Loss determination method and device for electric aviation power motor controller and medium

Abstract

The invention relates to the technical field of computer technology application, in particular to a loss determination method and device for an electric aviation power motor controller and a medium, and the method is executed by a computing device and comprises the steps: generating a time-varying operation condition sequence based on an expected flight profile; establishing a switching loss model through a time slicing algorithm; establishing an on-state loss model and a diode switching loss model; and integrating all models to calculate the total loss power. According to the method, through multi-time scale analysis and parameter fitting, high-precision prediction of the loss of the electric aviation power motor controller is realized, and an important basis is provided for system optimization design.

Description

Technical Field

[0001] This invention relates to the field of computer technology application technology, and in particular to a method, device and medium for determining the losses of an electric aircraft power motor controller. Background Technology

[0002] With the rapid development of electric aircraft, extremely stringent requirements have been placed on the performance, efficiency, and reliability of their core component, the power motor controller. The main power topology circuit in the controller typically uses silicon carbide (SiC) MOSFETs as power switching devices to achieve high power density and high-efficiency energy conversion. However, in aerospace applications, the controller faces challenges such as high switching frequencies, prolonged operation under heavy loads, drastically changing flight profiles, and harsh thermal environments. This makes the accurate calculation of switching and conduction losses of power devices, especially SiC MOSFETs, a core and challenging aspect of controller topology design, device selection, thermal management, and system reliability assessment.

[0003] The mainstream methods for modeling power device losses mainly include physical models, behavioral models, and analytical models. Physical models, based on semiconductor physics principles, offer high accuracy but are complex, difficult to extract parameters from, and computationally intensive, making them unsuitable for system-level simulation and real-time control. Behavioral models rely on external characteristics (such as current sources and capacitor networks) to fit device behavior; while offering fast simulation speeds, their accuracy heavily depends on the completeness of test data, and their physical meaning is unclear, resulting in poor generalization ability. Analytical models, on the other hand, seek a balance between complexity and accuracy, analytically describing voltage and current changes during switching processes using mathematical formulas to calculate energy losses, making them a more ideal method for current engineering applications.

[0004] However, existing analytical loss models still have significant shortcomings when applied to the specific field of electric aviation: Multi-scale dynamic response coupling problem: The flight profile of an aircraft consists of a series of rapidly changing operational control points, resulting in time-varying sequences of operating parameters such as on-state current, junction temperature, and switching frequency with different response time scales. Most existing models model steady-state or single-time-scale conditions, failing to effectively integrate this multi-scale dynamic characteristic, leading to significant biases in loss prediction under transient conditions.

[0005] Lack of interface with top-level system requirements: Existing models are mostly independent device-level models, failing to establish a clear modeling path from top-level aircraft mission requirements (such as specific flight profiles) to controller operating conditions (such as current and frequency commands), and then to device losses. The interface between model inputs and system-level external characteristics is unclear, making it difficult to accurately assess and optimize system-level performance (such as range and thermal management requirements) in the early stages of design.

[0006] Insufficient accuracy in multi-factor coupling modeling: Existing analytical models fall short when dealing with the strong coupling effects of multiple factors (such as junction temperature-dependent device parameters, nonlinear parasitic parameters, driving conditions, and load characteristics) under high-frequency and high-temperature conditions. In particular, dynamic modeling of switching losses requires decomposition of the detailed physical processes of switching transients and quantification of the influence of various parasitic parameters (such as driving resistance, parasitic inductance, and nonlinear capacitance). Existing methods often oversimplify these aspects, leading to high solution difficulty and making it difficult to guarantee model accuracy over a wide operating range.

[0007] Therefore, there is an urgent need for a high-precision loss modeling method that can closely integrate the characteristics of electric aviation applications, handle multi-scale dynamic operating conditions, establish a clear mapping from top-level requirements to bottom-level losses, and meticulously consider the coupling of multiple physical factors, so as to support the design and optimization of electric aviation power motor controllers with high reliability and high efficiency. Summary of the Invention

[0008] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows: According to a first aspect of the present invention, a method for determining the losses of an electric aircraft power motor controller is provided, the method being executed by a computing device and comprising the following steps: S100, based on the expected flight profile of the electric aircraft, extracts the time-varying operating condition sequence through multi-timescale time series analysis and stores the sequence in a memory. The time-varying operating condition sequence includes time series data of output phase current, switching frequency, modulation frequency and device junction temperature.

[0009] S200: The processor reads the time-varying operating condition sequence and calculates the energy loss of the SiC power device during the switching transient process based on numerical calculation methods, establishing a switching loss calculation model. The switching transient process is divided into multiple sub-stages using a time-slicing algorithm. The calculation of the switching loss calculation model depends on the time-varying operating condition sequence and the key device parameters of the power device. The key device parameters are represented as a function of the junction temperature in the time-varying operating condition sequence through a parameter fitting module.

[0010] S300, the processor executes the conduction loss calculation module to calculate the conduction loss of the SiC power device and the conduction loss of the freewheeling diode based on the time-varying operating condition sequence, and establishes the conduction loss calculation model of the SiC power device and the conduction loss calculation model of the freewheeling diode.

[0011] S400, the processor executes the freewheeling diode switching loss calculation module to calculate the switching loss of the freewheeling diode based on the time-varying operating condition sequence, and establishes a freewheeling diode switching loss calculation model.

[0012] S500 integrates the established loss calculation models through the model integration module, inputs the time-varying operating condition sequence, calculates and outputs the total loss power of the power motor controller within the entire expected flight profile.

[0013] According to a second aspect of the present invention, an electronic device is provided, including a processor and a memory; the processor executes the steps of the method described in the first aspect of the present invention by invoking a program or instructions stored in the memory.

[0014] According to a third aspect of the present invention, a computer-readable storage medium is provided that stores a program or instructions that cause a computer to perform the steps of the method described in the first aspect of the present invention.

[0015] The loss determination method for electric aircraft power motor controllers provided in this invention includes: generating a time-varying operating condition sequence based on a predicted flight profile; establishing a switching loss model using a time-slicing algorithm; establishing a conduction loss model and a diode switching loss model; and integrating all models to calculate the total power loss. This invention achieves high-precision prediction of the losses of electric aircraft power motor controllers through multi-timescale analysis and parameter fitting, providing an important basis for system optimization design.

[0016] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the loss determination method for an electric aircraft power motor controller provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the loss model; Figure 3 This is a half-bridge test circuit; Figure 4 The dominant physical mechanism is the switch. Figure 5 To initiate a transient process; Figure 6 This is to shut down the transient process. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0021] It should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the steps as sequential processes, many of these steps can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the steps can be rearranged. A process can be terminated when its operation is complete, but it may also have additional steps not included in the figures. A process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0022] The purpose of this invention is to provide a method for determining the losses of an electric aircraft power motor controller. Based on the single-tube loss model of the controller, multi-scale parameter extraction is performed by considering the aircraft operating conditions of the power flow path. This provides the required single-tube-level time-varying operating condition sequence dataset (including time-varying parameters such as single-tube on-state current, switching frequency, modulation frequency, and junction temperature) for the single-tube loss model. Ultimately, this method outputs the single-tube switching loss power and single-tube on-state loss power corresponding to each short-term operating point within the flight profile of the electric aircraft, providing refined data support for the thermal design, efficiency optimization, and reliability assessment of the controller's single-power devices.

[0023] Furthermore, this embodiment of the invention provides a method for determining the losses of an electric aircraft power motor controller. The method is executed by a computing device, the core task of which is to adapt to the calculation requirements of a single-tube loss model, and to complete the parameter processing, model calculation, and result output related to single-tube losses.

[0024] In this embodiment of the invention, the computing device used to execute the above-described loss determination method refers to a dedicated or general-purpose electronic computing device that possesses data processing, model calculation, parameter interaction, and result output capabilities adaptable to single-tube loss models and meets the high reliability and real-time requirements of electric aviation scenarios. Its core function is to automate the entire process of single-tube loss calculation for the power motor controller (including single-tube time-varying parameter analysis, single-tube switching / continuation loss sub-item calculation, loss result output, etc.) through the collaboration of hardware architecture (such as a dedicated unit supporting transient integral calculation) and software system (such as a single-tube loss calculation module).

