Voltage stabilization control method of automobile generator

Abstract

The invention provides a voltage stabilization control method for an automobile generator, and relates to the technical field of generators, and the method comprises the steps: defining a target function, carrying out the initialization 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 to perform hierarchical control on the target automobile generator, adjusting the target automobile generator through a global layer and a local layer, and generating global distribution parameters and local distribution parameters; correlation analysis is carried out based on the distribution parameters, and a voltage stabilization control task is generated; and executing the voltage stabilization control task to perform generator performance verification, optimizing the voltage stabilization control task according to the performance response state, and generating a voltage stabilization optimization control strategy. The technical problem that an existing automobile generator voltage stabilization control method lacks a global adjustment and local adjustment cooperation mechanism and cannot adjust generator control parameters in time according to actual working conditions is solved, generator voltage fluctuation is reduced, and power supply quality is improved.

Description

Technical Field

[0001] The present application relates to the technical field of generators, and in particular to a voltage stabilization control method for an automobile generator. Background Art

[0002] The automotive generator is the core component of the automotive power system. Its main function is to provide stable power output for the vehicle to meet the needs of various electrical equipment. As the load and speed of the vehicle are constantly changing during operation, the output voltage of the generator is prone to fluctuations, which may cause electrical equipment failure or battery overcharge and discharge.

[0003] Existing methods for voltage stabilization control of automotive generators mainly rely on fixed parameters or empirical parameters to adjust the output voltage through a single control model. These methods usually use feedback control technology to achieve voltage stabilization, and the control strategy is mostly a single mode of global regulation or local regulation. This type of method relies on human experience or fixed settings when determining the initialization control parameters, and it is difficult to dynamically adapt according to the actual operating status of the vehicle, resulting in limited control performance; on the other hand, this single-level control method lacks global regulation and local coordination mechanisms, which easily leads to insufficient voltage stabilization control accuracy and difficulty in effectively suppressing voltage fluctuations, which in turn affects the stable operation of automotive electronic equipment, and may reduce the service life of the generator and cause energy waste. Summary of the invention

[0004] The present application provides a voltage stabilization control method for an automobile generator, which solves the technical problem that the existing automobile generator voltage stabilization control method lacks a coordinated mechanism for global regulation and local regulation and cannot timely adjust the generator control parameters according to actual working conditions, thereby achieving the technical effect of reducing the voltage fluctuation of the automobile generator and improving the power supply quality.

[0005] In view of the above problems, on the one hand, the present application provides a voltage stabilization control method for an automobile generator, the method comprising: defining an objective function based on an offline parameter set of a target automobile generator, initializing control of the target automobile generator through the objective function, and determining 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 the analysis results in combination with the initial control parameter set, and constructing a voltage stabilization control mechanism, the voltage stabilization control mechanism comprising a global layer and a local layer; dynamically adjusting the target automobile generator through the global layer to generate global allocation parameters; performing real-time adjustment of the target automobile generator 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 stabilization control task; executing the voltage stabilization control task to perform performance verification of the target automobile generator, generating 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 perform intelligent voltage stabilization control on the target automobile generator.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The objective function is defined based on the offline parameter set of the target automobile generator, and the target orientation of the voltage stabilization control can be established according to the inherent characteristics of the generator. The target automobile generator is initialized and controlled by the objective function to determine the initial control parameter set, which provides the initial control basis for the subsequent voltage stabilization control. Voltage stabilization analysis is performed based on the initial control parameter set, and the target automobile generator is hierarchically controlled according to the analysis results and the initial control parameter set to construct a voltage stabilization control mechanism. The voltage stabilization control mechanism includes a global layer and a local layer. The global layer grasps the voltage stabilization demand of the generator as a whole and makes dynamic adjustments; the local layer focuses on local details and makes real-time adjustments. This hierarchical structure can perform voltage stabilization control on the generator in a more comprehensive and precise manner. The target automobile generator is dynamically adjusted by the global layer to generate global allocation parameters, and the operating state of the generator system is adjusted from a macro level to ensure the global stability of the generator and the rationality of the overall voltage distribution. The target automobile generator is adjusted in real time by the local layer to generate local allocation parameters, which can be adjusted for the local voltage fluctuations or special situations of the generator, supplement the local details that may be ignored by the global layer, and cooperate with the global allocation parameters to improve the dynamic response capability and voltage stabilization accuracy of the voltage stabilization control. Based on the correlation analysis of the global allocation parameters and the local allocation parameters, a voltage stabilization control task is generated. Through the correlation analysis, a global and local coordination mechanism is established so that the two can cooperate with each other in the control strategy to achieve coordinated and consistent voltage stabilization control. The voltage stabilization control task is executed to verify the performance of the target automobile generator, generate a performance response state, and optimize the voltage stabilization control task according to the performance response state to form a closed-loop control system. The generated voltage stabilization optimization control strategy can adapt to the actual working conditions of the generator. According to the voltage stabilization optimization control strategy, the target automobile generator is intelligently controlled to improve the accuracy and effectiveness of the voltage stabilization control.

[0007] In summary, this application has built an intelligent voltage stabilization control system through hierarchical control mechanism and objective function optimization, taking into account global stability and local dynamic response capabilities. Based on the collaborative analysis of global and local allocation parameters, accurate voltage stabilization control tasks are generated, and closed-loop optimization control is achieved in combination with performance feedback. The overall solution improves the accuracy, stability and response speed of voltage stabilization control, can effectively reduce voltage fluctuations, and make the generator output a more stable voltage. At the same time, the control strategy can also be adaptively adjusted according to the actual operating conditions of the generator, extending the service life of the generator, and improving energy utilization efficiency, thereby improving the overall performance of the vehicle electrical system.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic flow chart of a voltage stabilization control method for an automobile generator provided in an embodiment of the present application.

