Parallel charger current-sharing control method based on current loop prediction

Abstract

The present application relates to the technical field of power current sharing control, and particularly relates to a parallel charger current sharing control method based on current loop prediction, which comprises: collecting real-time current data and real-time voltage data of each charger according to a preset period, and preprocessing the real-time current data and real-time voltage data to obtain preprocessed voltage data and current data; determining a voltage output state sequence and an output error based on the preprocessed voltage data and current data; determining a predicted current at a next moment based on a dynamic weight, historical current data and the output error; determining an ideal current output value based on the predicted current, a cost function, the preprocessed voltage data and a preset full load current, and performing current sharing control on each charger in a parallel charger group based on the ideal current output value. The present application can maintain voltage stability while achieving high-precision current sharing control.

Description

Technical Field

[0001] This invention relates to the field of power supply current sharing control technology, and more specifically to a current sharing control method for parallel chargers based on current loop prediction. Background Technology

[0002] With the rapid development of industries such as electric vehicles, new energy storage, and data centers, the demand for high-power, high-efficiency, and high-reliability power supply systems is becoming increasingly urgent. The power rating of a single charger module is limited by semiconductor devices, magnetic components, and heat dissipation capabilities, often making it difficult to meet the ever-increasing power demands of the system. Connecting multiple charger modules in parallel allows for capacity expansion through power aggregation and improves system reliability through redundancy design. However, while parallel connection offers advantages, inherent differences in the parameters of components within each module, coupled with varying output line impedances, lead to severe current imbalances among the modules if directly connected in parallel. This current imbalance causes modules with high output current to operate under overload for extended periods, resulting in accelerated temperature rise, significantly shortened lifespan, and even damage due to overheating. Meanwhile, the total system output power is limited by the module that first reaches its current limit, failing to reach the rated capacity and resulting in resource waste.

[0003] Currently, traditional current sharing control methods, such as droop control, while alleviating current unevenness to some extent, have significant limitations. Droop control adjusts current sharing by introducing a voltage difference across the output impedance. To achieve high current sharing accuracy, a large droop coefficient is required. This means that even a slight difference in current results in a large voltage difference. However, a large droop coefficient leads to a severe drop in system output voltage when the load increases, resulting in poor voltage stability. Conversely, to maintain voltage stability, a small droop coefficient is needed, but this leads to a decrease in current sharing accuracy.

[0004] In other words, the current flow control accuracy is relatively low. Summary of the Invention

[0005] To address the technical problem of low current sharing control accuracy in existing current sharing control methods, the present invention aims to provide a current sharing control method for parallel chargers based on current loop prediction. The specific technical solution adopted is as follows:

[0006] In a first aspect, one embodiment of the present invention provides a current sharing control method for parallel chargers based on current loop prediction, the method comprising:

[0007] Real-time current and voltage data of each charger are collected according to a preset cycle, and the real-time current and voltage data are preprocessed to obtain preprocessed voltage and current data.

[0008] Based on the preprocessed voltage and current data, the voltage output state sequence and output error are determined; the output error is used to characterize the degree of deviation between the preprocessed voltage data and the voltage output state sequence.

[0009] Based on dynamic weights, historical current data, and output error, the predicted current for the next moment is determined; dynamic weights are used to characterize the reliability of historical current data for the predicted current.

[0010] Based on the predicted current, cost function, preprocessed voltage data, and preset full-load current, the ideal current output value is determined, and current sharing control is performed on each charger in the parallel charging unit based on the ideal current output value. The cost function is used to characterize the degree of deviation between the predicted current and the target current. The target current is used to characterize the ideal reference value for current sharing of the parallel charging unit. The preset full-load current is used to characterize the rated full-load current of each charger.

[0011] In one embodiment, determining the voltage output state sequence and output error based on preprocessed voltage and current data includes:

[0012] Based on the preprocessed voltage and current data, the variation coefficient and variability of the first operating condition are determined; the variability is used to characterize the difference between the operating conditions of a single charger and the operating conditions of a parallel charger unit.

[0013] Based on the degree of variation, the voltage output state equation is constructed from the preprocessed voltage data, and the voltage output state sequence is determined based on the voltage output state equation.

[0014] The output error is determined based on the preprocessed voltage data and the voltage output state sequence.

[0015] In one embodiment, determining the predicted current for the next moment based on dynamic weights, historical current data, and output error includes:

[0016] Dynamic weights are determined based on historical current data and the corresponding historical predicted current data.

[0017] The output error, dynamic weights, and historical current data are input into the current prediction model. Based on the function mapping relationship of the current prediction model, the predicted current at the next moment is obtained. The function mapping relationship is used to characterize the correspondence between the output error, dynamic weights, historical current data, and the predicted current at the next moment.

[0018] In one embodiment, determining the ideal current output value based on the predicted current, cost function, preprocessed voltage data, and preset full-load current includes:

[0019] Based on the target current and the predicted current, a cost function is constructed to characterize the degree of prediction deviation.

[0020] Based on the cost function, the preprocessed voltage data, and the preset full-load current, the ideal current output value is determined.

[0021] In one embodiment, determining the ideal current output value based on the cost function, preprocessed voltage data, and preset full-load current includes: averaging the cost functions of each charger to obtain the comprehensive cost value of the parallel charging unit; the comprehensive cost value is used to characterize the overall prediction deviation of the parallel charging unit.

[0022] Based on the comprehensive cost value, the preprocessed voltage data, and the preset full-load current, the ideal current output value is determined.

[0023] In one embodiment, determining the first operating condition variation coefficient and degree of variation based on preprocessed voltage and current data includes:

[0024] Based on the preprocessed voltage and current data, determine the discharge power change rate and the sum of historical discharge power change rates;

[0025] The first operating condition variation coefficient is obtained by statistically analyzing the proportion of the discharge power change rate at any given time to the total historical discharge power change rate.

[0026] The average value of the operating condition variation coefficients of each charger is calculated to obtain the second operating condition variation coefficient of the parallel charger group; the second operating condition variation coefficient is used to characterize the overall operating condition variation coefficient of the parallel charger group.

[0027] The degree of variation is determined based on the variation coefficients of the first and second operating conditions.

[0028] In one embodiment, determining the output error based on the preprocessed voltage data and the voltage output state sequence includes:

[0029] The preprocessed voltage data and voltage output state sequence are normalized to obtain normalized voltage data and normalized voltage output state sequence.

[0030] Calculate the first difference between the normalized voltage data and the normalized voltage output state sequence; the first difference is used to characterize the degree of deviation between the voltage data at the same time and the matching data points in the voltage output state sequence.

[0031] The average of the first difference for each data point is calculated to obtain the second difference within a preset period; the second difference is used to characterize the overall deviation value within the preset period.

[0032] The output error is determined based on the first and second differences.

[0033] In one embodiment, determining the dynamic weight based on historical current data and corresponding historical predicted current data includes:

[0034] Determine the absolute value of the difference between historical current data and the corresponding historical predicted current data.