[0025] In this embodiment of the invention, "single transistor" specifically refers to a single power semiconductor device constituting the power conversion circuit of the motor controller, which is the basic switching unit for realizing power conversion. It is mainly based on SiC MOSFET (silicon carbide metal-oxide-semiconductor field-effect transistor), with its anti-parallel SiC Schottky barrier diode (freewheeling diode) considered an inherent component of the single transistor. Because the two are physically integrated or functionally inseparable, they jointly participate in the switching process.

[0026] In power topologies such as half-bridge and full-bridge controllers, a single transistor exists as an independent switching unit, such as the upper or lower transistor in a half-bridge topology. It achieves power chopping and conversion through on / off state switching. The loss analysis of a single transistor focuses on its own energy dissipation, including the conduction and switching losses of the SiC MOSFET, as well as the conduction and switching losses of its anti-parallel diode. It is the smallest unit for calculating the total controller loss; the sum of the losses of multiple single transistors constitutes the overall controller loss.

[0027] like Figure 1 As shown in the figure, the loss determination method for an electric aircraft power motor controller provided by an embodiment of the present invention may include the following steps: S100, based on the expected flight profile of the electric aircraft, extracts the time-varying operating condition sequence through multi-timescale time series analysis and stores the sequence in a memory. The time-varying operating condition sequence includes time series data of output phase current, switching frequency, modulation frequency and device junction temperature.

[0028] Specifically, S100 may include the following steps: S101, Flight Profile Analysis and Mission Segmentation First, the expected flight profile of the electric aircraft is obtained. This flight profile defines the relationship between the aircraft's speed, altitude, acceleration, and time during a complete mission (e.g., takeoff, climb, cruise, descent, landing, and ground taxiing). Subsequently, the flight profile is divided into mission phases, and each phase is assigned a unique operational control point identifier.

[0029] S102, Multi-timescale runtime condition mapping For each task stage and its operation control point defined in S101, operating conditions are mapped based on the performance characteristics and control strategy of the motor and controller, including: (1) Generation of output phase current sequence: Based on the propulsion power and motor speed required for the flight profile, the time-varying command value sequence I of the output phase current of the motor controller is calculated through the motor map or torque-speed-current relationship. m (t), I m (t) represents the target phase current value that the controller should output at time point t. For example, at t=10s (takeoff acceleration phase), the current command may be large; at t=1800s (smooth cruise phase), the current command will be significantly reduced.

[0030] (2) Switching frequency sequence generation: Based on the pre-set switching loss-frequency optimization curve or thermal management strategy, generate a sequence of switching frequency command values ​​f that are associated with the current flight phase, output power and estimated thermal state. sw m (t). f sw m (t) represents the target switching frequency value given by the system decision at time point t. For example, in the low-speed, high-torque stage, a lower switching frequency command may be selected to reduce losses; when high dynamic response is required, a higher switching frequency command may be switched.

[0031] (3) Generation of modulation frequency / modulation ratio sequence: Based on the motor speed control requirements and the selected PWM modulation strategy (such as SPWM, SVPWM), the modulation wave frequency or modulation ratio command value sequence m is calculated. a (t) or f mod (t).

[0032] (4) Generation of initial junction temperature sequence of the device: Starting from the ambient temperature or initial thermal state, and combining the power change history of the flight profile, the time-varying sequence T of the junction temperature of the power device is initially estimated through a pre-constructed electrothermal coupling model. jpre (t).

[0033] S103, Time-series data synthesis and storage: The various operating condition command values ​​or predicted sequences generated in S102 are aligned and synthesized along a unified time axis to form the aforementioned multi-timescale time-varying operating condition sequence. This sequence fully describes the dynamic changes of all key external conditions for the operation of the power motor controller throughout the entire expected flight profile. Finally, this sequence is written into the memory of the computing device in the form of an array or table for subsequent modeling steps.

[0034] Optionally, the data points in the time-varying operating condition sequence are discretized and stored at a fixed sampling interval, which is dynamically adjusted according to the fastest changing stage in the flight profile to ensure that the sequence can accurately capture the transient characteristics of the operating conditions.

[0035] S200: The processor reads the time-varying operating condition sequence, calculates the energy loss of SiC power devices during switching transients based on numerical calculation methods, and establishes a switching loss calculation model.

[0036] The switching transient process is divided into multiple sub-stages using a time-slicing algorithm. The calculation of the switching loss calculation model depends on the time-varying operating condition sequence and the key device parameters of the power device. The key device parameters are represented as a function of the junction temperature in the time-varying operating condition sequence through a parameter fitting module.

[0037] In this embodiment of the invention, to consider the accuracy of multi-scale loss modeling, the device junction temperature sequence change is approximated as a piecewise first-order dynamic differential equation, specifically: dT Jt / dt=(T Jobj -T Jt ) / τ Jt Among them, T Jobj The desired steady-state junction temperature after the operating control point is applied, expressed in °C or K; τ Jt T represents the thermal time constant corresponding to the rise or fall of the junction temperature curve, in seconds. Jt Let t be the real-time junction temperature of the power device.

[0038] In this embodiment of the invention, the loss model qualitatively considers the influence of parasitic parameters on switching losses, requiring the introduction of drive resistance, parasitic inductance, and parasitic capacitance. As the gate drive resistance R... g As the inductance L increases, the switching speed decreases, leading to a corresponding increase in device switching losses; with the increase of source parasitic inductance L... s The increase of di during the switching process d As / dt decreases, switching losses increase. Turn-on losses are particularly affected; this is especially true as drain parasitic inductance L... d The increase in size, opening the process di d / dt decreases, while the shutdown process di d / dt remains essentially unchanged. Turn-on losses decrease, while turn-off losses increase. d This is the transient drain current.

[0039] With the gate-source nonlinear parasitic capacitance C gs The increase of dv during the switching process gs / dt、dv ds / dt、d idAs / dt decreases, turn-on losses increase and turn-off losses decrease. With the decrease of drain-source nonlinear parasitic capacitance C... ds The increase of dv during the switching process gs / dt、dv ds / dt、d id The switching losses remain essentially unchanged, and the parasitic parameters mentioned above are all inherent parameters of the initial circuit, which are already quantitatively expressed in the model.

[0040] Based on fixed DC bus voltage and drive source voltage, it is necessary to focus on the impact mechanism of four operating conditions on power device losses: output current, device junction temperature, switching frequency, and load variable frequency speed regulation frequency. The on-state resistance R of SiC power devices... ds(ON) Internal gate resistance R g(int) Transfer characteristics g fs Threshold voltage V th The forward on-state resistance RD of a Schottky barrier diode (freewheeling diode) (ON) Threshold voltage VD (th) All parameters are related to the junction temperature. The fitting interval is [25℃, 150℃], and R0... ds(ON) -T J It can be fitted using a quadratic polynomial function, specifically: .

[0041] Among them, TC1 and TC2 are both R ds(ON) -T J The quadratic fitting coefficient. T 25 =25℃.

[0042] R g(int) -T J The same principle applies to R in principle. ds(ON) -T J The quadratic function fitting often ignores the influence of temperature control, and it is modeled as a constant value. fs -T J V th -T J The curve can be fitted by a linear or higher-order function, specifically: ; .

[0043] T 150 =150℃.

[0044] R of a Schottky barrier diode D(ON) -T J VD (th) -T J The curve can be fitted with a linear function as follows: ; .

[0045] Among them, TC3 and TC4 are both R D(ON) -T J VD (th) -T J The linear fitting coefficients.

[0046] In this embodiment of the invention, a single-transistor loss model of the controller is constructed using a half-bridge circuit as an example. Figure 2 As shown: The left side of the figure shows the multi-scale loss modeling abstraction of a three-phase main power topology. The corresponding levels G0 and G1 are: top-level requirements related to electromechanical system simulation, power device modeling, etc. The four time-varying sequences of output phase current, switching frequency, device junction temperature, and speed regulation frequency are included in the discussion of operating conditions (G1 is an abstract expression of operating conditions), thus clearly anchoring the path logic of multi-scale loss modeling.

