Energy saving optimization method for controller production using current monitoring

Abstract

The application provides an energy-saving optimization method for controller production by current monitoring, relates to the technical field of controller production, and comprises the following steps: analyzing the application requirements of an electric vehicle controller production line, and constructing a controller current test parameter table; adopting the controller current test parameter table to perform current test monitoring on the controller, and obtaining a controller current test signal set; obtaining a controller production performance index set, performing multi-dimensional evaluation on the controller current test signal set, and obtaining a controller production performance parameter; constructing a controller production strategy parameter space to perform energy-saving optimization analysis on the controller production performance parameter, and obtaining a target controller production strategy parameter. The application solves the technical problem that the existing energy-saving method focuses on local optimization, lacks an optimization strategy from the global perspective, and thus the energy-saving potential is not fully tapped, and the overall energy utilization efficiency is affected, improves the energy utilization efficiency of the electric vehicle controller production process under the premise of ensuring the production quality.

Description

TECHNICAL FIELD

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

[0002] The controller is an important component of an electric vehicle, responsible for regulating and controlling the flow of current between the battery and the electric motor, ensuring the normal operation and performance of the vehicle. Currently, the production optimization of electric vehicle controllers mainly focuses on the following aspects: first, focusing on improving the energy utilization rate of production equipment, reducing energy consumption by upgrading and improving equipment efficiency; second, optimizing production processes by improving production processes, reducing unnecessary energy consumption and waste, thereby improving production efficiency. However, these methods mostly focus on local optimization, often lacking systematic optimization of overall production strategies. Such local optimization often fails to fully optimize energy-saving potential for each link in the production process, failing to maximize energy-saving potential. SUMMARY

[0003] The application provides a controller production energy-saving optimization method using current monitoring, which solves the technical problem that existing energy-saving methods focus on local optimization and lack overall optimization strategies, resulting in incomplete tapping of energy-saving potential and affecting overall energy utilization efficiency improvement. The technical effect of improving the energy utilization efficiency of the electric vehicle controller production process under the premise of ensuring production quality is achieved.

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

[0005] One or more technical solutions provided in the application have at least the following technical effects or advantages:

[0006] By analyzing the application requirements of the electric vehicle controller production line, a controller current test parameter table was constructed, providing standardized test parameters and methods for subsequent current testing and performance evaluation, ensuring the accuracy and effectiveness of the tests. After establishing the test parameter table, actual current testing was conducted to collect controller current test signals, which are key data for evaluating controller performance and subsequent optimization. The controller production performance targets were obtained and their indicators were decomposed, concretizing the abstract production performance targets into a set of quantifiable controller production performance indicators, providing clear goals and directions for subsequent performance evaluation and optimization. Based on the controller production performance indicator set, the controller current test signal set was evaluated in a multi-dimensional manner, comprehensively considering multiple performance indicators to conduct a comprehensive evaluation of the controller's performance and obtain more accurate controller production performance parameters. According to the electric vehicle controller production line, a controller production strategy parameter space was constructed, determining the parameter range of the controller production strategy, providing possible parameter selection and adjustment space for energy-saving optimization. The optimal production strategy parameters were obtained through energy-saving optimization analysis, and these parameters were applied to control the production process of the electric vehicle controller to achieve energy-saving optimization.

[0007] In summary, this application achieves coordinated improvement in energy saving and controller performance through precise data collection based on current monitoring, multi-dimensional production performance evaluation, and intelligent production strategy optimization. By constructing a 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 product quality consistency and long-term controller stability are enhanced, ultimately achieving continuous energy saving.

[0008] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0009] Figure 1 This is a schematic flowchart of an energy-saving optimization method for controller production using current monitoring, provided in an embodiment of this application.

[0010] Figure 2 This is a schematic diagram of the process for obtaining controller production performance parameters in the controller production energy-saving optimization method using current monitoring provided in the embodiments of this application.

[0011] Figure 3 This is a schematic diagram of the process for obtaining the target controller production strategy parameters in the controller production energy-saving optimization method using current monitoring provided in the embodiments of this application. Detailed Implementation

[0012] This application provides a method for energy-saving optimization of controller production using current monitoring. It constructs a controller current test parameter table, performs current testing and monitoring on the electric vehicle controller to obtain a current test signal set, and combines this with multi-dimensional evaluation of the controller's production performance targets to obtain controller production performance parameters. Furthermore, based on the controller's production strategy parameter space, it performs energy-saving optimization analysis on the production performance parameters to obtain target controller production strategy parameters, thereby achieving energy-saving optimized production control of the electric vehicle controller.

