Axial flux permanent magnet motor energy consumption testing device

Abstract

The invention relates to the technical field of motor testing, in particular to an axial flux permanent magnet motor energy consumption testing device which comprises a signal separation module, a decoupling component module, an eddy current loss module, a model construction module, a trend change module and a comprehensive report generation module. Decoupling analysis is carried out on the multiple paths of electric signals to obtain a phase current harmonic component, and a copper loss fluctuation component and a mechanical loss fluctuation component of the target motor in the operation process are separated; detecting a current response change caused by the high-frequency disturbance voltage signal, and analyzing a real-time eddy current loss component of the iron core; constructing a loss component mapping relation model; driving the test loop to simulate a load cycle including a power generation feedback state, and monitoring a collaborative change trend between loss components; evaluating operation efficiency and energy recovery characteristics to obtain a comprehensive energy consumption evaluation report; according to the invention, the efficiency of the axial flux permanent magnet motor energy consumption test can be improved.

Description

Technical Field

[0001] This invention relates to the field of motor testing technology, and in particular to an energy consumption testing device for axial flux permanent magnet motors. Background Technology

[0002] In the field of energy consumption testing for axial flux permanent magnet motors, existing technologies struggle to achieve high-precision synchronous acquisition of multiple electrical and vibration signals under various operating conditions. The lack of a timing drift correction mechanism results in insufficient timing consistency in signal acquisition. Furthermore, the decoupling analysis of phase current harmonic components and vibration signal energy at specific frequency bands is insufficient, making it impossible to accurately separate copper loss fluctuation components from mechanical loss fluctuation components. This leads to low accuracy in identifying loss components during energy consumption testing, impacting the accuracy of subsequent energy consumption assessments.

[0003] Current energy consumption testing technologies lack the ability to simulate load cycles under power generation feedback conditions, making it difficult to construct a model of loss component mapping relationships under all operating conditions and effectively monitor the coordinated changing trends between loss components. Furthermore, the lack of scientific quantitative basis for assessing motor operating efficiency and energy recovery characteristics leads to low efficiency in generating comprehensive energy consumption assessment reports, failing to provide comprehensive and reliable technical support for motor performance optimization. Therefore, improving the energy consumption testing efficiency of axial flux permanent magnet motors has become an urgent problem to be solved. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides an axial flux permanent magnet motor energy consumption testing device, characterized in that the device includes a signal separation module, a decoupling component module, an eddy current loss module, a model construction module, a trend change module, and a comprehensive report generation module, wherein:

[0005] The signal separation module is used to connect the target motor to the test circuit, so that the target motor runs under a preset multi-condition command sequence, and simultaneously collects multiple electrical signals and vibration signals of the target motor.

[0006] The decoupling component module is used to perform decoupling analysis on the multi-channel electrical signals to obtain the phase current harmonic components of the target motor, and, in combination with the specific frequency band energy in the vibration signal, separate the copper loss fluctuation components and mechanical loss fluctuation components of the target motor during operation.

[0007] The eddy current loss module is used to inject a controllable high-frequency disturbance voltage signal synchronized with the back EMF waveform of the target motor into the test circuit, and detect the change in current response caused by the high-frequency disturbance voltage signal to analyze the real-time eddy current loss component of the iron core in the target motor.

[0008] The model building module is used to perform multi-mode fitting on the copper loss fluctuation component, the mechanical loss fluctuation component, the real-time eddy current loss component, and the total input power of the target motor to build a loss component mapping relationship model of the target motor under all operating conditions.

[0009] The trend change module is used to drive the test loop to simulate a load cycle including power generation feedback state based on the loss component mapping relationship model, and in the process, monitor the coordinated change trend between loss components in the loss component mapping relationship model.

[0010] The comprehensive report generation module is used to evaluate the operating efficiency and energy recovery characteristics of the target motor based on the coordinated change trend, and obtain a comprehensive performance evaluation report of the target motor.

[0011] In a preferred embodiment, when the signal separation module connects the target motor to the test circuit, causing the target motor to operate under a preset multi-condition command sequence, and simultaneously acquires multiple electrical and vibration signals of the target motor, it is specifically used for:

[0012] A preset multi-condition command sequence is sent to the controller of the target motor, and a synchronous trigger signal for the target motor is generated according to the step change node of the preset multi-condition command sequence.

[0013] The synchronization trigger signal is split and transmitted to the acquisition channel of the target motor, and the acquisition channel is started synchronously using the synchronization trigger signal to continuously sample the target motor.

[0014] During continuous sampling, the key electrical signals in the acquisition channel are used as reference signals, and the high-precision time-scale sequence of the target motor is determined based on the zero-crossing events of the reference signals.

[0015] The timing drift of the target motor is obtained by comparing the high-precision time-stamped sequence with the expected timing of the synchronous trigger signal in real time.

[0016] Based on the time drift, the phase of the synchronization trigger signal in subsequent sampling periods is adjusted to dynamically correct the relative time synchronization relationship between different dimensional channels in the acquisition channel.

[0017] Based on the relative timing synchronization relationship, the target motor is synchronously acquired to obtain multiple electrical signals and vibration signals of the target motor.

[0018] In a preferred embodiment, when the decoupling component module performs decoupling analysis on the multi-channel electrical signals to obtain the phase current harmonic components of the target motor, and combines this with the specific frequency band energy in the vibration signal to separate the copper loss fluctuation components and mechanical loss fluctuation components of the target motor during operation, it is specifically used for:

[0019] Based on the torque command phase change command in the preset multi-condition command sequence, the phase current signal in the multi-channel electrical signal is decomposed in the time domain to obtain the first current component of the multi-channel electrical signal.

[0020] The first component of the current is removed from the phase current signal to obtain the phase current harmonic component of the phase current signal.

[0021] Based on the current electrical frequency of the target motor, a frequency selection network for the target motor is constructed, and the vibration signal is input into the frequency selection network to obtain the specific frequency band energy of the vibration signal;

[0022] The amplitude envelope of the phase current harmonic components is analyzed by similarity matching with the amplitude envelope of the specific frequency band energy.

[0023] When the change in the envelope amplitude of the phase current harmonic component dominates the change in the envelope amplitude of the energy in the specific frequency band, the energy fluctuation of the phase current harmonic component is classified as the copper loss fluctuation component of the target motor.

[0024] When the change in the envelope amplitude of the specific frequency band energy is independent of the change in the envelope amplitude of the phase current harmonic component, and is associated with the speed command step in the preset multi-condition command sequence, the energy fluctuation of the specific frequency band energy is classified as the mechanical loss fluctuation component of the target motor.

[0025] In a preferred embodiment, when the decoupling component module performs the following operations: constructing a frequency selection network for the target motor based on its current electrical frequency, and inputting the vibration signal into the frequency selection network to obtain the specific frequency band energy of the vibration signal, it is specifically used for:

[0026] Monitor the real-time electrical frequency of the target motor, and determine the fundamental frequency for the mechanical rotation synchronization of the rotor in the target motor based on the real-time electrical frequency;

[0027] Using the fundamental frequency as a reference, determine the passband frequency range for capturing mechanical state characteristics in the target motor;

[0028] Based on the passband frequency range, the frequency response characteristics of the frequency selection network are configured so that the frequency selection network attenuates vibration signal components outside the passband frequency range, thereby obtaining the initial attenuation signal of the target motor.

[0029] By filtering out the high-frequency components directly related to electromagnetic noise and the low-frequency drift components unrelated to the basic rotation in the rotor mechanical rotation synchronization of the preliminary attenuation signal, a specific frequency band energy signal of the target motor is obtained.

[0030] In a preferred embodiment, when the eddy current loss module performs the operation of injecting a controllable high-frequency disturbance voltage signal synchronized with the back EMF waveform of the target motor into the test circuit, and detecting the current response change caused by the high-frequency disturbance voltage signal to analyze the real-time eddy current loss component of the iron core in the target motor, it is specifically used for:

[0031] Obtain the reference back EMF waveform of the target motor when no high-frequency disturbance voltage signal is injected;

[0032] Based on the zero-crossing point and slope characteristics of the reference back EMF waveform, the high-frequency carrier signal of the target motor is determined;

[0033] The phase of the high-frequency carrier signal is dynamically phase-locked with the instantaneous value of a selected phase in the reference back EMF waveform to obtain the phase reference of the high-frequency disturbance voltage signal.

[0034] The phase reference and the preset disturbance amplitude are combined to form a high-frequency disturbance voltage signal for the target motor, and the high-frequency disturbance voltage signal is injected into the test circuit.

[0035] While injecting the high-frequency disturbance voltage signal, the phase current waveform flowing through the target motor is acquired, and the current response component with the same frequency as the high-frequency disturbance voltage signal in the phase current waveform is separated.

[0036] Based on the ratio of the amplitude between the current response component of the same frequency and the high-frequency disturbance voltage signal, and by performing differential analysis with the reference impedance of the winding parameters in the target motor, the differential result is used as the real-time eddy current loss component of the target motor.

[0037] In a preferred embodiment, when the model building module performs multi-mode fitting of the copper loss fluctuation component, the mechanical loss fluctuation component, the real-time eddy current loss component, and the total input power of the target motor to construct a loss component mapping model of the target motor under all operating conditions, it is specifically used for:

[0038] The copper loss fluctuation component, the mechanical loss fluctuation component, the real-time eddy current loss component, and the total input power of the target motor are standardized to obtain the copper loss standardization factor, mechanical loss standardization factor, eddy current loss standardization factor, and total power standardization factor of the target motor.

[0039] The copper loss normalization factor, the mechanical loss normalization factor, the eddy current loss normalization factor, and the total power normalization factor are linearly weighted and coupled to obtain a linear mapping relationship model of the target motor.

[0040] The linear mapping model is used as the loss component mapping model of the target motor under all operating conditions.

[0041] In a preferred embodiment, the calculation formula for the linear mapping relationship model is as follows:

[0042] ;

[0043] In the formula, This is the linear mapping relationship model. The weights of the copper loss normalization factor are... The weights of the mechanical loss normalization factor are... The weights of the eddy current loss normalization factor are... The weights of the total power normalization factor are... The copper loss normalization factor is... The mechanical loss standardization factor is... The eddy current loss normalization factor is... The total power normalization factor is... This is the coefficient of nonlinear synergistic effect.

[0044] In a preferred embodiment, when the trend change module executes the test loop simulation of a load cycle including power generation feedback based on the loss component mapping relationship model, and monitors the coordinated change trend between loss components in the loss component mapping relationship model during this process, it is specifically used for:

[0045] During the electric state phase of the load cycle, the real-time numerical change direction between the loss components in the loss component mapping relationship model is continuously recorded.

[0046] Based on the direction of the real-time numerical change, when the test circuit is driven into the power generation feedback state, the direction of the change of the copper loss fluctuation component is identified.

