Control method, system and equipment of Brayton cycle system and readable storage medium

Abstract

The invention provides a Brayton cycle system control method, system and device and a readable storage medium. A sensing, decision-making and execution closed loop is formed through at least one sensor, a main control unit, at least one actuator and a communication network. The main control unit is configured to be a basic control layer and an optimization control layer which operate at the same time, the basic control layer carries out rapid closed-loop adjustment based on an optimization target and real-time parameters, and the rapid response capability and basic stability of the system to dynamic changes are ensured. The optimization control layer carries out calculation according to the global operation parameters, and an output optimization target can guide the system to operate in a state with higher efficiency, better performance or better adaptability to working conditions. The hierarchical control strategy not only can quickly stabilize a local loop, but also can perform dynamic optimization based on global information, so that the control precision, the response speed and the overall operation efficiency are balanced and improved, and quick iterative optimization can be performed to adapt to dynamic control requirements under complex working conditions such as multivariable and nonlinear conditions.

Description

Technical Field

[0001] This specification relates to the field of new energy technology, and in particular to a control method, system, device and readable storage medium for a Brayton cycle system. Background Technology

[0002] The Brayton cycle is one of the core thermodynamic cycles that converts thermal energy into mechanical or electrical energy. Its basic processes include air compression, fuel combustion heating, high-temperature and high-pressure gas expansion for power generation, and exhaust cooling. This cycle is widely used in gas turbines, aero engines, micro gas turbines, and distributed energy systems, and is a key technology for achieving efficient energy exchange. With the increasing global demand for clean energy, distributed energy supply, and efficient power systems, the application scenarios of Brayton cycle systems are constantly expanding, and the requirements for their operating efficiency, stability, and automation control levels are also increasing.

[0003] Existing Brayton cycle control systems typically employ the traditional PID control method, achieving stable system operation through real-time monitoring and feedback control of key parameters such as temperature, pressure, and flow rate. The PID controller generates a control signal by performing proportional, integral, and derivative operations on the error signal to adjust the system's operating state. However, while traditional PID control exhibits good control performance when handling linear systems, its control accuracy and response speed are often insufficient when dealing with complex nonlinear or multivariable systems. Summary of the Invention

[0004] To address the aforementioned technical problems, this specification provides the following technical solutions: Firstly, a control method for a Brayton cycle system is provided, the method comprising: A control system for a Brayton cycle system, the system comprising: At least one sensor is used to collect the operating parameters of the Brayton cycle system; Main control unit; At least one actuator is provided for receiving control commands to adjust the operating state of the Brayton cycle system; A communication network is used to connect the sensor, the main control unit, and the actuator; The main control unit is configured as follows: The basic control layer is used to generate control commands for closed-loop regulation of the basic operating loop of the Brayton cycle system based on the operating parameters and optimization objectives. An optimization control layer is used to perform optimization calculations on the global operating state of the Brayton cycle system based on the operating parameters, and output the optimization target to the basic control layer.

[0005] In some embodiments, the main control unit has a built-in CAN controller, which is connected to the communication network.

[0006] In some embodiments, the main control unit has a built-in multi-channel analog-to-digital converter, and the main control unit is connected to the sensor through the analog-to-digital converter.

[0007] In some embodiments, the main control unit has a built-in floating-point arithmetic unit for providing hardware computing power for the basic control layer and the optimized control layer.

[0008] In some embodiments, the main control unit is an embedded control device; and / or; The main control unit is a multi-core microcontroller unit.

[0009] In some embodiments, the at least one sensor includes a vibration sensor and a signal processing board connected to the vibration sensor; The signal processing board is configured as follows: The vibration signal collected by the vibration sensor is preprocessed, and the processed vibration characteristic data is sent to the main control unit through the communication network.

[0010] In some embodiments, the main control unit has a built-in hardware-level fault detection module, which is configured to: Real-time diagnosis is performed based on the operating parameters and preset rule base, and protection actions are automatically triggered when a fault is diagnosed.

[0011] Secondly, a control method for a Brayton cycle system is provided, applied to the control system of the Brayton cycle system described in the first aspect, the method comprising: Receive operating parameters for at least one Brayton cycle system; The global operating state of the Brayton cycle system is optimized based on the operating parameters, and the optimization target is output. Based on the operating parameters and the optimization objective, control commands are generated to rapidly close-loop adjust the basic operating loop of the Brayton cycle system. The operation of the Brayton cycle system is controlled according to the control instructions.

[0012] In some embodiments, the step of optimizing the global operating state of the Brayton cycle system based on the operating parameters and outputting the optimization objective includes: Based on the operating parameters and a control algorithm combining model predictive control and / or fuzzy logic control, the global operating state of the Brayton cycle system is optimized and the optimization target is output.

[0013] In some embodiments, the operating parameters include vibration signals, and the method further includes: Receive processed vibration characteristic data, which is obtained by preprocessing the collected vibration signal; Based on the vibration characteristic data, fault diagnosis is performed on the Brayton cycle system.

[0014] In some embodiments, after receiving the operating parameters of at least one Brayton cycle system, the method further includes: Real-time diagnosis is performed based on the aforementioned operating parameters and a preset rule base. When a fault is detected, the protection action is automatically triggered.

[0015] Thirdly, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements a control method for a Brayton cycle system as described in any one of the second aspects.

[0016] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the control method for the Brayton cycle system described in the second aspect above.

