Intelligent drive control system and method for IGBT devices

Abstract

This invention provides an intelligent drive control system and method for IGBT devices, relating to the field of semiconductor device control technology. The system includes: an operation status analysis module that collects real-time operation data of the IGBT device to obtain operation status identification results; a drive signal generation module that performs hierarchical driving to generate multiple drive signals; a drive signal execution module that simulates and executes the multiple drive signals for drive control, generating simulated drive control results; and a drive control module that applies drive gain to the multiple drive signals to generate a drive control gain signal for drive feedback, performing iterative gain control for intelligent drive control of the IGBT device. This invention solves the technical problem in existing technologies where the lack of real-time monitoring and dynamic drive adjustment of IGBT devices leads to a decline in device performance and safety. It achieves precise drive control of IGBT devices, effectively improving device performance and safety.

Description

Technical Field

[0001] This invention relates to the field of semiconductor device control technology, and more specifically to an intelligent drive control system and method for IGBT devices. Background Technology

[0002] IGBT devices are widely used in various high-power, high-voltage power conversion scenarios due to their advantages such as high input impedance, low on-state voltage drop, fast switching speed, and high withstand voltage. In traditional driving technologies, IGBTs are typically controlled using a fixed drive voltage or simple overcurrent protection. This means the drive circuit provides a constant gate voltage or current, switching the IGBT device between on and off states. While this method offers simple circuit structure and low cost, the electrical characteristics of the IGBT device change under different load and temperature conditions. A fixed-parameter drive signal can lead to unstable switching characteristics, increased switching losses, and even overheating and damage. Furthermore, traditional drive control methods lack real-time monitoring and feedback adjustment capabilities for the IGBT device's operating status, making it difficult to adapt to dynamically changing operating environments, resulting in decreased IGBT device performance and safety.

[0003] Existing technologies suffer from a lack of real-time monitoring and dynamic drive adjustment for IGBT devices, leading to a decline in the operating performance and safety of IGBT devices. Summary of the Invention

[0004] The purpose of this application is to provide an intelligent drive control system and method for IGBT devices, which solves the technical problem that the lack of real-time monitoring and dynamic drive adjustment of IGBT devices in the prior art leads to a decline in the operating performance and safety of IGBT devices.

[0005] In view of the above problems, this application provides an intelligent drive control system and method for IGBT devices.

[0006] The first aspect of this application provides an intelligent drive control system for IGBT devices. The system includes: an operation status analysis module, used to collect real-time operation data of the IGBT device through a multi-parameter sensor network, obtain an operation parameter set for analyzing the operation status of the IGBT device, and obtain an operation status identification result, the operation status identification result containing N levels of operation status data; a drive signal generation module, used to perform hierarchical driving of the IGBT device based on the N levels of operation status data, generating multiple drive signals; a drive signal execution module, used to simulate the execution of the multiple drive signals to drive the IGBT device, generating a simulated drive control result; and a drive control module, used to activate a MOSFET switching circuit based on the simulated drive control result to increase the drive gain of the multiple drive signals, generate a drive control gain signal to drive the IGBT device, perform iterative gain calculation based on the feedback result, and perform intelligent drive control of the IGBT device based on the iterative gain result.

[0007] Optionally, the sampling frequency setting unit is used to dynamically adjust the sampling period of the multi-parameter sensing network by introducing the switching frequency of the IGBT device, and set a graded sampling frequency; the parameter acquisition unit is used to activate the multi-parameter sensing network to collect data on the IGBT device in real time according to the graded sampling frequency, and obtain multiple operating sensing parameters; the operating status data acquisition unit is used to perform spatiotemporal registration of the multiple operating sensing parameters according to the sensor association data of the multi-parameter sensing network, obtain an operating parameter set, perform operating status analysis, and obtain operating status data; the N-level operating status data construction unit is used to perform feature analysis based on the operating parameter set, extract dynamic feature quantities, divide the operating status data into multiple levels according to the dynamic feature quantities, and construct the N-level operating status data, where N is an integer greater than 1.

[0008] Optionally, the data boundary determination subunit is used to calculate the membership degree of the operating status data based on the dynamic feature quantity to determine multiple data boundaries; the operating status level definition subunit is used to define N operating status levels according to the multiple data boundaries; the operating parameter migration subunit is used to migrate the operating parameters of the operating status data according to the N operating status levels to generate multiple state migration data; the data filtering subunit is used to evaluate the migration effect based on the multiple state migration data and filter valid migration data according to the migration effect score; and the operating status level matching subunit is used to match the valid migration data according to the N operating status levels to construct the N-level operating status data.

[0009] Optionally, the associated configuration unit is used to retrieve the historical drive parameter set of the IGBT device, which contains multiple drive parameter combinations. It iterates through the N operating state levels and associates these drive parameter combinations to construct a state-drive mapping table. The multi-level drive analysis unit is used to perform multi-level drive analysis according to the state-drive mapping table and the N-level operating state data to construct multiple drive units. The reference drive signal generation unit is used to generate multiple reference drive signals through parallel driving of the multiple drive units. The compensation calculation unit is used to perform compensation calculations based on the multiple reference drive signals according to the N operating state levels to obtain multiple drive compensation amounts. The compensation update unit is used to update the multiple reference drive signals according to the multiple drive compensation amounts to generate the multiple drive signals.

[0010] Optionally, the coupling analysis unit is used to construct virtual test environment parameters based on multi-physics coupling analysis of IGBT devices; the priority evaluation unit is used to evaluate the priority of the multiple drive signals according to the N-level operating state data and construct a drive priority sequence; the signal response unit is used to load the multiple drive signals to respond according to the drive priority sequence based on the virtual test environment parameters and generate multiple electrical response waveform parameters; the correlation array construction unit is used to perform correlation analysis between the multiple electrical response waveform parameters and the multiple drive signals to construct a drive-response correlation array; the feature recognition unit is used to perform multi-dimensional co-simulation based on the drive-response correlation array, obtain a dynamic response simulation dataset, perform feature recognition, and obtain multiple performance feature parameters; and the performance evaluation unit is used to perform performance evaluation on the dynamic response simulation dataset according to the multiple performance feature parameters and generate the simulated drive control result.

[0011] Optionally, the weight allocation unit is used to allocate weights according to the virtual test environment parameters based on the simulated drive control results, generating multiple weight coefficients; the evaluation and analysis unit is used to evaluate and analyze the simulated drive control results according to the multiple weight coefficients, and determine the key evaluation data of the virtual test environment parameters based on the drive control evaluation value; the conflict analysis unit is used to trace the key evaluation data back to the multiple drive signals for conflict analysis, and determine the drive control conflict data; the drive gain allocation unit is used to activate the MOS transistor switching circuit based on the drive control conflict data to allocate the drive gain of the multiple drive signals, generating a drive gain collaborative control scheme; and the collaborative control scheme execution unit is used to execute the drive gain collaborative control scheme on the multiple drive signals, generating the drive control gain signal.

[0012] Optionally, the conflict classification subunit is used to classify the conflict based on the drive control conflict data to obtain multiple conflict levels; the feature extraction subunit is used to activate the MOS transistor switching circuit according to the multiple conflict levels to analyze the multiple drive signals and extract drive control conflict features; the gain analysis subunit is used to use the drive control conflict features as constraints to perform gain analysis on the multiple drive signals and determine multiple main gain signals and multiple auxiliary drive signals; the signal processing subunit is used to enhance the gain of the multiple main gain signals to generate a first gain control parameter and to coordinate the gain of the multiple auxiliary drive signals to generate a second gain control parameter; and the parameter addition subunit is used to add the first gain control parameter and the second gain control parameter to the drive gain coordinated control scheme.