[0047] In the power generation feedback state, the changing directions of the mechanical loss fluctuation component and the real-time eddy current loss component are compared, and a trend comparison analysis is performed with the changing direction of the copper loss fluctuation component to obtain the trend comparison result of the test circuit.

[0048] Based on the trend comparison results, the collaborative state identifier of the dynamic correlation in the loss components is determined.

[0049] In a preferred embodiment, when the trend change module executes the process of obtaining the collaborative state identifier of the dynamic correlation in the loss components based on the trend comparison results, it is specifically used for:

[0050] In the power generation feedback state, it is determined whether the direction of change of the mechanical loss fluctuation component is consistent with the direction of change of the copper loss fluctuation component;

[0051] The direction of change of the real-time eddy current loss component is determined to be consistent with the direction of change of the mechanical loss fluctuation component.

[0052] If the direction of change of the mechanical loss fluctuation component is opposite to the direction of change of the copper loss fluctuation component, and the change of the real-time eddy current loss component exhibits a lag characteristic to the change of the mechanical loss fluctuation component, then the cooperative state identifier is defined as the first type of cooperative mode of the loss component mapping relationship model.

[0053] If the direction of change of the mechanical loss fluctuation component is opposite to the direction of change of the copper loss fluctuation component, but the change of the real-time eddy current loss component shows an advance characteristic to the change of the mechanical loss fluctuation component, then the cooperative state identifier is defined as the second type of cooperative mode of the loss component mapping relationship model.

[0054] In the load cycle, the collaborative change trend of the loss component mapping relationship model is obtained based on the switching sequence between the first type of collaborative mode and the second type of collaborative mode.

[0055] In a preferred embodiment, when the comprehensive report generation module evaluates the operating efficiency and energy recovery characteristics of the target motor based on the coordinated change trend to obtain a comprehensive performance evaluation report of the target motor, it is specifically used for:

[0056] Identify the target state identifier corresponding to the power generation feedback state in the coordinated change trend;

[0057] Based on the target state identifier, the change profile between the mechanical loss fluctuation component and the copper loss fluctuation component is extracted from the load cycle;

[0058] The change profile is compared with the reference profile of the electric state stage to determine the mechanical loss attenuation characteristics and copper loss fluctuation stability of the target motor.

[0059] When mechanical losses decrease and copper loss fluctuations stabilize, a positive evaluation result for efficient energy recovery in the target motor is obtained.

[0060] When mechanical losses decrease and copper losses fluctuate drastically, a negative evaluation result is obtained indicating that coupling losses exist in the target motor.

[0061] By integrating the positive evaluation results, the negative evaluation results, and the loss component mapping model, a comprehensive performance evaluation report of the target motor is obtained.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] 1. This invention achieves high-precision synchronous acquisition of multiple electrical and vibration signals under various operating conditions through the dynamic timing correction mechanism of the signal separation module. Combined with the in-depth analysis of phase current harmonic components and vibration signal energy at specific frequency bands by the decoupling component module, it can accurately separate copper loss fluctuation components and mechanical loss fluctuation components. Simultaneously, the eddy current loss module efficiently analyzes the real-time eddy current loss components of the iron core by injecting a high-frequency disturbance voltage signal synchronized with the back EMF waveform, significantly improving the identification accuracy and data reliability of various loss components in energy consumption testing.

[0064] 2. This invention utilizes multimodal fitting to construct a loss component mapping model under all operating conditions. Combined with load cycle simulation including power generation feedback, it can comprehensively monitor the coordinated change trends among loss components. The comprehensive report generation module uses this trend to quantitatively evaluate the motor's operating efficiency and energy recovery characteristics. This not only improves the efficiency of generating comprehensive energy consumption assessment reports but also provides comprehensive and accurate technical basis for motor performance optimization, further expanding the applicable scenarios and application value of the energy consumption testing device. Attached Figure Description

[0065] Figure 1 This is a device architecture diagram of an axial flux permanent magnet motor energy consumption testing device provided in an embodiment of the present invention;

[0066] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments belong to some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “said” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0069] Depending on the context, the word "if" or "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0070] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0071] In practice, the server-side equipment deployed by the axial flux permanent magnet motor energy consumption testing device may consist of one or more devices. The aforementioned axial flux permanent magnet motor energy consumption testing device can be implemented as a business instance, a virtual machine, or a hardware device. For example, the axial flux permanent magnet motor energy consumption testing device can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, the axial flux permanent magnet motor energy consumption testing device can be understood as software deployed on a cloud node, used to provide axial flux permanent magnet motor energy consumption testing to various user terminals. Alternatively, the axial flux permanent magnet motor energy consumption testing device can also be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing various user terminals. Alternatively, the axial flux permanent magnet motor energy consumption testing device can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide axial flux permanent magnet motor energy consumption testing to various user terminals.

[0072] In terms of implementation, the axial flux permanent magnet motor energy consumption testing device and the user terminal are mutually compatible. That is, if the axial flux permanent magnet motor energy consumption testing device is implemented as an application installed on a cloud service platform, then the user terminal is implemented as a client that establishes a communication connection with the application; or if the axial flux permanent magnet motor energy consumption testing device is implemented as a website, then the user terminal is implemented as a webpage; or if the axial flux permanent magnet motor energy consumption testing device is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0073] like Figure 1 The diagram shown is a device architecture diagram of an axial flux permanent magnet motor energy consumption testing device provided in an embodiment of the present invention.

[0074] The axial flux permanent magnet motor energy consumption testing device 100 of this invention can be installed in a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed in the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed into a website. Depending on the functions implemented, the axial flux permanent magnet motor energy consumption testing device 100 may include a signal separation module 101, a decoupling component module 102, an eddy current loss module 103, a model building module 104, a trend change module 105, and a comprehensive report generation module 106. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can perform a fixed function, stored in the memory of the electronic device.

[0075] In this embodiment of the invention, each of the above-mentioned modules in the axial flux permanent magnet motor energy consumption testing device can be implemented independently and called upon other modules. This "calling" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the axial flux permanent magnet motor energy consumption testing device provided by this embodiment of the invention, the applicable scope of the axial flux permanent magnet motor energy consumption testing device architecture can be adjusted by adding modules and directly calling them without modifying the program code, achieving cluster-based horizontal expansion to quickly and flexibly expand the axial flux permanent magnet motor energy consumption testing device. In practical applications, the above-mentioned modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0076] The following describes the components and workflow of the axial flux permanent magnet motor energy consumption testing device, using specific embodiments as examples:

[0077] The signal separation module 101 is used to connect the target motor to the test circuit, so that the target motor runs under a preset multi-condition command sequence, and simultaneously collects multiple electrical signals and vibration signals of the target motor.

[0078] In this embodiment of the invention, when the signal separation module connects the target motor to the test circuit, causing the target motor to operate under a preset multi-condition command sequence, and simultaneously acquires multiple electrical and vibration signals of the target motor, it is specifically used for:

[0079] A preset multi-condition command sequence is sent to the controller of the target motor, and a synchronous trigger signal for the target motor is generated according to the step change node of the preset multi-condition command sequence.

[0080] The synchronization trigger signal is split and transmitted to the acquisition channel of the target motor, and the acquisition channel is started synchronously using the synchronization trigger signal to continuously sample the target motor.

[0081] During continuous sampling, the key electrical signals in the acquisition channel are used as reference signals, and the high-precision time-scale sequence of the target motor is determined based on the zero-crossing events of the reference signals.

[0082] The timing drift of the target motor is obtained by comparing the high-precision time-stamped sequence with the expected timing of the synchronous trigger signal in real time.

[0083] Based on the time drift, the phase of the synchronization trigger signal in subsequent sampling periods is adjusted to dynamically correct the relative time synchronization relationship between different dimensional channels in the acquisition channel.

[0084] Based on the relative timing synchronization relationship, the target motor is synchronously acquired to obtain multiple electrical signals and vibration signals of the target motor.

[0085] The signal separation module establishes a stable connection with the target motor's controller via a wired communication interface. The preset multi-condition command sequence is a pre-determined set of commands including different operating states such as start, acceleration, constant speed, deceleration, and stop. The signal separation module transmits the commands corresponding to each condition to the controller one by one according to the order of this command set, ensuring no packet loss and no delay during command transmission. While sending commands, the signal separation module monitors the parameter changes of each command in the preset multi-condition command sequence in real time. When it detects that the parameter value of a certain command suddenly changes from one fixed value to another, the moment of this change is the step change node of the preset multi-condition command sequence. At the instant the signal separation module identifies the step change node, it activates the internal trigger signal generation unit to generate a synchronous trigger signal corresponding one-to-one with the step change node. The synchronous trigger signal adopts a square wave form, and its rising edge completely coincides with the moment of the step change node, ensuring that the synchronous trigger signal can accurately mark the condition switching time of the preset multi-condition command sequence.

[0086] The signal separation module contains a signal splitting unit. This unit receives the generated synchronization trigger signal and copies it into multiple identical signals, matching the number of acquisition channels. Each signal corresponds to one acquisition channel. These multiple synchronization trigger signals are transmitted to the trigger terminals of their respective acquisition channels via independent shielded signal transmission lines, preventing signal interference during transmission. Upon receiving the rising edge of the synchronization trigger signal, the trigger terminal of each acquisition channel immediately activates its internal sampling circuit. The sampling circuit switches from standby to operating mode, and the signal receiving element in the sampling circuit is directly connected to the signal output terminal of the target motor. It then begins continuous signal acquisition of the electrical and vibration signals generated during the motor's operation. During acquisition, the operating voltage and sampling rate of the sampling circuit are kept stable to ensure signal consistency in continuous sampling, until the preset sampling period ends or a stop sampling command is received from the signal separation module.

[0087] During the continuous sampling of the target motor by the acquisition channel, the signal separation module selects any one phase current signal from all the acquired electrical signals as the key electrical signal. This key electrical signal is the reference signal, which is transmitted in real time to the time stamp generation unit of the signal separation module via the signal transmission line. The time stamp generation unit is equipped with a high-precision signal comparison circuit, which compares the reference signal with a preset zero-potential signal in real time. When the potential of the reference signal crosses from negative to positive or from positive to negative, it is determined that a zero-crossing event of the reference signal has been detected. Whenever a zero-crossing event is detected, the time stamp generation unit immediately calls the internally integrated high-precision clock module to record the system time at the moment the zero-crossing event occurs. The clock module uses a temperature-compensated crystal oscillator to provide the time reference, with a time accuracy of microseconds. The system times corresponding to each zero-crossing event are arranged in chronological order to form a high-precision time stamp sequence for the target motor.

