Voltage stabilizing control method for automobile generator

Abstract

The application provides a voltage stabilization control method for an automobile generator, relates to the technical field of generators, and comprises the following steps: defining a target function, initializing control of a target automobile generator, and determining an initial control parameter set; performing voltage stabilization analysis based on the initial control parameter set, constructing a voltage stabilization control mechanism, and performing hierarchical control of the target automobile generator, wherein the target automobile generator is adjusted through a global layer and a local layer respectively, and global distribution parameters and local distribution parameters are generated; performing correlation analysis based on the distribution parameters, and generating a voltage stabilization control task; performing generator performance verification by executing the voltage stabilization control task, optimizing the voltage stabilization control task according to a performance response state, and generating a voltage stabilization optimization control strategy. The application solves the technical problem that the existing voltage stabilization control method for the automobile generator lacks a cooperative mechanism of global regulation and local regulation, and cannot timely adjust the generator control parameters according to actual working conditions, reduces voltage fluctuation of the generator, and improves power supply quality.

Description

Technical Field

[0001] This application relates to the field of generator technology, specifically to a voltage regulation control method for automotive generators. Background Technology

[0002] The automotive alternator is a core component of the vehicle's electrical system. Its main function is to provide a stable power output to meet the needs of various electrical devices. Because the load and speed of a vehicle constantly change during operation, the alternator's output voltage is prone to fluctuations, which may lead to problems such as equipment malfunctions or battery overcharging and discharging.

[0003] Existing automotive alternator voltage regulation control methods primarily rely on fixed or empirical parameters, adjusting the output voltage through a single control model. These methods typically employ feedback control technology for voltage regulation, with control strategies often employing a single mode of global or local adjustment. Such methods depend on human experience or fixed settings when determining initial control parameters, making it difficult to dynamically adapt to the actual operating conditions of the vehicle, thus limiting control performance. Furthermore, this single-level control method lacks a coordinated mechanism between global and local adjustments, easily leading to insufficient voltage regulation accuracy, difficulty in effectively suppressing voltage fluctuations, and consequently affecting the stable operation of automotive electronic equipment. It may also reduce the alternator's lifespan and result in energy waste. Summary of the Invention

[0004] This application provides a voltage stabilization control method for automotive generators, which solves the technical problem that existing automotive generator voltage stabilization control methods lack a coordinated mechanism for global and local regulation, and cannot adjust generator control parameters in a timely manner according to actual working conditions. This achieves the technical effect of reducing voltage fluctuations in automotive generators and improving power supply quality.

[0005] In view of the above problems, this application provides a voltage regulation control method for an automotive alternator. The method includes: defining an objective function based on an offline parameter set of the target automotive alternator; performing initial control on the target automotive alternator using the objective function to determine an initial control parameter set; performing voltage regulation analysis based on the initial control parameter set; performing hierarchical control on the target automotive alternator based on the analysis results and the initial control parameter set to construct a voltage regulation control mechanism, which includes a global layer and a local layer; dynamically adjusting the target automotive alternator through the global layer to generate global allocation parameters; adjusting the target automotive alternator in real time through the local layer to generate local allocation parameters; performing correlation analysis based on the global allocation parameters and the local allocation parameters to generate a voltage regulation control task; executing the voltage regulation control task to verify the performance of the target automotive alternator, generating a performance response state; optimizing the voltage regulation control task based on the performance response state to generate a voltage regulation optimization control strategy for intelligent voltage regulation control of the target automotive alternator.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] Based on the offline parameter set of the target vehicle generator, an objective function is defined, enabling the establishment of a target-oriented voltage regulation control based on the inherent characteristics of the generator. Initial control of the target vehicle generator is performed using this objective function to determine the initial control parameter set, providing the initial control basis for subsequent voltage regulation control. Voltage regulation analysis is conducted based on this initial control parameter set. According to the analysis results and the initial control parameter set, hierarchical control is implemented on the target vehicle generator to construct a voltage regulation control mechanism. This mechanism includes a global layer and a local layer. The global layer grasps the overall voltage regulation requirements of the generator and makes dynamic adjustments; the local layer focuses on local details and makes real-time adjustments. This hierarchical structure allows for more comprehensive and precise voltage regulation control of the generator. The global layer dynamically adjusts the target vehicle generator to generate global allocation parameters, adjusting the generator system's operating state from a macroscopic level to ensure the generator's global stability and the rationality of the overall voltage distribution. The local layer makes real-time adjustments to the target vehicle generator to generate local allocation parameters, enabling adjustments for local voltage fluctuations or special conditions, supplementing local details that the global layer might overlook. This, combined with the global allocation parameters, improves the dynamic response capability and voltage regulation accuracy of the voltage regulation control. A voltage stabilization control task is generated based on the correlation analysis of the global and local allocation parameters. A global and local coordination mechanism is established through this correlation analysis, enabling them to cooperate in the control strategy and achieve coordinated voltage stabilization control. The voltage stabilization control task is executed to verify the performance of the target vehicle's generator, generating a performance response state. The voltage stabilization control task is then optimized based on this performance response state, forming a closed-loop control system. The generated optimized voltage stabilization control strategy adapts to the actual operating conditions of the generator. Intelligent voltage stabilization control is then performed on the target vehicle's generator according to this optimized control strategy, improving the accuracy and effectiveness of voltage stabilization control.

