Circuit design simulation method and system for angle sensor

Abstract

The invention provides a circuit design simulation method and system for an angle sensor, and relates to the technical field of circuit design simulation, and the method comprises the steps: constructing a circuit topological structure diagram of the angle sensor, carrying out the multi-physics coupling simulation, and obtaining an output characteristic curve of the angle sensor; performing nonlinear error analysis, constructing an error compensation mapping set, performing parameter iterative optimization, and generating a target compensation parameter combination; and the target compensation parameter combination is embedded into the circuit topological structure diagram for closed-loop verification, and a simulation verification report is generated. According to the method and the device, the technical problem that deviation exists between a simulation result and reality in a complex dynamic environment due to poor environmental adaptability and compensation lag existing in circuit design simulation is solved, intelligent compensation of nonlinear errors is realized through multi-physics field coupling simulation and segmented compensation, and the precision and the efficiency of circuit design simulation are improved.

Description

Technical Field

[0001] The present application relates to the technical field of circuit design simulation, and in particular to a circuit design simulation method and system for an angle sensor. Background Art

[0002] An angle sensor is a device that can detect changes in the rotation angle of an object and convert it into an electrical signal output. It needs to provide accurate and reliable angle measurement data under different environmental conditions. During the circuit design stage, a virtual circuit model is usually built to predict the electrical performance of the circuit under different conditions to assist in circuit design and optimization. However, existing circuit design simulation methods are mainly based on standard environmental parameters, and often ignore the problem of circuit performance drift caused by environmental changes in the actual working environment, resulting in significant deviations between the simulation results and the actual performance in complex dynamic environments. In addition, in circuit simulation, linear approximation compensation is usually used for detected deviations (such as temperature drift compensation, gain error correction, zero drift compensation, etc.), without fully considering dynamic response characteristics such as response delay and compensation error accumulation. This leads to a lag in the compensation effect, further exacerbating the deviation between the simulation prediction results and the actual performance.

[0003] In summary, the prior art has a technical problem in which the simulation results deviate from the actual results in a complex dynamic environment due to poor environmental adaptability and compensation lag in circuit design simulation. Summary of the Invention

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

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

[0006] In the first aspect, the present application provides a circuit design simulation method for an angle sensor, which is implemented by a circuit design simulation system for an angle sensor, wherein the circuit design simulation method for an angle sensor includes: constructing a circuit topology structure diagram of the angle sensor, performing multi-physical field coupling simulation on the circuit topology structure diagram, and obtaining an output characteristic curve of the angle sensor; performing nonlinear error analysis based on the output characteristic curve, constructing an error compensation mapping set, performing parameter iterative optimization on the error compensation mapping set, and 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.

[0007] Optionally, the mechanical structure parameters and electrical characteristic parameters of the angle sensor are retrieved, and the mechanical structure parameters and the electrical characteristic parameters are subjected to finite element analysis to obtain a three-dimensional electromagnetic field finite element model; the sensitive nodes of the angle sensor are calculated based on the three-dimensional electromagnetic field finite element model 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 structure diagram of the angle sensor.

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

[0009] Optionally, a nonlinear analysis is performed based on the output characteristic curve to extract a set of nonlinear distortion feature points, and error components are decomposed based on the set of nonlinear distortion feature points to obtain nonlinear error components; a 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; a global optimization is performed on the error compensation mapping set to generate an initial compensation parameter combination, a dynamic working 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, equiangularly spaced sampling points are set for the output characteristic curve, and sampling calculation is performed based on the equiangularly spaced 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 a distortion starting point, and forward tracing is performed along the output characteristic curve according to the distortion starting point to mark the distortion ending point; all sampling points from the distortion starting point to the distortion ending 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 modal 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 associated 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 screen out temperature drift-related components as temperature drift errors; the remaining components are transformed to extract the instantaneous phase to calculate the mechanical hysteresis error; the temperature drift error and the mechanical hysteresis error are used as the nonlinear error components.

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

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

[0014] Optionally, analysis is performed based on the parameter sensitivity matrix to extract the tolerance sensitivity sequence of key components; multi-dimensional mapping is performed on the preliminary verification index set and the parameter sensitivity matrix according to the tolerance sensitivity sequence of key components to determine the mapping weight space; dynamic and static analysis of the angle sensor is performed on 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 interactively integrated to construct the simulation verification report.

[0015] In the second aspect, the present application also provides a circuit design simulation system for an angle sensor, which is 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, which is used to construct a circuit topology structure diagram of the angle sensor, perform multi-physical field coupling simulation on the circuit topology structure diagram, and obtain the output characteristic curve of the angle sensor; an error compensation module, which 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; a closed-loop verification module, which is used to embed the target compensation parameter combination into the circuit topology structure 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: By constructing a circuit topology diagram of the angle sensor, performing a multi-physics coupling simulation on the circuit topology diagram, and obtaining an output characteristic curve of the angle sensor, a nonlinear error analysis is performed based on the output characteristic curve, an error compensation mapping set is constructed, and parameter iterative optimization is performed on the error compensation mapping set to generate a target compensation parameter combination; the target compensation parameter combination is embedded in the circuit topology diagram for closed-loop verification, and a simulation verification report is generated. In other words, by constructing a circuit topology diagram, performing a multi-physics coupling simulation, constructing an error compensation mapping set, and generating a target compensation parameter combination through parameter iterative optimization, real-time compensation for nonlinear errors is achieved, and the compensation parameters are embedded in the circuit topology diagram for closed-loop verification to ensure the compensation effect, which helps to promptly discover and correct deviations between the simulation model and reality, thereby improving the accuracy and efficiency of circuit design simulation.

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the description, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0019] Figure 1 This is a flow chart of a circuit design simulation method for an angle sensor of the present application; Figure 2 This is a structural diagram of a circuit design simulation system for an angle sensor in this application.