[0017] This application provides a control method for Brayton cycle systems, which, compared with the current methods for handling complex nonlinear or multivariable systems, has the following advantages: On the one hand, by forming a closed loop of perception, decision-making, and execution through at least one sensor, a main control unit, at least one actuator, and a communication network, the system can automatically collect operating parameters, automatically calculate and issue control commands, and automatically drive actuator actions, thereby achieving continuous and autonomous adjustment of the Brayton cycle operating state, significantly reducing the dependence on manual intervention and improving the system's automation level.

[0018] On the other hand, the main control unit is configured to operate simultaneously as a basic control layer and an optimization control layer. The basic control layer performs rapid closed-loop adjustment based on optimization objectives and real-time parameters, ensuring the system's rapid response to dynamic changes and basic stability. The optimization control layer calculates based on global operating parameters, and its output optimization objective guides the system towards a state with higher efficiency, better performance, or better adaptation to operating conditions. This hierarchical control strategy enables rapid stabilization of local loops and dynamic optimization based on global information, thereby achieving a balance and improvement in control accuracy, response speed, and overall operating efficiency. It also allows for rapid iterative optimization to adapt to the dynamic control requirements of complex operating conditions such as multivariable and nonlinear environments.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this specification. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings.

[0021] Figure 1 This is a schematic diagram of the control system architecture of a Brayton cycle system provided in this specification; Figure 2 This is a schematic diagram of the control system of a Brayton cycle system provided in this manual; Figure 3 This is a software architecture diagram of the main control unit of a Brayton cycle system control system provided in this manual; Figure 4 This is a structural diagram of the vibration signal processing board of a control system for a Brayton cycle system provided in this specification; Figure 5 This is a flowchart illustrating a control method for a Brayton cycle system provided in this specification. Figure 6 This is a control logic diagram of the optimized control layer of a control system for a Brayton cycle system provided in this specification; Figure 7 This is a logic diagram for fault diagnosis of a Brayton cycle system control system provided in this manual; Figure 8 This is another logic diagram for fault diagnosis of a Brayton cycle system control system provided in this manual; Figure 9 This is a schematic diagram of a computer-readable storage medium provided in this disclosure; Figure 10 This is a schematic diagram of the structure of a computing device provided in this disclosure. Detailed Implementation

[0022] This specification describes several technical solutions with different concepts. Each concept has one or more embodiments, and different concepts can be combined to form more embodiments. Those skilled in the art, after reading this specification, can combine different concepts to obtain new technical solutions, and these new technical solutions should also fall within the scope of this specification.

[0023] The technical solutions of these different concepts will be introduced in turn below. Some concepts may appear in multiple technical solutions of different concepts. For these concepts, this article will explain them when they first appear and will not repeat them in the following text.

[0024] Existing Brayton cycle control systems typically employ the traditional PID control method, which has significant drawbacks: In a Brayton cycle system, core parameters (such as turbine speed, combustion chamber temperature, and compressor outlet pressure) are not independent but interconnected and tightly coupled. For example, increasing fuel to improve power will lead to increased turbine speed and exhaust temperature, while also altering the compressor's back pressure. In traditional PID controllers, the fuel PID only controls fuel addition, while the temperature PID detects overheating and signals to reduce fuel intake. This can cause system oscillations, preventing the system from stabilizing at its optimal state. Furthermore, the behavior of a Brayton cycle system is not a simple linear change; adding the same unit of fuel under low and high load conditions will result in completely different temperature rises and speed changes.

[0025] Therefore, controlling the Brayton cycle system to rapidly iterate and optimize it to adapt to dynamic requirements under complex operating conditions (such as load fluctuations, changes in ambient temperature, etc.) is a challenging problem.

[0026] To address the aforementioned technical problems, this specification provides a control method, system, device, and readable storage medium for a Brayton cycle system.

[0027] The aim is to employ a hierarchical strategy, with the basic control layer responsible for executing specific instructions quickly and accurately, thus solving the problem of rapid stabilization of the basic loop. The optimization control layer, based on the global operating state of the Brayton cycle system, dynamically optimizes the control objectives of the basic control layer, thereby enabling the control system to possess global optimality and robustness. Through division of labor and collaboration, it effectively solves the multivariable and nonlinear problems of systems like the Brayton cycle.

[0028] Figure 1 This is a schematic diagram of the control system architecture for a Brayton cycle system provided in this specification. The control system of the Brayton cycle system includes: It includes at least one sensor, a main control unit, at least one actuator, and a communication network.

[0029] In the control system, at least one sensor is used to collect the operating parameters of the Brayton cycle system.

[0030] One or more physical measuring devices (such as temperature, pressure, flow, and vibration sensors) are deployed at key nodes of the Brayton cycle system to continuously and in real time acquire operating parameters characterizing the system's thermodynamic, mechanical, and fluid states. These parameters form the basis for the control system's decision-making. (Refer to...) Figure 2 , Figure 2 This is a schematic diagram of the control system of a Brayton cycle system provided in this specification. At least one sensor is included, but is not limited to, a flow sensor, a closed-loop low-pressure / high-pressure sensor, a CAN4022T voltage acquisition module, a T-type / K-type thermocouple temperature acquisition module, etc. The operating parameters include, but are not limited to, wheel speed, combustion chamber temperature, compressor outlet pressure, etc.

[0031] In the control system, the master control unit is the command center of the entire Brayton cycle system, undertaking all core information processing, decision-making and scheduling tasks in the system.

[0032] In this embodiment, the main control unit is an embedded control device.

[0033] With the development of embedded computing technology, real-time control algorithms and sensor technology, embedded controllers are used to perform real-time and intelligent control of Brayton cycle systems, thereby improving the system's control accuracy, response speed and robustness.

[0034] In this embodiment, the main control unit is a multi-core microcontroller unit.

[0035] In some cases, the core of this embedded control device is a multi-core microcontroller unit.

