Power supply fast charging pulse current control system and method based on current monitoring

Abstract

The present invention discloses a power supply fast charging pulse current control system and method based on current monitoring, which specifically relates to the field of power supply management technology. By collecting pulse current signals during the battery charging process, nonlinear decoupling analysis is performed to decompose the pulse current signals into a first characteristic subvector representing the actual charging state of the battery and a second characteristic subvector representing the high-frequency harmonic interference state. The battery electrochemical impedance change trend is predicted based on the first characteristic subvector to form an instant charging acceptance capability evaluation index. The harmonic interference degree is evaluated based on the second characteristic subvector to form a charging state misjudgment risk level index. Based on the battery instant charging acceptance capability evaluation index and the battery charging state misjudgment risk level index, the optimal pulse current control strategy is determined, and the power supply output parameters are adjusted in real time, effectively solving the state misjudgment problem caused by the coupling of high-frequency harmonic interference and electrochemical impedance, and improving the battery charging performance and long-term reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of power management, and more particularly to a power supply rapid charging pulse current control system and method based on current monitoring. Background Art

[0002] Existing pulse charging control technologies typically determine battery status based on current monitoring data and set pulse current parameters accordingly. However, this approach fails to fully account for the interference of high-frequency harmonic components in the pulse current on the measurement of battery electrochemical characteristics. During the battery charging process, a complex nonlinear coupling relationship exists between the high-frequency perturbation components carried by the current signal and the battery's electrochemical impedance, which can easily lead to misjudgment of the battery's true charging state and charge acceptance. This misjudgment directly affects the accuracy and adaptability of the control strategy, causing the pulse parameter settings to deviate from the battery's actual charging capacity. This not only reduces charging efficiency but can also accelerate battery performance degradation, affecting battery safety and long-term stability.

[0003] In order to solve the above problems, a technical solution is now provided. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a power supply fast charging pulse current control method and system based on current monitoring to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for controlling a power supply's rapid charging pulse current based on current monitoring includes the following steps:

[0007] S1: Collect the pulse current signal during battery charging, perform high-frequency noise filtering and feature parameter extraction to obtain the current feature vector;

[0008] S2: Perform nonlinear decoupling analysis on the current eigenvector to generate a first eigenvector representing the actual charging state of the battery and a second eigenvector representing the harmonic interference state;

[0009] S3: Perform a battery electrochemical impedance change trend prediction analysis on the first characteristic sub-vector to obtain an evaluation index of the battery's instant charge acceptance capability;

[0010] S4: Evaluate and analyze the high-frequency harmonic interference degree of the second characteristic sub-vector to generate a battery charging status misjudgment risk level indicator;

[0011] S5: Determine the optimal pulse charging current control strategy based on the battery instant charging acceptance capability evaluation index and the battery charging state misjudgment risk level index;

[0012] S6: Adjust the output current parameters of the pulse power supply in real time according to the optimal pulse charging current control strategy.

[0013] In a preferred embodiment, S1 is specifically:

[0014] The pulse current signal of the battery charging is sampled at equal time intervals to obtain the original sampling sequence of the pulse current;

[0015] Perform high-frequency noise filtering on the original sampling sequence of the pulse current and output the filtered current data sequence;

[0016] The filtered current data sequence is processed by sliding segmentation according to the preset time window, and the pulse current characteristic parameters are extracted in each time window;

[0017] The pulse current characteristic parameters extracted in each time window are arranged in time series to form a set of current characteristic vector sequences.

[0018] In a preferred embodiment, S2 is specifically:

[0019] Perform nonlinear principal component analysis on the current characteristic vector sequence to extract the characteristic component set with the maximum nonlinear independence;

[0020] Based on the characteristic component set and the frequency distribution characteristics of the pulse current signal during battery charging, a frequency domain decoupling model is constructed;

[0021] The current eigenvector sequence is separated by feature space mapping through a frequency domain decoupling model to obtain a first eigenvector and a second eigenvector.

[0022] In a preferred embodiment, S3 is specifically:

[0023] Extracting electrochemical impedance data points according to the first eigenvector;

[0024] The least square fitting algorithm is used to fit the curve of the corresponding relationship between the electrochemical impedance data points and the charging time to obtain the electrochemical impedance dynamic change curve;

[0025] The curve slope is calculated based on the dynamic change curve of electrochemical impedance, and the curve slope is used as an evaluation indicator for the battery's instant charging acceptance capability.

