Method and device for testing comprehensive characteristics of direct current charger

Abstract

The application relates to the technical field of comprehensive characteristic test of chargers, in particular to a comprehensive characteristic test method and device of a direct-current charger, wherein the method comprises the following steps: globally optimizing test cases by means of an improved particle swarm algorithm, combining a weighted data fitting and a sampling priority screening mechanism, greatly reducing global mechanical repeated sampling, and reducing the total test time; meanwhile, the algorithm optimizes data collection quality, and the sampling characteristics of parameters such as voltage, current and ripple under steady state, transient state and fault working conditions are complete, and the test instrument has higher capture precision for small fluctuations and implicit deterioration characteristics of the direct-current system. Therefore, the problems in the prior art that the comprehensive characteristic test cases of the direct-current charger are solidified, the sampling strategy is rigid, the working condition adaptability is poor, the global repeated sampling mechanism has a large redundancy, the test time is seriously consumed, and the efficiency is low are solved.

Description

Technical Field

[0001] This application relates to the field of comprehensive characteristic testing technology for chargers, and in particular to a method and apparatus for comprehensive characteristic testing of DC chargers. Background Technology

[0002] Currently, the comprehensive characteristic testing of power plant DC systems strictly follows relevant technical regulations and safety requirements to carry out routine tests such as steady-state parameter detection, battery verification discharge, insulation performance verification, and charging device accuracy testing. The mainstream testing methods in the industry take fixed operating condition testing, uniform and equal-interval sampling, fixed threshold data processing, and artificial operating condition configuration as core technical solutions. The supporting commercial DC comprehensive testers mostly adopt fixed test procedures and basic signal processing algorithms to complete data acquisition and index calculation.

[0003] The existing technology's overall workflow is as follows: Before testing, standard test cases are manually selected, and parameters such as fixed sampling frequency, discharge duration, and load level are preset; during the test, the tester adopts an equal-interval uniform sampling mode throughout the process to collect data such as DC bus voltage, output current, ripple, battery cell parameters, and insulation resistance to ground; after the test, simple data cleaning is performed using fixed threshold filtering and basic mean noise reduction methods, and the data is directly substituted into fixed formulas to calculate conventional indicators such as voltage and current regulation accuracy, ripple coefficient, battery capacity, and current sharing error, and finally, a standardized test report is output.

[0004] Existing traditional testing technologies can meet basic compliance testing requirements in engineering applications, but they have the following inherent technical shortcomings when facing the complex operating conditions and high-precision testing requirements of power plant DC systems: 1. Fixed test cases and rigid sampling strategies result in poor adaptability to operating conditions: Existing testing instruments all employ preset fixed test conditions and uniform sampling mechanisms, which cannot adaptively adjust sampling strategies according to dynamic conditions such as DC system float charging, equalization charging, load surges, AC power outages, and grounding disturbances. During steady-state operation, a large amount of repetitive and redundant sampling data exists, significantly increasing the equipment's storage burden and the workload for subsequent data analysis. Furthermore, critical transient fault conditions such as sudden changes in bus voltage, switching between charging and discharging modes, and short-term load surges are prone to sparse sampling and missed feature sampling, making it impossible to accurately capture latent degradation and transient anomalies in the DC system, resulting in insufficient test completeness.

[0005] 2. The global resampling mechanism has a large amount of redundancy, resulting in significant testing time consumption and extremely low efficiency: Traditional testing methods, in order to reduce random errors in single sampling, mechanically perform a fixed number of repeated samplings and mean calculations for all test points, including bus voltage, loop current, ripple parameters, battery internal resistance, and insulation impedance. This directly leads to a significant extension of the entire testing cycle for the DC system. It cannot meet the efficient operation and maintenance requirements of power plant DC equipment for rapid verification, batch inspection, and defect retesting.

[0006] In summary, in the existing technology, the comprehensive characteristic test cases of DC chargers are fixed, the sampling strategy is rigid, the adaptability to operating conditions is poor, and the redundancy of the global repeated sampling mechanism is large, resulting in serious test time consumption and low efficiency, which urgently need to be solved. Summary of the Invention

[0007] This application provides a comprehensive characteristic testing method and apparatus for DC chargers to solve the problems in the prior art, such as fixed test cases, rigid sampling strategies, poor adaptability to operating conditions, large redundancy in the global repeated sampling mechanism, serious test time consumption, and low efficiency.

[0008] The first aspect of this application provides a comprehensive characteristic testing method for a DC charger, comprising the following steps: based on multiple preset electrical test sampling conditions, collecting multi-dimensional measured electrical parameters output by the target charger, constructing a five-dimensional particle position vector and a five-dimensional particle velocity vector to establish a corresponding digital particle swarm algorithm; based on multiple DC indicators of the target charger and the multi-dimensional measured electrical parameters, constructing a particle fitness function, and performing velocity and position update operations on the particle iterations in the digital particle swarm algorithm based on a preset linearly decreasing inertia weight, and evaluating the fitness of the particles through the particle fitness function; after the velocity and position update operations meet the preset iteration termination condition, outputting multiple key sampling points corresponding to each set of electrical test sampling conditions, and determining the measured sampling values ​​and fitted calculation values ​​corresponding to the multiple key sampling points to establish an overall residual optimization objective function, iteratively solving the overall residual optimization objective function to obtain fitted correction data, and generating a test report corresponding to the target charger based on the fitted correction data.

[0009] Based on the above technical means, the embodiments of this application improve the particle swarm optimization algorithm to optimize the test case encoding, adaptively generate the optimal test path, and adopt the test case priority sorting and weighted fitting data processing method to intelligently discard low-priority steady-state redundant sampling, effectively reduce the number of invalid samplings, and significantly shorten the overall test time.

[0010] Optionally, in one embodiment of this application, the step of collecting multi-dimensional measured electrical parameters output by the target charger based on preset multiple sets of electrical test sampling conditions and constructing a five-dimensional particle position vector and a five-dimensional particle velocity vector includes: defining each set of electrical test sampling conditions as a particle, collecting multi-dimensional measured electrical parameters output by the target charger, and converting the multi-dimensional measured electrical parameters into corresponding standardized feature quantities to construct the five-dimensional particle position vector, and establishing a five-dimensional particle velocity vector corresponding to the five-dimensional particle position vector, wherein the multi-dimensional measured electrical parameters include the current output current, output voltage, test point, frequency, and output duration.