[0088] The signal separation module internally stores the expected timing sequence of the synchronization trigger signals. This expected timing sequence is a theoretical time sequence of the synchronization trigger signals that should appear, pre-set according to the step change nodes of the preset multi-condition command sequence. It includes the theoretical occurrence time corresponding to each synchronization trigger signal, and this theoretical timing sequence corresponds one-to-one with the condition switching order of the preset multi-condition command sequence. After receiving the high-precision time-stamped sequence and the expected timing sequence of the synchronization trigger signals, the timing comparison unit of the signal separation module matches each time-stamped data in the high-precision time-stamped sequence with the corresponding theoretical time data in the expected timing sequence in chronological order, ensuring that each time-stamped data can find a corresponding theoretical time reference. For each set of successfully matched time-stamped data and theoretical time data, the time difference between the actual time and the theoretical time data corresponding to the time-stamped data is calculated. This time difference is the timing offset at a single moment. All the timing offsets at single moments are arranged in chronological order to form a timing drift that can reflect the timing changes during the operation of the target motor. The positive or negative sign of the timing drift indicates whether the actual timing is ahead or behind the expected timing, and the magnitude of the value directly corresponds to the time length of the offset.

[0089] After receiving the timing drift, the phase adjustment unit of the signal separation module analyzes the trend and magnitude of the timing drift to determine the phase direction and specific adjustment amount of the synchronization trigger signal in subsequent sampling periods. The phase adjustment unit contains a phase offset control circuit, which adjusts the phase by changing the output delay of the synchronization trigger signal. If the timing drift is positive, indicating that the actual timing lags behind the expected timing, the phase offset control circuit advances the rising edge of the phase-shift trigger signal by a time equal to the timing drift value. If the timing drift is negative, indicating that the actual timing leads the expected timing, the phase offset control circuit delays the rising edge of the phase-shift trigger signal by a time equal to the absolute value of the timing drift. Through precise adjustment of the synchronization trigger signal phase, the sampling start time of each acquisition channel in subsequent sampling periods is kept consistent with the expected timing. This dynamically corrects the time difference in sampling start between acquisition channels of different dimensions, eliminates timing deviations caused by differences in hardware response speed, and ensures that the relative timing synchronization relationship between channels meets the preset synchronization standard.

[0090] After dynamically correcting the relative timing synchronization between the acquisition channels, the acquisition control unit of the signal separation module sends a unified sampling control command to all acquisition channels according to the corrected relative timing synchronization. This command includes key information such as the sampling start time and sampling duration. Under the control of the sampling control command, each acquisition channel simultaneously starts sampling. The electrical signal acquisition channel, connected to the circuit interface of the target motor, acquires multiple electrical signals such as three-phase current and three-phase voltage generated during the operation of the target motor. The vibration signal acquisition channel, through vibration sensors installed on key parts such as the target motor housing and bearings, acquires vibration signals generated during the operation of the target motor. The sampling frequency and sampling duration of all acquisition channels are strictly kept consistent to ensure the synchronization of the acquisition process. The acquired multiple electrical and vibration signals are transmitted in real time to the signal storage unit of the signal separation module through independent signal transmission lines. The signal storage unit classifies and stores the received signals, clearly marking them according to signal type and acquisition time to avoid signal confusion. Finally, a complete and synchronized set of multiple electrical and vibration signals of the target motor is formed, providing accurate and reliable raw data for subsequent signal processing.

[0091] The beneficial effects include ensuring the stable transmission of preset multi-condition command sequences to the target motor controller, accurately identifying step change nodes and generating synchronous trigger signals, providing a precise timing reference for subsequent signal acquisition. By splitting signal transmission and synchronously starting acquisition channels, transmission interference and sampling delays are avoided, ensuring signal consistency and integrity during continuous sampling. Using key electrical signals as reference signals, a high-precision time-stamp sequence is generated using zero-crossing events and a high-precision clock module to accurately mark the signal acquisition time. By comparing the high-precision time-stamp sequence with the expected timing of the synchronous trigger signal in real time, the timing drift is accurately obtained, providing a reliable basis for timing correction. The phase of the synchronous trigger signal is dynamically adjusted based on the timing drift, effectively eliminating relative timing deviations between acquisition channels of different dimensions and ensuring the synchronization of acquisition from each channel. Finally, signal acquisition is performed through the corrected relative timing synchronization relationship to obtain complete, synchronous, and accurate multi-channel electrical and vibration signals from the target motor, providing high-quality raw data for subsequent signal separation and analysis, and improving the accuracy and reliability of motor testing.

[0092] The decoupling component module 102 is used to perform decoupling analysis on the multi-channel electrical signals to obtain the phase current harmonic components of the target motor, and to separate the copper loss fluctuation components and mechanical loss fluctuation components of the target motor during operation by combining the specific frequency band energy in the vibration signal.

[0093] In this embodiment of the invention, when the decoupling component module performs decoupling analysis on the multi-channel electrical signals to obtain the phase current harmonic components of the target motor, and combines the specific frequency band energy in the vibration signal to separate the copper loss fluctuation components and mechanical loss fluctuation components of the target motor during operation, it is specifically used for:

[0094] Based on the torque command phase change command in the preset multi-condition command sequence, the phase current signal in the multi-channel electrical signal is decomposed in the time domain to obtain the first current component of the multi-channel electrical signal.

[0095] The first component of the current is removed from the phase current signal to obtain the phase current harmonic component of the phase current signal.

[0096] Based on the current electrical frequency of the target motor, a frequency selection network for the target motor is constructed, and the vibration signal is input into the frequency selection network to obtain the specific frequency band energy of the vibration signal;

[0097] The amplitude envelope of the phase current harmonic components is analyzed by similarity matching with the amplitude envelope of the specific frequency band energy.

[0098] When the change in the envelope amplitude of the phase current harmonic component dominates the change in the envelope amplitude of the energy in the specific frequency band, the energy fluctuation of the phase current harmonic component is classified as the copper loss fluctuation component of the target motor.

[0099] When the change in the envelope amplitude of the specific frequency band energy is independent of the change in the envelope amplitude of the phase current harmonic component, and is associated with the speed command step in the preset multi-condition command sequence, the energy fluctuation of the specific frequency band energy is classified as the mechanical loss fluctuation component of the target motor.

[0100] When the decoupling component module performs the following operations: constructs a frequency selection network for the target motor based on its current electrical frequency, and inputs the vibration signal into the frequency selection network to obtain the specific frequency band energy of the vibration signal, it is specifically used for:

[0101] Monitor the real-time electrical frequency of the target motor, and determine the fundamental frequency for the mechanical rotation synchronization of the rotor in the target motor based on the real-time electrical frequency;

[0102] Using the fundamental frequency as a reference, determine the passband frequency range for capturing mechanical state characteristics in the target motor;

[0103] Based on the passband frequency range, the frequency response characteristics of the frequency selection network are configured so that the frequency selection network attenuates vibration signal components outside the passband frequency range, thereby obtaining the initial attenuation signal of the target motor.

[0104] By filtering out the high-frequency components directly related to electromagnetic noise and the low-frequency drift components unrelated to the basic rotation in the rotor mechanical rotation synchronization of the preliminary attenuation signal, a specific frequency band energy signal of the target motor is obtained.

[0105] The decoupling component module first extracts the torque command phase change command from a preset multi-operational command sequence. This command reflects the synchronous change pattern of the target motor torque output. The decoupling component module acquires the phase current signal from multiple electrical signals through the signal transmission line and expands the phase current signal in time sequence to form a time-domain signal. The decoupling component module internally sets up a time-domain decomposition unit. This unit uses the change trend of the torque command phase change command as a reference, compares the numerical change of the phase current signal with the change trend of the torque command phase change command at each time step, and extracts the part of the phase current signal that is completely consistent with the change trend of the torque command phase change command. This part is the first current component of the multiple electrical signals, ensuring that the first current component can accurately reflect the phase current change characteristics corresponding to the torque command phase change.

[0106] The decoupling module establishes a timing correspondence between the phase current signal and the first current component. It directly subtracts the value of the phase current signal at each moment from the corresponding value of the first current component, maintaining the correspondence at each moment without shifting. This subtraction completely removes the portion of the phase current signal belonging to the first current component, leaving the phase current harmonic component. This component does not contain current components that change in phase with the torque command; it only retains the harmonic components of the phase current, ensuring the purity and accuracy of the phase current harmonic component.

[0107] The decoupling component module communicates in real time with the target motor's controller to obtain the electrical frequency of the target motor's current operating state. This electrical frequency is the alternating frequency of the current in the target motor's stator windings, directly reflecting the motor's operating speed-related characteristics. The decoupling component module internally includes a frequency selection network construction unit. Based on the acquired current electrical frequency, it determines the corresponding harmonic and division ranges, selects inductors, capacitors, and resistors of appropriate specifications, and constructs a frequency selection network through a fixed circuit connection method. This network only allows signals of a specific frequency band related to the current electrical frequency to pass through, attenuating or blocking signals of other frequency bands. The acquired vibration signal is input to this frequency selection network through the signal input port. After being filtered by the inductors and capacitors within the network, signals of non-specific frequency bands are filtered out, and only signals of the specific frequency band are retained and output. The energy carried by this output signal is the specific frequency band energy of the vibration signal.

[0108] The decoupling component module performs full-wave rectification on the phase current harmonic components. The rectified signal is then smoothed by a low-pass filter circuit using an RC filter structure. Resistors limit current changes, and capacitors store charge to achieve signal smoothing. After processing, the maximum amplitude of the phase current harmonic components at each moment is obtained. These maximum amplitudes are arranged in chronological order to form the amplitude envelope of the phase current harmonic components. Using the same full-wave rectification and RC low-pass filtering method, signals corresponding to specific frequency band energy are processed to obtain the maximum amplitude of the specific frequency band energy at each moment. These maximum amplitudes are then arranged in chronological order to form the amplitude envelope of the specific frequency band energy. The decoupling component module compares the numerical trends of the two amplitude envelopes moment-by-moment, observing whether changes in the phase current harmonic component amplitude envelope trigger synchronous changes in the amplitude envelope of the specific frequency band energy. Simultaneously, it determines the correlation between the amplitude changes of the two components, completing a similarity matching analysis.

[0109] During the similarity matching and parsing process, the decoupling component module continuously monitors the timing and magnitude of changes in the envelope amplitude of the phase current harmonic components and the envelope amplitude of the energy in a specific frequency band. When it detects that the envelope amplitude of the phase current harmonic components changes first, and the envelope amplitude of the energy in the specific frequency band follows suit with the same trend, and the magnitude of the change in the envelope amplitude of the energy in the specific frequency band is proportional to the magnitude of the change in the envelope amplitude of the phase current harmonic components—meaning the change in the envelope amplitude of the energy in the specific frequency band is entirely driven by the change in the envelope amplitude of the phase current harmonic components—the decoupling component module clearly classifies the energy fluctuations generated by the phase current harmonic components during their change as copper loss fluctuations of the target motor, ensuring that the classification result perfectly matches the component's essence.