[0020] Description of reference numerals: coupling simulation module 11 , error compensation module 12 , closed-loop verification module 13 . DETAILED DESCRIPTION

[0021] This application provides a circuit design simulation method and system for an angle sensor, solving the technical problem in the prior art of deviations between simulation results and actual results in complex dynamic environments due to poor environmental adaptability and compensation lag in circuit design simulation. By constructing a circuit topology diagram and performing multi-physics field coupling simulation, an error compensation mapping set is constructed, and a target compensation parameter combination is generated through parameter iterative optimization to achieve real-time compensation for nonlinear errors. The compensation parameters are embedded in the circuit topology diagram for closed-loop verification to ensure the compensation effect, helping to promptly discover and correct deviations between the simulation model and actual results, thereby improving the accuracy and efficiency of circuit design simulation.

[0022] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0023] For example, see the attached Figure 1 The present application provides a circuit design simulation method for an angle sensor, wherein the circuit design simulation method for the angle sensor is executed by a circuit design simulation system for the angle sensor, and the circuit design simulation method for the angle sensor specifically includes the following steps: S100: Constructing a circuit topology diagram of the angle sensor, performing multi-physics field coupling simulation on the circuit topology diagram, and obtaining an output characteristic curve of the angle sensor.

[0024] Furthermore, the present application S100 includes: The mechanical structure parameters and electrical characteristic parameters of the angle sensor are retrieved, and the mechanical structure parameters and the electrical characteristic parameters are subjected to finite element analysis to obtain a three-dimensional electromagnetic field finite element model; the sensitive nodes of the angle sensor are calculated based on the three-dimensional electromagnetic field finite element model 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.

[0025] Specifically, the mechanical structure parameters and electrical characteristic parameters of the angle sensor are retrieved. Among them, the mechanical structure parameters are physical parameters related to the mechanical structure of the angle sensor, such as the shaft diameter, shell material, coil layout, air gap size, etc., which affect the mechanical properties 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 value, mutual inductance coefficient, parasitic capacitance, etc., which directly determine its electrical response behavior.

[0026] Using finite element analysis tools, the 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, ensuring that the mesh density is sufficient to accurately simulate the electromagnetic field distribution. When setting boundary conditions, a simulation space of appropriate size is usually selected, and the boundary is defined as magnetically insulating or open. The electromagnetic field distribution within the model is calculated using the finite element method, and the electromagnetic field distribution inside and outside the entire angle sensor is obtained, thereby obtaining a three-dimensional electromagnetic field finite element model, including the distribution of magnetic induction intensity, electric field intensity, current density, and other information at each location.

[0027] The magnetic field distribution characteristics of the angle sensor's sensitive nodes—those nodes particularly sensitive to magnetic field changes—are calculated using a three-dimensional electromagnetic finite element model. Sensitive nodes are the locations within the angle sensor most sensitive to magnetic field changes and have the greatest impact on the final signal output. They are typically located in areas with dense magnetic flux, dramatic magnetic field gradient changes, or where the induction coil interacts with rotating components. Several sensitive nodes are selected within the three-dimensional electromagnetic finite element model, and magnetic field distribution data is extracted for each of them, including magnetic induction intensity values at different rotational angles. Changes in the magnetic field's direction and amplitude are also recorded to determine the magnetic field distribution characteristics of these multiple sensitive nodes.

[0028] Based on the magnetic field distribution characteristics, an equivalent circuit network topology is constructed, converting the sensor's physical properties into circuit components (such as inductors, capacitors, resistors, and mutual inductance) to simulate the actual electromagnetic behavior within the angle sensor. The equivalent circuit network topology is then calibrated for frequency domain response characteristics. This involves testing and calibrating the circuit's response at different frequencies to confirm that the network's dynamic behavior in the frequency domain is consistent with the actual sensor performance. This is accomplished by applying excitation signals of varying frequencies and measuring the circuit's amplitude-frequency and phase-frequency characteristics to complete the frequency domain (frequency space) calibration of the equivalent circuit. Based on the calibration results, a complete circuit topology is constructed, including the signal conditioning module, analog-to-digital conversion module, and nonlinear compensation module. The signal conditioning module performs processing such as filtering, amplification, and offset adjustment on the sensor's raw signal to improve signal quality. The analog-to-digital conversion module (ADC) converts the analog signal into a digital signal for subsequent digital processing. The nonlinear compensation module corrects for nonlinear errors in the angle sensor output, improving overall accuracy.

[0029] 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 nodes, a circuit topology diagram that can truly reflect the dynamic behavior of the angle sensor is derived.

[0030] Furthermore, the present application further comprises the following steps: Setting electromagnetic field-temperature field joint simulation boundary conditions, performing circuit simulation according to the electromagnetic field-temperature field joint simulation boundary conditions in combination with the circuit topology diagram, and obtaining electromagnetic field simulation results; mapping the electromagnetic field simulation results to circuit nodes in the circuit topology diagram, and constructing a parasitic parameter coupling matrix; performing signal conditioning based on the parasitic parameter coupling matrix, performing iterative correction of common mode suppression according to the signal conditioning results, and constructing the output characteristic curve.

[0031] Specifically, simulation conditions are set for the simultaneous effects of electromagnetic and temperature fields. Specifically, the electromagnetic load (e.g., operating current, excitation frequency, and rotation angle changes) of the sensor in operation, as well as the ambient temperature load (e.g., ambient temperature changes from 25°C to 80°C, or local temperature rise due to heating caused by induced current), are considered to allow the simulation to simultaneously account for 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 feedback and affect electromagnetic properties (e.g., changes in magnetic permeability and increased 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 joint simulation is run to obtain the distribution results of the electromagnetic and temperature fields, i.e., the electromagnetic field simulation results.

