Field Effect Transistor Switching Loss Evaluation Method Based on Gate Charge Analysis

Abstract

The present invention discloses a method for evaluating the switching loss of a field effect transistor based on gate charge analysis, which relates to the technical field of charge analysis. By collecting the voltage curve Vg(t) and the charge accumulation curve Qg(t) on the gate side of the field effect transistor, an original parameter data set RPS is constructed, and on this basis, a structured feature set FV that can reflect the change characteristics of different load attributes is extracted, thus avoiding the identification interference problem caused by the output side fluctuation. In addition, by constructing a charge behavior recognition model, a rapid determination of the load label LT is realized, and combined with the maximum recognition confidence value CM and the switching loss estimation value LE output by the model, a comprehensive state evaluation index SSE is generated, and the synergistic relationship between the recognition credibility and the actual energy consumption can be dynamically sensed before the strategy is executed, improving the sensitivity and robustness of the strategy decision-making, and significantly improving the dynamic adaptation ability and energy efficiency control ability of the system under different loads and different working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of charge analysis, and specifically to a method for evaluating the switching loss of a field effect transistor based on gate charge analysis. Background Art

[0002] In the macroscopic system of power electronics technology, power conversion has always been one of the core tasks, covering the efficient conversion of energy forms and voltage levels from the power grid to terminal devices. Especially in current new energy, high-efficiency power distribution, smart grid and other systems, power conversion not only occurs between traditional alternating current and alternating current (AC-AC), but also widely exists in the bidirectional, continuous, and high-speed regulation processes between alternating current and direct current (AC-DC) and between direct current and direct current (DC-DC). In order to achieve high-efficiency conversion in these complex energy transmission paths, power semiconductor devices (such as MOSFETs, IGBTs, GaNs, etc.) have become the key switching components for realizing energy control and scheduling.

[0003] The types of loads driven by field effect transistors are usually different. For example, inductive loads are mainly used in motor systems, resistive loads are mainly used in lighting systems, and capacitive characteristics are more prominent in energy storage systems. Under different types of loads, there are significant differences in the voltage and current waveform characteristics during the FET switching period, and these differences in turn will affect its switching loss, oscillation behavior, and EMI generation mechanism. Traditional technologies mainly rely on real-time sampling of current and voltage waveforms to judge the load attributes and adjust the driving strategy. However, this method is restricted by the sampling response speed on the one hand and is vulnerable to high-frequency noise interference on the other hand, making it difficult to balance the recognition accuracy and real-time performance. In addition, the waveform differences between different load characteristics often require complex filtering and judgment logic to draw conclusions, and the overall recognition and control process has a response delay and is difficult to quickly adapt to dynamically changing loads. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a method for evaluating the switching loss of a field effect transistor based on gate charge analysis, which solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for evaluating the switching loss of a field effect transistor based on gate charge analysis, including the following steps:

[0006] S1. Collect the gate voltage curve Vg(t) and charge accumulation curve Qg(t) that change with time during the switching operation from the gate drive channel of the MOSFET, calculate the charge response parameters, and then form the original parameter data set RPS together with the charge response change amount Zeta obtained within the perturbation range at the gate conduction critical point;

[0007] S2. Based on the original parameter dataset RPS, extract a set of feature vectors reflecting the changes in different load attributes to form a structured feature set FV;

[0008] S3. Based on the structured feature set FV, establish a charge behavior recognition model to identify the load type, output the load label LT under the task, and calculate the instantaneous switching loss estimation value LE through a multi-factor function;

[0009] S4. Perform a fusion process on the switching loss estimation value LE and the maximum recognition confidence value CM output by the charge behavior recognition model to generate a comprehensive state evaluation index SSE;

[0010] S5. Compare the comprehensive state evaluation index SSE with a preset state evaluation threshold TE, and make a judgment according to the comparison result to enter the drive control strategy adjustment process, and generate a drive strategy adjustment instruction set DCS;

[0011] S6. Apply the generated drive strategy adjustment instruction set DCS, re-collect the gate charge behavior data to form a new parameter set RPSN, extract a new feature vector FVN, obtain a differential feature vector DV, and then perform iterative optimization according to the differential feature vector DV.

[0012] Preferably, the S1 includes S11;

[0013] S11. During the normal working cycle of the metal-oxide-semiconductor field-effect transistor (MOSFET), collect the gate voltage curve Vg(t) and the charge accumulation curve Qg(t) that change with time during the switching operation from the gate drive channel of the MOSFET, and record the gate voltage curve Vg(t) and the charge accumulation curve Qg(t) as a structured time series vector to form a gate signal time series GSTS, and synchronously store the gate signal time series GSTS as stored historical data;

[0014] The gate signal time series GSTS is specifically GSTS = {(t, VG(t), Qg(t))|t ∈ [tstart, tend]}, where t represents the sampling moment, and tstart and tend respectively represent the sampling start moment and the sampling end moment.

[0015] Preferably, the S1 includes S12. Process the gate voltage curve Vg(t) and the charge accumulation curve Qg(t) to extract influence parameters reflecting the dynamic characteristics of the switching behavior. The influence parameters include the rise time Trise of the gate voltage, the fall time Tfall of the gate voltage, the average change rate davg of the gate charge in the effective drive interval, and the charge response change amount Zeta at the perturbation point near the gate conduction threshold;

[0016] Integrate the rise time Trise of the gate voltage, the fall time Tfall of the gate voltage, the average change rate davg, and the charge response change amount Zeta to obtain the original parameter data set RPS, and store it as historical data.