[0011] Based on the above technical means, this application embodiment introduces an intelligent optimization particle swarm algorithm. By deconstructing the standard test process of a power plant DC system, it extracts core test elements such as test item type, sampling frequency, continuous test duration, simulated load conditions, key test points, and fault verification scenarios. The "test elements" are transformed into feature values ​​of test cases, thereby constituting standardized feature values ​​that can participate in algorithm iteration, providing solid data guidance and basis for the subsequent construction of fitness functions and algorithm iteration.

[0012] Optionally, in one embodiment of this application, the step of constructing a particle fitness function based on multiple DC indicators of the target charger and the multi-dimensional measured electrical parameters, and performing velocity and position update operations on particle iterations in the digital particle swarm algorithm based on a preset linearly decreasing inertia weight, and evaluating the fitness of particles through the particle fitness function, includes: constructing a corresponding particle fitness function based on multiple DC indicators of the target charger and the multi-dimensional measured electrical parameters, combined with a preset test element condition coverage function and sampling strategy fit function, wherein the multiple DC indicators include current regulation accuracy, voltage regulation accuracy, and... Ripple coefficient; determine the combination of the historical best test conditions for a single particle and the global best test conditions for the population during the iteration process, and obtain the maximum value, minimum value and maximum number of iterations of the inertia weight during the iteration process, so as to calculate the linearly decreasing inertia weight corresponding to the current iteration round; based on the five-dimensional particle position vector, the five-dimensional particle velocity vector, the linearly decreasing inertia weight, the combination of the historical best test conditions for a single particle and the global best test conditions for the population, and in combination with the preset learning factor, perform velocity and position update operations on the particle iteration in the digitized particle swarm, and solve the particle fitness function to calculate the particle fitness value of the current iteration round.

[0013] Based on the above technical means, the embodiments of this application introduce linearly decreasing adaptive inertial weights to effectively balance the large-scale search capability under all working conditions with the local high-precision optimization capability, thereby eliminating defects such as premature convergence, getting trapped in local optima, and incomplete optimization under working conditions, and solving the problem of imbalance between global search and local optimization capabilities in traditional particle swarm optimization algorithms.

[0014] Optionally, in one embodiment of this application, after the speed and position update operation meets the preset iteration termination condition, outputting multiple key sampling points corresponding to each group of electrical test sampling conditions, and determining the measured sampling values ​​and fitted calculation values ​​corresponding to the multiple key sampling points to establish an overall residual optimization objective function, iteratively solving the overall residual optimization objective function to obtain fitting correction data, and generating a test report corresponding to the target charger based on the fitting correction data, includes: iteratively solving the particle fitness function, and determining whether the particle fitness value of the current iteration round is the target optimal solution, responding to the particle fitness value of the current iteration round. Given the optimal solution to the target value, output multiple key sampling points; determine the measured sampling value corresponding to each key sampling point, and use a least squares polynomial to fit and correct the multiple key sampling points to obtain the corresponding fitted calculated value; based on the measured sampling value, the fitted calculated value, and the number of effective sampling points, construct the overall residual optimization objective function, and iteratively solve the overall residual optimization objective function to obtain the fitted and corrected data; calculate the multiple DC indicators based on the fitted and corrected data, and statistically analyze the operating condition coverage, sampling optimization efficiency, performance deviation threshold, and disturbance impact degree to generate the test report.

[0015] Based on the above technical means, the embodiments of this application realize fully automated testing integrating test case optimization, adaptive sampling, and intelligent data processing, reducing manual intervention and human error. It can complete the full-dimensional accurate verification of the DC system before the unit is connected to the grid and the equipment is put into operation, and detect hidden anomalies in advance. It avoids rework and rectification and shutdown maintenance problems caused by incomplete testing and parameter mismatch after the equipment is put into operation, effectively improving the operational stability of the power plant's DC system and the overall safety and reliability of the unit.

[0016] Optionally, in one embodiment of this application, the step of calculating the plurality of DC indicators based on the fitting correction data includes: extracting the maximum fluctuation limit deviation value of the output current, the maximum fluctuation limit value of the output voltage, the peak value of DC voltage ripple, the valley value of DC voltage ripple, and the average value of DC voltage from the fitting correction data throughout the entire test process; calculating the current regulation accuracy based on the maximum fluctuation limit deviation value of the output current throughout the entire test process and a preset device output current setting value; calculating the voltage regulation accuracy based on the maximum fluctuation limit value of the output voltage during the test process and the preset device output voltage setting value; and calculating the ripple coefficient based on the peak value of DC voltage ripple, the valley value of DC voltage ripple, and the average value of DC voltage.

[0017] Based on the above technical means, this application embodiment conducts experimental research on the core steady-state performance of the DC charging float charging device in power plants, including current stabilization accuracy, voltage stabilization accuracy, and ripple coefficient, thereby clarifying the test conditions, test principles, and calculation methods for each test condition, and providing a standard basis for the quantitative evaluation of the comprehensive performance of the DC system.

[0018] Optionally, in one embodiment of this application, the mathematical expression of the particle fitness function is:

[0019] in, This represents the particle fitness value. For stable flow accuracy; For voltage regulation accuracy; Ripple coefficient; This represents the function representing the coverage of test element operating conditions. Represents the sampling strategy fit function; , Indicates the normalized weighting coefficient; Indicates the first i The particle positions corresponding to the test cases of the DC charger group.

[0020] Based on the above technical means, the embodiments of this application embed the three core quantitative indicators of DC charger current regulation accuracy, voltage regulation accuracy, and ripple coefficient into the fitness function, breaking the traditional fixed weight fitness mechanism, thereby realizing adaptive optimization under DC performance constraints and prioritizing the screening of high disturbance, high risk, and boundary limit conditions.

[0021] A second aspect of this application provides a comprehensive characteristic testing device for a DC charger, comprising: a data acquisition module, configured to acquire multi-dimensional measured electrical parameters output by a target charger based on multiple preset electrical test sampling conditions, and construct a five-dimensional particle position vector and a five-dimensional particle velocity vector to establish a corresponding digital particle swarm algorithm; an iteration module, configured to construct a particle fitness function based on multiple DC indicators of the target charger and the multi-dimensional measured electrical parameters, and perform velocity and position update operations on the particles in the digital particle swarm algorithm based on a preset linearly decreasing inertia weight, and evaluate the fitness of the particles through the particle fitness function; and a fitting module, configured to output multiple key sampling points corresponding to each set of electrical test sampling conditions after the velocity and position update operations meet preset iteration termination conditions, and determine the measured sampling values ​​and fitted calculation values ​​corresponding to the multiple key sampling points to establish an overall residual optimization objective function, iteratively solve the overall residual optimization objective function to obtain fitting correction data, and generate a test report corresponding to the target charger based on the fitting correction data.

[0022] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the comprehensive characteristic testing method for a DC charger as described in the above embodiments.