[0032] The electromagnetic field simulation results (such as the 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 each node in the circuit (usually indexed by node number and filled with the corresponding parasitic changes). The parasitic parameter coupling matrix is a matrix-like mathematical model that describes the changes in non-ideal parasitic effects (parasitic inductance, parasitic capacitance, parasitic resistance, etc.) between each node (or component) in the circuit due to changes in the electromagnetic field or temperature field, and their mutual influence. It includes the mutual coupling parameters formed between each node due to parasitic inductance, parasitic capacitance, and parasitic resistance, representing the inevitable small interference paths in the circuit. After the simulation is completed, the local electromagnetic quantity changes at each node within the sensor can be obtained, such as the magnetic induction intensity at node 1 changing from 0.6T to 0.58T, or the temperature at node 2 increasing from 25°C to 75°C.

[0033] In the circuit topology diagram, define the correspondence between each node and its physical location in the simulation model. 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 variations, calculate the parasitic parameter changes of the components corresponding to each node. These include changes in coil inductance due to changes in magnetic permeability caused by temperature and magnetic field variations; increases in material resistivity with temperature; and changes in capacitor spacing due to thermal expansion of certain structural components. The parasitic parameter changes between all node pairs (such as N1 and N2) are organized into a matrix, with each element representing the change in parasitic inductance, capacitance, or resistance between a particular pair of nodes.

[0034] Because parasitic effects can cause signal amplitude variations, phase shifts, and spurious common-mode interference, signal conditioning is required to compensate for these deviations. Based on the parasitic parameter coupling matrix, the circuit output signal is conditioned, such as amplification and filtering, to improve signal quality. In a simulation environment, the parasitic matrix is applied to the angle sensor's circuit topology to simulate the output response under real-world operating conditions. By inserting conditioning modules such as active filters and differential amplifiers, the gain and filter frequency are dynamically adjusted to eliminate signal distortion caused by parasitic parameters. Signal conditioning refers to the process of making the raw sensor signal more stable and easier to process through a series of circuit processing steps (such as filtering, amplification, and compensation).

[0035] After conditioning is complete, the common-mode signal is calculated (this can be achieved by extracting the differential and common-mode signal components). If common-mode suppression is insufficient, iterative corrections are 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 the set threshold. Iterative corrections are not a one-time calculation, but rather a process of continuously approaching the ideal output characteristic through multiple adjustments, such as gradually reducing the compensation amount until the error meets the set standard. The output characteristic curve depicts the relationship between the sensor's output signal and its physical input quantity (such as angle) and is an important indicator for evaluating sensor performance, such as linearity, sensitivity, and hysteresis.

[0036] For example, assume the design parameters of an angle sensor are as follows: mechanical structural parameters include a sensor diameter of 10 mm, a thickness of 5 mm, and a material of iron-nickel alloy; electrical characteristic parameters include coil resistance of 100 Ω, inductance of 10 mH, and magnetic permeability of 100 H / m. Simulation conditions are set as follows: operating voltage of 5 V, ambient temperature of 25°C, and temperature range of -40°C to 125°C. Electromagnetic field simulation results show a magnetic field strength of 0.5 T in the center of the sensor; temperature field simulation results show a maximum operating temperature of 75°C. In circuit simulation, the parasitic parameter coupling matrix shows a parasitic capacitance of 10 pF and a parasitic inductance of 1 μH between nodes. After signal conditioning, the common-mode rejection ratio is improved from 60 dB to 100 dB.

[0037] The electromagnetic field-temperature field joint simulation ensures the accuracy of sensor performance prediction in different environments, and the iterative correction of signal conditioning and common-mode suppression significantly improves the stability and accuracy of the sensor output signal.

[0038] S200: performing nonlinear error analysis based on the output characteristic curve, constructing an error compensation mapping set, performing parameter iterative optimization on the error compensation mapping set, and generating a target compensation parameter combination.

[0039] Furthermore, the present application S200 includes: A nonlinear analysis is performed based on the output characteristic curve to extract a set of nonlinear distortion feature points, and error components are decomposed based on the set of nonlinear distortion feature points to obtain nonlinear error components; a 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; a global optimization is performed on the error compensation mapping set to generate an initial compensation parameter combination, a dynamic working condition verification is performed based on the initial compensation parameter combination, and the target compensation parameter combination is determined based on the verification result.

[0040] Specifically, a nonlinear analysis is performed on the output characteristic curve to identify the nonlinear distortion region on the curve. All sampling points within the distortion region from the starting point to the end point 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 frequency and time characteristics. Temperature drift error and mechanical hysteresis error are identified to obtain nonlinear error components. The specific process is explained in detail in the corresponding sub-steps and will not be described here for the sake of brevity.

[0041] For the nonlinear error components, time domain and frequency domain characteristic analysis is performed separately, such as the law of signal amplitude change over time, or the energy distribution on different frequency components. The signal characteristics are observed simultaneously in the time domain and frequency domain to reveal the frequency components of the signal at different time points. Analyze in which time periods and frequency segments 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 to match each error component with its effective compensation strategy. 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. 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.

[0042] An improved particle swarm optimization algorithm (such as a multi-objective particle swarm optimization algorithm) 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. These initially optimized compensation parameters are embedded in the angle sensor simulation model and verified under a dynamically changing environment to observe whether the compensated output maintains error stability and reduces hysteresis under various operating conditions. Dynamic operating condition verification simulates the sensor's operating state under actual varying operating conditions (such as temperature, load changes, and vibration) to verify the compensation effect.

[0043] If error drift is observed under certain extreme operating conditions (such as during rapid temperature rise), the compensation parameters can be further fine-tuned or locally optimized. Ultimately, the target compensation parameter combination, fully validated under dynamic operating conditions, is obtained for deployment. Through nonlinear and time-frequency domain analysis, error components are accurately identified and decomposed, and an error compensation mapping set is constructed to achieve effective error compensation. Global optimization and dynamic operating condition validation ensure the optimality of the compensation parameters, improving the sensor's measurement accuracy and stability.