[0008] In summary, this application constructs an intelligent voltage regulation control system by employing a hierarchical control mechanism and objective function optimization, balancing global stability with local dynamic response capabilities. Based on the collaborative analysis of global and local parameter allocation, a precise voltage regulation control task is generated, and closed-loop optimization control is achieved by combining performance feedback. The overall solution improves the accuracy, stability, and response speed of voltage regulation control, effectively reducing voltage fluctuations and resulting in a more stable generator output voltage. Furthermore, it can adaptively adjust the control strategy according to the actual operating conditions of the generator, extending its service life, improving energy efficiency, and thus enhancing the overall performance of the automotive electrical system.

[0009] 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

[0010] Figure 1 This is a schematic flowchart of the voltage regulation control method for an automotive generator provided in an embodiment of this application.

[0011] Figure 2 This is a schematic diagram of the process for generating global allocation parameters in the voltage regulation control method for an automotive generator provided in this application embodiment.

[0012] Figure 3 This is a schematic diagram of the process for generating local allocation parameters in the voltage regulation control method for an automotive generator provided in this application embodiment. Detailed Implementation

[0013] This application provides a voltage regulation control method for automotive alternators. It defines an objective function to set initial control parameters, establishes a hierarchical control mechanism for global and local hierarchical control, performs correlation analysis on the global and local allocated parameters to generate voltage regulation control tasks, executes these tasks for performance verification and optimization, and ultimately achieves intelligent voltage regulation control of the automotive alternator. This application solves the technical problem of existing automotive alternator voltage regulation control methods lacking a coordinated mechanism for global and local adjustment, and being unable to adjust alternator control parameters in a timely manner according to actual operating conditions. It achieves the technical effect of reducing voltage fluctuations in automotive alternators and improving power supply quality.

[0014] like Figure 1 As shown in the embodiment of this application, a voltage regulation control method for an automotive generator is provided, the method comprising:

[0015] Step S1: Define an objective function based on the offline parameter set of the target vehicle generator, and perform initial control on the target vehicle generator through the objective function to determine the initial control parameter set.

[0016] Specifically, the target automotive generator is the control object in this application embodiment, and can be any automotive generator requiring voltage regulation control. The offline parameter set refers to a set of parameters obtained through experimental testing or historical data collection before the target automotive generator actually operates. These parameters may include the generator's rated voltage, rated speed, load characteristics, etc., and are typically static data. For example, the offline parameter set of a certain model of automotive generator includes: rated voltage 14V, rated speed 2000rpm, and load current range of 10A to 50A. During the voltage regulation control process, the objective function is an expression for minimizing voltage fluctuations constructed based on the target automotive generator's rated voltage, resistance, and other offline parameters. It is used to evaluate the merits of the control scheme or parameters and can be designed based on statistical methods (such as least squares method) or engineering indicators (such as steady-state error, dynamic response time).

[0017] Collect the offline parameter set of the target vehicle's generator and define the objective function based on this parameter set. For example, if the goal is to stabilize the generator output voltage near the rated voltage, the objective function might be a function that minimizes the sum of the squares of the differences between the actual output voltage and the rated voltage, such as f = (V - V0)², where V is the actual output voltage and V0 is the rated voltage. Next, initialize the target vehicle's generator using this objective function. Based on the parameter relationships in the objective function, determine the initial control parameter set. For example, using a simple PID (Proportional-Integral-Derivative) control algorithm, determine the initial values ​​of the proportional coefficient, integral time constant, and derivative time constant. This initial control parameter set will serve as the starting point for subsequent voltage regulation control.

[0018] Step S2: Perform voltage regulation analysis based on the initial control parameter set, and perform hierarchical control of the target vehicle generator according to the analysis results and the initial control parameter set to construct a voltage regulation control mechanism, which includes a global layer and a local layer.

[0019] Specifically, the voltage regulation performance of the target generator under different load and operating conditions is simulated or measured using an initial control parameter set to obtain various indicators related to voltage stability. Based on the voltage regulation analysis results and the initial control parameter set, the control task is divided into a global layer and a local layer, constructing a hierarchical control mechanism. The global layer is mainly responsible for large-scale adjustments based on overall load demand, such as adjusting the generator's total power output; the local layer is responsible for fine-tuning the adjustment, such as adjusting the excitation current to achieve voltage fine-tuning. By constructing a voltage regulation control mechanism that includes global and local layers, the precision and comprehensiveness of the target vehicle generator's voltage regulation control are improved.

[0020] Step S3: Dynamically adjust the target vehicle generator through the global layer to generate global allocation parameters.

[0021] Specifically, global allocation parameters are generated by the global layer after dynamically adjusting the target vehicle generator, such as overall load distribution, power configuration, and global target voltage value. These parameters reflect the generator's resource allocation and control strategy under global control.

[0022] The global layer continuously monitors the operating status of the target vehicle's generator and dynamically adjusts it based on factors such as the generator's load demand, power, and power configuration. For example, if a sudden increase in the generator's total load is detected, the global layer's control strategy may adjust the generator's excitation control based on factors such as the generator's rated power and current output voltage, generating global allocation parameters. These global parameters help coordinate the generator's overall power output and resource allocation, enabling the generator to maintain stable voltage output under complex load conditions and avoiding voltage fluctuations caused by global control lag.

[0023] Step S4: Adjust the target vehicle generator in real time through the local layer to generate local allocation parameters.

[0024] Specifically, local allocation parameters are parameters generated after the local layer makes real-time adjustments to the generator. They are usually adjustments made to specific circuits or parts, such as adjusting the excitation current or adjusting the set value of a certain voltage output. These parameters reflect the allocation of generator control resources at the local level.

