Model prediction-based gate voltage regulation control method and device, and medium
By employing a model-based gate voltage regulation control method, and utilizing piecewise linear models and multi-objective rolling optimization, adaptive regulation of IGBT drive voltage is achieved, solving the problems of current capacity improvement and safety, and enhancing the reliability and stability of power electronic devices.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-04
- Publication Date
- 2026-03-31
AI Technical Summary
Existing technologies struggle to achieve precise and adaptive control of IGBT drive voltage under complex operating environments, leading to insufficient current capacity or overheating damage, which affects the safety and reliability of power electronic devices.
A model-predictive gate voltage regulation control method is adopted. By obtaining the operating parameters of the IGBT, the optimal control sequence is determined by using a piecewise linear model for prediction and multi-objective rolling optimization. The control quantity is then applied through a programmable gate driver. Combined with online learning and closed-loop correction, adaptive control of the IGBT is achieved.
It achieves precise control of IGBTs in complex environments, improves current capacity, ensures the best balance between safety and dynamic performance, and has robustness in the face of device aging and environmental changes.
Smart Images
Figure CN121764288A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of integrated circuits, and in particular to a model-predictive gate voltage regulation control method, device, and medium. Background Technology
[0002] With the continuous development of semiconductor technology, the voltage withstand capability and current breaking capability of power devices have gradually improved. One of the current research focuses is to improve the turn-off reliability of power electronic devices, such as by using snubber circuits, low stray inductance design, and soft turn-off strategies to suppress overvoltage and dynamic avalanche effects. However, research on the current carrying capacity of devices in the on-state remains insufficient.
[0003] Currently, the common method to increase the current capacity of a single power electronic device is to increase the device's drive voltage. The maximum current that a power electronic device can safely carry is affected by multiple parameters, including its drive voltage, internal physical structure, and operating junction temperature. Some key parameters are not only difficult to measure accurately in real time, but also change dynamically with varying operating conditions, posing significant challenges to the precise adjustment of the gate voltage. If the drive voltage is insufficient, the device's current potential cannot be fully realized; if it is excessive, it can easily lead to overheating and damage, or even thermal runaway. Therefore, how to achieve precise and adaptive control of the IGBT drive voltage under complex operating environments, thereby maximizing its current capacity while ensuring safe operation, has become a problem that needs to be solved in the field of power electronics technology. Summary of the Invention
[0004] This application provides a model-predictive gate voltage regulation control method, device, and medium to solve the problems in the background art.
[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.
[0006] According to a first aspect of the embodiments of this application, a model-predictive gate voltage regulation control method is provided, comprising: Obtain the current operating parameters of the IGBT and determine the current operating status; Select a piecewise linear model from a pre-built model library based on the current working status; The piecewise linear model is used to predict the current operating state of the IGBT and obtain candidate values for the control sequence and future changes in current, temperature and voltage at future times. The optimal control sequence is determined through multi-objective rolling optimization; The first value in the optimal control sequence is applied to the IGBT through the programmable gate driver, and the operating state of the IGBT at the next moment is measured. The error between the operating state of the IGBT at the next moment and the predicted changes in current, temperature and voltage is calculated, and the error is used to correct the parameters of the piecewise linear model; the corrected piecewise linear model is used for prediction of the next cycle.
[0007] According to one embodiment of this application, obtaining the current IGBT operating parameters includes: Current collector current Casing temperature collector voltage Gate voltage and utilize the shell temperature Estimate the junction temperature .
[0008] According to one embodiment of this application, determining the current working state specifically includes: According to the junction temperature Estimate the current threshold voltage ; According to the junction temperature Calculate the temperature-dependent transconductance; Based on collector current Transconductance, threshold voltage Calculate the estimated value of the Miller plateau voltage. ; when The IGBT is determined to be in a completely off state at this time; when The IGBT is determined to be in a linear amplification state at this time; when It is assumed that the IGBT is in a saturated conduction state at this time; when When this occurs, it is determined to be in a thermal protection state; among which The preset temperature protection threshold.
[0009] According to one embodiment of this application, the step of selecting a piecewise linear model from a pre-established model library based on the current working state specifically includes: Select a piecewise linear model from a pre-built model library. Each operating state corresponds to pre-defined state-space equation parameters to ensure that the model matches the actual operating state of the IGBT.
[0010] According to one embodiment of this application, the step of using the piecewise linear model to predict based on the current operating state of the IGBT to obtain candidate values of the control sequence for future times and future changes in current, temperature, and voltage specifically includes: Using the acquired current IGBT operating parameters as initial prediction conditions, a set of future IGBT parameters is generated using a piecewise linear model. Candidate values of the control sequence at each time step ; The initial prediction conditions and control sequence are input into the selected piecewise linear prediction model, and multi-step forward simulation is performed to obtain the predicted future... Current response at time 1 Temperature change and voltage changes .