[0023] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for testing the comprehensive characteristics of a DC charger.

[0024] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, implements the above-described method for testing the comprehensive characteristics of a DC charger.

[0025] This application embodiment collects multi-dimensional measured electrical parameters output by the target charger based on multiple preset electrical test sampling conditions, constructs a five-dimensional particle position vector and a five-dimensional particle velocity vector, and establishes a corresponding digital particle swarm algorithm. Based on multiple DC indicators and multi-dimensional measured electrical parameters of the target charger, a particle fitness function is constructed, and based on a preset linearly decreasing inertia weight, velocity and position updates are performed on the particle iterations in the digital particle swarm algorithm. The particle fitness is evaluated through the particle fitness function. After the velocity and position update operations meet the preset iteration termination conditions, multiple key sampling points corresponding to each set of electrical test sampling conditions are output, and the measured sampling values ​​and fitted calculation values ​​corresponding to the multiple key sampling points are determined to establish an overall residual optimization objective function. The overall residual optimization objective function is iteratively solved to obtain fitted correction data, and a test report corresponding to the target charger is generated based on the fitted correction data. This application improves the particle swarm optimization algorithm to optimize test case encoding, adaptively generating the optimal test path. It also employs a test case priority ranking and weighted fitting data processing method to intelligently discard low-priority steady-state redundant samples, effectively reducing the number of invalid samples and significantly shortening the overall test time. This solves the problems in existing technologies, such as fixed comprehensive characteristic test cases for DC chargers, rigid sampling strategies, poor adaptability to operating conditions, and large redundancy in the global repeated sampling mechanism, resulting in significant test time consumption and low efficiency.

[0026] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0027] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a comprehensive characteristic testing method for a DC charger according to an embodiment of this application; Figure 2 This is a schematic diagram illustrating the execution logic of a comprehensive characteristic testing method for a DC charger according to an embodiment of this application; Figure 3 This is a block diagram of a comprehensive characteristic testing device for a DC charger provided according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0028] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0029] The following describes a comprehensive characteristic testing method and apparatus for a DC charger according to embodiments of this application, with reference to the accompanying drawings. Addressing the problems mentioned in the background section, this application provides a comprehensive characteristic testing method for a DC charger. In this method, based on multiple preset electrical test sampling conditions, multi-dimensional measured electrical parameters output by the target charger are collected to construct a five-dimensional particle position vector and a five-dimensional particle velocity vector, thereby establishing a corresponding digital particle swarm algorithm. Based on multiple DC indicators and multi-dimensional measured electrical parameters of the target charger, a particle fitness function is constructed. Based on a preset linearly decreasing inertia weight, velocity and position updates are performed on the particles in the digital particle swarm algorithm, and the particle fitness function is used to evaluate the particle fitness. After the velocity and position update operations meet preset iteration termination conditions, multiple key sampling points corresponding to each set of electrical test sampling conditions are output, and the measured sampling values ​​and fitted calculation values ​​corresponding to the multiple key sampling points are determined to establish an overall residual optimization objective function. The overall residual optimization objective function is iteratively solved to obtain fitted correction data, and a test report corresponding to the target charger is generated based on the fitted correction data. This application improves the particle swarm optimization algorithm to optimize test case encoding, adaptively generating the optimal test path. It also employs a test case priority ranking and weighted fitting data processing method to intelligently discard low-priority steady-state redundant samples, effectively reducing the number of invalid samples and significantly shortening the overall test time. This solves the problems in existing technologies, such as fixed comprehensive characteristic test cases for DC chargers, rigid sampling strategies, poor adaptability to operating conditions, and large redundancy in the global repeated sampling mechanism, resulting in significant test time consumption and low efficiency.

[0030] Specifically, Figure 1 This is a flowchart of a comprehensive characteristic testing method for a DC charger according to an embodiment of this application.

[0031] like Figure 1 As shown, the comprehensive characteristic testing method for this DC charger includes a biomass dual-pathway system comprising a biomass utilization branch and a biomass energy storage branch. The method includes the following steps: In step S101, based on multiple preset electrical test sampling conditions, multi-dimensional measured electrical parameters output by the target charger are collected, and a five-dimensional particle position vector and a five-dimensional particle velocity vector are constructed to establish the corresponding digital particle swarm algorithm.

[0032] The embodiments of this application firstly collect multi-dimensional measured electrical parameters output by the DC charger (i.e., the target charger) under multiple sets of electrical test sampling conditions (i.e., actual measurement sampling conditions) to construct a five-dimensional particle position vector and a five-dimensional particle velocity vector, and then construct the corresponding core test element digital particle swarm algorithm, thereby providing reliable technical support for subsequent comprehensive characteristic testing of the DC charger.

[0033] Optionally, in one embodiment of this application, based on multiple preset electrical test sampling conditions, multi-dimensional measured electrical parameters output by the target charger are collected to construct a five-dimensional particle position vector and a five-dimensional particle velocity vector. This includes: defining each set of electrical test sampling conditions as a particle, collecting multi-dimensional measured electrical parameters output by the target charger, and converting the multi-dimensional measured electrical parameters into corresponding standardized feature quantities to construct a five-dimensional particle position vector, and establishing a five-dimensional particle velocity vector corresponding to the five-dimensional particle position vector. The multi-dimensional measured electrical parameters include the current output current, output voltage, test point, frequency, and output duration.

[0034] It should be noted that, as Figure 2 As shown, when conducting DC charger current and voltage regulation tests, this embodiment of the application can collect the measured data output by the charger (i.e., multi-dimensional measured electrical parameters), including real-time output current, output voltage, test point, frequency and output duration, and directly define each set of measured sampling conditions as a particle. The particle position vector completely corresponds to the real electrical sampling parameters, realizing a one-to-one accurate mapping between algorithm particles and real electrical test conditions.

[0035] Secondly, the embodiments of this application can convert the collected multi-dimensional measured electrical parameters into standardized feature quantities that can be recognized by the algorithm, and build a five-dimensional particle position vector to fully cover the core test elements of the charger, as shown in equation (1): (1) in, For the first i The particle positions corresponding to the test cases of the DC charger group; This is the actual measured test current; This is the actual measured test voltage; For continuous testing duration; Encode the key test points; It is a dynamically adaptive sampling frequency.

[0036] Subsequently, in this embodiment, a five-dimensional particle velocity vector corresponding to the particle position vector can be established to characterize the dynamic optimization step size of the five test elements during the algorithm iteration process, thereby realizing the dynamic updating and optimization of test condition parameters. The specific model is shown in the following formula: (2) in, , , , , These are the iterative update steps for current, voltage, test duration, key points, and sampling frequency, respectively.