[0013] Optionally, the initial drive control execution unit is used to apply the drive control gain signal to the IGBT device to perform initial drive control and obtain dynamic response data; the feedback result generation unit is used to perform drive feedback on the IGBT device based on the dynamic response data and generate feedback results; the drive control index setting unit is used to perform drive performance evaluation based on the feedback results and set drive control indexes; the gain correction amount acquisition unit is used to calculate the deviation of the feedback results according to the drive control indexes and obtain the gain correction amount; and the iterative gain result generation unit is used to track iterative gain based on the gain correction amount of the feedback results and generate the iterative gain results.

[0014] Optionally, a stage division subunit is used to decompose the gain correction amount into multiple gain adjustment steps, divide the feedback result into stages according to the multiple gain adjustment steps, and construct an incremental gain adjustment strategy; a tracking iteration execution subunit is used to execute the incremental gain adjustment strategy to perform multiple rounds of tracking iteration on the feedback result until the feedback result meets the driving control index: S1: Based on the i-th iteration, the incremental gain adjustment strategy is executed to perform progressive gain tracking monitoring on the feedback result, generating the i-th round driving control result, where i is an integer greater than 0, and the i-th iteration... The iteration round is any one of the iteration rounds in the multi-round tracking iteration; S2: Evaluate the control stability of the i-th round drive control result and generate a control stability coefficient; S3: If the control stability coefficient is not in the expected stable range, the iteration stops, and the i-th iteration round is rolled back to the (i-1)-th iteration round for reverse adjustment until the control stability coefficient is in the expected stable range. The (i-1)-th iteration round is the previous iteration round of the i-th iteration round; S4: If the control stability coefficient is in the expected stable range, the iteration stops, and the i-th round drive control result is used as the iteration gain result.

[0015] A second aspect of this application provides an intelligent drive control method for IGBT devices. The method includes: real-time acquisition of IGBT device data via a multi-parameter sensor network to obtain a set of operating parameters for analyzing the operating state of the IGBT device and obtaining an operating state identification result, wherein the operating state identification result contains N levels of operating state data; hierarchical driving of the IGBT device based on the N levels of operating state data to generate multiple drive signals; simulated execution of the multiple drive signals by the IGBT device to perform drive control, generating a simulated drive control result; activating a MOSFET switching circuit based on the simulated drive control result to increase the drive gain of the multiple drive signals, generating a drive control gain signal to provide drive feedback to the IGBT device; iterative gain calculation based on the feedback result; and intelligent drive control of the IGBT device based on the iterative gain result.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0017] The system comprises the following modules: an operation status analysis module, which acquires real-time operational data of the IGBT device through a multi-parameter sensor network, obtains an operation parameter set for analyzing the IGBT device's operation status, and generates operation status identification results containing N levels of operation status data; a drive signal generation module, which performs hierarchical driving of the IGBT device based on the N-level operation status data, generating multiple drive signals; a drive signal execution module, which simulates the execution of the multiple drive signals to drive the IGBT device, generating simulated drive control results; and a drive control module, which activates the MOSFET switching circuit based on the simulated drive control results to adjust the drive gain of the multiple drive signals, generates a drive control gain signal for drive feedback to the IGBT device, iterates the gain based on the feedback results, and performs intelligent drive control of the IGBT device based on the iterative gain results. This achieves precise drive control of the IGBT device, effectively improving its operational performance and safety.

[0018] The above description is merely an overview of the technical solution of this application. To enable a clearer understanding of the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application 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 merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 A schematic diagram of the intelligent drive control system for IGBT devices provided in this application.

[0021] Figure 2 This is a flowchart illustrating the intelligent drive control method for IGBT devices provided in this application.

[0022] Explanation of reference numerals in the attached diagram: 11. Operation status analysis module; 12. Drive signal generation module; 13. Drive signal execution module; 14. Drive control module. Detailed Implementation

[0023] This application provides an intelligent drive control system and method for IGBT devices, addressing the technical problem of existing technologies lacking real-time monitoring and dynamic drive adjustment for IGBT devices, leading to a decline in IGBT device performance and safety. It achieves precise drive control of IGBT devices, effectively improving their operational performance and safety.

[0024] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0025] Example 1, as Figure 1 As shown, this application provides an intelligent drive control system for IGBT devices, the intelligent drive control system for IGBT devices comprising:

[0026] The operation status analysis module 11 is used to collect real-time operation data of the IGBT device through a multi-parameter sensor network, obtain an operation parameter set for operation status analysis of the IGBT device, and obtain operation status identification results, which include N levels of operation status data.

[0027] Furthermore, the operation status analysis module 11 includes: a sampling frequency setting unit, used to dynamically adjust the sampling period of the multi-parameter sensing network by introducing the switching frequency of the IGBT device, and setting a graded sampling frequency; a parameter acquisition unit, used to activate the multi-parameter sensing network to collect data on the IGBT device in real time according to the graded sampling frequency, and obtain multiple operation sensing parameters; an operation status data acquisition unit, used to perform spatiotemporal registration of the multiple operation sensing parameters according to the sensor association data of the multi-parameter sensing network, obtain an operation parameter set for operation status analysis, and obtain operation status data; and an N-level operation status data construction unit, used to perform feature analysis based on the operation parameter set, extract dynamic feature quantities, divide the operation status data into multiple levels according to the dynamic feature quantities, and construct the N-level operation status data, where N is an integer greater than 1.

[0028] Specifically, an IGBT (Insulated Gate Bipolar Transistor) is a composite, fully controllable, voltage-driven power semiconductor device that combines the advantages of metal-oxide-semiconductor field-effect transistors (MOSFETs) and bipolar transistors, featuring high input impedance, low on-state voltage drop, fast switching speed, and high withstand voltage. The structure of an IGBT can be viewed as a composite of a MOSFET and a bipolar transistor. The operating principle of an IGBT can be divided into three parts: the on-state, the off-state, and the switching process. In the on-state, when a positive voltage is applied to the gate relative to the emitter, the MOSFET partially conducts, forming an N-type channel. Electrons flow from the N+ source region to the N- drift region, forming a current. Due to the low resistance of the N- drift region, the current mainly flows through the N- drift region to the P-type base region. In the P-type base region, electrons and holes recombine, forming another current. Due to the amplification effect of the bipolar transistor, the on-state current of an IGBT is much larger than that of a MOSFET, and its on-state voltage drop is lower. In the off state, when a negative voltage or no voltage is applied to the gate relative to the emitter, the MOSFET is partially turned off, the N-type channel disappears, and since no electrons flow from the N+ source region to the N- drift region, the bipolar transistor is also turned off, and the IGBT is in the off state. The switching process includes a turn-on process and a turn-off process. When the gate voltage changes from negative to positive, the MOSFET gradually turns on, forming an N-type channel, and the current gradually increases, putting the IGBT into the on state. When the gate voltage changes from positive to negative, the MOSFET gradually turns off, the N-type channel disappears, the current gradually decreases, and the IGBT enters the off state.