[0044] Furthermore, the present application further comprises the following steps: The output characteristic curve is set with equally spaced sampling points, and sampling calculation is performed based on the equally spaced 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 a distortion starting point, and forward tracing is performed along the output characteristic curve according to the distortion starting point to mark the distortion ending point; all sampling points from the distortion starting point to the distortion ending 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.

[0045] Specifically, the output characteristic curve is divided into several equal parts, and sampling points are selected at equal angular intervals. In other words, the angular input range is sampled equally, with a sampling point taken at equal angular intervals. 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 the equally spaced sampling points. For each sampling point, the slope between it and the previous sampling point is calculated. The rate of change of the slope, that is, the degree of change in the slope between adjacent sampling points, is further calculated to detect sudden or irregular changes in the curve. The slope change rate refers to the degree of change in the slope of the output characteristic curve between two adjacent sampling points. The slope change rate is obtained by calculating the slope difference between adjacent sampling points.

[0046] If the slope change rate exceeds the preset slope change threshold, the current sampling point is marked as the distortion start point. Starting from the distortion start point, the output characteristic curve is tracked backward until the slope change rate returns to the steady-state range, that is, the slope change rate no longer exceeds the preset threshold. At this point, the current sampling point is marked as the distortion end point. Forward tracking refers to tracking forward along the curve from the distortion start point until the slope change returns to the normal range, that is, returns to the steady-state range. During the tracking process, when the slope change returns to a stable state, this point is the distortion end point. The distortion end point is the point marked after the output characteristic curve returns to the normal linear region, indicating the end of the distortion phenomenon.

[0047] All sampling points between the distortion start point and the distortion end point are extracted to form an initial feature point set, which represents the portion of the output curve where nonlinear distortion occurs. The initial feature point set is smoothed and denoised, and the sampling points are subjected to signal processing to remove high-frequency noise from the data, thereby making the data smoother. 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 the moving average method, for each sampling point, the average value of the points in its neighborhood is taken as the smoothed value to obtain the denoised data, which constitutes a nonlinear distortion feature point set, representing the nonlinear region in the output characteristic curve and accurately reflecting the distortion phenomenon.

[0048] By analyzing the slope change rate and marking the distortion starting and ending points, the nonlinear distortion in the sensor output characteristics can be identified, and effective error compensation can be performed to improve the measurement accuracy and reliability of the sensor.

[0049] Furthermore, the present application further comprises the following steps: Variational modal 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 associated 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 screen out temperature drift-related components as temperature drift errors; 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.

[0050] Specifically, variational mode decomposition is performed on the set of nonlinear distortion feature points to decompose them into multiple intrinsic mode function components with different center frequencies. Each intrinsic mode function reflects the different frequency components of the signal. Variational mode decomposition is a signal processing method used to decompose the signal into a series of intrinsic mode function components with different frequency characteristics, reflecting the changes in different frequency components in the signal. The intrinsic mode function is a signal component obtained after variational mode decomposition, representing the different frequency components in the signal. Each intrinsic mode function usually contains a center frequency and reflects the nature of the signal in the time-frequency domain of the signal.

[0051] By setting a set of preset temperature data and observing the angle sensor's frequency response at different temperatures, you can construct a temperature-frequency response curve. For example, you might find that as the temperature increases, the angle sensor's frequency response changes, manifesting as a frequency drift. In many sensors, temperature drift can cause changes in frequency response, so constructing a temperature-frequency response curve can help analyze the impact of temperature on sensor performance.

[0052] Correlation analysis is performed on the eigenmode function components with the preset temperature-frequency response curve to identify frequency components that are closely related to temperature changes. Correlation analysis is performed on the frequency change of each eigenmode function component with the temperature-frequency response curve by calculating the correlation coefficient between the frequency response of the eigenmode function component and the temperature change. For example, for eigenmode function component 1, whose center frequency is 50Hz, its frequency change at different temperatures is examined to determine whether it is consistent with the change in the temperature-frequency response curve. If the frequency response change is consistent with the trend of increasing temperature, then eigenmode function component 1 is determined to be related to temperature drift.

[0053] Through correlation analysis, the intrinsic mode function components that are highly correlated with temperature changes, i.e., the temperature drift-related components, are selected as the temperature drift error. For the remaining components unrelated to temperature drift, the instantaneous phase is further extracted. This is the phase information of the signal at each instant, typically 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 signal into an analytical signal, thereby obtaining the signal envelope and instantaneous phase. The extracted instantaneous phase values are mapped to the angle input values and plotted as a phase-angle curve. Ideally, the relationship between phase and angle should be monotonic, but due to mechanical hysteresis, the actual curve forms a closed loop. The width (horizontal distance) of this loop is the hysteresis bandwidth, which represents the phase difference between the rising and falling edges at the same angle. The instantaneous phase differences corresponding to the same angle on the hysteresis loop are selected, and the horizontal width of the loop is calculated. 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 with angle changes, that is, the size of the mechanical hysteresis. Generally, the wider the hysteresis loop, the greater the mechanical hysteresis error.

[0054] Mechanical hysteresis error is a lag in the sensor's angle-output characteristic curve caused by non-ideal effects such as elastic deformation of mechanical components, friction, and magnetic hysteresis. This means that the rising and falling edges of the sensor's output differ under the same angle change path, resulting in a phase offset. Temperature drift error and mechanical hysteresis error are considered nonlinear error components and, together with other errors in the original output characteristic curve, form the complete nonlinear error component. Variational mode decomposition and correlation analysis effectively identify the effects of temperature changes and mechanical hysteresis on angle sensors, avoiding the error neglect often seen in traditional methods. This allows for more precise error compensation and improves sensor performance.

