A method for real-time simulation of electro-thermal coupling of permanent magnet synchronous motor

Abstract

This invention discloses a real-time simulation method for electrothermal coupling of permanent magnet synchronous motors, specifically relating to the field of motors. The method employs an electrothermal coupling model for real-time simulation. The simulation method includes: outputting three-phase equivalent phase voltages from a drive circuit model; determining line voltages based on these three-phase equivalent phase voltages and inputting them into the motor body model; determining stator current and electromagnetic torque based on the motor body model; determining three-phase equivalent phase currents based on the stator current and inputting these three-phase equivalent phase currents into the drive circuit model; inputting the three-phase equivalent phase currents and flux linkage into a loss model to determine copper losses and iron losses, and correcting the electromagnetic torque based on the iron losses; determining the loss power based on the iron and copper losses, inputting the loss power into a thermal network model to determine the temperature of each node in the motor; and inputting the temperature of the corresponding node into an electrical parameter correction module to correct and update the stator resistance and flux linkage of the motor body model. This method ensures simulation accuracy.

Description

Technical Field

[0001] This invention relates to the field of electric motors, and more particularly to a real-time simulation method for electrothermal coupling of permanent magnet synchronous motors. Background Technology

[0002] The current real-time simulation of permanent magnet synchronous motors mainly faces two core difficulties: "insufficient model accuracy" and "limited solution efficiency". Specifically, it is manifested as follows: (1) Insufficient model accuracy. Most existing commercial real-time simulators usually use simplified lumped parameter models, which only focus on simulating the electrical transient behavior of the motor and forcibly assume that the motor operates at a constant temperature. However, in actual operation, the winding resistance of the motor increases significantly with temperature rise, and the magnetic flux of the permanent magnet decreases with temperature rise. Ignoring these electrothermal coupling characteristics will lead to serious distortion of the simulation results under long-term operation or high load conditions, and will not be able to truly reflect the thermal decay behavior and overheating risk of the motor. (2) High solution difficulty. High-precision physical models are difficult to meet the real-time requirements. Although finite element simulation (FEA) based on physical characteristics can model electrothermal behavior in detail, its computational complexity is extremely high. The single-step calculation time is usually on the order of minutes or even hours. It is only suitable for offline design verification and cannot meet the needs of microsecond-level real-time closed-loop testing. Even with optimized magnetic equivalent circuit (MEC) models, although efficiency has improved, complex nonlinear networks are often required to ensure accuracy, resulting in huge matrix solving overhead in real-time computing. Furthermore, the model parameters are highly dependent on the specific geometric design, leading to poor versatility and portability.

[0003] The reason why existing research struggles to effectively address the aforementioned problems in real-time simulation is not due to a lack of theoretical models describing electrothermal coupling behavior, but rather to the fundamental contradiction between "modeling accuracy" and "solution efficiency." On the one hand, high-precision multi-physics coupling models inevitably introduce complex systems of differential equations and nonlinear parameter calculations, significantly increasing the computational load. On the other hand, real-time simulation systems (especially CPU / DSP-based serial processors) cannot complete the iterative solution of massive amounts of data within a specified timeframe due to extremely short simulation step sizes. Therefore, how to construct a real-time model that accurately reflects the bidirectional electrothermal coupling characteristics of motors while operating efficiently, under limited computational resources and strict real-time step size constraints, is a pressing technical challenge that needs to be addressed. Summary of the Invention

[0004] The main purpose of this application is to provide a real-time simulation method for electrothermal coupling of permanent magnet synchronous motors, which aims to solve the problem that existing simulation models cannot accurately reflect the bidirectional electrothermal coupling characteristics of motors and achieve efficient operation.

[0005] To achieve the above objectives, this application provides a real-time simulation method for electrothermal coupling of permanent magnet synchronous motors. The method employs an electrothermal coupling model for real-time simulation, which includes an electrical model, a loss model, a thermal network model, and an electrical parameter correction module. The electrical model comprises a motor body model and a drive circuit model, with the drive circuit model including three half-bridge circuit models. The simulation method includes: outputting three-phase equivalent phase voltages through the drive circuit model; determining line voltages based on these three-phase equivalent phase voltages and inputting them into the motor body model; determining stator current and electromagnetic torque based on the motor body model, and determining the three-phase equivalent phase currents based on the stator current, and inputting these three-phase equivalent phase currents into the drive circuit model; inputting the three-phase equivalent phase currents and flux linkage into the loss model to determine copper losses and iron losses, and correcting the electromagnetic torque based on the iron losses; determining the loss power based on the iron losses and copper losses, inputting the loss power into the thermal network model to determine the temperature of each node of the motor; and inputting the temperature of the corresponding node into the electrical parameter correction module to correct and update the stator resistance and flux linkage of the motor body model.

[0006] Optionally, each half-bridge circuit model is established based on the equivalent conductance of the switching devices in the off state and on state, the equivalent phase voltage, the current flowing into the DC bus from the half-bridge circuit, the DC bus voltage, and the equivalent phase current.

[0007] Optionally, the motor body model includes stator voltage equations, flux linkage equations, electromagnetic torque equations, and mechanical motion equations.