[0010] Figure 2 A schematic diagram of a flow chart for generating global allocation parameters in a voltage stabilization control method for an automobile generator provided in an embodiment of the present application.

[0011] Figure 3 A schematic diagram of a flow chart for generating local distribution parameters in a voltage stabilization control method for an automobile generator provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] The embodiment of the present application provides a voltage stabilization control method for an automobile generator, which sets initial control parameters by defining an objective function, establishes a hierarchical control mechanism for global and local hierarchical control, performs correlation analysis on global and local allocation parameters to generate voltage stabilization control tasks, executes voltage stabilization control tasks for performance verification and optimization, and finally realizes intelligent voltage stabilization control of automobile generators. The embodiment of the present application solves the technical problem that the existing automobile generator voltage stabilization control method lacks a coordination mechanism for global and local regulation and cannot adjust the generator control parameters in time according to actual working conditions, and achieves the technical effect of reducing the voltage fluctuation of the automobile generator and improving the power supply quality.

[0013] like Figure 1 As shown, the embodiment of the present application provides a voltage stabilization control method for an automobile generator, the method comprising: Step S1: defining an objective function based on an offline parameter set of a target automobile generator, initializing control of the target automobile generator through the objective function, and determining an initial control parameter set.

[0014] Specifically, the target automobile generator is the control object of the embodiment of the present application, and can be any automobile generator that needs to be voltage-stabilized. The offline parameter set refers to a set of parameters obtained through experimental testing or historical data collection before the actual operation of the target automobile generator. These parameters may include the rated voltage, rated speed, load characteristics, etc. of the generator, which are usually static data. For example, the offline parameter set of a certain model of automobile generator includes: rated voltage 14V, rated speed 2000rpm, and load current range 10A to 50A. In the process of voltage stabilization control, the objective function is an expression for minimizing voltage fluctuations based on offline parameters such as the rated voltage and resistance of the target automobile generator, which is used to evaluate the pros and cons of control schemes 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).

[0015] Collect the offline parameter sets of the target vehicle generator and define the objective function based on these offline parameter sets. For example, if you want to stabilize the generator output voltage near the rated voltage, the objective function may be a function that minimizes the sum of the squares of the difference 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 control of the target vehicle generator through this objective function, and determine the initial control parameter set based on the parameter relationship in the objective function. For example, use a simple PID (proportional-integral-differential) control algorithm to determine the initial values ​​of the proportional coefficient, integral time constant, and differential time constant. These initial control parameter sets will serve as the starting basis for subsequent voltage stabilization control.

[0016] Step S2: Perform voltage stabilization analysis based on the initial control parameter set, perform hierarchical control on the target automobile generator according to the analysis result combined with the initial control parameter set, and construct a voltage stabilization control mechanism, which includes a global layer and a local layer.

[0017] Specifically, the voltage stabilization performance of the target generator under different loads and operating conditions is simulated or measured using the initial control parameter set to obtain various indicators of voltage stability. According to the voltage stabilization analysis results, the control task is divided into a global layer and a local layer in combination with the initial control parameter set to construct a hierarchical control mechanism. Among them, the global layer is mainly responsible for large-scale adjustments based on the overall load demand, such as adjusting the total power output of the generator; the local layer is responsible for detailed adjustments, such as fine-tuning the voltage by adjusting the excitation current. By constructing a voltage stabilization control mechanism that includes the global layer and the local layer, the refinement and comprehensiveness of the voltage stabilization control of the target automotive generator are improved.

[0018] Step S3: dynamically adjusting the target vehicle generator through the global layer to generate global allocation parameters.

[0019] Specifically, the global allocation parameters are the parameters generated by the global layer after dynamically adjusting the target vehicle generator, such as overall load distribution, power configuration, global target voltage value, etc. These parameters reflect the resource allocation and control strategy of the generator under global control as a whole.

[0020] The global layer continuously monitors the operating status of the target vehicle generator and makes dynamic adjustments based on the monitored generator load demand, power, and power configuration. For example, if the total load of the generator is detected to have increased suddenly, the global layer's control strategy may adjust the excitation control of the generator based on factors such as the rated power of the generator and the current output voltage to generate global allocation parameters. These global parameters help coordinate the overall power output and resource allocation of the generator, enabling the generator to maintain a stable voltage output under complex load conditions and avoid voltage fluctuations caused by global control lags.

[0021] Step S4: Real-time adjustment is performed on the target vehicle generator through the local layer to generate local allocation parameters.

[0022] Specifically, local allocation parameters are parameters generated by real-time adjustment of the generator at the local level, usually adjustments to specific circuits or specific 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.

[0023] The local layer monitors the voltage, load current and other key parameters of the generator in real time, analyzes the instantaneous changes in voltage and load, and determines whether the control strategy needs to be adjusted. Based on the instantaneous analysis results, the local layer generates local distribution parameters to adjust the working state of the generator to ensure that the generator can respond quickly when the voltage fluctuates. For example, if the voltage of the generator deviates from the target value at a certain moment, the local layer will adjust the output voltage by adjusting the excitation current in real time to restore it to the target range. The local layer ensures that the generator maintains a stable voltage within the local range through careful adjustment of these parameters, and the stability of the overall power supply will not be affected by local disturbances, thereby improving the dynamic responsiveness of the voltage stabilization control.

