Controller production energy-saving optimization method using current monitoring

Abstract

The invention provides a controller production energy-saving optimization method using current monitoring, and relates to the technical field of controller production, and the method comprises the steps: carrying out the application demand analysis of an electric vehicle controller production line, and constructing a controller current test parameter table; performing current test monitoring on the controller by adopting the controller current test parameter table to obtain a controller current test signal set; acquiring a controller production performance index set, and performing multi-dimensional evaluation on the controller current test signal set to obtain controller production performance parameters; and constructing a controller production strategy parameter space to perform energy-saving optimization analysis on the controller production performance parameters to obtain target controller production strategy parameters. The technical problems that an existing energy-saving method focuses on local optimization and lacks an optimization strategy starting from the overall situation, consequently, the energy-saving potential is not completely excavated, and improvement of the overall energy utilization efficiency is affected are solved, and the energy utilization efficiency of the electric vehicle controller in the production process is improved on the premise that the production quality is guaranteed.

Description

Technical Field

[0001] The present application relates to the technical field of controller production, and in particular to a controller production energy-saving optimization method using current monitoring. Background Art

[0002] The controller is an important component of electric vehicles, responsible for regulating and controlling the flow of current between the battery and the motor to ensure the normal operation and performance of the vehicle. At present, the production optimization of electric vehicle controllers mainly focuses on the following aspects: First, it focuses on improving the energy utilization rate of production equipment, reducing energy consumption by upgrading and improving equipment efficiency; second, it optimizes the production process, improves production efficiency by improving the production process and reducing unnecessary energy consumption and waste. However, most of these methods focus on local optimization and often lack systematic optimization of the overall production strategy. This kind of local optimization often makes it difficult to make sufficient energy-saving improvements for each link in the production process and cannot maximize the energy-saving potential. Summary of the invention

[0003] The present application provides an energy-saving optimization method for controller production using current monitoring, which solves the technical problem that the existing energy-saving methods focus on local optimization and lack an optimization strategy from a global perspective, resulting in the energy-saving potential not being fully tapped, thereby affecting the overall energy utilization efficiency improvement. The application achieves the technical effect of improving the energy utilization efficiency of the electric vehicle controller production process while ensuring production quality.

[0004] In view of the above problems, the present application provides a controller production energy-saving optimization method using current monitoring, the method comprising: obtaining an electric vehicle controller production line, performing application demand analysis on the electric vehicle controller production line, and constructing a controller current test parameter table; using the controller current test parameter table to perform current test monitoring on the target electric vehicle controller to obtain a controller current test signal set; obtaining a controller production performance target, performing indicator decomposition on the controller production performance target, and obtaining a controller production performance indicator set; performing a multi-dimensional evaluation of the controller current test signal set based on the controller production performance indicator set to obtain controller production performance parameters; constructing a controller production strategy parameter space based on the electric vehicle controller production line; performing energy-saving optimization analysis on the controller production performance parameters based on the controller production strategy parameter space to obtain target controller production strategy parameters, and performing energy-saving optimization production control of the electric vehicle controller through the target controller production strategy parameters.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages: By analyzing the application requirements of the electric vehicle controller production line, a controller current test parameter table is constructed, which provides standardized test parameters and methods for subsequent current testing and performance evaluation, ensuring the accuracy and effectiveness of the test. After the test parameter table is established, the current test signals of the controller are further collected through actual current test monitoring. These signals are key data for evaluating controller performance and subsequent optimization. The controller production performance target is obtained and the index is disassembled, and the abstract production performance target is concretized into a quantifiable controller production performance index set, which provides a clear goal and direction for subsequent performance evaluation and optimization. Based on the controller production performance index set, the controller current test signal set is evaluated in multiple dimensions, and multiple performance indicators are comprehensively considered to comprehensively evaluate the performance of the controller, so as to obtain more accurate controller production performance parameters. According to the electric vehicle controller production line, the controller production strategy parameter space is constructed, and the parameter range of the controller production strategy is determined, providing possible parameter selection and adjustment space for energy-saving optimization. The optimal production strategy parameters are obtained through energy-saving optimization analysis, and these parameters are used to control the production process of electric vehicle controllers to achieve energy-saving optimization.

[0006] In summary, this application achieves the coordinated improvement of energy-saving optimization and controller performance through accurate data collection based on current monitoring, multi-dimensional production performance evaluation and intelligent production strategy optimization. By constructing the controller production strategy parameter space and performing multi-level optimization based on this space, the energy utilization efficiency of the production process is significantly improved, unnecessary energy consumption is reduced, and the consistency of product quality and the long-term stability of the controller are improved, ultimately achieving the effect of continuous energy saving.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flow chart of a controller production energy-saving optimization method using current monitoring provided in an embodiment of the present application.