[0013] like Figure 1 As shown in the embodiment of this application, an energy-saving optimization method for production using a controller with current monitoring is provided. The method includes:

[0014] Step S1: Obtain the electric vehicle controller production line, analyze the application requirements of the electric vehicle controller production line, and construct a controller current test parameter table.

[0015] Specifically, an electric vehicle controller production line refers to automated or semi-automated production facilities and equipment used to produce electric vehicle controllers, encompassing all processes and equipment from raw material input to product completion. The controller current test parameter table is a set of parameters used to guide the current testing of electric vehicle controllers, defining various standards and objectives for current testing, such as rated current values, current fluctuation range, and maximum allowable current under different operating conditions (e.g., starting, acceleration, constant speed, deceleration).

[0016] The control system of the interactive electric vehicle controller production line acquires basic information such as the production capacity of the production line, the type of controller produced, and the types of electric vehicles applicable. Based on this basic information, it conducts a detailed analysis of the production line's work requirements, target performance, production process, and technical requirements to determine the key parameters and technical standards for each link. It also creates a controller current test parameter table, which details the standards required for current testing, the specific measurement points, and the range of each parameter.

[0017] The requirements analysis and the construction of the current test parameter table laid the foundation for subsequent testing and optimization, ensuring that the current test is targeted, accurate and comprehensive.

[0018] Step S2: Use the controller current test parameter table to perform current test monitoring on the target electric vehicle controller to obtain the controller current test signal set.

[0019] Specifically, the target electric vehicle controller refers to a specific controller selected as the 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.

[0020] The target electric vehicle controller is subjected to specific current tests using the current test parameter table constructed in step S1. These tests are performed under different operating states of the controller, such as startup, operation, and standby, to collect data on current changes. The controller's current data is monitored in real time using current testing equipment, forming a complete set of current test signals. For example, if the electric vehicle controller requires a large instantaneous current during startup, while the current remains at a low level during standby, these changes will be recorded and stored by the production line control system. Current testing monitoring provides accurate current signals, offering data support for subsequent performance evaluation and energy-saving optimization.

[0021] Step S3: Obtain the controller production performance target, decompose the controller production performance target into indicators, and obtain the controller production performance indicator set.

[0022] Specifically, controller production performance targets refer to the various performance requirements that electric vehicle controllers should achieve during the production process, such as output power, energy efficiency, and stability. The controller production performance index set is a collection of specific indicators related to controller production performance obtained after index decomposition. This includes indicators related to controller quality, such as power conversion efficiency, operating time at different temperatures, and failure rate under different loads, as well as indicators related to controller production energy consumption, such as energy consumption and raw material consumption at different production stages.

[0023] Pre-defined controller production performance targets are obtained from the controller production line control system. These performance targets are then broken down to generate multiple specific and actionable performance indicators, constructing a controller production performance indicator set. This indicator decomposition transforms high-level performance requirements into measurable metrics, ensuring that subsequent evaluations accurately reflect the performance of the production process.

[0024] Step S4: Perform a multi-dimensional evaluation of the controller current test signal set based on the controller production performance index set to obtain the controller production performance parameters.

[0025] Specifically, controller production performance parameters are the final parameters of controller production performance obtained through multi-dimensional evaluation. These parameters can be a comprehensive score, a performance level, or a set of specific performance values. The evaluation dimensions are determined based on the controller production performance index set, for example, evaluating power conversion efficiency, temperature stability, current stability, and power consumption. Then, data analysis is performed on the controller current test signal set from multiple evaluation dimensions. By comparing the analysis results of the controller current test signal set with the controller production performance index set, a set of specific production performance parameters is obtained. These parameters accurately reflect the various performance characteristics of the electric vehicle controller and provide a basis for subsequent energy-saving optimization. Multi-dimensional evaluation helps to comprehensively understand the production performance of the electric vehicle controller from multiple perspectives and provides accurate parameters for optimization.

[0026] Step S5: Based on the electric vehicle controller production line, construct the controller production strategy parameter space.

[0027] Specifically, the controller production strategy parameter space refers to the set of all possible strategy parameters during the production of electric vehicle controllers, including process parameters, production methods, and equipment parameters. The interactive controller production line obtains the parameter range of all adjustable parameters during production, including but not limited to the control parameter range of production equipment, production speed, and operating environment. The construction of the controller production strategy parameter space provides a theoretical framework and optimization space for subsequent energy-saving optimizations, ensuring that subsequent optimizations do not exceed the operational range of the production line.