[0024] Step S5: performing correlation analysis based on the global allocation parameter and the local allocation parameter to generate a voltage stabilization control task.

[0025] Specifically, the voltage stabilization control task is a specific control task generated according to the correlation analysis results of the global allocation parameters and the local allocation parameters, and is used to perform voltage stabilization operations, such as adjusting the excitation current and load distribution.

[0026] Perform correlation analysis on the control parameters of the global layer and the local layer to find out the relationship between the two. For example, there may be a correlation between the total excitation current in the global allocation parameters and the current of a local winding in the local allocation parameters. According to the results of the correlation analysis, determine how the control of different levels affects each other, so as to generate the voltage stabilization 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. Through the correlation analysis, a global and local coordination mechanism is established, so that the two can cooperate with each other in the control strategy to achieve coordinated and consistent voltage stabilization control.

[0027] Step S6: Execute the voltage stabilization control task to verify the performance of the target vehicle generator, generate a performance response state, optimize the voltage stabilization control task according to the performance response state, and generate a voltage stabilization optimization control strategy to perform intelligent voltage stabilization control on the target vehicle generator.

[0028] Specifically, the performance response state is the state information that reflects the performance of the target vehicle generator after executing the voltage stabilization control task, including voltage fluctuation range, output power stability, generator heating, etc. The voltage stabilization optimization control strategy is a control strategy obtained by optimizing the voltage stabilization control task based on the performance response state.

[0029] Apply the voltage stabilization control task generated in the previous step to the target automobile generator to simulate or measure its performance response. In the process of executing the control task, the performance response state of the generator is recorded in real time, and its voltage stability, dynamic response and load adaptability are evaluated to detect whether the output voltage of the generator is stable. If the performance response does not meet the requirements (for example, the voltage fluctuation is too large), the generated performance response state is analyzed to optimize the voltage stabilization control task, formulate a voltage stabilization optimization control strategy, and further improve the voltage stabilization control effect. For example, by analyzing the performance response state, it is found that the high frequency of voltage fluctuation is due to too frequent adjustment of the control parameters. The optimization process may adjust the adjustment step size or adjustment time interval of the control parameters. Use the voltage stabilization optimization control strategy to perform intelligent voltage stabilization control on the target automobile generator, so as to achieve efficient and stable voltage control under different loads, and ensure that the generator provides stable voltage output under complex working conditions.

[0030] Furthermore, step S2 of the embodiment of the present application further includes: Step S21: simulate the target automobile generator according to the initial control parameter set, extract load change parameters, and formulate generator operating conditions according to the load change parameters.

[0031] Step S22: performing voltage stabilization evaluation analysis on the initial control parameter set according to the generator operating conditions to generate the analysis result.

[0032] Step S23: performing cluster analysis on the initial control parameter set according to the analysis result to determine a plurality of parameter control categories.

[0033] Step S24: performing a global control analysis based on the multiple parameter control categories in combination with global load demand information, constructing a global voltage regulator controller, and adding the global voltage regulator controller to the global layer.

[0034] Step S25: Perform local control analysis based on the multiple parameter control categories combined with real-time pressure error information, construct a local voltage regulator controller, and add the local voltage regulator controller to the local layer.

[0035] Step S26: Establishing the voltage stabilization control mechanism according to the communication connection between the global layer and the local layer.

[0036] Specifically, the initial control parameter set is input into the simulation model of the generator for simulation operation. During the simulation process, the working state of the automobile generator under different load conditions is simulated to test the performance of the generator. The data acquisition function in the simulation software is used to extract key load change parameters (such as current demand, power fluctuation, etc.). Based on these load change parameters, the operating conditions of the generator are formulated, that is, the working state requirements that the generator should meet, including the ideal excitation current and output voltage under different load conditions.

[0037] Based on the determined generator operating conditions, evaluate the output voltage stability of the generator under different load conditions under the initial control parameter set. By analyzing the load change parameters, calculate the voltage fluctuation range, fluctuation frequency, recovery time and other indicators to determine whether the generator can stably output the required voltage and meet the specified generator operating conditions. Through these analysis results, we can understand the performance of the generator in actual applications and discover potential regulation problems.

[0038] Based on the analysis results of the previous step, cluster analysis is performed on the initial control parameter set, and the initial control parameter set is divided into different categories according to different generator output performance results. Exemplarily, the performance results obtained in the simulation show a variety of different voltage fluctuation modes, and these fluctuation modes are classified into several categories through cluster analysis, such as low load fluctuation, high load fluctuation, etc. The clustering process can use K-means algorithm, hierarchical clustering, etc. Through cluster analysis, the control parameters of the generator are divided into multiple categories, so that in subsequent control, corresponding control strategies can be more effectively adopted for different load conditions and voltage fluctuation modes.

[0039] Global load demand information refers to the power demand of the entire vehicle power system, which usually includes the current battery charging status, the power demand of the on-board equipment, etc. Global load demand information reflects the overall power consumption. Obtain the global load demand information, perform matching analysis in multiple control categories based on the global load information, and perform macro-control of the generator from an overall perspective to determine the overall power distribution and output voltage of the generator to meet the needs of the entire vehicle electrical system. Based on the global control analysis results, build a global voltage regulator controller, which is used to adjust and control the generator according to the global load demand information and coordinate the various parameters of the generator to ensure the stable operation of the overall vehicle power system. Add the global controller to the global layer to adjust the output and load distribution of the generator as a whole.