[0029] The operation status analysis module 11 includes a sampling frequency setting unit, a parameter acquisition unit, an operation status data acquisition unit, and an N-level operation status data construction unit. It is used to perform real-time monitoring and intelligent analysis of the operation status of IGBT devices through a multi-parameter sensor network, thereby obtaining operation status identification results reflecting the device's operating characteristics. The multi-parameter sensor network is constructed based on the electrical, thermal, and mechanical characteristics of the IGBT device and is used to collect dynamic operation data of the IGBT device from multiple dimensions. The multi-parameter sensor network includes, but is not limited to, voltage sensors, current sensors, temperature sensors, and stress and vibration sensors. The voltage sensors are used to detect the collector-emitter voltage, gate-emitter voltage, bus voltage, and switching node voltage change rate of the IGBT device. The current sensors include Hall current sensors and shunt resistor sensors, used to detect the device's on-current, peak current, and switching transient current change rate. The temperature sensors are used to detect the thermal characteristics of the IGBT device. The stress and vibration sensors are used to monitor the micro-vibration and stress changes of the device packaging or heat dissipation system to reflect mechanical stability. Multi-parameter sensors are connected via high-bandwidth buses such as SPI, I²C, CAN, or synchronous Ethernet to form a distributed sensor network with a unified time base. The sampling frequency setting unit dynamically adjusts the sampling period of multiple sensors in the multi-parameter sensor network based on the actual switching frequency and operating mode of the IGBT devices. The switching frequency is obtained from the control signals of the IGBT devices; for example, a Hall effect sensor or current sensor is used to monitor the switching action of the IGBT, and its frequency is calculated. Based on the switching frequency, the basic sampling period is calculated. To ensure sampling accuracy, the sampling period is set to 1 / 2 or 1 / 4 of the switching period. Multiple sampling frequencies are set according to different operating conditions. For example, a higher sampling frequency is used for rapidly changing voltage and current signals, while a lower sampling frequency can be used for slowly changing signals such as temperature or stress, achieving dynamic optimization of sampling resources.

[0030] The parameter acquisition unit, based on the configuration results of the sampling frequency setting unit, activates the multi-parameter sensor network to perform real-time data acquisition of the IGBT device, obtaining multiple operating sensor parameters across multiple dimensions, including electrical, thermal, and mechanical aspects. The operating status data acquisition unit performs spatiotemporal registration of the acquired operating sensor parameters according to the topology and time reference of the multi-parameter sensor network. Specifically, it uses time synchronization protocols and spatial interpolation methods, such as IEEE 1588 precise clock synchronization or timestamp alignment, to perform time and spatial alignment on different types of sensor data, combining the aligned parameter data into an operating parameter set. The multiple parameters in the operating parameter set are then normalized and denoised to eliminate dimensional differences and random interference. Based on time series analysis methods, the preprocessed multiple parameters are analyzed for trends and fluctuation ranges to obtain operating status data reflecting the real-time operating characteristics of the IGBT device. Operating status data refers to a multi-dimensional comprehensive index dataset characterizing the electrical, thermal, and mechanical characteristics of the IGBT device within a certain time window, including stability and fluctuation indices for each operating parameter. The N-level operating status data construction unit analyzes the operating parameter set using statistical analysis and signal processing methods, such as differential calculation, differential analysis, spectrum analysis, and waveform energy calculation, to extract dynamic features. These dynamic features are key quantitative indicators that reflect the dynamic changes of the device at the electrical, thermal, and mechanical levels, including the mean, variance, and peak value of current; the voltage fluctuation range; the rate of temperature change; and the on-state voltage drop fluctuation. Based on these dynamic features, the operating status data is divided into N levels using fuzzy clustering algorithms or membership calculation methods, where N is an integer greater than 1. Each level corresponds to a different device health level and operating risk range, such as the normal zone, light load zone, overload zone, critical zone, and degradation warning zone. This hierarchical approach enables refined identification of the IGBT's operating status.

[0031] The operation status analysis module 11 uses a multi-parameter sensor network to achieve hierarchical dynamic sampling and spatiotemporal registration fusion. Combined with dynamic feature extraction and multi-level state division algorithms, it realizes intelligent identification of IGBT device operation data and outputs N-level operation status identification results, providing reliable and comprehensive data support for intelligent drive control of IGBT devices.

[0032] Furthermore, the N-level operational status data construction unit includes: a data boundary determination subunit, used to calculate the membership degree of the operational status data based on the dynamic feature quantity, and determine multiple data boundaries; an operational status level definition subunit, used to define N operational status levels according to the multiple data boundaries; an operational parameter migration subunit, used to migrate operational parameters of the operational status data according to the N operational status levels, and generate multiple status migration data; a data filtering subunit, used to evaluate the migration effect based on the multiple status migration data, and filter valid migration data according to the migration effect score; and an operational status level matching subunit, used to match the valid migration data according to the N operational status levels, and construct the N-level operational status data.

[0033] Specifically, the data boundary determination subunit calculates the membership degree of operating states based on dynamic characteristic quantities. For example, using fuzzy logic, Gaussian membership functions, or triangular membership functions, it calculates the membership degree of multi-dimensional dynamic characteristics such as current, voltage, temperature, and stress under different operating states, and uses the intersection of membership curves of adjacent states as state boundaries to determine multiple data boundaries. The operating state level definition subunit defines N operating state levels based on the determined multiple data boundaries, combined with the variation range and stability parameters of multiple operating characteristic parameters, and sets a level threshold for each operating state level. Each operating state level corresponds to a specific operating range and characteristic distribution, such as different state levels like normal, slight deviation, warning, critical, and fault.

[0034] The operation parameter migration subunit traverses the operation status data, migrating each data point to the corresponding operation status level based on data boundaries, forming multiple state migration data sets. To ensure the effectiveness of the migration data, the data filtering subunit calculates a migration effect score for each state migration data set using weighted averages based on indicators such as migration duration, energy fluctuation amplitude, parameter change rate, and state consistency. This score assesses the stability and accuracy of the state migration data; stability evaluates the degree of fluctuation in the migration data over time, while accuracy evaluates the degree of matching between the migration data and the actual operation status. The migration effect scores are sorted in descending order, and a threshold is set according to actual needs. Only state migration data sets with a migration effect score greater than or equal to the threshold are retained. This threshold-based selection process identifies representative and stable valid migration data, eliminating noisy or short-term abrupt changes. The operation status level matching subunit matches and categorizes the selected valid migration data according to the level thresholds of N operation status levels, combining the matched data into N levels of operation status data.

[0035] The N-level operating status data construction unit analyzes the dynamic characteristics of the operating parameter set, determines the data boundary through membership calculation, defines the operating status level, performs operating parameter migration, filters effective migration data, and finally constructs N-level operating status data. This improves the accuracy of multi-dimensional operating status analysis of IGBT devices, thereby improving the effectiveness and reliability of IGBT device control.

[0036] The drive signal generation module 12 is used to drive the IGBT device in stages based on the N-level operating status data and generate multiple drive signals.

[0037] Furthermore, the drive signal generation module 12 includes: an association configuration unit, used to retrieve the historical drive parameter set of the IGBT device, the historical drive parameter set containing multiple drive parameter combinations, traversing the N operating state levels and associating the multiple drive parameter combinations to construct a state-drive mapping table; a multi-level drive analysis unit, used to perform multi-level drive analysis according to the state-drive mapping table and the N-level operating state data to construct multiple drive units; a reference drive signal generation unit, used to generate multiple reference drive signals by performing parallel drive through the multiple drive units; a compensation calculation unit, used to perform compensation calculation based on the multiple reference drive signals according to the N operating state levels to obtain multiple drive compensation amounts; and a compensation update unit, used to update the multiple reference drive signals according to the multiple drive compensation amounts to generate the multiple drive signals.