[0026] In a preferred embodiment, S4 is specifically:

[0027] Extracting a time domain harmonic interference data sequence according to the second eigenvector;

[0028] Performing fast Fourier transform on the time domain harmonic interference data sequence to obtain spectrum distribution information;

[0029] Extracting high-frequency harmonic amplitude components from the spectrum distribution information and calculating the ratio of the high-frequency harmonic amplitude to the fundamental amplitude as the harmonic distortion coefficient;

[0030] Divide risk levels according to harmonic distortion coefficient and preset risk threshold;

[0031] The risk level is used as an indicator of the risk level of misjudgment of the battery charging status.

[0032] In a preferred embodiment, S5 is specifically:

[0033] Based on the battery instant charge acceptance evaluation index, a charge acceptance response function is constructed;

[0034] Based on the battery charging status misjudgment risk level index, a misjudgment risk impact function is constructed;

[0035] The pulse charging current amplitude, pulse width and pulse interval are set as optimization variables, and a multi-objective optimization model is constructed with the charging acceptance response function as the benefit objective and the misjudgment risk impact function as the risk constraint.

[0036] A global iterative solution is performed based on a multi-objective optimization model to output the optimal pulse charging current control strategy parameter set.

[0037] In a preferred embodiment, S6 is specifically:

[0038] Adjusting the output current amplitude according to the pulse charging current amplitude in the optimal pulse charging current control strategy parameter set;

[0039] Controlling the duration of each pulse signal according to the pulse width in the optimal pulse charging current control strategy parameter set;

[0040] The time interval between adjacent pulses is controlled according to the pulse interval in the optimal pulse charging current control strategy parameter set.

[0041] On the other hand, the present invention provides a power supply fast charging pulse current control system based on current monitoring, comprising:

[0042] Current acquisition unit: collects the pulse current signal during battery charging, performs high-frequency noise filtering and characteristic parameter extraction to obtain the current characteristic vector;

[0043] Decoupling analysis unit: performs nonlinear decoupling analysis on the current eigenvector to generate a first eigenvector representing the actual charging state of the battery and a second eigenvector representing the harmonic interference state;

[0044] Acceptance capacity evaluation unit: performs battery electrochemical impedance change trend prediction analysis on the first characteristic sub-vector to obtain the battery instant charging acceptance capacity evaluation index;

[0045] Risk assessment unit: This unit evaluates and analyzes the degree of high-frequency harmonic interference of the second characteristic sub-vector and generates a risk level indicator for misjudgment of the battery charging status;

[0046] Strategy decision unit: Determines the optimal pulse charging current control strategy based on the battery instant charging acceptance capability evaluation index and the battery charging status misjudgment risk level index;

[0047] Current control unit: adjusts the pulse power supply output current parameters in real time according to the optimal pulse charging current control strategy.

[0048] The technical effects and advantages of the power supply fast charging pulse current control system and method based on current monitoring of the present invention are as follows:

[0049] By performing high-frequency noise filtering and extracting characteristic parameters on the battery charging pulse current signal, refined real-time monitoring of the charging process is achieved; nonlinear decoupling analysis is used to separate the actual charging state from the harmonic interference state, avoiding the interference of high-frequency harmonic interference on state identification; dynamic trend prediction of electrochemical impedance is performed based on the first characteristic subvector, and the instantaneous charging acceptance capability of the battery can be dynamically evaluated; the degree of harmonic interference is evaluated based on the second characteristic subvector, which can effectively warn of the potential misjudgment risk of battery charging state identification; based on the battery instant charging acceptance evaluation index and the battery charging state misjudgment risk level index, the optimal pulse charging current control strategy is determined and the pulse power supply output parameters are adjusted in real time to achieve precise control of charging behavior, improve the response accuracy of the pulse control strategy to the battery state, optimize the charging efficiency and extend the battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 Schematic diagram of the power supply fast charging pulse current control method based on current monitoring of the present invention;

[0051] Figure 2 This is a structural diagram of the power supply fast charging pulse current control system based on current monitoring of the present invention. DETAILED DESCRIPTION