[0040] Real-time pressure error information refers to the voltage error information monitored in real time, which is usually the difference between the current output voltage and the target voltage in the operating conditions of the generator. This information reflects the real-time voltage deviation of the generator in actual operation. The real-time voltage information is obtained by using sensors (such as voltage sensors, current sensors) of the target vehicle generator, and compared with the generator operating conditions to determine the real-time pressure error information. According to the real-time pressure error information, it is matched in multiple control categories to determine the corresponding control information, such as dynamically adjusting the excitation current or other parameters to correct the voltage deviation in a local range, thereby constructing a local voltage regulator controller. The local voltage regulator controller can respond quickly to voltage changes, adjust the local voltage according to the real-time voltage error, and ensure the stability of the local circuit voltage. The local voltage regulator controller is added to the local layer to be responsible for the adjustment of local details.

[0041] A data transmission and interaction channel is established between the global layer and the local layer, so that the global voltage regulator controller and the local voltage regulator controller can exchange information and control instructions with each other. By establishing a communication connection between the two control layers, a voltage regulation control mechanism is constructed, so that the overall voltage regulation control can be achieved according to the decision of the global layer and the real-time feedback of the local layer, ensuring the stable voltage output of the target vehicle generator in a complex environment.

[0042] Further, such as Figure 2 As shown, step S3 of the embodiment of the present application also includes: Step S31: performing on-board load monitoring on the target automobile generator based on the load change parameter to obtain the global load demand information.

[0043] Step S32: extracting the power configuration information of the target automobile generator and traversing it, performing calculation according to the global load demand information, and obtaining a global target voltage value.

[0044] Step S33: extracting the circuit configuration information of the target automobile generator, performing calculations based on the global target voltage value and the circuit configuration information, and obtaining a global target excitation current value.

[0045] Step S34: performing load analysis on the target vehicle generator according to the global target excitation current value, and formulating a load priority.

[0046] Step S35: Allocate power to the target vehicle generator according to the load priority and generate the global allocation parameter.

[0047] Specifically, the load information of each electrical device inside the vehicle, such as the power consumption requirements of headlights, air conditioners, audio systems, navigation devices, etc., is obtained through real-time sensor data or by connecting to the vehicle's electronic system, and global load demand information is generated by summarizing it.

[0048] The power configuration information refers to the operating parameters and configuration of the target vehicle generator, including the maximum output power, power factor, etc. of the generator. The power configuration information of the target vehicle generator is extracted from the technical manual of the vehicle generator or the vehicle electronic system. The global target voltage to be output is calculated based on the global load demand information obtained, that is, the ideal voltage value that the generator should output calculated based on the global load demand information. This voltage value must meet the normal operation of all electrical equipment and ensure the stable operation of the entire vehicle electrical system.

[0049] Circuit configuration information is parameter information related to the circuit structure of the automobile generator, including the resistance, inductance, capacitance, etc. of the circuit. The global target excitation current value is the current value that should flow through the generator excitation winding in order to achieve the global target voltage value, calculated based on the circuit configuration information. The magnitude of the excitation current directly affects the output voltage of the generator. The circuit configuration information of the target automobile generator is extracted from the circuit design drawing of the automobile generator or the vehicle electronic system, and the global target excitation current value is determined by calculation based on the global target voltage value and the circuit configuration information.

[0050] According to the global target excitation current value, the power demand of the vehicle power system is analyzed in detail, and the power equipment is sorted according to the importance and urgency of the load to determine the priority of different power equipment. Loads with high priority will be met first when power is distributed. For example, under high load conditions, the generator will give priority to supplying safety systems, power systems, etc., while non-urgent equipment such as car audio or air conditioning may temporarily reduce power.

[0051] The power of the generator is reasonably allocated according to the load priority and global load demand. For example, the total power of the generator is limited. When allocating power, high-priority devices, such as power systems and safety devices, are guaranteed to obtain the required power first, while lower-priority devices may have their power reduced. Through power allocation, global allocation parameters are generated, including the power allocation ratio of each device, the adjusted excitation current, and the adjusted voltage.

[0052] Through the above steps, it can be ensured that under different operating conditions, the generator can always power the vehicle in an optimal manner, ensure the normal operation of high-priority equipment, and balance the output of the generator.

[0053] Further, such as Figure 3 As shown, step S4 of the embodiment of the present application also includes: Step S41: collecting the real-time output voltage information of the target automobile generator, subtracting the real-time output voltage information from the global target voltage value, and determining the real-time pressure error information.

[0054] Step S42: Perform real-time control analysis based on the real-time pressure error information in combination with the load change parameter to generate an excitation current parameter to be adjusted.

[0055] Step S43: dynamically adjusting the load priority according to the excitation current parameter to be adjusted to generate a load update priority, locally allocating power to the target automobile generator according to the load update priority, and generating the local allocation parameter.

[0056] Specifically, a voltage sensor installed near the output terminal of the target vehicle generator is used to collect real-time output voltage information, and the collected real-time output voltage information is subtracted from the global target voltage value obtained in the previous step to determine the real-time pressure error information.

[0057] Real-time control analysis is performed based on the real-time pressure error information and load change parameters. The real-time pressure error indicates the deviation between the current voltage and the target voltage, and the load change parameters provide the dynamic changes in the current power demand of the vehicle. Based on these two, the excitation current parameters that need to be adjusted are calculated to compensate for the voltage error and adapt to the load demand.