[0047] The right side of the figure shows the state and mode representation of the half-bridge circuit, with corresponding levels G3, G4, and G5: G3 shows the three core operating modes of the half-bridge circuit: upper transistor on + lower transistor off mode, upper transistor off + lower transistor on mode, and both upper and lower transistors off mode; the mode switching process is accompanied by discrete switching events, and multiple transient stages are divided by time slicing technology; G4 and G5 further elaborate on the state sequence evolution under different transients, providing support for the refined analysis of the loss characteristics of the switching process.

[0048] Figure 3 This is a half-bridge test circuit. Figure 3 In the middle, V DC R is the DC bus voltage. p L is the parasitic resistance in the DC bus. p L is the parasitic inductance in the DC bus. d(ext) R is the external drain parasitic inductance. g(ext) L is the external gate parasitic resistance. g(ext) The external gate parasitic inductance is S1, and the switching transistor is V. drive L is the gate drive voltage. s(ext) S2 is the external source parasitic inductance, and S2 is the switching transistor 2. L R is the load current. L L is the load resistance. L For the load-side inductance, C L C is the parasitic capacitance of the load inductor. jD For the nonlinear parasitic capacitance of the freewheeling diode, D f L is the ideal symbol for a freewheeling diode in forward orientation. d(int) For the internal drain parasitic inductance, C gdL is the nonlinear parasitic capacitance on the gate-drain side. g(int) R is the internal gate parasitic inductance. g(int) C is the internal gate parasitic resistance. gs L is the nonlinear parasitic capacitance on the gate-source side. s(int) For internal source parasitic inductance, C ds D is the nonlinear parasitic capacitance on the drain-source side. body This is the ideal symbol for a body diode.

[0049] The resistance parameters in the single-tube loss model include: DC bus parasitic resistance R. p SiC MOSFET (S i The external gate resistance R g（ext） With internal gate resistance R g（int） The on-state resistance R of a SiC MOSFET ds(ON) And SiC Schottky barrier diode D f On-state resistance RD (ON) .

[0050] The parasitic inductance parameters of the single-tube loss model include: DC bus parasitic inductance L p SiC MOSFET (S i External gate parasitic inductance L g（ext） Internal gate parasitic inductance L g（int） Drain parasitic inductance L d（int） Parasitic inductance L inside the source electrode s（int） And SiC Schottky barrier diode D f parasitic inductance L sD ; among which, L g（int） and L sD Often, because its impact on characteristics is relatively low, it is ignored; therefore, L sD Not reflected in Figure 3 middle.

[0051] Parasitic capacitance parameters in the single-transistor loss model include: SiC MOSFET (S i The gate-source nonlinear parasitic capacitance C gs Drain-source nonlinear parasitic capacitance C ds The nonlinear parasitic capacitance C of a SiC Schottky barrier diode jD and load capacitance C L Among them, C gs and C L Modeled as a constant capacitor, C gd C ds Modeled as drain-source voltage v ds nonlinear function, C jD Modeled as reverse voltage v rThe nonlinear function. Furthermore, based on the gate-source voltage v... gs Drain-source voltage v ds Under different working conditions, C gd and C ds It will exhibit characteristics related to the device's operating region (such as the ohmic region, saturation region, and cutoff region); C jD The characteristics of the diode also change with the reverse voltage, corresponding to different operating states.

[0052] Assume the drain-source voltage v during the switching transient process ds and drain current i d Linear change, dv ignored ds / dt and di d The rate of change of / dt simplifies the calculation. The model starts with the law of conservation of energy and is expressed as: E loss =E source -E load -△E c Among them, E loss For losses, E source DC bus E sbus and E sdrive Energy; E load Energy consumed by the load; ΔE c This refers to the change in energy stored in the circuit. E sdrive =V CC ×Q g Among them, t p2 t is the starting time point in the process of driving the source pulse to turn on. p3 Q is the end time point in the process of driving the source pulse to start. g This represents the total gate charge.

[0053] Among them, the parasitic inductance L of the circuit stray For L p L d L s sum.

[0054] Change in energy stored in the circuit ΔE c With constant value capacitor C gs C L Nonlinear capacitor C gd C ds C jD The expression is related to different operating conditions. The change in energy stored in the circuit during the start-up process, ΔE, is related to this. c(ON) For E c(tp3) With E c(tp2) The difference, i.e., ΔE c（ON） =E c (t p3)-E c (t p2 ), where E c (t p3 ) for t p3 The total energy stored in the circuit at any given moment, E c (t p2 ) for t p2 The total energy stored in the circuit at any given moment, E c (t p3 ) = (1 / 2) × (L) p I o 2 +C issH V CC 2 ) + (1 / 2)(C jDL +C L )×V DC 2 E c (t p2 )=[(1 / 2)×(C gs V EE 2 +C OssL V DC 2 ) + (1 / 2)(C jDH +C L )×V FD 2 ], where L p For the parasitic inductance of the DC bus, C issH C represents the gate-drain parasitic capacitance of a SiC MOSFET in the ohmic region. jDL C is the parasitic capacitance of the freewheeling diode in the cutoff region. L C is the parasitic capacitance of the load inductor. gs C is the gate-source parasitic capacitance of the SiC MOSFET. OssL C is the output parasitic capacitance of the SiC MOSFET cutoff region. jDH V is the junction capacitance of the conduction region of a SiC Schottky diode. FD This is the voltage of the freewheeling diode.

[0055] The change in energy stored in the circuit during the turn-off process, ΔE c(OFF) For E c(tp1) With E c(tp0) The difference, i.e., ΔE c（OFF） =E c (t p1 )-E c (t p0 ), where t p0 t is the starting time point in the drive source pulse turn-off process. p1E is the end time point in the process of turning off the driving source pulse. c (t p1 ) for t p1 The total energy stored in the circuit at any given moment, E c (t p0 ) for t p0 The total energy stored in the circuit at any given moment, E c (t p1 ) = (1 / 2) × (L) p I o 2 +C issH V CC 2 ) + (1 / 2)(C jDL +C L )×V DC 2 E c (t p0 )=[(1 / 2)×(C gs V EE 2 +C OssL V DC 2 ) + (1 / 2)(C jDH +C L )×V FD 2 ], where L p For the parasitic inductance of the DC bus, C issH C represents the gate-drain parasitic capacitance of a SiC MOSFET in the ohmic region. jDL C is the parasitic capacitance of the freewheeling diode in the cutoff region. L C is the parasitic capacitance of the load inductor. gs C is the gate-source parasitic capacitance of the SiC MOSFET. ossL C is the output parasitic capacitance of the SiC MOSFET cutoff region. jDH V is the junction capacitance of the conduction region of a SiC Schottky diode. FD This is the voltage of the freewheeling diode.

[0056] In this embodiment of the invention, the switching loss calculation model for SiC power devices includes a turn-on loss calculation model and a turn-off loss calculation model, wherein the turn-on loss calculation model satisfies the following condition: P sw（ON） =E loss（ON） ×f sw ;P sw（ON） f represents the turn-on power loss of SiC power devices. sw E is the switching frequency. loss（ON） To enable energy dissipation, E loss（ON） =E loss1（ON） +E loss2（ON）, of which E loss1（ON） E represents the main energy loss during the start-up process. loss2（ON） Additional energy loss during the startup process. V DC i is the DC bus voltage. d For transient drain current, I o For load current, v ds t is the drain-source voltage. p2 t is the starting time point in the process of driving the source pulse to turn on. p3 E represents the end time point in the process of driving the source pulse to start; loss2（ON） =L stray I o 2 +V CC Q g -△E c（ON） L stray For the parasitic inductance of the line, V CC Q is the gate drive voltage. g For the total gate charge, ΔE c（ON） To store the energy change of the circuit during the start-up process.