[0036] As an example, the AURIX TC234LP is used as the main chip, integrating data acquisition, algorithm calculation, and communication control functions onto a single chip. This replaces the multi-module combination scheme of traditional PLCs or industrial control computers, achieving system miniaturization and low power consumption design. Other multi-core microcontrollers integrating data acquisition, algorithm calculation, and communication control functions onto a single chip can also be used; no specific limitations are made here.

[0037] When using a highly integrated embedded controller with the AURIX TC234LP as the main chip, compared with the fixed program architecture of traditional PLCs or industrial PCs, the control algorithm parameters can be flexibly adjusted to quickly adapt to the control requirements under different working conditions, significantly improving the efficiency of algorithm iteration.

[0038] In some embodiments, the main control unit is configured to: The basic control layer is used to generate control commands for closed-loop regulation of the basic operating loop of the Brayton cycle system based on the operating parameters and optimization objectives. An optimization control layer is used to perform optimization calculations on the global operating state of the Brayton cycle system based on the operating parameters, and output the optimization target to the basic control layer.

[0039] Reference Figure 3 , Figure 3 This is a software architecture diagram of the main control unit of a Brayton cycle system control system provided in this manual.

[0040] First, the main control unit's software architecture is divided into a basic control layer (lower layer) and an optimization control layer (upper layer). The software for the upper-layer optimization control layer is built using a first development toolchain (such as MATLAB / SIMULINK). This layer contains the algorithm logic for Model Predictive Control (MPC) and / or Fuzzy Logic Control (FLC), and algorithm code such as system_control.c / .h is generated using an automatic code generation tool. Simultaneously, the software for the lower-layer basic control layer is written, which includes PID algorithm logic, low-level communication, and hardware driver-related initialization and task management. The PID algorithm logic performs rapid closed-loop adjustment based on the optimization objective and real-time parameters to achieve stability of the basic operating loop. Low-level communication and hardware driver-related initialization and task management include communication initialization, sensor / actuator message exchange, and other hardware driver-related tasks, providing data transmission support for PID closed-loop adjustment and the upper-layer optimization logic.

[0041] Next, the code corresponding to the underlying software and the upper-level control software is integrated and compiled using a second development toolchain (such as the HIGHTEC compiler) to generate executable files (such as the VehicleControl.elf file) and writable files (such as the VehicleControl.hex file).

[0042] Finally, the above files are burned and solidified into the main control unit (ECU).

[0043] The solidified software starts in the ECU. The underlying software first completes communication initialization (Com_Init), and then realizes message interaction with external sensors and actuators through a 100ms cycle communication management task (Com_100msManage). At the same time, it calls the initialization (system_control_initialize) and step execution (system_control_step) interfaces of the upper-level control software to drive the hierarchical control logic to run, complete data processing and control command output.

[0044] Hierarchical control logic can effectively address the control requirements of multivariable and nonlinear systems, improving the system's control accuracy, response speed, and robustness.

[0045] Traditional PLCs or industrial PCs are based on a modular architecture with low integration. They require a large number of external interface circuits to achieve system control, which further increases the system size and cost. Moreover, their closed hardware architecture makes it difficult to modify and upgrade the control algorithm, making it difficult to adapt to the dynamic control requirements of Brayton cycle systems under diverse operating conditions.

[0046] Based on this, the main control unit of this application adopts a highly integrated embedded controller, which has a built-in multi-channel ADC, CAN controller and floating-point arithmetic unit, and can directly connect to sensor signals and actuators without the need for additional hardware expansion modules, effectively simplifying the system architecture.

[0047] In some embodiments, the main control unit has a built-in floating-point arithmetic unit for providing hardware computing power for the basic control layer and the optimized control layer.

[0048] In some embodiments, the main control unit has a built-in multi-channel analog-to-digital converter, and the main control unit is connected to the sensor through the analog-to-digital converter.

[0049] The control system collects key operating parameters, such as temperature, pressure, and flow rate, through multiple sensors. The collected analog signals are converted into digital signals by a multi-channel analog-to-digital converter (ADC) built into the main control unit. Subsequently, the main control unit executes a hierarchical control strategy based on these operating parameter data.

[0050] In this embodiment, the main control unit has a built-in ADC, which can directly interface with sensor signals without the need for additional hardware expansion modules. This effectively simplifies the system architecture, ensures that the system can accurately acquire real-time data, and provides a reliable foundation for subsequent control algorithms.

[0051] The main control unit has a built-in Controller Area Network (CAN) controller, which is connected to the communication network.

[0052] In the control system, a communication network is used to connect the sensor, the main control unit, and the actuator.

[0053] As an example, the communication network uses the CAN communication protocol to realize data transmission and control command sending between the main control unit and various sensors and actuators. In this embodiment, the CAN communication protocol has high reliability and real-time performance, which can meet the communication requirements of industrial control systems.

[0054] In this embodiment, the main control unit has a built-in multi-channel CAN controller. The data of the operating parameters collected by the sensor are converted from analog to digital and processed by signal processing before being transmitted to the main control unit via CAN communication.

[0055] As an example, CAN control includes a CAN FD controller. Continuing with the previous embodiment, the AURIX chip integrates a multi-channel CAN FD controller, which supports high-speed data transmission and redundant communication design, reducing communication latency by 30% compared to traditional industrial PC Ethernet communication solutions, while also reducing the power consumption of external communication modules.

[0056] After executing the hierarchical control strategy formed by the basic control layer and the optimized control layer, the main control unit also sends the calculated control commands to the actuators through the CAN communication network to adjust the operating state of the Brayton cycle system.