[0058] The load priority is adjusted according to the real-time pressure error information and the excitation current parameters to be adjusted, and the load update priority is generated, so that the power distribution can be more flexibly adapted to the local operation of the generator. Compared with the previous load priority, the generated load update priority can better reflect the current local operation requirements of the generator. According to the updated load priority, the power is redistributed in the local range of the generator, and the local distribution parameters are determined to guide how the generator distributes power to each load.

[0059] Furthermore, step S43 further includes: Step S431: Perform quantitative analysis based on the excitation current parameter to be adjusted to generate a load adjustment factor.

[0060] Step S432: dynamically update the load priority according to the load adjustment factor to generate a priority list.

[0061] Step S433: performing an impact evaluation on the load adjustment factor based on the load change parameter to obtain a load impact score.

[0062] Step S434: sorting the priority list in descending order according to the load impact score to determine the load update priority.

[0063] Specifically, firstly, a quantitative analysis is performed based on the excitation current parameter to be adjusted to generate a load adjustment factor for adjusting the load priority, which reflects the degree of influence of the excitation current parameter to be adjusted on the load priority. Exemplarily, the numerical range of the excitation current parameter to be adjusted can be divided into different intervals according to a pre-set rule, and each interval corresponds to a load adjustment factor. For example, the absolute value of the excitation current parameter to be adjusted is between 0A and 1A, and the load adjustment factor is 0.1; the absolute value of the excitation current parameter to be adjusted is between 1A and 2A, and the load adjustment factor is 0.2, etc.

[0064] Then, read the previous load priority list and adjust the priority of each power-consuming device according to the load adjustment factor to ensure that the most urgent load can obtain sufficient power. For example, the original priority of a power-consuming device is 3, and the load adjustment factor is 0.2. According to the set update rule, such as priority = original priority × (1-load adjustment factor), the new priority of the device is 3 × (1-0.2) = 2.4. After updating the priorities of all power-consuming devices, a new priority list is generated.

[0065] According to the load change parameters and load adjustment factors, the impact of the load change of each power device on the entire load system is evaluated to determine the corresponding load impact score. For example, an evaluation model based on fuzzy logic can be established, with the load change parameters and load adjustment factors as inputs, and a numerical value representing the impact degree, i.e., the load impact score, can be obtained through fuzzy reasoning.

[0066] The priority list is sorted in descending order according to the load impact score to obtain the load update priority, so as to further adjust the power distribution strategy to ensure that the most important loads are always given priority in power supply.

[0067] Furthermore, step S5 of the embodiment of the present application further includes: Step S51: performing a multivariate association analysis based on the global allocation parameters and the local allocation parameters to generate a plurality of parameter associations.

[0068] Step S52: Analyze the global allocation parameters and the local allocation parameters according to the multiple parameter associations to generate parameter fuzzy logic information.

[0069] Step S53: traverse the parameter fuzzy logic and match it with the load impact score to extract multiple impact parameters.

[0070] Step S54: taking the multiple influencing parameters as decomposition reference values, performing multi-task decomposition on the target automobile generator according to the decomposition reference values, generating multiple control tasks, coordinating the multiple control tasks according to multiple control directions, and generating a voltage stabilization control task.

[0071] Specifically, a multivariate correlation analysis is performed on the global allocation parameters and the local allocation parameters using statistical analysis software or algorithms, such as covariance analysis, correlation coefficient calculation, etc., to study the relationship between the global allocation parameters and the local allocation parameters, so as to determine the degree of correlation between the parameters, and obtain the parameter correlation degree indicating the degree of correlation between the global allocation parameters and the local allocation parameters. Exemplarily, for the total power in the global allocation parameter and the local current in the local allocation parameter, the covariance between the two is calculated, and then the correlation coefficient is calculated as the parameter correlation degree of the two according to the ratio of the covariance to the respective standard deviations.

[0072] The global allocation parameters and local allocation parameters are analyzed according to the correlation of multiple parameters, and the relationship between the global and local allocation parameters is mapped into the fuzzy logic model. The logic model handles the uncertainty of the parameters by defining fuzzy rules to generate parameter fuzzy logic information. The parameter fuzzy logic information is used to describe the fuzzy relationship and rules between the global and local allocation parameters, such as maintaining the current parameter configuration when the correlation is high and the load demand is stable. Exemplarily, the parameter correlation is used as input based on the algorithm and rule base of fuzzy logic. The membership function and rules of fuzzy logic are defined, for example, what kind of correlation range corresponds to concepts such as "strong correlation". According to the fuzzy logic rules, this relationship is mapped to a fuzzy set (such as fuzzy concepts such as "strong correlation" and "medium correlation"). Parameter fuzzy logic information is generated by similar processing of multiple parameter correlations.

[0073] Traverse the generated parameter fuzzy logic information. For each fuzzy logic relationship, match it with the load impact score and extract multiple impact parameters. These impact parameters include load change parameters, excitation current, output voltage, etc. For example, if a fuzzy logic relationship indicates that the relationship between a global allocation parameter and a local allocation parameter becomes more sensitive when the load impact score is higher, such as the correlation in the parameter fuzzy logic relationship changes significantly when the load impact score is higher than a certain threshold, the control parameters related to the load impact score are extracted as impact parameters.

