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

Abstract

The invention discloses a power supply rapid charging pulse current regulation and control system and method based on current monitoring, and particularly relates to the technical field of power supply management. The method comprises the following steps: acquiring a pulse current signal in a battery charging process, performing nonlinear decoupling analysis, and decomposing the pulse current signal into a first characteristic sub-vector representing a real charging state of the battery and a second characteristic sub-vector representing a high-frequency harmonic interference state; predicting the change trend of the electrochemical impedance of the battery based on the first feature sub-vector to form an instant charging acceptance evaluation index, and evaluating the harmonic interference degree based on the second feature sub-vector to form a charging state misjudgment risk level index; based on the battery instant charging acceptance assessment index and the battery charging state misjudgment risk grade index, the optimal pulse current regulation and control strategy is determined, the power output parameters are adjusted in real time, the state misjudgment problem caused by high-frequency harmonic interference and electrochemical impedance coupling is effectively solved, and the battery charging performance and the long-term reliability are improved.

Description

Technical Field

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

[0002] Existing pulse charging control technologies usually judge the battery state based on current monitoring data and set pulse current parameters accordingly, but do not fully consider the interference of high-frequency harmonic components in the pulse current on the measurement of battery electrochemical characteristics. During the battery charging process, there is a complex non-linear coupling relationship between the high-frequency disturbance components carried by the current signal and the battery electrochemical impedance, which easily leads to misjudgment of the true charging state and charging acceptance ability of the battery. Such misjudgment will directly affect the accuracy and adaptability of the regulation strategy, resulting in the deviation of the pulse parameter setting from the actual bearing capacity of the battery, not only reducing the charging efficiency, but also possibly accelerating the deterioration of the battery performance and affecting the safety and long-term stability of the battery.

[0003] To solve the above problems, a technical solution is provided now. 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 fast charging pulse current regulation method and system based on current monitoring to solve the problems proposed in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions: A power fast charging pulse current regulation method based on current monitoring, comprising the following steps: S1: Collect the pulse current signal during battery charging, and perform high-frequency noise filtering and feature parameter extraction to obtain a current feature vector; S2: Perform non-linear decoupling analysis on the current feature vector to generate a first feature sub-vector representing the true charging state of the battery and a second feature sub-vector representing the harmonic interference state; S3: Perform prediction analysis on the change trend of the battery electrochemical impedance for the first feature sub-vector to obtain an evaluation index of the battery's instant charging acceptance ability; S4: Perform evaluation analysis on the degree of high-frequency harmonic interference for the second feature sub-vector to generate a risk level index of misjudgment of the battery charging state; S5: Determine the optimal pulse charging current regulation strategy based on the evaluation index of the battery's instant charging acceptance ability and the risk level index of misjudgment of the battery charging state; S6: Adjust the output current parameters of the pulse power supply in real time according to the optimal pulse charging current regulation strategy.

[0006] In a preferred embodiment, S1 is specifically: Sampling the pulse current signal when charging the battery at equal time intervals to obtain the original sampling sequence of the pulse current; Performing high-frequency noise filtering on the original sampling sequence of the pulse current and outputting the filtered current data sequence; Performing sliding segmentation processing on the filtered current data sequence according to a preset time window, and extracting pulse current characteristic parameters within each time window; Arranging the pulse current characteristic parameters extracted within each time window in a time series to form a set of current characteristic vector sequences.

[0007] In a preferred embodiment, S2 is specifically: Performing non-linear principal component analysis on the current characteristic vector sequence to extract a set of characteristic components with the maximum non-linear independence; Based on the set of characteristic components, combining with the frequency distribution characteristics of the pulse current signal during battery charging, constructing a frequency-domain decoupling model; Performing feature space mapping separation on the current characteristic vector sequence through the frequency-domain decoupling model to obtain a first characteristic sub-vector and a second characteristic sub-vector.

[0008] In a preferred embodiment, S3 is specifically: Extracting electrochemical impedance data points according to the first characteristic sub-vector; Using the least squares fitting algorithm to perform curve fitting on the corresponding relationship between the electrochemical impedance data points and the charging time to obtain the dynamic change curve of the electrochemical impedance; Calculating the curve slope based on the dynamic change curve of the electrochemical impedance, and taking the curve slope as an evaluation index for the battery's instant charging acceptance ability.

