A circuit design simulation method and system of an angle sensor

Abstract

The application provides a circuit design simulation method and system of an angle sensor, and relates to the technical field of circuit design simulation. The method comprises the following steps: constructing a circuit topology structure diagram of the angle sensor, performing multi-physical field coupling simulation, obtaining an output characteristic curve of the angle sensor, performing nonlinear error analysis, constructing an error compensation mapping set, performing parameter iterative optimization, generating a target compensation parameter combination, embedding the target compensation parameter combination into the circuit topology structure diagram for closed-loop verification, and generating a simulation verification report. The application solves the technical problem that the simulation result deviates from the actual result in a complex dynamic environment due to poor environmental adaptability and compensation lag of circuit design simulation. Through multi-physical field coupling simulation and segmented compensation, intelligent compensation of nonlinear error is realized, and the precision and efficiency of circuit design simulation are improved.

Description

Technical Field

[0001] This application relates to the field of circuit design simulation technology, and in particular to a circuit design simulation method and system for an angle sensor. Background Technology

[0002] An angle sensor is a device that detects changes in the rotation angle of an object and converts them into an electrical signal output. It needs to provide accurate and reliable angle measurement data under different environmental conditions. In the circuit design phase, virtual circuit models are typically built to predict the electrical performance of the circuit under different conditions, thus aiding in circuit design and optimization. However, existing circuit design simulation methods are mainly based on standard environmental parameters, often neglecting the circuit performance drift caused by environmental changes in actual working environments. This leads to significant deviations between simulation results and actual performance in complex dynamic environments. Furthermore, in circuit simulation, detected deviations (such as temperature drift compensation, gain error correction, and zero-point drift compensation) are usually compensated using linear approximations, without fully considering dynamic response characteristics such as response delay and compensation error accumulation. This results in a lag in compensation effects, further exacerbating the deviation between simulation predictions and actual performance.

[0003] In summary, existing technologies suffer from poor environmental adaptability and compensation lag in circuit design simulation, leading to discrepancies between simulation results and actual conditions in complex dynamic environments. Summary of the Invention

[0004] The purpose of this application is to provide a circuit design simulation method and system for angle sensors, in order to solve the technical problem in the prior art that the simulation results deviate from the actual results in complex dynamic environments due to the poor environmental adaptability and compensation lag of the circuit design simulation.

[0005] In view of the above problems, this application provides a circuit design simulation method and system for an angle sensor.

[0006] In a first aspect, this application provides a circuit design simulation method for an angle sensor. This method is implemented through a circuit design simulation system for an angle sensor. The method includes: constructing a circuit topology diagram of the angle sensor; performing multi-physics coupling simulation on the circuit topology diagram to obtain the output characteristic curve of the angle sensor; performing nonlinear error analysis based on the output characteristic curve to construct an error compensation mapping set; performing parameter iterative optimization on the error compensation mapping set to generate a target compensation parameter combination; embedding the target compensation parameter combination into the circuit topology diagram for closed-loop verification; and generating a simulation verification report.

[0007] Optionally, the mechanical structure parameters and electrical characteristic parameters of the angle sensor are retrieved, and finite element analysis is performed on the mechanical structure parameters and electrical characteristic parameters to obtain a three-dimensional electromagnetic field finite element model; based on the three-dimensional electromagnetic field finite element model, the sensitive nodes of the angle sensor are calculated to obtain the magnetic field distribution characteristics of multiple sensitive nodes; an equivalent circuit network topology diagram is constructed according to the magnetic field distribution characteristics, and the frequency domain response characteristics of the equivalent circuit network topology diagram are calibrated to construct the circuit topology diagram of the angle sensor.

[0008] Optionally, set boundary conditions for the joint simulation of electromagnetic field and temperature field, perform circuit simulation according to the boundary conditions and the circuit topology diagram, and obtain electromagnetic field simulation results; map the electromagnetic field simulation results to the circuit nodes in the circuit topology diagram to construct a parasitic parameter coupling matrix; perform signal conditioning based on the parasitic parameter coupling matrix, perform iterative correction of common-mode suppression according to the signal conditioning results, and construct the output characteristic curve.

[0009] Optionally, nonlinear analysis is performed based on the output characteristic curve to extract a set of nonlinear distortion feature points. Error component decomposition is performed based on the set of nonlinear distortion feature points to obtain nonlinear error components. Time-frequency domain analysis is performed based on the nonlinear error components, and an error compensation mapping set is constructed based on the time-frequency domain characteristic data. Global optimization is performed on the error compensation mapping set to generate an initial compensation parameter combination. Dynamic operating condition verification is performed based on the initial compensation parameter combination, and the target compensation parameter combination is determined based on the verification results.

[0010] Optionally, sampling points with equal angular intervals are set for the output characteristic curve, and sampling calculations are performed based on the equal angular interval sampling points to obtain the slope change rate of adjacent sampling points; when the slope change rate exceeds a preset slope change threshold, the corresponding sampling point is marked as the distortion start point, and forward tracing is performed along the output characteristic curve according to the distortion start point to mark the distortion end point; all sampling points from the distortion start point to the distortion end point are extracted to construct an initial feature point set, and smoothing and denoising are performed based on the initial feature point set to generate the nonlinear distortion feature point set.

[0011] Optionally, variational mode decomposition is performed on the set of nonlinear distortion feature points to obtain intrinsic mode function components; preset temperature data is set, and the preset temperature data is correlated with the response frequency of the angle sensor to construct a preset temperature-frequency response curve; correlation analysis is performed on the intrinsic mode function components and the preset temperature-frequency response curve to select temperature drift-related components as temperature drift error; the remaining components are transformed to extract the instantaneous phase and calculate the mechanical hysteresis error; the temperature drift error and the mechanical hysteresis error are used as the nonlinear error components.

[0012] Optionally, global multi-objective optimization is performed on the error compensation mapping set to generate an initial compensation parameter combination; a composite dynamic working condition signal is injected to execute the initial compensation parameter group, and a first output dataset from the angle sensor is collected; a dynamic nonlinearity deviation spectrum is calculated based on the first output dataset, and parameter-sensitive frequency bands are identified according to the dynamic nonlinearity deviation spectrum; the initial compensation parameter combination is frequency-domain weighted and adjusted according to the parameter-sensitive frequency bands to generate the target compensation parameter combination.

[0013] Optionally, the target compensation parameter combination is embedded into the nonlinear compensation module of the circuit topology diagram to generate a parameterized simulation unit; a dynamic angle excitation signal and a multi-physics interference signal are applied to the parameterized simulation unit, and the second output dataset of the angle sensor before and after compensation is collected based on the target compensation parameter combination; closed-loop verification analysis is performed based on the second output dataset to generate a preliminary verification index set; the tolerance of the circuit components of the angle sensor is retrieved for testing to construct a parameter sensitivity matrix; the preliminary verification index set and the parameter sensitivity matrix are fused to construct the simulation verification report.

