charging management system may also perform any step of the method proposed in the first aspect.

[0068] Thirdly, the present invention also proposes an electronic device 300, such as... Figure 3 As shown, it includes a memory 310, a processor 320, and a computer program 311 stored on the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of the battery fast charging management method as described in any of the first aspects.

[0069] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the battery fast charging management method as described in any one of the first aspects.

[0070] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0071] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0073] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0075] This application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device performs the voice-based identity recognition process in the corresponding embodiment. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0076] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0078] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0079] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0080] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0081] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A battery fast charging management method, characterized in that, include: Before the battery starts charging, the initial battery voltage, ambient temperature and transient response parameters are collected, and a battery initial state benchmark model is established based on the initial voltage, ambient temperature and transient response parameters. During the constant current charging phase, the control unit periodically superimposes a micro-amplitude pulse disturbance current onto the charging current and simultaneously collects the battery voltage change before and after the disturbance in order to calculate the battery's dynamic equivalent internal resistance. The dynamic equivalent internal resistance is compared and analyzed with the battery initial state benchmark model, and a battery behavior feature vector is constructed by combining the temperature change rate, voltage change trend and voltage rebound time. The battery behavior feature vector is input into the safety assessment model to generate a charging safety coefficient, and the charging rate or charging power is dynamically adjusted based on the charging safety coefficient. When an abnormal deviation of the dynamic equivalent internal resistance relative to the battery initial state reference model is detected, or the temperature rise rate exceeds a preset threshold, at least one safety control strategy among current limiting, power reduction, pulse trickle charging, or power outage protection is automatically triggered.

2. The battery fast charging management method according to claim 1, characterized in that, The process of collecting the battery's initial voltage, ambient temperature, and transient response parameters, and establishing a battery initial state baseline model based on the initial voltage, ambient temperature, and transient response parameters, includes: When the battery enters the charging standby state, the initial voltage value of the battery port and the ambient temperature value of the space where the battery is located are acquired, and the acquired initial voltage value and ambient temperature value are time-synchronized and calibrated to form initial measurement reference data. A micro-step excitation current of a preset amplitude is applied to the battery while the battery is in a static state, and the transient voltage change curve generated during the loading of the micro-step excitation current is collected to obtain the transient response parameters. The transient response parameters are analyzed to extract the transient rise slope, steady-state recovery time, and voltage overshoot amplitude, and a set of battery transient response feature parameters is generated. The initial measurement baseline data and the transient response characteristic parameter set are fused together to construct a battery initial state baseline model for characterizing the electrochemical stability and thermal response characteristics of the battery during the initial charging stage.

3. The battery fast charging management method according to claim 1, characterized in that, During the constant current charging phase, the control unit periodically superimposes a micro-amplitude pulse disturbance current onto the charging current and simultaneously collects the battery voltage change before and after the disturbance to calculate the battery's dynamic equivalent internal resistance, including: During the constant current charging phase, the control unit superimposes a micro-pulse disturbance current with a preset amplitude and a preset duration onto the current charging current at preset time intervals, and records the first steady-state voltage value of the battery before the micro-pulse disturbance current is applied. During the loading of the micro-pulse disturbance current, the instantaneous voltage change data of the battery is collected in real time, and the second steady-state voltage value of the battery is recorded after the micro-pulse disturbance current is unloaded, so as to form a disturbance response voltage sequence. The dynamic voltage response difference of the battery is calculated based on the first steady-state voltage value, the second steady-state voltage value, and the change in current of the micro-pulse disturbance current. The dynamic equivalent internal resistance of the battery is obtained based on the dynamic voltage response difference and the change in current of the micro-pulse disturbance current.

4. The battery fast charging management method according to claim 1, characterized in that, The step involves comparing and analyzing the dynamic equivalent internal resistance with the battery's initial state baseline model, and constructing a battery behavior feature vector by combining the temperature change rate, voltage change trend, and voltage rebound time, including: Based on the real-time collected dynamic equivalent internal resistance, the dynamic equivalent internal resistance is compared with the corresponding reference internal resistance parameter in the battery initial state reference model to calculate the battery internal resistance offset and internal resistance change rate, so as to obtain the internal resistance offset characteristic parameter characterizing the degree of change in conductivity. The data sequence of battery temperature change over time is collected synchronously, and the differential operation is performed on the data sequence to obtain the rate of temperature change per unit time, thereby forming a temperature change characteristic parameter characterizing the intensity of thermal response. Slope analysis and fitting are performed on the time series data of battery terminal voltage to extract the voltage change trend parameters, and the voltage rebound time parameters are obtained by measuring the voltage stabilization process after pulse disturbance unloading. The internal resistance offset characteristic parameter, the temperature change characteristic parameter, the voltage change trend parameter, and the voltage rebound time parameter are fused in a multi-dimensional manner to construct a battery behavior feature vector that characterizes the degree of deviation of the real-time operating state of the battery relative to the battery initial state benchmark model.