[0009] In a preferred embodiment, S4 is specifically: Extracting the time-domain harmonic interference data sequence according to the second characteristic sub-vector; Performing fast Fourier transform on the time-domain harmonic interference data sequence to obtain the spectrum distribution information; Extracting the high-frequency harmonic amplitude component in the spectrum distribution information and calculating the ratio of the high-frequency harmonic amplitude to the fundamental wave amplitude as the harmonic distortion coefficient; Dividing the risk level according to the harmonic distortion coefficient and a preset risk threshold; Taking the risk level as an index for the risk level of misjudging the battery charging state.

[0010] In a preferred embodiment, S5 is specifically: Based on the evaluation index for the battery's instant charging acceptance ability, constructing a charging acceptance ability response function; Based on the index for the risk level of misjudging the battery charging state, constructing a misjudgment risk impact function; Set the amplitude of the pulsed charging current, the pulse width, and the pulse interval as optimization variables, and construct a multi-objective optimization model with the charging acceptance ability response function as the benefit objective and the misjudgment risk impact function as the risk constraint; Execute global iterative solution based on the multi-objective optimization model, and output the optimal pulsed charging current regulation strategy parameter set.

[0011] In a preferred embodiment, S6 is specifically: Adjust the output current amplitude according to the amplitude of the pulsed charging current in the optimal pulsed charging current regulation strategy parameter set; Control the duration of each pulse signal according to the pulse width in the optimal pulsed charging current regulation strategy parameter set; Control the time interval between adjacent pulses according to the pulse interval in the optimal pulsed charging current regulation strategy parameter set.

[0012] On the other hand, the present invention provides a pulsed current regulation system for fast charging of a power supply based on current monitoring, including: Current acquisition unit: Collect the pulsed current signal during battery charging, perform high-frequency noise filtering and feature parameter extraction, and obtain the current feature vector; Decoupling analysis unit: Perform non-linear decoupling analysis on the current feature vector to generate a first feature sub-vector representing the true charging state of the battery and a second feature sub-vector representing the harmonic interference state; Acceptance ability evaluation unit: Perform prediction analysis on the change trend of the battery electrochemical impedance for the first feature sub-vector to obtain the battery's instant charging acceptance ability evaluation index; Risk assessment unit: Perform evaluation analysis on the degree of high-frequency harmonic interference for the second feature sub-vector to generate an index of the misjudgment risk level of the battery charging state; Strategy decision-making unit: Determine the optimal pulsed charging current regulation strategy based on the battery's instant charging acceptance ability evaluation index and the misjudgment risk level index of the battery charging state; Current regulation unit: Adjust the output current parameters of the pulsed power supply in real time according to the optimal pulsed charging current regulation strategy.

[0013] The technical effects and advantages of the pulsed current regulation system and method for fast charging of a power supply based on current monitoring according to the present invention: By filtering the high-frequency noise of the battery charging pulse current signal and extracting the characteristic parameters, the refined real-time monitoring of the charging process is realized; the non-linear decoupling analysis is used to separate the real charging state from the harmonic interference state, avoiding the interference effect of high-frequency harmonic interference on the state recognition; based on the first eigenvector, the dynamic trend of the electrochemical impedance is predicted, and the instantaneous charging acceptance ability of the battery can be dynamically evaluated; based on the second eigenvector, the degree of harmonic interference is evaluated, and the potential misjudgment risk of the battery charging state recognition can be effectively warned; based on the battery instantaneous charging acceptance ability evaluation index and the battery charging state misjudgment risk level index, the optimal pulse charging current regulation strategy is determined and the output parameters of the pulse power supply are adjusted in real time, realizing the precise control of the charging behavior, improving the response accuracy of the pulse regulation strategy to the battery state, optimizing the charging efficiency and extending the service life of the battery. Description of the Drawings

[0014] Figure 1 Schematic diagram of the method for regulating the pulse current of the power supply for fast charging based on current monitoring according to the present invention; Figure 2 Schematic diagram of the structure of the system for regulating the pulse current of the power supply for fast charging based on current monitoring according to the present invention. Detailed Embodiments