[0037] Therefore, this application introduces an intelligent optimization particle swarm algorithm. By deconstructing the standard test process of a power plant DC system, it extracts core test elements such as test item type, sampling frequency, continuous test duration, simulated load conditions, key test points, and fault verification scenarios. These "test elements" are then transformed into feature values ​​of test cases, thereby forming standardized feature values ​​that can participate in algorithm iteration. This provides solid data guidance and basis for the subsequent construction of fitness functions and algorithm iteration.

[0038] In step S102, based on multiple DC indicators and multi-dimensional measured electrical parameters of the target charger, a particle fitness function is constructed, and based on a preset linearly decreasing inertia weight, the velocity and position of the particles in the digital particle swarm algorithm are updated. The particle fitness is evaluated through the particle fitness function.

[0039] Furthermore, embodiments of this application can integrate multiple DC indices to construct a new particle fitness function, and at the same time combine linearly decreasing inertial weights to improve the particle velocity and position iterative update formula, thereby performing velocity and position iterative updates on particles in the digital particle swarm algorithm and calculating the particle fitness value of each particle.

[0040] Optionally, in one embodiment of this application, a particle fitness function is constructed based on multiple DC indicators and multi-dimensional measured electrical parameters of the target charger. Based on a preset linearly decreasing inertia weight, velocity and position updates are performed on the particles in the digital particle swarm optimization algorithm. Furthermore, the particle fitness function is used to evaluate the fitness of the particles. This includes: constructing a corresponding particle fitness function based on multiple DC indicators and multi-dimensional measured electrical parameters of the target charger, combined with a preset test element condition coverage function and sampling strategy fit function. The multiple DC indicators include current regulation accuracy, voltage regulation accuracy, and ripple current accuracy. Wave coefficients; determine the combination of the historical best test conditions for a single particle and the global best test conditions for the population during the iteration process, and obtain the maximum value, minimum value and maximum number of iterations of the inertia weight during the iteration process to calculate the linearly decreasing inertia weight corresponding to the current iteration round; based on the five-dimensional particle position vector, five-dimensional particle velocity vector, linearly decreasing inertia weight, the combination of the historical best test conditions for a single particle and the global best test conditions for the population, and combined with the preset learning factor, perform velocity and position update operations on the iteration of particles in the digitized particle swarm, and solve the particle fitness function to calculate the particle fitness value of the current iteration round.

[0041] In actual implementation, the embodiments of this application can introduce linearly decreasing adaptive inertial weights (i.e., linearly decreasing inertial weights) to balance the algorithm's global working condition search capability and local high-precision optimization capability, and avoid the defects of premature convergence and local optima in traditional particle swarm optimization algorithms. The iterative expressions for the ion velocity and position are as shown in equation (3) and formula (4): (3) (4) in, This represents the number of iterations. For adaptive inertia weights; , For learning factors; , A random number in the interval [0,1]; This represents the best historical test condition for a single-particle experiment. This represents the optimal combination of test conditions for the entire population.

[0042] It should be noted that the calculation of the linearly decreasing inertia weight in this embodiment can be dynamically adjusted according to the iteration progress to ensure that the algorithm performs global traversal in the early stage and accurate convergence in the later stage. The calculation expression of the linearly decreasing inertia weight is as follows: (5) in, , These represent the maximum and minimum values ​​of the inertia weight, respectively. This represents the maximum number of iterations.

[0043] Therefore, the embodiments of this application effectively balance the large-scale search capability under all working conditions and the local high-precision optimization capability by introducing linearly decreasing adaptive inertial weights, thus eliminating the defects of premature convergence, getting stuck in local optima, and incomplete optimization under working conditions, and solving the problem of imbalance between global search and local optimization capabilities of traditional particle swarm optimization algorithms.

[0044] As one possible approach, such as Figure 2 As shown, in this embodiment of the application, a new particle fitness function can be constructed by integrating DC indices. The mathematical expression of this fitness function is as follows: (6) in, The coverage of the five major test elements (i.e., the test element coverage function) characterizes the completeness of the traversal of the charger under full load, full voltage fluctuation, and full test points. The sampling strategy fit (i.e., the sampling strategy fit function) characterizes the degree of matching between the sampling frequency, test duration and the current DC disturbance condition. , These are the normalized weighting coefficients; This represents the particle fitness value. For stable flow accuracy; For voltage regulation accuracy; Ripple coefficient; Indicates the first i The particle positions corresponding to the test cases of the DC charger group.

[0045] When the voltage and current fluctuations of the charger are greater, the ripple interference is stronger, and the performance deviation is more significant, the particle fitness value is higher. The algorithm automatically performs key iterations, encrypted sampling, and enhanced testing for this type of working condition, while eliminating stable and steady-state redundant tests and invalid sampling points.

[0046] Therefore, the embodiments of this application improve the current regulation accuracy of the DC charger. Voltage regulation accuracy ripple coefficient The three core quantitative DC indicators are embedded in the fitness function, breaking the traditional fixed-weight fitness mechanism, thereby realizing adaptive optimization under DC performance constraints and prioritizing the screening of high-disturbance, high-risk, and boundary extreme conditions.

[0047] In step S103, after the speed and position update operations meet the preset iteration termination conditions, multiple key sampling points corresponding to each group of electrical test sampling conditions are output, and the measured sampling values ​​and fitted calculation values ​​corresponding to the multiple key sampling points are determined to establish the overall residual optimization objective function. The overall residual optimization objective function is iteratively solved to obtain the fitted correction data, and a test report corresponding to the target charger is generated based on the fitted correction data.

[0048] Subsequently, based on the fitness function, after the speed and position update operations meet the preset iteration termination conditions (such as reaching the optimal solution or reaching the preset number of iterations), the embodiments of this application output multiple key sampling points corresponding to each group of electrical test sampling conditions, and use the least squares polynomial to fit and correct them, obtain the corresponding fitting calculation values, and then establish and solve the overall residual optimization objective function to obtain the fitting correction data, so as to generate the test report corresponding to the target charger.