[0009] Figure 2 A schematic diagram of a process for obtaining controller production performance parameters in a controller production energy-saving optimization method using current monitoring provided in an embodiment of the present application.

[0010] Figure 3 A schematic diagram of a process for obtaining target controller production strategy parameters in a controller production energy-saving optimization method using current monitoring provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The embodiment of the present application provides a controller production energy-saving optimization method using current monitoring, constructs a controller current test parameter table, performs current test monitoring on the electric vehicle controller, obtains a current test signal set, and performs multi-dimensional evaluation in combination with the controller production performance target to obtain the controller production performance parameters. Furthermore, based on the controller production strategy parameter space, the production performance parameters are analyzed for energy-saving optimization, the target controller production strategy parameters are obtained, and energy-saving optimization production control of the electric vehicle controller is realized.

[0012] like Figure 1 As shown, the embodiment of the present application provides a controller production energy saving optimization method using current monitoring, the method comprising: Step S1: obtaining an electric vehicle controller production line, analyzing application requirements for the electric vehicle controller production line, and constructing a controller current test parameter table.

[0013] Specifically, the electric vehicle controller production line refers to the automated or semi-automated production facilities and equipment used to produce electric vehicle controllers, including all process flows and equipment from raw material input to product production completion. The controller current test parameter table is a set of parameters used to guide the current test of electric vehicle controllers, defining various standards and objectives for current testing, such as current ratings, current fluctuation range, and maximum allowable current under different working conditions (such as starting, acceleration, constant speed, and deceleration).

[0014] The control system of the interactive electric vehicle controller production line obtains basic information such as the production capacity of the production line, the type of controller produced, and the applicable electric vehicle type. Based on this basic information, a detailed analysis is conducted on the work needs, target performance, production process and technical requirements of the production line to determine the key parameters and technical standards of each link, and create a controller current test parameter table, which lists in detail the standards required for the current test, the specific points of measurement, and the range of each parameter.

[0015] Through demand analysis and the construction of current test parameter tables, the foundation is laid for subsequent testing and optimization, ensuring that the current test is targeted, accurate and comprehensive.

[0016] Step S2: using the controller current test parameter table to perform current test monitoring on the target electric vehicle controller to obtain a controller current test signal set.

[0017] Specifically, the target electric vehicle controller refers to a specific controller selected as a test object in the electric vehicle controller production line. The current test signal set refers to a series of current-related signal data collected through current test monitoring, including current values, timestamps, current change frequencies, current waveforms, and other information at different test stages.

[0018] Use the current test parameter table constructed in step S1 to perform specific current tests on the target electric vehicle controller. These tests will be performed under different working conditions of the controller, such as startup, operation, standby, etc., to collect its current changes. The current data of the controller is monitored in real time by the current testing equipment to form a complete current test signal set. For example, if the electric vehicle controller requires a larger current at startup and the current remains at a lower level when in standby, these changes will be recorded and stored by the production line control system. The real current signal can be obtained through current test monitoring to provide data support for subsequent performance evaluation and energy-saving optimization.

[0019] Step S3: Obtain a controller production performance target, decompose the controller production performance target into indicators, and obtain a controller production performance indicator set.

[0020] Specifically, the controller production performance target refers to the various performance requirements that electric vehicle controllers should meet during the production process, such as output power, energy efficiency, stability, etc. The controller production performance indicator set is a set of specific indicators related to controller production performance obtained after indicator decomposition, including indicators related to controller quality, such as power conversion efficiency, working time at different temperatures, failure rate under different loads, etc., as well as indicators related to controller production energy consumption, such as power consumption and raw material consumption at different production stages.

[0021] Obtain the preset controller production performance targets from the controller production line control system, decompose these performance targets, generate multiple specific and actionable performance indicators, and build a controller production performance indicator set. Through indicator decomposition, high-level performance requirements are converted into measurable indicators to ensure that subsequent evaluations can accurately reflect the performance of the production process.

[0022] Step S4: performing a multi-dimensional evaluation on the controller current test signal set based on the controller production performance indicator set to obtain controller production performance parameters.

[0023] Specifically, the controller production performance parameters are the final parameters of the controller production performance obtained through multi-dimensional evaluation, which can be a comprehensive score, a performance level, or a set of specific performance values. The evaluation dimensions are determined according to the controller production performance indicator set, for example, from the dimensions of power conversion efficiency, temperature stability, current stability, production power consumption, etc. Then, the controller current test signal set is analyzed from multiple evaluation dimensions, and a set of specific production performance parameters is obtained by comparing the analysis results of the controller current test signal set with the controller production performance indicator set. These parameters can accurately reflect the various performances of the electric vehicle controller and provide a basis for subsequent energy-saving optimization. Multi-dimensional evaluation helps to fully understand the production performance of electric vehicle controllers from multiple perspectives and provide accurate parameters for optimization.