[0025] The local layer monitors the generator's voltage, load current, and other key parameters in real time, analyzes instantaneous changes in voltage and load, and determines whether adjustments to the control strategy are necessary. Based on the instantaneous analysis results, the local layer generates local allocation parameters to adjust the generator's operating state, ensuring a rapid response to voltage fluctuations. For example, if the generator's voltage deviates from the target value at a certain moment, the local layer adjusts the excitation current in real time to restore the output voltage to the target range. Through meticulous adjustments to these parameters, the local layer ensures that the generator maintains a stable voltage within a local range, preventing local disturbances from affecting the overall power supply stability and improving the dynamic responsiveness of voltage regulation control.

[0026] Step S5: Perform correlation analysis based on the global allocation parameters and the local allocation parameters to generate a voltage regulation control task.

[0027] Specifically, the voltage regulation control task is a specific control task generated based on the correlation analysis results of global and local allocation parameters, used to perform voltage regulation operations, such as adjusting excitation current and load distribution.

[0028] Correlation analysis is performed on the control parameters of the global and local layers to identify their interrelationships. For example, there may be a correlation between the total excitation current in the global allocation parameters and the current of a certain local winding in the local allocation parameters. Based on the results of the correlation analysis, it is determined how the control at different levels affects each other, thereby generating the voltage regulation control task. For example, the local layer may need to adjust the excitation current in real time, while the global layer may need to adjust the load priority. By establishing a global and local coordination mechanism through correlation analysis, the two can cooperate with each other in the control strategy to achieve coordinated and consistent voltage regulation control.

[0029] Step S6: Execute the voltage regulation control task to verify the performance of the target vehicle generator, generate a performance response state, optimize the voltage regulation control task based on the performance response state, and generate a voltage regulation optimization control strategy to perform intelligent voltage regulation control on the target vehicle generator.

[0030] Specifically, the performance response status reflects the performance of the target vehicle's generator after performing a voltage regulation control task, including various status indicators such as voltage fluctuation range, output power stability, and generator heat generation. The voltage regulation optimization control strategy is a control strategy obtained by optimizing the voltage regulation control task based on the performance response status.

[0031] The voltage regulation control task generated in the previous step is applied to the target automotive generator to simulate or measure its performance response. During the execution of the control task, the generator's performance response status is recorded in real time, and its voltage stability, dynamic response, and load adaptability are evaluated to detect whether the generator's output voltage is stable. If the performance response does not meet the requirements (e.g., excessive voltage fluctuations), the generated performance response status is analyzed to optimize the voltage regulation control task, and a voltage regulation optimization control strategy is formulated to further improve the voltage regulation control effect. For example, analysis of the performance response status may reveal that high voltage fluctuation frequency is due to overly frequent adjustments of control parameters; the optimization process may adjust the adjustment step size or adjustment time interval of the control parameters. Using the voltage regulation optimization control strategy, intelligent voltage regulation control is performed on the target automotive generator, thereby achieving efficient and stable voltage control under different loads and ensuring that the generator provides a stable voltage output under complex operating conditions.

[0032] Furthermore, step S2 in this embodiment of the application also includes:

[0033] Step S21: Simulate the target vehicle generator according to the initial control parameter set, extract load change parameters, and formulate generator operating conditions based on the load change parameters.

[0034] Step S22: Perform voltage stabilization evaluation and analysis on the initial control parameter set according to the generator operating conditions, and generate the analysis results.

[0035] Step S23: Perform cluster analysis on the initial control parameter set based on the analysis results to determine multiple parameter control categories.

[0036] Step S24: Based on the multiple parameter control categories and global load demand information, perform global control analysis, construct a global voltage regulator, and add the global voltage regulator to the global layer.

[0037] Step S25: Based on the multiple parameter control categories and real-time pressure error information, perform local control analysis, construct a local voltage regulator, and add the local voltage regulator to the local layer.

[0038] Step S26: Establish communication connections between the global layer and the local layer to construct the voltage regulation control mechanism.

[0039] Specifically, the initial set of control parameters is input into the generator's simulation model for simulation operation. During the simulation, the working state of the automotive generator under different load conditions is simulated to test the generator's performance. Key load change parameters (such as current demand, power fluctuations, etc.) are extracted using the data acquisition function of the simulation software. Based on these load change parameters, the generator's operating conditions are determined, that is, the operating state requirements that the generator should meet, including ideal excitation current, output voltage, etc., under different load conditions.

[0040] Based on the determined generator operating conditions, the output voltage stability of the generator under different load conditions is evaluated under the initial control parameter set. By analyzing load variation parameters, indicators such as voltage fluctuation range, fluctuation frequency, and recovery time are calculated to determine whether the generator can stably output the required voltage and meet the specified generator operating conditions. These analytical results allow us to understand the generator's performance in practical applications and identify potential regulation problems.

[0041] Based on the analysis results from the previous step, cluster analysis is performed on the initial control parameter set, categorizing it according to different generator output performance results. For example, the performance results obtained in the simulation show various voltage fluctuation patterns. Cluster analysis categorizes these fluctuation patterns into several classes, such as low load fluctuations and high load fluctuations. The clustering process can use algorithms such as K-means clustering and hierarchical clustering. By dividing the generator's control parameters into multiple categories through cluster analysis, appropriate control strategies can be more effectively adopted for different load conditions and voltage fluctuation patterns in subsequent control operations.