[0052] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] Example 1

[0054] Figure 1The present invention provides a method for controlling the rapid charging pulse current of a power supply based on current monitoring, which includes the following steps:

[0055] S1: Collect the pulse current signal during battery charging, perform high-frequency noise filtering and feature parameter extraction to obtain the current feature vector;

[0056] S2: Perform nonlinear decoupling analysis on the current eigenvector to generate a first eigenvector representing the actual charging state of the battery and a second eigenvector representing the harmonic interference state;

[0057] S3: Perform a battery electrochemical impedance change trend prediction analysis on the first characteristic sub-vector to obtain an evaluation index of the battery's instant charge acceptance capability;

[0058] S4: Evaluate and analyze the high-frequency harmonic interference degree of the second characteristic sub-vector to generate a battery charging status misjudgment risk level indicator;

[0059] S5: Determine the optimal pulse charging current control strategy based on the battery instant charging acceptance capability evaluation index and the battery charging state misjudgment risk level index;

[0060] S6: Adjust the output current parameters of the pulse power supply in real time according to the optimal pulse charging current control strategy.

[0061] S1: Collect the pulse current signal during battery charging, perform high-frequency noise filtering and feature parameter extraction to obtain the current feature vector, including:

[0062] The pulse current signal of the battery charging is sampled at equal time intervals to obtain the original sampling sequence of the pulse current;

[0063] Specifically, the pulse current signal generated during battery charging is measured in real time at a constant millisecond sampling interval. For example, current data is collected every one millisecond, and the continuously measured current data is recorded in sequence as a set of original sampling data of the pulse current in the order of time collection. Each data in the set represents the current value at the corresponding sampling moment.

[0064] Perform high-frequency noise filtering on the original sampling sequence of the pulse current and output the filtered current data sequence;

[0065] Specifically, an initial filter window length is determined based on the degree of current fluctuation and the required stability of the pulse current characteristics. For each data point in the original sampled data set, the median of multiple data points adjacent to the current data point is calculated as the output value of the filtered current data. The adaptive filter threshold is adjusted. When the difference between two adjacent data values ​​in the original sampled data of the pulse current exceeds the set threshold, the filter window length is increased to improve the accuracy of the filtered current data, thereby obtaining a filtered current data set.

[0066] The filtered current data sequence is processed by sliding segmentation according to the preset time window, and the pulse current characteristic parameters are extracted in each time window;

[0067] Specifically, sliding segmentation involves selecting a fixed-length millisecond time window as the time window. The time window length is determined by the characteristic period of the charging pulse current waveform. The fixed-length time window is then slid point by point from the data start position to the end position. Each slide interval is a fixed sampling period, sequentially generating multiple non-overlapping or partially overlapping data subsets.

[0068] A plurality of pulse current characteristic parameters are extracted from the filtered current data set within each window, including but not limited to the maximum amplitude parameter, minimum amplitude parameter, average amplitude parameter, rise rate parameter of the current rise process, fall rate parameter of the current fall process, and pulse cycle stability parameter. Among them, the current rise rate parameter is defined as the amplitude of the increase of the pulse current from the start of the window to the current reaching the maximum amplitude divided by the corresponding time difference; the current fall rate parameter is defined as the difference of the current amplitude from the maximum amplitude to the end position of the window divided by the corresponding time difference; the average amplitude parameter is defined as the sum of the values ​​of all current data in the window and then divided by the number of data in the window; the cycle stability parameter is defined as the ratio of the standard deviation of the lengths of multiple consecutive pulse cycles in the window to the average length of the pulse cycles, which is used to characterize the stability of the pulse current cycle.

[0069] The pulse current characteristic parameters extracted in each time window are arranged in time series to form a set of current characteristic vector sequences;

[0070] Specifically, each current characteristic vector includes a maximum amplitude parameter, a minimum amplitude parameter, an average amplitude parameter, a rising rate parameter, a falling rate parameter, and a period stability parameter. Multiple current characteristic vectors are arranged into a sequence in chronological order.