[0057] Figure 4 A schematic diagram illustrating the topology evolution of a transient circuit. Figure 5 A timing diagram for enabling transient waveforms. Figure 4 In the middle, the parasitic inductance of the line L stray The parasitic inductance effect originating from the DC bus and power device connections affects the oscillation characteristics during transient processes. drive is the gate drive voltage, providing the turn-on / turn-off drive signal for the MOSFET gate; D is a freewheeling diode (such as a SiC Schottky diode), providing a freewheeling path for current during switching transients. L is the load current, the current flowing through the load. Figure 5 In the middle, V gs0 V represents the voltage level of the gate drive circuit. Miller For Miller plateau voltage, because C gd The Miller effect, v gs Changes are slow at this stage. peak This represents the peak drain current, the maximum value of the drain current during the turn-on transient.

[0058] like Figure 4 and Figure 5 As shown, based on the dominant physical mechanism of the turn-on transient, the MOSFET turn-on process can be divided into three core stages: the gate circuit charging stage (gate capacitor charging, v...). gs The rising phase of drain current and the falling phase of drain-source voltage (dominated by Miller effect, where drain current gradually replaces diode freewheeling current, v) ds(Rapid decline), drain current oscillation and steady-state transition stage (circuit parasitic inductance and parasitic capacitance resonate, current / voltage oscillate and tend to steady state).

[0059] Using time slicing technology, the entire start-up transient is subdivided into seven sub-stages: t0-t1, t1-t2, t2-t3, t3-t4, t4-t5, t5-t6, and t6-t7 (e.g., ...). Figure 5 (as shown in the waveform timing diagram) t0-t1: Gate circuit to C gs Charging, v gs The voltage rises but does not reach the threshold voltage V th Drain current (i d =0), drain-source voltage v ds ≈V DC ; t1-t2: v gs DaV th i d The current begins to rise, and the freewheeling diode D remains conducting; v ds Maintain high level; t2-t3: i d The current continues to rise and gradually replaces diode freewheeling, v ds It begins to descend, v gs In Miller capacitance C gd Entering the "Miller Platform" (slow changes); t3-t4: i d Rise to peak I peak v ds Rapidly decreases to near the on-state pressure drop; t4-t5: Parasitic inductance of the line L par With parasitic capacitance (C) ds C jD (etc.) resonance, i d v ds Oscillations occurred; t5-t6: The oscillation gradually decays, i d The load current I tends to O v gs It disengages from the Miller plateau and rises to the drive voltage level; t6-t7: Entering the steady-state conduction phase, i d Stable at I O Nearby, v ds For the on-state voltage drop, v gs It is stable at the driving voltage.

[0060] After analyzing the voltage and current dynamic characteristics of each sub-stage, the turn-on loss E is then analyzed. loss1(ON) By performing integration, the analytical loss expression for the start-up process can be derived.

[0061] The turn-off loss calculation model satisfies the following conditions: P sw（OFF） =E loss（OFF） ×f sw ;P sw（OFF） For the turn-off power loss of SiC power devices, f sw E is the switching frequency. loss（OFF） To shut off energy loss, E loss（OFF） =E loss1（OFF） +E loss2（OFF） , of which E loss1（OFF） E represents the main energy loss during the turn-off process. loss2（OFF） This refers to the additional energy loss during the shutdown process. , t p0 t is the starting time point in the drive source pulse turn-off process. p1 This is the end time point in the drive source pulse turn-off process; E loss2（OFF） =-L stray I o 2 -V EE Q g -△E c（OFF） V EE The lower limit of the driving voltage, ΔE c（OFF） This stores the change in energy during the circuit's shutdown process.

[0062] like Figure 6 As shown, based on the dominant physical mechanism of the turn-off transient, the turn-off process of a SiC MOSFET can be divided into three core stages: the gate circuit discharge stage (energy storage and release of the gate capacitor, gate-source voltage v) and the gate-source voltage discharge stage. gs (decline), drain-source voltage rise phase (Miller capacitance C) gd Dominant, drain-source voltage v ds From the on-state voltage drop to the DC bus voltage V DC Rise, drain current i d (Gradually shifting towards freewheeling diodes), drain current and drain-source voltage oscillation stage (circuit parasitic inductance and parasitic capacitance resonate, current and voltage oscillate and tend towards steady state).

[0063] Using time slicing technology, the entire turn-off transient is subdivided into six sub-stages: t0-t1, t1-t2, t2-t3, t3-t4, t4-t5, and t5-t6 (combined with the current and voltage waveform timing in the figure). t0-t1: Gate circuit starts discharging, v gs From the high level of the drive, but still above the threshold voltage V th Drain current i d Maintaining load current I ODrain-source voltage v ds For on-state voltage drop; t1-t2: v gs Drop to Miller plateau voltage V Miller Miller capacitance C gd Significantly participates in charging and discharging, v ds As the on-state pressure drop increases rapidly, i d Still close to I O ; t2-t3: v ds Continued rise approaching V DC i d The current gradually shifts to the freewheeling diode and begins to decrease, v gs Because C gd It acts to maintain the Miller platform; t3-t4: v ds Approaching V DC i d Rapidly dropping to a lower value, v gs The voltage continues to drop towards the low level of the drive, detaching from the Miller platform. t4-t5: Parasitic inductance and capacitance of the circuit (such as drain-source parasitic capacitance C) ds Diode junction capacitance C jD (etc.) resonates, i d v ds Oscillations occurred; t5-t6: The oscillation gradually decays, i d Approaching 0 (freewheeling diode stable freewheeling current), v ds Stable at V DC Nearby, v gs Drop to drive low level (e.g., V) EE After analyzing the voltage and current dynamic characteristics of each sub-stage, the analytical loss expression for the turn-off process can be derived by integrating the turn-off loss.

[0064] S300, the processor executes the conduction loss calculation module to calculate the conduction loss of the SiC power device and the conduction loss of the freewheeling diode based on the time-varying operating condition sequence, and establishes the conduction loss calculation model of the SiC power device and the conduction loss calculation model of the freewheeling diode.

[0065] In this embodiment of the invention, the conduction loss calculation module is a functional module integrated into the processor. Its core function is to calculate, based on the input time-varying operating condition sequence (including but not limited to time-varying parameters such as output phase current, device junction temperature, and switching frequency), the energy loss (conduction loss) of SiC power devices (such as SiC MOSFETs) in the conduction state and the energy loss (conduction loss) of freewheeling diodes (such as SiC Schottky barrier diodes) in the forward conduction state. Furthermore, it solidifies the calculation logic into a reusable conduction loss calculation model (including the SiC power device conduction loss model and the freewheeling diode conduction loss model) through mathematical modeling. The core calculation logic of the module includes: By combining the on-state resistance (whose value changes nonlinearly with junction temperature and on-current) and on-time of SiC power devices, their on-state loss is calculated. Calculate the conduction loss of the freewheeling diode by combining its forward conduction resistance and forward voltage drop (which varies nonlinearly with junction temperature and forward current) with its conduction time. Based on the dynamic characteristics of time-varying operating condition sequences, time-series calculation of losses is realized (i.e., loss values ​​are updated in real time as operating conditions change).

[0066] By executing this module, on-state losses can be transformed from abstract physical processes into quantitative mathematical expressions, providing fundamental data support for total loss analysis and thermal design of power devices.

[0067] Considering a three-phase voltage-source main power topology, the on-state loss calculation model for SiC power devices satisfies the following conditions: P C，M =R ds（ON） ×I Drms 2 P C，M R represents the on-state loss of a SiC power device. ds（ON） I is the on-state resistance of the SiC power device. Drms I is the root mean square current of the SiC power device. Drms =I oc ×(1 / 8+(m a ×cosφ) / 3π)I oc The peak value of the output current; m a φ is the modulation index, which is the ratio of the peak voltage of the modulating wave to the peak voltage of the carrier wave. φ is the power factor angle.