[0015] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0016] Embodiment 1 Figure 1 The method for regulating the pulse current of the power supply for fast charging based on current monitoring according to the present invention is given, and it includes the following steps: S1: Collect the pulse current signal during battery charging, and perform high-frequency noise filtering and characteristic parameter extraction to obtain the current characteristic vector; S2: Perform non-linear decoupling analysis on the current characteristic vector to generate a first eigenvector representing the real charging state of the battery and a second eigenvector representing the harmonic interference state; S3: Perform prediction analysis on the change trend of the electrochemical impedance of the battery for the first eigenvector to obtain the evaluation index of the instantaneous charging acceptance ability of the battery; S4: Perform evaluation analysis on the degree of high-frequency harmonic interference for the second eigenvector to generate the misjudgment risk level index of the battery charging state; S5: Determine the optimal pulse charging current regulation strategy based on the evaluation index of the instantaneous charging acceptance ability of the battery and the misjudgment risk level index of the battery charging state; S6: Adjust the output current parameters of the pulse power supply in real time according to the optimal pulse charging current regulation strategy.

[0017] 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: Sample the pulse current signal during battery charging at equal time intervals to obtain the original sampling sequence of the pulse current; Specifically, the pulse current signal generated during battery charging is measured in real time at a constant millisecond-level sampling interval period. For example, the current data is collected every millisecond, and the continuously measured current data is sequentially recorded as the original sampling data set of the pulse current according to the time acquisition order. Each data in the set represents the current value at the corresponding sampling moment.

[0018] Perform high-frequency noise filtering on the original sampling sequence of the pulse current and output the filtered current data sequence; Specifically, determine an initial filtering window length, and the selection of the initial filtering window length is determined according to the requirements of current fluctuation degree and pulse current feature stability. For each data point in the original sampling data set, calculate the median of multiple adjacent data points of the current data point as the output value of the filtered current data; adjust the adaptive filtering threshold. When the difference between two adjacent data values in the original sampling data of the pulse current exceeds the set threshold, increase the filtering window length to improve the accuracy of the filtered current data and obtain the filtered current data set.

[0019] Perform sliding segmentation processing on the filtered current data sequence according to a preset time window, and extract pulse current feature parameters within each time window; Specifically, the sliding segmentation processing specifically refers to selecting a fixed-length millisecond-level time period as the time window, and the time window length is determined according to the charging pulse current waveform feature period. Use a fixed-length time window to slide point by point from the starting position to the ending position of the data, and the sliding interval each time is a fixed sampling period, and multiple non-overlapping or partially overlapping data subsets are generated in sequence.

[0020] Extract multiple pulsed current characteristic parameters from the filtered current data set within each window, including but not limited to the maximum amplitude parameter, minimum amplitude parameter, average amplitude parameter, rising rate parameter during the current rising process, falling rate parameter during the current falling process, and stability degree parameter of the pulse period. Among them, the current rising rate parameter is defined as the amplitude increase of the pulsed current from the start of the window to the maximum amplitude divided by the corresponding time difference; the current falling rate parameter is defined as the difference in current amplitude when the pulsed current drops 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 within the window divided by the number of data within the window; the period stability degree parameter is defined as the ratio of the standard deviation of the lengths of multiple consecutive pulse periods within the window to the average length of the pulse period, which is used to characterize the stability degree of the pulsed current period.

[0021] Arrange the pulsed current characteristic parameters extracted within each time window in a time series to form a set of current characteristic vector sequences; Specifically, each current characteristic vector includes the maximum amplitude parameter, minimum amplitude parameter, average amplitude parameter, rising rate parameter, falling rate parameter, and period stability degree parameter. Multiple current characteristic vectors form a sequence in chronological order.

[0022] S2: Perform non-linear decoupling analysis on the current characteristic vectors to generate a first characteristic sub-vector representing the true charging state of the battery and a second characteristic sub-vector representing the harmonic interference state, including: Perform non-linear principal component analysis on the current characteristic vector sequence to extract a set of characteristic components with the maximum non-linear independence; Specifically, use the non-linear principal component analysis algorithm, including the kernel function mapping process and eigenvalue decomposition of the current characteristic vector sequence. In order to fully reflect the non-linear characteristics of the pulsed current signal, select the radial basis kernel function as the kernel function for the mapping process. The calculation method of the radial basis kernel function is: by calculating the sum of the squares of the differences between any two current characteristic vectors in the current characteristic vector sequence, multiplying the sum of the squares of the differences by the negative kernel parameter and then taking the exponent to obtain the mapping result in the feature space; based on the kernel function mapping, use the matrix eigenvalue decomposition method to determine the importance of each characteristic component in the mapping space, sort the multiple characteristic components in the mapping space according to the eigenvalues from large to small, and select multiple characteristic components whose cumulative contribution degree of eigenvalues exceeds the preset percentage to form a set of characteristic components. Optimization parameters such as the kernel parameter and the contribution degree percentage are determined according to the discrimination effect of the characteristic components.