[0035] The dynamic weight is obtained by taking the negative exponent of the absolute value of the difference.

[0036] In one embodiment, the method further includes:

[0037] Calculate the weighted value of each historical current data and its corresponding dynamic weight, and the first sum of all dynamic weights, and obtain the second sum of the weighted values;

[0038] The target weighted value is determined based on the first sum and the second sum; the target weighted value is used to characterize the ratio of the second sum to the first sum.

[0039] Based on the function mapping relationship, the output error and the target weighted value are input into the current prediction model to output the predicted current at the next time step. The function mapping relationship includes: the predicted current at the next time step is equal to the sum of the output error and the target weighted value.

[0040] In one embodiment, the current sharing control of each charger in the parallel charging unit based on the ideal current output value includes:

[0041] Determine the current deviation between the real-time current data of each charger and the ideal current output value; the absolute value of the current deviation is positively correlated with the adjustment range of the output voltage.

[0042] The adjustment priority is determined based on the degree of variation of each charger at the same time; the degree of variation is positively correlated with the adjustment priority.

[0043] According to the adjustment priority and the adjustment range corresponding to the current deviation value, the output voltage of each charger is dynamically adjusted so that the real-time current of each charger approaches the ideal current output value.

[0044] Secondly, another embodiment of the present invention provides a parallel charger current sharing control system based on current loop prediction, the system comprising:

[0045] The preprocessing module is used to collect real-time current data and real-time voltage data of each charger according to a preset cycle, and to preprocess the real-time current data and real-time voltage data to obtain preprocessed voltage data and current data.

[0046] The current prediction module is used to determine the voltage output state sequence and output error based on preprocessed voltage and current data. The output error is used to characterize the degree of deviation between the preprocessed voltage data and the voltage output state sequence. Based on dynamic weights, historical current data, and output error, the predicted current for the next moment is determined. The dynamic weights are used to characterize the reliability of historical current data for the predicted current.

[0047] The current sharing control module is used to determine the ideal current output value based on the predicted current, cost function, preprocessed voltage data, and preset full-load current, and to perform current sharing control on each charger in the parallel charging unit based on the ideal current output value. The cost function is used to characterize the degree of deviation between the predicted current and the target current. The target current is used to characterize the ideal reference value for current sharing of the parallel charging unit. The preset full-load current is used to characterize the rated full-load current of each charger.

[0048] Thirdly, in another embodiment of the present invention, an electronic device is provided, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method described in the first aspect.

[0049] Fourthly, in another embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0050] The present invention has the following beneficial effects:

[0051] This invention first collects and preprocesses real-time current and voltage data from each charger according to a preset cycle. Then, based on the preprocessed voltage and current data, it determines the voltage output state sequence and output error. Subsequently, it predicts the current at the next moment by combining dynamic weights, historical current data, and output error. Finally, it derives the ideal current output value based on the predicted current, cost function, preprocessed voltage data, and preset full-load current. Using this as the target, it implements current sharing control for each charger in the parallel charging unit to ensure current balance among the chargers. In the process of current sharing control for each charger in the parallel charging unit based on the ideal current output value, it does not rely on a fixed droop coefficient. It can not only dynamically adapt to voltage changes and maintain voltage stability through the voltage output state equation, but also ensure high-precision current sharing control. Attached Figure Description

[0052] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a schematic flowchart of a current sharing control method for parallel chargers based on current loop prediction, provided as an embodiment of the present invention.

[0054] Figure 2 This is a schematic diagram of a current sharing control system for a parallel charger based on current loop prediction, provided as an embodiment of the present invention.

[0055] Figure 3 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present invention. Detailed Implementation

[0056] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a current-sharing control method for parallel chargers based on current loop prediction proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0058] The following description, in conjunction with the accompanying drawings, details a specific scheme for a current sharing control method for parallel chargers based on current loop prediction provided by the present invention.

[0059] This invention proposes a current sharing control method for parallel chargers based on current loop prediction. Please refer to [link to relevant documentation]. Figure 1 The diagram illustrates a schematic flowchart of a current sharing control method for parallel chargers based on current loop prediction, according to an embodiment of the present invention. The method includes the following steps:

[0060] Step S1: Collect real-time current data and real-time voltage data of each charger according to a preset cycle, and preprocess the real-time current data and real-time voltage data to obtain preprocessed voltage data and current data.

[0061] The preset period refers to a fixed time interval set in advance for the regular collection of current and voltage data from the charger. This period needs to be determined based on the charger's operating characteristics (such as switching frequency and load change rate) and control accuracy requirements. It must ensure real-time data acquisition (avoiding data lag due to excessively long periods) while preventing data redundancy and increased computational load due to excessively short periods. For example, if the charger's switching frequency is 20kHz, a preset period of 50μs can be set to ensure coverage of data acquisition at critical operating points.

[0062] For example, the physical quantities of current and voltage during the operation of the charger are acquired through hardware devices such as sensors, signal conditioning circuits, and analog-to-digital converters. The acquisition process needs to meet the requirements of synchronization (simultaneous sampling by multiple modules), accuracy (reducing sensor errors), and anti-interference (filtering out high-frequency noise). Specifically, this is achieved through devices such as Hall current sensors, resistor voltage dividers, and analog-to-digital converters (ADCs).

[0063] For example, the sampling time of the ADC is usually triggered by a specific event of the Pulse Width Modulation Timer (PWM Timer) (such as the counter returning to zero). This ensures that sampling is performed at a fixed point in the PWM cycle each time, maintaining the regularity and stability of the control.

[0064] Preprocessing refers to the process of processing and optimizing the acquired raw current and voltage data, removing invalid information, and standardizing the data format to provide high-quality data for subsequent operating condition analysis, current prediction, and current sharing control. Preprocessing is a key step in eliminating data noise, avoiding dimensional interference, and improving control accuracy.

[0065] Real-time current data refers to the current values ​​at the output terminals of each charger module, acquired in real time within a preset period by current acquisition devices (such as Hall current sensors, sampling resistors + differential amplifiers). This data directly reflects the power output status of the charger and is used to determine whether the current is balanced and whether the operating conditions are stable. Therefore, the real-time nature and accuracy of the data acquisition must be guaranteed.

[0066] Real-time voltage data refers to the input and output voltage values ​​of each charger module acquired in real time within a preset period using voltage acquisition equipment (such as a resistor divider). The input voltage reflects the power supply status of the charger, while the output voltage directly affects the current distribution. Together, they constitute important data for charger operating condition analysis, used to construct voltage state equations and calculate output errors.

[0067] Preprocessed voltage data refers to voltage data optimized through preprocessing steps such as data cleaning, power calculation, rate of change solution, and normalization. This process eliminates high-frequency noise, dimensional differences, and abnormal fluctuations in the original data.

[0068] Preprocessed current data refers to current data optimized through preprocessing steps such as data cleaning, filtering, and synchronization calibration. It removes interference signals mixed in during the sampling process and corrects errors caused by asynchronous sampling, and is used for subsequent calculation of change coefficients and current prediction (such as input to the Auto Regressive Integrated Moving Average Model (ARIMA) model).