[0028] Step S6: Based on the controller production strategy parameter space, perform energy-saving optimization analysis on the controller production performance parameters to obtain the target controller production strategy parameters, and use the target controller production strategy parameters to perform energy-saving optimization production control on the electric vehicle controller.

[0029] Specifically, the target controller production strategy parameters are determined after energy-saving optimization analysis to achieve energy-saving optimization. Energy-saving optimization analysis is performed based on the controller's production performance parameters within the controller's production strategy parameter space. Simulation software can be used to simulate the production process and energy-saving effects under different production strategy parameters. For example, in the simulation, parameters such as welding temperature and welding time of the welding equipment can be changed, and the impact on the controller's production performance parameters and energy-saving effects can be observed. Then, through analysis, the production strategy parameters with the best energy-saving effect are found, i.e., the target controller production strategy parameters. Applying the target controller production strategy parameters to the actual electric vehicle controller production process, the production line's automated control system controls the production equipment to operate according to the target parameters, achieving energy-saving optimized production management. By optimizing the production strategy parameters, the energy-saving target and the production target are balanced, achieving efficient energy-saving optimized production management.

[0030] Furthermore, step S1 in the embodiments of this application includes:

[0031] Step S11: Perform working condition analysis on the application scenario information of the electric vehicle controller production line to obtain a set of controller application working conditions.

[0032] Step S12: Perform simulated current analysis on the controller application condition set respectively to obtain the controller operating condition simulated current change set.

[0033] Step S13: Extract monitoring indicators from the electric vehicle controller production line to obtain a set of controller current monitoring indicators.

[0034] Step S14: Based on the controller operating condition simulation current change set and the controller current monitoring index set, perform parameter design and combination to construct the controller current test parameter table.

[0035] Specifically, application scenario information refers to the various environmental conditions and operating states of electric vehicle controllers in actual applications. Different driving modes (such as city driving and highway driving) and external environments (such as temperature and humidity) will affect the controller's performance. Application scenario information from the electric vehicle controller production line is collected, and this information is analyzed to determine the different operating environments the controller may face, such as driving roads, ambient temperature, humidity, and driving speed. This information is then compiled and summarized to generate a controller application condition set. Each set of data in the set corresponds to one application scenario.

[0036] The controller operating condition simulated current variation set is obtained by performing simulated current analysis on a set of controller application operating conditions, representing a collection of controller current variations under different operating conditions. For each operating condition in the controller application condition set, a corresponding circuit model is built using simulation software (such as MATLAB, Simulink, PSIM, etc.). Based on the working principle of the electric vehicle controller, the current changes of the controller under different operating conditions are simulated. For example, in a high-temperature environment, the controller may experience a slight increase in current due to the rise in temperature; in acceleration mode, the controller may experience a large current pulse. Through simulation analysis, the current changes of the controller under all operating conditions can be obtained, and this data is used as the operating condition simulated current variation set. Simulated current analysis provides expected data for subsequent current monitoring and optimization, avoiding the high cost and uncertainty of direct measurement.

[0037] Current monitoring metrics are key indicators used to monitor controller performance. Multiple metrics related to controller current monitoring, extracted after monitoring, form a controller current monitoring metric set. These metrics are extracted from actual operating data of the electric vehicle controller production line. These metrics help analyze the controller's operating status during production. These metrics include, but are not limited to, the maximum, minimum, average, and fluctuation range of the current; they are key parameters for evaluating controller performance. By extracting these metrics, effective monitoring of controller performance during production can be ensured, allowing for timely detection and resolution of potential problems.

[0038] Combining the simulated current change data obtained in step S12 and the current monitoring indicators extracted in step S13, the specific values ​​of each current indicator under different operating conditions are determined based on the current changes under different operating conditions. The simulated current change data reflects the current changes of the electric vehicle controller under ideal conditions. Data analysis software is used to analyze and calculate the monitoring indicators of the simulated current change data, obtaining the current monitoring indicator range for normal controller operation under each operating condition. Based on these current monitoring indicator ranges, a current test parameter table is constructed to adapt to different operating conditions and production monitoring needs, providing a standardized test framework for actual current testing and ensuring that the test results effectively reflect the controller's performance. For example, in the simulation, if the controller's current fluctuates significantly under acceleration conditions, the corresponding test cycle and current range are set according to the fluctuation patterns in the simulated test tree, thereby capturing this fluctuation in actual monitoring.