[0024] Step S5: constructing a controller production strategy parameter space according to the electric vehicle controller production line.

[0025] Specifically, the controller production strategy parameter space refers to the set of all possible strategy parameters in the production process of electric vehicle controllers, including process parameters, production methods, equipment parameters, etc. The interactive controller production line obtains the parameter range of all adjustable parameters in the production process, including but not limited to the control parameter range of production equipment, production speed, working environment, etc. The construction of the controller production strategy parameter space provides a theoretical framework and optimization space for subsequent energy-saving optimization, ensuring that subsequent optimization will not exceed the operational range of the production line.

[0026] Step S6: performing energy-saving optimization analysis on the controller production performance parameters based on the controller production strategy parameter space, obtaining target controller production strategy parameters, and performing energy-saving optimization production control and management on the electric vehicle controller through the target controller production strategy parameters.

[0027] Specifically, the target controller production strategy parameters are production strategy parameters for achieving energy-saving optimization determined after energy-saving optimization analysis. In the controller production strategy parameter space, energy-saving optimization analysis is performed according to the controller production performance parameters. Simulation software can be used to simulate the production process and energy-saving effects under different production strategy parameters. For example, in the simulation, the welding temperature, welding time and other parameters of the welding equipment are changed to observe the impact on the controller production performance parameters and energy-saving effects. Then, the production strategy parameters with the best energy-saving effect are found through analysis, that is, the target controller production strategy parameters. The target controller production strategy parameters are applied to the actual electric vehicle controller production process, and the production equipment is controlled by the production line automation control system to perform production operations according to the target parameters to achieve energy-saving optimization production control. By optimizing the production strategy parameters, the energy-saving goals and production goals can be balanced, and efficient energy-saving optimization production control can be achieved.

[0028] Furthermore, step S1 of the embodiment of the present application includes: Step S11: performing working condition analysis on the application scenario information of the electric vehicle controller production line to obtain a controller application working condition set.

[0029] Step S12: performing simulated current analysis on the controller application condition set respectively to obtain a controller condition simulated current change set.

[0030] Step S13: extracting monitoring indicators of the electric vehicle controller production line to obtain a controller current monitoring indicator set.

[0031] Step S14: performing parameter design combination based on the controller operating condition simulation current change set and the controller current monitoring index set to construct the controller current test parameter table.

[0032] Specifically, application scenario information refers to the various environmental conditions and working conditions of electric vehicle controllers in actual applications. Different driving modes (such as urban driving, high-speed driving) and external environments (such as temperature, humidity, etc.) will affect the performance of the controller. Collect application scenario information of the electric vehicle controller production line, analyze the working conditions of these application scenario information, and analyze the different working environments that electric vehicle controllers may face, such as driving roads, working environment temperature, humidity, driving speed, etc. Sort and summarize this information to generate a controller application condition set. Each set of data in the set corresponds to an application scenario.

[0033] The controller working condition simulation current change set is a set of controller current change conditions under different working conditions obtained after simulating current analysis on the controller application working condition set. For each working condition in the controller application working condition set, a corresponding circuit model is established using simulation software (such as MATLAB, Simulink, PSIM, etc.), and the current change of the controller under different working conditions is simulated based on the working principle of the electric vehicle controller. For example, in a high temperature environment, the controller may cause a slight increase in current due to temperature increase; in acceleration mode, the controller may have a large current pulse. Through simulation analysis, the current change of the controller under all working conditions can be obtained, and these data are used as the working condition simulation current change set. Simulated current analysis provides expected data for subsequent current monitoring and optimization, avoiding the high cost and uncertainty brought by direct measurement.

[0034] The current monitoring index is a key indicator for monitoring the performance of the controller. After the monitoring index is extracted, multiple indicators related to the controller current monitoring are obtained to form the controller current monitoring index set. The monitoring indicators are extracted from the actual operation data of the electric vehicle controller production line. These indicators can help analyze the working status of the controller during the production process. These indicators include but are not limited to the maximum value, minimum value, average value, fluctuation range, etc. of the current, which are key parameters for evaluating the performance of the controller. By extracting these indicators, it can ensure that the performance of the controller is effectively monitored during the production process, and possible problems can be discovered and solved in a timely manner.

[0035] Combined with the simulated current change data obtained in step S12 and the current monitoring index extracted in step S13, the specific values ​​of the corresponding current indicators under different working conditions are determined according to the current change conditions under different working conditions. The simulated current change data is the current change of the electric vehicle controller under an ideal state. The monitoring index analysis and calculation of the simulated current change data are performed using data analysis software to obtain the current monitoring index range when the controller is working normally under each working condition. According to these current monitoring index ranges, a current test parameter table that adapts to different working conditions and production monitoring needs is constructed to provide a standardized test framework for actual current testing, ensuring that the test results can effectively reflect the performance of the controller. For example, in the simulation, if the current of the controller under acceleration conditions has a large fluctuation, then according to the fluctuation law in the simulation test tree, the corresponding test cycle and current range are set to capture this fluctuation in actual monitoring.