[0014] Optionally, based on the parameter sensitivity matrix, the tolerance sensitivity sequence of key components is extracted through analysis; the preliminary verification index set and the parameter sensitivity matrix are mapped in a multidimensional manner according to the tolerance sensitivity sequence of key components to determine the mapping weight space; the preliminary verification index set and the parameter sensitivity matrix are used to perform dynamic and static analysis on the angle sensor according to the mapping weight space to obtain dynamic test data and static analysis results; the dynamic test data and the static analysis results are interactively integrated to construct the simulation verification report.

[0015] Secondly, this application also provides a circuit design simulation system for an angle sensor, used to execute a circuit design simulation method for an angle sensor as described in the first aspect, wherein the circuit design simulation system for an angle sensor includes: a coupling simulation module, used to construct a circuit topology diagram of the angle sensor, perform multi-physics coupling simulation on the circuit topology diagram, and obtain the output characteristic curve of the angle sensor; an error compensation module, used to perform nonlinear error analysis based on the output characteristic curve, construct an error compensation mapping set, perform parameter iterative optimization on the error compensation mapping set, and generate a target compensation parameter combination; and a closed-loop verification module, used to embed the target compensation parameter combination into the circuit topology diagram for closed-loop verification and generate a simulation verification report.

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

[0017] By constructing a circuit topology diagram of an angle sensor, multiphysics coupling simulation is performed on the circuit topology diagram to obtain the output characteristic curve of the angle sensor. Based on the output characteristic curve, nonlinear error analysis is performed to construct an error compensation mapping set. The error compensation mapping set is then iteratively optimized to generate a target compensation parameter combination. The target compensation parameter combination is embedded into the circuit topology diagram for closed-loop verification, generating a simulation verification report. In other words, by constructing a circuit topology diagram, performing multiphysics coupling simulation, constructing an error compensation mapping set, and generating a target compensation parameter combination through parameter iterative optimization, real-time compensation of nonlinear errors is achieved. Embedding the compensation parameters into the circuit topology diagram for closed-loop verification ensures the compensation effect, helps to promptly detect and correct deviations between the simulation model and reality, and improves the accuracy and efficiency of circuit design simulation.

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

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a schematic flowchart illustrating the circuit design simulation method for an angle sensor according to this application.

[0021] Figure 2 This is a schematic diagram of the circuit design simulation system for an angle sensor according to this application.

[0022] Figure labeling: Coupled simulation module 11, error compensation module 12, closed-loop verification module 13. Detailed Implementation

[0023] This application provides a circuit design simulation method and system for angle sensors, solving the technical problem in existing technologies where poor environmental adaptability and compensation lag in circuit design simulation lead to deviations between simulation results and actual values ​​in complex dynamic environments. By constructing a circuit topology diagram and performing multi-physics coupling simulation, an error compensation mapping set is built. Target compensation parameter combinations are generated through parameter iterative optimization, achieving real-time compensation for nonlinear errors. The compensation parameters are embedded in the circuit topology diagram for closed-loop verification, ensuring the compensation effect. This helps to promptly detect and correct deviations between the simulation model and reality, improving the accuracy and efficiency of circuit design simulation.

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

[0025] Example 1, please refer to the appendix. Figure 1 This application provides a circuit design simulation method for an angle sensor. The method is executed using an angle sensor circuit design simulation system and specifically includes the following steps:

[0026] S100: Construct the circuit topology diagram of the angle sensor, perform multi-physics coupling simulation on the circuit topology diagram, and obtain the output characteristic curve of the angle sensor.

[0027] Furthermore, this application S100 includes:

[0028] The mechanical structure parameters and electrical characteristic parameters of the angle sensor are retrieved, and finite element analysis is performed on the mechanical structure parameters and electrical characteristic parameters to obtain a three-dimensional electromagnetic field finite element model. Based on the three-dimensional electromagnetic field finite element model, the sensitive nodes of the angle sensor are calculated to obtain the magnetic field distribution characteristics of multiple sensitive nodes. An equivalent circuit network topology is constructed according to the magnetic field distribution characteristics, and the frequency domain response characteristics of the equivalent circuit network topology are calibrated to construct the circuit topology diagram of the angle sensor.

[0029] Specifically, the mechanical structure parameters and electrical characteristic parameters of the angle sensor are retrieved. The mechanical structure parameters are physical parameters related to the mechanical structure of the angle sensor, such as the diameter of the shaft, the material of the housing, the coil layout, and the air gap size, which affect the mechanical performance of the sensor. The electrical characteristic parameters are parameters related to the electrical performance of the angle sensor, such as the number of coil turns, inductance, resistance, mutual inductance coefficient, and parasitic capacitance, which directly determine its electrical response behavior.

[0030] Using finite element analysis tools, mechanical structure parameters and electrical characteristic parameters are input to establish a three-dimensional electromagnetic field finite element model of the angle sensor, which can simulate the electromagnetic field distribution inside the sensor. A simulation project is created using finite element simulation software. The geometric model of the sensor is created based on the mechanical structure parameters, and the electrical characteristic parameters are input. The geometric model is divided into a finite number of elements to ensure that the mesh density is sufficient to accurately simulate the electromagnetic field distribution. When setting boundary conditions, an appropriate simulation space is typically selected, and the boundaries are defined as magnetically insulated or open boundaries. The electromagnetic field distribution inside the model is calculated using the finite element method, obtaining the electromagnetic field distribution throughout the entire internal and external space of the angle sensor, thus obtaining a three-dimensional electromagnetic field finite element model, including the distribution of magnetic induction intensity, electric field intensity, current density, etc., at various locations.

[0031] For the sensitive nodes of the angle sensor, i.e., the nodes within the angle sensor that are particularly sensitive to changes in the magnetic field, the magnetic field distribution characteristics are calculated using a three-dimensional electromagnetic field finite element model. Sensitive nodes are the locations within the angle sensor that are most sensitive to changes in the magnetic field and have the greatest impact on the final signal output. They are typically located in areas with dense magnetic flux, drastic changes in the magnetic field gradient, or areas where the induction coil interacts with rotating components. Several sensitive nodes are selected in the three-dimensional electromagnetic field finite element model, and the magnetic field distribution data of these sensitive nodes are extracted one by one, including the magnetic induction intensity values ​​at different corner positions, and the changes in the direction and amplitude of the magnetic field are recorded, thus obtaining the magnetic field distribution characteristics of multiple sensitive nodes.

[0032] Based on the magnetic field distribution characteristics, an equivalent circuit network topology is drawn, converting the sensor's physical characteristics into circuit elements (such as inductors, capacitors, resistors, and mutual inductors) to simulate the actual electromagnetic behavior inside the angle sensor. The frequency domain response characteristics of the equivalent circuit network topology are calibrated, i.e., the circuit response is tested and calibrated at different frequencies to confirm that the dynamic behavior of the circuit network in the frequency domain is consistent with the actual sensor performance. In other words, by applying excitation signals of different frequencies, the amplitude-frequency and phase-frequency characteristics of the circuit are measured to complete the characteristic calibration of the equivalent circuit in the frequency domain (frequency space). Based on the calibration results, a complete circuit topology is constructed, including a signal conditioning module, an analog-to-digital converter (ADC) module, and a nonlinear compensation module. The signal conditioning module is used to filter, amplify, and adjust the bias of the sensor's raw signal to improve signal quality; the ADC module converts the analog signal into a digital signal for subsequent digital processing; and the nonlinear compensation module corrects the nonlinear error in the angle sensor output to improve overall accuracy.