[0042] Global load demand information refers to the power demand of the entire vehicle's electrical system, typically including the current battery charging status and the power requirements of onboard equipment. This information reflects the overall power consumption. By acquiring global load demand information and performing matching analysis across multiple control categories, the generator is macroscopically controlled from a holistic perspective to determine its overall power distribution and output voltage to meet the needs of the entire vehicle's electrical system. Based on the global control analysis results, a global voltage regulator is constructed. This regulator adjusts and controls the generator according to the global load demand information, coordinating various generator parameters to ensure the stable operation of the overall vehicle electrical system. The global controller is added to the global layer to adjust the generator's output and load distribution holistically.

[0043] Real-time pressure error information refers to the voltage error information monitored in real time, typically the difference between the current output voltage and the target voltage under the generator's operating conditions. This information reflects the real-time voltage deviation of the generator during actual operation. Real-time voltage information is acquired using sensors (such as voltage and current sensors) of the target vehicle generator and compared with the generator's operating conditions to determine the real-time pressure error information. Based on the real-time pressure error information, matching is performed across multiple control categories to determine the corresponding control information, such as dynamically adjusting the excitation current or other parameters to correct the voltage deviation within a local range, thereby constructing a local voltage regulator controller. This local voltage regulator controller can quickly respond to voltage changes and adjust the local voltage according to the real-time voltage error, ensuring the stability of the local circuit voltage. The local voltage regulator controller is added to the local layer to handle the adjustment of local details.

[0044] A data transmission and interaction channel is established between the global and local layers, enabling the global and local voltage regulators to exchange information and control commands. By establishing a communication connection between the two control layers, a voltage regulation control mechanism is constructed, which can coordinate based on the decisions of the global layer and the real-time feedback of the local layer to achieve overall voltage regulation control and ensure stable voltage output of the target vehicle generator in complex environments.

[0045] Furthermore, such as Figure 2 As shown, step S3 in this embodiment further includes:

[0046] Step S31: Based on the load change parameters, perform on-board load monitoring on the target vehicle generator to obtain the global load demand information.

[0047] Step S32: Extract the power configuration information of the target vehicle generator and traverse it. Calculate the global target voltage value according to the global load demand information.

[0048] Step S33: Extract the circuit configuration information of the target vehicle generator, and calculate the global target excitation current value based on the global target voltage value and the circuit configuration information.

[0049] Step S34: Perform load analysis on the target vehicle generator based on the global target excitation current value and determine the load priority.

[0050] Step S35: Distribute power to the target vehicle generator according to the load priority to generate the global allocation parameters.

[0051] Specifically, load information of various electrical devices inside the vehicle, such as the power consumption requirements of headlights, air conditioning, audio systems, and navigation devices, is obtained through real-time sensor data or by connecting to the vehicle's electronic system, and then aggregated to generate global load demand information.

[0052] Power configuration information refers to the operating parameters and configuration of the target vehicle's alternator, including the alternator's maximum output power and power factor. This power configuration information is extracted from the vehicle's alternator technical manual or the vehicle's electronic system. Based on the obtained global load demand information, the required global target output voltage is calculated; that is, the ideal voltage value that the alternator should output, calculated based on the global load demand information. This voltage value must ensure the normal operation of all electrical equipment and guarantee the stable operation of the entire vehicle's electrical system.

[0053] Circuit configuration information refers to parameter information related to the automotive generator circuit structure, including circuit resistance, inductance, and capacitance. The global target excitation current value is the current that should flow through the generator's excitation winding, calculated based on the circuit configuration information, to achieve the global target voltage value. The magnitude of the excitation current directly affects the generator's output voltage. The target automotive generator's circuit configuration information is extracted from the generator's circuit design drawings or the vehicle's electronic system. The global target excitation current value is then calculated based on the global target voltage value and the circuit configuration information.

[0054] Based on the global target excitation current value, a detailed analysis of the vehicle's electrical system's power demand is conducted. Electrical equipment is prioritized based on factors such as load importance and urgency, determining the priority of different equipment. Higher-priority loads are given priority in power allocation. For example, under high load conditions, the generator will prioritize supplying safety systems and power systems, while non-urgent equipment such as car audio systems or air conditioning may temporarily reduce power.

[0055] Based on load priority and global load demand, the generator power is rationally allocated. For example, if the total generator power is limited, power allocation prioritizes high-priority devices, such as power systems and safety equipment, ensuring they receive the necessary power, while lower-priority devices may receive reduced power. Through power allocation, global allocation parameters are generated, including the power allocation ratio for each device, the adjusted excitation current, and the adjusted voltage.

[0056] By following the steps above, it can be ensured that the generator can always supply power to the vehicle in the optimal way under different operating conditions, guarantee the normal operation of high-priority equipment, and balance the generator output.

[0057] Furthermore, such as Figure 3 As shown, step S4 in this embodiment further includes:

[0058] Step S41: Collect the real-time output voltage information of the target vehicle generator, and calculate the difference between the real-time output voltage information and the global target voltage value to determine the real-time pressure error information.

[0059] Step S42: Based on the real-time pressure error information and the load change parameters, perform real-time control analysis to generate the excitation current parameters to be adjusted.

[0060] Step S43: Dynamically adjust the load priority according to the excitation current parameter to be adjusted, generate a load update priority, perform local power allocation on the target vehicle generator according to the load update priority, and generate the local allocation parameter.