[0023] Based on the set of characteristic components, combined with the frequency distribution characteristics of the pulsed current signal during battery charging, construct a frequency domain decoupling model; Specifically, perform spectral analysis on the pulsed current signal during battery charging to determine the frequency composition and amplitude characteristic distribution of the pulsed current signal. The spectral analysis uses the fast Fourier transform, that is, converts the pulsed current signal into a frequency-domain signal through the fast Fourier transform and obtains the amplitudes corresponding to different frequency components. Based on the set of characteristic components, a frequency-domain decoupling model is constructed by combining the frequency characteristics in the spectral analysis. The model includes establishing a mathematical correlation between the characteristic components and the spectral characteristics. The frequency-domain decoupling model is a linear mixing model, and multiple characteristic components in the set of characteristic components are linearly combined through the decoupling coefficients to be determined to respectively represent the low-frequency components corresponding to the true battery charging state and the high-frequency components corresponding to the harmonic interference state. The decoupling coefficients in the linear mixing model are determined by optimizing according to the frequency amplitude distribution characteristics of the current signal and the differences in battery charging characteristics.

[0024] Perform feature space mapping separation on the current feature vector sequence through the frequency-domain decoupling model to obtain a first eigen-subvector and a second eigen-subvector; Specifically, input the current feature vector sequence into the frequency-domain decoupling model. The frequency-domain decoupling model uses the decoupling coefficients to calculate the input current feature vector sequence to achieve the separation of the characteristic components in the feature space. Specifically: each input current feature vector is multiplied separately with multiple characteristic components in the set of characteristic components, and then multiplied with the corresponding decoupling coefficients and then summed separately; according to the selection principle of the decoupling coefficients and the characteristic components, two independent eigen-subvector sequences are output. The characteristic components of one eigen-subvector sequence correspond to the low-frequency characteristics related to the true battery charging state, which is the first eigen-subvector; the characteristic components of the other eigen-subvector sequence correspond to the high-frequency characteristics related to the harmonic interference state, which is the second eigen-subvector.

[0025] S3: Perform prediction analysis on the change trend of the battery electrochemical impedance for the first eigen-subvector to obtain an evaluation index of the battery's instantaneous charging acceptance ability, including: Extract the electrochemical impedance data points according to the first eigen-subvector; Specifically, determine the corresponding relationship between each characteristic component in the first eigen-subvector and the battery electrochemical impedance; based on the corresponding relationship, select the characteristic components reflecting the battery electrochemical impedance state from each eigen-vector in the first eigen-subvector sequence respectively to form a set of electrochemical impedance data points, and each data point clearly represents the electrochemical impedance value of the battery at the corresponding acquisition moment.

[0026] The corresponding relationship is: by synchronizing the extraction moment of the current feature vector and the measurement moment of the electrochemical impedance during the same charging process of the same battery, each dimensional characteristic component in the current feature vector forms a correspondence with the impedance value measured by the electrochemical impedance spectrometer at the exactly same sampling moment.

[0027] The least - squares fitting algorithm is used to perform curve fitting on the corresponding relationship between the electrochemical impedance data points and the charging time, and the dynamic change curve of the electrochemical impedance is obtained; Specifically, the least - squares fitting algorithm is as follows: The charging time corresponding to each data point in the electrochemical impedance data point set is regarded as the independent variable, and the data point value in the electrochemical impedance data point set is regarded as the dependent variable; A fitting curve model is set, such as a polynomial fitting model, and the order of the polynomial is determined by the complexity of the electrochemical impedance change characteristics over time and the requirement for accurate fitting. The least - squares fitting algorithm determines the parameters of the fitting model by minimizing the sum of the squares of the deviations between the fitting curve and the data points; The calculation process of the sum of the squares of the deviations is: Calculate the square of the difference between the actual electrochemical impedance value of each data point and the predicted value at the corresponding position of the fitting curve, and add up the squared differences corresponding to all data points to obtain the total sum of the squares of the deviations. During the fitting process, by continuously adjusting the fitting curve parameters, the total sum of the squares of the deviations is minimized, so as to achieve the best fitting effect.