[0071] S2: Perform nonlinear decoupling analysis on the current eigenvector to generate a first eigenvector representing the actual battery charging state and a second eigenvector representing the harmonic interference state, including:

[0072] Perform nonlinear principal component analysis on the current characteristic vector sequence to extract the characteristic component set with the maximum nonlinear independence;

[0073] Specifically, a nonlinear principal component analysis algorithm is employed, including kernel function mapping of the current eigenvector sequence and eigenvalue decomposition. To fully reflect the nonlinear characteristics of the pulsed current signal, a radial basis kernel function is selected as the kernel function for the mapping process. The radial basis kernel function is calculated by summing the squares of the differences between any two current eigenvectors in the current eigenvector sequence, multiplying the sum of the squares by the negative kernel parameter, and then taking the exponential value to obtain the mapping result of the feature space. Based on the kernel function mapping, a matrix eigenvalue decomposition method is used to determine the importance of each eigencomponent in the mapping space. The multiple eigencomponents in the mapping space are sorted from large to small according to their eigenvalues. The eigencomponents whose cumulative eigenvalue contributions exceed a preset percentage are selected to form a set of eigencomponents. Optimization parameters such as the kernel parameter and contribution percentage are determined based on the effectiveness of the eigencomponent differentiation.

[0074] Based on the characteristic component set and the frequency distribution characteristics of the pulse current signal during battery charging, a frequency domain decoupling model is constructed;

[0075] Specifically, a spectrum analysis is performed on the pulse current signal during battery charging to determine the frequency composition and amplitude characteristic distribution of the pulse current signal. Spectral analysis uses fast Fourier transform, that is, the pulse current signal is converted into a frequency domain signal through fast Fourier transform, and the amplitudes corresponding to different frequency components are obtained. Based on the characteristic component set, a frequency domain decoupling model is constructed in combination with the frequency characteristics in the spectrum analysis. The model includes establishing a mathematical correlation between the characteristic components and the spectrum characteristics. The frequency domain decoupling model is a linear hybrid model. Multiple characteristic components in the characteristic component set are linearly combined through the decoupling coefficient to be determined to represent the low-frequency components corresponding to the actual charging state of the battery and the high-frequency components corresponding to the harmonic interference state. The decoupling coefficient in the linear hybrid model is determined based on the optimization of the frequency amplitude distribution characteristics of the current signal and the difference in battery charging characteristics.

[0076] Performing feature space mapping separation on the current eigenvector sequence through a frequency domain decoupling model to obtain a first eigenvector and a second eigenvector;

[0077] Specifically, the current eigenvector sequence is input into the frequency-domain decoupling model, which uses the decoupling coefficient to calculate the input current eigenvector sequence to achieve separation of the characteristic components in the characteristic space. Specifically, each input current eigenvector is multiplied by multiple characteristic components in the characteristic component set, multiplied by the corresponding decoupling coefficient, and then summed. Based on the selection principles of the decoupling coefficient and characteristic components, two independent characteristic sub-vector sequences are output. The characteristic components of one characteristic sub-vector sequence correspond to low-frequency characteristics related to the actual battery charging state, namely the first characteristic sub-vector; the characteristic components of the other characteristic sub-vector sequence correspond to high-frequency characteristics related to the harmonic interference state, namely the second characteristic sub-vector.

[0078] S3: Predict and analyze the trend of battery electrochemical impedance change for the first characteristic subvector to obtain evaluation indicators for the battery's instant charge acceptance capability, including:

[0079] Extracting electrochemical impedance data points according to the first eigenvector;

[0080] Specifically, the correspondence between each characteristic component in the first characteristic sub-vector and the electrochemical impedance of the battery is determined; based on the correspondence, the characteristic component reflecting the electrochemical impedance state of the battery is selected from each characteristic vector of the first characteristic sub-vector sequence to form a set of electrochemical impedance data points, each data point clearly representing the electrochemical impedance value of the battery at the corresponding acquisition moment.

[0081] The corresponding relationship is: by synchronizing the current feature vector extraction time and the electrochemical impedance measurement time of the same battery in the same charging process, each characteristic component in the current feature vector corresponds to the impedance value measured by the electrochemical impedance spectrometer at exactly the same sampling time.