[0008] Optionally, the stator current and electromagnetic torque are determined based on the motor body model, including: obtaining the stator voltage, stator resistance, and electric angular velocity; inputting the stator voltage, stator resistance, and electric angular velocity into the stator voltage equation to determine the relationship between flux linkage and stator current; inputting the relationship between flux linkage and stator current, and the excitation flux linkage into the flux linkage equation to determine the stator current and flux linkage. The stator current and flux linkage are input into the electromagnetic torque equation to determine the electromagnetic torque.

[0009] Optionally, the three-phase equivalent phase current and flux linkage are input into the loss model to determine copper loss and iron loss, including: obtaining the current flowing through the stator winding, determining copper loss based on the current flowing through the stator winding and the stator resistance; determining magnetization voltage and demagnetization voltage based on the electric angular velocity and flux linkage, and determining iron loss based on the magnetization voltage and demagnetization voltage.

[0010] Optionally, the nodes include the stator yoke, stator teeth, armature winding, permanent magnet, motor housing, and air.

[0011] Optionally, the power loss includes iron loss power and copper loss power. The power loss is determined based on iron loss and copper loss, and then input into the thermal network model to determine the temperature of each node of the motor. This includes: determining the iron loss power of the rotor permanent magnet, stator teeth, and stator yoke based on iron loss; determining the copper loss power of the stator winding based on copper loss; determining the thermal resistance between nodes based on the node's thermal conductivity, the area of ​​the node perpendicular to the heat flow direction, the length of the heat conduction path between nodes, the convective heat transfer coefficient of the node, and the effective contact area for convective heat transfer; determining the equivalent heat capacity of the node based on its mass and specific heat capacity; and inputting the power loss corresponding to each node, the thermal resistance between nodes, and the equivalent heat capacity of the node into the thermal network model to obtain the node's temperature.

[0012] Optionally, the stator resistance and flux linkage of the motor body model are corrected, including: correcting and updating the stator resistance based on the difference between the node temperature and the initial temperature, and the stator resistance at the initial moment; and correcting and updating the permanent magnet remanence at the current moment based on the difference between the node temperature and the initial temperature, and the permanent magnet remanence at the initial moment.

[0013] Optionally, the output of the motor body model is provided with a unit delay, which is used to input the three-phase equivalent phase current at the current moment to the drive circuit model at the next moment; the output of the drive circuit model is provided with a unit delay, which is used to input the three-phase equivalent phase voltage at the current moment to the motor body model at the next moment; the output of the thermal network model is provided with a unit delay, which is used to input the node temperature at the current moment to the electrical parameter correction module at the next moment.

[0014] Optionally, the electrothermal coupling model also includes a heat exchange interface, which is connected to the thermal network model and is used to cool the motor or exchange heat with an external system.

[0015] Compared with the prior art, the beneficial effects of this application are as follows: The present invention provides a real-time simulation method for electrothermal coupling of permanent magnet synchronous motors (PMSGs). This method simulates the dynamic evolution of the motor through an electrical model, a loss model, a thermal network model, and an electrical parameter correction module. It not only accurately characterizes the electrical performance and thermal response characteristics of the motor under different operating conditions but also deeply reveals the interaction mechanism and feedback relationship between the electrical and thermal physical domains. By decoupling the three-phase full-bridge circuit into three parallel half-bridge circuit models as the drive circuit model, parallel computation is achieved, improving solution efficiency and ensuring simulation accuracy. A collaborative architecture between the drive circuit and the motor body is adopted, using a hardware decoupling strategy based on unit delay to solve the problem of strongly coupled algebraic loops formed by the "voltage-current" interdependence between the PMSG motor and the drive circuit. This enables efficient parallel solution of the motor body model and the drive circuit model, thus ensuring simulation accuracy. In the computational logic of the electrical parameter correction module, a unit clock delay is inserted, successfully severing direct data dependencies within the same clock cycle without sacrificing model fidelity, fundamentally ensuring the timing convergence and synthesizability of the FPGA logic. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the electrothermal coupling model in the real-time simulation method for electrothermal coupling of a permanent magnet synchronous motor according to this application; Figure 2 This is a diagram illustrating the decoupling process of the drive circuit model in the real-time simulation method for electrothermal coupling of a permanent magnet synchronous motor according to this application. Figure 3 This is a flowchart illustrating a real-time simulation method for electrothermal coupling of a permanent magnet synchronous motor according to this application. Figure 4 This is a schematic diagram of the FPGA solver for a real-time simulation method of electrothermal coupling of a permanent magnet synchronous motor according to this application. Figure 5 This is a schematic diagram of the thermal network model in the real-time simulation method for electrothermal coupling of a permanent magnet synchronous motor according to this application; Figure 6 This is a schematic diagram of the algebraic loop in the electrothermal coupling model of the real-time simulation method for electrothermal coupling of a permanent magnet synchronous motor in this application; Figure 7 This is a schematic diagram of the fixed-point number representation method in the real-time simulation method of electrothermal coupling of a permanent magnet synchronous motor in this application; Figure 8 This is a waveform comparison diagram of the DC bus voltage in an embodiment of this application; Figure 9 The waveform comparison diagrams are of the equivalent phase currents in the embodiments of this application; Figure 10 This is a waveform comparison diagram of the electromagnetic torque in the embodiments of this application; Figure 11This is a waveform comparison diagram of copper loss in an embodiment of this application; Figure 12 This is a waveform comparison diagram of iron loss in an embodiment of this application; Figure 13 A waveform comparison diagram of the stator yoke temperature in an embodiment of this application; Figure 14 A waveform comparison diagram of stator tooth temperature in an embodiment of this application; Figure 15 A waveform comparison diagram of stator winding temperature in an embodiment of this application; Figure 16 This is a waveform comparison diagram of the rotor permanent magnet temperature in an embodiment of this application.