[0017] Preferably, S2 includes S21;

[0018] S21. Based on the rise time Trise of the gate voltage, the fall time Tfall of the gate voltage, the average change rate davg, and the charge response change amount Zeta in the original parameter data set RPS, obtain the original parameter data set RPS, perform mapping processing, and construct response factors, where the response factors include the average charge response rate factor Alpha1, the charge perturbation response magnification factor Alpha2, and the charge response stability factor Alpha3;

[0019] The average charge response rate factor Alpha1 is used to reflect the average change rate of the gate charge with time when the gate voltage is in the effective rising region. When constructing the average charge response rate factor Alpha1, determine the boundary points of the rising section of the gate voltage, and extract the change amount of the charge value and the time used within this interval to form the acquisition of the average charge response rate parameter;

[0020] The charge perturbation response magnification factor Alpha2 is used to measure the response ability of the system to the charge change rate of small voltage perturbations when the gate voltage is close to the conduction threshold. When constructing the average charge response rate factor Alpha1, extract the change trends of the gate charge in two adjacent regions near the conduction threshold voltage, and calculate the difference between the two to obtain;

[0021] The charge response stability factor Alpha3 is used to measure the stability of the gate charge response process within the effective working interval. Extract the gate charge curve within the entire interval from the start of the gate voltage rise to the saturation stage, and calculate the ratio of the fluctuation degree of the charge change to the average trend in this section of the curve to obtain.

[0022] Preferably, S2 includes S22. Based on the obtained average charge response rate factor Alpha1, the charge perturbation response magnification factor Alpha2, and the charge response stability factor Alpha3, reflect the eigenvectors of different load attribute changes, and form a structured feature set FV by integrating the average charge response rate factor Alpha1, the charge perturbation response magnification factor Alpha2, and the charge response stability factor Alpha3;

[0023] The specific structured feature set FV is FV = {Alpha1, Alpha2, Alpha3}.

[0024] Preferably, S3 includes S31;

[0025] S31. Based on the structured feature set FV, input it into a pre-established charge behavior recognition model for recognition and inference. The charge behavior recognition model is a set of feature judgment logics constructed based on a preset load sample library, used to reflect the distribution laws of different types of loads on the three features in the structured feature set FV, record the similarity between the load samples and each feature in the structured feature set FV, and then judge and assign the load type recognition result of the current cycle according to the similarity, marked as the load label LT;

[0026] Among them, in the process of recognition and inference in the charge behavior recognition model, according to each feature in the structured feature set FV, determine the load type to which each feature in the structured feature set FV belongs based on the feature space distribution obtained from the predefined classification boundary in the charge behavior recognition model;

[0027] The different types of loads include inductive loads, capacitive loads, and resistive loads;

[0028] The load label LT includes the resistive type Resistive, the inductive type Inductive, and the capacitive type Capacitive.

[0029] Preferably, S3 includes S32. Based on the obtained load label LT and the structured feature set FV, call the built-in energy consumption evaluation model. The energy consumption evaluation model is a set of energy consumption estimation algorithms constructed based on empirical data and switching behavior laws, used to judge the typical conduction and turn-off loss levels of field effect transistors under specific load characteristic conditions;

[0030] The energy consumption evaluation model has two input interfaces, and the input interfaces include a feature vector interface and a load label interface;

[0031] The feature vector interface is used to receive the structured feature set FV, representing the response features of the gate control behavior in the current cycle;

[0032] The load label interface is used to receive the load label LT as the basis for selecting the loss function path;

[0033] When the input interface receives data, the energy consumption evaluation model executes a pre-constructed multi-factor loss estimation function, performs weight combination and behavior fitting on each parameter factor input into the feature vector interface, and outputs the loss evaluation value in the current working cycle, marked as the instantaneous switching loss estimation value LE.

[0034] Preferably, the S4 includes S41;

[0035] S41. Perform a fusion process on the switch loss estimation value LE and the maximum recognition confidence value CM output by the charge behavior recognition model to generate a comprehensive state evaluation index SSE;

[0036] The fusion process is calculated by using the maximum recognition confidence value CM as the correction weight of the switch loss estimation value L to obtain the corrected switch loss estimation value L, marked as the comprehensive state evaluation index SSE;

[0037] Among them, the maximum recognition confidence value CM is obtained based on the maximum value output by the charge behavior recognition model in the different types of loads.

[0038] Preferably, the S5 includes S51;

[0039] S51. Compare the comprehensive state evaluation index SSE with a preset state evaluation threshold TE, and make a judgment based on the comparison result to enter the drive control strategy adjustment process, generating a drive strategy adjustment instruction set DCS;

[0040] The drive control strategy adjustment process is judged to enter through the following comparison results:

[0041] When the comprehensive state evaluation index SSE < the state evaluation threshold TE, it is obtained that the current control state is normal, and the original drive control strategy adjustment process is not adjusted;

[0042] When the comprehensive state evaluation index SSE ≥ the state evaluation threshold TE, it is obtained that the current control state is abnormal, indicating that there are problems with the loss and recognition credibility of the current drive control strategy, and the original drive control strategy adjustment process is adjusted. The adjustment includes the gate drive voltage correction value, the rising and falling edge timing adjustment parameters of the PWM drive signal, the fine adjustment amount of the PWM duty cycle and frequency, the soft start delay adjustment amount, and the control pulse edge filtering strategy switching flag.