[0082] The least square fitting algorithm is used to fit the curve of the corresponding relationship between the electrochemical impedance data points and the charging time to obtain the electrochemical impedance dynamic change curve;

[0083] Specifically, the least squares fitting algorithm is as follows: the charging time corresponding to each data point in the electrochemical impedance data set is treated as the independent variable, and the data point value in the electrochemical impedance data set is treated as the dependent variable; a fitting curve model is set, such as a polynomial fitting model, where the order of the polynomial is determined by the complexity of the time-varying characteristics of the electrochemical impedance and the accuracy required for the fitting. The least squares fitting algorithm determines the parameters of the fitting model by minimizing the sum of squared deviations between the fitting curve and the data points; the sum of squared deviations is calculated by calculating the square of the difference between the actual electrochemical impedance value of each data point and the predicted value at the corresponding position on the fitting curve, and then summing the squared differences corresponding to all data points to obtain the total sum of squared deviations. During the fitting process, the fitting curve parameters are continuously adjusted to minimize the total sum of squared deviations, thereby achieving the best fitting effect.

[0084] The slope of the curve is calculated based on the dynamic change curve of electrochemical impedance, and the slope of the curve is used as an evaluation indicator of the battery's instant charging acceptance capability;

[0085] Specifically, the slope of the curve is calculated by dividing the difference between the ordinates of adjacent data points on the curve by the corresponding difference in the abscissas, that is, the change in electrochemical impedance is divided by the corresponding charging time interval; in order to avoid accidental fluctuations caused by a single local change, the slope can be calculated by averaging multiple adjacent data points, that is, taking a certain number of consecutive data points, calculating the cumulative difference in the change in electrochemical impedance between the consecutive data points and dividing it by the total length of the charging time corresponding to the consecutive data points, so as to obtain a more stable slope value.

[0086] S4: Evaluate and analyze the high-frequency harmonic interference level of the second characteristic sub-vector to generate a battery charging status misjudgment risk level indicator, including:

[0087] Extracting a time domain harmonic interference data sequence according to the second eigenvector;

[0088] Specifically, current characteristic data is extracted from the second characteristic subvector and arranged sequentially according to a fixed sampling period to form a time-domain harmonic interference data sequence representing the harmonic interference state. The second characteristic subvector is then reconstructed into a time series, i.e., arranged in the time order of the original sampling moments of each characteristic subvector. Within each characteristic subvector, based on the correspondence between characteristic components and spectral positions established in the frequency-domain decoupling model, characteristic dimensions strongly correlated with high-frequency components are screened out, such as those representing current change rate, short-term amplitude oscillation, or signal mutation slope, which are directly related to harmonic disturbances. Data from the screened characteristic dimensions is extracted from all characteristic subvectors and arranged sequentially according to the sampling moments corresponding to each component, forming a time-domain harmonic interference data sequence. Each data point clearly represents the harmonic interference component in the current characteristic data at the sampling moment. The length of the sampling period is determined by the frequency characteristics of the harmonic interference signal and is typically based on satisfying the Nyquist sampling theorem, i.e., the sampling frequency is at least twice the highest frequency of the harmonic interference signal, to ensure that the time-domain harmonic interference data sequence fully reflects the harmonic characteristics of the original signal.

[0089] Performing fast Fourier transform on the time domain harmonic interference data sequence to obtain spectrum distribution information;

[0090] Specifically, the data points in the time-domain harmonic interference data sequence are transformed into the frequency domain using the Fast Fourier Transform (FFT) algorithm. This involves multiplying each data point by a Fourier transform basis function and then summing the products to obtain spectral distribution information. This spectral distribution information is output as a set of frequency-amplitude data points, representing the amplitudes corresponding to the harmonic interference components at different frequencies.

[0091] Extracting high-frequency harmonic amplitude components from the spectrum distribution information and calculating the ratio of the high-frequency harmonic amplitude to the fundamental amplitude as the harmonic distortion coefficient;

[0092] Specifically, the high-frequency harmonic amplitude component is defined as the amplitude of the frequency position in the selected spectrum distribution information that is higher than an integer multiple of the fundamental frequency; the fundamental amplitude component is defined as the amplitude of the lowest frequency position in the selected spectrum distribution information; the harmonic distortion coefficient is calculated by dividing the value of the high-frequency harmonic amplitude component by the value of the fundamental amplitude component to obtain an indicator reflecting the intensity of high-frequency harmonic interference in the battery charging state.