[0061] Specifically, a voltage sensor installed near the output terminal of the target vehicle's generator is used to collect real-time output voltage information. The difference between the collected real-time output voltage information and the global target voltage value obtained in the previous step is calculated to determine the real-time pressure error information.

[0062] Real-time control analysis is performed based on real-time pressure error information and load variation parameters. The real-time pressure error indicates the deviation between the current voltage and the target voltage, while the load variation parameters provide information on the dynamic changes in the vehicle's current power demand. Based on these two factors, the excitation current parameters that need to be adjusted are calculated to compensate for voltage errors and adapt to load requirements.

[0063] Based on real-time pressure error information and the excitation current parameters to be adjusted, load priorities are modified to generate updated load priorities, enabling power allocation to more flexibly adapt to the local operating conditions of the generator. The generated updated load priorities better reflect the current local operating needs of the generator compared to the previous load priorities. Based on the updated load priorities, power is redistributed within the local area of ​​the generator to determine local allocation parameters, which guide how the generator distributes power to each load.

[0064] Furthermore, step S43 also includes:

[0065] Step S431: Perform quantitative analysis based on the excitation current parameters to be adjusted to generate a load adjustment factor.

[0066] Step S432: Dynamically update the load priority according to the load adjustment factor to generate a priority list.

[0067] Step S433: Evaluate the impact of the load adjustment factor based on the load change parameters to obtain a load impact score.

[0068] Step S434: Sort the priority list in descending order according to the load impact score to determine the load update priority.

[0069] Specifically, the process begins with a quantitative analysis of the excitation current parameter to be adjusted, generating a load adjustment factor to determine load priority. This factor reflects the degree of influence of the excitation current parameter on load priority. For example, the numerical range of the excitation current parameter to be adjusted can be divided into different intervals according to pre-defined rules, with each interval corresponding to a load adjustment factor. For instance, if the absolute value of the excitation current parameter is between 0A and 1A, the load adjustment factor is 0.1; if the absolute value is between 1A and 2A, the load adjustment factor is 0.2, and so on.

[0070] Next, the previous load priority list is read, and the priority of each electrical device is adjusted according to the load adjustment factor to ensure that the most urgent loads receive sufficient power. For example, if an electrical device originally had a priority of 3 and a load adjustment factor of 0.2, according to the set update rule, such as priority = original priority × (1 - load adjustment factor), then the new priority of this device is 3 × (1 - 0.2) = 2.4. After updating the priorities of all electrical devices, a new priority list is generated.

[0071] The impact of load changes on the entire load system of each electrical device is assessed based on load change parameters and load adjustment factors to determine the corresponding load impact score. For example, an evaluation model based on fuzzy logic can be established, taking load change parameters and load adjustment factors as input, and obtaining a numerical value representing the degree of impact, i.e., the load impact score, through fuzzy inference.

[0072] The priority list is sorted in descending order according to the load impact score to obtain the load update priority, thereby further adjusting the power allocation strategy to ensure that the most important loads are always given priority in power supply.

[0073] Furthermore, step S5 in this embodiment of the application also includes:

[0074] Step S51: Perform multivariate correlation analysis based on the global allocation parameters and the local allocation parameters to generate multiple parameter correlation degrees.

[0075] Step S52: Analyze the global allocation parameters and the local allocation parameters according to the correlation of the multiple parameters to generate parameter fuzzy logic information.

[0076] Step S53: Iterate through the parameters, combine the fuzzy logic with the load impact score to match and extract multiple impact parameters.

[0077] Step S54: Using the multiple influencing parameters as decomposition benchmark values, perform multi-task decomposition on the target vehicle generator based on the decomposition benchmark values ​​to generate multiple control tasks, and coordinate the multiple control tasks according to multiple control directions to generate a voltage stabilization control task.

[0078] Specifically, statistical analysis software or algorithms, such as analysis of covariance and correlation coefficient calculation, are used to conduct multivariate correlation analysis on global and local allocation parameters to study the interrelationship between them, determine the degree of correlation between each parameter, and obtain the parameter correlation degree, which represents the degree of correlation between global and local allocation parameters. For example, for the total power in the global allocation parameters and the local current in the local allocation parameters, the covariance between them is calculated, and then the correlation coefficient is calculated based on the ratio of the covariance to their respective standard deviations as the parameter correlation degree.

[0079] The global and local allocation parameters are analyzed based on the correlation of multiple parameters, mapping their relationship to a fuzzy logic model. The logic model handles parameter uncertainty by defining fuzzy rules, generating parameter fuzzy logic information. This information describes the fuzzy relationships and rules between global and local allocation parameters, such as maintaining the current parameter configuration when the correlation is high and the load demand is stable. For example, a fuzzy logic-based algorithm and rule base uses parameter correlation as input. Membership functions and rules for the fuzzy logic are defined, such as defining what correlation range corresponds to concepts like "strong correlation." Based on the fuzzy logic rules, this relationship is mapped to fuzzy sets (such as fuzzy concepts like "strong correlation" and "moderate correlation"). Through similar processing of multiple parameter correlations, parameter fuzzy logic information is generated.

[0080] The generated fuzzy logic information is iterated. For each fuzzy logic relation, it is matched with the load impact score to extract multiple impact parameters. These impact parameters include load variation parameters, excitation current, output voltage, etc. For example, if a fuzzy logic relation indicates that the relationship between a certain global allocation parameter and a local allocation parameter becomes more sensitive when the load impact score is high, such as if the correlation in the parameter fuzzy logic relation changes significantly when the load impact score is above a certain threshold, the control parameters related to that load impact score are extracted as impact parameters.