5. The battery fast charging management method according to claim 1, characterized in that, The step of inputting the battery behavior feature vector into the safety assessment model to generate a charging safety coefficient, and dynamically adjusting the charging rate or charging power based on the charging safety coefficient, includes: The battery behavior feature vector is input into a pre-built safety assessment model, and a weighted calculation is performed based on the internal resistance offset feature parameter, temperature change feature parameter, voltage change trend parameter and voltage rebound time parameter contained in the battery behavior feature vector to generate a charging safety coefficient that characterizes the current battery safety state. Based on the charging safety factor and the preset safety threshold range, range mapping is performed to determine the corresponding charging adjustment level, and a target charging rate parameter or target charging power parameter matching the charging adjustment level is generated. The charging control signal output by the charging control unit is corrected in real time according to the target charging rate parameter or the target charging power parameter, so as to dynamically adjust the output amplitude of the charging current or the output level of the charging voltage, so that the charging process is always in a safe charging range that is compatible with the real-time operating state of the battery.

6. The battery fast charging management method according to claim 5, characterized in that, The security assessment model includes a feature normalization processing unit, a weight mapping unit, a multi-dimensional security scoring unit, a threshold decision unit, and a security coefficient output unit. The feature normalization processing unit is used to standardize the dynamic equivalent internal resistance offset, temperature change rate, voltage change trend and voltage rebound time in the battery behavior feature vector. The weight mapping unit is used to automatically determine the application scenario type based on the identification results of the current operating mode and thermal response behavior of the device, and to construct the scenario feature mapping relationship by combining the internal resistance offset feature parameter and temperature change feature parameter in the battery behavior feature vector, so as to generate the scenario adaptive weight matrix. The multidimensional security scoring unit is used to perform weighted fusion calculations on the normalized feature parameters to form a comprehensive security score; The threshold decision unit is used to generate a corresponding security level range based on the comprehensive security score; The safety factor output unit is used to output the charging safety factor and serve as the basis for adjusting the charging control strategy.

7. The battery fast charging management method according to claim 1, characterized in that, When the battery fast charging management method is applied to a power bank, based on the detection results of the external load output state, the dynamic equivalent internal resistance offset characteristic parameter and the voltage change trend parameter are preferentially used as the dominant evaluation factors to construct a battery behavior feature vector. A first charging safety control strategy focusing on voltage stability is then generated through the safety evaluation model to ensure efficient and safe fast charging under fluctuating external load conditions; or... When the battery fast charging management method is applied to a hand warmer, based on the detection results of the working status and surface temperature distribution of the heating component, the temperature change characteristic parameter and the voltage rebound time parameter are preferentially used as the dominant evaluation factors to construct a battery behavior feature vector. A second charging safety control strategy focusing on thermal safety is generated through the safety evaluation model to achieve thermal risk suppression and energy synergistic regulation under the parallel operation of charging and heating.

8. A battery fast charging management system, characterized in that, include: The data acquisition unit is used to acquire the battery's initial voltage, ambient temperature, and transient response parameters before the battery starts charging, and to establish a battery initial state reference model based on the initial voltage, ambient temperature, and transient response parameters. The calculation unit is used to periodically superimpose a micro-amplitude pulse disturbance current onto the charging current during the constant current charging stage, and simultaneously collect the battery voltage change before and after the disturbance, so as to calculate the dynamic equivalent internal resistance of the battery. The construction unit is used to compare and analyze the dynamic equivalent internal resistance with the battery initial state benchmark model, and construct a battery behavior feature vector by combining the temperature change rate, voltage change trend and voltage rebound time. The generation unit is used to input the battery behavior feature vector into the safety assessment model, generate a charging safety coefficient, and dynamically adjust the charging rate or charging power based on the charging safety coefficient. An abnormal handling unit is used to automatically trigger at least one safety control strategy among current limiting, power reduction, pulse trickle charging, or power failure protection when an abnormal deviation of the dynamic equivalent internal resistance relative to the battery initial state reference model is detected or the temperature rise rate exceeds a preset threshold.

9. An electronic device, comprising: The memory and processor are characterized in that the processor, when executing a computer program stored in the memory, implements the steps of the battery fast charging management method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the battery fast charging management method as described in any one of claims 1-7.

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