[0043] Preferably, the S6 includes S61;

[0044] S61. Apply the generated drive strategy adjustment instruction set DCS, and re-collect the gate charge behavior data to form a new parameter set RPSN, extract a new feature vector FVN, obtain a differential feature vector DV by comparing the difference between the structured feature set FV and the feature vector FVN, and then perform iterative optimization according to the differential feature vector DV;

[0045] When the differential feature vector DV is in a positive trend, it indicates that the drive policy adjustment instruction set DCS is effective, and the drive policy adjustment instruction set DCS is recorded as an effective parameter configuration;

[0046] When the differential feature vector DV is in a negative trend, it indicates that the drive policy adjustment instruction set DCS is ineffective, and the drive policy adjustment instruction set DCS is not recorded as an effective parameter configuration, and S1 to S5 are continued to be executed for iterative optimization.

[0047] The present invention provides a method for evaluating the switching loss of a field effect transistor based on gate charge analysis, which has the following beneficial effects:

[0048] (1) By collecting the voltage curve Vg(t) and the charge accumulation curve Qg(t) on the gate side of the field effect transistor, an original parameter data set RPS is constructed, and on this basis, a structured feature set FV that can reflect the change characteristics of different load attributes is extracted, thus avoiding the identification interference problem caused by the output side fluctuation. In addition, by constructing a charge behavior recognition model, the load label LT can be quickly determined, and combined with the maximum recognition confidence value CM and the switching loss estimation value LE output by the model, a comprehensive state evaluation index SSE is generated. The synergistic relationship between the recognition credibility and the actual energy consumption can be dynamically sensed before the strategy is executed, improving the sensitivity and robustness of the strategy decision-making. When the comprehensive state evaluation index SSE exceeds the preset state evaluation threshold TE, the system can trigger the drive policy adjustment instruction set DCS, and after execution, the gate response is re-collected to generate a new parameter set RPSN and a new feature vector FVN. By constructing a differential feature vector DV, the quantitative evaluation and iterative optimization of the strategy effect are realized, effectively solving the problems that the control strategy in the traditional method cannot adapt to the load change and is difficult to quantitatively feedback and optimize, realizing an information-driven control mechanism based on gate charge analysis, and establishing a strategy optimization determination process based on the feature vector difference, significantly improving the dynamic adaptation ability and energy efficiency management ability of the system under different loads and different working conditions, and having the comprehensive technical advantages of high real-time performance, high recognition performance and high stability.

[0049] (2)By collecting the gate voltage curve Vg(t) and the charge accumulation curve Qg(t), a gate signal time series GSTS is constructed, enabling the system to record the entire process of control behavior in a unified structured time series format, with time domain continuity and historical traceability. This time series is not only used for current evaluation but also for multi-cycle behavior trend comparison and modeling analysis. On this basis, by extracting key influencing parameters including the rise time Trise, fall time Tfall, average charge change rate davg of the gate voltage, and the charge response change amount Zeta under the threshold perturbation interval, and integrating them into the original parameter dataset RPS, it is possible to reflect the true response state of the MOSFET to the drive signal only through the gate terminal signal behavior of the MOSFET without opening an external sampling probe or modifying the hardware structure, realizing a low-intrusion, high-resolution, behavior modeling-oriented control state construction mechanism, which is particularly suitable for working scenarios with device response ability degradation, frequent load changes, or high control lag risk during actual operation. Therefore, it not only realizes the in-situ acquisition and structural reconstruction of the MOSFET switching behavior but also lays the key data foundation for subsequent processes such as feature extraction, load discrimination, and strategy adaptation of the system, and is particularly suitable for power electronic control system applications with embedded, space-constrained, or high real-time requirements.

[0050] (3)By performing targeted mapping processing on the key dynamic behavior parameters in the original parameter dataset RPS, a three-factor feature set including the average charge response rate factor Alpha1, charge perturbation response magnification factor Alpha2, and charge response stability factor Alpha3 is constructed, enabling the expression of load behavior differences in a more discriminative structural manner. Compared with the traditional method that uses a single time or voltage boundary parameter as the discrimination basis, the constructed factor system has stronger dynamic information dimension analysis ability and physical behavior interpretability, can reflect the essential response characteristics closely related to load coupling in the MOSFET drive behavior, realizes the orderly compression expression of the original response characteristics, and also enables the subsequent recognition model to more accurately distinguish different load types in the high-dimensional feature space, especially suitable for multi-source response recognition in complex load mixing scenarios, providing a more reliable basic input for subsequent recognition accuracy, loss calculation accuracy, and control strategy adaptability.

[0051] (4) By constructing a comprehensive state evaluation index SSE based on the fusion of energy consumption estimation and recognition confidence, and using the comparison result between the comprehensive state evaluation index SSE and the preset state evaluation threshold TE as the basis for policy decision-making, the system is equipped with dynamic state perception and quantitative judgment capabilities during the control strategy adaptation process. The switching loss estimation LE and the maximum recognition confidence value CM output by the charge behavior recognition model are fused to form the comprehensive state evaluation index SSE, which can not only reflect the current energy efficiency level of the device but also quantify the credibility of the recognition result, ensuring that the subsequent control strategy judgment is not only based on energy consumption performance but also fully considers the stability of the decision-making basis. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Schematic diagram of the steps of the field effect transistor switching loss evaluation method based on gate charge analysis according to the present invention;

[0053] Figure 2 Schematic diagram of the comparison of three-factor eigenvalue under different load types;