[0017] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] It should be noted that the real-time simulation method for electrothermal coupling of permanent magnet synchronous motors of this invention has a universal model and core architecture, applicable to all types of permanent magnet synchronous motors (including motors and generators). The following explanation uses a permanent magnet synchronous generator (PMSG) as an example.

[0020] Embodiments of the present invention provide a real-time simulation method for electrothermal coupling of permanent magnet synchronous motors, which uses an electrothermal coupling model for real-time simulation, such as... Figure 1 The electrothermal coupling model shown includes an electrical model, a loss model, a thermal network model, and an electrical parameter correction module. Furthermore, it also includes a heat exchange interface connected to the thermal network model. The heat exchange interface is used to cool the motor or exchange heat with an external system. The electrical model includes a motor body model and a drive circuit model.

[0021] For PMSG active rectification operation, this invention implements parallel computing by network decoupling of the three-phase bridge circuit, specifically as follows: Figure 2As shown, the stator windings of the PMSG are equivalent to three independent current sources for the rectifier bridge, and the DC-side voltage regulator bus is equivalent to a voltage source. Since the three-phase half-bridge structures are independent of each other, the three-phase full-bridge circuit can be decomposed into three independent half-bridge circuits and modeled separately. Therefore, the drive circuit model in this embodiment includes three half-bridge circuit models. In real-time simulation of power electronic systems, switching devices are usually simplified using a binary resistor model. When the device is in the off state, its equivalent resistance is taken as a large value. When the device is turned on, a smaller value is taken. To adapt to the modeling of node current equations, the switching characteristics are described in terms of conductance, and the following definition is used: The equivalent conductance in the on state is... The equivalent conductance in the off state is given. Within this model framework, each binary resistor model corresponds to a combination of a fully controllable power semiconductor device and its anti-parallel diode. The on and off states of the fully controllable device can be directly controlled by its gate drive signal, while the on state of the anti-parallel diode is determined by the polarity of the voltage across its terminals.

[0022] Therefore, each half-bridge circuit model is established based on the equivalent conductance of the switching devices in the off-state and on-state, the equivalent phase voltage, the current flowing into the DC bus from the half-bridge circuit, the DC bus voltage, and the equivalent phase current. For example, taking phase A as an example, the half-bridge circuit model is as follows:

[0023] In the formula, This is the equivalent phase voltage at the midpoint of the half-bridge (PMSG phase A). This refers to the current flowing into the DC bus from the half-bridge. This is the DC bus voltage. This is the equivalent phase current of phase A in the PMSG. Under rectified operation, the current... The current flows from the A-phase winding of the PMSG into the midpoint of the half-bridge.

[0024] like Figure 3-4 As shown, the simulation method specifically includes the following steps: Step S1: Output the three-phase equivalent phase voltage through the drive circuit model, determine the line voltage based on the three-phase equivalent phase voltage, and input it into the motor body model; wherein, the line voltage... and The expression is:

[0025] In the formula, The equivalent phase voltage of phase B of PMSG. This is the equivalent phase voltage of phase C in PMSG.

[0026] Step S2: Determine the stator current and electromagnetic torque based on the motor body model, and determine the three-phase equivalent phase current based on the stator current. Input the three-phase equivalent phase current into the drive circuit model. The motor body model includes stator voltage equations, flux linkage equations, electromagnetic torque equations, and mechanical motion equations. The equivalent phase current can be obtained by performing inverse Park transform and inverse Clark transform on the stator current.

[0027] Specifically, obtain the stator voltage, stator resistance, and electric angular velocity. Input the stator voltage, stator resistance, and electric angular velocity into the stator voltage equation to determine the relationship between the magnetic flux linkage and the stator current. The stator voltage equation is:

[0028] In the formula, , These are the stator voltages along the d-axis and q-axis, respectively. For stator resistance, , For the stator current in the corresponding direction, , For the corresponding direction of magnetic flux linkage, Let be the electric angular velocity of the motor.

[0029] By inputting the relationship between flux linkage and stator current, and the excitation flux linkage, into the flux linkage equation, the stator current and flux linkage are determined. The specific expression of the flux linkage function can be selected based on the required modeling accuracy: if it is assumed that the motor is in a linear magnetization state, the d-axis and q-axis flux linkages and current can be approximated as linear, i.e. and If nonlinear factors such as saturation are considered, the flux linkage can be represented by a fitting function or by generating a lookup table (LUT) from the finite element analysis (FEA) results, thus achieving a fast and accurate description of the nonlinear characteristics. For example, the flux linkage equation can be:

[0030] In the formula, , Let be the inductances about the d-axis and q-axis, respectively, and be a function of the currents about the d-axis and q-axis. The excitation flux is provided by the permanent magnet.