[0039] Furthermore, step S3 in this embodiment includes:

[0040] Step S31: Decompose the controller production performance target into attributes to obtain controller production performance attributes, which include production quality attributes and production energy consumption attributes.

[0041] Step S32: Based on the production quality attribute and production energy consumption attribute, extract related indicators to construct a generalized set of related production quality indicators and a generalized set of related production energy consumption indicators.

[0042] Step S33: Quantify 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 the correlation degree set of production quality indicators and the correlation degree set of production energy consumption indicators.

[0043] Step S34: Set a preset correlation threshold, and filter and integrate the production quality index correlation set and the production energy consumption index correlation set that are within the preset correlation threshold to obtain the controller production performance index set.

[0044] Specifically, the production performance targets of the controller are broken down into 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. Production energy consumption attributes involve the amount of energy consumed during production, including electricity consumption and energy consumption per unit of product. By breaking down these attributes, these macro-level targets can be transformed into specific, actionable production standards, providing guidance for subsequent indicator design and evaluation.

[0045] The generalized relational set of production quality indicators refers to a set of indicators extracted from production quality attributes that can reflect the overall production quality status, such as product qualification rate and rework rate. The generalized relational set of production energy consumption indicators refers to a set of indicators extracted from production energy consumption attributes that can reflect the overall energy efficiency status, such as unit production energy consumption and total production line energy consumption. For the decomposed production quality and production energy consumption attributes, it is necessary to further extract specific indicators that reflect these two attributes. Data mining algorithms are used to analyze the identified production quality and production energy consumption attributes, extract specific indicators associated with these two attributes, and construct the generalized relational set of production quality and production energy consumption indicators.

[0046] The production quality indicator correlation set is a collection obtained by quantifying the correlation of indicators in the generalized correlation production quality indicator set, where each element represents the correlation value of the corresponding indicator. Similarly, the production energy consumption indicator correlation set is a collection obtained by quantifying the correlation of indicators in the generalized correlation production energy consumption indicator set, containing the correlation values ​​of each energy consumption indicator. Data analysis is performed on each indicator in both the generalized correlation production quality indicator set and the generalized correlation production energy consumption indicator set to calculate the correlation degree between each indicator in each set. Correlation quantification can employ statistical analysis methods, such as correlation coefficient analysis. For example, two indicators can be extracted from the generalized correlation production quality indicator set, and the correlation coefficient between these two indicators can be calculated by collecting historical production data, thus quantifying the correlation degree. This method is then used to quantify the correlation of each indicator in both the generalized correlation production quality indicator set and the generalized correlation production energy consumption indicator set, respectively, and the quantified correlation values ​​are then organized into the production quality indicator correlation set and the production energy consumption indicator correlation set.

[0047] A predefined correlation threshold is used to filter out indicators with strong relationships and significant impact on overall production performance. For example, based on historical production experience, the correlation threshold is set to 0.6; indicators with a correlation of 0.6 or higher are considered strongly correlated. Indicators in the correlation sets of production quality indicators and production energy consumption indicators are then filtered to select those whose correlation values ​​meet the predefined correlation threshold. The filtered production quality indicators and production energy consumption indicators are then integrated to form the controller's production performance indicator set. This filtering and integration ensures that the final production performance indicator set includes the most influential and relevant indicators, thus providing accurate guidance for subsequent energy-saving optimization and quality improvement.

[0048] Furthermore, such as Figure 2 As shown, step S4 in this embodiment includes:

[0049] Step S41: Analyze the noise characteristics and filtering requirements of the controller current test signal set to obtain signal noise characteristic information and filtering requirement information.

[0050] Step S42: Based on the signal noise characteristics information and filtering requirements information, perform filter characteristic parameter analysis to determine the digital filter for the current signal.

[0051] Step S43: Use 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.

[0052] Step S44: Based on the controller production performance index set, perform a correlation multidimensional evaluation on the standard controller current test signal set to obtain the controller production performance parameters.

[0053] Specifically, noise characteristics refer to the properties of interference components present in the controller current test signal, such as the frequency range, amplitude, and type of noise. Filtering requirements refer to the specific requirements of the filtering operations needed to remove noise from the signal, including the frequency range of noise to be filtered out and the required accuracy of the filtered signal. Detailed noise characteristics and filtering requirements analysis of the controller current test signal set is performed using signal analysis software (such as MATLAB). This process identifies noise components in the signal, assesses the impact of this noise on signal quality, and determines the required filter characteristics to meet specific signal processing objectives. It generates signal noise characteristic information and filtering requirement information, providing necessary information for subsequent filter design and signal preprocessing, ensuring that the filtered signal accurately reflects the controller's performance.