[0110] The decoupling component module continues to monitor the relationship between the changes in the two amplitude envelopes. When it is found that the change in the envelope amplitude of the specific frequency band energy has no temporal or amplitude correlation with the change in the envelope amplitude of the phase current harmonic component, that is, regardless of whether the envelope amplitude of the phase current harmonic component changes, the envelope amplitude of the specific frequency band energy changes independently according to its own law. At the same time, by comparing the time of the change in the envelope amplitude of the specific frequency band energy with the time of the step change in the speed command in the preset multi-condition command sequence, it is confirmed that the two are completely coincident. That is, the change in the envelope amplitude of the specific frequency band energy occurs exactly at the moment when the speed command steps from one fixed value to another fixed value, and the trend of change is consistent with the step direction of the speed command. At this time, the decoupling component module clearly classifies the energy fluctuation generated by the change in the specific frequency band energy as the mechanical loss fluctuation component of the target motor, ensuring that the classification result conforms to the essential characteristics of mechanical loss and speed change.

[0111] The decoupling component module establishes a wired communication connection with the controller of the target motor. This connection uses a stable serial communication protocol to ensure the real-time performance and integrity of data transmission. Through this connection, the decoupling component module continuously receives the target motor's operating status data from the controller. It then filters out real-time electrical frequency data characterizing the alternating frequency of the stator winding current from this data. During reception, the data is verified in real time to eliminate any abnormal data that may have occurred during transmission, ensuring the accuracy of the real-time electrical frequency data. Since the electromagnetic rotation frequency of the target motor is strictly synchronized with the rotor's mechanical rotation frequency, there is a fixed correspondence between the rotor's mechanical rotation frequency and the real-time electrical frequency. Without additional calculations, the monitored real-time electrical frequency is directly determined as the fundamental frequency for the rotor's mechanical rotation synchronization in the target motor, ensuring that the fundamental frequency accurately reflects the synchronization characteristics of the rotor's mechanical rotation.

[0112] Using a defined fundamental frequency as the core reference, and considering the mechanical structural characteristics of the target motor, the frequency signals corresponding to mechanical state characteristics such as rotor imbalance, bearing wear, and shaft bending are all distributed around the fundamental frequency. The passband frequency range must cover all frequency intervals where these mechanical state characteristics may occur. Centered on the fundamental frequency, the passband frequency range extends towards higher frequencies to the highest frequency interval where the mechanical fault characteristics corresponding to the fundamental frequency may occur, and towards lower frequencies to the lowest frequency interval where the basic mechanical operating characteristics corresponding to the fundamental frequency may occur, forming a continuous and complete passband frequency range. This range can comprehensively capture the frequency signals corresponding to various mechanical state characteristics during the operation of the target motor, avoiding the omission of key mechanical characteristic information.

[0113] A frequency selective network is constructed from inductors, capacitors, and resistors in a specific topology. Its frequency response characteristics are determined by the specifications and connection methods of the components. Based on a defined passband frequency range, inductors, capacitors, and resistors matching this range are selected. The inductors must exhibit low impedance characteristics within the passband frequency range, the capacitors must exhibit low capacitive reactance characteristics, and the resistors are used to stabilize the circuit's operating state. The selected components are assembled by soldering them in a series and parallel topology to form a complete frequency selective network. This network exhibits low attenuation characteristics for signals within the passband frequency range, allowing vibration signal components within the passband to pass smoothly. For vibration signal components outside the passband frequency range, the inductive reactance of the inductors and the capacitive reactance of the capacitors increase the signal transmission resistance, significantly reducing the signal amplitude, thereby achieving attenuation of signals outside the passband. The vibration signal processed by this network is the initial attenuation signal for the target motor.

[0114] Two stages of filtering circuits are connected in series at the output of the frequency selection network. The first stage is a high-pass filter circuit, and the second stage is a low-pass filter circuit. The two stages are connected sequentially to form a continuous filtering link. The high-pass filter circuit adopts a structure of series capacitor and parallel resistor. The capacitance value is precisely selected so that low-frequency drift components unrelated to the basic rotation during rotor mechanical rotation synchronization cannot pass through the capacitor. These low-frequency drift components are guided to the ground terminal by the parallel resistor and consumed, thus achieving the filtering out of low-frequency drift components. The low-pass filter circuit adopts a structure of series inductor and parallel capacitor. The inductance value is precisely selected so that high-frequency components directly related to electromagnetic noise cannot pass through the inductor. These high-frequency components are guided to the ground terminal by the parallel capacitor and consumed, thus achieving the filtering out of high-frequency components. The initially attenuated signal passes through the high-pass filter circuit and the low-pass filter circuit in sequence. After two stages of filtering, the high-frequency components and low-frequency drift components are completely filtered out. The remaining signal, containing only the mechanical state characteristics within the passband frequency range, is the specific frequency band energy signal of the target motor.

[0115] The beneficial effects include accurately extracting the first component of the torque-related current and efficiently obtaining the harmonic components of the pure phase current; constructing a network based on the motor's electrical frequency to obtain energy in a specific frequency band, and through envelope matching analysis, clearly distinguishing the fluctuation components of copper loss and mechanical loss, thus providing accurate and reliable component data for motor loss analysis.

[0116] By capturing the motor's electrical frequency in real time and directly determining the fundamental frequency for rotor mechanical rotation synchronization, a precise benchmark is provided for signal screening, ensuring reliable frequency anchoring associated with mechanical characteristics. A clearly defined passband frequency range, referenced to the fundamental frequency, comprehensively covers the frequency intervals corresponding to the motor's mechanical state characteristics, avoiding the omission of critical mechanical information. Frequency selection network characteristics are configured based on the passband range, enabling low-attenuation signal passage within the passband and significant attenuation of signals outside the passband, resulting in targeted preliminary attenuation signals. Furthermore, two-stage filtering precisely removes high-frequency components related to electromagnetic noise and low-frequency drift components unrelated to fundamental rotation, effectively stripping away interference signals. Ultimately, precise acquisition of specific frequency band energy signals containing only mechanical state characteristics is obtained, providing pure and reliable core data support for motor mechanical state analysis.

[0117] The eddy current loss module 103 is used to inject a controllable high-frequency disturbance voltage signal synchronized with the back EMF waveform of the target motor into the test circuit, and detect the change in current response caused by the high-frequency disturbance voltage signal to analyze the real-time eddy current loss component of the iron core in the target motor.

[0118] In this embodiment of the invention, when the eddy current loss module performs the operation of injecting a controllable high-frequency disturbance voltage signal synchronized with the back EMF waveform of the target motor into the test circuit, and detecting the current response change caused by the high-frequency disturbance voltage signal to analyze the real-time eddy current loss component of the iron core in the target motor, it is specifically used for:

[0119] Obtain the reference back EMF waveform of the target motor when no high-frequency disturbance voltage signal is injected;

[0120] Based on the zero-crossing point and slope characteristics of the reference back EMF waveform, the high-frequency carrier signal of the target motor is determined;

[0121] The phase of the high-frequency carrier signal is dynamically phase-locked with the instantaneous value of a selected phase in the reference back EMF waveform to obtain the phase reference of the high-frequency disturbance voltage signal.

[0122] The phase reference and the preset disturbance amplitude are combined to form a high-frequency disturbance voltage signal for the target motor, and the high-frequency disturbance voltage signal is injected into the test circuit.

[0123] While injecting the high-frequency disturbance voltage signal, the phase current waveform flowing through the target motor is acquired, and the current response component with the same frequency as the high-frequency disturbance voltage signal in the phase current waveform is separated.

[0124] Based on the ratio of the amplitude between the current response component of the same frequency and the high-frequency disturbance voltage signal, and by performing differential analysis with the reference impedance of the winding parameters in the target motor, the differential result is used as the real-time eddy current loss component of the target motor.

[0125] The eddy current loss module first disconnects the injection channel of the high-frequency disturbance voltage signal to ensure that only the electrical signal generated by the normal operation of the target motor exists in the test circuit. Through the voltage acquisition probe connected to the output terminal of the stator winding of the target motor, the terminal voltage signal of the target motor under the current operating condition is acquired in real time. During the acquisition process, the connection between the voltage acquisition probe and the winding output terminal is kept stable to avoid signal distortion caused by poor contact. The acquired continuous terminal voltage signals are recorded completely in time sequence to form the reference back EMF waveform of the target motor when there is no high-frequency disturbance voltage signal injection.

[0126] The eddy current loss module performs time-by-time signal analysis on the reference back EMF waveform. When the amplitude of the reference back EMF waveform changes from negative to positive and crosses zero potential, this moment is recorded as a positive zero-crossing point; when the amplitude changes from positive to negative and crosses zero potential, this moment is recorded as a reverse zero-crossing point. All zero-crossing points are arranged in chronological order to form a zero-crossing point sequence. At the same time, the ratio of the amplitude change to the time change of the reference back EMF waveform between two adjacent zero-crossing points is calculated to obtain the slope characteristics of the corresponding interval. By analyzing the slope characteristics of all intervals, the variation law of the reference back EMF waveform is clarified. A sine wave of a fixed frequency is selected as the high-frequency carrier signal. The frequency of this high-frequency carrier signal is higher than the frequency of the reference back EMF waveform, and the variation period maintains a fixed proportional relationship with the zero-crossing interval of the reference back EMF waveform.

[0127] The phase corresponding to the moment when the amplitude reaches its maximum value after the positive zero crossing of the reference back EMF waveform is selected as the selected phase. The instantaneous amplitude of the reference back EMF waveform corresponding to the selected phase is recorded. The phase-locked unit inside the eddy current loss module collects the phase information of the high-frequency carrier signal and the instantaneous value of the selected phase in the reference back EMF waveform in real time and compares the two in real time. When there is a deviation between the phase of the high-frequency carrier signal and the instantaneous value of the selected phase, the phase-locked unit adjusts the phase change rate of the high-frequency carrier signal so that the phase of the high-frequency carrier signal always tracks the instantaneous value change of the selected phase, keeping the phase difference between the two constant. After continuous tracking and adjustment, a stable phase reference for the high-frequency disturbance voltage signal is obtained.

[0128] The preset disturbance amplitude is a predetermined fixed voltage amplitude that will not affect the normal operation of the target motor and can induce a detectable change in current response. The signal synthesis unit inside the eddy current loss module uses the phase reference as the phase basis of the high-frequency disturbance voltage signal and the preset disturbance amplitude as the amplitude basis. The two are fused into a continuous electrical signal in the form of a sine wave signal. This electrical signal is the high-frequency disturbance voltage signal of the target motor. The high-frequency disturbance voltage signal is smoothly injected into the test circuit through the signal injection port connected in series with the test circuit. During the injection process, the signal amplitude and phase are kept stable to avoid signal distortion.