[0028] Based on the dynamic change curve of the electrochemical impedance, the curve slope is calculated, and the curve slope is used as an evaluation index for the battery's instant charging acceptance ability; Specifically, the curve slope is calculated by dividing the difference in the ordinate between adjacent data points on the curve by the corresponding difference in the abscissa, that is, the change value of the electrochemical impedance divided by the corresponding charging time interval; In order to avoid the interference of accidental fluctuations caused by single - local changes, the average method of multiple adjacent data points can be used to calculate the slope, that is, a certain number of consecutive data points are taken, and the result of dividing the cumulative difference in the electrochemical impedance change between the consecutive data points by the total length of the charging time corresponding to the consecutive data points is calculated to obtain a more stable slope value.

[0029] S4: Evaluate and analyze the degree of high - frequency harmonic interference of the second eigen - vector to generate an index for the risk level of misjudging the battery charging state, including: Extract the time - domain harmonic interference data sequence according to the second eigen - vector; Specifically, current feature data is extracted from the second eigenvector and arranged in sequence according to a fixed sampling period to form a time-domain harmonic interference data sequence representing the harmonic interference state. Time series reconstruction is performed on the second eigenvector, that is, arranged in the time order of the original sampling time of each eigenvector; in each eigenvector, according to the corresponding relationship between the feature components and the spectral positions established in the frequency-domain decoupling model, the feature dimensions strongly correlated with the high-frequency components are selected, such as the feature dimensions directly related to harmonic disturbances, such as the current change rate, short-term amplitude oscillation, or signal mutation slope; the data of the selected feature dimensions in all eigenvectors are extracted and arranged in sequence according to the sampling time order corresponding to each component to form a time-domain harmonic interference data sequence. Each data clearly represents the harmonic interference component in the current feature data at the sampling time; the length of the sampling period is determined by the frequency characteristics of the harmonic interference signal, usually based on satisfying the Nyquist sampling theorem, that is, the sampling frequency is higher than twice the highest frequency of the harmonic interference signal, to ensure that the time-domain harmonic interference data sequence completely reflects the harmonic characteristics of the original signal.

[0030] Perform a fast Fourier transform on the time-domain harmonic interference data sequence to obtain spectral distribution information; Specifically, the data points in the time-domain harmonic interference data sequence are subjected to frequency-domain conversion processing according to the fast Fourier transform algorithm, that is, each data point in the data sequence is multiplied by the basis function of the Fourier transform, and then the results of the products are accumulated to obtain spectral distribution information. The spectral distribution information is output in the form of a frequency-amplitude data point set, representing the amplitudes corresponding to the harmonic interference components of different frequencies.

[0031] Extract the high-frequency harmonic amplitude component from the spectral distribution information and calculate the ratio of the high-frequency harmonic amplitude to the fundamental wave amplitude as the harmonic distortion coefficient; Specifically, the high-frequency harmonic amplitude component is defined as the amplitude at the frequency position higher than an integer multiple of the fundamental wave frequency in the selected spectral distribution information; the fundamental wave amplitude component is defined as the amplitude at the lowest frequency position in the selected spectral distribution information; the calculation method of the harmonic distortion coefficient is the value of the high-frequency harmonic amplitude component divided by the value of the fundamental wave amplitude component, to obtain an index reflecting the high-frequency harmonic interference intensity in the battery charging state.

[0032] Divide the risk level according to the harmonic distortion coefficient and the preset risk threshold; Specifically, the risk threshold is determined by the statistical distribution of historical battery experiment measurement data. The risk level is clearly divided into three levels: low risk level, medium risk level, and high risk level. The division rule is: if the harmonic distortion coefficient is less than the preset risk threshold, it is determined as the low risk level; if the harmonic distortion coefficient is equal to the preset risk threshold, it is determined as the medium risk level; if the harmonic distortion coefficient is greater than the preset risk threshold, it is determined as the high risk level.