[0069] It should be noted that in the implementation of this invention, firstly, real-time current and voltage data (including input / output voltage and output current) of each charger are synchronously acquired through professional acquisition equipment at a predetermined reasonable time interval; then, the raw data is preprocessed by cleaning, filtering, normalizing, and calculating the rate of change to eliminate noise, dimensions and other interference factors, and obtain high-quality standardized data; this data is used for subsequent quantification of operating condition differences (calculation of change coefficient and degree of variation), construction of voltage state equation, current prediction and dynamic current sharing adjustment.

[0070] Step S2: Based on the preprocessed voltage and current data, determine the voltage output state sequence and output error; the output error is used to characterize the degree of deviation between the preprocessed voltage data and the voltage output state sequence.

[0071] The preprocessed voltage data refers to the standardized data obtained from the original charger voltage data (including input and output voltages) after processing and optimization. The preprocessing process includes data cleaning (removing outliers), filtering (removing high-frequency noise), power calculation, rate of change calculation, and normalization (eliminating dimensional influences). The aim is to eliminate interference factors in the original data, ensuring that the data meets the requirements of subsequent model calculations and analysis, and providing high-quality input for constructing the voltage output state sequence and solving the output error.

[0072] Preprocessed current data refers to standardized data obtained by processing and optimizing the raw output current data of the charger. Preprocessing steps include data cleaning, synchronization calibration, and filtering, which can correct errors caused by asynchronous sampling, remove high-frequency noise caused by switching frequency, and are used to calculate the operating condition variation coefficient and build a current prediction model (such as the ARIMA model). Preprocessed current data will affect the accuracy of current sharing control.

[0073] A voltage output state sequence refers to a continuous data sequence formed by arranging state equations based on the charger's variability, input voltage, and output voltage in chronological order. Its core is to quantify the differences in operating conditions and the dynamic correlation between input and output voltages through state equations. Each data point corresponds to the charger's voltage output state at a specific moment under the current operating conditions, fully characterizing the voltage change pattern of the charger during the discharge process.

[0074] For example, the voltage output state equation can be expressed as: In the formula, Indicates the first The charger is in the first Voltage output status at any given time. Indicates the first The charger is in the first The degree of variation over time, Indicates the first The charger is in the first The preprocessed input voltage at time 10:00. Indicates the first The charger is in the first The preprocessed output voltage at time 10:00. Indicates to Normalization is performed.

[0075] Output error refers to the deviation between the actual voltage output of the charger and the ideal state, and is an indicator for measuring the historical regulation stability of the charger. Its calculation requires first normalizing the pre-processed voltage data and the voltage output state sequence, then calculating the difference between the two data points at the same time, and finally obtaining the mean absolute value of the deviation between the difference and the mean of the difference, which directly reflects the degree of deviation between the pre-processed voltage data and the voltage output state sequence.

[0076] For example, the output error can be expressed as: In the formula, Indicates the first The output error of each charger Indicates the first The first difference between the voltage data at a given time and the corresponding data point in the voltage output state sequence data. This represents the mean of the differences among all data points, i.e., the second difference. This indicates the number of data points.

[0077] The degree of deviation refers to the magnitude of the difference between the preprocessed voltage data and the voltage output state sequence at the same moment. The greater the degree of deviation, the more significant the deviation between the actual voltage output of the charger and the ideal state, reflecting stronger instability in the historical adjustment process; conversely, it indicates that the actual voltage output is closer to the ideal state, and the adjustment stability is better.

[0078] It should be noted that, in the implementation of this invention, based on the preprocessed voltage and current data, a voltage output state sequence is first constructed in chronological order by fusing the state equations of variability, input voltage, and output voltage; then, the output error is solved by normalization, difference calculation, and mean deviation analysis; finally, the deviation between the preprocessed actual voltage data and the ideal voltage output state sequence is quantified by the output error, providing a basis for correction of the subsequent current prediction model, helping to improve the accuracy of current prediction, and laying a data foundation for dynamic current sharing regulation.

[0079] Furthermore, determining the voltage output state sequence and output error based on the preprocessed voltage and current data includes:

[0080] Based on the preprocessed voltage and current data, the variation coefficient and degree of variation of the first operating condition are determined; the degree of variation is used to characterize the difference between the operating conditions of a single charger and the operating conditions of a parallel charger unit.

[0081] The first operating condition variation coefficient is used to quantify the degree of drastic change in the operating condition of a single charger at a specific moment. It is calculated by the ratio of the current discharge power change rate to the sum of the discharge power change rates over the recent period (preset n+1 moments). Essentially, it represents the proportion of the current instantaneous power change to the recent overall trend. The higher the proportion (e.g., exceeding 30%), the more drastic the change in the charger's operating condition, and the more important it is to adjust in the current sharing control.

[0082] For example, the variation coefficient of the first operating condition can be expressed as: In the formula, Indicates the first The coefficients of change of current and voltage under the first operating condition at any given time. Indicates the first The rate of change of discharge power at time , Indicates the first The rate of change of discharge power at time , This indicates the number of data points; the default value can be 10. This represents the sum of the historical discharge power change rates. denominator We need to add a very small positive number to ensure that the denominator is not 0.

[0083] The parallel charging unit operating condition refers to the overall operating state of the entire system at a specific moment when multiple chargers are running in parallel. It is determined by the operating conditions of all chargers and is quantitatively characterized by the average value of the variation coefficients of each charger.

[0084] The degree of difference refers to the extent of deviation between the operating conditions of a single charger and the overall operating conditions of the parallel charger group. The greater the degree of difference, the lower the compatibility between the charger and the overall system operating state, the more unbalanced the current distribution, and the more necessary it is to adjust the output voltage to reduce the difference in operating conditions with other modules.

[0085] Based on the degree of variation, the voltage output state equation is constructed from the preprocessed voltage data, and the voltage output state sequence is determined based on the voltage output state equation.

[0086] Among them, the voltage output state equation is a dynamic correlation model built based on the variability of a single charger and the preprocessed input / output voltage. It is used to map the dynamic relationship between operating condition differences, input voltage, and output voltage; it is used to quantify the matching degree between input voltage and output voltage under current operating condition differences, and can characterize the comprehensive index of system operating status.

[0087] A voltage output state sequence refers to a continuous data sequence formed by arranging the voltage output states of a single charger at different times, calculated using the voltage output state equation, in chronological order. This sequence fully records the dynamic trajectory of the voltage output state as the charger changes with operating conditions during the discharge process, revealing the influence of different operating conditions on the voltage output.

[0088] The output error is determined based on the preprocessed voltage data and the voltage output state sequence.

[0089] For example, output error refers to an index calculated by the normalized difference and mean difference between the preprocessed voltage data and the voltage output state sequence. It is used to quantify the deviation between the actual voltage output of the charger and the ideal state, and to reflect the stability of the charger's historical regulation process. The larger the output error, the worse the stability of the historical regulation, and this deviation needs to be included in the subsequent current prediction for correction to improve the accuracy of current sharing control.