[0074] Take multiple influencing parameters as the reference values ​​for decomposition, such as the current parameters or voltage adjustment parameters that a certain load needs to meet first. According to these parameters, the control task of the generator is decomposed into multiple control tasks such as dynamic excitation current adjustment task, load priority adjustment task and voltage stabilization task. Next, these tasks are classified and coordinated according to the control direction of the task (such as voltage stability, current coordination), and a set of voltage stabilization control tasks are generated to achieve intelligent voltage stabilization control of the target generator. For example, if a certain control direction needs to be processed first, the relevant tasks will be arranged at the forefront of the execution process.

[0075] Furthermore, step S54 of the embodiment of the present application further includes: Step S541: constructing task decomposition rules through association analysis algorithm.

[0076] Step S542: Decompose the target automobile generator into multiple tasks according to the task decomposition rule and the decomposition reference value to generate multiple control tasks, wherein the multiple control tasks include an excitation current adjustment task, a dynamic load management task, and a power generation efficiency optimization task.

[0077] Step S543: Parameter mapping is performed based on the excitation current adjustment task, the dynamic load management task, and the power generation efficiency optimization task to obtain multiple control directions.

[0078] Step S544: the excitation current adjustment task, the dynamic load management task, and the power generation efficiency optimization task are coordinated according to the multiple control directions to generate the voltage stabilization control task.

[0079] Specifically, the global allocation parameters, local allocation parameters, influencing parameters, etc. in the previous steps are used to construct task decomposition rules through the association analysis algorithm. The task decomposition rule defines how to decompose the overall control task of the target vehicle generator into multiple subtasks. For example, a regression analysis algorithm can be used to analyze the relationship between power generation efficiency and parameters such as excitation current and load. If it is found that the power generation efficiency has a quadratic function relationship with the excitation current and a linear relationship with the load, then the task decomposition rule can be constructed based on this relationship. For example, when it is necessary to improve the power generation efficiency, the task can be decomposed according to the load size and the target value of the excitation current. The amplitude of the excitation current can be adjusted to a smaller value at low loads and to a larger value at high loads.

[0080] According to the conditions and logic in the task decomposition rules and the specific values ​​of the decomposition benchmark values, the overall control task of the target vehicle generator is decomposed into multiple control tasks including the excitation current adjustment task, the dynamic load management task, and the power generation efficiency optimization task. The excitation current adjustment task is a subtask specifically for adjusting the excitation current of the target vehicle generator, and the purpose is to achieve goals such as voltage stability and power generation efficiency optimization by changing the excitation current. The dynamic load management task is a subtask for dynamically managing the load on the vehicle, including adjusting the power distribution and priority according to the changes in the load to ensure that the generator can supply power stably. The power generation efficiency optimization task is a subtask for improving the power generation efficiency of the target vehicle generator, by adjusting various parameters (such as excitation current, load distribution, etc.) so that the generator can generate power at a higher efficiency under different working conditions. For example, if a parameter in the decomposition benchmark value indicates that the current load is in a high load state and the power generation efficiency is low, according to the task decomposition rules, an excitation current adjustment task will be generated, the goal is to appropriately increase the excitation current to improve the power generation efficiency; at the same time, a dynamic load management task is generated to readjust the power distribution under high load; and a power generation efficiency optimization task is generated to optimize the power generation process by comprehensively considering various parameters.

[0081] Parameter mapping is performed on the decomposed control tasks. For example, the corresponding parameter of the excitation current adjustment task is the target current value, the mapping parameters of the dynamic load management task are the load priority and real-time pressure error information, and the mapping parameters of the power generation efficiency optimization task are the energy consumption rate and load utilization rate. Through this mapping, the system can assign tasks to different control directions for subsequent collaborative processing.

[0082] By coordinating different control directions, various tasks are integrated to form a voltage stabilization control task. For example, when the vehicle air conditioning and braking systems require power support at the same time, the air conditioning load is prioritized according to the priority in the dynamic load management task, while the excitation current is adjusted to meet the rapid response requirements of the braking system, and the 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.

[0083] Furthermore, the multiple control directions include voltage control direction, power distribution direction, and efficiency control direction. Step S544 also includes: Step S544 - 1 : Execute and analyze the excitation current adjustment task according to the voltage control direction to generate a first execution result.

[0084] Step S544-2: Execute and analyze the dynamic load management task according to the power allocation direction to generate a second execution result.

[0085] Step S544-3: Execute and analyze the power generation efficiency optimization task according to the efficiency control direction to generate a third execution result.

[0086] Step S544-4: collaboratively analyze the first execution result, the second execution result, and the third execution result to generate a collaborative analysis result, and associate and coordinate the excitation current adjustment task, the dynamic load management task, and the power generation efficiency optimization task according to the collaborative analysis result to generate the voltage stabilization control task.

[0087] Specifically, multiple control directions include voltage control direction, power distribution direction, and efficiency control direction. Among them, the voltage control direction focuses on the specific control direction of adjusting the output voltage of the generator, focusing on ensuring the stability of the output voltage by adjusting the excitation current of the generator to meet the voltage target. The power distribution direction will manage the power distribution of various loads on the car. The efficiency control direction is controlled around improving the power generation efficiency of the car generator.

[0088] The execution analysis of the excitation current adjustment task in the voltage control direction is performed, and the required excitation current adjustment amount is calculated based on the real-time pressure error information to generate a first execution result. The execution result includes the excitation current adjustment amount after the excitation current adjustment task is executed under the voltage control requirement, as well as information such as the voltage change.

[0089] When executing the dynamic load management task, the real-time load priority is adjusted according to the power allocation direction and the load power demand is calculated to generate a second execution result. The execution result includes the power allocation after the dynamic load management task is executed under the power allocation requirement, and the impact on the load operation state and other information.