[0033] Use the risk level as the risk level index for misjudging the battery charging state; Specifically, output the obtained risk level as the risk level index for misjudging the battery charging state.

[0034] S5: Based on the battery's instantaneous charging acceptance ability evaluation index and the risk level index for misjudging the battery charging state, determine the optimal pulse charging current regulation strategy, including: Construct a charging acceptance ability response function based on the battery's instantaneous charging acceptance ability evaluation index; Specifically, determine the quantitative relationship between the battery's instantaneous charging acceptance ability evaluation index and the three variables of the pulse charging current amplitude, pulse width, and pulse interval. Obtain the values of the battery's instantaneous charging acceptance ability evaluation index under the setting conditions of multiple different combinations of the pulse charging current amplitude, pulse width, and pulse interval through experimental methods; use the multiple regression analysis algorithm for function fitting. The function fitting process is specifically to minimize the sum of the squares of the differences between the values of the experimentally measured instantaneous charging acceptance ability evaluation index and the predicted values of the response function, and then determine the mathematical expression of the charging acceptance ability response function; the output result of the function characterizes the trend of the battery's charging acceptance ability changing with the charging parameters.

[0035] Construct a misjudgment risk influence function based on the risk level index for misjudging the battery charging state; Specifically, the method for establishing the misjudgment risk influence function is as follows: quantitatively assign values to the risk level index for misjudging the battery charging state obtained under different harmonic distortion coefficient conditions; determine the functional relationship between the misjudgment risk influence function and the pulse charging current amplitude, pulse width, and pulse interval. The functional relationship is achieved through the multi-variable function fitting method, and the fitting method uses the multiple regression analysis algorithm; the function fitting process is carried out by minimizing the sum of the squares of the differences between the predicted values of the misjudgment risk influence function and the actual risk level assignment, and then determine the mathematical expression of the misjudgment risk influence function; the output result of the function characterizes the degree of influence of the charging parameter selection on the risk of misjudging the battery charging state.

[0036] Set the pulse charging current amplitude, pulse width, and pulse interval as optimization variables, and construct a multi-objective optimization model with the charging acceptance ability response function as the benefit objective and the misjudgment risk influence function as the risk constraint; Specifically, the amplitude of the pulsed charging current represents the current intensity applied to the battery each time a pulse is applied, the pulse width represents the duration for which each pulse current lasts, and the pulse interval represents the intermittent duration 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 charging acceptance ability response function within the allowable range of variables, that is, to obtain the highest value of the battery's instantaneous charging acceptance ability index; the optimization constraint is clearly defined as the risk level value output by the misjudgment risk influence function being less than or equal to a pre-set risk level safety threshold to ensure that the misjudgment risk during charging does not exceed the allowable range; the risk level safety threshold is determined through experimental measurement and verification of the charging effect to avoid the occurrence of adverse charging states; the above optimization objective and optimization constraint together constitute the multi-objective optimization model.

[0037] Based on the multi-objective optimization model, perform global iterative solution to output the optimal pulsed charging current regulation strategy parameter set; Specifically, the global iterative optimization solution method is implemented by selecting the genetic algorithm. The specific implementation process of the genetic algorithm is as follows: in the initial stage, randomly generate multiple different combinations of pulsed charging current parameters (including the amplitude of the pulsed charging current, the pulse width, and the pulse interval) to form an initial population, and sequentially perform fitness evaluation, selection operation, crossover operation, and mutation operation through an iterative method, and continuously iterate and optimize until the preset convergence condition is met. In the fitness evaluation stage, calculate the charging acceptance ability response function value and the misjudgment risk influence function value corresponding to each parameter combination to determine whether the parameter combination meets the objective function and constraint conditions in the multi-objective optimization model; the selection operation is based on the fitness evaluation result, and select the parameter combination with better performance to enter the next generation of iterative process; the crossover operation is to select the parameters of multiple parameter combinations for exchange to generate new combinations; the mutation operation is to randomly change some parameters to jump out of the local optimal solution. Finally, through the above iterative optimization process, determine a combination of the optimal pulsed charging current amplitude, pulse width, and pulse interval that maximizes the charging acceptance ability and whose risk level meets the constraint conditions, and form the optimal pulsed charging current regulation strategy parameter set.