[0090] It should be noted that in the implementation of this invention, firstly, based on the preprocessed voltage and current data, the first operating condition variation coefficient is obtained through power change rate correlation calculation. Then, the variation degree of a single charger and the whole unit (i.e., the degree of operating condition difference) is quantified by combining the average value of the variation coefficients of each charger. Next, based on the degree of variation and the preprocessed voltage data, a voltage output state equation reflecting the correlation between operating condition difference and voltage output is constructed, and a voltage output state sequence is generated in chronological order. Finally, by comparing the preprocessed voltage data with the voltage output state sequence, the output error is determined through normalization, difference calculation, and other steps, providing a basis for correction of the subsequent current prediction model. Ultimately, precise current sharing control can be achieved while also achieving a balance between current sharing accuracy and voltage stability.

[0091] Furthermore, determining the variation coefficient and degree of variability of the first operating condition based on the preprocessed voltage and current data includes:

[0092] Based on the preprocessed voltage and current data, determine the discharge power change rate and the sum of historical discharge power change rates.

[0093] Among them, the discharge power change rate refers to the difference in discharge power of a single charger at adjacent moments; its value reflects the stability of the charger's operating conditions. The larger the change rate, the more violent the power output fluctuation and the more unstable the operating conditions; the smaller the change rate, the more stable the operating conditions.

[0094] The sum of historical discharge power change rates refers to the cumulative value obtained by summing the discharge power change rates of a single charger over a preset number of past periods. It is used to provide a historical reference benchmark to avoid interference with the judgment of operating conditions due to abnormal power fluctuations at a single moment, and to ensure the objectivity and accuracy of the calculation of the operating condition change coefficient.

[0095] The first operating condition variation coefficient is obtained by statistically analyzing the proportion of the discharge power change rate at any given time to the total historical discharge power change rate.

[0096] For example, the first operating condition variation coefficient is calculated as the ratio of the rate of change of discharge power at any given time to the sum of the historical rates of change of discharge power. It is a key indicator for quantifying the degree of drastic change in the operating condition of a single charger at a specific time. The higher the ratio (e.g., exceeding 30%), the greater the impact of the charger's operating condition change on the recent overall trend, and the more unstable the operating condition. It requires close attention and adjustment in current sharing control.

[0097] The average value of the operating condition variation coefficients of each charger is used to obtain the second operating condition variation coefficient of the parallel charger group; the second operating condition variation coefficient is used to characterize the overall operating condition variation coefficient of the parallel charger group.

[0098] The second operating condition variation coefficient refers to the result of averaging the first operating condition variation coefficients of all parallel-operating chargers at the same moment. It is used to characterize the overall operating condition variation level of the parallel-operating charger unit and reflects the operating stability of the entire unit. The larger the average value, the more obvious the fluctuation of the overall operating condition of the unit. The smaller the average value, the more stable the overall operation of the unit. It provides a quantitative benchmark for judging the difference between the operating condition of a single charger and the overall operating condition of the unit.

[0099] The degree of variation is determined based on the variation coefficients of the first and second operating conditions.

[0100] Among them, the degree of variation refers to the comprehensive index obtained by multiplying the first operating condition variation coefficient of a single charger and the second operating condition variation coefficient of the unit by an exponential function to amplify the deviation between the two, and then multiplying it by the second operating condition variation coefficient. It is used to quantify the degree of difference between the operating condition of a single charger and the overall operating condition of the parallel charging unit: the greater the deviation between the two, the more significant the degree of variation value, indicating that the charger is less compatible with the overall operating state of the unit.

[0101] For example, the degree of variation can be calculated using the following formula: In the formula, Indicates the first The charger is in the first The degree of variation over time, Indicates the first The charger is in the first The coefficient of change of the first operating condition at time t, Indicates the first The mean of the variation coefficients of all chargers at any given time is the second operating condition variation coefficient of the parallel charger units.

[0102] It should be noted that, in the embodiments of the present invention, the discharge power change rate (instantaneous fluctuation index) and the sum of historical discharge power change rates (historical reference benchmark) of a single charger can first be calculated based on the preprocessed voltage and current data. Then, the first operating condition change coefficient, which characterizes the drastic change in the operating condition of a single charger, is obtained by the ratio of the two. Next, the average of the first operating condition change coefficients of all chargers is calculated to obtain the second operating condition change coefficient, which reflects the overall operating stability of the unit. Finally, based on the first operating condition change coefficient of a single module and the second operating condition change coefficient of the unit, the degree of variation characterizing the difference between the operating conditions of a single charger and the overall operating conditions of the unit is determined by exponentially amplifying the deviation and combining the average to quantify the global anomaly. This provides a precise quantitative basis for the subsequent construction of the voltage output state equation and the realization of dynamic current sharing regulation, ensuring that the current sharing control is both consistent with the operating conditions of a single module and matched with the overall operating state of the unit.

[0103] Further, determining the output error based on the preprocessed voltage data and the voltage output state sequence includes:

[0104] The preprocessed voltage data and voltage output state sequence are normalized to obtain normalized voltage data and normalized voltage output state sequence.

[0105] It should be noted that the normalization process is performed using the maximum and minimum value normalization function, and its mapping interval is [0,1].

[0106] Normalization refers to the process of mapping the preprocessed voltage data and voltage output state sequence to a uniform numerical range (such as (0,1) or (-1,1)) through a specific mathematical transformation. This is used to eliminate the comparison deviation caused by the different dimensions and numerical magnitudes of the two types of data, and to ensure the rationality and accuracy of subsequent difference calculations. For example, voltage data may be in units of V, and voltage output state sequence may be a comprehensive correlation value. After normalization, deviation comparison under the same scale can be achieved, avoiding interference from the error judgment due to the difference in dimensions.

[0107] Normalized voltage data refers to the standardized data obtained after normalizing the preprocessed voltage data. It retains the variation trend and relative relationship of the original voltage data, but eliminates the influence of absolute numerical magnitude. It can be directly compared with the normalized voltage output state sequence to calculate data point deviation.

[0108] The normalized voltage output state sequence refers to the standardized data sequence obtained after the voltage output state sequence has been normalized. It maintains the dynamic correlation law of the voltage output state at each time in the original sequence, and at the same time adapts to the comparison scale with the voltage data, ensuring that the data points at the same time are comparable, and is used to quantify the deviation between the actual voltage and the ideal state.

[0109] Calculate the first difference between the normalized voltage data and the normalized voltage output state sequence; the first difference is used to characterize the degree of deviation between the voltage data and the matching data points in the voltage output state sequence at the same time.

[0110] The first difference refers to the difference between the normalized voltage data and the corresponding data point at the same moment in the normalized voltage output state sequence. It can characterize the degree of instantaneous deviation between the actual voltage output (normalized) and the ideal voltage output state (normalized) of the charger at a single moment. The larger the difference, the lower the degree of fit between the actual voltage and the ideal state at that moment, and the more obvious the instantaneous adjustment deviation.