[0055] Furthermore, the present application further comprises the following steps: A global multi-objective optimization is performed on the error compensation mapping set to generate an initial compensation parameter combination; a composite dynamic operating condition signal is injected to execute the initial compensation parameter group, and a first output data set of the angle sensor is collected; a dynamic nonlinearity deviation spectrum is calculated based on the first output data set, and a parameter-sensitive frequency band is identified according to the dynamic nonlinearity deviation spectrum; and a frequency-domain weighted adjustment is performed on the initial compensation parameter combination according to the parameter-sensitive frequency band to generate the target compensation parameter combination.

[0056] Specifically, based on the error compensation mapping set, through multi-objective global optimization, while considering minimizing multiple indicators such as measurement error, temperature drift, and hysteresis, the optimal or near-optimal solution is found in 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, and are usually set according to the application scenario, such as 0.4, 0.3, and 0.3, M is minimizing angle measurement error, Z is minimizing dynamic response hysteresis, and T is minimizing temperature drift error.

[0057] Initialize the particle swarm, with each particle representing a compensation parameter combination. Calculate each particle's fitness based on the objective function, and update both the individual optimal position of each particle and the global optimal position of the entire swarm. Update each particle's velocity based on the individual optimal, global optimal, and current velocity, and adjust each particle's position based on the updated velocity (i.e., the compensation parameter combination). Perform a non-dominated sort on the particles, identify the non-dominated solution (Pareto optimal solution), and update the global optimal position based on the non-dominated solution. Repeat these steps until convergence is achieved, such as reaching the maximum number of iterations or no significant improvement in optimal fitness after 20 consecutive generations. From the final Pareto optimal solution, select the solution with the smallest overall error as the initial compensation parameter combination. This solution is the current optimal or near-optimal combination in terms of overall performance.

[0058] In the simulation environment, we applied complex dynamic operating conditions, such as a temperature gradient from -20°C to +80°C, an accelerated angular velocity from 10 rpm to 300 rpm, and superimposed random vibration disturbances ranging from 5 Hz to 50 Hz. We then ran the angle sensor simulation, applied the initial compensation parameter combination, and collected the response output to form the first output data set. This first output data set contains the angle sensor output response data collected after applying the initial compensation parameter combination and the complex dynamic operating conditions.

[0059] The dynamic nonlinearity deviation spectrum is a spectrum obtained by performing frequency domain analysis of the nonlinear error of the output data as it varies with frequency. It reflects the magnitude and variation of the sensor's dynamic nonlinear error at different frequencies. A fast Fourier transform is performed on the raw output signal of the first output data set to obtain a frequency domain representation. Simultaneously, a fast Fourier transform is performed on the theoretical (ideal) output signal. Based on the two frequency domain representations, the deviation spectrum is calculated as the deviation intensity, which is then normalized to obtain the dynamic nonlinearity deviation spectrum. Within the dynamic nonlinearity deviation spectrum, peaks and frequency points with abnormally high spikes are identified.

[0060] A sensitivity threshold is set to identify the frequency range that has a significant impact on sensor performance, known as the parameter-sensitive frequency band. Frequency intervals that continuously or isolatedly exceed the threshold are considered parameter-sensitive frequency bands. Parameter-sensitive frequency bands are frequency ranges where the compensation parameters have the most significant impact on output error. Within these frequency bands, compensation parameters are adjusted more strongly (weighted) to prioritize correcting the error contribution. This frequency-domain weighted parameter set becomes the final target compensation parameter combination, resulting in better dynamic response performance and smoother, more stable overall error.

[0061] Through multi-objective optimization, while taking into account multiple 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 traditional methods ignoring local frequency band instability, and being used for actual compensation of sensors to improve their performance.

[0062] S300: Embed the target compensation parameter combination into the circuit topology diagram for closed-loop verification, and generate a simulation verification report.

[0063] Furthermore, the present application S300 includes: The target compensation parameter combination is embedded in the nonlinear compensation module of the circuit topology diagram to generate a parameterized simulation unit; a dynamic angle excitation signal and a multi-physical field interference signal are applied to the parameterized simulation unit to collect a second output data set of the angle sensor before and after compensation of the target compensation parameter combination; a closed-loop verification analysis is performed based on the second output data set to generate a preliminary verification index set; the circuit element tolerance of the angle sensor is retrieved for testing to construct a parameter sensitivity matrix; the preliminary verification index set is integrated with the parameter sensitivity matrix to construct the simulation verification report.

[0064] Furthermore, the present application further comprises the following steps: Based on the parameter sensitivity matrix, analysis is performed to extract the tolerance sensitivity sequence of key components; according to the tolerance sensitivity sequence of key components, the preliminary verification index set and the parameter sensitivity matrix are multi-dimensionally mapped 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.

[0065] 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, and the target compensation parameter combination is encoded into an XML-formatted configuration file. The register mapping table of the compensation module is written in batches through the script interface, the multi-physics 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 that is specifically used to correct nonlinear errors (such as temperature drift, mechanical hysteresis, etc.) and usually contains programmable registers or parameter interfaces.

[0066] A dynamic angle excitation signal and a multi-physics interference signal simulating actual operating conditions are applied to the parametric simulation unit, and a second output data set of the sensor is collected. The dynamic angle excitation signal applies a time-varying angle input to the angle sensor, such as a periodic angle change or a set trajectory, to test the sensor's response under dynamic operating conditions. The multi-physics interference signal simulates various physical field interferences that the sensor may encounter in actual operation, such as temperature changes (such as heating and cooling cycles), mechanical vibrations (such as random vibration and impact vibration), electromagnetic interference, and other external environmental changes. The output data of the angle sensor before and after compensation are collected to form the second output data set.