[0057] Compared to traditional PLCs or industrial PCs, which are based on modular architectures, have low integration, and require a large number of external interface circuits to achieve system control, the control system of this application can directly connect to sensor signals and actuators through the multi-channel ADC, CAN controller, and floating-point arithmetic unit built into the main control unit. This eliminates the need for additional hardware expansion modules, effectively simplifies the system architecture, ensures that the system can accurately acquire real-time data, and provides a reliable foundation for subsequent control algorithms.

[0058] In the control system, at least one actuator is used to receive control commands to adjust the operating state of the Brayton cycle system.

[0059] Actuators include, but are not limited to, fuel valves, frequency converters, etc.

[0060] Continue to refer to Figure 2 In the control system of the Brayton cycle system, various sensors and acquisition modules are responsible for acquiring the key operating parameters of the system. For example, the flow sensor acquires the closed-loop flow (outputs a 0-5V voltage signal), the low-pressure / high-pressure sensor acquires the closed-loop pressure (transmits data via CAN), the T-type / K-type thermocouple with the temperature acquisition module acquires the closed-loop high and low temperatures (outputs a mV voltage signal), the voltage acquisition module acquires the bus voltage, the generator controller feeds back physical signals such as generator speed and motor temperature, the electric heating cabinet feeds back electrical signals such as heating rod voltage and current, the bidirectional load feeds back the status such as bus current, and the valves feed back the actual and target status. After the underlying signals are processed by ADC conversion and CAN message encapsulation, they are transmitted to the main control unit ECU via USB-CAN or CAN link. The ECU, as the core control unit, receives and processes the data and outputs control commands. The ECU and the host computer communicate via TCP messages through the RJ-45 interface. The host computer can obtain DC power data through a power analyzer and can also receive CAN message data transmitted by the ECU, enabling remote monitoring and command issuance of the system. The motor controller and other actuators receive commands from the ECU, adjust the motor's operating state, and form a complete control closed loop of data acquisition, command calculation, and execution feedback.

[0061] Based on the above embodiments, in the control system of the Brayton cycle system, the at least one sensor includes a vibration sensor and a signal processing board connected to the vibration sensor.

[0062] The signal processing board is configured to preprocess the vibration signals collected by the vibration sensor and send the processed vibration characteristic data to the main control unit through the communication network.

[0063] In this embodiment, the vibration sensor may be an eddyNCDT 3070.

[0064] In this embodiment, a high-precision vibration sensor is used to collect the main frequency signal of the turbine generator, and a dedicated signal processing board is designed to transmit the data to the main control unit (ECU) via CAN communication, thereby improving the system status monitoring capability.

[0065] Reference Figure 4 , Figure 4 This is a structural diagram of a vibration signal processing board for a Brayton cycle system control system, as provided in this manual. The signal processing board integrates a signal interface for the vibration sensor, a filtering unit, a feature extraction module, and a CAN communication interface.

[0066] The vibration signal of the main frequency of the turbine generator in the Brayton cycle system is collected by a vibration sensor and the original vibration signal is transmitted to the signal processing board. The signal processing board filters the original vibration signal to remove environmental interference noise, and then uses a built-in algorithm to extract the features of the vibration signal and select the effective main frequency feature data related to the turbine's operating state. The processed vibration characteristic data is encapsulated into CAN messages and transmitted to the ECU main control unit via the onboard CAN communication interface, providing the ECU's fault detection module with key monitoring data on the turbine mechanical status.

[0067] Based on the above embodiments, in the control system of the Brayton cycle system, the main control unit has a built-in hardware-level fault detection module.

[0068] The fault detection module is configured to perform real-time diagnosis based on the operating parameters and a preset rule base, and automatically trigger protection actions when a fault is detected.

[0069] In this embodiment, compared to PID control, which lacks a diagnostic and protection mechanism for system faults and abnormal conditions and is prone to unstable operation of the system under extreme conditions, this application uses the fault diagnosis logic on the main control unit and the built-in hardware self-test function of the chip to perform real-time health monitoring and protection of the entire control system, ensuring the safe operation of the system.

[0070] In the control system of the Brayton cycle system presented in this application, the shortcomings of traditional Brayton cycle control methods in multivariable and nonlinear systems, as well as the problems of large size, high cost, high energy consumption, and poor algorithm flexibility of traditional controllers, are solved by adopting a hierarchical control strategy and a highly integrated hardware architecture. This provides an efficient, reliable, and intelligent control system solution. A specific example is given, using the AURIX TC234LP as the main chip, integrating data acquisition, algorithm calculation, and communication control functions into a single chip, replacing the multi-module combination scheme of traditional PLCs or industrial control computers, and achieving system miniaturization and low power consumption design.

[0071] The modules / hardware in the control system of the Brayton cycle system are used to implement the control method of the Brayton cycle system as described below.

[0072] Figure 5 This is a flowchart illustrating a control method for a Brayton cycle system provided in this manual.

[0073] The control method for the Brayton cycle system is applied to the main control unit of the control system for the Brayton cycle system described above. In this embodiment, the main control unit (ECU) is an embedded controller used to implement the control method for the Brayton cycle system.

[0074] In the first embodiment, the control method for the Brayton cycle system includes: Step 102: Receive operating parameters for at least one Brayton cycle system.

[0075] Key operating parameters of the Brayton cycle system, such as temperature, pressure, and flow rate, are collected by multiple sensors. The collected analog signals are converted into digital signals by an analog-to-digital converter (ADC), and after processing, the digital signals are transmitted to the main control unit via CAN communication.