[0049] Therefore, this application embodiment utilizes standardized feature values ​​that can participate in algorithm iteration to design a corresponding fitness function. Ultimately, through iterative optimization of the algorithm, the optimal test path for the comprehensive characteristics of the DC charger is found, thereby solving the problems of fixed test cases, rigid sampling strategies, and poor adaptability to operating conditions for the comprehensive characteristics of the DC charger. Secondly, this application embodiment innovatively adopts a dual optimization mechanism of weighted fitting of sampled data and intelligent sorting of test case priorities. Differentiated weights are assigned to the original sampled data, and curve fitting is completed based on the weighted dataset to dynamically prioritize all sampled test cases. Redundant sampling tasks with extremely low priority, no fluctuation in operating status, and no potential defect risks are automatically identified and discarded in the long term. Under the premise of strictly meeting the power testing procedures and ensuring test accuracy and operating condition integrity, the invalid sampling process is simplified to the maximum extent, reducing the number of samplings, compressing test time, and reducing data redundancy from the root, effectively solving the problems of large redundancy, serious test time consumption, and low efficiency of the full-domain repeated sampling mechanism.

[0050] Optionally, in one embodiment of this application, after the speed and position update operations meet the preset iteration termination condition, multiple key sampling points corresponding to each group of electrical test sampling conditions are output, and the measured sampling values ​​and fitted calculation values ​​corresponding to the multiple key sampling points are determined to establish an overall residual optimization objective function. The overall residual optimization objective function is iteratively solved to obtain fitting correction data, and based on the fitting correction data, a test report corresponding to the target charger is generated, including: iteratively solving the particle fitness function, and determining whether the particle fitness value of the current iteration round is the target optimal solution, responding to the particle fitness value of the current iteration round. Given the sub-fitness value as the target optimal solution, output the corresponding multiple key sampling points; determine the measured sampling value corresponding to each key sampling point, and use the least squares polynomial to fit and correct the multiple key sampling points to obtain the corresponding fitted calculated value; based on the measured sampling value, the fitted calculated value, and the number of effective sampling points, construct the overall residual optimization objective function, and iteratively solve the overall residual optimization objective function to obtain the fitted correction data, and calculate multiple DC indicators based on the fitted correction data, and statistically analyze the operating condition coverage, sampling optimization efficiency, performance deviation threshold, and disturbance impact degree to generate a test report.

[0051] It should be noted that the key sampling points after particle swarm optimization (i.e., the key sampling points output through the fitness function iteration) are discrete feature points. To eliminate on-site electromagnetic interference, random sampling jitter, and single-point sampling errors, this embodiment can use least squares polynomial fitting to continuously reconstruct the discrete voltage and current sequences to restore the dynamic performance change trend of the charger, as shown in the following formula: (7) in, The voltage / current electrical quantities after fitting at time t; For sampling timing; The fitting coefficients are denoted as .

[0052] Secondly, this embodiment of the application takes minimizing the overall residual as the optimization objective, constructs and iteratively solves the overall residual optimization objective function to obtain the optimal polynomial fitting coefficients, thereby maximizing the fit to the real working condition data. The mathematical expression of the overall residual optimization objective function is as follows: (8) in, These are measured sample values; The calculated value is used for fitting; N is the number of effective sampling points, and finally a continuous and smooth electrical characteristic curve is obtained, providing high-precision data support for performance quantitative evaluation.

[0053] Finally, the embodiments of this application can automatically calculate the three core performance indicators of DC chargers—current regulation accuracy, voltage regulation accuracy, and ripple coefficient—based on the high-precision data after fitting and correction (i.e., fitting and correction data), and statistically analyze the operating condition coverage, sampling optimization efficiency, performance deviation threshold, and disturbance impact degree, thereby automatically generating a standardized DC charger test report with data traceability, compliance judgment, and performance rating, achieving a fully unmanned automated closed loop.

[0054] It is understood that the embodiments of this application optimize test case encoding by improving the particle swarm optimization algorithm (i.e., digital particle swarm optimization algorithm) and adaptively generating the optimal test path, thus solving the problems of fixed test conditions, poor adaptability, and easy omission of transient features in traditional test systems. This allows for dynamic adjustment of sampling strategies based on different operating conditions such as DC system float charging, equalization charging, load disturbances, and fault transients, significantly improving test condition coverage and dynamic defect identification capabilities. Secondly, the embodiments of this application employ a test case priority sorting and weighted fitting data processing method, abandoning the traditional full-domain repeated sampling mode. Low-priority steady-state redundant sampling is intelligently discarded, effectively reducing the number of invalid samples and significantly shortening the overall test time. This solves the technical problems of cumbersome traditional test processes, high sampling redundancy, and low test efficiency. Furthermore, the embodiments of this application realize fully automated testing integrating test case optimization, adaptive sampling, and intelligent data processing, reducing manual intervention and human error. It can complete the full-dimensional accurate verification of the DC system before the unit is connected to the grid and the equipment is put into operation, identify hidden anomalies in advance, and avoid rework, rectification, and shutdown maintenance problems caused by incomplete testing and parameter mismatch after the equipment is put into operation, effectively improving the operational stability of the power plant's DC system and the overall safety and reliability of the unit.

[0055] Optionally, in one embodiment of this application, multiple DC indicators are calculated based on the fitting correction data, including: extracting the maximum fluctuation limit deviation value of the output current, the maximum fluctuation limit value of the output voltage, the peak value of DC voltage pulsation, the valley value of DC voltage pulsation, and the average value of DC voltage through the fitting correction data; calculating the current regulation accuracy based on the maximum fluctuation limit deviation value of the output current and the preset device output current setting value; calculating the voltage regulation accuracy based on the maximum fluctuation limit value of the output voltage and the preset device output voltage setting value; and calculating the ripple coefficient based on the peak value of DC voltage pulsation, the valley value of DC voltage pulsation, and the average value of DC voltage.

[0056] In actual implementation, this application embodiment conducts experimental research on the core steady-state performance of the DC charging float charging device in power plants, mainly including three key tests: current regulation accuracy, voltage regulation accuracy, and ripple coefficient, as detailed below.

[0057] 1. Current stability accuracy test The test was conducted to evaluate the performance of the constant current operation mode of the floating charging device. During the test, the AC input voltage was dynamically varied within the range of +15% to -10% of the rated value. The output voltage of the device was adjusted to any operating point within the full charging voltage adjustment range. At the same time, the output current was stabilized at any value between 20% and 100% of the rated value to simulate the constant current operation state of the power plant's DC system under different loads and different grid voltage fluctuations.

[0058] Steady-state output current data is collected using an optimized adaptive sampling strategy, and the output current limit deviation under operating condition fluctuations is recorded. Finally, the current stabilization accuracy of the device is calculated using a formula, as shown in equation (9): (9) in, Indicates the accuracy of the device's current stabilization. This represents the maximum fluctuation limit deviation of the output current throughout the entire test. This indicates the device's output current setting value.

[0059] In the embodiments of this application, the smaller the constant current accuracy value, the better the constant current control performance of the device and the stronger the DC power supply stability. This can effectively avoid abnormal battery charging and discharging and secondary protection sampling deviation caused by current fluctuations, and ensure the stable operation of the relay protection control cabinet.