[0054] Figure 3 Schematic diagram of the change trend of each dimension of the differential feature vector DV during the policy iteration process. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0056] Embodiment 1

[0057] The present invention provides a field effect transistor switching loss evaluation method based on gate charge analysis. Please refer to Figure 1 and includes the following steps:

[0058] S1. Collect the gate voltage curve Vg(t) and the charge accumulation curve Qg(t) that change with time during the switching operation from the gate drive channel of the MOSFET, calculate the charge response parameters, and then combine them with the charge response change amount Zeta within the perturbation range at the gate conduction critical point to form the original parameter data set RPS;

[0059] S2. Based on the original parameter data set RPS, extract the feature vector set reflecting different load attribute changes to form the structured feature set FV;

[0060] S3. Based on the structured feature set FV, establish a charge behavior recognition model to identify the load type, output the load tag LT under the task, and calculate the instantaneous switching loss estimation value LE through a multi-factor function;

[0061] S4. Fuse the switching loss estimation value LE and the maximum recognition confidence value CM output by the charge behavior recognition model to generate a comprehensive state evaluation index SSE;

[0062] S5. Compare the comprehensive state evaluation index SSE with a preset state evaluation threshold TE, and make a judgment according to the comparison result to enter the drive control strategy adjustment process, and generate a drive strategy adjustment instruction set DCS;

[0063] S6. Apply the generated drive strategy adjustment instruction set DCS, re-collect the gate charge behavior data, form a new parameter set RPSN, extract a new feature vector FVN, obtain a differential feature vector DV, and then perform iterative optimization according to the differential feature vector DV.

[0064] In this embodiment, by collecting the voltage curve Vg(t) and the charge accumulation curve Qg(t) on the gate side of the field effect transistor, an original parameter data set RPS is constructed, and on this basis, a structured feature set FV that can reflect the change characteristics of different load attributes is extracted, thus avoiding the recognition interference problem caused by the output side fluctuation. In addition, by constructing a charge behavior recognition model, the rapid determination of the load tag LT is realized, and combined with the maximum recognition confidence value CM and the switching loss estimation value LE output by the model, a comprehensive state evaluation index SSE is generated, and the synergistic relationship between the recognition credibility and the actual energy consumption can be dynamically sensed before the strategy is executed, improving the sensitivity and robustness of the strategy decision-making. When the comprehensive state evaluation index SSE exceeds the preset state evaluation threshold TE, the system can trigger the drive strategy adjustment instruction set DCS, and re-collect the gate response after execution to generate a new parameter set RPSN and a new feature vector FVN, and realize the quantitative evaluation and iterative optimization of the strategy effect by constructing a differential feature vector DV, effectively solving the problems that the control strategy in the traditional method cannot adapt to the load change and is difficult to quantitatively feedback and optimize.

[0065] Therefore, not only an information-driven control mechanism based on gate charge analysis is realized, but also a strategy optimization determination process based on the difference of feature vectors is established, significantly improving the dynamic adaptation ability and energy efficiency management and control ability of the system under different loads and different working conditions, and having comprehensive technical advantages of high real-time performance, high recognition performance and high stability.

[0066] Embodiment 2

[0067] This embodiment is an explanatory description based on Embodiment 1, please refer to Figure 1, specifically: S1 includes S11;

[0068] S11. During the normal operating cycle of the metal-oxide-semiconductor field-effect transistor (MOSFET), by collecting the gate voltage curve Vg(t) and the charge accumulation curve Qg(t) that change with time during the switching operation from the gate drive channel of the MOSFET, and recording the gate voltage curve Vg(t) and the charge accumulation curve Qg(t) as a structured time series vector to form a gate signal time series GSTS, and synchronously storing the gate signal time series GSTS as stored historical data;

[0069] The gate signal time series GSTS is specifically GSTS = {(t, VG(t), Qg(t))|t ∈ [tstart, tend]}, where t represents the sampling time, and tstart and tend represent the sampling start time and the sampling end time respectively.

[0070] S1 includes S12. Processing the gate voltage curve Vg(t) and the charge accumulation curve Qg(t) to extract influence parameters reflecting the dynamic characteristics of the switching behavior. The influence parameters include the rise time Trise of the gate voltage, the fall time Tfall of the gate voltage, the average change rate davg of the gate charge in the effective drive interval, and the charge response change amount Zeta at the perturbation point near the gate conduction threshold;

[0071] Integrating the rise time Trise of the gate voltage, the fall time Tfall of the gate voltage, the average change rate davg, and the charge response change amount Zeta to obtain an original parameter data set RPS, and storing it as historical data;

[0072] Among them, the rise time Trise of the gate voltage reflects the gate drive speed during the turn-on process of the MOSFET. The shorter the time, the faster the response;

[0073] The fall time Tfall of the gate voltage reflects the voltage fall edge during the turn-off process of the MOSFET. The shorter the time, the faster the response;

[0074] The average change rate davg reflects the average growth rate of the gate charge in the effective charging interval, indirectly reflecting the response ability of the gate to the drive;

[0075] The charge response change amount Zeta reflects the sensitive response ability to the gate charging rate near the threshold, and is used to evaluate the dynamic response factor of the load type difference.