[0068] The on-state loss calculation model of the freewheeling diode satisfies the following conditions: P C，D =u D0 ×I Fav +RD （ON） ×I Frms 2 PC，D For the conduction loss of the freewheeling diode, u D0 I is the forward voltage drop of the freewheeling diode. Fav I is the average current of the freewheeling diode. Fav =I oc ×(1 / 2π-(m a ×cosφ) / 8)I Frms I is the root mean square current of the freewheeling diode. Frms =I oc ×(1 / 8-(m a (×cosφ) / 3π).

[0069] S400, the processor executes the freewheeling diode switching loss calculation module to calculate the switching loss of the freewheeling diode based on the time-varying operating condition sequence, and establishes a freewheeling diode switching loss calculation model.

[0070] In this embodiment of the invention, the processor is a core hardware unit with data processing, module scheduling, and logic control capabilities. Its core function is to serve as the "central control and processing carrier" for the loss calculation process. Specifically: Functional positioning: Receive and parse the time-varying operating condition sequence (such as dynamic parameters such as output phase current, switching frequency, device junction temperature, speed regulation frequency, etc.) output by the top-level requirements, and schedule the execution of functional modules such as "freewheeling diode switching loss calculation module" and "conduction loss calculation module" in sequence according to the preset logic; Core capabilities: Possesses high-speed data processing capabilities, which can support complex computational tasks involved in switching loss calculations, such as transient physical process modeling, nonlinear parameter fitting (e.g., the effect of junction temperature on device characteristics), and integral operations (e.g., time integration of energy loss). Output function: Integrate the calculation results of each module (such as the quantification data of freewheeling diode switching loss and the parameters of loss calculation model) to provide data support for subsequent total loss analysis, device thermal design and controller optimization.

[0071] The freewheeling diode switching loss calculation module is a dedicated function module integrated into the processor. Its core function is to quantify the energy loss of a freewheeling diode (such as a SiC Schottky barrier diode) during the switching process (turn-on transient and turn-off transient) and establish a reusable switching loss calculation model. The specific definitions are as follows: Module Positioning: This is a loss calculation function unit specifically developed for the switching physical characteristics of freewheeling diodes (such as the forward recovery process during turn-on, the reverse recovery process during turn-off, transient voltage / current oscillations, etc.). It is a core component of the "switching loss branch" in the total loss calculation system. Input parameters: The core input is a time-varying sequence of operating conditions, specifically including: Electrical parameters: forward conduction current, reverse blocking voltage, switching frequency (determines the number of switching cycles), and gate drive parameters (such as gate voltage and gate resistance, which affect the switching speed). Thermal parameters: Device junction temperature (junction temperature significantly affects the reverse recovery charge, forward voltage drop, and other characteristics of the diode, thereby changing the switching losses); Inherent parameters of the device: nonlinear characteristic parameters of the freewheeling diode (such as parasitic inductance, parasitic capacitance, reverse recovery time, reverse recovery charge, etc.); Calculation logic: Based on the transient physical mechanism of freewheeling diode switching (such as the three-stage process of "forward current decrease → reverse current peak → reverse current decay" during turn-off), the transient process is decomposed into multiple sub-stages using the time slicing method. For each sub-stage, a dynamic relationship model of voltage (such as forward / reverse voltage across the diode), current (such as forward conduction current and reverse recovery current) and time is established. By integrating the "voltage-current-time" relationship of each sub-stage, the quantized values ​​of turn-on loss and turn-off loss within a single switching cycle are obtained. This is combined with the dynamic changes of time-varying operating conditions (such as the increase in junction temperature leading to Q...). rr Increase), correct the calculation parameters of each sub-stage, and realize the time-series dynamic calculation of loss; Output results: Quantitative data: Real-time switching loss value of freewheeling diode (including turn-on loss, turn-off loss and total switching loss) that varies with operating conditions; Calculation model: The above calculation logic (including transient stage division, parameter correction rules, integral formula, etc.) is solidified into a "freewheeling diode switching loss calculation model", which can be directly used for subsequent integrated analysis of total loss (conduction loss + switching loss), as well as the controller's prediction and optimization of device loss.

[0072] Furthermore, the turn-on loss of the freewheeling diode is mainly caused by the reverse recovery process, and the calculation model for the switching loss of the freewheeling diode satisfies the following conditions: P sw，D = (1 / 4) × Q rr ×U Drr ×f sw , where P sw，D Q is the turn-on power loss of the freewheeling diode. rr To recover the charge in the reverse direction, U Drr f is the voltage across the diode during reverse recovery. sw This refers to the switching frequency.

[0073] P sw，D The derivation process is as follows: Obtain the turn-on loss energy E of the freewheeling diode loss（ON），D .

[0074] in, Where, tri is the time it takes for the drain current to rise from 0 to the on-state current Io of the SiC device; tfu is the time it takes for the drain-source voltage to drop from UDD to R. ds(on) •I o Time. E loss（ON），Drr This refers to the reverse recovery loss during the turn-on phase of the freewheeling diode.

[0075] Due to the energy loss E when the freewheeling diode is turned off. loss(OFF),D Generally ignored, therefore P sw，D =(E loss（ON），D +E loss（OFF），D )×f sw ≈E loss（ON），D ×f sw = (1 / 4) × Q rr ×U Drr ×f sw .

[0076] The impact of inductive loads on the operating mode of the main power topology circuit can be categorized into two cases: power factor angle 0 < Φ < π / 3 and Φ > π / 3. In these cases, the loss calculation must consider the effect of the freewheeling diode D. When considering 0 < Φ < π / 3, there are two operating modes: 3S+0D and 2S+1D. The commutation process under inductive loads cannot be completed instantaneously; the load current must flow through the freewheeling diode of the target commutation switch. The target commutation switch only turns on when the load current drops to 0. When considering Φ > π / 3, there are two operating mode categories: 2S+1D and 1S+2D.

[0077] S500 integrates the established loss calculation models through the model integration module, inputs the time-varying operating condition sequence, calculates and outputs the total loss power of the power motor controller within the entire expected flight profile.

[0078] The model integration module is the core functional module responsible for the closed-loop logic of loss calculation in this invention. Its core function is to organically integrate the previously established independent loss calculation models (including the on-state loss model of SiC power devices, the on-state loss model of freewheeling diodes, the switching loss model of SiC power devices, and the switching loss model of freewheeling diodes). Through timing matching, dynamic correlation, and energy superposition, it achieves integrated calculation of the total loss of the power motor controller. As the "final integration unit" of the loss calculation system, the model integration module connects the various sub-loss models with the total loss output, solving problems such as timing asynchrony and weak parameter correlation between independent models, and ensuring the accuracy and dynamism of the total loss calculation.

[0079] The input parameters for the model integration module include: Output results of each subdivided loss model: including turn-on switching loss power and turn-off switching loss power of SiC power devices, conduction loss power of SiC power devices, conduction loss power of freewheeling diodes, and switching loss power of freewheeling diodes. Original time-varying operating condition sequence: used to verify the temporal consistency of each loss model and ensure that the integrated result matches the actual operating state (e.g., the switching frequency and phase current changes in different flight stages need to be reflected in the total loss synchronously). Model-related parameters, such as the duty cycle of each device and the switching cycle overlap coefficient, are used to correct the loss superposition logic when multiple devices are working simultaneously.

[0080] The core function of the model integration module is: Timing alignment: Synchronize the loss data output by each sub-model along the time axis to eliminate timing deviations caused by differences in calculation granularity (e.g., switching loss is calculated in transient units, while on-state loss is calculated in steady-state units, and timing needs to be unified through time slice correspondence). Dynamic superposition: based on the total power loss formula P=P sw（ON） +P sw（OFF） +P C，M +P C，D +P sw，D The various loss components are dynamically accumulated according to real-time operating conditions to generate a time-series curve of total power loss. Boundary correction: For special operating conditions (such as overlapping intervals where devices are simultaneously turned on, and nonlinear changes in loss under extreme junction temperatures), a correction coefficient is introduced to adjust the superposition results to ensure consistency with physical reality; Profile adaptation: The total loss calculation results are mapped to the entire cycle of the "expected flight profile" (such as takeoff, cruise, hovering, landing, etc.), and the total loss characteristic values ​​(average, peak, integral energy, etc.) of each stage are output.