[0111] The average of the first difference for each data point is calculated to obtain the second difference within a preset period; the second difference is used to characterize the overall deviation value within the preset period.

[0112] The second difference refers to the result obtained by taking the arithmetic mean of the first differences of all data points within the preset period. In essence, it is a comprehensive quantitative index of the instantaneous deviation within the preset period, used to characterize the overall deviation level of the charger voltage output from the ideal state within the period. The larger the second difference, the more significant the comprehensive deviation of the actual voltage output within the entire period, and the worse the stability of the historical adjustment process. Conversely, it indicates that the overall adjustment effect within the period is more stable.

[0113] The output error is determined based on the first and second differences.

[0114] The output error refers to the index calculated by the average absolute deviation of the first and second differences at various times within a preset period. It is used to quantify the overall fluctuation deviation of the charger's voltage output within the preset period. It considers both the instantaneous deviation at individual moments and corrects for the impact of local abnormal fluctuations through the comprehensive deviation, ultimately reflecting the stability level of historical regulation and providing a basis for correcting subsequent current prediction models.

[0115] It should be noted that, in the embodiments of the present invention, firstly, the difference in dimensions and magnitude between voltage data and voltage output state sequence is eliminated by normalization processing to obtain standardized data that can be directly compared; then, the first difference of data points at the same time is calculated to quantify the degree of instantaneous deviation; then, the second difference is obtained by the mean of the first difference to characterize the comprehensive deviation level within a preset period; finally, based on the degree of deviation between the instantaneous deviation (first difference) and the comprehensive deviation of the period (second difference) at each time, the output error reflecting the historical regulation stability is calculated.

[0116] Furthermore, determining the dynamic weights based on historical current data and corresponding historical predicted current data includes:

[0117] Determine the absolute value of the difference between the historical current data and the corresponding historical predicted current data.

[0118] Among them, historical current data refers to the actual current value collected by the charger at multiple consecutive sampling times in the past and after preprocessing. It can reflect the past operating status of the charger and record the current change trajectory under historical operating conditions of the charger, providing a reference for the current prediction model and ensuring that the prediction results can fit the actual operating rules of the charger.

[0119] Historical predicted current data refers to the current values ​​predicted by the ARIMA model at corresponding sampling times in the past. These are theoretical values ​​calculated based on historical current data prior to the corresponding time and the output error, used to compare with historical current data actually collected during the same period, thus quantifying the accuracy of the model's past predictions.

[0120] The absolute value of the difference refers to the result of taking the absolute value of the difference between historical current data (actual value) and historical predicted current data (predicted value) at the same historical moment. It can quantify the degree of deviation between the predicted value and the actual value at a single historical moment, eliminate the offsetting effect of positive and negative deviations, and reflect the magnitude of the error in the model prediction at that moment. The larger the absolute value, the lower the prediction accuracy at that moment; conversely, the smaller the absolute value, the more accurate the prediction.

[0121] The dynamic weight is obtained by taking the negative exponent of the absolute value of the difference.

[0122] For example, a negative exponent refers to a mathematical operation with the natural constant e as the base and the absolute value of the difference raised to a negative exponent. It is used to perform a non-linear transformation on the absolute value of the difference, achieving a dynamic mapping where the smaller the error, the greater the weight: when the absolute value of the difference approaches 0 (accurate prediction), the negative exponent result approaches 1, and the weight is the largest; when the absolute value of the difference increases (large prediction deviation), the negative exponent result approaches 0, and the weight is the smallest, thus highlighting the reference value of accurate prediction data and weakening the impact of large error data on the model.

[0123] Dynamic weights refer to weighting coefficients that are dynamically adjusted based on historical prediction errors, calculated by taking the negative exponent of the absolute value of the difference. They are not fixed values ​​but adaptively adjusted according to the prediction accuracy of each historical data point. The smaller the prediction error, the larger the weight, and the higher the contribution of that historical data to the current prediction; conversely, the larger the prediction error, the smaller the weight, and the lower the contribution. The introduction of dynamic weights enables the ARIMA model to adaptively select high-quality historical data, improving the accuracy of current current predictions and avoiding error accumulation caused by fixed weights.

[0124] It should be noted that, in the embodiments of the present invention, the historical current data (actual value) and the corresponding historical predicted current data (predicted value) of the charger are first extracted. By calculating the absolute value of the difference between the two, the error magnitude of the prediction model at each historical moment is quantified. Subsequently, a negative exponential operation is performed on the absolute value of the difference to convert the error magnitude into a dynamically adjusted weight coefficient. That is, an adaptive mechanism is achieved through nonlinear transformation, where the more accurate the prediction, the greater the weight. The resulting dynamic weight will be used for the current prediction of the ARIMA model at the current moment, enabling the model to focus on high-quality historical data, weaken the interference of error data, and improve the accuracy of the current prediction at the next moment.

[0125] Step S3: Based on the dynamic weight, historical current data, and output error, determine the predicted current for the next moment; the dynamic weight is used to characterize the reliability of the historical current data for the predicted current.

[0126] The predicted current for the next moment refers to the theoretical current value of the charger at a subsequent sampling moment, calculated using an ARIMA model with dynamic weights. This allows for the prediction of current change trends in advance, providing data for the dynamic adjustment of the charger's output voltage and avoiding current fluctuations and power imbalances caused by adjustment lag.

[0127] Reliability refers to the reference value of historical current data for predicting the current at the next moment, and is directly quantified by dynamic weights. The magnitude of the dynamic weight is positively correlated with the reliability; the higher the weight, the smaller the prediction error of the historical data, the higher its reference value, and the greater its contribution to the predicted current; conversely, the lower the reliability, the smaller the contribution, ensuring that the prediction process can select high-quality historical data to support it.

[0128] It should be noted that, in the embodiments of the present invention, dynamic weights, historical current data, and output errors are input into an ARIMA model with dynamic weights to calculate the predicted current for the next moment. The dynamic weights serve as a quantitative basis for the reliability of historical data, prioritizing the adoption of high-quality historical data and mitigating errors. The output error is used to correct deviations caused by historical adjustment instability. The synergistic effect of these three factors ensures that the predicted current not only conforms to the historical operating patterns of the charger but also offsets the influence of past adjustment deviations, ultimately yielding an accurate prediction of the current for the next moment.

[0129] Furthermore, the step of determining the predicted current at the next moment based on dynamic weights, historical current data, and output error includes: determining dynamic weights based on historical current data and historical predicted current data corresponding to the historical current data.

[0130] The dynamic weight refers to the adaptive coefficient obtained by taking the negative exponent of the absolute value of the difference between historical current data (actual value) and historical predicted current data (predicted value) at the same historical moment. It can be dynamically adjusted according to the prediction error. The smaller the prediction error, the closer the weight is to 1, and the higher the reference value of the corresponding historical current data. The larger the error, the closer the weight is to 0, and the lower the reference value, thus achieving priority screening of high-quality historical data.