[0011] According to one embodiment of this application, determining the optimal control sequence through multi-objective rolling optimization specifically includes: In the control time domain Find the optimal one sequence Under the given constraints, the defined cost function J is minimized; where the cost function is... , This represents the predicted value of the collector current. This is a reference value for the collector current. This is the predicted value for the casing temperature. The safe temperature threshold for the casing. This represents the rate of change of the gate voltage.
[0012] According to one embodiment of this application, in multi-objective rolling optimization, when When the current is large, increase the current weight. To ensure The tracking effect; when the temperature rises, the effect increases. Prioritize temperature protection; reduce temperature when a rapid response is required. Allowing for faster change.
[0013] According to one embodiment of this application, the constraints include: The hard constraint is: , The soft constraint is: , ;in, These are slack variables.
[0014] According to a second aspect of this application, an electronic device is provided, comprising: At least one processor; and a memory communicatively connected to said at least one processor; The memory stores instructions executable by the at least one processor, which executes the instructions stored in the memory to perform the method described in any of the first aspects.
[0015] According to a third aspect of this application, a computer-readable storage medium is provided for storing instructions that, when executed, cause the method described in the first aspect to be implemented.
[0016] Compared with existing technologies, the beneficial effects of adopting the above technical solution are as follows: 1. This invention adopts a piecewise linear model, which can identify the working mode in real time based on the current, temperature and other conditions and switch to the optimal prediction model, thus solving the problem of strong nonlinear modeling of power devices.
[0017] 2. This invention designs a dynamic weight optimization mechanism, which enables the cost function to adaptively adjust the priority of the control target according to the different operating states of the power device, thereby achieving the best balance between safety and dynamic performance.
[0018] 3. This invention establishes an online learning and closed-loop correction architecture, enabling the system to have long-term robustness in the face of device aging and environmental changes. Attached Figure Description
[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.
[0020] Figure 1 This is a flowchart of the model prediction-based gate voltage regulation control method according to an embodiment of this application.
[0021] Figure 2 This is a schematic diagram illustrating the implementation of the model prediction-based gate voltage regulation control method according to an embodiment of this application.
[0022] Figure 3 This is a schematic diagram of the IGBT structure according to an embodiment of this application.
[0023] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation
[0024] The embodiments of this application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar modules or modules having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application. Rather, the embodiments of this application include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.
[0025] To address the shortcomings of existing technologies, this application proposes a model-predictive gate voltage regulation control method to improve the current capacity of individual power electronic devices. This method features adaptive adjustment of the device's own parameter variations. Please refer to... Figure 1 , Figure 2 The specific steps are as follows: S101. Obtain the current operating parameters of the IGBT and determine the current operating status.
[0026] like Figure 3 As shown, the maximum collector current that can flow through an IGBT With junction temperature collector-emitter voltage Gate voltage It is related to its own parameters. Among them, the junction temperature can only be obtained from the casing temperature. Indirect measurement, and the fact that its own parameters are often difficult to estimate, leads to collector current... Difficult to pass the gate voltage To control.
[0027] In this embodiment, The current collector current is obtained through the sensor at all times. Casing temperature collector voltage Gate voltage And estimate the junction temperature .
[0028] After determining the working parameters, the current working status is further identified, specifically including: First estimate the current threshold voltage : (1) in, This represents the threshold voltage at a junction temperature of 25°C and can be obtained from the IGBT manufacturer's datasheet. It is the IGBT threshold voltage Temperature coefficient.
[0029] Then, the temperature-dependent transconductance value is obtained: (2) in, This represents the transconductance at a junction temperature of 25°C and can be obtained from the IGBT manufacturer's datasheet. It is the transconductance of IGBT Temperature coefficient.
[0030] Finally, the estimated value of the Miller plateau voltage was calculated. : (3) when The IGBT is determined to be in a fully off state at this time, and the off-behavior model is selected for subsequent prediction. The IGBT is determined to be in a linear amplification state, and a linear model is selected for prediction. It is assumed that the IGBT is in a saturated conduction state at this time, and a saturation model is selected for prediction. When the casing temperature... When this condition is detected, the system is determined to be in a thermal protection state, and the thermal limitation protection model is selected. Temperature protection thresholds set manually.
[0031] S102. Select a piecewise linear model from the pre-established model library based on the current working status.
[0032] In practical applications, based on the determined working state, a piecewise linear model can be selected from a pre-established model library, with the model parameters being... Each operating mode corresponds to specific state-space equation parameters, ensuring that the model matches the actual operating mode of the IGBT.
[0033] S103. Using the piecewise linear model, predict the current operating state of the IGBT to obtain candidate values of the control sequence for future moments and future changes in current, temperature and voltage.