[0033] By establishing a highly consistent three-dimensional electromagnetic field model based on the actual mechanical structure and electrical characteristics of the sensor, and accurately extracting the magnetic field characteristics of the sensitive node, a circuit topology diagram that can truly reflect the dynamic behavior of the angle sensor is derived.

[0034] Furthermore, this application also includes the following steps:

[0035] Set the boundary conditions for the joint simulation of electromagnetic field and temperature field, and perform circuit simulation according to the boundary conditions and the circuit topology diagram to obtain the electromagnetic field simulation results; map the electromagnetic field simulation results to the circuit nodes in the circuit topology diagram to construct the parasitic parameter coupling matrix; perform signal conditioning based on the parasitic parameter coupling matrix, and perform iterative correction of common mode suppression according to the signal conditioning results to construct the output characteristic curve.

[0036] Specifically, simulation conditions are set up under the simultaneous influence of electromagnetic and temperature fields. This includes the electromagnetic load on the sensor during operation (such as changes in operating current, excitation frequency, and rotation angle) and the ambient temperature load (such as ambient temperature variations from 25°C to 80°C, or localized temperature rise due to induced current). This allows the simulation to simultaneously consider the coupled effects of electromagnetic and thermal effects. Simultaneously, thermal convection and thermal radiation boundary conditions are applied during the simulation to ensure that temperature changes can influence electromagnetic properties (such as changes in permeability and increases in resistance). In other words, the sensor's geometric model and material properties are imported into the software; boundary conditions for the electromagnetic and temperature fields are defined; and a co-simulation is run to obtain the distribution results of the electromagnetic and temperature fields, i.e., the electromagnetic field simulation results.

[0037] The electromagnetic field simulation results (such as magnetic field distribution) are mapped to the corresponding nodes in the circuit topology diagram to construct a parasitic parameter coupling matrix. This matrix describes the parasitic capacitance and inductance parameters generated by electromagnetic coupling between nodes in the circuit (usually indexed by node number, with the corresponding parasitic changes filled in). The parasitic parameter coupling matrix is ​​a matrix-based mathematical model that describes the changes in non-ideal parasitic effects (parasitic inductance, capacitance, resistance, etc.) between nodes (or components) due to changes in the electromagnetic or temperature fields, and their interrelationships. It includes the mutual coupling parameters formed between nodes due to parasitic inductance, capacitance, and resistance, and represents unavoidable small interference paths in the circuit. After the simulation, the local electromagnetic quantity changes at each node inside the sensor can be obtained, such as the magnetic flux density at node 1 changing from 0.6T to 0.58T, and the temperature at node 2 rising from 25℃ to 75℃.

[0038] In the circuit topology diagram, the correspondence between each node and its physical location in the simulation model is defined. For example, one end of the induction coil corresponds to node N1, and the input end of the signal conditioning circuit corresponds to node N2. Based on the simulated magnetic field strength, electric field distribution, and temperature changes, the parasitic parameter changes of the corresponding components at each node are calculated. These include the influence of temperature and magnetic field changes on the permeability, leading to changes in coil inductance; the increase in material resistivity with increasing temperature; and the thermal expansion of some structural components causing changes in capacitor spacing. The parasitic parameter changes between all node pairs (such as N1 and N2) are organized into a matrix, where each element represents the change in parasitic inductance, parasitic capacitance, or parasitic resistance between a certain pair of nodes.

[0039] Parasitic effects cause signal amplitude variations, phase shifts, and spurious common-mode interference, necessitating signal conditioning to compensate for these deviations. Based on the parasitic parameter coupling matrix, the circuit output signal is conditioned through processes such as amplification and filtering to improve signal quality. In a simulation environment, the parasitic matrix is ​​applied to the circuit topology of the angle sensor to simulate the output response under real-world operating conditions. By inserting conditioning modules such as active filters and differential amplifiers, the gain and filtering frequency are dynamically adjusted to eliminate signal distortion caused by parasitic parameters. Signal conditioning refers to the process of making the original sensor signal more stable and easier to process through a series of circuit operations (such as filtering, amplification, and compensation).

[0040] After conditioning, the magnitude of the common-mode signal is calculated (this can be achieved by extracting the differential and common-mode signal components). If the common-mode suppression is insufficient, iterative correction is performed, such as fine-tuning the feedback resistor and common-mode feedback network parameters in the circuit. After each adjustment, the simulation is repeated, and the output curve is re-extracted until the common-mode component in the output characteristic curve is less than a set threshold. Iterative correction refers to a process that is not completed in one go, but through multiple cyclic adjustments, continuously approaching the ideal output characteristics. For example, the compensation amount is reduced slightly each time until the error meets the set standard. The output characteristic curve is the curve showing the relationship between the sensor's output signal and its physical input quantity (such as angle). It is an important indicator for evaluating sensor performance, such as linearity, sensitivity, and hysteresis.

[0041] For example, suppose the design parameters of a certain angle sensor are as follows: mechanical structure parameters include a sensor diameter of 10mm, a thickness of 5mm, and a material of iron-nickel alloy; electrical characteristic parameters include a coil resistance of 100Ω, an inductance of 10mH, and a permeability of 100H / m. Simulation conditions are set as follows: operating voltage 5V, ambient temperature 25℃, and temperature range -40℃ to 125℃. Electromagnetic field simulation results show that the magnetic field strength in the central region of the sensor is 0.5T; temperature field simulation results show that the highest temperature of the sensor during operation is 75℃; in circuit simulation, the parasitic parameter coupling matrix shows that the parasitic capacitance between nodes is 10pF, and the parasitic inductance is 1μH. After signal conditioning, the common-mode rejection ratio (CMRR) is improved from 60dB to 100dB.

[0042] The joint simulation of electromagnetic and temperature fields ensured the accuracy of sensor performance prediction under different environments, and the iterative correction of signal conditioning and common-mode suppression significantly improved the stability and accuracy of the sensor output signal.

[0043] S200: Based on the output characteristic curve, perform nonlinear error analysis, construct an error compensation mapping set, perform parameter iterative optimization on the error compensation mapping set, and generate a target compensation parameter combination.

[0044] Furthermore, this application S200 includes:

[0045] Nonlinear analysis is performed based on the output characteristic curve to extract a set of nonlinear distortion feature points. Error component decomposition is then performed based on the set of nonlinear distortion feature points to obtain nonlinear error components. Time-frequency domain analysis is performed based on the nonlinear error components, and an error compensation mapping set is constructed based on the time-frequency domain characteristic data. Global optimization is performed on the error compensation mapping set to generate an initial compensation parameter combination. Dynamic operating condition verification is performed based on the initial compensation parameter combination, and the target compensation parameter combination is determined based on the verification results.

[0046] Specifically, nonlinear analysis is performed on the output characteristic curve to identify the nonlinear distortion region on the curve. All sampling points from the start point to the end point within the distortion region are extracted and smoothed to obtain a set of nonlinear distortion feature points. For the extracted set of nonlinear distortion feature points, variational mode decomposition is used to decompose the total nonlinear distortion into several components with different frequencies and time characteristics, identifying temperature drift error and mechanical hysteresis error to obtain the nonlinear error component. The specific process is explained in detail in the corresponding weighted steps, and will not be elaborated here for the sake of brevity.