[0038] S6: According to the optimal pulsed charging current regulation strategy, adjust the output current parameters of the pulsed power supply in real time, including: Adjust the output current amplitude according to the amplitude of the pulsed charging current in the optimal pulsed charging current regulation strategy parameter set; Specifically, according to the amplitude of the pulsed charging current, control the current regulation circuit in the pulsed charging power supply in real time, such as the duty cycle adjustment link of a current-mode switching converter, so that its output current stably reaches the specified amplitude of the pulsed charging current.

[0039] Control the duration of each pulse signal according to the pulse width in the optimal pulsed charging current regulation strategy parameter set; Specifically, the pulse control logic module uses the pulse width as the control standard for the duration of the energized stage within each pulse period. The pulse control logic module activates the timing logic, sets the conduction duration within each period to the pulse width value, and maintains the conduction state of the power switch element within the duration, so as to enable the power supply to output a stable pulsed current signal.

[0040] Control the time interval between adjacent pulses according to the pulse interval in the optimal pulsed charging current regulation strategy parameter set; 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 the maintenance state to achieve the intermittency of the charging current. The pulse control logic module analyzes the pulse interval value in the optimal strategy parameter set and sets it as the duration of the non-conduction stage within the period. The pulse control logic module forms a complete on-off-on cyclic control signal through an internal dual-timing mechanism. On the one hand, it sets the pulse width as the conduction time, and on the other hand, it sets the pulse interval as the off time. The pulse output of each complete period 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.

[0041] Embodiment 2 The difference between Embodiment 2 and Embodiment 1 of the present invention is that this embodiment introduces a pulsed current regulation system for fast charging of a power supply based on current monitoring.

[0042] Figure 2 The structural schematic diagram of the pulsed current regulation system for fast charging of a power supply based on current monitoring according to the present invention is given. The pulsed current regulation system for fast charging of a power supply based on current monitoring includes: Current acquisition unit: Acquire the pulsed current signal during battery charging, perform high-frequency noise filtering and feature parameter extraction, and obtain the current feature vector; Decoupling analysis unit: Perform non-linear decoupling analysis on the current feature vector to generate a first feature sub-vector representing the true charging state of the battery and a second feature sub-vector representing the harmonic interference state; Acceptability evaluation unit: Perform prediction analysis on the change trend of the battery's electrochemical impedance for the first feature sub-vector to obtain the battery's instant charging acceptability evaluation index; Risk assessment unit: Perform evaluation analysis on the degree of high-frequency harmonic interference for the second feature sub-vector to generate an index for the risk level of misjudging the battery's charging state; Strategy decision-making unit: Determine the optimal pulsed charging current regulation strategy based on the battery's instant charging acceptability evaluation index and the index for the risk level of misjudging the battery's charging state; Current regulation unit: Adjust the output current parameters of the pulsed power supply in real time according to the optimal pulsed charging current regulation strategy.

[0043] The above formulas are all dimensionless and only take their numerical values for calculation. The formulas are obtained by collecting a large amount of data and performing software simulations to get a formula that is closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0044] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. 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 includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. 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 a website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0045] Those of ordinary skill in the art can realize that the modules and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0046] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0047] In several embodiments provided by the present 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 illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, 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 displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces. The indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0048] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0049] In addition, in each embodiment of the present application, the functional modules can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0050] If the function is implemented in the form of a software functional module 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 the present application, in essence, or the part that contributes to the prior art, or a part of this 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 enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0051] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0052] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for regulating the pulsed current of a power supply for fast charging based on current monitoring, characterized in that, It includes the following steps: 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 non-linear decoupling analysis on the current feature vector to generate a first feature sub-vector representing the true charging state of the battery and a second feature sub-vector representing the harmonic interference state; S3: Perform prediction analysis on the change trend of the battery's electrochemical impedance for the first feature sub-vector to obtain an evaluation index for the battery's instantaneous charging acceptance ability; S4: Perform evaluation analysis on the degree of high-frequency harmonic interference for the second feature sub-vector to generate an index for the risk level of misjudgment of the battery charging state; S5: Based on the evaluation index for the battery's instantaneous charging acceptance ability and the index for the risk level of misjudgment of the battery charging state, determine the optimal pulse charging current regulation strategy; S6: According to the optimal pulse charging current regulation strategy, adjust the output current parameters of the pulse power supply in real time.