[0076] The main control unit receives operating parameters of at least one Brayton cycle system and processes them based on the control algorithm thereon, providing a data foundation for subsequent control of the Brayton cycle system.

[0077] In some embodiments, the master control unit acquires data from the sensing module periodically or event-triggered through its hardware interface (such as a built-in ADC, CAN controller, etc.).

[0078] In this embodiment, the operating parameters are a complete set, including but not limited to: core thermodynamic parameters such as compressor outlet pressure and temperature, combustion chamber temperature, turbine inlet / outlet temperature, and fuel flow rate. These form the basis for evaluating cycle status and efficiency.

[0079] Mechanical and power parameters: turbine / generator speed.

[0080] These parameters together constitute a complete set of information describing the global operating status of the system.

[0081] Step 104: Optimize the global operating state of the Brayton cycle system based on the operating parameters and output the optimization target.

[0082] The optimization calculation in the main control unit is a dynamic decision based on the system's multivariate coupling model and real-time operating parameters.

[0083] In this embodiment, the main control unit adopts a hierarchical control strategy, including a bottom basic control layer and an upper optimization control layer.

[0084] In some embodiments, the basic control layer is responsible for basic PID control, while the optimization control layer employs a combination of model predictive control (MPC) and fuzzy logic control (FLC).

[0085] In this embodiment, a multi-layered hybrid control architecture is formed by combining the underlying PID control with the upper-level model predictive control (MPC) and fuzzy logic control (FLC).

[0086] Among them, the upper-level control (MPC+FLC) is responsible for global optimization and intelligent decision-making, while the lower-level control (PID) is responsible for fast response to basic parameter adjustment, balancing control accuracy and response speed to adapt to the nonlinear and multivariable characteristics of the Brayton system.

[0087] In some embodiments, the step of optimizing the global operating state of the Brayton cycle system based on the operating parameters and outputting the optimization objective includes: Based on the operating parameters and a control algorithm combining model predictive control and / or fuzzy logic control, the global operating state of the Brayton cycle system is optimized and the optimization target is output.

[0088] Reference Figure 6 , Figure 6This is a control logic diagram of the optimized control layer of a Brayton cycle system control system provided in this manual. This diagram represents the control logic module of the optimized control layer built in the SIMULINK environment, focusing on the connection between data interaction and control algorithms. It serves as the concrete implementation carrier for the upper-layer MPC and / or FLC control strategies. It includes a module for receiving and parsing messages and inputting analog signal acquisition results, a data processing and control algorithm module, and a module for constructing messages and outputting control signals.

[0089] The specific processing logic is as follows: The message parsing and analog quantity acquisition result input module connects to the CAN message transmitted by the ECU's underlying communication module and parses out the analog quantity acquisition results such as temperature, pressure, flow rate, and vibration collected by the sensor. The parsed raw data is transmitted to the data processing and control algorithm module to perform preprocessing such as data verification and standardization transformation, and to convert it into an input quantity that the control algorithm can recognize. The preprocessed data is input into the algorithm of the optimization control layer (which selects model predictive control (MPC), fuzzy logic control (FLC), or a combination of both control logic according to the operating conditions). The algorithm combines the preset model and fuzzy rules to complete the calculation and generate the optimization target. Subsequently, the target input is optimized and fed into the algorithm of the basic control layer. The algorithm generates control quantities for the actuators. The control quantities are encapsulated into CAN control messages by the message construction and control output module and sent to actuators such as regulating valves and frequency converters through the underlying communication interface. At the same time, some operating data is sent back to the host computer to realize the closed-loop execution of control commands.

[0090] In this embodiment, Model Predictive Control (MPC) establishes a mathematical model of the system and, using the built-in model, predicts the changing trends of key parameters (such as temperature, pressure, and efficiency) over a future period, i.e., predicts the behavior of the Brayton cycle system, based on the current operating parameters as initial conditions. Furthermore, considering physical constraints (such as upper temperature limits and safe pressure ranges), it solves for a series of future control action sequences that optimize system performance (such as efficiency and load tracking capability).

[0091] The optimization objective output in this step is usually the target value of the first action in the sequence. For example, the optimal combustion chamber temperature setpoint T_set_new at the next moment.

[0092] Therefore, it can be seen that MPC can handle multivariable and nonlinear systems, improving the control accuracy and robustness of the system.

[0093] Fuzzy logic control (FLC) evaluates operating parameters based on preset fuzzy logic rules, handling uncertainties and fuzzy concepts. If it determines that there is a risk in the operating condition that is not covered by the model, FLC will output a corrective or protective target, thus achieving intelligent control of the system.

[0094] As an example, uncertainty information and fuzzy concepts can be parameters such as vibrations and rates of change that reflect uncertainty and abnormal trends.

[0095] Therefore, FLC can effectively cope with the uncertainty and complexity of the system, and improve the system's adaptability and reliability.

[0096] The outputs of MPC and FLC in the main control unit undergo collaborative decision-making to ultimately form a safe and feasible optimization objective that integrates optimality and robustness. It should be noted that the collaborative decision-making can be achieved by MPC and FLC operating in a specific order, with one algorithm optimizing the result of the other to obtain the final optimization objective; or by parallel optimization, where the final results of both algorithms are arbitrated to determine the final optimization objective. Further specific limitations are not provided here. The main control unit (such as the AURIX TC234LP main chip) supports online programming and dynamic algorithm loading, allowing the algorithm execution logic to be set according to actual needs, including but not limited to selection based on system computing power, response efficiency, and scenario requirements.

[0097] By combining MPC and FLC, multivariable and nonlinear systems can be effectively handled, improving control accuracy.