[0054] A digital current signal filter is a filter that digitally processes current signals to remove noise from current test signals. Based on signal noise characteristics and filtering requirements, the filter's characteristic parameters are analyzed to determine parameters such as cutoff frequency and passband gain. A suitable digital filter type is then selected based on these parameters. For example, a finite impulse response (FIR) filter can be chosen if linear phase characteristics are required; an infinite impulse response (IOR) filter can be chosen if filtering efficiency and lower computational complexity are more important.

[0055] A standard controller current test signal set refers to a set of current test signals that has undergone filtering preprocessing. The controller current test signal set is input into a pre-defined digital filter for current signals. The digital filter processes the input signal according to its set characteristic parameters, removing noise components. Finally, the standard controller current test signal set after filtering preprocessing is obtained. These signals are clearer and more stable, with unnecessary interference 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 signal waveform becomes smoother, eliminating fluctuations caused by the external environment. Through filtering, noise and irrelevant signal components in the controller current test signal set can be removed, improving signal quality and making subsequent performance evaluations more accurate.

[0056] Based on a filtered set of standard controller current test signals, the current signals are evaluated from multiple dimensions (such as signal stability, production efficiency, and energy efficiency) to analyze whether they meet production performance targets. 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, thus providing effective data support for subsequent optimization.

[0057] Furthermore, step S44 includes:

[0058] Step S441: Based on the controller production performance index set, perform correlation and current splitting on the standard controller current test signal set to obtain the controller index correlation test signal set.

[0059] Step S442: Use the controller production performance index set to perform multi-dimensional evaluation on the controller index associated test signal set to obtain the controller performance index evaluation parameter set.

[0060] Step S443: Assign weights to each performance index in the controller production performance index set to determine the production performance index decision factor set.

[0061] Step S444: Based on the set of production performance index decision factors, the set of controller performance index evaluation parameters is weighted and corrected to obtain the controller production performance parameters.

[0062] Specifically, the controller performance indicator-related test signal set is a set of current test signals after correlation and current splitting. Each subset corresponds to a specific production performance target or operating condition, facilitating subsequent detailed analysis. The relationship between the controller production performance indicator set and the standard controller current test signal set is analyzed. For example, if the controller production performance indicator set includes indicators related to current stability, then related current fluctuation data and other signals are found in the standard controller current test signal set. Then, the standard controller current test signal set is classified according to these relationships, categorizing signals into different subsets based on different correlation relationships. Finally, these classified signal subsets are combined to form the controller performance indicator-related test signal set.

[0063] The controller performance evaluation parameter set is a set of parameters obtained through multi-dimensional evaluation. These parameters reflect the evaluation results of the controller in various performance indicators. Based on the shunted controller indicator-correlated test signal set, combined with the controller production performance indicator set, a multi-dimensional evaluation is performed on each shunted 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 operating efficiency from multiple dimensions. For example, in the evaluation of current signals, the signal fluctuation amplitude, frequency response, and timing may be analyzed to reflect the quality and efficiency performance of the controller during production. Through multi-dimensional evaluation, a controller performance evaluation parameter set containing all key evaluation results is finally obtained.

[0064] The set of decision factors for production performance indicators is a set obtained after weighting, where each element is a performance indicator with a weight, which serves as the basis for weighted adjustments. The Analytic Hierarchy Process (AHP) is used to analyze the impact of each indicator on the controller's production process, assigning a corresponding weight value to each production performance indicator, and determining a set of decision factors for production performance indicators. These factors will form the basis of the decision-making process.

[0065] Each decision factor (a weighted performance indicator) in the set of production performance indicator decision factors is mapped to a set of controller performance indicator evaluation parameters. For example, if the decision factor set includes a current stability indicator and its weight, then the corresponding current stability evaluation parameter is found in the controller performance indicator evaluation parameter set. Then, a weighted calculation is performed. Each performance indicator evaluation parameter is multiplied by its corresponding weight to adjust each indicator. All weighted results are summed to obtain the controller's production performance parameters. This weighted adjustment ensures that different performance indicators (such as quality and energy efficiency) are appropriately amplified or reduced according to their importance during comprehensive evaluation, and the final production performance parameters accurately reflect the overall performance of the electric vehicle controller.