[0081] The output of the model integration module is the total power loss time series data and characteristic parameters of the power motor controller throughout the entire expected flight profile, providing core basis for controller thermal design, efficiency optimization and energy management.

[0082] Furthermore, the S500 specifically includes: S501 receives the loss components output by each subdivision model, and combines them with the input time-varying operating condition sequence (such as phase current, switching frequency, and junction temperature data at each stage of the flight profile), aligning all loss data along the time axis (for example, establishing a correspondence between the transient value of switching loss and the steady-state range value of on-state loss through time slices). S502, based on the total power loss formula, dynamically superimposes each loss component to obtain the total power loss; S503, for special operating conditions in the flight profile (such as high current surges during takeoff and high-frequency switching during cruise), introduces boundary correction coefficients (such as those for R caused by junction temperature rise). ds(ON) Increase the correction factor) to adjust the superposition results to match the actual physical properties; The S504 ultimately outputs the total power loss time-series curve of the power motor controller throughout the entire expected flight profile, as well as the total peak, average, and cumulative energy loss for each flight phase (takeoff, cruise, landing, etc.), providing complete loss data support for the controller's heat dissipation design, efficiency optimization, and endurance assessment.

[0083] Furthermore, the method provided in this embodiment of the invention further includes the following steps: S600, through the temperature sensor and current / voltage sensor integrated in the motor controller, monitors and collects the junction temperature T of the SiC power device in real time. j Load current I o and drain-source voltage V ds , as real-time monitoring data.

[0084] This invention achieves real-time acquisition of key electro-thermal parameters of SiC power devices through a high-reliability sensor array integrated near the main power topology of the electric aero-engine controller, specifically including: 1. Sensor selection and installation design Temperature sensor: An NTC thermistor conforming to avionics environmental standards (such as DO-160G) with an accuracy of ±1℃ and a measurement range of -40℃ to 175℃ is selected and placed in close contact with the package shell of the SiC power device (such as the bottom heat dissipation surface of the TO-247-4 package). The gap is filled with thermal grease to reduce thermal resistance error. Each SiC power device is independently equipped with one NTC sensor to ensure the spatial resolution of junction temperature monitoring (avoiding the averaging error caused by multiple devices sharing the same sensor).

[0085] Current sensor: A Hall effect current sensor (bandwidth ≥ 1MHz, accuracy ± 0.5%FS, range 0~500A) is used, connected in series in the drain circuit of the SiC power device, and the parasitic inductance between the sensor and the main circuit is controlled to ≤ 5nH (to avoid measurement noise generated by high-frequency switching current); for a three-phase topology, an additional auxiliary current sensor is configured on the output side of each phase to cross-verify the consistency of the load current.

[0086] Voltage sensor: A differential voltage sensor (input impedance ≥100MΩ, common-mode rejection ratio ≥80dB, measurement range 0~1000V) is used. Its two acquisition terminals are connected to the drain and source of the SiC power device, respectively, and the lead length is controlled to ≤5cm (to reduce high-frequency voltage measurement deviation caused by lead parasitic capacitance). The sensor output signal is transmitted to the signal conditioning module through shielded twisted pair cable to avoid the influence of electromagnetic interference (EMI) on the data in the aviation environment.

[0087] 2. Data acquisition timing and synchronization control The controller generates a synchronous trigger signal based on its master clock (frequency 100MHz) to ensure that the acquisition time deviation of temperature, current and voltage data is ≤1μs (to avoid the "junction temperature-voltage-current" matching error caused by asynchronous timing, such as using the junction temperature at time t to correspond to the voltage data at t+5μs); the acquisition frequency is dynamically adjusted according to the operating conditions: the acquisition frequency is set to 1MHz during the switching transient stage (such as takeoff and landing) and 100kHz during the steady-state operation stage (such as cruise), balancing data accuracy and storage overhead.

[0088] Preprocess the collected raw data: Current / voltage data: High-frequency noise is removed by moving average filtering (window size 10 sampling points), and then pulse interference generated by switching transients is removed by peak rejection algorithm (the threshold is set to 1.5 times the rated value); Temperature data: The slow changes in junction temperature are smoothed by first-order hysteresis filtering (time constant 0.1s) to avoid instantaneous fluctuations caused by the sensor's own thermal inertia.

[0089] 3. Data storage and anomaly marking The preprocessed real-time monitoring data (including acquisition timestamps, parameter type identifiers, and sensor IDs) is stored in the controller's non-volatile memory (NVM) using a cyclic overwrite strategy (storage capacity ≥ 1GB, capable of saving data from the most recent 24 hours). Simultaneously, the data is marked as abnormal: when the acquired value exceeds the preset range (e.g., junction temperature > 175℃, current > 500A, voltage > 1000V), it is automatically marked as "abnormal data" and an alarm signal is triggered. Subsequent parameter identification steps will skip this type of data to avoid incorrect input.

[0090] S700 dynamically updates the parameters of the key components based on the real-time monitoring data using a parameter recognition algorithm.

[0091] Based on real-time monitoring data preprocessed by S600, key parameters of SiC power devices are dynamically updated through a phased parameter identification algorithm, with a core focus on the functional relationship between on-state resistance and junction temperature. Specifically, during the steady-state conduction phase of the device, the real-time on-state resistance is estimated according to a preset estimation method, and the estimated real-time on-state resistance is bound to the current junction temperature to update the functional relationship between on-state resistance and junction temperature.

[0092] The conditions for determining the conduction steady-state period of SiC power devices can be screened by combining multiple conditions, including: load current I. o The fluctuation range of 100 consecutive sampling points is ≤ ±2% (to avoid V caused by current transients). ds (Nonlinear variation); drain-source voltage V ds : Fluctuation range of 100 consecutive sampling points ≤ ±3% (ensuring the device operates in the ohmic conduction region, not the saturation region); Switching state: The PWM drive signal output by the controller is continuously high (duration ≥ 100μs, covering at least 2 current / voltage acquisition cycles).

[0093] The time period that meets all the above conditions is marked as the "conduction steady-state stage". The average drain-source voltage Avg (V) during the conduction steady-state stage is extracted. ds ), average load current Avg (I o ), average junction temperature Avg (T j This serves as the basic data for parameter identification.

[0094] In this embodiment of the invention, the real-time on-state resistance R can be calculated during the steady-state conduction phase according to Ohm's law: ds(ON) new =(Avg(V ds -V th (T) j )) / I o , where V th (T) j ) is the threshold voltage of the SiC power device at the current junction temperature (based on the initially fitted V). th -T j Function calculation, such as V th (T) j )=V th25 +TC Vth ×(Avg(T j -25)), V th25 TC is the threshold voltage at 25℃. Vth (For the threshold voltage temperature coefficient); introduce V th (T) j The correction term can eliminate the effect of the threshold voltage on the drain-source voltage, making R... ds(ON) new The estimation error was reduced from ±8% to within ±3%.

[0095] In this embodiment of the invention, the estimated real-time on-state resistance and the current junction temperature are combined to form a new sample pair, and R is updated using the sliding window least squares method. ds(on) -T j The functional relationship can specifically include: Sample pair construction: Estimating R ds(ON) new With the corresponding Avg(T) j New "resistance-junction temperature" sample pairs are formed and added to the sample pool. The sample pool adopts a sliding window mechanism with a window size of 100 samples (taking into account both data timeliness and statistical reliability). When a new sample is added, the oldest sample is removed.

[0096] Least squares fitting: Weighted least squares is performed on the sample pairs within the sliding window (assigning higher weights to the nearest 30 samples, with the weight coefficients decreasing linearly over time), and the fitting R is obtained. ds(ON) -T j Quadratic polynomial function: R ds(ON) (T j )=a·T j 2 +b·T j +c, where a, b, and c are fitting coefficients; after fitting, the residual (the deviation between the actual sample value and the fitted value) is calculated. If the residual is ≤ ±5%, the function relationship is updated; if the residual is > ±5%, anomaly diagnosis is triggered (such as checking whether the sensor is offset or whether the device is aging).