[0076] In this embodiment, by collecting the gate voltage curve Vg(t) and the charge accumulation curve Qg(t), a gate signal time series GSTS is constructed, enabling the system to record the entire process of control behavior in a unified structured timing format, with time-domain continuity and historical traceability. This time series is not only used for current evaluation but also for multi-cycle behavior trend comparison and modeling analysis. On this basis, by extracting key influencing parameters including the rise time Trise, fall time Tfall, average charge change rate davg of the gate voltage, and the charge response change amount Zeta under the threshold perturbation interval, and integrating them into the original parameter data set RPS, it is possible to reflect the true response state of the MOSFET to the drive signal only through the gate terminal signal behavior of the MOSFET without opening an external sampling probe or modifying the hardware structure, realizing a low-intrusion, high-resolution, behavior-modeling-oriented control state construction mechanism, which is particularly suitable for working scenarios where the device response ability degrades, the load changes frequently, or the control lag risk is high during actual operation. Therefore, not only the in-situ acquisition and structural reconstruction of the MOSFET switching behavior are achieved, but also the key data foundation for subsequent processes such as feature extraction, load discrimination, and strategy adaptation of the system is laid, which is particularly suitable for power electronic control system applications with embedded, space-constrained, or high real-time requirements.

[0077] Embodiment 3

[0078] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 and Figure 2 , specifically: S2 includes S21;

[0079] S21. Based on the rise time Trise, fall time Tfall, average change rate davg, and charge response change amount Zeta of the gate voltage in the original parameter data set RPS, the original parameter data set RPS is obtained, and after mapping processing, a response factor is constructed. The response factor includes an average charge response rate factor Alpha1, a charge perturbation response magnification factor Alpha2, and a charge response stability factor Alpha3;

[0080] The average charge response rate factor Alpha1 is used to reflect the average change rate of gate charge over time when the gate voltage is in the effective rising region. When constructing the average charge response rate factor Alpha1, the boundary points of the rising section of the gate voltage are determined, and the change amount of the charge value and the time used are extracted within this interval to form the acquisition of the average charge response rate parameter. The larger this parameter is, the faster the device's turn-on response and the smaller the inertia of the load to the drive response. It can usually be used to identify resistive or low-inductance loads;

[0081] The charge perturbation response magnification factor Alpha2 is used to measure the ability of the system to respond to the rate of charge change of a small voltage perturbation when the gate voltage is close to the turn-on threshold. When constructing the average charge response rate factor Alpha1, the change trends of the gate charge are extracted in two adjacent regions near the turn-on threshold voltage (one preset perturbation amount higher and lower than the threshold respectively), and the difference between the two is calculated to obtain it. The larger this value is, the more non-linear amplification characteristics the device exhibits near the threshold point, which is often associated with inductive loads, energy storage devices, or high-voltage feedback paths;

[0082] The charge response stability factor Alpha3 is used to measure the stability of the gate charge response process within the effective operating range. In the entire range from the start to the saturation stage of the gate voltage, the gate charge curve is extracted, and the ratio between the fluctuation degree of the charge change and the average trend in this section of the curve is calculated to obtain it. If the value of this factor is large, it indicates that there are jitters, oscillations, or unstable characteristics caused by load feedback in the response process, which are common in capacitive loads or composite loads with stray coupling.

[0083] The S2 includes S22, a feature vector that reflects the changes in different load attributes based on the obtained average charge response rate factor Alpha1, the charge perturbation response magnification factor Alpha2, and the charge response stability factor Alpha3, and forms a structured feature set FV by integrating the average charge response rate factor Alpha1, the charge perturbation response magnification factor Alpha2, and the charge response stability factor Alpha3;

[0084] The structured feature set FV is specifically FV = {Alpha1, Alpha2, Alpha3}.

[0085] In this embodiment, by performing targeted mapping processing on the key dynamic behavior parameters in the original parameter dataset RPS, a three-factor feature set including the average charge response rate factor Alpha1, the charge perturbation response magnification factor Alpha2, and the charge response stability factor Alpha3 is constructed, enabling the expression of load behavior differences in a more discriminative structural manner. Compared with the traditional method that uses a single time or voltage boundary parameter as the discrimination basis, the constructed factor system has stronger dynamic information dimension analysis ability and physical behavior interpretability, and can reflect the essential response characteristics closely related to load coupling in the MOSFET driving behavior. Among them, the average charge response rate factor Alpha1 can quantitatively measure the charge driving rate during conduction, the charge perturbation response magnification factor Alpha2 can reveal the nonlinear gain characteristics of the device in the threshold perturbation region, and the charge response stability factor Alpha3 is used to reflect the consistency and stability during the charge charging and discharging process. By integrating these three factors to form a structured feature set FV, not only the orderly compression expression of the original response characteristics is realized, but also the subsequent recognition model can more accurately distinguish different load types in the high-dimensional feature space, especially suitable for multi-source response recognition in complex load mixing scenarios, providing a more reliable basic input for subsequent recognition accuracy, loss calculation accuracy, and control strategy adaptability.

[0086] Embodiment 4

[0087] This embodiment is an explanatory description carried out in Embodiment 3. Please refer to Figure 1 , specifically: S3 includes S31;

[0088] S31. Based on the structured feature set FV, input it into the pre-established charge behavior recognition model for recognition and inference. The charge behavior recognition model is a feature judgment logic based on a preset load sample library, used to reflect the distribution laws of different types of loads on the three features in the structured feature set FV, record the similarity between the load samples and each feature in the structured feature set FV, and then determine the load type recognition result of the current cycle according to the similarity and mark it as the load label LT;

[0089] Among them, during the process of recognition and inference in the charge behavior recognition model, according to each feature in the structured feature set FV, determine the load type to which each feature in the structured feature set FV belongs according to the feature space distribution obtained from the predefined classification boundary in the charge behavior recognition model;

[0090] The different types of loads include inductive loads, capacitive loads, and resistive loads;

[0091] The load tag LT includes a resistive type, an inductive type, and a capacitive type.