[0036] Furthermore, step S3 of the embodiment of the present application includes: Step S31: Decomposing the controller production performance target by attributes to obtain controller production performance attributes, wherein the controller production performance attributes include production quality attributes and production energy consumption attributes.

[0037] Step S32: extracting related indicators based on the production quality attributes and the production energy consumption attributes, and constructing a generalized related production quality indicator set and a generalized related production energy consumption indicator set.

[0038] Step S33: quantifying the correlation of each indicator information in the generalized correlated production quality indicator set and the generalized correlated production energy consumption indicator set to obtain a production quality indicator correlation degree set and a production energy consumption indicator correlation degree set.

[0039] Step S34: setting a preset correlation threshold, screening and integrating the indicators of the production quality indicator correlation set and the production energy consumption indicator correlation set within the preset correlation threshold, and obtaining the controller production performance indicator set.

[0040] Specifically, the production performance goals of the controller are disassembled to identify two main attributes: production quality and production energy consumption. Production quality attributes include indicators such as product consistency, defect rate, and stability, which are key factors in measuring product quality; while production energy consumption attributes involve the amount of energy consumed in the production process, including electricity consumption, unit product energy consumption, etc. Through attribute disassembly, these macro goals can be transformed into specific and actionable production standards, providing guidance for subsequent indicator design and evaluation.

[0041] The generalized associated production quality indicator set refers to an indicator set extracted from production quality attributes that can reflect the overall production quality status, such as product qualification rate, rework rate, etc. The generalized associated production energy consumption indicator set refers to an indicator set extracted from production energy consumption attributes that can reflect the overall energy efficiency status, such as unit production energy consumption, total energy consumption of the production line, etc. For the disassembled production quality attributes and production energy consumption attributes, it is necessary to further extract specific indicators that can reflect these two attributes. The data mining algorithm is used to analyze the determined production quality attributes and production energy consumption attributes, extract specific indicators associated with the two attributes, and construct a generalized associated production quality indicator set and production energy consumption indicator set.

[0042] The production quality index correlation set is a set obtained by quantifying the correlation of the indicators in the generalized correlation production quality index set, in which each element represents the correlation value of the corresponding indicator. The production energy consumption index correlation set is a set obtained by quantifying the correlation of the indicators in the generalized correlation production energy consumption index set, and contains the correlation values ​​of each energy consumption index. Data analysis is performed on each indicator in the generalized correlation production quality index set and the generalized correlation production energy consumption index set, and the correlation between each indicator in each set is calculated. The correlation quantification can adopt statistical analysis methods, such as correlation coefficient analysis methods. For example, two indicators are extracted from the generalized correlation production quality index set, and the correlation coefficient between the two indicators is calculated by collecting historical production data, which is used as the quantitative correlation. According to this method, the correlation of each indicator in the generalized correlation production quality index set and the generalized correlation production energy consumption index set is quantified, and the quantified correlation values ​​are sorted into the production quality index correlation set and the production energy consumption index correlation set.

[0043] A correlation threshold is defined in advance to screen out those indicators that have a strong relationship and a greater impact on the overall production performance. For example, based on historical production experience, the correlation threshold is set to 0.6, and indicators with a correlation greater than or equal to 0.6 are considered to be strongly correlated indicators. The indicators in the production quality indicator correlation set and the production energy consumption indicator correlation set are screened, and the indicators whose correlation values ​​meet the preset correlation threshold are selected. The screened production quality indicators and production energy consumption indicators are integrated to form a controller production performance indicator set. Through this screening and integration, it can be ensured that the final production performance indicator set contains the most influential and relevant indicators, thereby providing accurate guidance for subsequent energy-saving optimization and quality improvement.

[0044] Further, such as Figure 2 As shown, step S4 of the embodiment of the present application includes: Step S41: performing noise characteristic and filtering requirement analysis on the controller current test signal set to obtain signal noise characteristic information and filtering requirement information.

[0045] Step S42: Analyze filter characteristic parameters based on the signal noise characteristic information and filtering requirement information to determine a current signal digital filter.

[0046] Step S43: using the current signal digital filter to perform filtering preprocessing on the controller current test signal set to obtain a standard controller current test signal set.

[0047] Step S44: performing an associated multi-dimensional evaluation on the standard controller current test signal set based on the controller production performance indicator set to obtain the controller production performance parameters.