[0031] Input the stator current and flux linkage into the electromagnetic torque equation to determine the electromagnetic torque; the electromagnetic torque equation is:

[0032] The mechanical motion equations can be used to observe and analyze the relationship between the electromagnetic torque generated by the motor and the load torque under a given speed and control strategy, or to calculate the equivalent load torque. The mechanical motion equations are:

[0033] In the formula, This represents the number of pole pairs of the motor. For mechanical angular velocity, The moment of inertia of the rotor. This represents the applied load torque, which is positive (resistance) under electric motor operation and negative under generator operation. Its absolute value represents the driving torque provided by the prime mover. is the viscous damping coefficient.

[0034] The three-phase equivalent phase current is determined based on the stator current, and the three-phase equivalent phase current is input into the drive circuit model.

[0035] This invention employs a collaborative architecture of drive circuit and motor body. However, the strong coupling algebraic loop formed between the PMSG motor and drive circuit due to the "voltage-current" interdependence prevents the two models from being computed in parallel. To address this, this invention designs a hardware decoupling strategy based on unit delay. Specifically, the output of the motor body model is set with a unit delay to input the current three-phase equivalent phase current to the drive circuit model at the next moment; the output of the drive circuit model is set with a unit delay to input the current three-phase equivalent phase voltage to the motor body model at the next moment. That is, when calculating the current simulation step, the input voltage required by the motor body model is taken from the output voltage of the drive circuit model in the previous simulation step; similarly, the input current required by the drive circuit model is taken from the output current of the motor body model at the previous moment. Since the simulation step is extremely small, the phase error introduced by this unit delay is negligible, but it fundamentally breaks the hardware algebraic loop, thereby achieving efficient parallel solution of the motor model and drive circuit model.

[0036] To ensure that all the above computational tasks can be completed within the same extremely small time step, this embodiment deploys the entire drive circuit-motor collaborative solution architecture within a single-cycle time loop (SCTL) structure of a LabVIEW FPGA. By configuring the SCTL clock frequency to 5MHz, all parallel pipeline stages (including electrical, loss, and thermal calculations) are forced to deterministically complete a full state update within a single 200ns cycle. The SCTL structure not only guarantees the simultaneous and absolute synchronization of electromagnetic transients and thermal dynamics, but also ensures the stable operation of the entire complex multiphysics model under extremely stringent timing constraints through automatic optimization of the logic path by the compiler.

[0037] Step S3: Input the three-phase equivalent phase current and flux linkage into the loss model, determine the copper loss and iron loss, and correct the electromagnetic torque based on the iron loss; Specifically, copper losses are mainly caused by the current flowing through the stator windings and are one of the main sources of heat generation in the motor. The copper losses for any single phase winding are determined as follows.

[0038] Step S31: Obtain the current flowing through the stator winding, and determine the copper loss based on the current flowing through the stator winding and the stator resistance; the expression for copper loss is:

[0039] In the formula, The current flowing through the stator winding is selected from any one of the three-phase equivalent phase currents. This is the stator resistance, i.e., the equivalent resistance of this phase winding.

[0040] Iron loss can be divided into two components: one part originates from the main magnetization path, and the other part is related to the demagnetization path during the weakening magnetic process. The iron loss in the main magnetization path is related to the induced stator voltage and can be controlled by the magnetization voltage. The polynomial function is used for approximation. Another part is the iron loss along the demagnetizing path, which is related to the demagnetizing magnetic field strength applied to the stator and its corresponding demagnetizing voltage. "Relevant" can also be expressed as "about". The polynomial function. These two losses are modeled and parameterized based on two-dimensional finite element magnetic field analysis (FEA) of the motor under open-circuit and short-circuit conditions, respectively. The specific iron loss model is as follows.

[0041] Step S32: Determine the magnetization voltage and demagnetization voltage based on the electric angular velocity and magnetic flux linkage; determine the iron loss based on the magnetization voltage and demagnetization voltage; the iron loss model is as follows:

[0042] In the formula, and These are the open-circuit loss and short-circuit loss of iron loss, respectively. , , To and Correlation coefficient vector. Magnetization voltage With demagnetizing voltage It can be calculated using the following formula:

[0043] The iron loss under a certain operating condition is the sum of the two parts mentioned above:

[0044] Step S33: Correct the electromagnetic torque based on iron losses. Since iron losses affect the electromagnetic characteristics of the motor and reduce the power used for electromagnetic conversion, the electromagnetic conversion power... , It is electromagnetic torque, It is the mechanical angular velocity. Therefore, combining the torque equation of PMSG, the modified electromagnetic torque equation after considering energy conservation is:

[0045] In the formula, mechanical angular velocity This is to prevent excessive iron loss correction at low speeds.

[0046] Step S4: Determine the power loss based on iron loss and copper loss, input the power loss into the thermal network model, and determine the temperature of each node of the motor; wherein, the power loss includes iron loss power and copper loss power; for example Figure 5 As shown in the figure, the thermal network model of this embodiment divides the motor into six key nodes: stator yoke, stator teeth, armature winding, permanent magnet, motor housing, and air. As can be seen from the figure, each temperature node of the LPTN contains an independent heat source input, all derived from power loss; each temperature node also needs to include heat capacity to describe the dynamic process of temperature change; furthermore, the nodes are connected by thermal resistance to form an equivalent thermal network. The power loss, heat capacity, thermal resistance, and temperature of each node are determined as follows.