[0047] For nonlinear error components, time-domain and frequency-domain characteristic analyses are performed separately. For example, the signal amplitude varies with time, or the energy distribution at different frequency components is analyzed. Signal characteristics are observed simultaneously in both the time and frequency domains to reveal the frequency components of the signal at different time points. The analysis identifies the time periods and frequency ranges where the main energy of each component is concentrated, and whether these frequencies drift or change over time. Based on the data obtained from the time-frequency domain analysis, an error compensation mapping set is constructed, matching each error component with its effective compensation strategy. This process involves performing time-frequency domain analysis on the nonlinear error components and constructing an error compensation mapping set based on the time-frequency domain characteristic data.

[0048] An improved particle swarm optimization algorithm (such as multi-objective particle swarm optimization) is used to globally optimize the compensation parameters of the error compensation mapping set, obtaining a set of optimal compensation parameters as the initial compensation parameter combination. The preliminarily optimized compensation parameters are embedded into the angle sensor simulation model and verified under dynamically changing environments to observe whether the compensated output maintains error stability and reduces hysteresis under various operating conditions. Dynamic operating condition verification refers to simulating the sensor's operating state under actual changing conditions (such as temperature, load changes, and vibration environment) to verify the compensation effect.

[0049] If error drift is still observed under extreme conditions (such as rapid temperature rise), the compensation parameters can be further fine-tuned or locally optimized again. The final target compensation parameter combination, fully validated under dynamic conditions, is then used for practical deployment. Through nonlinear analysis and time-frequency domain analysis, error components are accurately identified and decomposed, an error compensation mapping set is constructed, and effective error compensation is achieved. Global optimization and dynamic condition validation ensure the optimality of the compensation parameters, improving the sensor's measurement accuracy and stability.

[0050] Furthermore, this application also includes the following steps:

[0051] Equal-angle interval sampling points are set for the output characteristic curve. Sampling calculation is performed based on the equal-angle interval sampling points to obtain the slope change rate of adjacent sampling points. When the slope change rate exceeds a preset slope change threshold, the corresponding sampling point is marked as the distortion start point. The distortion end point is marked by forward tracing along the output characteristic curve according to the distortion start point. All sampling points from the distortion start point to the distortion end point are extracted to construct an initial feature point set. Smoothing and denoising are performed based on the initial feature point set to generate the nonlinear distortion feature point set.

[0052] Specifically, the output characteristic curve is divided into several equal parts, and sampling points with equal angular intervals are selected. That is, the angular input range is sampled equally, with one sampling point taken at every equal angular interval. For example, if the total angular range is 0° to 360°, a sampling point can be set every 10°. Sampling calculations are performed based on these equally spaced sampling points. For each sampling point, the slope between it and the previous sampling point is calculated, and the rate of change of the slope is further calculated, i.e., the degree of change in the slope between adjacent sampling points. This is used to detect abrupt or irregular changes in the curve. The rate of change of slope refers to the degree of change in the slope of the output characteristic curve between two adjacent sampling points, and is obtained by calculating the slope difference between adjacent sampling points.

[0053] If the rate of change of the slope exceeds a preset slope change threshold, the current sampling point is marked as the distortion start point. From this point, the output characteristic curve is traced backward until the rate of change of the slope returns to the steady-state range, meaning it no longer exceeds the preset threshold. At this point, the current sampling point is marked as the distortion termination point. Forward tracing refers to moving forward along the curve from the distortion start point until the slope change returns to the normal range, i.e., to the steady-state range. During the tracing process, when the slope change becomes stable, that point is the distortion termination point. The distortion termination point is the point marked after the output characteristic curve returns to the normal linear region, indicating the end of the distortion phenomenon.

[0054] All sampling points between the distortion start point and the distortion end point are extracted to form an initial feature point set, representing the portion of the output curve where nonlinear distortion occurs. The initial feature point set is then smoothed and denoised. Signal processing is performed on the sampling points to remove high-frequency noise from the data, thus making the data more stable. The purpose of smoothing and denoising is to remove high-frequency noise from the initial feature point set, making the nonlinear distortion feature point set smoother. For example, using a moving average method, the average value of the points in the neighborhood of each sampling point is taken as the smoothed value. The resulting denoised data constitutes the nonlinear distortion feature point set, representing the nonlinear region in the output characteristic curve and accurately reflecting the distortion phenomenon.

[0055] By analyzing the rate of change of slope and marking the start and end points of distortion, nonlinear distortion in the sensor output characteristics can be identified, enabling effective error compensation and improving the measurement accuracy and reliability of the sensor.

[0056] Furthermore, this application also includes the following steps:

[0057] Variational mode decomposition is performed on the set of nonlinear distortion feature points to obtain intrinsic mode function components; preset temperature data is set, and the preset temperature data is correlated with the response frequency of the angle sensor to construct a preset temperature-frequency response curve; correlation analysis is performed on the intrinsic mode function components and the preset temperature-frequency response curve to select temperature drift-related components as temperature drift error; the remaining components are transformed to extract the instantaneous phase and calculate the mechanical hysteresis error; the temperature drift error and the mechanical hysteresis error are used as the nonlinear error components.

[0058] Specifically, variational mode decomposition (VMD) is performed on the set of nonlinear distortion feature points, decomposing it into multiple eigenmode function (EMF) components with different center frequencies. Each EMF reflects a different frequency component of the signal. VMD is a signal processing method used to decompose a signal into a series of EMF components with different frequency characteristics, reflecting the variations in different frequency components of the signal. EMFs are signal components obtained after VMD, representing different frequency components in the signal. Each EMF typically contains a center frequency and reflects the essence of the signal in the time-frequency domain.

[0059] Set a set of preset temperature data and observe the frequency response of the angle sensor at different temperatures to construct a temperature-frequency response curve. For example, it may be observed that the frequency response of the angle sensor changes as the temperature increases, exhibiting a frequency drift. In many sensors, temperature drift causes changes in frequency response; therefore, constructing a temperature-frequency response curve can help analyze the impact of temperature on sensor performance.

[0060] Correlation analysis is performed between the intrinsic mode function (IMF) components and a pre-defined temperature-frequency response curve to identify frequency components closely related to temperature changes. This correlation analysis is conducted by calculating the correlation coefficient between the frequency response of each IMF component and the temperature response curve. For example, for IMF component 1, with a center frequency of 50 Hz, its frequency change at different temperatures is examined to determine if it matches the change in the temperature-frequency response curve. If the frequency response change matches the trend of increasing temperature, then IMF component 1 is determined to be related to temperature drift.