[0093] Divide risk levels according to harmonic distortion coefficient and preset risk threshold;

[0094] Specifically, the risk threshold is determined by the statistical distribution of historical battery test measurement data. Risk levels are clearly divided into three levels: low, medium, and high. The classification rules are as follows: if the harmonic distortion coefficient is less than the preset risk threshold, the risk level is determined to be low; if the harmonic distortion coefficient is equal to the preset risk threshold, the risk level is determined to be medium; if the harmonic distortion coefficient is greater than the preset risk threshold, the risk level is determined to be high.

[0095] The risk level is used as an indicator of the risk level of misjudgment of the battery charging status;

[0096] Specifically, the risk level obtained by classification is output as a battery charging state misjudgment risk level indicator.

[0097] S5: Determine the optimal pulse charging current control strategy based on the battery instant charging acceptance capability evaluation index and the battery charging state misjudgment risk level index, including:

[0098] Based on the battery instant charge acceptance evaluation index, a charge acceptance response function is constructed;

[0099] Specifically, the quantitative relationship between the battery's instant charge acceptance capability evaluation index and the three variables of pulse charging current amplitude, pulse width and pulse interval is determined, and the value of the battery's instant charge acceptance capability evaluation index under the setting conditions of multiple different combinations of pulse charging current amplitude, pulse width and pulse interval is obtained through experiments; a multiple regression analysis algorithm is used to perform function fitting, and the function fitting process is specifically to minimize the sum of the squares of the differences between the values ​​of the instant charge acceptance capability evaluation index obtained by experimental measurement and the predicted values ​​of the response function, thereby determining the mathematical expression of the charge acceptance capability response function; the output result of the function represents the trend of the battery's charge acceptance capability changing with the charging parameters.

[0100] Based on the battery charging status misjudgment risk level index, a misjudgment risk impact function is constructed;

[0101] Specifically, the method for establishing the misjudgment risk impact function is: quantitatively assigning values ​​to the battery charging status misjudgment risk level indicators obtained under different harmonic distortion coefficient conditions; determining the functional relationship between the misjudgment risk impact function and the pulse charging current amplitude, pulse width and pulse interval, and the functional relationship is realized by a multivariate function fitting method, and the fitting method adopts a multiple regression analysis algorithm; the function fitting process is carried out by minimizing the sum of squares of the difference between the predicted value of the misjudgment risk impact function and the actual risk level assignment, and then determining the mathematical expression of the misjudgment risk impact function; the output result of the function represents the degree of influence of the charging parameter selection on the battery charging status misjudgment risk.

[0102] The pulse charging current amplitude, pulse width and pulse interval are set as optimization variables, and a multi-objective optimization model is constructed with the charging acceptance response function as the benefit objective and the misjudgment risk impact function as the risk constraint.

[0103] Specifically, the pulse charging current amplitude represents the current intensity applied to the battery during each pulse, the pulse width represents the duration of each pulse current, and the pulse interval represents the duration of the pause between two adjacent pulses. The mathematical form of the multi-objective optimization model consists of an optimization objective and an optimization constraint. The optimization objective is to maximize the output value of the charge acceptance response function within the allowed range of the variables, that is, to achieve the highest value for the battery's instantaneous charge acceptance index. The optimization constraint specifies that the risk level value output by the misjudgment risk impact function is less than or equal to the pre-set risk level safety threshold, ensuring that the misjudgment risk during charging does not exceed the allowable range. The risk level safety threshold is determined through experimental measurement and charging effect verification to avoid the occurrence of adverse charging conditions. The above optimization objectives and optimization constraints together constitute the multi-objective optimization model.

[0104] Perform global iterative solution based on the multi-objective optimization model and output the optimal pulse charging current control strategy parameter set;

[0105] Specifically, a genetic algorithm was chosen as the global iterative optimization solution. The genetic algorithm's implementation process is as follows: In the initial stage, multiple different pulse charging current parameter combinations (including pulse charging current amplitude, pulse width, and pulse interval) are randomly generated to form an initial population. Fitness evaluation, selection, crossover, and mutation operations are then iteratively performed, with optimization continuing until the preset convergence criteria are met. The fitness evaluation stage calculates the charge acceptance response function and misjudgment risk impact function for each parameter combination to determine whether the parameter combination meets the objective function and constraints of the multi-objective optimization model. The selection operation selects the best performing parameter combination based on the fitness evaluation results to advance to the next iteration. The crossover operation swaps parameters across multiple parameter combinations to generate new combinations. The mutation operation randomly changes some parameters to escape local optimal solutions. Ultimately, this iterative optimization process determines the optimal combination of pulse charging current amplitude, pulse width, and pulse interval that maximizes charge acceptance while meeting the risk level constraints, forming the optimal pulse charging current control strategy parameter set.