[0047] Specifically, in step S41, the iron loss power of the rotor permanent magnet, stator teeth, and stator yoke is determined according to the iron loss; the copper loss power of the stator winding is determined according to the copper loss; the specific expression is:

[0048] In the formula, This refers to the copper loss power of the stator winding. This represents the effective value of the phase current in the three-phase equivalent phase current. , , The table shows the iron loss power of the rotor permanent magnet, stator teeth, and stator yoke, respectively. , and Open circuit iron loss Distribution coefficients among permanent magnets, stator teeth, and stator yoke. , and Short-circuit iron loss The corresponding allocation coefficients all take values ​​between 0 and 1, and the sum of the coefficients equals 1.

[0049] For a thermal network, each temperature node, in addition to the heat source input, also experiences heat exchange with other nodes. This heat exchange can be modeled using a unified "thermal conduction / thermal resistance" model, and its physical essence is divided into heat conduction between solids and heat convection at the solid-fluid interface. Here, we directly calculate heat conduction and heat convection instead of calculating thermal resistance.

[0050] For the heat conduction path between solid nodes, the heat flow calculation formula is:

[0051] In the formula, For a unit of time from node i Flow to Node j heat flow, The thermal conductivity of the component corresponding to the node. S c Let the area be perpendicular to the direction of heat flow. D This is the length of the heat conduction path between nodes (or the thickness of the component corresponding to the node).

[0052] The thermal convection path between a solid node and the fluid medium can be calculated using the following formula:

[0053] In the formula, For a unit of time from node i Flow to fluid nodes j convective heat flow, The convective heat transfer coefficient, The effective contact area for convective heat transfer. The thermal resistance between nodes where heat conduction and convection are the primary modes of heat transfer in a localized region is determined as follows.

[0054] Step S42: Determine the thermal resistance between nodes based on the node's thermal conductivity, the area of ​​the node perpendicular to the heat flow direction, the length of the heat conduction path between nodes, the node's convective heat transfer coefficient, and the effective contact area for convective heat transfer; the expression is:

[0055] Step S43: Determine the equivalent heat capacity of the node based on its mass and specific heat capacity;

[0056] In the formula, It is the change in heat absorbed / released by the node. It is the change in temperature. It refers to the mass of the component corresponding to the node. It is the specific heat capacity of the component corresponding to the node.

[0057] Step S44: Input the power loss of each node, the thermal resistance between nodes, and the equivalent heat capacity of the nodes into the thermal network model to obtain the node temperature; the transient temperature equation used to describe the node thermal equilibrium, i.e., the thermal network model, is as follows:

[0058] In the formula, Let be the equivalent heat capacity of the node. and They are nodes i and j temperature, For nodes i and j Thermal resistance between For nodes i The heat source input power.

[0059] Step S5: Input the node temperature into the electrical parameter correction module to correct and update the stator resistance and flux linkage of the motor body model.

[0060] Specifically, the stator resistance is corrected and updated based on the difference between the temperature of the node (stator winding SW) and the initial temperature, and the product of the initial stator resistance; the correction formula for the stator resistance is:

[0061] The change in flux linkage amplitude is reflected by the remanence of the permanent magnet; therefore, flux linkage is corrected by correcting the remanence of the permanent magnet. Specifically, the remanence of the permanent magnet at the current moment is corrected and updated based on the product of the difference between the temperature of the permanent magnet and the initial temperature, and the remanence of the permanent magnet at the initial moment. The correction formula is:

[0062] In the formula, is the temperature coefficient of remanence.

[0063] Based on the above mathematical model, the temperature-sensitive parameters of the motor can be updated in real time, and the corrected parameters can be fed back to the electrical model, thereby realizing the information closed loop and coupled calculation between the electric and thermal models, and improving the accuracy and stability of the entire system model under different operating conditions.

[0064] Due to the strong coupling characteristics of "electricity generating heat and thermal parameter change," an algebraic loop will be generated. For example... Figure 6 As shown, the current at the current moment Loss Loss leads to temperature The temperature changes, and in turn, corrects the resistance. Resistance directly affects the current. The calculations form a momentary closed loop, causing the combinational logic to be unable to determine the final state. Therefore, this embodiment proposes an algebraic loop decoupling mechanism based on hardware register delay. In thermal parameters (such as resistance) In the computational logic of ), an explicit unit clock delay is inserted. Specifically, the output of the thermal network model is configured with a unit delay to input the current node temperature into the electrical parameter correction module at the next time step. This means that the current simulation step size is calculated during the simulation process. k resistance value At that time, the temperature used is not the one calculated at the current step size. Instead, it is latched by the register for the previous simulation step size ( k -1) temperature value Mathematically, this is represented as:

[0065] Due to the distance from the walking distance Extremely small and The difference is negligible. Therefore, this method successfully cuts off the direct data dependency within the same clock cycle without sacrificing model fidelity, fundamentally ensuring the timing convergence and synthesizability of FPGA logic.