[0066] Furthermore, such as Figure 3 As shown, step S6 in this embodiment includes:

[0067] Step S61: Based on the controller production performance index set, perform production simulation fitting on the controller production strategy parameter space to construct a controller production performance simulation module.

[0068] Step S62: Based on the controller production strategy parameter space, perform energy-saving optimization analysis on the controller production performance parameters to obtain the production strategy parameter selection threshold.

[0069] Step S63: Randomly select multiple production strategy parameters within the selected production strategy parameter threshold, and use the controller production performance simulation module to simulate and evaluate the multiple production strategy parameters to obtain the predicted production performance of the multiple parameters.

[0070] Step S64: Based on the production performance prediction effect of the multiple parameters, perform global optimization within the selected threshold of the production strategy parameters, and output the target controller production strategy parameters.

[0071] Specifically, the controller production performance simulation module is a module built using simulation tools and algorithms to simulate the performance of the controller in the production process. This module can simulate based on different strategy parameters and predict the controller's performance, including energy consumption, production efficiency, and quality. By constructing simulation models and combining various production strategy parameters with the actual performance of the production process, it predicts the controller's production effects and energy efficiency under different strategy configurations.

[0072] Based on the controller's production strategy parameter space and combined with the controller's production performance parameters, energy-saving optimization analysis is performed to analyze the impact of different production strategy parameters on energy-saving effects, while ensuring that these strategies can meet the controller's production quality requirements. Through optimization analysis, a threshold for selecting production strategy parameters is obtained, which represents the range of strategy parameters that can achieve energy-saving optimization while maintaining the controller's production quality standards. This threshold will limit the subsequent selection of production strategy parameters, thereby ensuring that energy-saving optimization does not reduce production quality.

[0073] Multiple production strategy parameters are randomly selected from the threshold range of production strategy parameters. These randomly selected parameters are then input one by one into the controller's production performance simulation module for simulation evaluation. By simulating different production environments and operating conditions, the production performance indicators under these parameter configurations are predicted. These production performance indicators and their corresponding production strategy parameters are then integrated to obtain the predicted production performance effects for multiple parameters.

[0074] Based on the predicted effects of multiple production strategy parameters, global optimization is performed. By searching the parameter space, considering all possible production strategy parameters and analyzing their comprehensive impact on production performance, an optimal combination of production strategies is found, enabling the controller's production process to achieve the best energy-saving effect. This optimal combination of production strategies is output as the target controller's production strategy parameters and used in the actual production process. Through controller production strategy optimization, the most suitable energy-saving production strategy can be found, maximizing energy utilization efficiency while ensuring production quality and efficiency.

[0075] Furthermore, step S64 includes:

[0076] Step S641: Determine the center of the production strategy parameter cluster based on the production performance prediction effect of the multiple parameters.

[0077] Step S642: Cluster the multiple production strategy parameters based on the production strategy parameter cluster center to generate a controller production strategy parameter cluster.

[0078] Step S643: Optimize and update the remaining production strategy parameters in the controller production strategy parameter cluster to the production strategy parameter cluster center to obtain the controller production strategy optimization parameter cluster.

[0079] Step S644: Perform global optimization based on the controller production strategy parameter set, and output the target controller production strategy parameters.

[0080] Specifically, the center of a production strategy parameter cluster refers to a central point or representative value 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 within that cluster. By inputting the production performance prediction effects of multiple parameters as input data into a clustering analysis algorithm, the algorithm automatically determines the center of the production strategy parameter cluster based on the data distribution.

[0081] Based on the identified cluster centers of production strategy parameters, multiple production strategy parameters are clustered. Parameter combinations with similar performance prediction results are grouped into one cluster. The parameter combinations within a cluster have similar energy-saving benefits or performance indicators, thus facilitating unified optimization of the strategies within the cluster.

[0082] The controller production strategy optimization parameter cluster is an optimized and updated production strategy parameter cluster. The updated cluster is more focused on high-performance parameter combinations, resulting in higher energy efficiency or production performance. Based on the previously determined production strategy parameter cluster center, the remaining parameters in the cluster are optimized and updated. Through parameter adjustment, smoothing, or weighted correction, all parameter combinations in the cluster are made to converge towards the cluster center, improving the overall performance of the cluster. This makes the parameter configuration within the cluster more focused on the optimal production strategy, thus determining the controller production strategy optimization parameter cluster and improving the overall energy-saving effect.

[0083] Based on the optimized and updated controller production strategy parameter set, global optimization is performed. By comparing the performance of different parameter sets in the entire parameter space, each parameter set is searched and evaluated to find a production strategy that can maximize energy saving effect in the global scope. The output is the target controller production strategy parameter.