[0106] S6: Adjust the output current parameters of the pulse power supply in real time according to the optimal pulse charging current control strategy, including:

[0107] Adjusting the output current amplitude according to the pulse charging current amplitude in the optimal pulse charging current control strategy parameter set;

[0108] Specifically, according to the pulse charging current amplitude, the current regulating circuit in the pulse charging power supply, such as the duty cycle regulating link of the current-mode switching converter, is controlled in real time so that its output current stably reaches the specified pulse charging current amplitude.

[0109] Controlling the duration of each pulse signal according to the pulse width in the optimal pulse charging current control strategy parameter set;

[0110] Specifically, the pulse control logic module uses pulse width as the control criterion for the duration of the power-on phase within each pulse cycle. The pulse control logic module activates the timing logic, sets the on-time duration within each cycle to the pulse width value, and maintains the power switch element in the on state for the duration, thereby ensuring that the power supply outputs a stable pulse current signal.

[0111] Control the time interval between adjacent pulses according to the pulse interval in the optimal pulse charging current control strategy parameter set;

[0112] Specifically, the pulse interval parameter refers to the non-conduction time between two adjacent pulses. During the non-conduction time, the output current is zero or drops to a maintenance state to achieve intermittent charging current. The pulse control logic module parses the pulse interval value in the optimal strategy parameter set and sets it as the duration of the non-conduction phase within the cycle. The pulse control logic module uses an internal dual timing mechanism to set the pulse width as the conduction time on the one hand and the pulse interval as the off time on the other hand, forming a complete on-off-on cycle control signal. The pulse output of each complete cycle is determined by the sum of the conduction time and the off time, where the pulse interval determines the frequency structure of the output waveform and the average charging current.

[0113] Example 2

[0114] The difference between Example 2 of the present invention and Example 1 is that this example introduces a power supply fast charging pulse current control system based on current monitoring.

[0115] Figure 2 The present invention provides a schematic structural diagram of a power supply fast charging pulse current control system based on current monitoring, which includes:

[0116] Current acquisition unit: collects the pulse current signal during battery charging, performs high-frequency noise filtering and characteristic parameter extraction to obtain the current characteristic vector;

[0117] Decoupling analysis unit: performs nonlinear decoupling analysis on the current eigenvector to generate a first eigenvector representing the actual charging state of the battery and a second eigenvector representing the harmonic interference state;

[0118] Acceptance capacity evaluation unit: performs battery electrochemical impedance change trend prediction analysis on the first characteristic sub-vector to obtain the battery instant charging acceptance capacity evaluation index;

[0119] Risk assessment unit: This unit evaluates and analyzes the degree of high-frequency harmonic interference of the second characteristic sub-vector and generates a risk level indicator for misjudgment of the battery charging status;

[0120] Strategy decision unit: Determines the optimal pulse charging current control strategy based on the battery instant charging acceptance capability evaluation index and the battery charging status misjudgment risk level index;

[0121] Current control unit: adjusts the pulse power supply output current parameters in real time according to the optimal pulse charging current control strategy.