[0066] The following describes the complete simulation process of the electrothermal coupling model based on the present invention on FPGA hardware.

[0067] Step S10 involves reconstructing the data using a unified numerical integration algorithm and a fixed-point conversion strategy. Specifically, Numerical integration methods (such as the forward Euler method) are employed to uniformly discretize all global continuous equations. The electrical differential equations and the state-space equations of the higher-order thermal network are all transformed into discrete difference iterative structures suitable for execution by hardware multiply-accumulate units. This method has the advantages of computational simplicity and ease of hardware pipelined implementation. Taking the stator voltage equation of the motor's d-axis as an example, after forward Euler discretization, it is reconstructed into the following difference equation form suitable for FPGA iterative calculation:

[0068] in, For discrete time steps, This refers to the current moment.

[0069] Fixed-point digital long programming and quantization optimization are performed on all state variables, model parameters, and coefficient matrices. This avoids the huge resource consumption and timing delays caused by floating-point operations without significantly sacrificing computational accuracy, laying the foundation for real-time solution with minimal step sizes. Figure 7 As shown, taking the motor current variable as an example, it is represented using the Q15.16 format (i.e., 1 sign bit, 15 integer bits, and 16 decimal bits). By accurately simulating and calibrating the physical range of each variable, and rationally planning the word length and decimal places of each variable, all complex floating-point operations are transformed into efficient hardware integer multiplication and shift operations while ensuring that the calculation accuracy meets engineering requirements.

[0070] Step S20: Identify the strongly coupled algebraic loop in the system caused by electric heat generation, thermal parameter change, and parameter-electrical change. That is, within the same clock cycle, the current electrical state depends on the current temperature, and the current temperature depends on the current electrical state.

[0071] In the thermal parameter correction loop, an explicit hardware-level register delay (unit clock delay) is inserted. This mechanism cleverly cuts off direct data dependencies within the same cycle by leveraging the register's ability to latch data on the clock edge, ensuring the compilability and timing convergence of the multi-physical domain coupled model, and solving the algebraic loop deadlock problem that inevitably arises in FPGA combinational logic synthesis due to electro-thermal closed-loop feedback.

[0072] Step S30 involves deploying all three parallelized half-bridge circuit models within a single-cycle timed loop structure or equivalent hardware pipeline control logic. By setting an extremely small discrete time step for this loop, a hard real-time step constraint at the microsecond or nanosecond level is imposed on the entire solver.

[0073] Timing closure and automatic pipeline optimization: Under the aforementioned hard real-time step size constraints, the FPGA compiler or synthesis tool automatically performs pipeline optimization on complex combinational logic within loops, segmenting critical paths by inserting registers to ensure that all computational tasks can be deterministically completed within a specified single cycle. This mechanism guarantees concurrent computation of electromagnetic transients and thermal dynamics at the same frequency, ultimately achieving ultra-low latency hard real-time simulation.

[0074] Step S40: Start the FPGA solver to update the state of the entire motor-converter system in real time and deterministically under internal closed-loop control. Operation flow: Within each discrete time step, the FPGA solver receives the target control input from the higher level (such as target DC voltage, target output power, or target torque command). The virtual controller inside the model generates a PWM drive signal in real time based on the target and the feedback state variables, and drives the converter model. Subsequently, the pipeline sequentially completes current calculation, loss generation, temperature update, and online reconstruction of thermistor parameters. Output results: This method ultimately enables real-time and deterministic output of high-fidelity electrical waveforms (such as three-phase current and bus voltage ripple) and transient temperatures of key internal nodes under internal closed-loop control with ultra-low latency in the hundreds of nanoseconds.

[0075] The real-time simulation method for electrothermal coupling of permanent magnet synchronous motors of this invention enables modularization and parallel deployment of the algorithm. Specifically, on the drive circuit side: the drive circuit is further decoupled into three independent half-bridge sub-circuit models. In the FPGA, the solution logic of these three sub-circuits is mapped to different hardware resources, realizing parallel concurrent computation of three phases. On the motor side: the motor electrothermal coupling model is decomposed into three independent calculation modules: electrical, loss, and thermal. These modules are organized into a deep pipeline within the FPGA. The electrical calculation level completes coordinate transformation and discrete solution of the stator voltage equation, outputting the dq-axis current. The loss calculation level receives the current / voltage results from the electrical calculation level and calculates copper and iron losses in parallel. The thermal calculation level receives the heat source power from the loss calculation level, completes the multiplication and addition operations of the thermal network matrix, and outputs the temperature of each node. The parameter feedback level receives the temperature results from the thermal calculation level and updates thermistor parameters such as resistance and flux linkage for use by the electrical calculation level in the next cycle.

[0076] By constructing the aforementioned deep pipeline-based concurrent solver, the discrete difference equations of all physical domains (electrical, loss, and thermal) are forced to be updated synchronously within the same minimal time step. This architecture ensures that the generation of electromagnetic losses and the accumulation of heat are absolutely synchronous on the microscopic time axis, avoiding pseudo-dynamic effects caused by interpolation or downsampling. Utilizing the massive parallelism of FPGAs, high-speed, synchronous evolution of the global state is achieved without sacrificing the computational frequency of any physical domain, significantly improving simulation fidelity.