[0048] Specifically, noise characteristics refer to the characteristics of interference components in the controller current test signal, such as the frequency range, amplitude, and type of noise. Filtering requirements refer to the specific requirements for filtering operations required to remove noise from the signal, including the frequency range of noise that needs to be filtered out, the accuracy requirements of the filtered signal, etc. Use signal analysis software (such as MATLAB) to perform detailed noise characteristics and filtering requirements analysis on the controller current test signal set, identify the noise components in the signal, evaluate the impact of these noises on signal quality, and determine the required filter characteristics to meet specific signal processing goals. Generate signal noise characteristic information and filtering requirements information, provide necessary information for subsequent filter design and signal preprocessing, and ensure that the filtered signal can accurately reflect the performance of the controller.

[0049] The current signal digital filter is a filter that filters the current signal in a digital way and is used to remove noise from the current test signal. The characteristic parameters of the filter are analyzed according to the signal noise characteristic information and the filtering requirement information, and the filter cutoff frequency, passband gain and other parameters are determined. The appropriate digital filter type is selected according to the determined filter parameters. For example, if there is a requirement for linear phase characteristics, a finite impulse response filter can be selected; if more attention is paid to filtering efficiency and lower computational complexity, an infinite impulse response filter can be selected.

[0050] The standard controller current test signal set refers to the current test signal set that has been pre-processed by filtering. The controller current test signal set is input into the determined current signal digital filter. The digital filter processes the input signal according to its set characteristic parameters to remove the noise components. Finally, the standard controller current test signal set after filtering pre-processing is obtained. These signals are clearer and more stable, and unnecessary interference is removed, making them suitable for further production performance evaluation and analysis. For example, the original current signal may contain interference from power supply noise. After filtering, the waveform of the signal becomes smoother, eliminating fluctuations caused by the external environment. Through filtering processing, the noise and irrelevant signal components in the controller current test signal set can be removed, the quality of the signal can be improved, and the subsequent performance evaluation can be more accurate.

[0051] Based on the filtered standard controller current test signal set, the current signal is evaluated from multiple dimensions (such as signal stability, production efficiency, energy efficiency, etc.) to analyze whether it meets the production performance target. Through multi-dimensional evaluation, the production performance parameters of the controller can be obtained. These parameters can accurately reflect the relationship between the current signal and the production process, thereby providing effective data support for subsequent optimization.

[0052] Further, step S44 includes: Step S441: Correlation and shunting the standard controller current test signal set based on the controller production performance indicator set to obtain a controller indicator correlation test signal set.

[0053] Step S442: using the controller production performance indicator set to perform multi-dimensional evaluation on the controller indicator associated test signal set to obtain a controller performance indicator evaluation parameter set.

[0054] Step S443: performing weight assignment on each performance indicator in the controller production performance indicator set to determine a production performance indicator decision factor set.

[0055] Step S444: performing weighted correction on the controller performance indicator evaluation parameter set based on the production performance indicator decision factor set to obtain the controller production performance parameter.

[0056] Specifically, the controller indicator association test signal set is a current test signal set after association and diversion. Each subset corresponds to a specific production performance target or operating condition, which is convenient for subsequent detailed analysis. Analyze the relationship between the controller production performance indicator set and the standard controller current test signal set. For example, if there are indicators about current stability in the controller production performance indicator set, then find related signals such as current fluctuation data in the standard controller current test signal set. Then, classify and divide the standard controller current test signal set according to these relationships, and classify the signals into different subsets according to different association relationships. Finally, combine these classified signal subsets to form a controller indicator association test signal set.

[0057] The controller performance index evaluation parameter set is a set of parameters obtained through multi-dimensional evaluation, which reflects the evaluation results of the controller in various performance indicators. Based on the diverted controller index association test signal set and combined with the controller production performance index set, a multi-dimensional evaluation is performed on each diverted signal subset. This evaluation not only focuses on a single performance indicator, but also comprehensively examines key performance parameters such as signal stability, power consumption, and work efficiency from multiple dimensions. For example, in the evaluation process of the current signal, the signal fluctuation amplitude, frequency response, timing, etc. may be analyzed to reflect the quality and efficiency performance of the controller in the production process. Through multi-dimensional evaluation, a controller performance index evaluation parameter set containing all key evaluation results is finally obtained.

[0058] The production performance index decision factor set is a set obtained after weight assignment, in which each element is a performance index with a weight, and this weight will be used as the basis for weighted correction. The hierarchical analysis method is used to analyze the influence of each index on the controller production process, and a corresponding weight value is assigned to each production performance index to determine a production performance index decision factor set. These factors will become the basis of the decision-making process.