[0067] A closed-loop verification analysis is performed on the second output data set to evaluate the compensation effectiveness and generate a preliminary verification metric set, including indicators such as accuracy, stability, and response time. This preliminary verification metric set is a set of quantitative performance indicators derived from the closed-loop verification analysis and is used to determine the performance of the target compensation parameter combination. The tolerances of the angle sensor's circuit components are extracted, including key components in the signal conditioning module, analog-to-digital conversion module, and nonlinear compensation module. Circuit component tolerance is the allowable deviation of the actual value of various components (such as resistors, capacitors, inductors, and amplifiers) from their nominal value. It is typically expressed as a percentage and reflects the impact of component manufacturing errors and environmental changes on performance.

[0068] Using the Monte Carlo analysis method, circuit components are tested for tolerance by randomly perturbing their parameters within a set tolerance range and performing multiple simulations. A simulation model is built within a circuit simulation environment based on the existing circuit topology, and input stimulus signals and measurement node definitions are prepared. The Monte Carlo analysis task is set up, defining the random offset range for component parameters within each simulation sample, ensuring that component values vary randomly within their tolerance range according to a set statistical distribution (typically a normal or uniform distribution). For a 10kΩ ± 1% resistor, for each simulation sample, the resistor value is randomly sampled between 9.9kΩ and 10.1kΩ. Monte Carlo simulations are typically run with at least 1000 simulation samples to ensure confidence in the statistical analysis. After each simulation run, key output data from the angle sensor is collected, such as output signal amplitude, phase shift, nonlinearity, and dynamic response error. During the simulation, the dynamic stimulus signal and multi-physics interference conditions (such as temperature gradients and electromagnetic interference) should be kept consistent to more closely resemble actual operating conditions. After multiple Monte Carlo simulations, a database containing all sample output metrics is generated. Statistical analysis is performed based on the simulation data to calculate the sensitivity of each component parameter change to the output metric—the ratio of the standard deviation of the output performance change to the standard deviation of the component parameter change. Each sensitivity result is normalized, unifying the different metrics to the same magnitude using a dimensionless form. This yields a parameter sensitivity matrix, where rows represent each performance metric, columns represent each circuit component, and the matrix element values represent the corresponding sensitivity values.

[0069] The parameter sensitivity matrix is parsed to extract the sensitivity sequence of key component tolerances—a list of circuit components arranged from high to low sensitivity. Based on this sensitivity sequence of key component tolerances, a multidimensional mapping is performed between the preliminary verification indicator set and the parameter sensitivity matrix. A unified weight relationship is established between different performance indicators and key component tolerances, thus forming a mapping weight space. This multidimensional mapping is more than just a simple correspondence; it also comprehensively considers factors such as sensitivity and performance indicator priority (e.g., static accuracy versus dynamic response). Ultimately, a comprehensive weight distribution system is constructed that simultaneously reflects multiple performance requirements. The goal of this mapping is to unify different indicators and sensitivity data into the same weight space.

[0070] Based on the mapping weight space, a preliminary validation set of metrics and a parameter sensitivity matrix were applied to the angle sensor for both static and dynamic analysis. Static analysis primarily evaluated steady-state performance, such as zero-position error, linearity, and hysteresis. Dynamic testing focused on metrics such as response speed, amplitude-frequency characteristics, phase delay, and dynamic nonlinearity. Dynamic test data is typically collected by stimulating an angle-varying signal and superimposing complex physical disturbances (such as vibration and temperature drift). Static analysis, on the other hand, relies more on steady-state excitation and stable environmental conditions. Based on this, performance data sets corresponding to both dynamic and static operating conditions were generated.

[0071] Dynamic test data and static analysis results are interactively integrated and normalized in a unified weight space to ensure consistent data dimensions. They are then combined into a comprehensive performance evaluation set using a pre-defined fusion strategy (such as weighted averaging). Based on this integrated data, a detailed simulation verification report is constructed, including dynamic and static performance analysis, the impact of key component tolerances, and compensation effectiveness evaluation.

[0072] The target compensation parameter combination obtained from the previous global multi-objective optimization search is embedded into the circuit topology diagram. This target compensation parameter combination is typically imported in a structured data format and written to the corresponding register mapping table in batches using a scripting tool to ensure that all compensation parameters are accurately mapped to circuit nodes. Closed-loop verification testing is performed based on the circuit topology after parameter injection. During closed-loop verification, dynamic angle excitation signals are 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 complex, real-world operating conditions. Compared to open-loop testing, closed-loop verification can more comprehensively reflect the actual operating characteristics of the system and is particularly important for comprehensively evaluating the effects of nonlinear dynamic errors, hysteresis, and temperature drift. Output data from the angle sensor is collected, focusing on metrics such as zero error, linearity error, resolution, and dynamic response characteristics, and quantitatively evaluated according to pre-set accuracy requirements. If individual metrics deviate slightly from the preset accuracy requirements, fine-tuning and optimization can be performed based on the feedback. All verification data is collated and a simulation verification report is generated, including 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.

[0073] By extracting key components based on sensitivity analysis, uniformly constructing a weight space, and integrating multi-dimensional dynamic and static performance mapping and interactive fusion, this approach enables in-depth simulation verification of the angle sensor's comprehensive performance under multiple operating conditions. Ultimately, a simulation verification report containing a comprehensive performance evaluation is constructed, achieving a deep understanding and verification of sensor performance. By embedding the target compensation parameter combination 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 the angle sensor can be effectively improved.