[0061] Correlation analysis is used to identify intrinsic mode function components highly correlated with temperature changes, i.e., temperature drift-related components, as the temperature drift error. For the remaining components uncorrelated with temperature drift, the instantaneous phase is further extracted, i.e., the phase information of the signal at each moment, usually obtained through time-frequency analysis. A Hilbert transform is applied to the remaining components to extract the instantaneous phase at each time point. The Hilbert transform essentially converts a real-valued signal into an analytic signal, thereby obtaining the signal envelope and instantaneous phase. The extracted instantaneous phase values ​​are mapped to the angle input values ​​to create a phase-angle curve. Ideally, the phase and angle should have a monotonic relationship, but due to mechanical hysteresis, the actual curve presents a closed loop. The width (horizontal distance) of this loop is the hysteresis bandwidth, representing the phase difference between the rising and falling edges at the same angle. The instantaneous phase difference corresponding to the same angle on the hysteresis loop is selected, the horizontal width of the loop is calculated, and the maximum horizontal width is taken as the mechanical hysteresis error value. The mechanical hysteresis error is calculated by measuring the width of the phase-angle hysteresis loop. The width of the hysteresis loop reflects the degree of phase lag as the angle changes, i.e., the magnitude of the mechanical hysteresis. Generally, the wider the hysteresis loop, the greater the mechanical hysteresis error.

[0062] Mechanical hysteresis error is a phenomenon where the angle-output characteristic curve of a sensor lags due to non-ideal effects such as elastic deformation, friction, and magnetic hysteresis of mechanical components. Specifically, the rising and falling edges of the output along the same angle change path differ, resulting in a phase shift. Temperature drift error and mechanical hysteresis error are treated as nonlinear error components, forming a complete nonlinear error component along with other errors in the original output characteristic curve. Variational mode decomposition and correlation analysis effectively identify the effects of temperature changes and mechanical hysteresis on the angle sensor, avoiding the error neglect common in traditional methods. This facilitates more accurate error compensation and improves sensor performance.

[0063] Furthermore, this application also includes the following steps:

[0064] Global multi-objective optimization is performed on the error compensation mapping set to generate an initial compensation parameter combination; a composite dynamic working condition signal is injected to execute the initial compensation parameter group, and a first output dataset from the angle sensor is collected; a dynamic nonlinearity deviation spectrum is calculated based on the first output dataset, and parameter-sensitive frequency bands are identified according to the dynamic nonlinearity deviation spectrum; the initial compensation parameter combination is frequency-domain weighted and adjusted according to the parameter-sensitive frequency bands to generate the target compensation parameter combination.

[0065] Specifically, based on the error compensation mapping set, multi-objective global optimization is used to simultaneously consider minimizing multiple indicators such as measurement error, temperature drift, and hysteresis, seeking the optimal or near-optimal solution across the entire parameter space. For example, multiple performance objectives are determined, including minimizing angle measurement error, minimizing dynamic response hysteresis, and minimizing temperature drift error. Based on this, an objective function is constructed, such as F = a*M + b*Z + c*T, where F is the fitness function, a, b, and c are the weights of angle measurement error, response hysteresis error, and temperature drift error, respectively, typically set according to the application scenario, such as 0.4, 0.3, and 0.3, M is for minimizing angle measurement error, Z is for minimizing dynamic response hysteresis, and T is for minimizing temperature drift error.

[0066] Initialize the particle swarm, with each particle representing a combination of compensation parameters. Calculate the fitness value of each particle according to the objective function, and update the individual optimal position of each particle and the global optimal position of the entire swarm. Update the velocity of each particle based on its individual optimality, global optimality, and current velocity, and adjust the position of each particle (i.e., the compensation parameter combination) according to the updated velocity. Perform non-dominated sorting on the particles, and select the non-dominated solution (Pareto optimal solution). Update the global optimal position based on the non-dominated solution. Repeat the above steps until convergence conditions are met, such as reaching the maximum number of iterations or no significant improvement in the optimal fitness for 20 consecutive generations, at which point the process terminates. Select the solution with the smallest overall error from the final Pareto optimal solution as the initial compensation parameter combination, which is currently optimal or near optimal in terms of overall performance.

[0067] A complex dynamic operating condition signal is applied in the simulation environment, such as a temperature gradient change from -20°C to +80°C, an acceleration change in angular velocity from 10 rpm to 300 rpm, and superimposed random vibration interference of 5 Hz to 50 Hz. The angle sensor simulation is run, and the initial compensation parameter combination is applied to collect the response output, forming the first output dataset. The first output dataset is the angle sensor output response data collected after executing the initial compensation parameter combination and applying the complex dynamic operating condition.

[0068] The dynamic nonlinearity deviation spectrum is a spectrum obtained by frequency domain analysis of the nonlinear error of the output data as a function of frequency. It reflects the magnitude and variation of the sensor's dynamic nonlinearity error at different frequencies. A Fast Fourier Transform (FFT) is performed on the original output signal of the first output dataset to obtain its frequency domain representation. Simultaneously, a FFT is also performed on the theoretical (ideal) output signal. The deviation spectrum between the two frequency domain representations is calculated as the deviation intensity and standardized to obtain the dynamic nonlinearity deviation spectrum. Peak values ​​and abruptly increasing frequency points are then identified within the dynamic nonlinearity deviation spectrum.

[0069] A sensitivity threshold is set to identify the frequency range that significantly impacts sensor performance, i.e., the parameter-sensitive frequency band. Frequency intervals that continuously or isolatedly exceed the threshold are defined as parameter-sensitive frequency bands. The parameter-sensitive frequency band refers to the frequency range where the compensation parameters have the most significant impact on output error. Within the parameter-sensitive frequency band, the compensation parameters are strengthened (weighted) to prioritize correcting this part of the error. The parameter set after frequency domain weighting is the final target compensation parameter combination, which has better dynamic response performance and a smoother and more stable overall error.

[0070] By optimizing multiple objectives and taking into account various error sources, the final parameter combination is more balanced in all aspects. Through composite dynamic working condition simulation verification, it is ensured that the compensation effect is not only applicable to static environments. Through deviation spectrum analysis and frequency domain weighting, the suppression of errors in sensitive frequency bands is particularly strengthened, avoiding the problem of local frequency band instability ignored by traditional methods. This is used for actual compensation of sensors to improve their performance.

[0071] S300: Embed the target compensation parameter combination into the circuit topology diagram for closed-loop verification and generate a simulation verification report.

[0072] Furthermore, this application S300 includes:

[0073] The target compensation parameter combination is embedded into the nonlinear compensation module of the circuit topology diagram to generate a parameterized simulation unit; a dynamic angle excitation signal and a multi-physics interference signal are applied to the parameterized simulation unit, and the second output dataset of the angle sensor before and after compensation is collected; closed-loop verification analysis is performed based on the second output dataset to generate a preliminary verification index set; the tolerance of the circuit components of the angle sensor is retrieved for testing to construct a parameter sensitivity matrix; the preliminary verification index set and the parameter sensitivity matrix are fused to construct the simulation verification report.

[0074] Furthermore, this application also includes the following steps:

[0075] Based on the parameter sensitivity matrix, the tolerance sensitivity sequence of key components is extracted through analysis. The preliminary verification index set and the parameter sensitivity matrix are then mapped in a multidimensional manner according to the key component tolerance sensitivity sequence to determine the mapping weight space. The angle sensor is then subjected to dynamic and static analysis using the preliminary verification index set and the parameter sensitivity matrix according to the mapping weight space to obtain dynamic test data and static analysis results. The dynamic test data and the static analysis results are then interactively integrated to construct the simulation verification report.