[0038] Specifically, in the implementation of the drive signal generation module 12, the historical drive parameter set of the IGBT device is first retrieved from the storage device through the associated configuration unit. This historical drive parameter set includes multiple combinations of drive parameters under different operating conditions, such as gate voltage, current drive capability, gate resistance, dead time, slope limit, and drive delay. It iterates through N operating state levels and calculates the multiple drive parameter combinations for each operating state level and the historical drive parameter set using a similarity calculation method. Each operating state level is then associated with these historical drive parameter combinations. For example, each operating state level is represented as a multi-dimensional feature vector, including mean current, current variance, voltage fluctuation range, junction temperature change rate, and on-state voltage drop fluctuation. The historical drive parameter combinations are also represented as corresponding feature vectors, including parameters such as drive voltage, gate resistance, drive current, and dead time. A similarity calculation method, such as Euclidean distance, weighted Manhattan distance, or cosine similarity, is used to calculate the multiple drive parameter combinations for each operating state level and the historical drive parameter set. Based on the similarity calculation results, for each operating state level, one or more combinations of drive parameters with the highest similarity are selected to construct a state-drive mapping table, which reflects the mapping relationship between different operating states and drive combinations. On this basis, the multi-level drive analysis unit, according to the state-drive mapping table and combined with N levels of operating state data, uses a multi-objective optimization algorithm and cost function evaluation method to perform multi-dimensional analysis of drive requirements at different levels, including parameters such as switching speed, energy loss, temperature response, and electromagnetic compatibility. Multiple drive units are then constructed, each corresponding to a set of feasible drive strategies to achieve the best drive control effect under different states.

[0039] The reference drive signal generation unit performs parallel driving based on the parameter configuration of multiple drive units, generating multiple reference drive signals. Each reference drive signal includes parameters such as gate turn-on voltage, turn-off voltage, gate resistance, drive current, and dead time. The compensation calculation unit performs compensation calculations on the multiple reference drive signals based on the dynamic characteristic parameters of each level in the N-level operating state data, obtaining multiple drive compensation amounts. For example, compensation calculations are performed using multi-dimensional signal analysis methods, performing differential calculations on the reference drive signals and corresponding N-level operating state data according to the time series to obtain the short-time change trend and transient deviation of the reference drive signals. Differential analysis is used to calculate the rate of change of the reference drive signals, reflecting the response characteristics of the IGBT device during dynamic switching. Simultaneously, spectral analysis is performed on the reference drive signals, extracting high-frequency components and harmonic components through fast Fourier transform or wavelet transform to identify oscillation, parasitic resonance, or EMI-sensitive areas. Combining the differential, differential, and spectral analysis results, a multi-objective compensation function is established, mapping each analysis index to a corresponding compensation amount, including voltage compensation, gate impedance adjustment, and slope correction, forming multiple drive compensation amounts. The compensation update unit updates multiple reference drive signals based on various drive compensation values. It employs a hierarchical weighting and progressive adjustment strategy to dynamically correct the reference drive signals, ensuring that the corrected drive signals meet safe operating constraints. Through this update, multiple drive signals are obtained for hierarchical intelligent driving of the IGBT device, achieving dynamic optimal control under different operating conditions and effectively improving the stability of the IGBT device operation.

[0040] The drive signal execution module 13 is used to simulate the execution of the multiple drive signals of the IGBT device to drive and control it, and generate simulated drive control results.

[0041] Furthermore, the drive signal execution module 13 includes: a coupling analysis unit for constructing virtual test environment parameters based on multi-physics coupling analysis of IGBT devices; a priority evaluation unit for prioritizing the multiple drive signals according to the N-level operating state data and constructing a drive priority sequence; a signal response unit for loading the multiple drive signals according to the drive priority sequence based on the virtual test environment parameters and generating multiple electrical response waveform parameters; an association array construction unit for performing association analysis between the multiple electrical response waveform parameters and the multiple drive signals to construct a drive-response association array; a feature recognition unit for performing multi-dimensional co-simulation based on the drive-response association array, obtaining a dynamic response simulation dataset, performing feature recognition, and obtaining multiple performance feature parameters; and a performance evaluation unit for performing performance evaluation on the dynamic response simulation dataset according to the multiple performance feature parameters and generating the simulated drive control result.

[0042] Specifically, the main function of the drive signal execution module 13 is to simulate the execution of multiple drive signals for IGBT device drive control and generate simulated drive control results. In the specific implementation of the drive signal execution module 13, the coupling analysis unit first establishes a multiphysics coupling model based on the electrical, thermal, and mechanical characteristics of the IGBT device. Electrical parameters include voltage, current, inductance, and capacitance; thermal parameters include temperature and heat flux density; and mechanical parameters include stress. Parameters such as current, voltage, inductance, capacitance, and switching characteristics are incorporated into the simulation environment. Using finite element analysis software or multiphysics simulation tools, virtual test environment parameters are constructed to simulate the dynamic response characteristics of the IGBT device under different operating states. The priority evaluation unit prioritizes multiple drive signals based on N-level operating state data, comprehensively analyzing the urgency of the operating level and the impact of the drive signals on the IGBT device performance. The evaluated drive signals are then sorted by priority to construct a drive priority sequence. The drive signal with the highest priority in the sequence is placed at the beginning to guide drive signal loading and response evaluation. Among these, the urgency of the operating status level, such as overload status having higher priority than normal operation status, and the impact of the drive signal on the performance of the IGBT device, such as high current drive signal having higher priority than low current drive signal.

[0043] The signal response unit loads multiple drive signals sequentially into the virtual test environment according to the drive priority sequence. In the virtual test environment, the response of the IGBT device to each drive signal is simulated, generating multiple electrical response waveform parameters such as current, voltage, and conduction loss. The correlation array construction unit uses correlation analysis and regression analysis to perform correlation analysis between each drive signal and its corresponding electrical response waveform parameters, determining the relationship between the drive signal and the electrical response. Based on the correlation analysis results, a drive-response correlation array is formed. Based on this drive-response correlation array, the feature recognition unit performs multi-dimensional co-simulation combining electrical, thermal, and mechanical dimensions to obtain a dynamic response simulation dataset. Statistical analysis methods are used to identify features in the dynamic response simulation dataset, obtaining multiple characteristic performance parameters, including voltage fluctuation range, peak current, temperature change rate, conduction voltage drop fluctuation, and vibration amplitude, to reflect the performance of the IGBT under different drive signals. The performance evaluation unit performs performance evaluation on the dynamic response simulation dataset based on multiple performance characteristic parameters. For example, performance evaluation indicators such as root mean square error, maximum deviation, and stability indicators are used to evaluate each performance characteristic parameter, obtaining performance evaluation results. Multiple performance evaluation results are aggregated to generate simulated drive control results, which include a performance score for each drive signal. These results guide the actual drive control strategy and ensure that the IGBT device operates in its optimal state.

[0044] By simulating and analyzing multiple drive signals for the IGBT device through the drive signal execution module 13, the accuracy and reliability of drive control are improved, and precise intelligent drive control of the IGBT device is realized.

[0045] The drive control module 14 is used to activate the MOS transistor switching circuit to drive multiple drive signals based on the analog drive control result, generate a drive control gain signal to drive the IGBT device, perform iterative gain according to the feedback result, and perform intelligent drive control of the IGBT device according to the iterative gain result.