2. The method for regulating the pulsed current of fast charging of a power supply based on current monitoring according to claim 1, wherein S1 is specifically as follows: Sample the pulse current signal during battery charging 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; Perform sliding segmentation processing on the filtered current data sequence according to a preset time window, and extract the pulse current feature parameters within each time window; Arrange the pulse current feature parameters extracted within each time window in a time series to form a set of current feature vector sequences.

3. The method for regulating the pulsed current of the power supply for fast charging based on current monitoring according to claim 2, characterized in that, S2 is specifically as follows: Perform non-linear principal component analysis on the current feature vector sequence to extract a set of feature components with the maximum non-linear independence; Based on the set of feature components, combined with the frequency distribution characteristics of the pulse current signal during battery charging, construct a frequency domain decoupling model; Perform feature space mapping separation on the current feature vector sequence through the frequency domain decoupling model to obtain the first feature sub-vector and the second feature sub-vector.

4. The method for regulating the pulsed current of the power supply for fast charging based on current monitoring according to claim 3, characterized in that, S3 is specifically as follows: Extract the electrochemical impedance data points according to the first feature sub-vector; Use the least squares fitting algorithm to perform curve fitting on the corresponding relationship between the electrochemical impedance data points and the charging time to obtain the dynamic change curve of the electrochemical impedance; Calculate the curve slope based on the dynamic change curve of the electrochemical impedance and use the curve slope as the evaluation index for the battery's instantaneous charging acceptance ability.

5. The method for regulating the pulsed current of fast charging of a power supply based on current monitoring according to claim 4, wherein, S4 is specifically as follows: Extract the time domain harmonic interference data sequence according to the second feature sub-vector; Perform fast Fourier transform on the time domain harmonic interference data sequence to obtain the frequency spectrum distribution information; Extract the high-frequency harmonic amplitude component in the frequency spectrum distribution information and calculate the ratio of the high-frequency harmonic amplitude to the fundamental wave amplitude as the harmonic distortion coefficient; Divide the risk level according to the harmonic distortion coefficient and the preset risk threshold; Use the risk level as the index for the risk level of misjudgment of the battery charging state.

6. The method for regulating the pulsed current of the power supply for fast charging based on current monitoring according to claim 5, characterized in that, S5 is specifically as follows: Based on the evaluation index for the battery's instantaneous charging acceptance ability, construct a charging acceptance ability response function; Based on the index for the risk level of misjudgment of the battery charging state, construct a misjudgment risk impact function; Set the pulse charging current amplitude, pulse width, and pulse interval as optimization variables, and construct a multi-objective optimization model with the charging acceptance ability response function as the benefit target 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 regulation strategy parameter set.

7. The method for regulating pulse current for fast charging of a power supply based on current monitoring according to claim 6, characterized in that, S6, specifically: Adjust the output current amplitude according to the pulse charging current amplitude in the optimal pulse charging current regulation strategy parameter set; Control the duration of each pulse signal according to the pulse width in the optimal pulse charging current regulation strategy parameter set; Control the time interval between adjacent pulses according to the pulse interval in the optimal pulse charging current regulation strategy parameter set.

8. A power fast charging pulse current regulation system based on current monitoring is used to implement the power fast charging pulse current regulation method based on current monitoring according to any one of claims 1-7, and is characterized in that, Include: Current acquisition unit: Collect the pulse current signal during battery charging, perform high-frequency noise filtering and feature parameter extraction to obtain the current feature vector; Decoupling analysis unit: Perform non-linear decoupling analysis on the current feature vector to generate a first feature sub-vector representing the true charging state of the battery and a second feature sub-vector representing the harmonic interference state; Acceptability evaluation unit: Perform prediction analysis on the change trend of the battery electrochemical impedance for the first feature sub-vector to obtain the battery instant charging acceptability evaluation index; Risk assessment unit: Perform evaluation analysis on the degree of high-frequency harmonic interference for the second feature sub-vector to generate the battery charging state misjudgment risk level index; Strategy decision-making unit: Determine the optimal pulse charging current regulation strategy based on the battery instant charging acceptability evaluation index and the battery charging state misjudgment risk level index; Current regulation unit: Real-time adjust the pulse power supply output current parameters according to the optimal pulse charging current regulation strategy.

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