[0074] In summary, the circuit design simulation method for an angle sensor provided in this application has the following beneficial effects: By constructing a circuit topology diagram of the angle sensor, performing a multi-physics coupling simulation on the circuit topology diagram, and obtaining an output characteristic curve of the angle sensor, a nonlinear error analysis is performed based on the output characteristic curve, an error compensation mapping set is constructed, and parameter iterative optimization is performed on the error compensation mapping set to generate a target compensation parameter combination; the target compensation parameter combination is embedded in the circuit topology diagram for closed-loop verification, and a simulation verification report is generated. In other words, by constructing a circuit topology diagram, performing a multi-physics coupling simulation, constructing an error compensation mapping set, and generating a target compensation parameter combination through parameter iterative optimization, real-time compensation for nonlinear errors is achieved, and the compensation parameters are embedded in the circuit topology diagram for closed-loop verification to ensure the compensation effect, which helps to promptly discover and correct deviations between the simulation model and reality, thereby improving the accuracy and efficiency of circuit design simulation.

[0075] In the second embodiment, based on the same inventive concept as the circuit design simulation method of an angle sensor in the first embodiment, the present application also provides a circuit design simulation system for an angle sensor, see the attached Figure 2 , the circuit design simulation system of the angle sensor includes: The coupling simulation module 11 is used to construct a circuit topology diagram of the angle sensor, perform multi-physical field coupling 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.

[0076] Furthermore, the coupling simulation module 11 in the circuit design simulation system for the angle sensor is further configured to: The mechanical structure parameters and electrical characteristic parameters of the angle sensor are retrieved, and the mechanical structure parameters and the electrical characteristic parameters are subjected to finite element analysis to obtain a three-dimensional electromagnetic field finite element model; the sensitive nodes of the angle sensor are calculated based on the three-dimensional electromagnetic field finite element model 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.

[0077] Furthermore, the coupling simulation module 11 in the circuit design simulation system for the angle sensor is further configured to: Setting electromagnetic field-temperature field joint simulation boundary conditions, performing circuit simulation according to the electromagnetic field-temperature field joint simulation boundary conditions in combination with the circuit topology diagram, and obtaining electromagnetic field simulation results; mapping the electromagnetic field simulation results to circuit nodes in the circuit topology diagram, and constructing a parasitic parameter coupling matrix; performing signal conditioning based on the parasitic parameter coupling matrix, performing iterative correction of common mode suppression according to the signal conditioning results, and constructing the output characteristic curve.

[0078] Furthermore, the error compensation module 12 in the circuit design simulation system for the angle sensor is further configured to: A nonlinear analysis is performed based on the output characteristic curve to extract a set of nonlinear distortion feature points, and error components are decomposed based on the set of nonlinear distortion feature points to obtain nonlinear error components; a 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; a global optimization is performed on the error compensation mapping set to generate an initial compensation parameter combination, a dynamic working condition verification is performed based on the initial compensation parameter combination, and the target compensation parameter combination is determined based on the verification result.

[0079] Furthermore, the error compensation module 12 in the circuit design simulation system for the angle sensor is further configured to: The output characteristic curve is set with equally spaced sampling points, and sampling calculation is performed based on the equally spaced 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 a distortion starting point, and forward tracing is performed along the output characteristic curve according to the distortion starting point to mark the distortion ending point; all sampling points from the distortion starting point to the distortion ending 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.

[0080] Furthermore, the error compensation module 12 in the circuit design simulation system for the angle sensor is further configured to: Variational modal 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 associated 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 screen out temperature drift-related components as temperature drift errors; 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.

[0081] Furthermore, the error compensation module 12 in the circuit design simulation system for the angle sensor is further configured to: A global multi-objective optimization is performed on the error compensation mapping set to generate an initial compensation parameter combination; a composite dynamic operating condition signal is injected to execute the initial compensation parameter group, and a first output data set of the angle sensor is collected; a dynamic nonlinearity deviation spectrum is calculated based on the first output data set, and a parameter-sensitive frequency band is identified according to the dynamic nonlinearity deviation spectrum; and a frequency-domain weighted adjustment is performed on the initial compensation parameter combination according to the parameter-sensitive frequency band to generate the target compensation parameter combination.

[0082] Furthermore, the closed-loop verification module 13 in the circuit design simulation system for the angle sensor is further configured to: The target compensation parameter combination is embedded in the nonlinear compensation module of the circuit topology diagram to generate a parameterized simulation unit; a dynamic angle excitation signal and a multi-physical field interference signal are applied to the parameterized simulation unit to collect a second output data set of the angle sensor before and after compensation of the target compensation parameter combination; a closed-loop verification analysis is performed based on the second output data set to generate a preliminary verification index set; the circuit element tolerance of the angle sensor is retrieved for testing to construct a parameter sensitivity matrix; the preliminary verification index set is integrated with the parameter sensitivity matrix to construct the simulation verification report.

[0083] Furthermore, the closed-loop verification module 13 in the circuit design simulation system for the angle sensor is further configured to: Based on the parameter sensitivity matrix, analysis is performed to extract the tolerance sensitivity sequence of key components; according to the tolerance sensitivity sequence of key components, the preliminary verification index set and the parameter sensitivity matrix are multi-dimensionally mapped 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.

[0084] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The circuit design simulation method and specific examples of an angle sensor in Example 1 are also applicable to a circuit design simulation system of an angle sensor in this embodiment. Through the above 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, so for the sake of brevity of the specification, it will not be described in detail here.

[0085] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0086] Obviously, for those skilled in the art, several improvements and modifications can be made to the present application without departing from the principles of the present application, and these improvements and modifications also fall within the scope of protection of the present application.

Claims

1. A circuit design simulation method for an angle sensor, characterized in that: include: Constructing a circuit topology diagram of the angle sensor, performing multi-physics field coupling simulation on the circuit topology diagram, and obtaining an output characteristic curve of the angle sensor; Performing nonlinear error analysis based on the output characteristic curve, constructing an error compensation mapping set, and performing parameter iterative optimization on the error compensation mapping set to generate a target compensation parameter combination; The target compensation parameter combination is embedded in the circuit topology diagram for closed-loop verification, and a simulation verification report is generated.