[0081] Multiple influencing parameters are used as the baseline values ​​for decomposition, such as the current or voltage regulation parameters that a certain load needs to prioritize. Based on these parameters, the generator control task is decomposed into multiple control tasks, including dynamic excitation current regulation, load priority adjustment, and voltage stabilization. Next, these tasks are classified and coordinated according to their control direction (e.g., voltage stability, current coordination) to generate a set of voltage regulation control tasks for achieving intelligent voltage regulation control of the target generator. For example, if a certain control direction needs to be prioritized, the relevant tasks will be scheduled at the forefront of the execution flow.

[0082] Furthermore, step S54 in this embodiment of the application also includes:

[0083] Step S541: Construct task decomposition rules using association analysis algorithms.

[0084] Step S542: Decompose the target vehicle generator into multiple tasks according to the task decomposition rules and the decomposition benchmark value to generate multiple control tasks, including excitation current adjustment task, dynamic load management task, and power generation efficiency optimization task.

[0085] Step S543: Based on the excitation current adjustment task, the dynamic load management task, and the power generation efficiency optimization task, perform parameter mapping to obtain multiple control directions.

[0086] Step S544: Coordinate the excitation current adjustment task, the dynamic load management task, and the power generation efficiency optimization task according to the multiple control directions to generate the voltage stabilization control task.

[0087] Specifically, using the global allocation parameters, local allocation parameters, and influencing parameters from previous steps, task decomposition rules are constructed through correlation analysis algorithms. These rules define how to break down the overall control task of the target automotive generator into multiple sub-tasks. For example, regression analysis algorithms can be used to analyze the relationship between generator efficiency and parameters such as excitation current and load. If it is found that generator efficiency has a quadratic function relationship with excitation current and a linear relationship with load, then task decomposition rules can be constructed based on this relationship. For instance, when it is necessary to improve generator efficiency, the task can be decomposed according to the load size and the target value of the excitation current; the adjustment of the excitation current can be smaller under low load and larger under high load.

[0088] Based on the conditions and logic in the task decomposition rules, and the specific values ​​of the decomposition baseline, the overall control task of the target vehicle generator is decomposed into multiple control tasks, including excitation current adjustment, dynamic load management, and power generation efficiency optimization. The excitation current adjustment task is a sub-task specifically designed to adjust the excitation current of the target vehicle generator, aiming to achieve goals such as voltage stability and power generation efficiency optimization by changing the excitation current. The dynamic load management task is a sub-task that dynamically manages the load on the vehicle, including adjusting power allocation and priorities based on load changes to ensure stable power supply from the generator. The power generation efficiency optimization task is a sub-task designed to improve the power generation efficiency of the target vehicle generator by adjusting various parameters (such as excitation current and load allocation) to ensure the generator generates power at a higher efficiency under different operating conditions. For example, if a parameter in the decomposition baseline indicates that the current load is under high load and the power generation efficiency is low, according to the task decomposition rules, an excitation current adjustment task will be generated, aiming to appropriately increase the excitation current to improve power generation efficiency; simultaneously, a dynamic load management task will be generated to readjust the power allocation under high load; and a power generation efficiency optimization task will be generated to comprehensively consider various parameters to optimize the power generation process.

[0089] The decomposed control tasks are mapped with parameters. For example, the excitation current adjustment task corresponds to the target current value, the dynamic load management task is mapped with load priority and real-time pressure error information, and the power generation efficiency optimization task is mapped with energy consumption rate and load utilization rate. Through this mapping, the system can allocate tasks to different control directions for subsequent coordinated processing.

[0090] By coordinating different control directions, various tasks are integrated to form a voltage stabilization control task. For example, when the vehicle's air conditioning and braking systems both require power, the air conditioning load is prioritized according to the priority in the dynamic load management task. At the same time, the excitation current is adjusted to meet the rapid response requirements of the braking system, and power generation efficiency is optimized to ensure maximum energy utilization. Through multi-task collaboration, the overall goal can be optimized while meeting short-term needs.

[0091] Furthermore, the plurality of control directions includes a voltage control direction, a power distribution direction, and an efficiency control direction. Step S544 further includes:

[0092] Step S544-1: Analyze the excitation current adjustment task according to the voltage control direction to generate the first execution result.

[0093] Step S544-2: Analyze the dynamic load management task according to the power allocation direction to generate a second execution result.

[0094] Step S544-3: Perform the power generation efficiency optimization task according to the efficiency control direction and generate a third execution result.

[0095] Step S544-4: Perform collaborative analysis on the first execution result, the second execution result, and the third execution result to generate a collaborative analysis result. Based on the collaborative analysis result, associate and coordinate the excitation current adjustment task, the dynamic load management task, and the power generation efficiency optimization task to generate the voltage stabilization control task.

[0096] Specifically, the multiple control directions include voltage control, power distribution, and efficiency control. Voltage control focuses on regulating the generator's output voltage, aiming to ensure its stability by adjusting the generator's excitation current to meet voltage targets. Power distribution manages the power allocation across various loads on the vehicle. Efficiency control focuses on improving the generator's power generation efficiency.

[0097] An execution analysis is performed on the excitation current adjustment task in the voltage control direction. Based on real-time pressure error information, the required excitation current adjustment amount is calculated, and a first execution result is generated. This execution result includes information such as the excitation current adjustment amount after the excitation current adjustment task is executed under voltage control requirements, as well as the voltage change.