[0077] In FPGA architecture design, by designing electrical, loss, and thermal models as independent logical entities with clearly defined input / output ports, users can quickly configure the model according to actual motor parameters or replace specific sub-modules (such as replacing the 6-node thermal model with a higher-order node model). In particular, the reserved external heat exchange interface enables this motor simulation model to function as a standardized virtual heat source, seamlessly integrating into larger-scale system-level thermal management simulations, and possessing the potential to become a core component of digital twins. Each model has reserved standardized interfaces, enhancing the model's engineering scalability. This solves the problems of difficult code modification and reusability in traditional integrated modeling.

[0078] Example The following section uses a 120kW high-speed permanent magnet synchronous motor (PMSG) as an example to verify the real-time simulation method for electrothermal coupling of the PMSG in this invention. The Simulink SPS model library contains standardized PMSG modules capable of describing the electrothermal characteristics of the PMSG. Based on these modules, an equivalent thermal network for the PMSG is constructed as a reference model for verifying the accuracy of this real-time simulation method. Furthermore, the PMSG system simulation models based on LabVIEW FPGA and MATLAB / Simulink platforms are implemented in a closed-loop manner under their respective controllers; therefore, the accuracy verification process includes the errors of the controller models.

[0079] The test conditions are set as follows: Initial state of the motor: initial temperature of each node of the motor T 0 is set to 25℃.

[0080] Operating command: The controller drives the motor to run at a constant speed of 60,000 r / min, and the DC bus voltage is... The target is 540V.

[0081] Dynamic load profile: Half load is applied during the period from 0 to 0.6 seconds; during the period from 0.6 to 1.0 seconds, the load is switched to the rated full load.

[0082] Perform steps S1-5 and verify the accuracy of the electrical model, loss model, and thermal network model. The verification results are as follows.

[0083] The electrical model was verified by testing the DC bus voltage. Equivalent phase current and electromagnetic torque The test was conducted, and the test results are as follows: Figure 8-10 As shown.

[0084] like Figure 8 As shown, under closed-loop control, the DC bus voltage (voltage) quickly stabilizes to the target value of 540V. The load current is 111A in the first 0-0.6s, rises to 222A in 0.6s, and increases further with the load. After a sudden voltage drop, the voltage quickly recovers and stabilizes at 540V. The voltage waveform generated by the FPGA real-time model of this invention matches well with the Simulink reference model. As shown in the enlarged view, the FPGA model can stably regulate the DC bus voltage to the target value, and its control performance is highly similar to that of the Simulink model.

[0085] Figure 9 This invention demonstrates the equivalent phase current of phase A in FPGA real-time simulation and Simulink reference model. The comparison results within 0.5-0.7s show that the phase current is closely related to the winding losses of the motor. Initially, the phase current stabilizes rapidly during the half-load phase. At 0.6s, as the load increases from half-load to rated load, the phase current increases rapidly and then tends to stabilize. The magnified view shows the phase current generated by the FPGA real-time model. It maintains a high degree of consistency with the Simulink reference model.

[0086] Figure 10 The electromagnetic torque obtained by FPGA real-time simulation and Simulink reference model of this invention was compared. As can be seen from the graph, the load current jumps from 111A at half load to 222A at full load in 0.6 seconds, and the torque rises rapidly. It can be observed that the waveforms of the two models almost completely overlap, the dynamic response is smooth, and the steady-state ripple is minimal.

[0087] The loss model was validated through copper loss analysis. and iron loss Conduct tests and verify the results as follows: Figure 11-12 As shown. From Figure 11 As can be seen, the copper loss of the motor is determined by the phase current; when the phase current is stable, the copper loss is also relatively stable. As shown in the enlarged image, the copper loss calculated by the real-time simulation based on FPGA matches very well with the copper loss calculated by the Simulink reference model. Furthermore, Figure 12 The iron loss simulation results shown further confirm the high consistency between the FPGA real-time simulation and the Simulink reference model. It can be seen that copper losses change rapidly with current steps, while iron losses adjust slowly with load. These results demonstrate that the loss model in the constructed real-time simulation model of the permanent magnet synchronous motor has high accuracy, and the real-time simulation method constructed in this invention has extremely high fidelity.

[0088] The thermal network model was validated by comparing the temperature response of four representative nodes (stator yoke, stator teeth, stator windings, and rotor permanent magnets) with the temperature response of the Simulink reference model. The results are as follows: Figure 13-16 As shown in the figure, the node temperature rise waveforms of the FPGA real-time model and the Simulink reference model exhibit high consistency, indicating that the thermal network model in the constructed real-time simulation model of the permanent magnet synchronous motor has high accuracy. Furthermore, it can be observed that the temperature rise rate significantly accelerates during the full-load stage. This demonstrates that the real-time simulation method constructed in this invention has extremely high fidelity.

[0089] This embodiment demonstrates that the waveform derived in real time by the present invention closely matches the reference waveform obtained offline under ideal conditions using standard industrial design software, proving that the real-time simulation method constructed by the present invention has extremely high fidelity. The high-frequency data output from the real-time simulation using the method of the present invention can be used for hardware-in-the-loop testing or as the core driving engine of a digital twin system.