[0059] Match each decision factor (performance indicator with weight) in the production performance indicator decision factor set with the controller performance indicator evaluation parameter set. For example, if there is a current stability indicator and its weight in the decision factor set, then find the corresponding current stability evaluation parameter in the controller performance indicator evaluation parameter set. Then, perform weighted calculations. Multiply each performance indicator evaluation parameter by its corresponding weight and adjust each indicator. Add all weighted results to obtain the controller production performance parameters. Weighted correction ensures that different performance indicators (such as quality, energy efficiency, etc.) are appropriately enlarged or reduced according to their importance during the comprehensive evaluation, and the final production performance parameters can accurately reflect the overall performance of the electric vehicle controller.

[0060] Further, such as Figure 3 As shown, step S6 of the embodiment of the present application includes: Step S61: performing production simulation fitting on the controller production strategy parameter space based on the controller production performance indicator set to construct a controller production performance simulation module.

[0061] Step S62: performing energy-saving optimization analysis on the controller production performance parameters based on the controller production strategy parameter space to obtain a production strategy parameter selection threshold.

[0062] Step S63: randomly selecting multiple production strategy parameters within the production strategy parameter selection threshold, and performing simulation evaluation on the multiple production strategy parameters through the controller production performance simulation module to obtain multiple parameter production performance prediction effects.

[0063] Step S64: Based on the production performance prediction effects of the multiple parameters, a global optimization is performed within the production strategy parameter selection threshold, and the target controller production strategy parameters are output.

[0064] Specifically, the controller production performance simulation module is a module used to simulate the performance of the controller production process through simulation tools and algorithms. This module can simulate based on different strategy parameters and predict the performance of the controller, including energy consumption, production efficiency and quality. By building a simulation model, various production strategy parameters are combined with the actual performance of the production process to predict the production effect and energy efficiency of the controller under different strategy configurations.

[0065] Based on the controller production strategy parameter space, energy-saving optimization analysis is performed in combination with the controller production performance parameters to analyze the impact of different production strategy parameters on energy-saving effects, while ensuring that these strategies can meet the production quality requirements of the controller. Through optimization analysis, a production strategy parameter selection threshold is obtained, that is, the strategy parameter range that can achieve energy-saving optimization while maintaining the controller production quality standards. This selection threshold will limit the subsequent selected production strategy parameters, thereby ensuring that energy-saving optimization will not reduce production quality.

[0066] Multiple production strategy parameters are randomly selected from the production strategy parameter selection threshold range, and these randomly selected production strategy parameters are input into the controller production performance simulation module one by one for simulation evaluation. By simulating different production environments and working conditions, the production performance indicators under these parameter configurations are predicted. These production performance indicators and corresponding production strategy parameters are integrated to obtain the prediction effect of multiple parameter production performance.

[0067] Based on the predicted effects of multiple production strategy parameters, a global optimization is performed. By searching in the parameter space, considering all possible production strategy parameters and analyzing their comprehensive impact on production performance, an optimal production strategy combination is found so that the controller's production process achieves the best energy-saving effect. This optimal production strategy combination is output as the target controller production strategy parameter and used in the actual production process. Through the controller production strategy optimization, the most suitable production strategy for energy saving can be found to maximize energy utilization efficiency while ensuring production quality and efficiency.

[0068] Further, step S64 includes: Step S641: Determine the center of the production strategy parameter cluster according to the production performance prediction effects of the multiple parameters.

[0069] Step S642: clustering the multiple production strategy parameters based on the production strategy parameter cluster center to generate a controller production strategy parameter cluster.

[0070] Step S643: Optimizing and updating the remaining production strategy parameters in the controller production strategy parameter cluster toward the center of the production strategy parameter cluster to obtain a controller production strategy optimization parameter cluster.

[0071] Step S644: Perform global optimization based on the controller production strategy parameter cluster and output the target controller production strategy parameters.

[0072] Specifically, the production strategy parameter cluster center refers to a central point or representative value determined among all production strategy parameters. This central point is the core of all production strategy parameter clusters and can represent the performance characteristics of all parameters in the cluster. The predicted effects of production performance of multiple parameters are used as input data and input into the cluster analysis algorithm. The algorithm will automatically determine the production strategy parameter cluster center based on the distribution of the data.

[0073] According to the determined production strategy parameter cluster center, multiple production strategy parameters are clustered. Parameter combinations with similar performance prediction results are divided into a cluster. The parameter combinations contained in the cluster have similar energy-saving benefits or performance indicators, which facilitates unified optimization of the strategies within the cluster.

[0074] The controller production strategy optimization parameter cluster is the production strategy parameter cluster that has been optimized and updated. The updated cluster is more focused on the parameter combination with excellent performance and has higher energy-saving efficiency or production performance. Based on the center of the production strategy parameter cluster determined previously, the remaining parameters in the cluster are optimized and updated. Through parameter adjustment, smoothing or weighted correction, all parameter combinations in the cluster are moved closer to the center of the cluster, improving the performance of the entire cluster, making the parameter configuration within the cluster more focused on the optimal production strategy, determining the controller production strategy optimization parameter cluster, and thus improving the overall energy-saving effect.