[0076] Specifically, in circuit simulation, the target compensation parameter combination is embedded into the nonlinear compensation module of the circuit topology diagram. The original circuit topology diagram is imported into the circuit simulation software, the parameter configuration interface of the nonlinear compensation module is located, the target compensation parameter combination is encoded into an XML format configuration file, and written in batches to the register mapping table of the compensation module through a script interface. The multiphysics coupling simulation engine is activated, and the mechanical vibration model and temperature field distribution data are loaded synchronously to generate a parameterized simulation unit that includes parasitic parameter effects. The nonlinear compensation module is a circuit or logic unit in the circuit structure specifically used to correct nonlinear errors (such as temperature drift, mechanical hysteresis, etc.), and usually contains programmable registers or parameter interfaces.

[0077] Dynamic angle excitation signals and multi-physics interference signals simulating actual working conditions are applied to the parametric simulation unit, and the second output dataset of the sensor is acquired. The dynamic angle excitation signal is a time-varying angle input applied to the angle sensor, such as an angle that changes periodically with time or follows a set trajectory, used to test the sensor's response under dynamic conditions. The multi-physics interference signal simulates various physical field interferences that the sensor may encounter in actual operation, such as temperature changes (e.g., heating and cooling cycles), mechanical vibrations (e.g., random vibrations, impact vibrations), electromagnetic interference, and other external environmental changes. The output data of the angle sensor before and after compensation are acquired to form the second output dataset.

[0078] Closed-loop verification analysis is performed based on the second output dataset to evaluate the compensation effect and generate a preliminary verification index set including metrics such as accuracy, stability, and response time. This preliminary verification index set is a set of quantitative performance index results obtained through closed-loop verification analysis, used to judge the merits of the target compensation parameter combination. The tolerances of the circuit components of the angle sensor are extracted, including key components in the signal conditioning module, analog-to-digital conversion module, and nonlinear compensation module. Circuit component tolerances are the allowable deviation range of the actual values ​​of various components in the circuit (such as resistors, capacitors, inductors, amplifiers, etc.) from their nominal values, usually expressed as a percentage, reflecting the impact of component manufacturing errors and environmental changes on performance.

[0079] The Monte Carlo analysis method is employed to randomly perturb the parameters of each component within a set tolerance range, conducting multiple simulations to test the tolerance of the circuit components. A simulation model is built based on the existing circuit topology in the circuit simulation environment, and the input excitation signal and measurement node definitions are prepared. The Monte Carlo analysis task is set, defining the random offset range of component parameters in each simulation sample, so that the component values ​​vary randomly within their tolerance range according to a set statistical distribution (usually a normal or uniform distribution). Taking a resistance of 10kΩ ±1% as an example, the resistance value in each sample is randomly sampled between 9.9kΩ and 10.1kΩ. Monte Carlo simulations are typically performed with at least 1000 simulation samples to ensure the confidence level of the statistical analysis. After each simulation run, key output data from the angle sensor are collected, such as output signal amplitude, phase change, nonlinearity index, and dynamic response error. During the simulation, consistency between the dynamic excitation signal and multi-physics interference conditions (such as temperature gradient and electromagnetic interference) should be maintained to more closely approximate actual operating conditions. After multiple Monte Carlo simulations, a database containing all sample output metrics is obtained. Statistical analysis is performed based on the simulation data to calculate the sensitivity of each component parameter change to the output metric, i.e., the ratio of the standard deviation of the output performance change to the standard deviation of the component parameter change. The sensitivity results are then normalized, unifying different metrics into the same order of magnitude in a dimensionless form, resulting in a parameter sensitivity matrix. Rows represent performance metrics, columns represent circuit components, and matrix elements represent the corresponding sensitivity values.

[0080] The parameter sensitivity matrix is ​​analyzed to extract the sensitivity sequence of key component tolerances, i.e., a list of circuit components arranged from highest to lowest sensitivity value. Based on this key component tolerance sensitivity sequence, a multi-dimensional mapping is performed between the preliminary verification index set and the parameter sensitivity matrix. A unified weight relationship is established between different performance indices and key component tolerances, thus forming a mapped weight space. This multi-dimensional mapping is not merely a simple correspondence; it also requires comprehensive consideration of factors such as sensitivity magnitude and performance index priority (e.g., static accuracy priority or dynamic response priority), ultimately constructing a comprehensive weight distribution system that simultaneously reflects multiple performance requirements. The purpose of this mapping is to unify different indices and sensitivity data into the same weight space.

[0081] Based on the mapped weight space, the preliminary verification index set and the parameter sensitivity matrix are applied together to the angle sensor for dynamic and static analysis. In static analysis, steady-state performance such as zero-position error, linearity, and hysteresis are mainly evaluated; in dynamic testing, the focus is on indicators such as response speed, amplitude-frequency characteristics, phase delay, and dynamic nonlinearity changes. Dynamic test data is generally collected by exciting angle change signals and superimposing composite physical disturbances (such as vibration and temperature drift), while static analysis relies more on steady-state excitation and stable environmental conditions. Based on this, performance datasets corresponding to dynamic and static operating conditions were obtained.

[0082] Dynamic test data and static analysis results are interactively integrated, and both are normalized in a unified weight space to ensure data dimension consistency. Then, they are integrated into a single overall performance evaluation set according to a pre-defined fusion strategy (such as weighted averaging). Based on the integrated data, a detailed simulation verification report is constructed, including dynamic and static performance analysis, the impact of key component tolerances, and compensation effect evaluation.

[0083] The target compensation parameter combinations obtained from the preliminary global multi-objective optimization are embedded into the circuit topology diagram. These parameter combinations are typically imported in a structured data format and batch-written into the corresponding register mapping table using script tools, ensuring that all compensation parameters accurately correspond to circuit nodes. Based on the circuit topology after parameter injection, closed-loop verification testing is performed. In closed-loop verification, a dynamic angle excitation signal is applied to the angle sensor, and multi-physics interference signals (such as vibration, temperature drift, and electromagnetic interference) are superimposed to simulate its response behavior under actual complex operating conditions. Compared to open-loop testing, closed-loop verification more comprehensively reflects the system's true operating characteristics, especially for the comprehensive evaluation of nonlinear dynamic errors, hysteresis effects, and temperature drift. The output data of the angle sensor is collected, and its zero-position error, linearity error, resolution, and dynamic response characteristics are evaluated, quantified according to pre-set accuracy requirements. If individual indicators deviate slightly from the preset accuracy requirements, fine-tuning and optimization can be performed based on feedback. All verification data are compiled and a simulation verification report is generated, which includes the conformity analysis of various performance indicators with preset targets, dynamic and static characteristic curves, sensitivity assessment, failure mode analysis (if any), and the final comprehensive performance score.

[0084] By extracting key components through sensitivity analysis, constructing a unified weight space, and employing multi-dimensional dynamic and static performance mapping and interactive fusion, a deep simulation verification of the comprehensive performance of angle sensors under multiple operating conditions was achieved. This resulted in a simulation verification report containing a comprehensive performance evaluation, enabling a thorough understanding and verification of the sensor's performance. By embedding the target compensation parameters into the circuit topology, performing closed-loop verification, and generating a simulation verification report that meets preset accuracy indicators, the dynamic stability, static accuracy, and environmental adaptability of angle sensors can be effectively improved.