[0060] 2. Voltage regulation accuracy test The voltage regulation accuracy test was conducted in the voltage regulation mode of the floating charging device, simulating AC grid disturbances and large-scale DC load fluctuations in a power plant. The test controlled the AC input voltage to continuously vary within the range of +15% to -10% of the rated value, while adjusting the output current to switch between 0% and 100% of the rated value across the full load range. The output voltage was stabilized within any value within the floating charging voltage regulation range to test the device's voltage regulation control performance.

[0061] Data on DC bus output voltage fluctuations under multiple operating conditions were collected, the maximum voltage deviation was extracted, and the voltage regulation accuracy was calculated as shown in equation (10). (10) in, Indicates the voltage regulation accuracy of the device; This indicates the maximum fluctuation limit of the output voltage during the test; it also indicates the set value of the device's output voltage.

[0062] Understandably, voltage regulation accuracy is a core indicator for evaluating the stability of DC system bus voltage and ensuring the reliable operation of secondary equipment.

[0063] 3. Ripple coefficient test The ripple coefficient test is used to detect the smoothness of the DC output voltage and the power quality of the floating charging device. It is also carried out under the floating charging voltage regulation state of the device. The test conditions are set as follows: the AC input voltage is maintained at +15% to -10% of the rated value, the output current varies within the full load range of 0% to 100% of the rated value, and the output voltage is fixed at any operating point within the floating charging voltage adjustment range. The DC voltage ripple waveform data across the resistive load is collected.

[0064] After purifying the original waveform and removing electromagnetic interference noise using a wavelet adaptive denoising algorithm, the peak, valley, and average values ​​of the DC voltage ripple are extracted. The ripple coefficient is calculated using the following formula: (11) in, Indicates the DC output voltage ripple factor; Indicates the peak value of DC voltage ripple; Indicates the DC voltage ripple valley value; This represents the average value of the DC voltage.

[0065] Understandably, a smaller ripple coefficient indicates higher quality DC output power, which can effectively avoid problems such as battery overheating and aging, and interference and malfunction of secondary measurement and control equipment caused by excessive ripple.

[0066] The embodiments of this application can extract the maximum fluctuation limit deviation of the output current, the maximum fluctuation limit of the output voltage, the peak value of DC voltage pulsation, the valley value of DC voltage pulsation and the average value of DC voltage through fitting correction data throughout the test process, and calculate the three DC indicators of current regulation accuracy, voltage regulation accuracy and ripple coefficient according to equations (9), (10) and (11).

[0067] Therefore, this application embodiment conducts experimental research on the core steady-state performance of the DC charging float charging device in power plants, including current stabilization accuracy, voltage stabilization accuracy, and ripple coefficient, thereby clarifying the test conditions, test principles, and calculation methods, and providing a standard basis for the quantitative evaluation of the comprehensive performance of DC systems.

[0068] In summary, based on the traditional fixed repeated sampling test scheme and the intelligent algorithm-integrated adaptive sampling test scheme of this application, the traditional fixed sampling test method is simple to operate, has no special requirements for the test instrument hardware, and the basic indicators of the sampled data under steady-state conditions can meet the basic acceptance standards. However, the traditional method involves a large number of repeated samples, long testing time, and lacks an adaptive filtering mechanism, requiring strict control of sampling time and test range.

[0069] The algorithm-integrated adaptive sampling test scheme in this application utilizes an improved particle swarm optimization algorithm to globally optimize test cases, coupled with weighted data fitting and sampling priority filtering mechanisms, significantly reducing mechanical repetitive sampling across the entire domain and lowering the total test time. Simultaneously, the algorithm optimizes data acquisition quality, ensuring complete sampling characteristics of parameters such as voltage, current, and ripple under steady-state, transient, and fault conditions. The tester achieves higher accuracy in capturing minute fluctuations and latent degradation features in the DC system. However, this scheme places certain demands on the tester's main control computing power and multi-channel synchronous acquisition performance. If the hardware computing power of older testers in the field is insufficient, the algorithm iteration optimization time will increase slightly, and a short-term surge in the amount of instantaneous high-frequency sampling data can easily cause local storage cache overflow.

[0070] According to the comprehensive characteristic testing method for DC chargers proposed in this application, the method collects multi-dimensional measured electrical parameters of the target charger output based on multiple preset electrical test sampling conditions, constructs a five-dimensional particle position vector and a five-dimensional particle velocity vector, and establishes a corresponding digital particle swarm algorithm. Based on multiple DC indicators and multi-dimensional measured electrical parameters of the target charger, a particle fitness function is constructed, and based on a preset linearly decreasing inertia weight, the velocity and position of the particles in the digital particle swarm algorithm are updated. The particle fitness is evaluated through the particle fitness function. After the velocity and position update operations meet the preset iteration termination conditions, multiple key sampling points corresponding to each set of electrical test sampling conditions are output, and the measured sampling values ​​and fitted calculation values ​​corresponding to the multiple key sampling points are determined to establish an overall residual optimization objective function. The overall residual optimization objective function is iteratively solved to obtain fitted correction data, and a test report corresponding to the target charger is generated based on the fitted correction data. This application improves the particle swarm optimization algorithm to optimize test case encoding, adaptively generates the optimal test path, and adopts a test case priority sorting and weighted fitting data processing method to intelligently discard low-priority steady-state redundant samples, effectively reducing the number of invalid samples and significantly shortening the overall test time.

[0071] Next, referring to the accompanying drawings, a comprehensive characteristic testing apparatus for a DC charger according to an embodiment of this application is described.

[0072] Figure 3 This is a block diagram of a comprehensive characteristic testing device for a DC charger provided according to an embodiment of this application.

[0073] like Figure 3 As shown, the comprehensive characteristic testing device 10 for the DC charger includes a biomass utilization branch and a biomass energy storage branch in the dual biomass route. The comprehensive characteristic testing device 10 for the DC charger includes: a data acquisition module 100, an iteration module 200 and a fitting module 300.

[0074] The acquisition module 100 is used to acquire multi-dimensional measured electrical parameters output by the target charger based on multiple preset electrical test sampling conditions, and to construct a five-dimensional particle position vector and a five-dimensional particle velocity vector in order to establish a corresponding digital particle swarm algorithm.

[0075] The iteration module 200 is used to construct a particle fitness function based on multiple DC indicators and multi-dimensional measured electrical parameters of the target charger, and to perform velocity and position update operations on the particle iteration in the digital particle swarm algorithm based on a preset linearly decreasing inertia weight, and to evaluate the fitness of the particles through the particle fitness function.