[0129] While the high-frequency disturbance voltage signal is injected into the test circuit, the eddy current loss module collects the phase current signal flowing through the target motor through a current sensor connected in series with the stator winding of the target motor. The current sensor is tightly connected to the winding to accurately capture all current changes. The collected phase current signal is recorded in time sequence to form a complete phase current waveform. A reference signal with the same frequency as the high-frequency disturbance voltage signal is constructed. The phase current waveform is compared with the reference signal at each moment, and the signal part in the phase current waveform that has the same frequency and phase correlation with the reference signal is extracted. This part is the current response component in the phase current waveform that is in the same frequency as the high-frequency disturbance voltage signal.

[0130] The eddy current loss module measures the amplitude of the current response component at the same frequency and the amplitude of the high-frequency disturbance voltage signal. The amplitude of the current response component at the same frequency is divided by the amplitude of the high-frequency disturbance voltage signal to obtain the ratio of the two amplitudes. The reference impedance of the winding parameters in the target motor is the impedance composed only of the winding's own resistance and inductance when there is no high-frequency disturbance and no core eddy current loss. This reference impedance is obtained by applying a low-frequency voltage signal of a fixed frequency to the winding when the target motor is not running and measuring the corresponding current signal, and then calculating the amplitude ratio. The impedance value corresponding to the amplitude ratio is compared with the reference impedance of the winding parameters. The difference obtained by subtracting the reference impedance of the winding parameters from the impedance value corresponding to the amplitude ratio is the real-time eddy current loss component of the target motor.

[0131] The beneficial effects are as follows: by capturing the reference back EMF waveform when the motor is free of high-frequency disturbances, a precise anchor point is provided for the synchronous design of disturbance signals, ensuring that subsequent operations are deeply adapted to the motor's own operating characteristics. Based on the zero-crossing point and slope characteristics of the reference back EMF, a high-frequency carrier is determined, and its phase is dynamically phase-locked with the selected instantaneous phase value of the back EMF. This achieves strict synchronization between the high-frequency disturbance voltage signal and the back EMF waveform, avoiding analytical errors caused by phase deviations at the source. The disturbance signal, synthesized from the phase reference and a preset amplitude, is injected into the circuit, ensuring signal stability and controllability without interfering with normal motor operation, while effectively stimulating a precisely identifiable current response. After acquiring the phase current waveform, the current response component with the same frequency as the disturbance signal is separated, allowing for precise location of the current change associated with the disturbance and the removal of irrelevant signal interference. By using the amplitude ratio of this current response to the disturbance voltage, combined with the winding reference impedance for differential analysis, the influence of the winding's own impedance can be directly eliminated, accurately extracting the real-time eddy current loss component of the core. This provides reliable core data for motor loss assessment, operating status monitoring, and efficiency optimization, significantly improving the accuracy and real-time performance of motor loss analysis.

[0132] The model building module 104 is used to perform multi-mode fitting on the copper loss fluctuation component, the mechanical loss fluctuation component, the real-time eddy current loss component, and the total input power of the target motor to build a loss component mapping relationship model of the target motor under all operating conditions.

[0133] In this embodiment of the invention, when the model building module performs multi-mode fitting of the copper loss fluctuation component, the mechanical loss fluctuation component, the real-time eddy current loss component, and the total input power of the target motor to construct a loss component mapping model of the target motor under all operating conditions, it is specifically used for:

[0134] The copper loss fluctuation component, the mechanical loss fluctuation component, the real-time eddy current loss component, and the total input power of the target motor are standardized to obtain the copper loss standardization factor, mechanical loss standardization factor, eddy current loss standardization factor, and total power standardization factor of the target motor.

[0135] The copper loss normalization factor, the mechanical loss normalization factor, the eddy current loss normalization factor, and the total power normalization factor are linearly weighted and coupled to obtain a linear mapping relationship model of the target motor.

[0136] The linear mapping model is used as the loss component mapping model of the target motor under all operating conditions.

[0137] The calculation formula for the linear mapping relationship model is as follows:

[0138] ;

[0139] In the formula, This is the linear mapping relationship model. The weights of the copper loss normalization factor are... The weights of the mechanical loss normalization factor are... The weights of the eddy current loss normalization factor are... The weights of the total power normalization factor are... The copper loss normalization factor is... The mechanical loss standardization factor is... The eddy current loss normalization factor is... The total power normalization factor is... This is the coefficient of nonlinear synergistic effect.

[0140] The model building module acquires the copper loss fluctuation component, mechanical loss fluctuation component, and real-time eddy current loss component of the target motor from the signal separation module, decoupling component module, and eddy current loss module through a data transmission link. Simultaneously, it establishes a connection with the power acquisition unit in the test loop to obtain the total input power of the target motor under the corresponding operating conditions. All components and the total input power are arranged in a continuous data sequence according to the acquisition time order to ensure data integrity and timing consistency. For each data sequence, the model building module traverses all data points in the sequence, recording the data point with the largest and smallest values ​​to determine the value range of each data sequence. Then, for each data point in each data sequence, the minimum value of the corresponding sequence is subtracted from the value of that data point, and the difference is divided by the difference between the largest and smallest values ​​of the sequence. Through this fixed process, the copper loss fluctuation component, mechanical loss fluctuation component, real-time eddy current loss component, and total input power are converted into standardized data sequences with uniform numerical ranges. The corresponding sequences are the copper loss standardization factor, mechanical loss standardization factor, eddy current loss standardization factor, and total power standardization factor of the target motor, respectively.

[0141] The model building module predetermines four fixed weight values ​​based on the target motor's design parameters, winding structure, and core material characteristics. These weight values ​​correspond to the copper loss normalization factor, mechanical loss normalization factor, eddy current loss normalization factor, and total power normalization factor, respectively. Each weight value characterizes the influence of the corresponding normalization factor in the mapping relationship. The model building module internally includes a weighted calculation unit. This unit first multiplies each data point in the copper loss normalization factor sequence with its corresponding weight value to obtain the copper loss weighted component sequence; then it multiplies each data point in the mechanical loss normalization factor sequence with its corresponding weight value to obtain the mechanical loss weighted component sequence; next, it multiplies each data point in the eddy current loss normalization factor sequence with its corresponding weight value to obtain the eddy current loss weighted component sequence; finally, it multiplies each data point in the total power normalization factor sequence with its corresponding weight value to obtain the total power weighted component sequence. The weighted operation unit superimposes the values ​​of the four weighted component sequences at the same time node, that is, the values ​​of the four weighted components corresponding to each time node are added together to form a new continuous data sequence. The relationship represented by this data sequence is the linear mapping relationship model of the target motor.

[0142] Since the copper loss fluctuation component, mechanical loss fluctuation component, and real-time eddy current loss component are all collected under a preset multi-condition command sequence, covering all operating conditions of the target motor such as start-up, acceleration, constant speed, deceleration, and stop, and the total input power also synchronously covers the corresponding full operating condition range, the standardization process eliminates the influence of the differences in the numerical range of each component and the total input power under different operating conditions. The linear weighted coupling process establishes a stable quantitative correlation between each loss standardization factor and the total power standardization factor. This correlation remains consistent in all collected operating conditions and can accurately reflect the correspondence between each loss component and the total input power under the full operating conditions. Therefore, this linear mapping relationship model is directly determined as the loss component mapping relationship model of the target motor under the full operating conditions. This model can quickly obtain the corresponding loss component values ​​by inputting the total input power under any subsequent operating condition of the target motor.

[0143] The weight of the copper loss normalization factor is determined by analyzing the correlation strength between the copper loss normalization factor and the output of the linear mapping model. To determine this, we first collect the copper loss normalization factor data and the corresponding actual values ​​of the model output under different operating conditions. We then calculate the magnitude of the change in the model output value caused by the change in the copper loss normalization factor in each set of data, and calculate the proportion of this magnitude to the total output change. This proportion is quantified into a specific value, which is the weight of the copper loss normalization factor. The greater the impact of copper loss on the model output, the larger the weight value.

[0144] The copper loss standardization factor is the result of standardizing the actual copper loss value of the equipment. To obtain it, the actual copper loss value of the winding during equipment operation is first measured using a special testing device. The rated copper loss value of the equipment under rated operating conditions is collected. The actual copper loss value is divided by the rated copper loss value, and the resulting ratio is the copper loss standardization factor. This process eliminates the influence of the magnitude difference in copper loss values ​​under different equipment or different operating conditions.

[0145] The weight of the mechanical loss standardization factor is determined based on the degree of influence of the mechanical loss standardization factor on the model output. When determining it, multiple sets of data corresponding to the mechanical loss standardization factor and the actual value of the model output are collected. The change in model output corresponding to each unit change in the mechanical loss standardization factor is analyzed. Combined with the contribution ratio of this change in the overall output change, the contribution ratio is converted into a specific value. This value is the weight of the mechanical loss standardization factor. The more significant the impact of mechanical loss, the larger the weight value.

[0146] The mechanical loss standardization factor is obtained by standardizing the actual mechanical loss value. During measurement, the actual mechanical loss value generated by bearing friction, fan resistance, etc. during equipment operation is obtained through detection equipment. The rated mechanical loss value under the rated operating conditions of the equipment is retrieved. The actual mechanical loss value is divided by the rated mechanical loss value, and the result is the mechanical loss standardization factor, which realizes the unified dimension of mechanical loss value.

[0147] The weight of the eddy current loss normalization factor is determined through statistical analysis of historical operating data. Data on the eddy current loss normalization factor of the equipment under various operating conditions and the actual values ​​of the model output are collected. The correlation coefficient between the eddy current loss normalization factor and the model output value is calculated. The influence of eddy current loss on the model output is quantified according to the magnitude of the correlation coefficient. This influence is converted into a specific value, which is the weight of the eddy current loss normalization factor. The higher the correlation coefficient, the larger the weight value.

[0148] Eddy current loss standardization factor is the product of standardizing the actual value of eddy current loss. During testing, the actual value of eddy current loss generated by components such as the iron core of the equipment is measured using the principle of electromagnetic induction. The rated value of eddy current loss under the rated operating conditions of the equipment is obtained. The ratio of the actual value of eddy current loss to the rated value of eddy current loss is used as the eddy current loss standardization factor to ensure the comparability of eddy current loss data under different scenarios.

[0149] The weight of the total power normalization factor is determined based on the correlation between the total power normalization factor and the model output. To determine this, the total power normalization factor data and the corresponding actual values ​​of the model output are collected under different input powers of the device. The driving force of the change in the total power normalization factor on the model output is analyzed, and this force is quantified into a specific value. This value is the weight of the total power normalization factor. The stronger the influence of the total power on the output, the larger the weight value.

[0150] The total power standardization factor is obtained by standardizing the actual total power value. During measurement, a power detection instrument is used to obtain the actual total input power of the equipment, and the rated total input power of the equipment is retrieved. The ratio obtained by dividing the actual total input power by the rated total input power is the total power standardization factor, which eliminates the influence of differences in the magnitude of power values.