[0085] In summary, the circuit design and simulation method for an angle sensor provided in this application has the following beneficial effects:

[0086] By constructing a circuit topology diagram of an angle sensor, multiphysics coupling simulation is performed on the circuit topology diagram to obtain the output characteristic curve of the angle sensor. Based on the output characteristic curve, nonlinear error analysis is performed to construct an error compensation mapping set. The error compensation mapping set is then iteratively optimized to generate a target compensation parameter combination. The target compensation parameter combination is embedded into the circuit topology diagram for closed-loop verification, generating a simulation verification report. In other words, by constructing a circuit topology diagram, performing multiphysics coupling simulation, constructing an error compensation mapping set, and generating a target compensation parameter combination through parameter iterative optimization, real-time compensation of nonlinear errors is achieved. Embedding the compensation parameters into the circuit topology diagram for closed-loop verification ensures the compensation effect, helps to promptly detect and correct deviations between the simulation model and reality, and improves the accuracy and efficiency of circuit design simulation.

[0087] Example 2: Based on the same inventive concept as the circuit design simulation method for an angle sensor in Example 1, this application also provides a circuit design simulation system for an angle sensor. Please refer to the appendix. Figure 2 The circuit design simulation system for the angle sensor includes:

[0088] The coupled simulation module 11 is used to construct the circuit topology diagram of the angle sensor, perform multi-physics coupled simulation on the circuit topology diagram, and obtain the output characteristic curve of the angle sensor; the error compensation module 12 is used to perform nonlinear error analysis based on the output characteristic curve, construct an error compensation mapping set, perform parameter iterative optimization on the error compensation mapping set, and generate a target compensation parameter combination; the closed-loop verification module 13 is used to embed the target compensation parameter combination into the circuit topology diagram for closed-loop verification and generate a simulation verification report.

[0089] Furthermore, the coupling simulation module 11 in the circuit design simulation system for the angle sensor is also used for:

[0090] The mechanical structure parameters and electrical characteristic parameters of the angle sensor are retrieved, and finite element analysis is performed on the mechanical structure parameters and electrical characteristic parameters to obtain a three-dimensional electromagnetic field finite element model. Based on the three-dimensional electromagnetic field finite element model, the sensitive nodes of the angle sensor are calculated to obtain the magnetic field distribution characteristics of multiple sensitive nodes. An equivalent circuit network topology is constructed according to the magnetic field distribution characteristics, and the frequency domain response characteristics of the equivalent circuit network topology are calibrated to construct the circuit topology diagram of the angle sensor.

[0091] Furthermore, the coupling simulation module 11 in the circuit design simulation system for the angle sensor is also used for:

[0092] Set the boundary conditions for the joint simulation of electromagnetic field and temperature field, and perform circuit simulation according to the boundary conditions and the circuit topology diagram to obtain the electromagnetic field simulation results; map the electromagnetic field simulation results to the circuit nodes in the circuit topology diagram to construct the parasitic parameter coupling matrix; perform signal conditioning based on the parasitic parameter coupling matrix, and perform iterative correction of common mode suppression according to the signal conditioning results to construct the output characteristic curve.

[0093] Furthermore, the error compensation module 12 in the circuit design simulation system for the angle sensor is also used for:

[0094] Nonlinear analysis is performed based on the output characteristic curve to extract a set of nonlinear distortion feature points. Error component decomposition is then performed based on the set of nonlinear distortion feature points to obtain nonlinear error components. Time-frequency domain analysis is performed based on the nonlinear error components, and an error compensation mapping set is constructed based on the time-frequency domain characteristic data. Global optimization is performed on the error compensation mapping set to generate an initial compensation parameter combination. Dynamic operating condition verification is performed based on the initial compensation parameter combination, and the target compensation parameter combination is determined based on the verification results.

[0095] Furthermore, the error compensation module 12 in the circuit design simulation system for the angle sensor is also used for:

[0096] Equal-angle interval sampling points are set for the output characteristic curve. Sampling calculation is performed based on the equal-angle interval sampling points to obtain the slope change rate of adjacent sampling points. When the slope change rate exceeds a preset slope change threshold, the corresponding sampling point is marked as the distortion start point. The distortion end point is marked by forward tracing along the output characteristic curve according to the distortion start point. All sampling points from the distortion start point to the distortion end point are extracted to construct an initial feature point set. Smoothing and denoising are performed based on the initial feature point set to generate the nonlinear distortion feature point set.

[0097] Furthermore, the error compensation module 12 in the circuit design simulation system for the angle sensor is also used for:

[0098] Variational mode decomposition is performed on the set of nonlinear distortion feature points to obtain intrinsic mode function components; preset temperature data is set, and the preset temperature data is correlated with the response frequency of the angle sensor to construct a preset temperature-frequency response curve; correlation analysis is performed on the intrinsic mode function components and the preset temperature-frequency response curve to select temperature drift-related components as temperature drift error; the remaining components are transformed to extract the instantaneous phase and calculate the mechanical hysteresis error; the temperature drift error and the mechanical hysteresis error are used as the nonlinear error components.

[0099] Furthermore, the error compensation module 12 in the circuit design simulation system for the angle sensor is also used for:

[0100] Global multi-objective optimization is performed on the error compensation mapping set to generate an initial compensation parameter combination; a composite dynamic working condition signal is injected to execute the initial compensation parameter group, and a first output dataset from the angle sensor is collected; a dynamic nonlinearity deviation spectrum is calculated based on the first output dataset, and parameter-sensitive frequency bands are identified according to the dynamic nonlinearity deviation spectrum; the initial compensation parameter combination is frequency-domain weighted and adjusted according to the parameter-sensitive frequency bands to generate the target compensation parameter combination.

[0101] Furthermore, the closed-loop verification module 13 in the circuit design simulation system for the angle sensor is also used for:

[0102] The target compensation parameter combination is embedded into the nonlinear compensation module of the circuit topology diagram to generate a parameterized simulation unit; a dynamic angle excitation signal and a multi-physics interference signal are applied to the parameterized simulation unit, and the second output dataset of the angle sensor before and after compensation is collected; closed-loop verification analysis is performed based on the second output dataset to generate a preliminary verification index set; the tolerance of the circuit components of the angle sensor is retrieved for testing to construct a parameter sensitivity matrix; the preliminary verification index set and the parameter sensitivity matrix are fused to construct the simulation verification report.

[0103] Furthermore, the closed-loop verification module 13 in the circuit design simulation system for the angle sensor is also used for:

[0104] Based on the parameter sensitivity matrix, the tolerance sensitivity sequence of key components is extracted through analysis. The preliminary verification index set and the parameter sensitivity matrix are then mapped in a multidimensional manner according to the key component tolerance sensitivity sequence to determine the mapping weight space. The angle sensor is then subjected to dynamic and static analysis using the preliminary verification index set and the parameter sensitivity matrix according to the mapping weight space to obtain dynamic test data and static analysis results. The dynamic test data and the static analysis results are then interactively integrated to construct the simulation verification report.