[0098] When executing a dynamic load management task, the real-time load priority is adjusted according to the power allocation direction, and the load power demand is calculated, generating a second execution result. This execution result includes information such as the power allocation situation after the execution of the dynamic load management task under the power allocation requirements, and the impact on the load operating status.

[0099] When performing a power generation efficiency optimization task, the generator's operating parameters (such as excitation current and speed) are adjusted to optimize generator efficiency while meeting load requirements, generating a third execution result. This execution result includes information on the improvement in power generation efficiency after the power generation efficiency optimization task is performed under efficiency control, as well as the adjustments made to relevant parameters (such as excitation current and load).

[0100] The execution results of the three tasks are analyzed collaboratively. First, the data from the first execution result (voltage changes), the second execution result (load status after power allocation), and the third execution result (improvement in power generation efficiency) are integrated. This analysis examines whether voltage changes affect power allocation and power generation efficiency, whether changes in power allocation conversely affect voltage and power generation efficiency, and whether improvements in power generation efficiency contribute to voltage stabilization. Based on the analysis results, a collaborative approach is determined. For example, if the voltage is low and there is room for improvement in power generation efficiency, the parameters of the excitation current adjustment task can be adjusted, along with the power allocation strategy in the dynamic load management task, to improve both power generation efficiency and voltage. These collaborative approaches and parameter adjustments are used as the results of the collaborative analysis to establish a collaborative relationship between the excitation current adjustment task, the dynamic load management task, and the power generation efficiency optimization task. This generates a voltage stabilization control task, ensuring that voltage stability, load balancing, and efficiency optimization all achieve the expected results.

[0101] In summary, the voltage regulation control method for automotive generators provided in this application has the following technical effects:

[0102] An objective function is defined using an offline parameter set, and this objective function is used to initialize the vehicle's alternator control. This process establishes a target-oriented approach for voltage regulation control based on the alternator's inherent characteristics, providing an initial control parameter set for the entire voltage regulation control process. Voltage regulation analysis is performed on the initial control parameter set, and a voltage regulation control mechanism is constructed based on the analysis results and the initial control parameter set. The hierarchical control mechanism is divided into a global layer and a local layer, responsible for overall and local regulation, respectively. The global layer dynamically adjusts the alternator according to global load requirements, while the local layer performs fine-tuning based on real-time feedback (such as voltage and pressure errors). This hierarchical structure allows for more comprehensive and precise voltage regulation control of the alternator, ensuring the target vehicle alternator remains stable under varying loads and operating conditions. Dynamic adjustment of the target vehicle alternator at the global layer involves analyzing load variation parameters and adjusting the alternator's overall power distribution and excitation current according to global load requirements, generating global distribution parameters to ensure the alternator maintains a stable voltage under different load conditions. In the local layer, the target vehicle generator is adjusted in real time. By monitoring and analyzing the real-time voltage, and combining the global target voltage value with the real-time pressure error, the excitation current parameters to be adjusted are generated. This process can adjust the local load in real time, ensuring the accuracy of the voltage output. The local adjustment mechanism improves the response speed of the target vehicle generator to sudden load changes, enabling the generator to remain stable under dynamic operating conditions. Based on the correlation analysis of global and local allocation parameters, the control information of the global and local layers is integrated to generate multi-dimensional control tasks. These tasks, through multivariate correlation analysis, enable the global and local layers to work collaboratively during the control process, ensuring reasonable control allocation under complex operating conditions to achieve optimal voltage stabilization. The voltage stabilization control task is executed, and the performance response state is generated through performance verification. The voltage stabilization control task is optimized based on the performance response state. Through multiple optimizations, the final voltage stabilization optimization control strategy can be obtained. Based on the voltage stabilization optimization control strategy, intelligent voltage stabilization control is performed on the target vehicle generator, improving the accuracy and effectiveness of voltage stabilization control.

[0103] Overall, this application's embodiments construct an intelligent voltage regulation control system by employing a hierarchical control mechanism and objective function optimization, balancing global stability with local dynamic response capabilities. Based on the collaborative analysis of global and local allocation parameters, precise voltage regulation control tasks are generated, and closed-loop optimization control is achieved through performance feedback. The method described in this application's embodiments improves the accuracy, stability, and response speed of voltage regulation control, effectively reducing voltage fluctuations and resulting in a more stable generator output voltage. Furthermore, the control strategy can be continuously adjusted and optimized based on the actual operating conditions of the generator, significantly improving the voltage regulation control performance of the automotive generator, extending its service life, increasing energy efficiency, and thus enhancing the overall performance of the automotive electrical system.