[0151] The nonlinear synergistic effect coefficient is a parameter used to quantify the synergistic effect of the copper loss normalization factor and the mechanical loss normalization factor on the model output. To determine it, a large amount of data corresponding to the copper loss normalization factor, the mechanical loss normalization factor, the total power normalization factor, and the actual values ​​of the model output are collected. The correlation between the ratio of the product of the copper loss normalization factor and the mechanical loss normalization factor to the total power normalization factor and the actual values ​​of the model output is analyzed. This correlation is quantified into a specific value, which is the nonlinear synergistic effect coefficient. The stronger the synergistic effect, the larger the coefficient value.

[0152] The linear mapping relationship model is a mathematical model that comprehensively quantifies the relationship between copper loss, mechanical loss, eddy current loss, total power and target output. Its output results can accurately reflect the comprehensive influence of each input factor on the target quantity.

[0153] The weight of the copper loss normalization factor is multiplied by the copper loss normalization factor to obtain the linear contribution value of copper loss to the model output. This contribution value directly reflects the degree of influence of the copper loss factor on the target quantity when it acts alone. The size of the weight determines the importance of the copper loss factor in the overall correlation.

[0154] The weight of the mechanical loss standardization factor is multiplied by the mechanical loss standardization factor to form the linear contribution value of mechanical loss to the model output. This value quantifies the impact of mechanical loss on the target quantity when it acts alone. The larger the weight, the more prominent the effect of mechanical loss.

[0155] The weight of the eddy current loss normalization factor is multiplied by the eddy current loss normalization factor to obtain the linear contribution value of eddy current loss. This part accurately reflects the role of eddy current loss in the overall correlation, and the weight value corresponds to the strength of its influence.

[0156] The weight of the total power normalization factor is multiplied by the total power normalization factor to form the linear contribution value of the total power. This contribution value reflects the basic influence of the input total power on the target quantity and is an important component of the model correlation.

[0157] Multiplying the copper loss normalization factor and the mechanical loss normalization factor, and then dividing by the total power normalization factor, yields the normalized result of their synergistic effect. This result is multiplied by the nonlinear synergistic effect coefficient to form the nonlinear synergistic contribution value. This part quantifies the additional impact of the interaction between copper loss and mechanical loss on the target quantity, making up for the insufficiency of a simple linear term in reflecting the synergistic effect between factors.

[0158] The linear contribution values ​​of copper loss, mechanical loss, eddy current loss, and total power are added together with the nonlinear synergistic contribution values ​​of copper loss and mechanical loss. The result obtained is the output of the linear mapping relationship model. This output fully integrates the individual and interactive effects of each input factor, and can accurately describe the complex relationship between each loss factor, total power, and target quantity, providing a reliable quantitative basis for equipment operation status assessment and loss optimization.

[0159] The beneficial effects include comprehensively acquiring all loss components and total input power of the motor while ensuring data consistency in time series, providing a complete and reliable data foundation for model construction. Standardization eliminates the influence of differences in parameter numerical ranges, making the standardized factors for copper loss, mechanical loss, eddy current loss, and total power comparable, thus improving the accuracy of subsequent calculations. By determining weights based on the motor's own characteristics and performing linear weighted coupling, a stable quantitative correlation is effectively established between each loss factor and the total power factor. The resulting linear mapping model accurately reflects the correspondence between each loss component and the total input power. Because this model covers all motor operating conditions, it can quickly obtain the values ​​of each loss component by inputting the total input power under any subsequent motor operating condition, providing a convenient and reliable basis for motor loss monitoring, efficiency optimization, and operating status evaluation, significantly improving the practicality and efficiency of motor loss analysis.

[0160] The trend change module 105 is used to drive the test circuit to simulate a load cycle including power generation feedback state based on the loss component mapping relationship model, and in the process, monitor the coordinated change trend between loss components in the loss component mapping relationship model.

[0161] In this embodiment of the invention, when the trend change module executes the test loop to simulate a load cycle including power generation feedback state based on the loss component mapping relationship model, and monitors the coordinated change trend between loss components in the loss component mapping relationship model during this process, it is specifically used for:

[0162] During the electric state phase of the load cycle, the real-time numerical change direction between the loss components in the loss component mapping relationship model is continuously recorded.

[0163] Based on the direction of the real-time numerical change, when the test circuit is driven into the power generation feedback state, the direction of the change of the copper loss fluctuation component is identified.

[0164] In the power generation feedback state, the changing directions of the mechanical loss fluctuation component and the real-time eddy current loss component are compared, and a trend comparison analysis is performed with the changing direction of the copper loss fluctuation component to obtain the trend comparison result of the test circuit.

[0165] Based on the trend comparison results, the collaborative state identifier of the dynamic correlation in the loss components is determined.

[0166] When the trend change module executes the process of obtaining the collaborative state identifier of the dynamic correlation in the loss components based on the trend comparison results, it is specifically used for:

[0167] In the power generation feedback state, it is determined whether the direction of change of the mechanical loss fluctuation component is consistent with the direction of change of the copper loss fluctuation component;

[0168] The direction of change of the real-time eddy current loss component is determined to be consistent with the direction of change of the mechanical loss fluctuation component.

[0169] If the direction of change of the mechanical loss fluctuation component is opposite to the direction of change of the copper loss fluctuation component, and the change of the real-time eddy current loss component exhibits a lag characteristic to the change of the mechanical loss fluctuation component, then the cooperative state identifier is defined as the first type of cooperative mode of the loss component mapping relationship model.

[0170] If the direction of change of the mechanical loss fluctuation component is opposite to the direction of change of the copper loss fluctuation component, but the change of the real-time eddy current loss component shows an advance characteristic to the change of the mechanical loss fluctuation component, then the cooperative state identifier is defined as the second type of cooperative mode of the loss component mapping relationship model.

[0171] In the load cycle, the collaborative change trend of the loss component mapping relationship model is obtained based on the switching sequence between the first type of collaborative mode and the second type of collaborative mode.

[0172] The trend change module controls the load controller in the test loop to adjust the load to a resistance load with the same direction as the output torque of the target motor. This puts the target motor in an electric state where it consumes electrical energy to drive the load. This stage lasts for a period covering the entire electric range of the load cycle. During this process, the trend change module calls the loss component mapping model in real time and extracts the values ​​of copper loss fluctuation component, mechanical loss fluctuation component, and real-time eddy current loss component from the model at fixed time intervals. It compares the value of each loss component extracted at each moment with the corresponding value at the previous moment. If the value at the current moment is greater than the value at the previous moment, the real-time value change direction of the loss component is recorded as increasing; if the value at the current moment is less than the value at the previous moment, it is recorded as decreasing; if the value remains unchanged, it is recorded as stable. This process is continuously completed to record the change direction of each loss component at all moments.

[0173] The trend change module summarizes the real-time numerical changes of each loss component during the motoring phase, forming a complete sequence of change directions. This sequence serves as the basis for sending a state switching command to the load controller in the test loop. Upon receiving the command, the load controller adjusts its internal circuit topology, switching the load type to a braking load opposite to the output torque direction of the target motor. This causes the target motor to switch from motoring to generating mode. The electrical energy generated by the motor is fed back to the energy storage unit in the test loop via a feedback circuit, achieving the switching of the generating feedback state. During the state switching process, the trend change module maintains real-time monitoring of the copper loss fluctuation component, extracting the value of the copper loss fluctuation component at fixed intervals and comparing the values ​​before and after the switch to determine whether the copper loss fluctuation component changes in direction after the state switch—increasing, decreasing, or remaining stable.

[0174] After entering the power generation feedback state, the trend change module continuously extracts the values ​​of the mechanical loss fluctuation component and the real-time eddy current loss component from the loss component mapping relationship model. Using the same comparison method as in the motoring state stage, it compares the current value of the mechanical loss fluctuation component with the previous value moment to determine its direction of change. Similarly, it compares the current value of the real-time eddy current loss component with the previous value moment to determine its direction of change. Subsequently, the trend change module compares the directions of change of the mechanical loss fluctuation component and the real-time eddy current loss component with the direction of change of the copper loss fluctuation component, respectively, to clarify whether there are identical, opposite, or unrelated changes among the three. For example, does the mechanical loss fluctuation component increase synchronously when the copper loss fluctuation component increases, or does the real-time eddy current loss component decrease in the opposite direction? The module fully records the correlation of the three directions of change, forming the trend comparison results of the test loop.

[0175] The trend change module pre-sets the judgment rules for the cooperative state identifier, which are based on the correlation of the change directions of the loss components. If the trend comparison results show that the change directions of the copper loss fluctuation component, mechanical loss fluctuation component, and real-time eddy current loss component are completely consistent, that is, all three increase simultaneously, decrease simultaneously, or stabilize simultaneously, it is judged as a positive cooperative state, and a positive cooperative identifier is generated accordingly. If the trend comparison results show that the change directions of the three are completely opposite, that is, when one increases, the other two decrease, or when one decreases, the other two increase, it is judged as a reverse cooperative state, and a reverse cooperative identifier is generated accordingly. If the trend comparison results show that the change directions of the three are not fixedly correlated, that is, some are consistent and some are opposite, or they change independently, it is judged as a non-cooperative state, and a non-cooperative identifier is generated accordingly. According to the above rules, the trend change module matches and judges the obtained trend comparison results, and finally determines the cooperative state identifier of the dynamic correlation in the loss components.

[0176] During the period when the target motor is in the power generation feedback state, the trend change module extracts continuous values ​​of the mechanical loss fluctuation component and the copper loss fluctuation component in real time through the data acquisition link. The values ​​of the two components are then organized into independent numerical sequences according to time order, ensuring that each time point has a corresponding component value. For the numerical sequence of the mechanical loss fluctuation component, the current value is compared with the previous value moment by moment. If the current value is greater than the previous value, it is marked as an increasing direction; if the current value is less than the previous value, it is marked as a decreasing direction; if the value remains unchanged, it is marked as a stable direction. This yields the moment-by-moment change direction of the mechanical loss fluctuation component. The same moment-by-moment comparison method is used to obtain the moment-by-moment change direction of the copper loss fluctuation component. The change directions of the two components at the same time point are compared one-to-one. If the change directions of the two components are the same at the same time point, they are determined to be consistent; if the change directions of the two components are opposite at the same time point, they are determined to be inconsistent.

[0177] The trend change module continues to extract continuous values ​​of the real-time eddy current loss component under power generation feedback conditions, organizes them into a numerical sequence in chronological order, and uses a method of comparing the current value with the previous value moment by moment to determine the direction of change of the real-time eddy current loss component. The direction of change of the real-time eddy current loss component is compared one-to-one with the direction of change of the mechanical loss fluctuation component. At the same time point, if the two change directions are the same, it is marked as consistent at that moment; if the change directions are opposite, it is marked as inconsistent at that moment. This comparison is continuously completed at all time points to comprehensively determine the consistency of the two changes.