[0097] Fitting interval adaptation: when Avg(T) j When the temperature exceeds the initial fitting range ([25℃, 150℃]) (e.g., under high-temperature conditions, Avg(T) j (=160℃), automatically expanding the fitted model to a cubic polynomial: R ds(ON) (T j )=a·T j 3 +b·T j 2 +c·T j +d ensures the validity of the functional relationship over a wide junction temperature range (-40℃ to 175℃), where d is the fitting coefficient.

[0098] S800 feeds back the updated key device parameters into the loss calculation model established by S200, S300, and S400, enabling online correction and adaptive prediction of model parameters to correct deviations caused by device aging and junction temperature drift. After calibration, the accuracy is improved to within ±5%.

[0099] In S800, the key device parameters updated from S700 (the core is R) are...ds(ON) (T j The feedback is fed back to the loss calculation model, and adaptive optimization of loss prediction is achieved through hierarchical correction and dynamic verification. The specific steps are as follows: 1. Parameter model adaptation and substitution Switching loss model correction: updated R ds(ON) (T j The "Additional Energy Loss" item used to correct switching losses: In the turn-on loss E loss2(ON) In the middle, the steady-state value of the drain-source voltage after the device is turned on changes from R ds(ON) old ·I o Updated to R ds(ON) new ·I o This leads to adjustments in the calculation of the change in circuit energy storage (due to ΔE). c(ON) (Related to the steady-state value of the drain-source voltage); similarly, in the turn-off loss, the initial steady-state value of the drain-source voltage is corrected to ensure that the switching loss calculation matches the current device state.

[0100] On-state loss model correction: directly update R ds(ON) (T j Substituting this into the SiC power device conduction loss formula, and based on R... ds(ON) (T j The changing trend of the freewheeling diode is used to adjust its on-state resistance. Since the aging trends of the two are correlated, R is set accordingly. D(ON) new =R D(ON) old · (R) ds(ON) new / R ds(ON) old This ensures consistency in the calculation of on-state losses for the two types of devices.

[0101] Diode switching loss model correction: Based on R ds(ON) (T j The reverse recovery charge Q reflects the degree of device aging and is dynamically adjusted. rr Correction factor: If R ds(ON) new / R ds(ON) old A value ≥1.2 indicates severe device aging, therefore Q should be adjusted accordingly. rr Increase by 5% (because aging can enhance the carrier storage effect in diodes), and the corrected diode switching loss formula is: P sw，D = (1 / 4) × Q rr ×(1+k)U Drr ×f sw k is the aging correction factor, 0≤k≤0.1.

[0102] 2. Dynamic verification of prediction accuracy Real-time loss comparison verification: The "predicted total loss power Ppred" output by the corrected loss model is compared with the "measured total loss power Pmeas" of the controller. Pmeas is calculated by the difference between "input power and output power" (input power is the product of DC bus voltage and current, and output power is the product of motor three-phase voltage and current, both calculated based on S600 sensor data). Accuracy adjustment mechanism: If |Ppred-Pmeas|≤5% (meets aerospace-grade accuracy requirements), the current model parameters are maintained; if 5%<|Ppred-Pmeas|≤10%, the parameter update cycle of S700 is shortened (from 10s / time to 5s / time); if |Ppred-Pmeas|>10%, "emergency correction" is triggered, the current sample pool is cleared, 20 sets of conducting steady-state sample pairs are re-acquired for fitting, and the sensor is self-checked for faults (such as checking the zero-point drift of the voltage sensor).

[0103] 3. Engineering Implementation of Adaptive Prediction The corrected loss model is integrated into the controller's real-time operating system (RTOS), employing a "segmented prediction + rolling update" strategy: For each flight phase of the aircraft (such as takeoff and cruise), the loss power curve for that phase is predicted 10 seconds in advance based on the time-varying operating condition sequence (S100 output); every second, based on the latest sensor data (S600 output) and updated parameters (S700 output), the loss prediction curve for the remaining phases is rolled over to ensure that the prediction results are always synchronized with the actual operating conditions; the prediction data is transmitted in real time to the "energy management module" of the aero-engine system, providing dynamic basis for range estimation, cooling fan speed adjustment, and power allocation strategy optimization (e.g., when the loss is predicted to increase during the cruise phase, the power allocation of non-critical systems is reduced in advance to ensure endurance).

[0104] The S600 to S800 series systems enable online self-calibration and adaptive prediction of loss models throughout their entire lifecycle, significantly improving the long-term accuracy and reliability of the models.

[0105] Traditional loss models, once established, have fixed parameter values, failing to reflect parameter drift and aging conditions during device use. This invention, by introducing real-time data monitoring and online parameter identification algorithms, dynamically captures and updates the relationship between key parameters such as on-state resistance and threshold voltage and their changes with junction temperature and aging degree. This transforms the loss prediction model from a "static snapshot" into a "dynamic video," maintaining high accuracy not only in the initial stages of controller deployment but also enabling self-calibration throughout its entire lifecycle, continuously providing reliable loss predictions and laying a solid foundation for predictive health management (PHM) and fault early warning.

[0106] (Example) First, clarify the core conditions for benchmark testing, including the device junction temperature T. J DC bus voltage V DS Gate drive source voltage range V GS Load current I D External gate drive resistor R gext) and internal gate drive resistor R g(int) Key variables, the physical meaning of each variable and the corresponding circuit / device location, as follows: Figure 3 As shown.

[0107] Secondly, through actual testing, numerical simulation, and data from the device datasheet, the characteristic parameters of the external circuit and devices are obtained, including: External circuit characteristic parameters: DC bus equivalent parasitic inductance L stray , load parallel capacitor C L Load inductance L load Bus equivalent resistance R p wait.

[0108] SiC MOSFET device characteristic parameters: Gate-source parasitic capacitance C gs The gate-drain parasitic capacitance C in the cutoff region gdL Ohmic region gate-drain parasitic capacitance C gdH The drain-source parasitic capacitance C in the cutoff region dsL Ohmic region drain-source parasitic capacitance C dsH On-state resistance R ds(on) Transfer characteristics k fs Threshold voltage V th0 Total gate charge Q g wait.

[0109] SiC Schottky barrier diode characteristic parameters: parasitic capacitance C in the cutoff region. jDH Ohmic region diode parasitic capacitance C jDL Diode on-state resistance R D(on) Diode forward voltage constant quantity V D(on) wait.

[0110] Finally, we conduct calculations and extended analysis of switching losses: (1) Based on Figure 5 (Activate transient) Figure 6 The time slice stage (turn-off transient) is divided, and the voltage and current dynamics of each sub-stage are analyzed. By integrating the "voltage-current-time" relationship, the turn-on energy loss E of the main switch process is calculated. loss1(ON) and the energy loss during shutdown E loss2(ON) ; (2) Execute E loss1(ON) Eloss2(OFF) Supplementary calculations: Supplementary calculations address "additional losses beyond the main switching process," with typical scenarios including: Oscillation loss caused by the resonance of parasitic inductance and parasitic capacitance (additional energy dissipation caused by the interaction of parasitic parameters in the later stage of switching transients). The charging and discharging loss of the gate drive circuit (the power loss generated by the gate resistor charging and discharging the gate capacitor when the drive signal is switched). Edge region losses caused by non-ideal device characteristics (such as near the threshold voltage, the on / off switching transition region, and additional losses caused by device characteristics deviating from the ideal model). (3) Combined with the switching frequency f sw The sum of "main switch energy loss + supplementary energy loss" is converted into average switch power loss (total switch energy loss × switching frequency). (4) If it is necessary to analyze the effects of different operating conditions, the above steps can be repeated to adjust key variables (such as the on-state resistance R of the SiC device). ds(ON) Internal gate resistance R g(int) Transfer characteristics g fs Threshold voltage V th The forward on-state resistance R of a Schottky barrier diode D(ON) Threshold voltage V D(th) (e.g., parameters), and recalculate the loss characteristics.