[0090] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A real-time simulation method for electrothermal coupling of a permanent magnet synchronous motor, characterized in that, Real-time simulation is performed using an electrothermal coupling model, which includes an electrical model, a loss model, a thermal network model, and an electrical parameter correction module. The electrical model includes a motor body model and a drive circuit model, and the drive circuit model includes three half-bridge circuit models. The simulation method includes: The three-phase equivalent phase voltage is output through the drive circuit model, the line voltage is determined based on the three-phase equivalent phase voltage, and then input into the motor body model; The stator current and electromagnetic torque are determined based on the motor body model, and the three-phase equivalent phase current is determined based on the stator current. The three-phase equivalent phase current is then input into the drive circuit model. The three-phase equivalent phase current and flux linkage input loss model is used to determine copper loss and iron loss, and the electromagnetic torque is corrected based on the iron loss. The power loss is determined based on iron loss and copper loss, and then input into the thermal network model to determine the temperature of each node of the motor. Input the temperature of the corresponding node into the electrical parameter correction module to correct and update the stator resistance and flux linkage of the motor body model.

2. The real-time simulation method for electrothermal coupling of permanent magnet synchronous motors according to claim 1, characterized in that, Each half-bridge circuit model is established based on the equivalent conductance of the switching devices in the off state and on state, the equivalent phase voltage, the current flowing into the DC bus from the half-bridge circuit, the DC bus voltage, and the equivalent phase current.

3. The real-time simulation method for electrothermal coupling of permanent magnet synchronous motors according to claim 1, characterized in that, The motor body model includes stator voltage equation, flux linkage equation, electromagnetic torque equation, and mechanical motion equation.

4. The real-time simulation method for electrothermal coupling of permanent magnet synchronous motors according to claim 3, characterized in that, The step of determining the stator current and electromagnetic torque based on the motor body model includes: Obtain the stator voltage, stator resistance, and electric angular velocity. Input the stator voltage, stator resistance, and electric angular velocity into the stator voltage equation to determine the relationship between the magnetic flux and the stator current. Input the relationship between the flux linkage and the stator current, and the excitation flux linkage into the flux linkage equation to determine the stator current and flux linkage; The stator current and flux linkage are input into the electromagnetic torque equation to determine the electromagnetic torque.

5. The real-time simulation method for electrothermal coupling of permanent magnet synchronous motors according to claim 1, characterized in that, The step of inputting the equivalent phase current and flux linkage of the three phases into the loss model to determine copper loss and iron loss includes: Obtain the current flowing through the stator winding, and determine the copper loss based on the current flowing through the stator winding and the stator resistance; The magnetization voltage and demagnetization voltage are determined based on the electric angular velocity and magnetic flux linkage, and the iron loss is determined based on the magnetization voltage and demagnetization voltage.

6. The real-time simulation method for electrothermal coupling of permanent magnet synchronous motors according to claim 1, characterized in that, The node includes a stator yoke, stator teeth, armature winding, permanent magnet, motor housing, and air.

7. The real-time simulation method for electrothermal coupling of a permanent magnet synchronous motor according to claim 1 or 6, characterized in that, The power loss includes iron loss power and copper loss power; the process of determining the power loss based on iron loss and copper loss, inputting the power loss into the thermal network model, and determining the temperature of each node of the motor includes: The iron loss power of the rotor permanent magnet, stator teeth and stator yoke is determined according to the iron loss. Determine the copper loss power of the stator winding based on the copper loss; The thermal resistance between nodes is determined based on the node's thermal conductivity, the area of ​​the node perpendicular to the heat flow direction, the length of the heat conduction path between nodes, the node's convective heat transfer coefficient, and the effective contact area for convective heat transfer. The equivalent heat capacity of a node is determined based on its mass and specific heat capacity. The temperature of a node is obtained by inputting the power loss of each node, the thermal resistance between nodes, and the equivalent heat capacity of the node into the thermal network model.

8. The real-time simulation method for electrothermal coupling of permanent magnet synchronous motors according to claim 1, characterized in that, The correction of the stator resistance and flux linkage of the motor body model includes: The stator resistance is corrected and updated based on the difference between the node temperature and the initial temperature, and the stator resistance at the initial moment. Based on the difference between the node's temperature and the initial temperature, and the remanence of the permanent magnet at the initial moment, the remanence of the permanent magnet at the current moment is corrected and updated.

9. The real-time simulation method for electrothermal coupling of permanent magnet synchronous motors according to claim 1, characterized in that, The output terminal of the motor body model is equipped with a unit delay, which is used to input the three-phase equivalent phase current at the current moment to the drive circuit model at the next moment. The output of the drive circuit model is set with a unit delay, which is used to input the three-phase equivalent phase voltage at the current moment to the motor body model at the next moment; The output of the thermal network model is equipped with a unit delay, which is used to input the temperature of the node at the current moment into the electrical parameter correction module at the next moment.

10. The real-time simulation method for electrothermal coupling of a permanent magnet synchronous motor according to claim 1, characterized in that, The electrothermal coupling model also includes a heat exchange interface, which is connected to the thermal network model. The heat exchange interface is used to cool the motor or exchange heat with an external system.

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