[0092] S3 includes S32, which calls a built-in energy consumption evaluation model based on the obtained load tag LT and the structured feature set FV. The energy consumption evaluation model is a set of energy consumption estimation algorithms constructed based on empirical data and switching behavior rules, and is used to judge the typical on and off loss levels of the field effect transistor under specific load characteristic conditions.

[0093] The energy consumption evaluation model has two input interfaces, and the input interfaces include a feature vector interface and a load tag interface.

[0094] The feature vector interface is used to receive the structured feature set FV, representing the response characteristics of the gate control behavior in the current cycle.

[0095] The load tag interface is used to receive the load tag LT as the basis for selecting the loss function path.

[0096] After the input interface receives the data, the energy consumption evaluation model executes a pre-constructed multi-factor loss estimation function, performs weight combination and behavior fitting on each parameter factor input into the feature vector interface, and outputs the loss evaluation value in the current working cycle, marked as the instantaneous switching loss estimate LE.

[0097] In this embodiment, by inputting the structured feature set FV into a pre-established charge behavior recognition model for recognition and inference, the system can complete the automatic classification and judgment of the actual load attributes in the current cycle based on the statistical distribution characteristics of different types of loads in the three-dimensional feature space in the load sample library. Compared with the traditional load recognition method that uses current waveforms or voltage gradients for threshold judgment, this step realizes a non-linear recognition mechanism based on the distribution law of the feature space by calculating the similarity between each feature parameter in the structured feature set FV and the load sample set, effectively improving the ability to distinguish the subtle behavioral differences between inductive loads, capacitive loads, and resistive loads. Further, by combining the recognized load label LT with the structured feature set FV and invoking the built-in energy consumption evaluation model, the multi-factor loss estimation function can be executed based on the coupling relationship between specific load attributes and behavioral characteristics, and the instantaneous switching loss estimation value LE of the current working cycle can be output. This evaluation mechanism not only considers the self-response characteristics of the control behavior but also integrates the recognized load feature path, realizing an energy loss evaluation method that models by type branch, thus significantly improving the adaptability and accuracy of loss prediction. Through the series application of this double-layer model structure, while achieving high-accuracy load recognition, the dynamic perception ability of the actual loss level under the current control conditions can be constructed, providing a directly quantifiable input data basis for subsequent state evaluation and strategy matching.

[0098] Embodiment 5

[0099] This embodiment is an explanatory description carried out in Embodiment 4. Please refer to Figure 1 and Figure 3 , specifically: S4 includes S41;

[0100] S41. Perform fusion processing on the switching loss estimation value LE and the maximum recognition confidence value CM output by the charge behavior recognition model to generate a comprehensive state evaluation index SSE;

[0101] The fusion processing is calculated by using the maximum recognition confidence value CM as the correction weight of the switching loss estimation value L to obtain the corrected switching loss estimation value L, denoted as the comprehensive state evaluation index SSE;

[0102] Among them, the maximum recognition confidence value CM is obtained based on the maximum value output by the charge behavior recognition model for different types of loads.

[0103] S5 includes S51;

[0104] S51. Compare the comprehensive state evaluation index SSE with a preset state evaluation threshold TE, and make a judgment according to the comparison result to enter the drive control strategy adjustment process, and generate a drive strategy adjustment instruction set DCS;

[0105] The driving control strategy adjustment process is judged to enter through the following comparison results:

[0106] When the comprehensive state evaluation index SSE < the state evaluation threshold TE, it is obtained that the current control state is normal, and the original driving control strategy adjustment process is not adjusted;

[0107] When the comprehensive state evaluation index SSE ≥ the state evaluation threshold TE, it is obtained that the current control state is abnormal, indicating that there are losses in the current driving control strategy and problems with the recognition credibility. The original driving control strategy adjustment process is adjusted. The adjustments include the correction value of the gate driving voltage, the rising and falling edge timing adjustment parameters of the PWM driving signal, the fine-tuning amount of the PWM duty cycle and frequency, the soft start delay adjustment amount, and the control pulse edge filtering strategy switching flag.

[0108] The S6 includes S61;

[0109] S61. Apply the generated driving strategy adjustment instruction set DCS, and re-collect the gate charge behavior data to form a new parameter set RPSN. Extract the new feature vector FVN. By comparing the difference between the structured feature set FV and the feature vector FVN, obtain the differential feature vector DV, and then perform iterative optimization according to the differential feature vector DV;

[0110] When the differential feature vector DV is in a positive trend, it indicates that the driving strategy adjustment instruction set DCS is effective, and record the driving strategy adjustment instruction set DCS as an effective parameter configuration;

[0111] When the differential feature vector DV is in a negative trend, it indicates that the driving strategy adjustment instruction set DCS is ineffective, and do not record the driving strategy adjustment instruction set DCS as an effective parameter configuration. Continue to execute S1 to S5 for iterative optimization.