[0076] The fitting module 300 is used to output multiple key sampling points corresponding to each group of electrical test sampling conditions after the speed and position update operations meet the preset iteration termination conditions, and to determine the measured sampling values ​​and fitted calculation values ​​corresponding to the multiple key sampling points in order to establish an overall residual optimization objective function, iteratively solve the overall residual optimization objective function, obtain fitting correction data, and generate a test report corresponding to the target charger based on the fitting correction data.

[0077] Optionally, in one embodiment of this application, the acquisition module 100 includes: a conversion unit, used to define each group of electrical test sampling conditions as a particle, and to acquire multi-dimensional measured electrical parameters output by the target charger, and to convert the multi-dimensional measured electrical parameters into corresponding standardized feature quantities to construct a five-dimensional particle position vector, and to establish a five-dimensional particle velocity vector corresponding to the five-dimensional particle position vector, wherein the multi-dimensional measured electrical parameters include the current output current, output voltage, test point, frequency and output duration.

[0078] Optionally, in one embodiment of this application, the iteration module 200 includes: a function construction unit, a calculation unit, and an update unit.

[0079] The function construction unit is used to construct the corresponding particle fitness function based on multiple DC indicators and multi-dimensional measured electrical parameters of the target charger, combined with the preset test element operating condition coverage function and sampling strategy adaptation function. The multiple DC indicators include current regulation accuracy, voltage regulation accuracy and ripple coefficient.

[0080] The calculation unit is used to determine the combination of the historical best test conditions for a single particle and the global best test conditions for the population during the iteration process, and to obtain the maximum value, minimum value and maximum number of iterations of the inertial weight during the iteration process, so as to calculate the linearly decreasing inertial weight corresponding to the current iteration round.

[0081] The update unit is used to perform velocity and position update operations on particles in the digitized particle swarm based on the five-dimensional particle position vector, the five-dimensional particle velocity vector, the linearly decreasing inertia weight, the combination of the single particle historical best test condition and the swarm global best test condition, and in combination with the preset learning factor. It also solves the particle fitness function and calculates the particle fitness value of the current iteration.

[0082] Optionally, in one embodiment of this application, the fitting module 300 includes: a solving unit, a correction unit, and a generation unit.

[0083] The solution unit is used to iteratively solve the particle fitness function and determine whether the particle fitness value of the current iteration is the target optimal solution. In response to the particle fitness value of the current iteration being the target optimal solution, it outputs multiple corresponding key sampling points.

[0084] The calibration unit is used to determine the measured sample value corresponding to each key sampling point, and to use the least squares polynomial to fit and correct multiple key sampling points to obtain the corresponding fitted calculated value.

[0085] The generation unit is used to construct an overall residual optimization objective function based on measured sampled values, fitted calculated values, and the number of effective sampling points, and iteratively solve the overall residual optimization objective function to obtain fitted correction data. Based on the fitted correction data, it calculates multiple DC indicators and statistically analyzes operating condition coverage, sampling optimization efficiency, performance deviation threshold, and disturbance impact degree to generate a test report.

[0086] Optionally, in one embodiment of this application, the generation unit includes: an extraction subunit, a current regulation accuracy calculation subunit, a voltage regulation accuracy calculation subunit, and a ripple coefficient calculation subunit.

[0087] The extraction subunit is used to extract the maximum fluctuation limit deviation of the output current, the maximum fluctuation limit of the output voltage, the peak value of DC voltage pulsation, the valley value of DC voltage pulsation, and the average value of DC voltage through fitting and correction data throughout the entire test process.

[0088] The current stabilization accuracy calculation subunit is used to calculate the current stabilization accuracy based on the maximum fluctuation limit deviation of the output current and the preset device output current setting value throughout the test.

[0089] The voltage regulation accuracy calculation subunit is used to calculate the voltage regulation accuracy based on the maximum fluctuation limit of the output voltage during the test and the preset output voltage setting value of the device.

[0090] The ripple coefficient calculation subunit is used to calculate the ripple coefficient based on the peak value, valley value, and average value of the DC voltage ripple.

[0091] Optionally, in one embodiment of this application, the mathematical expression for the particle fitness function is:

[0092] in, This represents the particle fitness value. For stable flow accuracy; For voltage regulation accuracy; Ripple coefficient; This represents the function representing the coverage of test element operating conditions. Represents the sampling strategy fit function; , Indicates the normalized weighting coefficient; Indicates the first i The particle positions corresponding to the test cases of the DC charger group.

[0093] It should be noted that the explanation of the aforementioned embodiment of the comprehensive characteristic test method for DC chargers also applies to the comprehensive characteristic test device for DC chargers in this embodiment, and will not be repeated here.

[0094] The comprehensive characteristic testing device for DC chargers proposed in this application includes a data acquisition module 100, used to acquire multi-dimensional measured electrical parameters output by the target charger based on multiple preset electrical test sampling conditions, and construct a five-dimensional particle position vector and a five-dimensional particle velocity vector to establish a corresponding digital particle swarm algorithm; an iteration module 200, used to construct a particle fitness function based on multiple DC indicators and multi-dimensional measured electrical parameters of the target charger, and perform velocity and position update operations on the particle iteration in the digital particle swarm algorithm based on a preset linearly decreasing inertia weight, and evaluate the fitness of the particles through the particle fitness function; and a fitting module 300, used to output multiple key sampling points corresponding to each set of electrical test sampling conditions after the velocity and position update operations meet the preset iteration termination conditions, and determine the measured sampling values ​​and fitted calculation values ​​corresponding to the multiple key sampling points to establish an overall residual optimization objective function, iteratively solve the overall residual optimization objective function to obtain fitting correction data, and generate a test report corresponding to the target charger based on the fitting correction data. This application improves the particle swarm optimization algorithm to optimize test case encoding, adaptively generates the optimal test path, and adopts a test case priority sorting and weighted fitting data processing method to intelligently discard low-priority steady-state redundant samples, effectively reducing the number of invalid samples and significantly shortening the overall test time.

[0095] Figure 4 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. The electronic device may include: The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0096] When the processor 402 executes the program, it implements the comprehensive characteristic testing method for the DC charger provided in the above embodiments.

[0097] Furthermore, electronic devices also include: Communication interface 403 is used for communication between memory 401 and processor 402.

[0098] The memory 401 is used to store computer programs that can run on the processor 402.

[0099] Memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0100] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0101] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0102] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0103] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for testing the comprehensive characteristics of a DC charger.

[0104] This application also provides a computer program product, including a computer program that, when executed, implements the above-described method for testing the comprehensive characteristics of a DC charger.