[0075] Based on the optimized and updated controller production strategy parameter cluster, a global optimization is performed. By comparing the performance of different parameter clusters in the entire parameter space, each parameter cluster is searched and evaluated to find a production strategy that can maximize the energy saving effect in the global scope, and the output is the target controller production strategy parameters.

[0076] Through the above clustering, optimization update and global optimization operations, multiple production strategy parameters are refined and optimized to find a set of production strategies with the best energy-saving effect, thereby maximizing the energy-saving effect.

[0077] Further, step S643 includes: Step S643-1: Analyze the optimization direction of the controller production strategy parameter cluster and set parameter variation rules and parameter crossover rules.

[0078] Step S643-2: Perform cross-mutation optimization on the controller production strategy parameter cluster based on the parameter variation rule and the parameter crossover rule to obtain the controller production strategy optimization parameter cluster.

[0079] Specifically, analyze the distribution of parameters in the controller production strategy parameter cluster and the current production performance. For example, if the production strategy parameters in a cluster perform poorly in terms of product quality but perform well in terms of energy consumption, then the optimization direction may be to adjust the parameter part related to product quality. Then, set the parameter variation rule according to the optimization direction. For example, if you decide to adjust a parameter related to product quality, such as the proportion of a certain component in the raw material ratio, then you can set the variation rule to randomly change the value of the ratio within a certain range (such as ±10%). Next, set the parameter crossover rule. The crossover method can be determined based on the structure and characteristics of the parameters. For example, if the production strategy parameter is in the form of a vector (such as containing multiple elements such as temperature, time, raw material ratio, etc.), you can set the crossover rule to randomly select two parameter vectors and exchange the values ​​of some of their middle elements.

[0080] Traverse each production strategy parameter in the controller production strategy parameter cluster. For each parameter, perform mutation operation according to the parameter mutation rule. Then, perform cross operation on the mutated parameters according to the parameter cross rule. Recombine all production strategy parameters after cross mutation optimization into a controller production strategy optimization parameter cluster. By performing optimization direction analysis, parameter mutation and cross operation on the controller production strategy parameter cluster, systematically adjust and reorganize the existing production strategy parameters to ensure the balance between energy-saving optimization and production quality. The final controller production strategy optimization parameter cluster can effectively improve the energy-saving effect and meet the quality requirements in the production process.

[0081] In summary, the controller production energy-saving optimization method using current monitoring provided in the embodiment of the present application has the following technical effects: By analyzing the application requirements of the electric vehicle controller production line, a controller current test parameter table is constructed, which provides standardized test parameters and methods for subsequent current testing and performance evaluation, ensuring the accuracy and effectiveness of the test. After the test parameter table is established, the current test signals of the controller are further collected through actual current test monitoring. These signals are key data for evaluating controller performance and subsequent optimization. The controller production performance target is obtained and the index is disassembled, and the abstract production performance target is concretized into a quantifiable controller production performance index set, which provides a clear goal and direction for subsequent performance evaluation and optimization. Based on the controller production performance index set, the controller current test signal set is evaluated in multiple dimensions, and multiple performance indicators are comprehensively considered to comprehensively evaluate the performance of the controller, so as to obtain more accurate controller production performance parameters. According to the electric vehicle controller production line, the controller production strategy parameter space is constructed, and the parameter range of the controller production strategy is determined, providing possible parameter selection and adjustment space for energy-saving optimization. The optimal production strategy parameters are obtained through energy-saving optimization analysis, and these parameters are used to control the production process of electric vehicle controllers to achieve energy-saving optimization.

[0082] In summary, this application achieves the coordinated improvement of energy-saving optimization and controller performance through accurate data collection based on current monitoring, multi-dimensional production performance evaluation and intelligent production strategy optimization. By constructing the controller production strategy parameter space and performing multi-level optimization based on this space, the energy utilization efficiency of the production process is significantly improved, unnecessary energy consumption is reduced, and the consistency of product quality and the long-term stability of the controller are improved, ultimately achieving the effect of continuous energy saving.

[0083] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A controller production energy saving optimization method using current monitoring, characterized in that: The method comprises: Obtain an electric vehicle controller production line, perform application demand analysis on the electric vehicle controller production line, and construct a controller current test parameter table; Using the controller current test parameter table to perform current test monitoring on a target electric vehicle controller to obtain a controller current test signal set; Obtaining a controller production performance target, decomposing the controller production performance target into indicators, and obtaining a controller production performance indicator set; Performing a multi-dimensional evaluation on the controller current test signal set based on the controller production performance indicator set to obtain controller production performance parameters; According to the electric vehicle controller production line, construct a controller production strategy parameter space; The controller production performance parameters are analyzed for energy-saving optimization based on the controller production strategy parameter space to obtain target controller production strategy parameters, and the electric vehicle controller is controlled for energy-saving optimization production through the target controller production strategy parameters.