[0098] Step 106: Based on the operating parameters and the optimization objective, generate control commands for rapid closed-loop adjustment of the basic operating loop of the Brayton cycle system.

[0099] In some embodiments, the basic control layer is responsible for basic PID control.

[0100] A PID controller generates a control signal by performing proportional, integral, and derivative operations on the error signal, thereby achieving basic control of the system. Therefore, PID control can quickly respond to system deviations and maintain stable system operation.

[0101] The specific implementation process is as follows: The basic control layer of the main control unit contains multiple PID control loops. Each loop receives two types of inputs: real-time measured values ​​of the operating parameters from sensors, and target values ​​for the operating parameters. These target values ​​refer to the normal range of the operating parameters under the current operating conditions. This normal range can be a single numerical value or a range with upper and lower limits. In this embodiment, a dynamic target value, adapted to the current operating conditions of the Brayton cycle system, is calculated by the upper-level optimization control layer; this is the optimization target.

[0102] Next, based on the error between the real-time measured values ​​of the operating parameters and the optimization target, a specific control command that can directly drive the actuator is generated by adjusting the rules. For example, through rapid proportional-integral-derivative calculations, a control command is generated to increase the opening of the fuel regulating valve by 3%. This process runs at a frequency much higher than that of the optimization layer, ensuring accurate and rapid tracking of the optimization target, thereby translating the overall strategic intent of the upper layer into precise control actions at the lower level.

[0103] In summary, the main control unit employs a hierarchical control strategy. The lower level uses PID control to adjust basic parameters, while the upper level combines Model Predictive Control (MPC) and Fuzzy Logic Control (FLC) to optimize the system's dynamic performance. This enables the Brayton cycle system to maintain stable operation under various conditions and reduces system instability caused by improper control.

[0104] Step 108: Control the operation of the Brayton cycle system according to the control command.

[0105] The generated control commands are sent to the corresponding actuators via a communication network (such as a CAN bus). Actuators include, but are not limited to, fuel proportional valves, compressor guide vane actuators, and frequency converters.

[0106] The actuator converts electrical signals into physical actions (such as changing valve opening or adjusting blade angle), thereby directly regulating the working fluid flow, pressure, and energy input of the Brayton cycle system. This action alters the physical state of the system, and these state changes are detected by the sensor module, generating new operating parameters and initiating the next control cycle. The entire process operates in a closed-loop, real-time cycle, dynamically maintaining the system at an optimized, safe, and stable operating point under various conditions.

[0107] In some embodiments, the main control unit can be a controller based on the AURIX TC234LP as the main chip. On one hand, based on the open programming architecture of the AURIX TC234LP, the control algorithm can be upgraded online to expand its functionality without replacing hardware, solving the problems of traditional PID control algorithms being fixed and difficult to modify. Alternatively, other multi-core microcontroller units can be used, with configurations identical to the AURIX TC234LP, which will not be elaborated further here. On the other hand, controllers using the AURIX TC234LP main chip are more than 60% smaller in size, 40% lower in hardware cost, and 25% lower in power consumption compared to traditional PLC or industrial PC solutions, significantly improving system deployment flexibility and economy.

[0108] Based on the first embodiment described above, a second embodiment of the control method for the Brayton cycle system is proposed.

[0109] In some embodiments, after receiving the operating parameters of at least one Brayton cycle system, the method further includes: Real-time diagnosis is performed based on the aforementioned operating parameters and a preset rule base. When a fault is detected, the protection action is automatically triggered.

[0110] The main control unit has a built-in hardware-level fault detection module, on which a fault diagnosis and protection mechanism runs, which can promptly detect and handle system faults and ensure the safe operation of the system.

[0111] Specifically, the system quickly identifies potential system faults (such as over-temperature, over-pressure, and abnormal vibration) by monitoring changes in operating parameters in real time and combining them with a preset rule base for fault diagnosis.

[0112] The fault diagnosis mechanism includes preset threshold judgment and dynamic rule matching.

[0113] For example, if the turbine outlet temperature exceeds a threshold, an over-temperature fault is triggered.

[0114] For example, if the amplitude of the dominant vibration frequency increases and is accompanied by the appearance of specific high-frequency components, while the lubricating oil temperature is abnormal, then an early warning of bearing wear is triggered. In some cases, the confidence level of this diagnostic conclusion can also be given for reference.

[0115] The diagnostic module scans the latest operating parameters at an extremely high frequency (e.g., milliseconds) and matches them with conditions in the rule base, ensuring that the system can diagnose anomalies in a timely manner.

[0116] Reference Figure 7 , Figure 7 This is a logic diagram for fault diagnosis of a Brayton cycle system control system provided in this manual.

[0117] Algorithms (such as fitting models based on historical data or output from the MPC prediction module) are used to calculate the expected motor speed in advance, serving as a benchmark for subsequently determining whether the actual speed is abnormal. Faults are detected by comparing the predicted values ​​with the real-time feedback values.

[0118] For each operating parameter or core operating parameter (motor speed, motor current, motor power, battery current, battery power, motor overspeed, etc.), determine whether to trigger the fault flag bit (e.g., 1 = fault, 0 = no fault, or Motor_Speed_Error indicates motor speed fault, etc.).

[0119] Then, the fault flag bits are integrated into "ECU Fault State" (ECU_Error_State) through "Bitwise OR". That is, as long as one fault flag bit is 1, "ECU Fault State" will become "Fault (non-zero value)" and when all flag bits are 0, "ECU Fault State" will be "No Fault (0 value)".

[0120] For example, if "motor overspeed fault (1)" and "battery current fault (1)" are triggered at the same time, and other flag bits are 0, then "ECU fault flag" = 1, indicating that there is an emergency fault in the system.