[0105] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The circuit design simulation method and specific examples of an angle sensor in Embodiment 1 are also applicable to the circuit design simulation system of an angle sensor in this embodiment. Through the foregoing detailed description of the circuit design simulation method of an angle sensor, those skilled in the art can clearly understand the circuit design simulation system of an angle sensor in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0106] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0107] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A circuit design simulation method for an angle sensor, characterized in that, include: Constructing the circuit topology diagram of the angle sensor, performing multiphysics coupling simulation on the circuit topology diagram, and obtaining the output characteristic curve of the angle sensor, including: The mechanical structure parameters and electrical characteristic parameters of the angle sensor are retrieved, and the mechanical structure parameters and electrical characteristic parameters are subjected to finite element analysis to obtain a three-dimensional electromagnetic field finite element model. The magnetic field distribution characteristics of multiple sensitive nodes are obtained by calculating the sensitive nodes of the angle sensor based on the three-dimensional electromagnetic field finite element model. An equivalent circuit network topology is constructed according to the magnetic field distribution characteristics. The frequency domain response characteristics of the equivalent circuit network topology are calibrated, and the circuit topology of the angle sensor is constructed. Set the boundary conditions for the joint simulation of electromagnetic field and temperature field, and perform circuit simulation according to the boundary conditions and the circuit topology diagram to obtain the electromagnetic field simulation results. The electromagnetic field simulation results are mapped to the circuit nodes in the circuit topology diagram to construct the parasitic parameter coupling matrix; Signal conditioning is performed based on the parasitic parameter coupling matrix, and common-mode suppression is iteratively corrected based on the signal conditioning results to construct the output characteristic curve. Based on the output characteristic curve, nonlinear error analysis is performed to construct an error compensation mapping set. The error compensation mapping set is then iteratively optimized to generate a target compensation parameter combination. The target compensation parameters are combined and embedded into the circuit topology diagram for closed-loop verification, and a simulation verification report is generated.

2. The circuit design simulation method for an angle sensor as described in claim 1, characterized in that, Based on the output characteristic curve, nonlinear error analysis is performed to construct an error compensation mapping set. The error compensation mapping set is then iteratively optimized to generate a target compensation parameter combination, including: Nonlinear analysis is performed based on the output characteristic curve to extract a set of nonlinear distortion feature points. Error component decomposition is then performed based on the set of nonlinear distortion feature points to obtain the nonlinear error components. Time-frequency domain analysis is performed based on the nonlinear error components, and an error compensation mapping set is constructed based on the time-frequency domain characteristic data. The error compensation mapping set is globally optimized to generate an initial compensation parameter combination. Dynamic operating condition verification is performed based on the initial compensation parameter combination, and the target compensation parameter combination is determined according to the verification results.

3. The circuit design simulation method for an angle sensor as described in claim 2, characterized in that, Based on the output characteristic curve, nonlinear analysis is performed to extract a set of nonlinear distortion feature points, including: Set equal-angle interval sampling points for the output characteristic curve, and perform sampling calculations based on the equal-angle interval sampling points to obtain the slope change rate of adjacent sampling points; When the slope change rate exceeds the preset slope change threshold, the corresponding sampling point is marked as the distortion start point, and the distortion termination point is marked by forward tracing along the output characteristic curve according to the distortion start point. Extract all sampling points from the distortion start point to the distortion end point to construct an initial feature point set, and perform smoothing and denoising based on the initial feature point set to generate the nonlinear distortion feature point set.

4. The circuit design simulation method for an angle sensor as described in claim 2, characterized in that, Error component decomposition is performed based on the set of nonlinear distortion feature points to obtain nonlinear error components, including: Variational mode decomposition is performed on the set of nonlinear distortion feature points to obtain the intrinsic mode function components; Set preset temperature data, correlate the preset temperature data with the response frequency of the angle sensor, and construct a preset temperature-frequency response curve; Correlation analysis is performed between the intrinsic mode function components and the preset temperature-frequency response curve to screen out temperature drift-related components as temperature drift error. Transform the remaining components to extract the instantaneous phase and calculate the mechanical hysteresis error; The temperature drift error and the mechanical hysteresis error are used as the nonlinear error components.

5. The circuit design simulation method for an angle sensor as described in claim 2, characterized in that, Global optimization is performed on the error compensation mapping set to generate an initial compensation parameter combination. Dynamic operating condition verification is then performed based on the initial compensation parameter combination. The target compensation parameter combination is determined according to the verification results, including: Global multi-objective optimization is performed on the error compensation mapping set to generate an initial compensation parameter combination; Inject the composite dynamic working condition signal to execute the initial compensation parameter set, and collect the first output dataset of the angle sensor; Calculate the dynamic nonlinearity deviation spectrum based on the first output dataset, and identify the parameter sensitive frequency bands based on the dynamic nonlinearity deviation spectrum; The initial compensation parameter combination is frequency-domain weighted and adjusted according to the parameter sensitive frequency band to generate the target compensation parameter combination.

6. The circuit design simulation method for an angle sensor as described in claim 1, characterized in that, The target compensation parameter combination is embedded into the circuit topology diagram for closed-loop verification, generating a simulation verification report, including: The target compensation parameters are combined and embedded into the nonlinear compensation module of the circuit topology diagram to generate a parameterized simulation unit; A dynamic angle excitation signal and a multi-physics interference signal are applied to the parameterized simulation unit, and the second output dataset of the angle sensor before and after compensation is collected by combining the target compensation parameters. Based on the second output dataset, a closed-loop verification analysis is performed to generate a preliminary verification index set. The tolerances of the circuit components of the angle sensor were tested to construct a parameter sensitivity matrix; The preliminary verification index set is fused with the parameter sensitivity matrix to construct the simulation verification report.

7. The circuit design simulation method for an angle sensor as described in claim 6, characterized in that, The simulation verification report is constructed by fusing the preliminary verification index set with the parameter sensitivity matrix, including: Based on the parameter sensitivity matrix, the tolerance sensitivity sequence of key components is extracted through analysis. The preliminary verification index set and the parameter sensitivity matrix are mapped in a multidimensional way according to the key component tolerance sensitivity sequence to determine the mapping weight space. According to the mapping weight space, the preliminary verification index set and the parameter sensitivity matrix are used to perform dynamic and static analysis on the angle sensor to obtain dynamic test data and static analysis results. The dynamic test data and the static analysis results are interactively integrated to construct the simulation verification report.

8. A circuit design simulation system for an angle sensor, characterized in that, The steps for implementing the circuit design simulation method for an angle sensor according to any one of claims 1 to 7, wherein the circuit design simulation system for the angle sensor comprises: The coupling simulation module is used to construct the circuit topology diagram of the angle sensor, perform multi-physics coupling simulation on the circuit topology diagram, and obtain the output characteristic curve of the angle sensor. The error compensation module is used to perform nonlinear error analysis based on the output characteristic curve, construct an error compensation mapping set, perform parameter iterative optimization on the error compensation mapping set, and generate a target compensation parameter combination. The closed-loop verification module is used to embed the target compensation parameter combination into the circuit topology diagram for closed-loop verification and generate a simulation verification report.

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