[0178] When the first two steps determine that the direction of change of the mechanical loss fluctuation component is opposite to that of the copper loss fluctuation component, the trend change module begins to analyze the change characteristics of the real-time eddy current loss component relative to the mechanical loss fluctuation component. It extracts all time points where the mechanical loss fluctuation component changes direction; these points are the moments when the value changes from increasing to decreasing, from decreasing to increasing, or from stable to increasing / decreasing. For each such time point, it finds the time points where the real-time eddy current loss component changes direction in the same way and compares them. If the time point where the real-time eddy current loss component changes direction is always later than the time point where the mechanical loss fluctuation component changes direction, and this time difference is maintained within each change cycle—that is, the change of the real-time eddy current loss component always lags behind the change of the mechanical loss fluctuation component—then it is determined that the change of the real-time eddy current loss component exhibits a lag characteristic relative to the change of the mechanical loss fluctuation component. At this point, the trend change module explicitly defines the cooperative state identifier as the first type of cooperative mode in the loss component mapping relationship model.

[0179] If the direction of change of the mechanical loss fluctuation component is determined to be opposite to that of the copper loss fluctuation component, and when analyzing the changing characteristics of the real-time eddy current loss component and the mechanical loss fluctuation component, all time nodes in which the direction of change of the mechanical loss fluctuation component occurs are extracted, and the corresponding time nodes in which the real-time eddy current loss component changes in the same direction are found. Through comparison, it is found that the time node in which the direction of change of the real-time eddy current loss component occurs is always earlier than the time node in which the direction of change of the mechanical loss fluctuation component occurs, and this time difference remains stable within each change cycle. That is, the change of the real-time eddy current loss component always occurs before the change of the mechanical loss fluctuation component. Therefore, it is determined that the change of the real-time eddy current loss component exhibits a characteristic of leading the change of the mechanical loss fluctuation component. At this time, the trend change module explicitly defines the cooperative state identifier as the second type of cooperative mode in the loss component mapping relationship model.

[0180] Throughout the load cycle, the trend change module monitors the changes in the collaborative status indicator in real time. Whenever the collaborative status indicator switches from the first collaborative mode to the second collaborative mode, or vice versa, it records the time of each switch and the collaborative mode type before and after the switch. Following the chronological order of the load cycle, all switching events are arranged sequentially by time point, forming a complete switching sequence including the switching time, the preceding mode, and the following mode. This switching sequence is then analyzed to observe the frequency of switching, the patterns of switching intervals, and the proportion of the two modes in the entire load cycle. Based on these observed patterns, the collaborative change trend of the loss component mapping relationship model is summarized.

[0181] The beneficial effects include fully recording the changing directions of each loss component during the motoring phase, providing a reliable data benchmark for subsequent state switching and trend analysis. Based on the precise driving direction of the motoring state change, the test circuit completes the power generation feedback state switching, while clearly identifying the changing direction of the copper loss fluctuation component after the state switch, ensuring the continuity of state transition and parameter monitoring. In the power generation feedback state, by comparing the changing directions of mechanical losses, eddy current losses, and copper losses, the correlation characteristics between loss components are comprehensively captured, and the resulting trend comparison results can intuitively reflect the dynamic relationship of each loss. Based on preset rules, the trend comparison results are accurately determined, and the generated collaborative state identifier can clearly define the collaborative type of dynamic correlation of loss components, providing clear characteristic basis for subsequent motor operation state optimization, loss control, and fault prediction, improving the pertinence and reliability of the full-condition motor operation analysis.

[0182] By accurately comparing the consistency of the changing directions of mechanical losses and copper losses under power generation feedback conditions, the correlation characteristics between the two are clearly defined, laying the foundation for determining the cooperative mode. By analyzing the changing characteristics of real-time eddy current losses relative to mechanical losses, the distinction criteria between lag and lead characteristics are clarified, enabling precise definitions of the two types of cooperative modes and ensuring the accuracy of dynamic correlation classification of loss components. Combining the switching sequences of the two types of cooperative modes in the load cycle, the system systematically sorts out the switching rules and mode proportion characteristics, comprehensively extracting the cooperative change trend of the loss component mapping relationship model. This provides precise dynamic characteristic basis for motor loss control, operating status optimization, and fault early warning, significantly improving the depth and relevance of the full-condition operation analysis of the motor.

[0183] The comprehensive report generation module 106 is used to evaluate the operating efficiency and energy recovery characteristics of the target motor based on the coordinated change trend, and obtain a comprehensive performance evaluation report of the target motor.

[0184] In this embodiment of the invention, when the comprehensive report generation module evaluates the operating efficiency and energy recovery characteristics of the target motor based on the coordinated change trend to obtain a comprehensive performance evaluation report of the target motor, it is specifically used for:

[0185] Identify the target state identifier corresponding to the power generation feedback state in the coordinated change trend;

[0186] Based on the target state identifier, the change profile between the mechanical loss fluctuation component and the copper loss fluctuation component is extracted from the load cycle;

[0187] The change profile is compared with the reference profile of the electric state stage to determine the mechanical loss attenuation characteristics and copper loss fluctuation stability of the target motor.

[0188] When mechanical losses decrease and copper loss fluctuations stabilize, a positive evaluation result for efficient energy recovery in the target motor is obtained.

[0189] When mechanical losses decrease and copper losses fluctuate drastically, a negative evaluation result is obtained indicating that coupling losses exist in the target motor.

[0190] By integrating the positive evaluation results, the negative evaluation results, and the loss component mapping model, a comprehensive performance evaluation report of the target motor is obtained.

[0191] The comprehensive report generation module obtains the coordinated change trend output by the trend change module through a data interface. This trend includes the coordinated status identifiers and corresponding time node information of the dynamic correlation of loss components throughout the load cycle. The module internally pre-defines the time interval determination rules for the power generation feedback state. Based on the load cycle state switching records, it locates all time segments in the power generation feedback state, matches the corresponding coordinated status identifier within each time segment, and filters out these coordinated status identifiers that precisely correspond to the power generation feedback state time segments. These are then determined as the target status identifiers corresponding to the power generation feedback state, ensuring that the target status identifiers accurately reflect the loss coordination characteristics of the power generation feedback stage.

[0192] The comprehensive report generation module extracts data from the time interval corresponding to the target state identifier, retrieving mechanical loss fluctuation component data and copper loss fluctuation component data within that time interval from the complete data record of the load cycle. All data are arranged in chronological order of acquisition time to ensure temporal integrity. The module plots the trajectory of the mechanical loss fluctuation component value changing over time as a continuous curve, and simultaneously plots a continuous curve of the time change of the copper loss fluctuation component in the same way. The two curves together form the change contour between the mechanical loss fluctuation component and the copper loss fluctuation component, which fully presents the dynamic change correlation of the two types of loss components under the power generation feedback state.

[0193] The integrated report generation module extracts the mechanical loss fluctuation components and copper loss fluctuation components from the load cycle data for the electric state phase. It then uses the same data processing method to create a reference profile for the electric state phase. The reference profile and the change profile for the power generation feedback state use the same time axis scale and numerical calibration standard. The module overlays and compares the change profile with the reference profile moment by moment, observing the numerical differences of the mechanical loss fluctuation components in the two profiles. If the values ​​of the mechanical loss fluctuation components in the change profile are generally lower than the values ​​in the reference profile at the corresponding time, and show a continuous decline or remain in a low value range, it is determined to be a mechanical loss attenuation characteristic. Simultaneously, it observes the amplitude of the copper loss fluctuation components' numerical fluctuations in the change profile. If the values ​​change slightly around a fixed range without significant sudden increases or decreases, it is determined to be a stable copper loss fluctuation.

[0194] After determining the characteristics of mechanical loss attenuation and the stability of copper loss fluctuations, when both mechanical loss attenuation and copper loss fluctuation stability are met, the comprehensive report generation module combines the core evaluation logic of energy recovery. Mechanical loss attenuation means that the ineffective consumption of mechanical energy during the power generation feedback process is reduced, and copper loss fluctuation stability indicates that the loss during the power conversion process is controlled within a reasonable range. Both characteristics point to efficient loss control during the energy recovery process. Therefore, a positive evaluation result of efficient energy recovery in the target motor is directly generated. This result clearly states that the energy recovery efficiency in the power generation feedback stage is excellent and the loss control meets the standards.

[0195] When the judgment result is mechanical loss attenuation but copper loss fluctuates drastically, the comprehensive report generation module analyzes the inherent relationship between the two types of characteristics. Although mechanical loss attenuation reflects a reduction in mechanical energy consumption, the drastic fluctuation in copper loss indicates that there are unstable factors in the electromagnetic conversion during the power generation feedback process. The imbalance in the coordination between current and voltage leads to irregular and large changes in copper loss. The essence of this phenomenon is that there is energy coupling interference between mechanical loss and copper loss, which generates additional coupling loss. Therefore, a negative assessment result of coupling loss in the target motor is generated. This result clearly points out the existence and performance characteristics of coupling loss.

[0196] The comprehensive report generation module pre-sets a fixed structure for the comprehensive performance consumption assessment report, including three core parts: loss component analysis, state assessment conclusion, and comprehensive performance judgment. The module fills the state assessment conclusion section with positive or negative assessment results, detailing the assessment basis and characteristics. It then fills the loss component analysis section with the core data of the loss component mapping model, the proportion of each loss component, and their correlation patterns, providing data support for the assessment conclusion. Finally, in the comprehensive performance judgment section, it combines the loss component data and assessment results to summarize the target motor's operating efficiency and energy recovery characteristics, clarifying its strengths and weaknesses, and forming a complete comprehensive performance consumption assessment report for the target motor through structured integration.

[0197] The beneficial effects include accurately identifying the target state markers corresponding to the power generation feedback state, anchoring core data ranges for subsequent performance evaluation, and ensuring the relevance of the assessment. Extracting the loss component change contours based on the markers and comparing them with the electric state reference contours clearly defines the characteristics of mechanical loss attenuation and copper loss fluctuation stability, providing an intuitive basis for the evaluation. Providing positive and negative evaluation results based on these characteristics accurately pinpoints the advantages of high-efficiency energy recovery and coupled loss issues. Integrating the evaluation results with the loss component mapping model generates a comprehensive performance evaluation report with a complete structure and sufficient data support, fully reflecting the motor's operating efficiency and energy recovery characteristics. This provides a reliable decision-making basis for motor performance optimization, loss control, and application improvement, enhancing the practicality and guidance of the evaluation.