[0105] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0106] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0107] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0108] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0109] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0110] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0111] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0112] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A comprehensive characteristic testing method for a DC charger, characterized in that, Includes the following steps: Based on multiple preset electrical test sampling conditions, multi-dimensional measured electrical parameters of the target charger output are collected, and a five-dimensional particle position vector and a five-dimensional particle velocity vector are constructed to establish the corresponding digital particle swarm algorithm. Based on multiple DC indicators of the target charger and the multi-dimensional measured electrical parameters, a particle fitness function is constructed. Based on a preset linearly decreasing inertia weight, the velocity and position of the particles in the digital particle swarm algorithm are updated. The particle fitness is evaluated through the particle fitness function. After the speed and position update operations meet the preset iteration termination conditions, multiple key sampling points corresponding to each group of electrical test sampling conditions are output, and the measured sampling values ​​and fitted calculation values ​​corresponding to the multiple key sampling points are determined to establish an overall residual optimization objective function. The overall residual optimization objective function is iteratively solved to obtain fitted correction data, and a test report corresponding to the target charger is generated based on the fitted correction data.

2. The method according to claim 1, characterized in that, The method involves collecting multi-dimensional measured electrical parameters from the target charger based on preset multiple sets of electrical test sampling conditions, and constructing a five-dimensional particle position vector and a five-dimensional particle velocity vector, including: Each electrical test sampling condition is defined as a particle, and multi-dimensional measured electrical parameters of the target charger output are collected. The multi-dimensional measured electrical parameters are converted into corresponding standardized feature quantities to construct the five-dimensional particle position vector, and a five-dimensional particle velocity vector corresponding to the five-dimensional particle position vector is established. The multi-dimensional measured electrical parameters include the current output current, output voltage, test point, frequency, and output duration.

3. The method according to claim 2, characterized in that, Based on multiple DC parameters of the target charger and the multi-dimensional measured electrical parameters, a particle fitness function is constructed. Based on a preset linearly decreasing inertia weight, velocity and position updates are performed on the particles in the digital particle swarm algorithm. Furthermore, the particle fitness function is used to evaluate the fitness of the particles, including: Based on the multiple DC indicators of the target charger and the multi-dimensional measured electrical parameters, combined with the preset test element condition coverage function and sampling strategy adaptation function, a corresponding particle fitness function is constructed. The multiple DC indicators include current regulation accuracy, voltage regulation accuracy and ripple coefficient. Determine the combination of the historical best test conditions for a single particle and the global best test conditions for the population during the iteration process, and obtain the maximum value, minimum value and maximum number of iterations of the inertial weight during the iteration process, so as to calculate the linearly decreasing inertial weight corresponding to the current iteration round; Based on the five-dimensional particle position vector, the five-dimensional particle velocity vector, the linearly decreasing inertia weight, the combination of the single-particle historical best test condition and the population global best test condition, and combined with the preset learning factor, the velocity and position of the particles in the digitized particle swarm are updated iteratively, and the particle fitness function is solved to calculate the particle fitness value of the current iteration.

4. The method according to claim 3, characterized in that, After the speed and position update operations meet the preset iteration termination condition, multiple key sampling points corresponding to each group of electrical test sampling conditions are output, and the measured sampling values ​​and fitted calculation values ​​corresponding to the multiple key sampling points are determined to establish an overall residual optimization objective function. The overall residual optimization objective function is iteratively solved to obtain fitted correction data, and based on the fitted correction data, a test report corresponding to the target charger is generated, including: The particle fitness function is solved iteratively, and it is determined whether the particle fitness value in the current iteration is the target optimal solution. If the particle fitness value in the current iteration is the target optimal solution, multiple corresponding key sampling points are output. The measured sample value corresponding to each key sampling point is determined, and the multiple key sampling points are fitted and corrected using the least squares polynomial to obtain the corresponding fitted calculated value. Based on the measured sampled values, the fitted calculated values, and the number of effective sampling points, the overall residual optimization objective function is constructed, and the overall residual optimization objective function is iteratively solved to obtain the fitted correction data. The multiple DC indicators are calculated based on the fitted correction data, and the operating condition coverage, sampling optimization efficiency, performance deviation threshold, and disturbance impact degree are statistically analyzed to generate the test report.

5. The method according to claim 4, characterized in that, The calculation of the plurality of DC indices based on the fitted correction data includes: The maximum fluctuation limit deviation of the output current, the maximum fluctuation limit of the output voltage, the peak value of DC voltage pulsation, the valley value of DC voltage pulsation, and the average value of DC voltage are extracted from the fitted and corrected data throughout the entire test process. The current stabilization accuracy is calculated based on the maximum fluctuation limit deviation of the output current during the entire test and the preset device output current setting value. The voltage regulation accuracy is calculated based on the maximum output voltage fluctuation limit during the test and the preset device output voltage setting value. The ripple coefficient is calculated based on the peak value of the DC voltage ripple, the valley value of the DC voltage ripple, and the average value of the DC voltage.

6. The method according to claim 3, characterized in that, The mathematical expression for the particle fitness function is: in, This represents the particle fitness value. For stable flow accuracy; For voltage regulation accuracy; Ripple coefficient; This represents the function representing the coverage of test element operating conditions. Represents the sampling strategy fit function; , Indicates the normalized weighting coefficient; Indicates the first i The particle positions corresponding to the test cases of the DC charger group.

7. A comprehensive characteristic testing device for a DC charger, characterized in that, include: The acquisition module is used to acquire multi-dimensional measured electrical parameters output by the target charger based on multiple preset electrical test sampling conditions, and to construct a five-dimensional particle position vector and a five-dimensional particle velocity vector in order to establish a corresponding digital particle swarm algorithm. The iteration module is used to construct a particle fitness function based on multiple DC indicators of the target charger and the multi-dimensional measured electrical parameters, and to perform velocity and position update operations on the particle iteration in the digital particle swarm algorithm based on a preset linearly decreasing inertia weight, and to evaluate the fitness of the particles through the particle fitness function. The fitting module is used to output multiple key sampling points corresponding to each group of electrical test sampling conditions after the speed and position update operations meet the preset iteration termination conditions, and to determine the measured sampling values ​​and fitted calculation values ​​corresponding to the multiple key sampling points in order to establish an overall residual optimization objective function, iteratively solve the overall residual optimization objective function to obtain fitting correction data, and generate a test report corresponding to the target charger based on the fitting correction data.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the comprehensive characteristic testing method for a DC charger as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the comprehensive characteristic test method for the DC charger as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the program is executed, it is used to implement the comprehensive characteristic test method for the DC charger as described in any one of claims 1-6.

Images