[0104] 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 voltage control method for an automobile generator, characterized by, The method comprises: Defining a target function based on an offline parameter set of a target automobile generator, initializing control of the target automobile generator through the target function to determine an initial control parameter set; Performing voltage stabilization analysis based on the initial control parameter set, performing hierarchical control of the target automobile generator according to an analysis result combined with the initial control parameter set, and constructing a voltage stabilization control mechanism, wherein the voltage stabilization control mechanism comprises a global layer and a local layer; Performing dynamic adjustment of the target automobile generator through the global layer to generate global distribution parameters; Performing real-time adjustment of the target automobile generator through the local layer to generate local distribution parameters; Performing correlation analysis based on the global distribution parameters and the local distribution parameters to generate a voltage stabilization control task; Performing performance verification of the target automobile generator by executing the voltage stabilization control task to generate a performance response state, optimizing the voltage stabilization control task according to the performance response state, and generating a voltage stabilization optimization control strategy to intelligently control the target automobile generator; Wherein, the method for performing voltage stabilization analysis based on the initial control parameter set and performing hierarchical control of the target automobile generator according to an analysis result combined with the initial control parameter set comprises: Performing simulation operation of the target automobile generator according to the initial control parameter set, extracting load change parameters, and formulating generator operation conditions according to the load change parameters; Performing voltage stabilization evaluation analysis of the initial control parameter set according to the generator operation conditions to generate the analysis result; Performing cluster analysis of the initial control parameter set according to the analysis result to determine a plurality of parameter control categories; Performing global control analysis based on the plurality of parameter control categories combined with global load demand information to construct a global voltage stabilization controller, and adding the global voltage stabilization controller to the global layer; Performing local control analysis based on the plurality of parameter control categories combined with real-time pressure error information to construct a local voltage stabilization controller, and adding the local voltage stabilization controller to the local layer; Performing communication connection according to the global layer and the local layer to construct the voltage stabilization control mechanism; Wherein, the method for performing dynamic adjustment of the target automobile generator through the global layer to generate global distribution parameters comprises: Performing vehicle-mounted load monitoring of the target automobile generator based on the load change parameters to obtain the global load demand information; Extracting power configuration information of the target automobile generator for traversal, calculating according to the global load demand information to obtain a global target voltage value; Extracting circuit configuration information of the target automobile generator, calculating according to the global target voltage value and the circuit configuration information to obtain a global target excitation current value; Performing load analysis of the target automobile generator according to the global target excitation current value to formulate a load priority; Performing power distribution of the target automobile generator according to the load priority to generate the global distribution parameters; Wherein, the method for performing real-time adjustment of the target automobile generator through the local layer to generate local distribution parameters comprises: Collecting real-time output voltage information of the target automobile generator, and determining real-time pressure error information by subtracting the real-time output voltage information from the global target voltage value; Performing real-time control analysis based on the real-time pressure error information and the load change parameter to generate an adjustable excitation current parameter; According to the adjustable excitation current parameter, dynamically adjusting the load priority to generate an updated load priority, and performing power local distribution on the target automobile generator according to the updated load priority to generate the local distribution parameter.

2. The voltage stabilization control method of an automobile generator according to claim 1, characterized by, According to the adjustable excitation current parameter, dynamically adjusting the load priority to generate an updated load priority, and performing power local distribution on the target automobile generator according to the updated load priority to generate the local distribution parameter, the method comprising: According to the adjustable excitation current parameter, performing quantitative analysis to generate a load adjustment factor; According to the load adjustment factor, dynamically updating the load priority to generate a priority list; Based on the load change parameter, evaluating the influence of the load adjustment factor to obtain a load influence score; According to the load influence score, sorting the priority list in descending order to determine the updated load priority.

3. The voltage stabilization control method of an automobile generator according to claim 2, characterized by, Based on the global distribution parameter and the local distribution parameter, performing correlation analysis to generate a voltage stabilization control task, the method comprising: Based on the global distribution parameter and the local distribution parameter, performing multivariate correlation analysis to generate a plurality of parameter correlation degrees; According to the plurality of parameter correlation degrees, analyzing the global distribution parameter and the local distribution parameter to generate parameter fuzzy logic information; Traversing the parameter fuzzy logic in combination with the load influence score to match a plurality of influence parameters; Taking the plurality of influence parameters as decomposition reference values, performing multi-task decomposition on the target automobile generator according to the decomposition reference values to generate a plurality of control tasks, and coordinating the plurality of control tasks according to a plurality of control directions to generate the voltage stabilization control task.

4. The voltage stabilization control method of an automobile generator according to claim 3, characterized by, Taking the plurality of influence parameters as decomposition reference values, performing multi-task decomposition on the target automobile generator according to the decomposition reference values to generate a plurality of control tasks, and coordinating the plurality of control tasks according to a plurality of control directions to generate the voltage stabilization control task, the method comprising: Constructing a task decomposition rule through correlation analysis algorithm; According to the task decomposition rule and the decomposition reference values, performing multi-task decomposition on the target automobile generator to generate a plurality of control tasks, the plurality of control tasks including an excitation current adjustment task, a dynamic load management task, and a power generation efficiency optimization task; Based on the excitation current adjustment task, the dynamic load management task, and the power generation efficiency optimization task, performing parameter mapping to obtain a plurality of control directions; According to the excitation current adjustment task, the dynamic load management task, and the power generation efficiency optimization task, coordinating the plurality of control directions to generate the voltage stabilization control task.

5. The voltage stabilization control method of an automobile generator according to claim 4, characterized by, According to the excitation current adjustment task, the dynamic load management task, and the power generation efficiency optimization task, coordinating the plurality of control directions to generate the voltage stabilization control task. The multiple control directions include a voltage control direction, a power distribution direction, and an efficiency control direction; The excitation current adjustment task is analyzed according to the voltage control direction to generate a first execution result; The dynamic load management task is analyzed according to the power distribution direction to generate a second execution result; The power generation efficiency optimization task is analyzed according to the efficiency control direction to generate a third execution result; The first execution result, the second execution result, and the third execution result are analyzed cooperatively to generate a cooperative analysis result, the excitation current adjustment task, the dynamic load management task, and the power generation efficiency optimization task are associated and cooperated according to the cooperative analysis result to generate the voltage stabilization control task.

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