[0121] Once a fault is detected, the system will take corresponding measures and automatically trigger protection actions. These include, but are not limited to, adjusting control signals and switching to backup equipment to enhance system robustness.

[0122] Reference Figure 8 , Figure 8 This is another logic diagram for fault diagnosis of a Brayton cycle system control system provided in this manual.

[0123] The triggering conditions for protective actions include the following two: One is that the "ECU fault status" is "fault (non-zero value)"; Secondly, "PC FaultStop enable" is "on (1)". When PC FaultStop enable is enabled (also known as on state), the system can only perform shutdown or emergency protection actions after detecting a fault; if it is disabled (or off state), even if a fault exists, the system will not trigger shutdown and will only issue an alarm.

[0124] In other words, if the fault state persists and no reset signal (ECU Error State ResetSignal) is received, the ECU fault action (ECU_Error_Act) is triggered, which links the PC to enable FaultStop, enabling protective measures such as automatic system shutdown or switching to backup equipment.

[0125] It should be noted that in other examples, the protection action is triggered only if the first condition above is met, namely, the "ECU fault status" is "fault (non-zero value)".

[0126] In some embodiments, if a reset signal is received and the fault is cleared, the fault state is cleared and the system is restored to normal operation.

[0127] The triggering conditions for fault reset include the following: All single-parameter fault flags are reset to 0 (the fault is actually resolved); Received "ECU Error State Reset Signal". This signal can be sent manually via a host computer or automatically generated by the system after detecting that the parameters are normal.

[0128] After a fault reset is performed, the "ECU Fault State (ECU_Error_State)" is reset to 0, the "ECU Fault Action (ECU_Error_Act)" stops, the system exits protection mode, and resumes normal operation.

[0129] In some embodiments, the response is tiered according to the severity of the fault. The ECU outputs a "fault action command (ECU_Error_Act)," and the specific action depends on the fault type / severity: For early warnings or minor anomalies, the system may automatically fine-tune operating parameters through a hierarchical control strategy that combines the basic control layer and the optimization control layer of the main control unit. For example, if it is diagnosed that a slight decrease in efficiency may be related to scaling, the MPC can automatically adjust other parameters to compensate; or based on the vibration trend, the FLC can proactively and gradually reduce the load.

[0130] When a fault is detected in a sensor or actuator, the system can automatically switch to a backup device or isolate the faulty component.

[0131] In the event of a serious malfunction, the system will trigger the highest level of protection, such as emergency fuel supply cut-off or motor power switching to trigger a shutdown.

[0132] The automatic triggering mechanism can eliminate the delay caused by manual judgment and operation, prevent the escalation of the fault within milliseconds, and minimize the risk of accidents.

[0133] As an example, the AURIX TC234LP main chip has a built-in hardware-level fault detection module. The AURIX chip's fast processing capability can monitor the chip's power supply, clock, and communication status in real time. Combined with software-level fault diagnosis logic, it forms a multi-level protection mechanism, which improves the response speed by more than 50% compared to the external relay protection scheme of traditional PLCs.

[0134] In some embodiments, the operating parameters include vibration signals, and the method further includes: Receive processed vibration characteristic data, which is obtained by preprocessing the collected vibration signal; Based on the vibration characteristic data, fault diagnosis is performed on the Brayton cycle system.

[0135] At least one sensor includes a vibration sensor, which acquires the main frequency signal of the turbine generator, i.e., the vibration signal. As an example, the vibration sensor could be an eddyNCDT 3070.

[0136] This control system also includes a signal processing board for processing data from vibration sensors. The signal processing board preprocesses the acquired vibration signals to obtain vibration characteristic data. Preprocessing includes, but is not limited to, filtering and feature extraction, to obtain key indicators that directly reflect the system's health status, such as the peak displacement and dominant frequency amplitude of shaft vibration.

[0137] The processed vibration characteristic data is transmitted to the main control unit (ECU) via CAN communication, which greatly reduces the processing burden of the main control unit and provides higher quality information, thereby improving the condition monitoring capability of the Brayton cycle system.

[0138] When a fault is diagnosed, the diagnosis is immediately incorporated into the overall system decision-making.

[0139] For example, triggering an alert to notify maintenance personnel.

[0140] For example, diagnostic results can be fed back to the upper-level optimization control algorithm (such as fuzzy logic control FLC) in real time. The FLC will then adjust its optimization objectives, such as proactively and gradually reducing the speed or load to prevent potential faults from worsening and to achieve predictive maintenance.

[0141] This embodiment elevates vibration signals from conventional condition monitoring to key parameters deeply integrated with control algorithms, and integrates them into the main control unit via the CAN bus, thereby enhancing the accuracy and foresight of fault diagnosis.

[0142] This application provides a control method for Brayton cycle systems, which, compared with the current methods for handling complex nonlinear or multivariable systems, has the following advantages: On the one hand, by forming a closed loop of perception, decision-making, and execution through at least one sensor, a main control unit, at least one actuator, and a communication network, the system can automatically collect operating parameters, automatically calculate and issue control commands, and automatically drive actuator actions, thereby achieving continuous and autonomous adjustment of the Brayton cycle operating state, significantly reducing the dependence on manual intervention and improving the system's automation level.

[0143] On the other hand, the main control unit is configured to operate simultaneously as a basic control layer and an optimization control layer. The basic control layer performs rapid closed-loop adjustment based on optimization objectives and real-time parameters, ensuring the system's rapid response to dynamic changes and basic stability. The optimization control layer calculates based on global operating parameters, and its output optimization objective guides the system towards a state with higher efficiency, better performance, or better adaptation to operating conditions. This hierarchical control strategy can both quickly stabilize local loops and dynamically optimize based on global information, thereby achieving a balance and improvement in control accuracy, response speed, and overall operating efficiency. It can rapidly iterate and optimize to adapt to the dynamic control requirements of complex operating conditions such as multivariable and nonlinear conditions.