[0090] When executing the power generation efficiency optimization task, on the basis of meeting the load demand, the operating parameters of the generator (such as excitation current, speed, etc.) are adjusted to optimize the generator efficiency and generate the third execution result. The execution result includes the improvement of power generation efficiency after the power generation efficiency optimization task is executed under the efficiency control direction, as well as the adjustment of related parameters (such as excitation current, load, etc.).

[0091] The execution results of the three tasks are analyzed collaboratively. First, the data of the first execution result (voltage change), the second execution result (load status after power distribution) and the third execution result (generation efficiency improvement) are integrated. Analyze whether voltage changes will affect power distribution and power generation efficiency, whether changes in power distribution will in turn affect voltage and power generation efficiency, and whether the improvement of power generation efficiency will help stabilize voltage. Determine the coordination method based on the analysis results. 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, and the power allocation strategy in the dynamic load management task can be adjusted at the same time to improve the voltage while improving power generation efficiency. Take these coordination methods, parameter adjustment and other information as the results of the collaborative analysis, establish the associated collaborative relationship between the excitation current adjustment task, the dynamic load management task, and the power generation efficiency optimization task, so as to generate the voltage stabilization control task, and ensure that the voltage stability, load balancing, and efficiency optimization all achieve the expected results.

[0092] In summary, the voltage stabilization control method for an automobile generator provided in the embodiment of the present application has the following technical effects: The objective function is defined by an offline parameter set, and the objective function is used to initialize the control of the automobile generator. This process can establish the target orientation of voltage stabilization control based on the inherent characteristics of the generator and provide an initial control parameter set for the entire voltage stabilization control. The voltage stabilization control mechanism is constructed by performing voltage stabilization analysis on the initial control parameter set and combining the initial control parameter set based on the analysis results. The hierarchical control mechanism is divided into a global layer and a local layer, which are responsible for overall regulation and local regulation respectively. The global layer is used to dynamically adjust the generator according to the global load demand, and the local layer finely adjusts the generator according to real-time feedback (such as voltage and pressure errors). This hierarchical structure can more comprehensively and finely control the voltage stabilization of the generator, ensuring that the target automobile generator remains stable under variable loads and working conditions. The target automobile generator is dynamically adjusted at the global layer. By analyzing the load change parameters, the overall power distribution and excitation current of the generator are adjusted according to the global load demand, and the global distribution parameters are generated, thereby ensuring that the generator can maintain a stable voltage under different load conditions. The target automobile generator is adjusted in real time at the local layer. By monitoring and analyzing the real-time voltage, the excitation current parameters to be adjusted are generated in combination with the global target voltage value and the real-time pressure error. This process can adjust the local load in real time to ensure the accuracy of the voltage output. The local adjustment mechanism improves the reaction speed of the target automobile generator to sudden load changes, so that the generator can still remain stable under dynamic conditions. Based on the correlation analysis of the global allocation parameters and the local allocation parameters, the control information of the global layer and the local layer are integrated to generate multi-dimensional control tasks. Through multivariate correlation analysis, these tasks enable the global layer and the local layer to operate in coordination during the control process, ensuring reasonable control allocation under complex conditions to achieve the optimal voltage stabilization effect. The voltage stabilization control task is executed, and the performance response state is generated through performance verification, and the voltage stabilization control task is optimized according to the performance response state. Through multiple optimizations, the final voltage stabilization optimization control strategy can be obtained, and the target automobile generator is intelligently controlled according to the voltage stabilization optimization control strategy to improve the accuracy and effectiveness of the voltage stabilization control.

[0093] In general, the embodiment of the present application constructs an intelligent voltage stabilization control system by optimizing the hierarchical control mechanism and the objective function, taking into account both global stability and local dynamic response capabilities. Based on the collaborative analysis of global and local allocation parameters, an accurate voltage stabilization control task is generated, and closed-loop optimization control is achieved in combination with performance feedback. The method described in the embodiment of the present application improves the accuracy, stability and response speed of the voltage stabilization control, can effectively reduce voltage fluctuations, and make the generator output a more stable voltage. At the same time, the control strategy can also be continuously adjusted and optimized according to the actual operating conditions of the generator, which significantly improves the voltage stabilization control performance of the automobile generator, extends the service life of the generator, and improves energy utilization efficiency, thereby improving the overall performance of the automobile electrical system.

[0094] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A voltage stabilization control method for an automobile generator, characterized in that: The method comprises: Defining an objective function based on an offline parameter set of a target automobile generator, initializing control of the target automobile generator through the objective function, and determining an initial control parameter set; Based on the initial control parameter set, voltage stabilization analysis is performed, and hierarchical control is performed on the target automobile generator according to the analysis result and the initial control parameter set, so as to construct a voltage stabilization control mechanism, wherein the voltage stabilization control mechanism includes a global layer and a local layer; Dynamically adjusting the target vehicle generator through the global layer to generate global allocation parameters; The target vehicle generator is adjusted in real time through the local layer to generate local allocation parameters; Performing correlation analysis based on the global allocation parameter and the local allocation parameter to generate a voltage stabilization control task; The voltage stabilization control task is executed to verify the performance of the target vehicle generator, a performance response state is generated, the voltage stabilization control task is optimized according to the performance response state, and a voltage stabilization optimization control strategy is generated to perform intelligent voltage stabilization control on the target vehicle generator.