[0122] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0123] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0124] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0125] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0126] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0127] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0128] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0129] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0130] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0131] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling the rapid charging pulse current of a power supply based on current monitoring, characterized in that: The steps include: S1: Collect the pulse current signal during battery charging, perform high-frequency noise filtering and feature parameter extraction to obtain the current feature vector; S2: Perform nonlinear decoupling analysis on the current eigenvector to generate a first eigenvector representing the actual charging state of the battery and a second eigenvector representing the harmonic interference state; S3: Perform a battery electrochemical impedance change trend prediction analysis on the first characteristic sub-vector to obtain an evaluation index of the battery's instant charge acceptance capability; S4: Evaluate and analyze the high-frequency harmonic interference degree of the second characteristic sub-vector to generate a battery charging status misjudgment risk level indicator; Extracting a time domain harmonic interference data sequence according to the second eigenvector; Performing fast Fourier transform on the time domain harmonic interference data sequence to obtain spectrum distribution information; Extracting high-frequency harmonic amplitude components from the spectrum distribution information and calculating the ratio of the high-frequency harmonic amplitude to the fundamental amplitude as the harmonic distortion coefficient; Divide risk levels according to harmonic distortion coefficient and preset risk threshold; The risk level is used as an indicator of the risk level of misjudgment of the battery charging status; S5: Determine the optimal pulse charging current control strategy based on the battery instant charging acceptance capability evaluation index and the battery charging state misjudgment risk level index; Based on the battery instant charge acceptance evaluation index, a charge acceptance response function is constructed; Based on the battery charging status misjudgment risk level index, a misjudgment risk impact function is constructed; The pulse charging current amplitude, pulse width and pulse interval are set as optimization variables, and a multi-objective optimization model is constructed with the charging acceptance response function as the benefit objective and the misjudgment risk impact function as the risk constraint. Perform global iterative solution based on the multi-objective optimization model and output the optimal pulse charging current control strategy parameter set; S6: Adjust the output current parameters of the pulse power supply in real time according to the optimal pulse charging current control strategy.

2. The method for controlling the rapid charging pulse current of a power supply based on current monitoring according to claim 1, characterized in that: S1, specifically: The pulse current signal of the battery charging is sampled at equal time intervals to obtain the original sampling sequence of the pulse current; Perform high-frequency noise filtering on the original sampling sequence of the pulse current and output the filtered current data sequence; The filtered current data sequence is processed by sliding segmentation according to the preset time window, and the pulse current characteristic parameters are extracted in each time window; The pulse current characteristic parameters extracted in each time window are arranged in time series to form a set of current characteristic vector sequences.

3. The method for controlling the rapid charging pulse current of a power supply based on current monitoring according to claim 2, characterized in that: S2, specifically: Perform nonlinear principal component analysis on the current characteristic vector sequence to extract the characteristic component set with the maximum nonlinear independence; Based on the characteristic component set and the frequency distribution characteristics of the pulse current signal during battery charging, a frequency domain decoupling model is constructed; The current eigenvector sequence is separated by feature space mapping through a frequency domain decoupling model to obtain a first eigenvector and a second eigenvector.

4. The method for controlling the rapid charging pulse current of a power supply based on current monitoring according to claim 3, characterized in that: S3, specifically: Extracting electrochemical impedance data points according to the first eigenvector; The least square fitting algorithm is used to fit the curve of the corresponding relationship between the electrochemical impedance data points and the charging time to obtain the electrochemical impedance dynamic change curve; The curve slope is calculated based on the dynamic change curve of electrochemical impedance, and the curve slope is used as an evaluation indicator for the battery's instant charging acceptance capability.

5. The method for controlling the rapid charging pulse current of a power supply based on current monitoring according to claim 4, characterized in that: S6, specifically: Adjusting the output current amplitude according to the pulse charging current amplitude in the optimal pulse charging current control strategy parameter set; Controlling the duration of each pulse signal according to the pulse width in the optimal pulse charging current control strategy parameter set; The time interval between adjacent pulses is controlled according to the pulse interval in the optimal pulse charging current control strategy parameter set.

6. A power supply fast charging pulse current control system based on current monitoring, used to implement the power supply fast charging pulse current control method based on current monitoring according to any one of claims 1 to 5, characterized in that: include: Current acquisition unit: collects the pulse current signal during battery charging, performs high-frequency noise filtering and characteristic parameter extraction to obtain the current characteristic vector; Decoupling analysis unit: performs nonlinear decoupling analysis on the current eigenvector to generate a first eigenvector representing the actual charging state of the battery and a second eigenvector representing the harmonic interference state; Acceptance capacity evaluation unit: performs battery electrochemical impedance change trend prediction analysis on the first characteristic sub-vector to obtain the battery instant charging acceptance capacity evaluation index; Risk assessment unit: This unit evaluates and analyzes the degree of high-frequency harmonic interference of the second characteristic sub-vector and generates a risk level indicator for misjudgment of the battery charging status; Strategy decision unit: Determines the optimal pulse charging current control strategy based on the battery instant charging acceptance capability evaluation index and the battery charging status misjudgment risk level index; Current control unit: adjusts the pulse power supply output current parameters in real time according to the optimal pulse charging current control strategy.

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