[0144] Figure 9 This is a schematic diagram of a computer-readable storage medium 140 provided in this disclosure, on which a computer program is stored, which, when executed by a processor, implements the method of any embodiment of this disclosure.

[0145] This disclosure also provides a computing device, including a memory and a processor; the memory is used to store computer instructions that can be executed on the processor, and the processor is used to implement the methods of any embodiment of this disclosure when executing the computer instructions.

[0146] Figure 10 This is a schematic diagram of the structure of a computing device provided in this disclosure, such as... Figure 10 As shown, the computing device 15 may include, but is not limited to: a processor 151, a memory 152, and a bus 153 connecting different system components (including the memory 152 and the processor 151).

[0147] The memory 152 stores computer instructions that can be executed by the processor 151, enabling the processor 151 to perform the control method of the Brayton cycle system according to any embodiment of this disclosure. The memory 152 may include a random access memory (RAM) 1521, a cache memory 1522, and / or a read-only memory (ROM) 1523. The memory 152 may also include a program tool 1525 having a set of program modules 1524, including but not limited to: an operating system, one or more application programs, other program modules, and program data. One or more combinations of these program modules may include an implementation of a network environment.

[0148] Bus 153 may include, for example, a data bus, an address bus, and a control bus. The computing device 15 can also communicate with external devices 155 via I / O interface 154, such as a keyboard or a Bluetooth device. The computing device 15 can also communicate with one or more networks via network adapter 156, such as a local area network (LAN), a wide area network (WAN), or a public network. As shown in the figure, the network adapter 156 can also communicate with other modules of the computing device 15 via bus 153.

[0149] Furthermore, although the operations of the methods disclosed herein are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0150] While the spirit and principles of this disclosure have been described with reference to several specific embodiments, it should be understood that this disclosure is not limited to the disclosed specific embodiments, and the division of aspects does not imply that features in these aspects cannot be combined for benefit; such division is merely for convenience of expression. This disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A control system for a Brayton cycle system, characterized in that, The system includes: At least one sensor is used to collect the operating parameters of the Brayton cycle system; Main control unit; At least one actuator is provided for receiving control commands to adjust the operating state of the Brayton cycle system; A communication network is used to connect the sensor, the main control unit, and the actuator; The main control unit is configured as follows: The basic control layer is used to generate control commands for closed-loop regulation of the basic operating loop of the Brayton cycle system based on the operating parameters and optimization objectives. An optimization control layer is used to perform optimization calculations on the global operating state of the Brayton cycle system based on the operating parameters, and output the optimization target to the basic control layer.

2. The control system of the Brayton cycle system as described in claim 1, characterized in that, The main control unit has a built-in CAN controller, which is connected to the communication network.

3. The control system of the Brayton cycle system as described in claim 1, characterized in that, The main control unit has a built-in multi-channel analog-to-digital converter, and the main control unit is connected to the sensor through the analog-to-digital converter.

4. The control system of the Brayton cycle system as described in claim 1, characterized in that, The main control unit has a built-in floating-point arithmetic unit, which provides hardware computing power for the basic control layer and the optimized control layer.

5. The control system of the Brayton cycle system as described in claim 1, characterized in that, The main control unit is an embedded control device; and / or; The main control unit is a multi-core microcontroller unit.

6. The control system of the Brayton cycle system as described in claim 1, characterized in that, The at least one sensor includes a vibration sensor and a signal processing board connected to the vibration sensor; the signal processing board is configured to: preprocess the vibration signal collected by the vibration sensor, and send the processed vibration characteristic data to the main control unit through the communication network; and / or, The main control unit has a built-in hardware-level fault detection module, which is configured to perform real-time diagnosis based on the operating parameters and a preset rule base, and automatically trigger protection actions when a fault is detected.

7. A control method for a Brayton cycle system, characterized in that, The method, applied to the control system of the Brayton cycle system of claim 1, comprises: Receive operating parameters for at least one Brayton cycle system; The global operating state of the Brayton cycle system is optimized based on the operating parameters, and the optimization target is output. Based on the operating parameters and the optimization objective, control commands are generated to rapidly close-loop adjust the basic operating loop of the Brayton cycle system. The operation of the Brayton cycle system is controlled according to the control instructions.

8. The control method for a Brayton cycle system as described in claim 7, characterized in that, The optimization calculation of the global operating state of the Brayton cycle system based on the operating parameters, and the output of the optimization objective, includes: Based on the operating parameters and a control algorithm combining model predictive control and / or fuzzy logic control, the global operating state of the Brayton cycle system is optimized and the optimization target is output.

9. The control method for the Brayton cycle system as described in claim 7, characterized in that, The operating parameters include vibration signals, and the method further includes: Receive processed vibration characteristic data, which is obtained by preprocessing the collected vibration signal; Based on the vibration characteristic data, fault diagnosis is performed on the Brayton cycle system.

10. The control method for a Brayton cycle system as described in claim 7, characterized in that, After receiving the operating parameters of at least one Brayton cycle system, the method further includes: Real-time diagnosis is performed based on the aforementioned operating parameters and a preset rule base. When a fault is detected, the protection action is automatically triggered.

11. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements a control method for a Brayton cycle system as described in any one of claims 7 to 10.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed, implements the control method for the Brayton cycle system as described in any one of claims 7 to 10.

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