2. The voltage stabilization control method of an automobile generator according to claim 1, characterized in that: Based on the initial control parameter set, voltage stabilization analysis is performed, and hierarchical control is performed on the target automobile generator according to the analysis result combined with the initial control parameter set. The method includes: Performing simulation operation on the target automobile generator according to the initial control parameter set, extracting load variation parameters, and formulating generator operation conditions according to the load variation parameters; Performing voltage stabilization evaluation analysis on the initial control parameter set according to the generator operating conditions to generate the analysis result; Performing cluster analysis on the initial control parameter set according to the analysis results to determine a plurality of parameter control categories; Performing a global control analysis based on the multiple parameter control categories combined with global load demand information, constructing a global voltage regulator controller, and adding the global voltage regulator controller to the global layer; Performing local control analysis based on the multiple parameter control categories combined with real-time pressure error information, constructing a local voltage regulator controller, and adding the local voltage regulator controller to the local layer; The voltage stabilization control mechanism is constructed by communicating with the global layer and the local layer.

3. The voltage stabilization control method of an automobile generator as claimed in claim 2, characterized in that: The target vehicle generator is dynamically adjusted through the global layer to generate global allocation parameters, and the method includes: Performing on-board load monitoring on the target automobile generator based on the load change parameter to obtain the global load demand information; Extract the power configuration information of the target automobile generator and traverse it, calculate it according to the global load demand information, and obtain the global target voltage value; Extracting circuit configuration information of the target automobile generator, performing calculations based on the global target voltage value and the circuit configuration information to obtain a global target excitation current value; Perform load analysis on the target vehicle generator according to the global target excitation current value and formulate load priority; Power is distributed to the target vehicle generator according to the load priority, and the global distribution parameter is generated.

4. The voltage stabilization control method of an automobile generator as claimed in claim 3, characterized in that: The target vehicle generator is adjusted in real time through the local layer to generate local allocation parameters, and the method includes: Collecting real-time output voltage information of the target automobile generator, subtracting the real-time output voltage information from the global target voltage value, and determining real-time pressure error information; Based on the real-time pressure error information and the load change parameter, real-time control analysis is performed to generate an excitation current parameter to be adjusted; The load priority is dynamically adjusted according to the excitation current parameter to be adjusted to generate a load update priority, and power is locally distributed to the target automobile generator according to the load update priority to generate the local distribution parameter.

5. The voltage stabilization control method of an automobile generator as claimed in claim 4, characterized in that: The load priority is dynamically adjusted according to the excitation current parameter to be adjusted to generate a load update priority, and the power of the target automobile generator is locally distributed according to the load update priority to generate the local distribution parameter. The method includes: Performing quantitative analysis based on the excitation current parameter to be adjusted to generate a load adjustment factor; Dynamically update the load priority according to the load adjustment factor to generate a priority list; Performing an impact evaluation on the load adjustment factor based on the load change parameter to obtain a load impact score; The priority list is sorted in descending order according to the load impact score to determine the load update priority.

6. The voltage stabilization control method of an automobile generator as claimed in claim 5, characterized in that: Based on the correlation analysis between the global allocation parameter and the local allocation parameter, a voltage stabilization control task is generated, the method comprising: Performing a multivariate association analysis based on the global allocation parameter and the local allocation parameter to generate a plurality of parameter associations; Analyze the global allocation parameters and the local allocation parameters according to the multiple parameter associations to generate parameter fuzzy logic information; Traversing the parameter fuzzy logic and matching it with the load impact score to extract multiple impact parameters; The multiple influencing parameters are used as decomposition reference values, and the target automobile generator is decomposed into multiple tasks according to the decomposition reference values ​​to generate multiple control tasks. The multiple control tasks are coordinated according to multiple control directions to generate a voltage stabilization control task.

7. The voltage stabilization control method of an automobile generator as claimed in claim 6, characterized in that: The method uses the multiple influencing parameters as decomposition reference values, performs multi-task decomposition on the target automobile generator according to the decomposition reference values, generates multiple control tasks, and coordinates the multiple control tasks according to multiple control directions to generate a voltage stabilization control task, including: Construct task decomposition rules through association analysis algorithm; Decomposing the target automobile generator into multiple tasks according to the task decomposition rule and the decomposition reference value to generate multiple control tasks, wherein the multiple control tasks include an excitation current adjustment task, a dynamic load management task, and a power generation efficiency optimization task; Parameter mapping is performed based on the excitation current adjustment task, the dynamic load management task, and the power generation efficiency optimization task to obtain multiple control directions; The excitation current adjustment task, the dynamic load management task, and the power generation efficiency optimization task are coordinated according to the multiple control directions to generate the voltage stabilization control task.

8. The voltage stabilization control method of an automobile generator as claimed in claim 7, characterized in that: The excitation current adjustment task, the dynamic load management task, and the power generation efficiency optimization task are coordinated according to the multiple control directions to generate the voltage stabilization control task, and the method includes: The multiple control directions include voltage control direction, power distribution direction, and efficiency control direction; Execute and analyze the excitation current adjustment task according to the voltage control direction to generate a first execution result; Performing an execution analysis on the dynamic load management task according to the power allocation direction to generate a second execution result; Performing an execution analysis on the power generation efficiency optimization task 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 collaboratively analyzed to generate a collaborative analysis result; and based on the collaborative analysis result, the excitation current adjustment task, the dynamic load management task, and the power generation efficiency optimization task are associated and coordinated to generate the voltage stabilization control task.

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