[0034] The measured state vector As initial prediction conditions, a set of future predictions is generated using a piecewise linear model. Candidate values of the control sequence at each time step .
[0035] The initial state and control sequence are then input into the selected piecewise linear prediction model for multi-step forward simulation to obtain the predicted future. Current response at time 1 Temperature change and voltage changes .
[0036] S104. Determine the optimal control sequence through multi-objective rolling optimization.
[0037] In this embodiment, the optimizer is used in the control time domain. Find the optimal one sequence The cost function is defined as follows: (4) in, This represents the predicted value of the collector current. This is a reference value for the collector current. This is the predicted value for the casing temperature. This is the safe temperature threshold for the casing. This represents the rate of change of the gate voltage.
[0038] when When the current is large, the current weight should be increased. To ensure The tracking effect. As the temperature increases, the... Prioritize temperature protection. When a rapid response is required, appropriately reduce... Allowing for faster Changes. The hard constraints are: , The soft constraint is: , . Let be a slack variable, a tiny positive value, indicating that under extreme conditions, the current limit can be briefly and slightly exceeded. and voltage change rate limit.
[0039] S105. Apply the first value in the optimal control sequence to the IGBT through the programmable gate driver, and measure the operating state of the IGBT at the next moment.
[0040] In this embodiment, the optimal control quantity is applied, that is, only the first value in the optimal control sequence is used. The sequence is discarded by applying a programmable gate driver to the IGBT. Subsequent control inputs will be re-optimized based on new measurements in the next sampling period. Control decisions and execution times are recorded for performance analysis and fault diagnosis. S106. Calculate the error between the operating state of the IGBT at the next moment and the predicted changes in current, temperature and voltage, and use the error to correct the parameters of the piecewise linear model; the corrected piecewise linear model is used for prediction of the next cycle.
[0041] Finally, feedback correction and model updates are also required. Constantly acquire new actual measurement data, i.e., remeasure the collector current. Casing temperature collector-emitter voltage Gate voltage And calculate the error between the actual state and the predicted state. Based on the prediction error, adjust the model parameters... Perform online corrections. Use the corrected model for prediction optimization in the next cycle to form adaptive closed-loop control.
[0042] This invention has the following advantages: 1. This invention adopts a piecewise linear model, which can identify the working mode in real time based on the current, temperature and other conditions and switch to the optimal prediction model, thus solving the problem of strong nonlinear modeling of power devices.
[0043] 2. This invention designs a dynamic weight optimization mechanism, which enables the cost function to adaptively adjust the priority of the control target according to the different operating states of the power device, thereby achieving the best balance between safety and dynamic performance.
[0044] 3. This invention establishes an online learning and closed-loop correction architecture, enabling the system to have long-term robustness in the face of device aging and environmental changes.
[0045] Based on the same technical concept, embodiments of this application also provide an electronic device that can implement the model prediction-based gate voltage regulation control method provided in the above embodiments of the present invention. In one embodiment, the electronic device can be a server, a terminal device, or other electronic equipment. Figure 4 As shown, the electronic device may include: At least one processor and a memory connected to the at least one processor. In this embodiment of the invention, the specific connection medium between the processor and the memory is not limited. Figure 4 The example used is the connection between the processor and memory via a bus. The bus... Figure 4 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. Buses can be divided into address buses, data buses, control buses, etc., but for ease of representation, [the specific bus type is not shown here]. Figure 4 The processor is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, a processor can also be called a controller; there are no restrictions on the name.
[0046] In this embodiment of the invention, the memory stores instructions executable by at least one processor. By executing the instructions stored in the memory, the at least one processor can perform the model prediction-based gate voltage regulation control method described above. The processor can implement... Figure 4 The functions of each module in the device shown.
[0047] The processor is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory and calling data stored in memory, it can monitor the device's various functions and process data, thereby enabling overall monitoring of the device.
[0048] In an alternative design, the processor may include one or more processing units. The processor may integrate an application processor and a modem processor, wherein the application processor primarily handles the operating system, user interface, and applications, while the modem processor primarily handles wireless communication. It is understood that the modem processor may also not be integrated into the processor. In some embodiments, the processor and memory may be implemented on the same chip; in some embodiments, they may also be implemented separately on separate chips.
[0049] The processor can be a general-purpose processor, such as a CPU, digital signal processor, application-specific integrated circuit, field-programmable array, or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the model prediction-based gate voltage regulation control method disclosed in the embodiments of this invention can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.
[0050] Memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory can include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic memory, magnetic disk, optical disk, etc. Memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures, and accessible by a computer, but is not limited thereto. In embodiments of the present invention, memory can also be a circuit or any other device capable of implementing storage functions, used to store program instructions and / or data.