[0198] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0199] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application device that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0200] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An energy consumption testing device for an axial flux permanent magnet motor, characterized in that, The device includes a signal separation module, a decoupling component module, an eddy current loss module, a model building module, a trend change module, and a comprehensive report generation module, wherein: The signal separation module is used to connect the target motor to the test circuit, so that the target motor runs under a preset multi-condition command sequence, and simultaneously collects multiple electrical signals and vibration signals of the target motor. The decoupling component module is used to decouple and analyze the multi-channel electrical signals to obtain the phase current harmonic components of the target motor, and combine the specific frequency band energy in the vibration signal to separate the copper loss fluctuation components and mechanical loss fluctuation components of the target motor during operation. The eddy current loss module is used to inject a controllable high-frequency disturbance voltage signal synchronized with the back EMF waveform of the target motor into the test circuit, and detect the change in current response caused by the high-frequency disturbance voltage signal to analyze the real-time eddy current loss component of the iron core in the target motor. The model building module is used to perform multi-mode fitting on the copper loss fluctuation component, the mechanical loss fluctuation component, the real-time eddy current loss component, and the total input power of the target motor to build a loss component mapping relationship model of the target motor under all operating conditions. The trend change module is used to drive the test loop to simulate a load cycle including power generation feedback state based on the loss component mapping relationship model, and in the process, monitor the coordinated change trend between loss components in the loss component mapping relationship model. The comprehensive report generation module is used to evaluate the operating efficiency and energy recovery characteristics of the target motor based on the coordinated change trend, and obtain a comprehensive performance evaluation report of the target motor.

2. The energy consumption testing device for axial flux permanent magnet motors as described in claim 1, characterized in that, When the signal separation module connects the target motor to the test circuit, causing the target motor to operate under a preset multi-condition command sequence, and simultaneously acquires multiple electrical and vibration signals from the target motor, it is specifically used for: A preset multi-condition command sequence is sent to the controller of the target motor, and a synchronous trigger signal for the target motor is generated according to the step change node of the preset multi-condition command sequence. The synchronization trigger signal is split and transmitted to the acquisition channel of the target motor, and the acquisition channel is started synchronously using the synchronization trigger signal to continuously sample the target motor. During continuous sampling, the key electrical signals in the acquisition channel are used as reference signals, and the high-precision time-scale sequence of the target motor is determined based on the zero-crossing events of the reference signals. The timing drift of the target motor is obtained by comparing the high-precision time-stamped sequence with the expected timing of the synchronous trigger signal in real time. Based on the aforementioned timing drift, the phase of the synchronization trigger signal in subsequent sampling periods is adjusted to dynamically correct the relative timing synchronization relationship between channels of different dimensions in the acquisition channel. Based on the relative timing synchronization relationship, the target motor is synchronously acquired to obtain multiple electrical signals and vibration signals of the target motor.

3. The axial flux permanent magnet motor energy consumption testing device as described in claim 1, characterized in that, The decoupling component module, when performing decoupling analysis on the multi-channel electrical signals to obtain the phase current harmonic components of the target motor, and combining this with the specific frequency band energy in the vibration signal to separate the copper loss fluctuation components and mechanical loss fluctuation components of the target motor during operation, is specifically used for: Based on the torque command phase change command in the preset multi-condition command sequence, the phase current signal in the multi-channel electrical signal is decomposed in the time domain to obtain the first current component of the multi-channel electrical signal. The first component of the current is removed from the phase current signal to obtain the phase current harmonic component of the phase current signal. Based on the current electrical frequency of the target motor, a frequency selection network for the target motor is constructed, and the vibration signal is input into the frequency selection network to obtain the specific frequency band energy of the vibration signal; The amplitude envelope of the phase current harmonic components is analyzed by similarity matching with the amplitude envelope of the specific frequency band energy. When the change in the envelope amplitude of the phase current harmonic component dominates the change in the envelope amplitude of the energy in the specific frequency band, the energy fluctuation of the phase current harmonic component is classified as the copper loss fluctuation component of the target motor. When the change in the envelope amplitude of the specific frequency band energy is independent of the change in the envelope amplitude of the phase current harmonic component, and is associated with the speed command step in the preset multi-condition command sequence, the energy fluctuation of the specific frequency band energy is classified as the mechanical loss fluctuation component of the target motor.

4. The axial flux permanent magnet motor energy consumption testing device as described in claim 3, characterized in that, When the decoupling component module performs the following operations: constructs a frequency selection network for the target motor based on its current electrical frequency, and inputs the vibration signal into the frequency selection network to obtain the specific frequency band energy of the vibration signal, it is specifically used for: Monitor the real-time electrical frequency of the target motor, and determine the fundamental frequency for the mechanical rotation synchronization of the rotor in the target motor based on the real-time electrical frequency; Using the fundamental frequency as a reference, determine the passband frequency range for capturing mechanical state characteristics in the target motor; Based on the passband frequency range, the frequency response characteristics of the frequency selection network are configured so that the frequency selection network attenuates vibration signal components outside the passband frequency range, thereby obtaining the initial attenuation signal of the target motor. By filtering out the high-frequency components directly related to electromagnetic noise and the low-frequency drift components unrelated to the basic rotation in the rotor mechanical rotation synchronization of the preliminary attenuation signal, a specific frequency band energy signal of the target motor is obtained.

5. The energy consumption testing device for an axial flux permanent magnet motor as described in claim 1, characterized in that, The eddy current loss module, when performing the operation of injecting a controllable high-frequency disturbance voltage signal synchronized with the back EMF waveform of the target motor into the test circuit, and detecting the current response change caused by the high-frequency disturbance voltage signal to analyze the real-time eddy current loss component of the iron core in the target motor, is specifically used for: Obtain the reference back EMF waveform of the target motor when no high-frequency disturbance voltage signal is injected; Based on the zero-crossing point and slope characteristics of the reference back EMF waveform, the high-frequency carrier signal of the target motor is determined; The phase of the high-frequency carrier signal is dynamically phase-locked with the instantaneous value of a selected phase in the reference back EMF waveform to obtain the phase reference of the high-frequency disturbance voltage signal. The phase reference and the preset disturbance amplitude are combined to form a high-frequency disturbance voltage signal for the target motor, and the high-frequency disturbance voltage signal is injected into the test circuit. While injecting the high-frequency disturbance voltage signal, the phase current waveform flowing through the target motor is acquired, and the current response component with the same frequency as the high-frequency disturbance voltage signal in the phase current waveform is separated. Based on the ratio of the amplitude between the current response component of the same frequency and the high-frequency disturbance voltage signal, and by performing differential analysis with the reference impedance of the winding parameters in the target motor, the differential result is used as the real-time eddy current loss component of the target motor.

6. The energy consumption testing device for an axial flux permanent magnet motor as described in claim 1, characterized in that, When the model building module performs multimodal fitting of the copper loss fluctuation component, the mechanical loss fluctuation component, the real-time eddy current loss component, and the total input power of the target motor to construct a loss component mapping model of the target motor under all operating conditions, it is specifically used for: The copper loss fluctuation component, the mechanical loss fluctuation component, the real-time eddy current loss component, and the total input power of the target motor are standardized to obtain the copper loss standardization factor, mechanical loss standardization factor, eddy current loss standardization factor, and total power standardization factor of the target motor. The copper loss normalization factor, the mechanical loss normalization factor, the eddy current loss normalization factor, and the total power normalization factor are linearly weighted and coupled to obtain a linear mapping relationship model of the target motor. The linear mapping model is used as the loss component mapping model of the target motor under all operating conditions.

7. The energy consumption testing device for an axial flux permanent magnet motor as described in claim 6, characterized in that, The calculation formula for the linear mapping relationship model is as follows: ； In the formula, This is the linear mapping relationship model. The weights of the copper loss normalization factor are... The weights of the mechanical loss normalization factor are... The weights of the eddy current loss normalization factor are... The weights of the total power normalization factor are... The copper loss normalization factor is... The mechanical loss standardization factor is... The eddy current loss normalization factor is... The total power normalization factor is... This is the coefficient of nonlinear synergistic effect.

8. The energy consumption testing device for an axial flux permanent magnet motor as described in claim 1, characterized in that, When the trend change module executes the test loop simulation of a load cycle including power generation feedback based on the loss component mapping relationship model, and monitors the coordinated change trend between loss components in the loss component mapping relationship model during this process, it is specifically used for: During the electric state phase of the load cycle, the real-time numerical change direction between the loss components in the loss component mapping relationship model is continuously recorded. Based on the direction of the real-time numerical change, when the test circuit is driven into the power generation feedback state, the direction of the change of the copper loss fluctuation component is identified. In the power generation feedback state, the changing directions of the mechanical loss fluctuation component and the real-time eddy current loss component are compared, and a trend comparison analysis is performed with the changing direction of the copper loss fluctuation component to obtain the trend comparison result of the test circuit. Based on the trend comparison results, the collaborative state identifier of the dynamic correlation in the loss components is determined.

9. The energy consumption testing device for an axial flux permanent magnet motor as described in claim 8, characterized in that, When the trend change module executes the process of obtaining the collaborative state identifier of the dynamic correlation in the loss components based on the trend comparison results, it is specifically used for: In the power generation feedback state, it is determined whether the direction of change of the mechanical loss fluctuation component is consistent with the direction of change of the copper loss fluctuation component; The direction of change of the real-time eddy current loss component is determined to be consistent with the direction of change of the mechanical loss fluctuation component. If the direction of change of the mechanical loss fluctuation component is opposite to the direction of change of the copper loss fluctuation component, and the change of the real-time eddy current loss component exhibits a lag characteristic to the change of the mechanical loss fluctuation component, then the cooperative state identifier is defined as the first type of cooperative mode of the loss component mapping relationship model. If the direction of change of the mechanical loss fluctuation component is opposite to the direction of change of the copper loss fluctuation component, but the change of the real-time eddy current loss component shows an advance characteristic to the change of the mechanical loss fluctuation component, then the cooperative state identifier is defined as the second type of cooperative mode of the loss component mapping relationship model. In the load cycle, the collaborative change trend of the loss component mapping relationship model is obtained based on the switching sequence between the first type of collaborative mode and the second type of collaborative mode.

10. The energy consumption testing device for an axial flux permanent magnet motor as described in claim 1, characterized in that, When the comprehensive report generation module evaluates the operating efficiency and energy recovery characteristics of the target motor based on the coordinated change trend to obtain a comprehensive performance evaluation report for the target motor, it is specifically used for: Identify the target state identifier corresponding to the power generation feedback state in the coordinated change trend; Based on the target state identifier, the change profile between the mechanical loss fluctuation component and the copper loss fluctuation component is extracted from the load cycle; The change profile is compared with the reference profile of the electric state stage to determine the mechanical loss attenuation characteristics and copper loss fluctuation stability of the target motor. When mechanical losses decrease and copper loss fluctuations stabilize, a positive evaluation result for efficient energy recovery in the target motor is obtained. When mechanical losses decrease and copper losses fluctuate drastically, a negative evaluation result is obtained indicating that coupling losses exist in the target motor. By integrating the positive evaluation results, the negative evaluation results, and the loss component mapping model, a comprehensive performance evaluation report of the target motor is obtained.

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