[0084] Through the above clustering, optimization and update, and global optimization operations, multiple production strategy parameters were refined and a set of production strategies with optimal energy-saving effect was found, thereby maximizing the energy-saving effect.

[0085] Furthermore, step S643 includes:

[0086] Step S643-1: Perform optimization direction analysis on the controller production strategy parameter cluster, and set parameter variation rules and parameter crossover rules.

[0087] Step S643-2: Based on the parameter mutation rules and parameter crossover rules, perform cross-mutation optimization on the controller production strategy parameter cluster to obtain the controller production strategy optimized parameter cluster.

[0088] Specifically, the analysis involves distributing parameters within the controller's production strategy parameter cluster and assessing current production performance. For instance, if a parameter cluster performs poorly in terms of product quality but well in terms of energy consumption, the optimization direction might be adjusting the parameters related to product quality. Then, parameter variation rules are set based on the optimization direction. For example, if it's determined that a product quality-related parameter, such as the proportion of a certain component in the raw material ratio, needs adjustment, the variation rule can be set to randomly change the value of that proportion within a certain range (e.g., ±10%). Next, parameter crossover rules are set. The crossover method can be determined based on the parameter's structure and characteristics. For example, if the production strategy parameter is a vector (containing multiple elements such as temperature, time, and raw material ratio), the crossover rule can be set to randomly select two parameter vectors and swap some of their middle elements.

[0089] The process iterates through each production strategy parameter in the controller's production strategy parameter family. For each parameter, a mutation operation is performed according to the parameter mutation rules. Then, a cross operation is performed on the mutated parameters according to the parameter cross operation rules. All production strategy parameters optimized through cross mutation are recombined into the controller's optimized production strategy parameter family. By performing optimization direction analysis, parameter mutation, and cross operation on the controller's production strategy parameter family, the existing production strategy parameters are systematically adjusted and reorganized, ensuring a balance between energy-saving optimization and production quality. The final optimized controller production strategy parameter family effectively improves energy-saving performance and meets the quality requirements of the production process.

[0090] In summary, the energy-saving optimization method for controller production using current monitoring provided in this application has the following technical effects:

[0091] By analyzing the application requirements of the electric vehicle controller production line, a controller current test parameter table was constructed, providing standardized test parameters and methods for subsequent current testing and performance evaluation, ensuring the accuracy and effectiveness of the tests. After establishing the test parameter table, actual current testing was conducted to collect controller current test signals, which are key data for evaluating controller performance and subsequent optimization. The controller production performance targets were obtained and their indicators were decomposed, concretizing the abstract production performance targets into a set of quantifiable controller production performance indicators, providing clear goals and directions for subsequent performance evaluation and optimization. Based on the controller production performance indicator set, the controller current test signal set was evaluated in a multi-dimensional manner, comprehensively considering multiple performance indicators to conduct a comprehensive evaluation of the controller's performance and obtain more accurate controller production performance parameters. According to the electric vehicle controller production line, a controller production strategy parameter space was constructed, determining the parameter range of the controller production strategy, providing possible parameter selection and adjustment space for energy-saving optimization. The optimal production strategy parameters were obtained through energy-saving optimization analysis, and these parameters were applied to control the production process of the electric vehicle controller to achieve energy-saving optimization.

[0092] In summary, this application achieves coordinated improvement in energy saving and controller performance through precise data collection based on current monitoring, multi-dimensional production performance evaluation, and intelligent production strategy optimization. By constructing a 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 product quality consistency and long-term controller stability are enhanced, ultimately achieving continuous energy saving.

[0093] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily 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 this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing energy production using a controller based on current monitoring, characterized in that, The method includes: Obtain the electric vehicle controller production line, analyze the application requirements of the electric vehicle controller production line, and construct a controller current test parameter table. The current test parameter table of the controller is used to perform current test monitoring on the target electric vehicle controller to obtain the controller current test signal set; Obtain the controller production performance target, and decompose the controller production performance target into indicators to obtain the controller production performance indicator set. Based on the controller production performance index set, the controller current test signal set is evaluated in multiple dimensions to obtain the controller production performance parameters. Based on the electric vehicle controller production line, construct the controller production strategy parameter space; Based on the controller production strategy parameter space, the controller production performance parameters are analyzed for energy-saving optimization to obtain the target controller production strategy parameters, and the electric vehicle controller is controlled for energy-saving optimization production using the target controller production strategy parameters.