[0051] By designing and programming the processor, the code corresponding to the model prediction-based gate voltage regulation control method described in the foregoing embodiments can be embedded into the chip, enabling the chip to execute the steps of the method described in the foregoing embodiments during operation. How to design and program a processor is a technique well-known to those skilled in the art and will not be elaborated upon here.
[0052] Based on the same inventive concept, embodiments of the present invention also provide a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform a model prediction-based gate voltage regulation control method described above.
[0053] In some alternative embodiments, the present invention also provides a model prediction-based gate voltage regulation control method that can also be implemented as a program product comprising program code that, when the program product is run on a device, causes the control device to perform the steps of a model prediction-based gate voltage regulation control method according to various exemplary embodiments of the present invention as described in this specification.
[0054] It should be noted that although several units or sub-units of the apparatus have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the invention, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units. Furthermore, although the operation of the method of the invention is described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all 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.
[0055] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0056] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a server, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0057] Program code for performing the operations of this invention can be written using any combination of one or more programming languages, including object-oriented programming languages such as Java and C++, as well as conventional procedural programming languages such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.
[0058] In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).
[0059] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0060] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0061] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A gate voltage regulation control method based on model prediction, characterized in that, include: Obtain the current operating parameters of the IGBT and determine the current operating status; Select a piecewise linear model from a pre-built model library based on the current working status; The piecewise linear model is used to predict the current operating state of the IGBT and obtain candidate values for the control sequence and future changes in current, temperature and voltage at future times. The optimal control sequence is determined through multi-objective rolling optimization; The first value in the optimal control sequence is applied to the IGBT through the programmable gate driver, and the operating state of the IGBT at the next moment is measured. The error between the operating state of the IGBT at the next moment and the predicted changes in current, temperature and voltage is calculated, and the error is used to correct the parameters of the piecewise linear model; the corrected piecewise linear model is used for prediction of the next cycle.
2. The gate voltage regulation control method based on model prediction according to claim 1, characterized in that, The process of obtaining the current IGBT operating parameters includes: Current collector current Casing temperature collector voltage Gate voltage and utilize the shell temperature Estimate the junction temperature .
3. The model-predictive gate voltage regulation control method according to claim 2, characterized in that, Determining the current working status specifically includes: According to junction temperature Estimate the current threshold voltage ; According to the junction temperature Calculate the temperature-dependent transconductance; Based on collector current Transconductance, threshold voltage Calculate the estimated value of the Miller plateau voltage. ; when The IGBT is determined to be in a completely off state at this time; when The IGBT is determined to be in a linear amplification state at this time; when It is assumed that the IGBT is in a saturated conduction state at this time; when When this occurs, it is determined to be in a thermal protection state; among which The preset temperature protection threshold.
4. The model-predictive gate voltage regulation control method according to claim 1, characterized in that, The step of selecting a piecewise linear model from a pre-established model library based on the current working status specifically includes: Select a piecewise linear model from a pre-built model library. Each operating state corresponds to pre-defined state-space equation parameters to ensure that the model matches the actual operating state of the IGBT.
5. The model-predictive gate voltage regulation control method according to claim 1, characterized in that, The step of using the piecewise linear model to predict based on the current operating state of the IGBT to obtain candidate values for the control sequence at future times and future changes in current, temperature, and voltage specifically includes: Using the acquired current IGBT operating parameters as initial prediction conditions, a set of future IGBT parameters is generated using a piecewise linear model. Candidate values of the control sequence at each time step ; The initial prediction conditions and control sequence are input into the selected piecewise linear prediction model, and multi-step forward simulation is performed to obtain the predicted future... Current response at time 1 Temperature change and voltage changes .
6. The gate voltage regulation control method based on model prediction according to claim 5, characterized in that, The process of determining the optimal control sequence through multi-objective rolling optimization specifically includes: In the control time domain Find the optimal one sequence Under the given constraints, the defined cost function J is minimized; where the cost function is... , This represents the predicted value of the collector current. This is a reference value for the collector current. This is the predicted value for the casing temperature. The safe temperature threshold for the casing. This represents the rate of change of the gate voltage.
7. The model-predictive gate voltage regulation control method according to claim 6, characterized in that, In multi-objective scrolling optimization, when When the current is large, increase the current weight. To ensure The tracking effect; when the temperature rises, the effect increases. Prioritize temperature protection; reduce temperature when a rapid response is required. Allowing for faster change.
8. The model-predictive gate voltage regulation control method according to claim 6 or 7, characterized in that, The constraints include: The hard constraint is: , The soft constraint is: , ;in, These are slack variables.
9. An electronic device, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, which executes the instructions stored in the memory to perform the method as described in any one of claims 1-8.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store instructions that, when executed, cause the method as described in any one of claims 1-8 to be implemented.