[0131] For example, dynamic weights can be represented by the following formula: In the formula, Indicates the first Dynamic weights of each data point Indicates the first The actual historical current data for each data point Indicates the first Historical predicted current data for each data point.

[0132] The output error, dynamic weights, and historical current data are input into the current prediction model. Based on the function mapping relationship of the current prediction model, the predicted current at the next moment is obtained. The function mapping relationship is used to characterize the correspondence between the output error, dynamic weights, historical current data, and the predicted current at the next moment.

[0133] The current prediction model refers to a mathematical model used to predict the charger's current at the next moment, such as the ARIMA model with dynamic weights. It can integrate output error, dynamic weights, and historical current data, and derive the theoretical current value at the next moment through a preset mathematical mapping relationship.

[0134] The function mapping relationship refers to the fixed mathematical association rules set within the current prediction model that characterize the output error, dynamic weights, historical current data, and the predicted current at the next moment. This relationship clarifies how each input parameter contributes to the prediction result, the dynamic weights are weighted and allocated to different historical current data, the output error corrects historical adjustment deviations, and finally, the predicted current is obtained through linear combination and other operations.

[0135] Step S4: Based on the predicted current, cost function, preprocessed voltage data, and preset full-load current, determine the ideal current output value, and perform current sharing control on each charger in the parallel charging unit based on the ideal current output value; the cost function is used to characterize the degree of deviation between the predicted current and the target current; the target current is used to characterize the ideal reference value for current sharing of the parallel charging unit; the preset full-load current is used to characterize the rated full-load current of each charger.

[0136] The target current refers to the ideal current reference value that each charger should reach when the parallel charging units achieve perfect current sharing. For example, it is determined based on the ratio of the total load current to the number of charger modules (such as when 3 modules are connected in parallel and the total load current is 30A, the target current is 10A), which provides a benchmark for measuring the rationality of the predicted current.

[0137] The preset full-load current refers to the maximum rated output current of each charger module. It is the upper limit threshold for the safe operation of the charger and can provide a safety benchmark for the ideal current output value. This ensures that the derived ideal current does not exceed the rated capacity of the module, avoids overload and overheating damage to the charger due to excessive adjustment, and ensures the safety of equipment operation.

[0138] The cost function is a quantitative index built based on the predicted current and the target current. It is used to characterize the degree of deviation between the predicted current and the target current. Its value is positively correlated with the degree of deviation. The smaller the value, the closer the predicted current is to the ideal reference of current sharing, and the more reliable the control strategy is. The larger the value, the greater the prediction deviation, and the more necessary it is to correct the ideal current through subsequent calculations.

[0139] For example, the cost function can be expressed as: In the formula, Indicates the first Predicted current at time of day Indicates the target current; This represents the linear normalization function; the smaller the value of the cost function, the closer the predicted current is to the target current, and the better the control effect.

[0140] The ideal current output value refers to the optimal current value that each charger should approach, calculated based on a preset full-load current as a safety benchmark, combined with a cost function and the current voltage. It aims to ensure that the current in each module converges towards the target current, without exceeding the charger's rated full-load current, while also correcting prediction deviations through the cost function. This is the direct objective of current sharing control.

[0141] For example, the ideal current output value can be expressed as: In the formula, This represents the ideal current output value. Indicates the preset full-load current. This represents the preprocessed voltage data. This represents the overall cost of the parallel charging units.

[0142] Current sharing control refers to the control process that, based on the ideal current output value, dynamically adjusts the output voltage of each module in a parallel charging unit to force the actual output current of each charger to approach the ideal current output value. Current balance is achieved through voltage adjustment: the output voltage of charger modules with insufficient current is increased, while the output voltage of charger modules with excessive current is decreased. Ultimately, this eliminates current imbalances caused by differences in components and line impedance among the modules, maximizing the total system output power and ensuring safe and stable equipment operation.

[0143] It should be noted that, in the embodiments of the present invention, firstly, using the target current as a reference, the deviation between the predicted current and the target current is quantified through a cost function; then, using the preset full-load current as a constraint, and combining the cost function and preprocessed voltage data, an ideal current output value that balances safety and current sharing is derived; finally, using the ideal current output value as the control target, current sharing control is achieved by dynamically adjusting the output voltage of each charger module to converge the actual current of each module to the ideal value. This solves the contradiction between current sharing accuracy and voltage stability in traditional control, and ensures equipment safety through the preset full-load current, ultimately achieving efficient, stable, and balanced operation of the parallel charging unit.

[0144] Furthermore, determining the ideal current output value based on the predicted current, cost function, preprocessed voltage data, and preset full-load current includes:

[0145] Based on the target current and the predicted current, a cost function is constructed to characterize the degree of prediction deviation.

[0146] The cost function is a quantitative model built based on the deviation between the predicted current and the target current, used to characterize the degree of deviation between the predicted current and the ideal current-sharing reference (target current). Its value is positively correlated with the degree of deviation; the smaller the value, the closer the predicted current is to the target, and the higher the reliability of the subsequent control strategy; the larger the value, the more significant the prediction deviation, and the more necessary it is to correct the deviation using the ideal current output value.

[0147] Based on the cost function, the preprocessed voltage data, and the preset full-load current, the ideal current output value is determined.

[0148] It should be noted that, in the embodiments of the present invention, the target current is first used as a reference, and the deviation between the predicted current and the target current is quantified by constructing a cost function, and the deviation is converted into a calculable quantitative index; then, with the preset full-load current as a safety constraint, the ideal current output value that balances current sharing accuracy and equipment safety is derived through mathematical calculations, combined with the cost function and the preprocessed voltage data.

[0149] Furthermore, determining the ideal current output value based on the cost function, the preprocessed voltage data, and the preset full-load current includes:

[0150] The average cost function of each charger is calculated to obtain the comprehensive cost value of the parallel charging unit; the comprehensive cost value is used to characterize the overall prediction deviation of the parallel charging unit.

[0151] For example, the comprehensive cost value refers to the result obtained by taking the arithmetic average of the cost functions of all chargers in the parallel charging unit. It can integrate the prediction deviation of a single module into a system-level comprehensive deviation, avoiding over-adjustment due to the extreme deviation of a single module, and ensuring the systematicness and stability of the current sharing control. The smaller the comprehensive cost value, the closer the overall predicted current of the unit is to the target current, and the more reliable the basis of the current sharing control. Conversely, it indicates that the overall deviation is large and needs to be corrected specifically by using the ideal current output value.

[0152] Based on the comprehensive cost value, the preprocessed voltage data, and the preset full-load current, the ideal current output value is determined.

[0153] It should be noted that, in the embodiments of the present invention, the prediction deviation of a single module is first integrated into a comprehensive cost value that characterizes the overall prediction deviation of the unit by averaging the cost function of all chargers, so as to avoid the interference of local deviations on global control. Then, with the preset full-load current as a safety constraint, the ideal current output value that takes into account both safety and current sharing requirements is derived through mathematical calculations, combining the comprehensive cost value and the preprocessed voltage data.