2. The controller production energy saving optimization method using current monitoring as claimed in claim 1, characterized in that: The controller current test parameter table is constructed, including: Performing working condition analysis on the application scenario information of the electric vehicle controller production line to obtain a controller application working condition set; Performing simulated current analysis on the controller application condition set respectively to obtain a controller condition simulated current change set; Extracting monitoring indicators from the electric vehicle controller production line to obtain a controller current monitoring indicator set; Based on the controller working condition simulation current change set and the controller current monitoring indicator set, parameter design combination is performed to construct the controller current test parameter table.

3. The controller production energy saving optimization method using current monitoring as claimed in claim 1, characterized in that: The obtaining of the controller production performance indicator set comprises: Decomposing the controller production performance target by attributes to obtain the controller production performance attributes, wherein the controller production performance attributes include production quality attributes and production energy consumption attributes; Extracting related indicators based on the production quality attributes and production energy consumption attributes, and constructing a generalized related production quality indicator set and a generalized related production energy consumption indicator set; Quantifying the correlation of each indicator information in the generalized correlation production quality indicator set and the generalized correlation production energy consumption indicator set to obtain a production quality indicator correlation degree set and a production energy consumption indicator correlation degree set; A preset correlation threshold is set, and the indicators of the production quality indicator correlation set and the production energy consumption indicator correlation set within the preset correlation threshold are screened and integrated to obtain the controller production performance indicator set.

4. The controller production energy saving optimization method using current monitoring as claimed in claim 1, characterized in that: The controller production performance parameters are obtained, including: Performing noise characteristic and filtering requirement analysis on the controller current test signal set to obtain signal noise characteristic information and filtering requirement information; Perform filter characteristic parameter analysis based on the signal noise characteristic information and filtering requirement information to determine a current signal digital filter; Using the current signal digital filter to perform filtering preprocessing on the controller current test signal set to obtain a standard controller current test signal set; Based on the controller production performance indicator set, the standard controller current test signal set is associated with a multi-dimensional evaluation to obtain the controller production performance parameters.

5. The controller production energy saving optimization method using current monitoring as claimed in claim 4 is characterized in that: The obtaining of the controller production performance parameters includes: Based on the controller production performance indicator set, the standard controller current test signal set is correlated and shunted to obtain a controller indicator correlation test signal set; Using the controller production performance indicator set to perform multi-dimensional evaluation on the controller indicator associated test signal set to obtain a controller performance indicator evaluation parameter set; Allocating weights to the various performance indicators in the controller production performance indicator set to determine a production performance indicator decision factor set; The controller performance indicator evaluation parameter set is weightedly modified based on the production performance indicator decision factor set to obtain the controller production performance parameter.

6. The controller production energy saving optimization method using current monitoring as claimed in claim 1, characterized in that: The obtaining of the target controller production strategy parameters includes: Performing production simulation fitting on the controller production strategy parameter space based on the controller production performance indicator set to construct a controller production performance simulation module; Performing energy-saving optimization analysis on the production performance parameters of the controller based on the production strategy parameter space of the controller to obtain a production strategy parameter selection threshold; Randomly selecting multiple production strategy parameters within the production strategy parameter selection threshold, and performing simulation evaluation on the multiple production strategy parameters through the controller production performance simulation module to obtain multiple parameter production performance prediction effects; Based on the production performance prediction effects of the multiple parameters, a global optimization is performed within the production strategy parameter selection threshold, and the target controller production strategy parameters are output.

7. The controller production energy saving optimization method using current monitoring as claimed in claim 6, characterized in that: The output target controller produces strategy parameters, including: Determine the center of the production strategy parameter cluster according to the production performance prediction effects of the multiple parameters; Clustering the multiple production strategy parameters based on the production strategy parameter cluster center to generate a controller production strategy parameter cluster; Optimizing and updating the remaining production strategy parameters in the controller production strategy parameter cluster toward the center of the production strategy parameter cluster to obtain a controller production strategy optimization parameter cluster; A global optimization is performed based on the controller production strategy parameter cluster to output the target controller production strategy parameters.

8. The controller production energy saving optimization method using current monitoring as claimed in claim 7, characterized in that: The step of obtaining a controller production strategy optimization parameter cluster includes: Analyze the optimization direction of the controller production strategy parameter cluster, and set parameter variation rules and parameter crossover rules; Based on the parameter variation rule and the parameter crossover rule, the controller production strategy parameter cluster is cross-varied and optimized to obtain the controller production strategy optimization parameter cluster.

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