[0112] In this embodiment, by constructing a comprehensive state evaluation index SSE based on the fusion of energy consumption estimation and recognition confidence, and using the comparison result between the comprehensive state evaluation index SSE and the preset state evaluation threshold TE as the basis for strategy decision-making, the system has the ability of dynamic state perception and quantitative judgment during the control strategy adaptation process. The switching loss estimation LE and the maximum recognition confidence value CM output by the charge behavior recognition model are fused to form the comprehensive state evaluation index SSE, which can not only reflect the current energy efficiency level of the device, but also quantify the credibility of the recognition result, ensuring that the subsequent control strategy judgment is not only based on the energy consumption performance, but also fully considers the stability of the decision-making basis.

[0113] When the comprehensive state evaluation index SSE is higher than the state evaluation threshold TE, a drive strategy adjustment instruction set DCS is generated according to the set strategy adjustment process, covering key drive control parameters such as the correction value of the gate drive voltage, the adjustment parameters of the rising and falling edge timings of the PWM drive signal, the fine-tuning amounts of the PWM duty cycle and frequency, the adjustment amount of the soft start delay, and the switching flag of the control pulse edge filtering strategy, so as to achieve multi-dimensional linkage correction of the control behavior. By re-collecting the charge behavior data after the control strategy is adjusted, the feature vector FVN is extracted, and the differential feature vector DV is constructed with the historical feature set FV. According to the trend of DV, it is judged whether the current strategy achieves the optimization goal, so as to realize the self-feedback verification of the effectiveness of the control strategy. This mechanism not only endows the system with the ability to self-evaluate and screen strategies after the strategy is executed, but also enables the control strategy to automatically adapt to different load environments and dynamically iterate and update during long-term operation, significantly enhancing the stability, self-recovery and adjustment flexibility of the control system in multi-state disturbance scenarios.

[0114] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made therein without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the switching loss of a field effect transistor based on gate charge analysis, characterized in that: Including the following steps: S1. Collect the gate voltage curve Vg(t) and charge accumulation curve Qg(t) that vary with time during the switching operation from the gate drive channel of the MOSFET, calculate the charge response parameters, and then combine them with the charge response change amount Zeta obtained within the perturbation range at the gate conduction critical point to form the original parameter dataset RPS; S2. Based on the original parameter dataset RPS, extract the set of feature vectors reflecting the changes in different load attributes to form the structured feature set FV; S3. Based on the structured feature set FV, establish a charge behavior recognition model to identify the load type, output the load label LT under the task, and calculate the instantaneous switching loss estimate LE through a multi-factor function; S3 includes S32; S32. Invoke the built-in energy consumption evaluation model based on the obtained load label LT and the structured feature set FV. The energy consumption evaluation model is a set of energy consumption estimation algorithms constructed based on empirical data and switching behavior rules, and is used to judge the typical conduction and turn-off loss levels of the field-effect transistor under specific load characteristic conditions; The energy consumption evaluation model is provided with two input interfaces, and the input interfaces include a feature vector interface and a load label interface; The feature vector interface is used to receive the structured feature set FV, representing the response characteristics of the gate control behavior in the current cycle; The load label interface is used to receive the load label LT as the basis for selecting the loss function path; After the input interface receives the data, the energy consumption evaluation model executes the pre-constructed multi-factor loss estimation function, performs weight combination and behavior fitting on each parameter factor input into the feature vector interface, and outputs the loss evaluation value in the current working cycle, marked as the instantaneous switching loss estimate LE; S4. Perform fusion processing on the switching loss estimate LE and the maximum recognition confidence value CM output by the charge behavior recognition model to generate the comprehensive state evaluation index SSE; S5. Compare the comprehensive state evaluation index SSE with the preset state evaluation threshold TE, and make a judgment according to the comparison result to enter the drive control strategy adjustment process, and generate the drive strategy adjustment instruction set DCS; S6. Apply the generated drive strategy adjustment instruction set DCS, re-collect the gate charge behavior data to form a new parameter set RPSN, extract the new feature vector FVN, obtain the differential feature vector DV, and then perform iterative optimization according to the differential feature vector DV.

2. The method for evaluating the switching loss of a field effect transistor based on gate charge analysis according to claim 1, wherein: S1 includes S11; S11. During the normal working cycle of the field-effect transistor MOSFET, collect the gate voltage curve Vg(t) and charge accumulation curve Qg(t) that vary with time during the switching operation from the gate drive channel of the field-effect transistor MOSFET, record the gate voltage curve Vg(t) and the charge accumulation curve Qg(t) as a structured time series vector to form the gate signal time series GSTS, and synchronously store the gate signal time series GSTS as the stored historical data; The gate signal time series GSTS is specifically GSTS = {(t, VG(t), Qg(t))|t ∈ [tstart, tend]}, where t represents the sampling moment, and tstart and tend represent the sampling start moment and the sampling end moment, respectively.

3. The method for evaluating the switching loss of a field effect transistor based on gate charge analysis according to claim 2, wherein: The S1 includes S12; S12 processes the gate voltage curve Vg(t) and the charge accumulation curve Qg(t) to extract influence parameters reflecting the dynamic characteristics of the switching behavior. The influence parameters include the rise time Trise of the gate voltage, the fall time Tfall of the gate voltage, the average change rate davg of the gate charge in the effective driving interval, and the charge response change amount Zeta at the perturbation point near the gate conduction threshold; Integrate the rise time Trise of the gate voltage, the fall time Tfall of the gate voltage, the average change rate davg, and the charge response change amount Zeta to obtain the original parameter data set RPS, and store it as historical data.