[0154] Furthermore, the method also includes:

[0155] Calculate the weighted value of each historical current data point and its corresponding dynamic weight, and the first sum of all dynamic weights, and obtain the second sum of the weighted values.

[0156] For example, the weighted value refers to the product of a single historical current data point and its corresponding dynamic weight. For instance, if the dynamic weight of a historical current data point is close to 1 (small prediction error, high reliability), the weighted value is closer to the historical current data point itself and contributes more to subsequent calculations. If the dynamic weight is close to 0 (large prediction error, low reliability), the weighted value approaches 0 and its contribution is weakened, thus prioritizing the adoption of high-quality historical data.

[0157] The second sum of weighted values ​​refers to the cumulative result obtained by summing the weighted values ​​corresponding to all historical current data. It can integrate the effective information of all historical current data, eliminate the interference of low-quality data, and form a comprehensive quantitative index that reflects the historical operating pattern of the charger, providing data support for current prediction.

[0158] The target weighted value is determined based on the first sum and the second sum; the target weighted value is used to characterize the ratio of the second sum to the first sum.

[0159] For example, the target weighted value can be expressed as: In the formula, Indicates weight, Indicates the first The current value at time [time]. Indicates the first The current value at time [time]. This represents the first sum of all dynamic weights. This represents the second sum of the weighted values.

[0160] In other words, the target weighted value can be obtained by normalizing the second sum of the weighted values.

[0161] Based on the function mapping relationship, the output error and the target weighted value are input into the current prediction model to output the predicted current at the next time step. The function mapping relationship includes: the predicted current at the next time step is equal to the sum of the output error and the target weighted value.

[0162] For example, the function mapping relationship can be represented as: In the formula, Indicates the first The current value at time [time]. Indicates the first The output error of each charger Indicates weight, Indicates the first The current value at time [time]. Indicates the first The current value at a given time.

[0163] It should be noted that, in the embodiments of the present invention, firstly, a multiplication operation is performed on each historical current data and its corresponding dynamic weight to obtain a weighted value reflecting the effective contribution of each historical data. Then, all weighted values ​​are summed to integrate the high-quality information of the historical current data. Subsequently, according to the function mapping relationship, the output error that corrects the historical adjustment deviation and the sum of the weighted values ​​of the normalized historical effective information are input into the current prediction model, and finally the predicted current at the next moment is output.

[0164] Furthermore, the current sharing control of each charger in the parallel charging unit based on the ideal current output value includes:

[0165] Determine the current deviation between the real-time current data of each charger and the ideal current output value; the absolute value of the current deviation is positively correlated with the adjustment range of the output voltage.

[0166] The current deviation value refers to the difference (positive or negative) between the real-time current data of a single charger and the ideal current output value. It quantifies the degree of deviation between the charger's current and the target current. Its sign reflects the direction of the deviation (positive deviation indicates that the real-time current is greater than the ideal value, and negative deviation indicates that the real-time current is less than the ideal value), and the absolute value directly determines the adjustment range of the output voltage.

[0167] The adjustment range refers to the numerical range (e.g., increasing by 0.2V, decreasing by 0.3V) required to adjust the output voltage to bring the charger's real-time current closer to the ideal output value. It is positively correlated with the absolute value of the current deviation; the larger the absolute value of the deviation, the larger the adjustment range, ensuring rapid compensation of large current deviations. Conversely, the smaller the absolute value of the deviation, the smaller the adjustment range, avoiding over-adjustment that could cause current fluctuations and ensuring the smoothness of the adjustment process.

[0168] The adjustment priority is determined based on the degree of variation of each charger at the same time; the degree of variation is positively correlated with the adjustment priority.

[0169] Among them, the adjustment priority refers to the order of voltage adjustment determined based on the degree of variation of each charger at the same time. It is positively correlated with the degree of variation. The greater the degree of variation, the more significant the difference between the operating conditions of the charger and the overall operating conditions of the unit, and the more unbalanced the current distribution. The higher the adjustment priority, the more important it is to adjust the voltage first. Conversely, the lower the priority, the more it can be delayed or adjusted slightly to avoid system imbalance caused by large adjustments of multiple modules at the same time.

[0170] According to the adjustment priority and the adjustment range corresponding to the current deviation value, the output voltage of each charger is dynamically adjusted so that the real-time current of each charger approaches the ideal current output value.

[0171] Dynamic adjustment refers to the continuous optimization of the charger's output voltage control based on real-time updates of current deviation, variability, and adjustment priority. The voltage adjustment amplitude and timing are flexibly adjusted according to dynamic changes in current deviation and operating conditions, ensuring rapid convergence to a current-sharing state even when unit operating conditions fluctuate, thus avoiding the lag or overshoot problems caused by traditional fixed adjustment methods.

[0172] It should be noted that, in the embodiments of the present invention, firstly, the current deviation value between the real-time current data of each charger and the ideal current output value is calculated to quantify the current deviation of a single module, and the larger the absolute value of the deviation, the larger the output voltage adjustment range; at the same time, the adjustment priority is determined according to the degree of variation of each charger at the same time. The higher the degree of variation (the more significant the difference from the overall operating condition of the unit), the higher the priority, and the more priority should be adjusted; finally, according to the rule of adjusting high priority first and low priority later, combined with the adjustment range corresponding to the current deviation value, the output voltage of each charger is dynamically optimized, and ultimately the real-time current of all chargers is driven to converge towards the ideal current output value.

[0173] In summary, this invention first collects and preprocesses real-time current and voltage data from each charger according to a preset cycle. Then, based on the preprocessed voltage and current data, it determines the voltage output state sequence and output error. Subsequently, it predicts the current at the next moment by combining dynamic weights, historical current data, and output error. Finally, it derives the ideal current output value based on the predicted current, cost function, preprocessed voltage data, and preset full-load current. Using this as the target, it implements current sharing control for each charger in the parallel charging unit to ensure current balance among the chargers. In the process of current sharing control for each charger in the parallel charging unit based on the ideal current output value, it does not rely on a fixed droop coefficient. It can not only dynamically adapt to voltage changes and maintain voltage stability through the voltage output state equation, but also ensure high-precision current sharing control.

[0174] This invention proposes a current sharing control system for parallel chargers based on current loop prediction. Please refer to [link / reference]. Figure 2 The diagram illustrates a schematic of a current sharing control system 200 for a parallel charger based on current loop prediction, according to an embodiment of the present invention. The system includes:

[0175] The preprocessing module 201 is used to collect real-time current data and real-time voltage data of each charger according to a preset cycle, and to preprocess the real-time current data and real-time voltage data to obtain preprocessed voltage data and current data.

[0176] The current prediction module 202 is used to determine the voltage output state sequence and output error based on the preprocessed voltage data and current data; the output error is used to characterize the degree of deviation between the preprocessed voltage data and the voltage output state sequence; based on dynamic weights, historical current data and output error, the predicted current at the next moment is determined; the dynamic weights are used to characterize the reliability of historical current data for the predicted current.