2. The energy-saving optimization method for controllers using current monitoring as described in claim 1, characterized in that, The constructed controller current test parameter table includes: The application scenario information of the electric vehicle controller production line is analyzed to obtain a set of controller application conditions. Simulated current analysis was performed on the controller application condition set to obtain the controller operating condition simulated current change set; The monitoring indicators of the electric vehicle controller production line are extracted to obtain a set of controller current monitoring indicators; Based on the set of simulated current changes under the controller operating conditions and the set of controller current monitoring indicators, the parameters are designed and combined to construct the controller current test parameter table.

3. The energy-saving optimization method for controllers using current monitoring as described in claim 1, characterized in that, The obtained controller production performance index set includes: The controller production performance target is decomposed into attributes to obtain controller production performance attributes, which include production quality attributes and production energy consumption attributes. Based on the aforementioned production quality attributes and production energy consumption attributes, correlation indicators are extracted to construct a generalized correlation production quality indicator set and a generalized correlation production energy consumption indicator set. The correlation metric of each indicator in the generalized correlation production quality indicator set and the generalized correlation production energy consumption indicator set is quantified to obtain the correlation degree set of production quality indicators and the correlation degree set of production energy consumption indicators. A preset correlation threshold is set, and the indicators within the preset correlation threshold of the production quality indicator correlation set and the production energy consumption indicator correlation set are screened and integrated to obtain the controller production performance indicator set.

4. The energy-saving optimization method for controller production using current monitoring as described in claim 1, characterized in that, The obtained controller production performance parameters include: Noise characteristics and filtering requirements are analyzed on the controller current test signal set to obtain signal noise characteristic information and filtering requirement information; Based on the signal noise characteristics and filtering requirements, filter characteristic parameters are analyzed to determine the digital filter for the current signal. The controller current test signal set is preprocessed by the current signal digital filter to obtain a standard controller current test signal set. Based on the controller production performance index set, the standard controller current test signal set is correlated and multidimensionally evaluated to obtain the controller production performance parameters.

5. The energy-saving optimization method for controller production using current monitoring as described in claim 4, characterized in that, The process of obtaining the controller's production performance parameters includes: Based on the controller production performance index set, the standard controller current test signal set is correlated and split to obtain the controller index correlated test signal set; The controller performance index set is used to perform a multi-dimensional evaluation of the controller index-correlated test signal set to obtain the controller performance index evaluation parameter set. Weights are assigned to each performance index in the controller's production performance index set to determine the set of production performance index decision factors. The controller production performance parameters are obtained by weighting and correcting the controller performance evaluation parameter set based on the set of production performance indicator decision factors.

6. The energy-saving optimization method for controller production using current monitoring as described in claim 1, characterized in that, The process of obtaining the target controller production strategy parameters includes: Based on the controller production performance index set, a production simulation fitting is performed on the controller production strategy parameter space to construct a controller production performance simulation module. Based on the controller production strategy parameter space, the energy-saving optimization analysis of the controller production performance parameters is performed to obtain the production strategy parameter selection threshold. Multiple production strategy parameters are randomly selected within the threshold range of the production strategy parameters, and the production performance simulation module of the controller is used to simulate and evaluate the multiple production strategy parameters to obtain the production performance prediction effect of multiple parameters. Based on the production performance prediction effect of the multiple parameters, global optimization is performed within the selected threshold of the production strategy parameters, and the production strategy parameters of the target controller are output.

7. The energy-saving optimization method for controller production using current monitoring as described in claim 6, characterized in that, The output target controller production strategy parameters include: Based on the predicted production performance of the multiple parameters, the center of the production strategy parameter cluster is determined; Based on the production strategy parameter cluster center, the multiple production strategy parameters are clustered to generate a controller production strategy parameter cluster; The remaining production strategy parameters in the controller production strategy parameter cluster are optimized and updated to the center of the production strategy parameter cluster to obtain the controller production strategy optimization parameter cluster. Global optimization is performed based on the controller production strategy parameter set, and the target controller production strategy parameters are output.

8. The energy-saving optimization method for controller production using current monitoring as described in claim 7, characterized in that, The obtained controller production strategy optimization parameter set includes: Optimization direction analysis is performed on the parameter family of the controller production strategy, and parameter variation rules and parameter crossover rules are set; Based on the parameter mutation rules and parameter crossover rules, the controller production strategy parameter cluster is cross-mutated and optimized to obtain the controller production strategy optimized parameter cluster.

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