4. The method for evaluating the switching loss of a field effect transistor based on gate charge analysis according to claim 3, characterized in that: The S2 includes S21; S21 obtains the original parameter data set RPS based on the rise time Trise of the gate voltage, the fall time Tfall of the gate voltage, the average change rate davg, and the charge response change amount Zeta in the original parameter data set RPS, and constructs a response factor after mapping processing. The response factor includes the average charge response rate factor Alpha1, the charge perturbation response magnification factor Alpha2, and the charge response stability factor Alpha3; The average charge response rate factor Alpha1 is used to reflect the average change rate of the gate charge with time when the gate voltage is in the effective rising region. When constructing the average charge response rate factor Alpha1, determine the boundary points of the rising section of the gate voltage, and extract the change amount of the charge value and the time used in this interval to form the acquisition of the average charge response rate parameter; The charge perturbation response magnification factor Alpha2 is used to measure the charge change rate response ability of the system to small voltage perturbations when the gate voltage is close to the conduction threshold. When constructing the average charge response rate factor Alpha1, extract the change trends of the gate charge in two adjacent regions near the conduction threshold voltage, and calculate the difference between the two to obtain; The charge response stability factor Alpha3 is used to measure the stability degree of the gate charge response process in the effective working interval. In the entire interval from the start of the gate voltage rising to the saturation stage, extract the gate charge curve, and calculate the ratio between the fluctuation degree of the charge change and the average trend in this section of the curve to obtain.

5. The method for evaluating the switching loss of a field effect transistor based on gate charge analysis according to claim 4, wherein: The S2 includes S22; S22. Reflect the eigenvectors of different load attribute changes based on the obtained average charge response rate factor Alpha1, charge perturbation response magnification factor Alpha2, and charge response stability factor Alpha3, and form a structured feature set FV by integrating the average charge response rate factor Alpha1, charge perturbation response magnification factor Alpha2, and charge response stability factor Alpha3; The structured feature set FV is specifically FV = {Alpha1, Alpha2, Alpha3}.

6. The method for evaluating the switching loss of a field effect transistor based on gate charge analysis according to claim 1, wherein: The S3 includes S31; S31. Based on the structured feature set FV, input it into a pre-established charge behavior recognition model for recognition and inference. The charge behavior recognition model is based on a set of feature judgment logics constructed based on a preset load sample library, used to reflect the distribution laws of different types of loads on the three features in the structured feature set FV, record the similarity between the load samples and each feature in the structured feature set FV, and then determine the load type recognition result of the current cycle according to the similarity judgment, marked as the load label LT; Among them, during the process of recognition and inference in the charge behavior recognition model, determine the load type to which each feature in the structured feature set FV belongs according to the feature space distribution obtained from each feature in the structured feature set FV and the predefined classification boundary in the charge behavior recognition model; The different types of loads include inductive loads, capacitive loads, and resistive loads; The load label LT includes the resistive type Resistive, inductive type Inductive, and capacitive type Capacitive.

7. The method for evaluating the switching loss of a field effect transistor based on gate charge analysis according to claim 1, wherein: The S4 includes S41; S41. Perform fusion processing on the switch loss estimation value LE and the maximum recognition confidence value CM output by the charge behavior recognition model to generate a comprehensive state evaluation index SSE; The fusion processing is calculated by using the maximum recognition confidence value CM as the correction weight of the switch loss estimation value L to obtain the corrected switch loss estimation value L, marked as the comprehensive state evaluation index SSE; Among them, the maximum recognition confidence value CM is obtained based on the maximum value output by the charge behavior recognition model for different types of loads.

8. The method for evaluating the switching loss of a field effect transistor based on gate charge analysis according to claim 1, wherein: The S5 includes S51; S51. Compare the comprehensive state evaluation index SSE with a preset state evaluation threshold TE, and make a judgment according to the comparison result to enter the drive control strategy adjustment process, and generate a drive strategy adjustment instruction set DCS; The drive control strategy adjustment process is judged to enter through the following comparison results: When the comprehensive state evaluation index SSE < the state evaluation threshold TE, it is obtained that the current control state is normal, and the original drive control strategy adjustment process is not adjusted; When the comprehensive state evaluation index SSE ≥ the state evaluation threshold TE, it is obtained that the current control state is abnormal, indicating that there are losses in the current drive control strategy and problems with the recognition credibility. The adjustment process of the original drive control strategy is adjusted, and the adjustment includes the gate drive voltage correction value, the PWM drive signal rise and fall edge timing adjustment parameter, the PWM duty cycle and frequency fine-tuning amount, the soft start delay adjustment amount, and the control pulse edge filtering strategy switching flag.

9. The method for evaluating the switching loss of a field effect transistor based on gate charge analysis according to claim 1, wherein: The S6 includes S61; S61. Apply the generated drive strategy adjustment instruction set DCS, and re-collect the gate charge behavior data to form a new parameter set RPSN, extract a new feature vector FVN. By comparing the difference between the structured feature set FV and the feature vector FVN, obtain the differential feature vector DV, and then perform iterative optimization according to the differential feature vector DV; When the differential feature vector DV is in a positive trend, it indicates that the drive strategy adjustment instruction set DCS is effective, and record the drive strategy adjustment instruction set DCS as the effective parameter configuration; When the differential feature vector DV is in a negative trend, it indicates that the drive strategy adjustment instruction set DCS is ineffective, and do not record the drive strategy adjustment instruction set DCS as the effective parameter configuration, and continue to execute S1 to S5 for iterative optimization.

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