2. The circuit design simulation method of an angle sensor according to claim 1, wherein: Constructing a circuit topology diagram of the angle sensor, performing multi-physics field coupling simulation on the circuit topology diagram, and obtaining an output characteristic curve of the angle sensor, including: Retrieving mechanical structure parameters and electrical characteristic parameters of the angle sensor, performing finite element analysis on the mechanical structure parameters and the electrical characteristic parameters to obtain a three-dimensional electromagnetic field finite element model; Calculating the sensitive nodes of the angle sensor based on the three-dimensional electromagnetic field finite element model to obtain magnetic field distribution characteristics of the 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 structure diagram of the angle sensor.

3. The circuit design simulation method of an angle sensor according to claim 1, wherein: Performing a multi-physics field coupling simulation on the circuit topology diagram to obtain an output characteristic curve of the angle sensor includes: Setting electromagnetic field-temperature field joint simulation boundary conditions, performing circuit simulation according to the electromagnetic field-temperature field joint simulation boundary conditions in combination with the circuit topology diagram, and obtaining electromagnetic field simulation results; Mapping the electromagnetic field simulation results to circuit nodes in the circuit topology diagram to construct a parasitic parameter coupling matrix; Signal conditioning is performed based on the parasitic parameter coupling matrix, and iterative correction of common mode suppression is performed according to the signal conditioning result to construct the output characteristic curve.

4. The circuit design simulation method for an angle sensor according to claim 1, wherein: Performing nonlinear error analysis based on the output characteristic curve, constructing an error compensation mapping set, and iteratively optimizing parameters of the error compensation mapping set to generate a target compensation parameter combination, including: Performing nonlinear analysis based on the output characteristic curve to extract a set of nonlinear distortion feature points, and performing error component decomposition based on the set of nonlinear distortion feature points to obtain nonlinear error components; Performing time-frequency domain analysis based on the nonlinear error component and constructing an error compensation mapping set according to the time-frequency domain characteristic data; A global optimization is performed on the error compensation mapping set to generate an initial compensation parameter combination, a dynamic working condition verification is performed based on the initial compensation parameter combination, and the target compensation parameter combination is determined according to the verification result.

5. The circuit design simulation method for an angle sensor according to claim 4, wherein: Performing nonlinear analysis based on the output characteristic curve to extract a set of nonlinear distortion feature points includes: Setting sampling points at equal angle intervals for the output characteristic curve, performing sampling calculation based on the sampling points at equal angle intervals, and obtaining slope change rates of adjacent sampling points; When the slope change rate exceeds a preset slope change threshold, marking the corresponding sampling point as a distortion starting point, performing forward tracing along the output characteristic curve according to the distortion starting point, and marking a distortion ending point; All sampling points from the distortion starting point to the distortion ending 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.

6. The circuit design simulation method for an angle sensor according to claim 4, wherein: Decomposing error components according to the nonlinear distortion feature point set to obtain nonlinear error components includes: Performing variational mode decomposition on the nonlinear distortion feature point set to obtain intrinsic mode function components; Setting preset temperature data, correlating the preset temperature data with a response frequency of the angle sensor, and constructing a preset temperature-frequency response curve; Performing a correlation analysis on the intrinsic mode function components and the preset temperature-frequency response curve, and screening out temperature drift-related components as temperature drift errors; 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.

7. The circuit design simulation method for an angle sensor according to claim 4, wherein: Performing global optimization on the error compensation mapping set to generate an initial compensation parameter combination, performing dynamic working condition verification based on the initial compensation parameter combination, and determining the target compensation parameter combination according to the verification result, including: Performing global multi-objective optimization on the error compensation mapping set to generate an initial compensation parameter combination; injecting a composite dynamic operating condition signal to execute the initial compensation parameter group and collecting a first output data set of the angle sensor; calculating a dynamic nonlinearity deviation spectrum based on the first output data set, and identifying a parameter-sensitive frequency band according to the dynamic nonlinearity deviation spectrum; The initial compensation parameter combination is subjected to frequency domain weighted adjustment according to the parameter-sensitive frequency band to generate the target compensation parameter combination.

8. The circuit design simulation method for an angle sensor according to claim 1, wherein: The target compensation parameter combination is embedded in the circuit topology diagram for closed-loop verification, and a simulation verification report is generated, including: Embedding the target compensation parameter combination into the nonlinear compensation module of the circuit topology diagram to generate a parameterized simulation unit; Applying a dynamic angle excitation signal and a multi-physics field interference signal to the parametric simulation unit, and collecting a second output data set of the angle sensor before and after compensation of the target compensation parameter combination; Performing closed-loop validation analysis based on the second output data set to generate a preliminary validation indicator set; The circuit component tolerances of the angle sensor are retrieved for testing and a parameter sensitivity matrix is constructed; The preliminary verification indicator set is integrated with the parameter sensitivity matrix to construct the simulation verification report.

9. The circuit design simulation method for an angle sensor according to claim 8, wherein: The preliminary verification indicator set is integrated with the parameter sensitivity matrix to construct the simulation verification report, including: Analyze based on the parameter sensitivity matrix to extract the key component tolerance sensitivity sequence; Performing multi-dimensional mapping between the preliminary verification indicator set and the parameter sensitivity matrix according to the key component tolerance sensitivity sequence to determine a mapping weight space; Performing dynamic and static analysis on the angle sensor using the preliminary verification indicator 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 interactively integrated to construct the simulation verification report.

10. A circuit design simulation system for an angle sensor, characterized in that: The method for implementing a circuit design simulation method for an angle sensor according to any one of claims 1 to 9 is configured such that the circuit design simulation system for the angle sensor comprises: A coupling simulation module is used to construct a circuit topology diagram of the angle sensor, perform multi-physics field coupling simulation on the circuit topology diagram, and obtain an output characteristic curve of the angle sensor; an error compensation module, configured 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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