[0177] The current sharing control module 203 is used to determine the ideal current output value based on the predicted current, cost function, preprocessed voltage data and preset full-load current, and to perform current sharing control on each charger in the parallel charging unit based on the ideal current output value; the cost function is used to characterize the degree of deviation between the predicted current and the target current; the target current is used to characterize the ideal reference value for current sharing of the parallel charging unit; and the preset full-load current is used to characterize the rated full-load current of each charger.

[0178] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the parallel charger current sharing control system based on current loop prediction and the parallel charger current sharing control method based on current loop prediction provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiments and will not be repeated here.

[0179] This invention also provides an electronic device. Please refer to [link to relevant documentation]. Figure 3 The electronic device may include a processor 301, a memory 302, and a program 3021 stored in the memory 302 and capable of running on the processor 301.

[0180] When program 3021 is executed by processor 301, it can achieve the following: Figure 1 Any steps in the corresponding method embodiments and the achievement of the same beneficial effects will not be repeated here.

[0181] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by hardware related to program instructions, and the program can be stored in a readable medium.

[0182] This invention also provides a readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described functions. Figure 1 Any step in the corresponding method embodiment can achieve the same technical effect, and will not be repeated here to avoid repetition.

[0183] The computer-readable storage medium of this invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0184] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0185] The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0186] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or terminal. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0187] This invention also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement the current sharing control method for parallel chargers based on current loop prediction provided in the above embodiments.

[0188] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0189] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A current sharing control method for parallel chargers based on current loop prediction, characterized in that, The method includes: Real-time current and voltage data of each charger are collected according to a preset cycle, and the real-time current and voltage data are preprocessed to obtain preprocessed voltage and current data. Based on the preprocessed voltage and current data, a voltage output state sequence and an output error are determined; the output error is used to characterize the degree of deviation between the preprocessed voltage data and the voltage output state sequence. Based on dynamic weights, historical current data, and the output error, the predicted current for the next moment is determined; the dynamic weights are used to characterize the reliability of the historical current data for the predicted current. Based on the predicted current, cost function, preprocessed voltage data, and preset full-load current, an ideal current output value is determined, and current sharing control is performed on each charger in the parallel charging unit based on the ideal current output value; the cost function is used to characterize the degree of deviation between the predicted current and the target current; the target current is used to characterize the ideal reference value for current sharing of the parallel charging unit; the preset full-load current is used to characterize the rated full-load current of each charger; The process of determining the ideal current output value based on the predicted current, cost function, preprocessed voltage data, and preset full-load current includes: Based on the target current and the predicted current, a cost function is constructed to characterize the degree of prediction deviation. Based on the cost function, the preprocessed voltage data, and the preset full-load current, the ideal current output value is determined. The step of determining the ideal current output value based on the cost function, the preprocessed voltage data, and the preset full-load current includes: The average cost function of each charger is calculated to obtain the comprehensive cost value of the parallel charging unit; the comprehensive cost value is used to characterize the overall prediction deviation of the parallel charging unit. Based on the comprehensive cost value, the preprocessed voltage data, and the preset full-load current, the ideal current output value is determined.

2. The parallel charger current sharing control method based on current loop prediction according to claim 1, characterized in that, The step of determining the voltage output state sequence and output error based on the preprocessed voltage and current data includes: Based on the preprocessed voltage and current data, the variation coefficient and degree of variation of the first operating condition are determined; the degree of variation is used to characterize the difference between the operating condition of a single charger and the operating condition of a parallel charger unit. Based on the degree of variation, a voltage output state equation is constructed from the preprocessed voltage data, and a voltage output state sequence is determined based on the voltage output state equation. The output error is determined based on the preprocessed voltage data and the voltage output state sequence.

3. The parallel charger current sharing control method based on current loop prediction according to claim 1, characterized in that, The process of determining the predicted current for the next moment based on dynamic weights, historical current data, and the output error includes: Dynamic weights are determined based on historical current data and the corresponding historical predicted current data. The output error, the dynamic weight, and the historical current data are input into the current prediction model. Based on the function mapping relationship of the current prediction model, the predicted current at the next moment is obtained. The function mapping relationship is used to characterize the correspondence between the output error, the dynamic weight, the historical current data, and the predicted current at the next moment.

4. The parallel charger current sharing control method based on current loop prediction according to claim 2, characterized in that, The determination of the first operating condition variation coefficient and degree of variation based on the preprocessed voltage and current data includes: Based on the preprocessed voltage and current data, determine the discharge power change rate and the sum of historical discharge power change rates; The first operating condition variation coefficient is obtained by calculating the proportion of the discharge power change rate at any given time to the sum of the historical discharge power change rates. The average value of the first operating condition variation coefficient of each charger is calculated to obtain the second operating condition variation coefficient of the parallel charger group; the second operating condition variation coefficient is used to characterize the overall operating condition variation coefficient of the parallel charger group. The degree of variation is determined based on the variation coefficients of the first and second operating conditions.

5. The parallel charger current sharing control method based on current loop prediction according to claim 2, characterized in that, The step of determining the output error based on the preprocessed voltage data and the voltage output state sequence includes: The preprocessed voltage data and the voltage output state sequence are normalized to obtain normalized voltage data and normalized voltage output state sequence. Calculate a first difference between the normalized voltage data and the normalized voltage output state sequence; the first difference is used to characterize the degree of deviation between the voltage data and the matching data points in the voltage output state sequence at the same time. The average of the first differences among the data points is calculated to obtain the second difference within a preset period; the second difference is used to characterize the overall deviation value within the preset period. The output error is determined based on the first difference and the second difference.

6. The parallel charger current sharing control method based on current loop prediction according to claim 3, characterized in that, The step of determining dynamic weights based on historical current data and corresponding historical predicted current data includes: Determine the absolute value of the difference between the historical current data and the historical predicted current data corresponding to the historical current data; The dynamic weight is obtained by taking the negative exponent of the absolute value of the difference.

7. The parallel charger current sharing control method based on current loop prediction according to claim 3, characterized in that, The method further includes: Calculate the weighted value of each historical current data and its corresponding dynamic weight, sum all dynamic weights to obtain the first sum, and sum all weighted values ​​to obtain the second sum; A target weighted value is determined based on the first sum and the second sum; the target weighted value is used to characterize the ratio of the second sum to the first sum. Based on the function mapping relationship, the output error and the target weighted value are input into the current prediction model to output the predicted current at the next time step; the function mapping relationship includes: the predicted current at the next time step is equal to the sum of the output error and the target weighted value.

8. The parallel charger current sharing control method based on current loop prediction according to claim 2, characterized in that, The current sharing control of each charger in the parallel charging unit based on the ideal current output value includes: The current deviation between the real-time current data of each charger and the ideal current output value is determined; the absolute value of the current deviation is positively correlated with the adjustment range of the output voltage. The adjustment priority is determined based on the degree of variation of each charger at the same time; the degree of variation is positively correlated with the adjustment priority. According to the adjustment priority, the output voltage of each charger is dynamically adjusted based on the adjustment range corresponding to the current deviation value, so that the real-time current of each charger approaches the ideal current output value.

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