[0046] Furthermore, the drive control module 14 includes: a weight allocation unit, used to allocate weights according to the virtual test environment parameters based on the simulated drive control results, generating multiple weight coefficients; an evaluation and analysis unit, used to evaluate and analyze the simulated drive control results according to the multiple weight coefficients, and determine the key evaluation data of the virtual test environment parameters based on the drive control evaluation value; a conflict analysis unit, used to trace the key evaluation data back to the multiple drive signals for conflict analysis, and determine drive control conflict data; a drive gain allocation unit, used to activate the MOS transistor switching circuit based on the drive control conflict data to allocate drive gain to the multiple drive signals, generating a drive gain collaborative control scheme; and a collaborative control scheme execution unit, used to execute the drive gain collaborative control scheme on the multiple drive signals, generating the drive control gain signal.

[0047] Specifically, the drive control module 14 intelligently controls the IGBT device by converting the simulated drive control results into actual drive gain signals and combining them with real-time feedback. The weight allocation unit assigns weights to the simulated drive control results based on factors such as switching efficiency, thermal response, reliability, and electromagnetic compatibility (EMC) in the virtual test environment, generating multiple weighting coefficients. For example, through feature quantization, various performance indicators are converted into measurable parameter values. Switching efficiency aims to minimize the sum of conduction loss and switching loss; thermal response is measured by junction temperature rise rate and steady-state temperature; reliability is evaluated by lifetime decay coefficient under repetitive pulse stress; and EMC is based on EMI spectral density and interference coupling strength. Fuzzy weighted hierarchical analysis is used to calculate the contribution of each performance indicator to the overall control objective and normalizes it to obtain multiple weighting coefficients. The evaluation and analysis unit performs a weighted evaluation of the simulated drive control results according to these multiple weighting coefficients to obtain the drive control evaluation value. The system selects the top K parameters with the highest drive control evaluation values ​​as the key evaluation data for the virtual test environment. These key evaluation data refer to the K parameters that have the greatest impact on IGBT device performance, where K is a positive integer. The key evaluation data is adjusted according to different application scenarios of the IGBT device. The conflict analysis unit traces the key evaluation data back to the corresponding drive signals, analyzes the impact of each drive signal on the key evaluation data, and generates drive control conflict data. This data includes conflict signal pairs, conflict types, and conflict strengths. Conflict types include electrical conflicts, thermal conflicts, and timing conflicts.

[0048] The drive gain allocation unit, based on the generated drive control conflict data, activates the MOSFET switching circuit to allocate the gain of multiple drive signals. The drive signals are grouped according to the conflict type in the drive control conflict data; for example, electrical conflict signals are prioritized for adjusting the voltage slope and gate impedance, while thermal conflict signals are prioritized for adjusting the on-time and drive current amplitude. Based on the conflict intensity, an optimal allocation algorithm, such as linear weighting or particle swarm optimization, is used to calculate the gain adjustment ratio for each drive signal, generating a main gain signal and an amplitude gain signal. The MOSFET switching circuit adjusts the gate current, on-delay, and turn-off speed of the drive path according to the allocation results, achieving multi-channel coordinated gain control, thus forming a drive gain coordinated control scheme. This scheme enhances the gain of the main drive signal while coordinating the gain of the auxiliary drive signals, ensuring that each drive signal meets the IGBT device performance optimization goals while avoiding conflicts. The collaborative control scheme execution unit distributes the collaborative control scheme of drive gain to multiple drive signals, generates drive control gain signals, realizes the actual drive of IGBT devices, and simultaneously collects real-time feedback on the response of IGBT devices. Based on the feedback results, it calculates the deviation and generates a gain correction amount. The iterative gain subunit performs multiple rounds of tracking and adjustment of the drive signals according to the correction amount until the feedback results reach the preset drive control index, thereby realizing intelligent drive control of IGBT devices.

[0049] The drive control module 14 achieves intelligent hierarchical driving of IGBT devices through weight allocation, evaluation analysis, conflict analysis, drive gain allocation and cooperative control scheme execution, ensuring the performance optimization and stable operation of IGBT devices under different operating conditions, and improving the stability and safety of IGBT device operation.

[0050] Furthermore, the drive gain allocation unit includes: a conflict classification subunit, used to classify conflicts based on the drive control conflict data to obtain multiple conflict levels; a feature extraction subunit, used to activate the MOS transistor switching circuit according to the multiple conflict levels to analyze the multiple drive signals and extract drive control conflict features; a gain analysis subunit, used to use the drive control conflict features as constraints to perform gain analysis on the multiple drive signals and determine multiple main gain signals and multiple auxiliary drive signals; a signal processing subunit, used to enhance the gain of the multiple main gain signals to generate a first gain control parameter, and to coordinate the gain of the multiple auxiliary drive signals to generate a second gain control parameter; and a parameter addition subunit, used to add the first gain control parameter and the second gain control parameter to the drive gain coordinated control scheme.

[0051] Specifically, the conflict grading subunit analyzes the collected drive control conflict data, calculates a conflict score based on the conflict's intensity, duration, and frequency, and classifies the conflict into multiple conflict levels, such as minor, moderate, severe, and critical. The feature extraction subunit activates the MOSFET switching circuit based on the classified conflict levels and performs dynamic analysis on the corresponding drive signals. Specifically, it evaluates the impact of changes in each drive signal parameter, such as gate resistance, drive voltage, and turn-off slope, on key performance indicators through correlation calculations, such as Pearson correlation coefficient or partial correlation analysis. When multiple drive signals exhibit opposite or superimposed effects on the same key evaluation data under the same operating state, leading to excessive current peaks, voltage overshoot exceeding limits, or rapid rise in device junction temperature, a drive control conflict is identified. Drive control conflict features characterizing voltage stress, current overshoot, and thermal coupling are extracted using differential analysis and spectral analysis. The gain analysis subunit uses the characteristics of drive control conflicts as constraints. Through optimal allocation algorithms, such as linear weighting or particle swarm optimization, it performs gain analysis and optimization calculations on multiple drive signals to determine the optimal gain allocation value for each drive signal. It identifies the main gain signal that significantly impacts IGBT device performance and several auxiliary drive signals that require coordinated adjustment. For example, by constructing a multi-objective optimization function with the goals of maximizing drive efficiency, minimizing thermal load, and minimizing signal interference, a particle swarm optimization algorithm is used to globally search and optimize the gain allocation of multiple drive signals. Through dynamic iterative updates of the particle swarm, the gain allocation parameters are adjusted in real time, allowing the multi-objective optimization function to gradually approach the optimal solution. Based on the gain allocation results, the main gain signal that significantly impacts IGBT device performance and contributes highly to the gain, and the auxiliary drive signals that require coordinated adjustment to maintain stable IGBT device operation, are identified. The signal processing subunit performs gain enhancement operations on the main gain signal based on the gain analysis results. By increasing the gate drive current, improving the conduction speed, or optimizing the switching slope, the driving capability and response accuracy of the main gain signal are enhanced, generating the first gain control parameters. Simultaneously, gain coordination is performed on multiple auxiliary drive signals. Based on the changing trend of the main signal, the gain amplitude, delay compensation, or phase relationship of the auxiliary drive signals is dynamically adjusted to generate a second gain control parameter, achieving optimal drive control under the synergistic effect of the main and auxiliary signals. Finally, a parameter addition subunit integrates the first and second gain control parameters into the drive gain coordination control scheme.

[0052] The drive gain allocation unit identifies the conflict level and extracts conflict features based on drive control conflict data, and uses them as constraints to allocate gain to multiple drive signals, generate scalable gain control parameters, and incorporate them into the drive gain control scheme. This enables dynamic gain allocation and coordinated control of multiple drive signals, improves the accuracy and reliability of IGBT device drive control, and allows IGBT devices to maintain high efficiency, low stress, and high reliability operation under high dynamic load conditions.