[0111] In summary, the present invention has at least the following beneficial effects: (1) It realizes the accurate mapping and dynamic evaluation of the loss of the aircraft from the top-level mission requirements to the bottom-level device losses.

[0112] Traditional loss models are mostly isolated device-level models, disconnected from system-level operational requirements. This invention, by extracting time-varying operating condition sequences across multiple time scales based on the expected flight profile, establishes for the first time a clear and quantitative modeling path from aircraft flight missions (e.g., climb, cruise, landing) to specific controller operating points (current, frequency, junction temperature), and finally to device-level losses. This enables accurate prediction of total energy consumption and thermal load throughout the entire flight profile during the design phase, providing unprecedented data support for system-level optimization (e.g., range estimation, thermal management system design).

[0113] (2) Significantly improves the accuracy and reliability of loss prediction under complex dynamic working conditions.

[0114] To address the extreme conditions of high frequency, high temperature, and transient operation in aerospace applications, this invention characterizes key device parameters (such as Rds(on) and Vth) as dynamic functions of junction temperature and employs a time-slicing algorithm for refined modeling of switching transients. This fundamentally solves the accuracy problem caused by the neglect of parameter temperature characteristics and transient details in traditional analytical models. This method can accurately capture the coupled effects of junction temperature changes and parasitic parameters on the switching process, thus maintaining extremely high prediction accuracy over a wide temperature range and under various load conditions. It effectively avoids device overheating damage or system reliability degradation due to loss estimation errors.

[0115] (3) A high-fidelity modeling solution that balances computational efficiency and engineering practicality is provided.

[0116] Compared to computationally intensive physical models and behavioral models lacking physical meaning, the analytical model of this invention significantly reduces computational complexity while maintaining high accuracy. The model established using this method is easily embedded into system-level simulation toolchains, enabling rapid and iterative simulation calculations and greatly shortening the design verification cycle. It provides efficient and reliable theoretical tools and design basis for component selection, heat dissipation design, efficiency optimization, and lifespan prediction of motor controllers, which is of great significance for promoting the development of high-power-density, high-reliability electric aerospace propulsion systems.

[0117] This invention also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform the method described in this invention.

[0118] This invention also provides a computer-readable storage medium storing computer-executable instructions for performing the methods described in this invention.

[0119] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0120] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining the losses of an electric aircraft power motor controller, characterized in that, The method is executed by a computing device and includes the following steps: S100, based on the expected flight profile of the electric aircraft, extracts the time-varying operating condition sequence through multi-timescale time series analysis and stores the sequence in memory. The time-varying operating condition sequence includes time series data of output phase current, switching frequency, modulation frequency and device junction temperature. S200: The processor reads the time-varying operating condition sequence and calculates the energy loss of the SiC power device during the switching transient process based on numerical calculation methods, establishing a switching loss calculation model. The switching transient process is divided into multiple sub-stages using a time-slicing algorithm. The calculation of the switching loss calculation model depends on the time-varying operating condition sequence and the key device parameters of the power device. The key device parameters are represented as a function of the junction temperature in the time-varying operating condition sequence through a parameter fitting module. S300, the processor executes the conduction loss calculation module to calculate the conduction loss of SiC power devices and the conduction loss of freewheeling diodes based on the time-varying operating condition sequence, and establishes the conduction loss calculation model of SiC power devices and the conduction loss calculation model of freewheeling diodes. S400, the processor executes the freewheeling diode switching loss calculation module to calculate the switching loss of the freewheeling diode based on the time-varying operating condition sequence and establishes a freewheeling diode switching loss calculation model; S500 integrates the established loss calculation models through the model integration module, inputs the time-varying operating condition sequence, calculates and outputs the total loss power of the power motor controller within the entire expected flight profile.

2. The method according to claim 1, characterized in that, The key parameters include the on-state resistance, threshold voltage, and transfer characteristics of SiC power devices, as well as the on-state resistance and threshold voltage of freewheeling diodes.

3. The method according to claim 2, characterized in that, The switching loss calculation model for SiC power devices includes a turn-on loss calculation model and a turn-off loss calculation model. The turn-on loss calculation model satisfies the following conditions: P sw（ON） =E loss（ON） ×f sw ;P sw（ON） f represents the turn-on power loss of SiC power devices. sw E is the switching frequency. loss（ON） To enable energy dissipation, E loss（ON） =E loss1（ON） +E loss2（ON） , of which E loss1（ON） E represents the main energy loss during the start-up process. loss2（ON） Additional energy loss during the startup process. V DC i is the DC bus voltage. d For transient drain current, I o For load current, v ds t is the drain-source voltage. p2 t is the starting time point in the process of driving the source pulse to turn on. p3 E represents the end time point in the process of driving the source pulse to start; loss2（ON） =L stray I o 2 +V CC Q g -△E c（ON） L stray For the parasitic inductance of the line, V CC Q is the gate drive voltage. g For the total gate charge, ΔE c（ON） To store the energy change of the circuit during the start-up process; The turn-off loss calculation model satisfies the following conditions: P sw（OFF） =E loss（OFF） ×f sw ;P sw（OFF） For the turn-off power loss of SiC power devices, f sw E is the switching frequency. loss（OFF） To shut off energy loss, E loss（OFF） =E loss1（OFF） +E loss2（OFF） , of which E loss1（OFF） E represents the main energy loss during the turn-off process. loss2（OFF） This refers to the additional energy loss during the shutdown process. , t p0 t is the starting time point in the drive source pulse turn-off process. p1 This is the end time point in the drive source pulse turn-off process; E loss2（OFF） =-L stray I o 2 -V EE Q g -△E c（OFF） V EE The lower limit of the driving voltage, ΔE c（OFF） This stores the change in energy during the circuit's shutdown process.

4. The method according to claim 1, characterized in that, The method further includes the following steps: S600, through the temperature sensor and current / voltage sensor integrated in the motor controller, monitors and collects the junction temperature T of the SiC power device in real time. j Load current I o and drain-source voltage V ds As real-time monitoring data; S700 dynamically updates the parameters of the key components based on the real-time monitoring data through a parameter recognition algorithm; S800 feeds back the updated key component parameters into the loss calculation model established by S200, S300, and S400, enabling online correction and adaptive prediction of model parameters.

5. The method according to claim 4, characterized in that, Specifically, S700 includes: during the steady-state conduction phase of power devices, estimating the real-time on-state resistance according to a preset estimation formula, binding the estimated real-time on-state resistance to the current junction temperature, and updating the functional relationship between on-state resistance and junction temperature.

6. The method according to claim 2, characterized in that, The on-state loss calculation model for SiC power devices satisfies the following conditions: P C，M =R ds（ON） ×I Drms 2 P C，M R represents the on-state loss of a SiC power device. ds（ON） I is the on-state resistance of the SiC power device. Drms This represents the root mean square current of the SiC power device.

7. The method according to claim 2, characterized in that, The on-state loss calculation model of the freewheeling diode satisfies the following conditions: P C，D =u D0 ×I Fav +RD （ON） ×I Frms 2 P C，D For the conduction loss of the freewheeling diode, u D0 I is the forward voltage drop of the freewheeling diode. Fav I is the average current of the freewheeling diode. Frms RD is the root mean square current of the freewheeling diode. （ON） This is the forward on-state resistance of the freewheeling diode.

8. The method according to claim 2, characterized in that, The calculation model for the switching loss of the freewheeling diode satisfies the following conditions: P sw，D = (1 / 4) × Q rr ×U Drr ×f sw , where P sw，D Q is the turn-on power loss of the freewheeling diode. rr To recover the charge in the reverse direction, U Drr f is the voltage across the diode during reverse recovery. sw This refers to the switching frequency.

9. An electronic device, characterized in that, Including processor and memory; The processor executes the steps of the method as described in any one of claims 1 to 8 by invoking programs or instructions stored in the memory.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a program or instructions that cause a computer to perform the steps of the method as described in any one of claims 1 to 8.

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