[0053] Furthermore, the drive control module 14 further includes: an initial drive control execution unit, used to apply the drive control gain signal to the IGBT device to perform initial drive control and obtain dynamic response data; a feedback result generation unit, used to perform drive feedback on the IGBT device based on the dynamic response data and generate feedback results; a drive control index setting unit, used to perform drive performance evaluation based on the feedback results and set drive control indexes; a gain correction amount acquisition unit, used to perform deviation calculation on the feedback results according to the drive control indexes and obtain a gain correction amount; and an iterative gain result generation unit, used to perform iterative gain tracking on the feedback results based on the gain correction amount and generate the iterative gain result.

[0054] Specifically, the initial drive control execution unit applies a drive control gain signal to the IGBT device and executes initial drive control through the MOSFET switching circuit. Simultaneously, it monitors the IGBT device's current, voltage, temperature, and switching waveforms in real time, acquiring dynamic response data reflecting the IGBT device's dynamic behavior. The feedback result generation unit extracts and evaluates the state based on the acquired dynamic response data, calculating operating parameters such as on-state voltage drop, switching losses, and temperature rise rate, generating feedback results. These feedback results reflect the IGBT device's operating state and performance under the current drive signal. The drive control index setting unit performs a multi-dimensional evaluation of the IGBT device's drive performance based on the feedback results, establishing drive control indices including efficiency, safety, and reliability indices. Efficiency indices include switching energy consumption and turn-on time; safety indices include junction temperature limits and voltage margins; and reliability indices include thermal cycling stability and breakdown probability. These established drive control indices serve as the target benchmark for drive optimization. The gain correction acquisition unit calculates the gain correction for each driving parameter by comparing the deviation between the actual feedback result and the preset target based on the driving control index. This calculation is performed using differential calculation or least squares method, quantifying the gap between the driving signal and the ideal control target. The iterative gain result generation unit tracks and adjusts the feedback result based on the calculated gain correction. It uses an adaptive iterative algorithm, such as gradient descent optimization, to continuously correct the gain parameters of the driving signal until the feedback result meets the set driving control index requirements, generating a stable iterative gain result.

[0055] Through a closed-loop iterative control process, the drive control module 14 achieves intelligent and adaptive drive optimization for the IGBT device, ensuring that the device always maintains a high-efficiency, safe and stable operating state under different working conditions.

[0056] Furthermore, the iterative gain result generation unit includes: a stage division subunit, used to decompose the gain correction amount into multiple gain adjustment steps, divide the feedback result into stages according to the multiple gain adjustment steps, and construct an incremental gain adjustment strategy; and a tracking iteration execution subunit, used to execute the incremental gain adjustment strategy to perform multiple rounds of tracking iteration on the feedback result until the feedback result meets the driving control index: S1: Based on the i-th iteration round, execute the incremental gain adjustment strategy to perform progressive gain tracking monitoring on the feedback result, and generate the i-th round driving control result, where i is greater than S1: An integer equal to 0, and the i-th iteration round is any iteration round in the multi-round tracking iteration; S2: Evaluate the control stability of the i-th round drive control result and generate a control stability coefficient; S3: If the control stability coefficient is not in the expected stable range, the iteration stops, and the i-th iteration round is rolled back to the (i-1)-th iteration round for reverse adjustment until the control stability coefficient is in the expected stable range, where the (i-1)-th iteration round is the previous iteration round of the i-th iteration round; S4: If the control stability coefficient is in the expected stable range, the iteration stops, and the i-th round drive control result is used as the iteration gain result.

[0057] Specifically, the stage division subunit decomposes the gain correction amount into multiple gain adjustment steps based on its numerical range and trend. Each step corresponds to a different degree of drive signal correction. For example, amplitude analysis is performed on the gain correction amount to determine the minimum, maximum, and average change amplitude. Combined with the gain change trend, and based on the rise or fall rate and fluctuation characteristics of the gain correction amount, the intervals with faster changes or larger fluctuations are divided into smaller gain adjustment steps, while the intervals with gentler changes are divided into larger steps, thus obtaining multiple gain adjustment steps that adapt to dynamic response. The feedback results are divided into stages according to multiple gain adjustment steps, constructing an incremental gain adjustment strategy with gradual convergence characteristics. This ensures that each round of gain adjustment is within a controllable range, avoiding over-correction that could lead to IGBT device oscillation or control instability. The tracking iteration execution subunit performs multiple rounds of tracking iterations on the feedback results according to the incremental gain adjustment strategy. During the i-th iteration, the startup state of the IGBT device is tested based on the current incremental gain adjustment strategy. The feedback results are monitored using progressive gain tracking to generate the corresponding i-th round of drive control results, which include the adjusted drive signal and the actual operating data of the IGBT device. Here, i is an integer greater than 0, and the i-th iteration round is any iteration round in the multi-round tracking iteration. The i-th round of drive control results are then evaluated for control stability based on indicators such as overshoot of voltage and current waveforms, rate of temperature change, and response time. A weighted algorithm is used to calculate the control stability coefficient, which reflects the operating performance of the IGBT device.

[0058] Based on the electrical and thermal safety indicators and dynamic response characteristics of IGBT devices, a desired stability range is set. For example, key parameters such as voltage fluctuation range, peak current, junction temperature change rate, and switching delay can be comprehensively considered. These indicators are normalized to form a comprehensive stability score, and an upper and lower limit range is defined as the desired stability range. If the control stability coefficient falls within the desired stability range, it indicates that the IGBT device response meets the design requirements. If it exceeds the desired stability range, it is considered that the current gain adjustment may cause overshoot, oscillation, or abnormal thermal stress, thereby triggering a backoff mechanism for reverse correction to ensure that the iterative gain result eventually falls within the safe stability range. Based on this, the control stability coefficient is compared with the desired stability range. When the control stability coefficient is not within the desired stability range, it is determined that the current iteration result has not met the expected performance requirements. The backoff mechanism is automatically triggered, the iteration stops, and the current i-th iteration round is backed to the (i-1)-th iteration round, which is the previous iteration round of the i-th iteration round. Reverse adjustment is performed, and the gain parameter is corrected in the opposite direction to offset the effect of over-adjustment. A new control output is then recalculated. By continuously adjusting the gain in both forward and reverse directions, a balance between stability and performance is achieved. The iteration stops when the control stability coefficient reaches the desired stable range—that is, when the IGBT device achieves optimal performance under the current operating conditions—and the result of the i-th drive control round is used as the iterative gain result. Through a progressively refined and dynamically backtracking iterative mechanism, adaptive optimization of the IGBT drive signal is achieved, enabling intelligent and adaptive drive control of the IGBT device. This improves the IGBT device's operating performance while simultaneously enhancing its stability and safety, and strengthening its adaptability to different operating conditions.

[0059] Example 2, based on the same inventive concept as the intelligent drive control system for IGBT devices in the foregoing examples, such as... Figure 2 As shown, this application provides an intelligent drive control method for IGBT devices, wherein the intelligent drive control method for IGBT devices includes:

[0060] Real-time operation data of IGBT devices is collected through a multi-parameter sensor network to obtain an operational parameter set for IGBT device operation status analysis, resulting in an operation status identification result containing N levels of operation status data. Based on the N levels of operation status data, the IGBT devices are driven in stages to generate multiple drive signals. The IGBT devices are simulated to execute the multiple drive signals for drive control, generating simulated drive control results. Based on the simulated drive control results, the MOSFET switching circuit is activated to increase the drive gain of the multiple drive signals, generating a drive control gain signal for drive feedback to the IGBT devices. Iterative gain is performed based on the feedback results, and intelligent drive control of the IGBT devices is performed based on the iterative gain results.

[0061] Furthermore, a multi-parameter sensor network is used to collect real-time operational data of the IGBT device, obtaining an operational parameter set for analyzing the IGBT device's operational status and obtaining operational status identification results. These operational status identification results contain N levels of operational status data. The method includes: dynamically adjusting the sampling period of the multi-parameter sensor network by introducing the switching frequency of the IGBT device, setting a graded sampling frequency; activating the multi-parameter sensor network to collect real-time operational data of the IGBT device according to the graded sampling frequency, obtaining multiple operational sensing parameters; performing spatiotemporal registration of the multiple operational sensing parameters according to the sensor association data of the multi-parameter sensor network, obtaining an operational parameter set for operational status analysis, and obtaining operational status data; performing feature analysis based on the operational parameter set, extracting dynamic feature quantities, and dividing the operational status data into multiple levels according to the dynamic feature quantities to construct the N levels of operational status data, where N is an integer greater than 1.

[0062] Furthermore, feature analysis is performed based on the set of operating parameters to extract dynamic feature quantities. The operating status data is then divided into multiple levels according to these dynamic feature quantities to construct the N-level operating status data. The method includes: calculating the membership degree of the operating status data based on the dynamic feature quantities to determine multiple data boundaries; defining N operating status levels according to the multiple data boundaries; migrating the operating parameters of the operating status data according to the N operating status levels to generate multiple state migration data; evaluating the migration effect based on the multiple state migration data and selecting effective migration data based on the migration effect score; and matching the effective migration data according to the N operating status levels to construct the N-level operating status data.

[0063] Furthermore, based on the N-level operating state data, the IGBT device is driven in a hierarchical manner to generate multiple drive signals. The method includes: retrieving the historical drive parameter set of the IGBT device, which contains multiple drive parameter combinations; traversing the N operating state levels and associating them with the multiple drive parameter combinations to construct a state-drive mapping table; performing multi-level drive analysis according to the state-drive mapping table and the N-level operating state data to construct multiple drive units; driving in parallel through the multiple drive units to generate multiple reference drive signals; performing compensation calculations based on the multiple reference drive signals according to the N operating state levels to obtain multiple drive compensation amounts; and updating the multiple reference drive signals according to the multiple drive compensation amounts to generate the multiple drive signals.

[0064] Furthermore, the method for simulating the execution of the multiple drive signals to drive the IGBT device and generating simulated drive control results includes: constructing virtual test environment parameters based on multiphysics coupling analysis of the IGBT device; prioritizing the multiple drive signals according to the N-level operating state data to construct a drive priority sequence; loading the multiple drive signals according to the drive priority sequence based on the virtual test environment parameters to generate multiple electrical response waveform parameters; performing correlation analysis between the multiple electrical response waveform parameters and the multiple drive signals to construct a drive-response correlation array; performing multi-dimensional co-simulation based on the drive-response correlation array to obtain a dynamic response simulation dataset for feature identification to obtain multiple performance characteristic parameters; and evaluating the performance of the dynamic response simulation dataset according to the multiple performance characteristic parameters to generate the simulated drive control results.

[0065] Furthermore, based on the simulated drive control results, the MOS transistor switching circuit is activated to apply drive gain to multiple drive signals to generate a drive control gain signal. The method includes: weighting the simulated drive control results according to the virtual test environment parameters to generate multiple weight coefficients; evaluating and analyzing the simulated drive control results according to the multiple weight coefficients, and determining the key evaluation data of the virtual test environment parameters based on the drive control evaluation values; backtracking the key evaluation data to the multiple drive signals for conflict analysis to determine drive control conflict data; activating the MOS transistor switching circuit based on the drive control conflict data to apply drive gain to the multiple drive signals to generate a drive gain collaborative control scheme; and executing the drive gain collaborative control scheme on the multiple drive signals to generate the drive control gain signal.

[0066] Furthermore, based on the drive control conflict data, the MOSFET switching circuit is activated to allocate drive gain for the multiple drive signals, generating a drive gain coordinated control scheme. The method includes: classifying the conflict based on the drive control conflict data to obtain multiple conflict levels; activating the MOSFET switching circuit according to the multiple conflict levels to analyze the multiple drive signals and extract drive control conflict features; using the drive control conflict features as constraints to perform gain analysis on the multiple drive signals to determine multiple main gain signals and multiple auxiliary drive signals; enhancing the gain of the multiple main gain signals to generate a first gain control parameter, and coordinating the gain of the multiple auxiliary drive signals to generate a second gain control parameter; adding the first gain control parameter and the second gain control parameter to the drive gain coordinated control scheme.

[0067] Furthermore, a drive control gain signal is generated to drive the IGBT device for feedback, and iterative gain is performed based on the feedback result. The method includes: applying the drive control gain signal to the IGBT device to perform initial drive control and obtain dynamic response data; driving the IGBT device based on the dynamic response data to generate feedback results; evaluating drive performance based on the feedback results and setting drive control indicators; calculating the deviation of the feedback results according to the drive control indicators to obtain a gain correction amount; and performing iterative gain tracking based on the gain correction amount to generate the iterative gain result.

[0068] Furthermore, based on the gain correction amount, the feedback result is used to track the iterative gain and generate the iterative gain result. The method includes:

[0069] The gain correction is decomposed into multiple gain adjustment steps. The feedback result is divided into stages according to the multiple gain adjustment steps to construct an incremental gain adjustment strategy. The incremental gain adjustment strategy is executed to perform multiple rounds of tracking iteration on the feedback result until the feedback result meets the driving control index: S1: Based on the i-th iteration round, the incremental gain adjustment strategy is executed to perform progressive gain tracking and monitoring on the feedback result, generating the i-th round driving control result, where i is an integer greater than 0, and the i-th iteration round is any iteration round in the multiple tracking iterations; S2: The control stability of the i-th round driving control result is evaluated to generate a control stability coefficient; S3: If the control stability coefficient is not in the expected stability range, the iteration stops, and the i-th iteration round is rolled back to the (i-1)-th iteration round for reverse adjustment until the control stability coefficient is in the expected stability range, where the (i-1)-th iteration round is the previous iteration round of the i-th iteration round; S4: If the control stability coefficient is in the expected stability range, the iteration stops, and the i-th round driving control result is used as the iterative gain result.

[0070] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0071] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. An intelligent drive control system for an IGBT device, characterized by, The system comprises: a running state analysis module for collecting the running parameters of the IGBT device through the multi-parameter sensing network, analyzing the running state of the IGBT device based on the running parameter set, and obtaining the running state recognition result, wherein the running state recognition result comprises N-level running state data; a driving signal generation module for driving the IGBT device based on the N-level running state data and generating a plurality of driving signals; a driving signal execution module for executing the driving control of the IGBT device based on the plurality of driving signals, and generating the simulation driving control result; a driving control module for activating the MOS tube switch circuit to drive the gain of the plurality of driving signals based on the simulation driving control result, generating the driving control gain signal to drive the feedback of the IGBT device, and iteratively increasing the gain based on the feedback result, and intelligently driving and controlling the IGBT device based on the iterative gain result; an N-level running state data construction unit comprising: a data boundary determination subunit for calculating the membership of the running state data based on the dynamic characteristic quantity, and determining a plurality of data boundaries; a running state level definition subunit for defining N running state levels according to the plurality of data boundaries; a running state level matching subunit for matching the effective migration data according to the N running state levels, and constructing the N-level running state data; the driving signal generation module comprises: an association configuration unit for calling the historical driving parameter set of the IGBT device, wherein the historical driving parameter set comprises a plurality of driving parameter combinations, and the N running state levels are associated with the plurality of driving parameter combinations to construct a state-driving mapping table; a multi-level driving analysis unit for performing multi-level driving analysis according to the state-driving mapping table and the N-level running state data, and constructing a plurality of driving units; a reference driving signal generation unit for generating a plurality of reference driving signals through the plurality of driving units in parallel, wherein each reference driving signal comprises a gate turn-on voltage, a gate turn-off voltage, a gate resistance, a driving current, and a dead time; a compensation calculation unit for performing compensation calculation according to the N running state levels based on the plurality of reference driving signals, and obtaining a plurality of driving compensation amounts; a compensation update unit for updating the plurality of reference driving signals based on the plurality of driving compensation amounts, and generating the plurality of driving signals.

2. The intelligent drive control system for an IGBT device of claim 1, wherein, the running state analysis module comprises: a sampling frequency setting unit for dynamically adjusting the sampling period of the multi-parameter sensing network by introducing the switching frequency of the IGBT device, and setting a hierarchical sampling frequency; a parameter obtaining unit for activating the multi-parameter sensing network to collect the running parameters of the IGBT device in real time according to the hierarchical sampling frequency, and obtaining a plurality of running sensing parameters; a running state data obtaining unit for performing space-time registration on the plurality of running sensing parameters according to the sensor association data of the multi-parameter sensing network, obtaining a running parameter set for running state analysis, and obtaining running state data. The N-level operation state data construction unit is configured to perform feature analysis based on the operation parameter set, extract dynamic characteristic quantities, perform multi-level division on the operation state data according to the dynamic characteristic quantities, and construct the N-level operation state data, wherein N is an integer greater than 1.

3. The intelligent drive control system for an IGBT device of claim 2, wherein, The N-level operation state data construction unit further comprises: An operation parameter migration subunit configured to perform operation parameter migration on the operation state data according to the N operation state levels, and generate a plurality of state migration data; A data screening subunit configured to perform migration effect evaluation based on the plurality of state migration data, and screen effective migration data according to a migration effect score.

4. The intelligent drive control system for an IGBT device of claim 1, wherein, The drive signal execution module comprises: A coupling analysis unit configured to perform multi-physical field coupling analysis based on an IGBT device to construct a virtual test environment parameter; A priority evaluation unit configured to perform priority evaluation on the plurality of drive signals according to the N-level operation state data, and construct a drive priority sequence; A signal response unit configured to load the plurality of drive signals according to the drive priority sequence based on the virtual test environment parameter to perform response, and generate a plurality of electrical response waveform parameters; An association array construction unit configured to perform association analysis on the plurality of electrical response waveform parameters and the plurality of drive signals, and construct a drive-response association array; A feature recognition unit configured to perform multi-dimensional collaborative simulation based on the drive-response association array, obtain dynamic response simulation data sets for feature recognition, and obtain a plurality of performance characteristic parameters; A performance evaluation unit configured to perform performance evaluation on the dynamic response simulation data sets according to the plurality of performance characteristic parameters, and generate the simulation drive control result.

5. The intelligent drive control system for an IGBT device of claim 4, wherein, The drive control module comprises: A weight distribution unit configured to perform weight distribution on the simulation drive control result according to the virtual test environment parameter, and generate a plurality of weight coefficients; An evaluation analysis unit configured to perform evaluation analysis on the simulation drive control result according to the plurality of weight coefficients, determine evaluation key data of the virtual test environment parameter according to a drive control evaluation value; A conflict analysis unit configured to backtrack the evaluation key data to the plurality of drive signals for conflict analysis, and determine drive control conflict data; A drive gain distribution unit configured to activate a MOS tube switch circuit based on the drive control conflict data to perform drive gain distribution on the plurality of drive signals, and generate a drive gain collaborative control scheme; A collaborative control scheme execution unit configured to execute the drive gain collaborative control scheme on the plurality of drive signals, and generate the drive control gain signal.

6. The intelligent drive control system for an IGBT device of claim 5, wherein, The drive gain distribution unit comprises: A conflict grading subunit configured to perform conflict grading based on the drive control conflict data, and obtain a plurality of conflict levels; A feature extraction subunit configured to activate a MOS tube switch circuit according to the plurality of conflict levels to analyze the plurality of drive signals, and extract drive control conflict features; A gain analysis subunit configured to take the drive control conflict features as constraint conditions to perform gain analysis on the plurality of drive signals, and determine a plurality of main gain signals and a plurality of auxiliary drive signals. The signal processing subunit is configured to perform gain enhancement on the plurality of main gain signals, generate a first gain control parameter, and perform gain coordination on the plurality of auxiliary drive signals, generate a second gain control parameter. The parameter adding subunit is configured to add the first gain control parameter and the second gain control parameter to the drive gain coordination control scheme.

7. The intelligent drive control system for an IGBT device of claim 1, wherein, The drive control module further comprises: The initial drive control execution unit is configured to apply the drive control gain signal to the IGBT device to perform initial drive control and obtain dynamic response data. The feedback result generation unit is configured to perform drive feedback on the IGBT device based on the dynamic response data and generate a feedback result. The drive control index setting unit is configured to evaluate drive performance according to the feedback result and set a drive control index. The gain correction amount acquisition unit is configured to calculate the deviation of the feedback result according to the drive control index and obtain a gain correction amount. The iterative gain result generation unit is configured to track and iterate the gain of the feedback result based on the gain correction amount and generate the iterative gain result.

8. The intelligent drive control system for an IGBT device of claim 7, wherein, The iterative gain result generation unit comprises: The stage division subunit is configured to divide the gain correction amount into a plurality of gain adjustment steps, divide the feedback result into stages according to the plurality of gain adjustment steps, and construct an incremental gain adjustment strategy. The tracking and iteration execution subunit is configured to perform a plurality of rounds of tracking and iteration on the feedback result according to the incremental gain adjustment strategy until the feedback result meets the drive control index. S1: based on the incremental gain tracking of the feedback result according to the incremental gain adjustment strategy in the i-th iteration round, generate the i-th round of drive control result, where i is an integer greater than 0, and the i-th iteration round is any iteration round in the plurality of rounds of tracking and iteration. S2: evaluate the control stability of the i-th round of drive control result and generate a control stability coefficient. S3: if the control stability coefficient is not in the expected stability interval, stop iteration, and perform reverse adjustment from the i-th iteration round to the i-1-th iteration round until the control stability coefficient is in the expected stability interval, where the i-1-th iteration round is the previous iteration round of the i-th iteration round. S4: if the control stability coefficient is in the expected stability interval, stop iteration, and take the i-th round of drive control result as the iterative gain result.

9. A method of intelligent drive control for an IGBT device, characterized by, The intelligent drive control method for IGBT devices comprises: Collecting running parameters of the IGBT device through a multi-parameter sensing network to obtain a set of running parameters for analyzing the running state of the IGBT device and obtaining a running state recognition result, wherein the running state recognition result comprises N-level running state data. Performing hierarchical drive on the IGBT device based on the N-level running state data to generate a plurality of drive signals. Performing drive control on the IGBT device by simulating the plurality of drive signals to generate a simulation drive control result. Based on the simulation driving control result, MOS tube switch circuit is activated to drive multiple driving signals to generate driving control gain signals to drive IGBT devices for feedback, and iteration gain is carried out according to the feedback result, and the IGBT devices are intelligently driven and controlled according to the iteration gain result.

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