Blast motor energy efficiency control method and system based on load identification

Abstract

The invention discloses a blast motor energy efficiency control method and system based on load identification, and relates to the field of motor energy efficiency control, and the method comprises the steps: firstly collecting multi-dimensional operation parameters of a blast motor in real time, and estimating a basic electromagnetic torque based on a rotor flux linkage reference value; meanwhile, by analyzing the change characteristics of the mechanical angular velocity of the motor, a steady-state discrimination mechanism and a pneumatic load reference model are constructed, so that the current pneumatic load reference torque is accurately obtained during stable operation. Furthermore, a thermal drift parameter observer is constructed by using the difference between the pneumatic load reference torque and the basic estimation torque, and rotor flux linkage attenuation caused by temperature change is dynamically captured and corrected in real time. And finally, an energy efficiency optimal control algorithm is executed based on the corrected real-time flux linkage parameters, and an optimal shaft current instruction is solved. In this way, interference of temperature changes on load identification precision can be effectively eliminated, and therefore precise load matching and energy efficiency optimal operation of the blast motor under the wide temperature range and complex working conditions are achieved.

Description

Technical Field

[0001] This application relates to the field of motor energy efficiency control, and more specifically, to a method and system for energy efficiency control of blower motors based on load identification. Background Technology

[0002] With the automotive industry's profound transformation towards electrification and intelligence, the vehicle's thermal management system, as a key component of overall vehicle energy consumption, directly impacts the vehicle's range and energy efficiency. The blower motor, as the core actuator of the air conditioning system, primarily overcomes duct resistance to deliver airflow. This duct resistance is often influenced by factors such as damper opening, filter dirt levels, and vehicle speed, exhibiting non-linear variations. To meet the thermal comfort requirements of the passenger compartment while minimizing energy consumption, modern motor control systems urgently need to adopt refined control strategies based on load identification. By sensing the blower's aerodynamic load status in real time, the control system can dynamically adjust the drive strategy, such as implementing maximum torque-to-current ratio (MTPA) control, to ensure the motor outputs the target torque with minimal current loss under different operating conditions. Therefore, constructing a high-precision, load-identification-based energy efficiency control scheme is crucial for improving the intelligence level and energy-saving effect of the vehicle's thermal management system.

[0003] However, existing blower motor control technologies generally rely on offline calibration parameters from the motor's factory setting when achieving load identification and energy efficiency optimization. This static approach has revealed significant shortcomings in practical applications. Traditional solutions typically measure the motor's rotor flux linkage or back EMF constant at room temperature (e.g., 20°C) and embed these parameters as fixed constants in the control algorithm. However, in actual automotive operation, the operating temperature range of blower motors is extremely wide (typically covering -40°C to 85°C), and the motor itself generates significant temperature rise during operation. Because commonly used permanent magnet materials (such as ferrite) have a negative remanence temperature coefficient, their rotor flux linkage amplitude physically decreases with increasing temperature. When the control system ignores this "thermal drift" characteristic and still estimates electromagnetic torque based on room-temperature static parameters, the calculated torque value will deviate significantly from the actual output torque of the motor. This parameter mismatch directly misleads the load identification algorithm, preventing it from accurately interpreting the current aerodynamic drag characteristics. This leads to the energy efficiency control strategy deviating from the optimal operating point, resulting in decreased system control accuracy and unnecessary energy loss.

[0004] Therefore, there is an urgent need for an optimized energy efficiency control method and system for blowers based on load identification. Summary of the Invention

[0005] This application is made in order to solve the above-mentioned technical problems.

[0006] According to one aspect of this application, a blower motor energy efficiency control method based on load identification is provided, comprising: Acquire the motor's three-phase current sampling values, bus voltage, motor electrical angular velocity, motor mechanical angular velocity, and rotor flux linkage reference value; The basic electromagnetic torque is estimated by sampling the three-phase current of the motor and the reference value of the rotor flux linkage. Steady-state discrimination and aerodynamic load reference torque calculation are performed based on the motor mechanical angular velocity to obtain the steady-state flag and aerodynamic load reference torque; Based on the steady-state flag, thermal drift parameters are observed for the aerodynamic load reference torque and the estimated electromagnetic torque to obtain the corrected real-time rotor flux linkage. Based on the corrected real-time rotor flux linkage, the target torque command is subjected to energy-efficient optimal control to obtain the optimal dq-axis current command.

[0007] According to another aspect of this application, a blower motor energy efficiency control system based on load identification is provided, comprising: The multi-parameter synchronous acquisition module is used to acquire the sampled values ​​of the motor's three-phase current, bus voltage, motor electrical angular velocity, motor mechanical angular velocity, and rotor flux linkage reference value. The electromagnetic torque estimation module is used to perform basic electromagnetic torque estimation on the sampled values ​​of the three-phase current of the motor and the reference value of the rotor flux linkage to obtain the estimated electromagnetic torque. The steady-state discrimination and torque calculation module is used to perform steady-state discrimination and aerodynamic load reference torque calculation based on the motor mechanical angular velocity to obtain the steady-state flag and aerodynamic load reference torque; The thermal drift parameter observation module is used to observe the thermal drift parameters of the aerodynamic load reference torque and the estimated electromagnetic torque based on the steady-state flag to obtain the corrected real-time rotor flux linkage. The energy efficiency optimal control module is used to perform energy efficiency optimal control on the target torque command based on the corrected real-time rotor flux linkage to obtain the optimal dq axis current command.

[0008] Compared with existing technologies, this application provides a blower motor energy efficiency control method and system based on load identification. First, it collects multi-dimensional operating parameters of the blower motor in real time and estimates the basic electromagnetic torque based on the rotor flux linkage reference value. Simultaneously, by analyzing the variation characteristics of the motor's mechanical angular velocity, a steady-state discrimination mechanism and an aerodynamic load reference model are constructed, thereby accurately determining the current aerodynamic load reference torque during stable operation. Furthermore, utilizing the difference between the aerodynamic load reference torque and the basic estimated torque, a thermal drift parameter observer is constructed to dynamically capture and correct rotor flux linkage attenuation caused by temperature changes in real time. Finally, based on the corrected real-time flux linkage parameters, an energy efficiency optimal control algorithm is executed to calculate the optimal shaft current command. This effectively eliminates the interference of temperature changes on the load identification accuracy, thereby achieving accurate load matching and optimal energy efficiency operation of the blower motor under wide temperature ranges and complex operating conditions. Attached Figure Description

[0009] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0010] Figure 1 This is a flowchart of a blower motor energy efficiency control method based on load identification according to an embodiment of this application.

[0011] Figure 2 This is a data flow diagram of a blower motor energy efficiency control method based on load identification according to an embodiment of this application.

[0012] Figure 3 This is a flowchart of sub-step S2 of the blower motor energy efficiency control method based on load identification according to an embodiment of this application.

[0013] Figure 4 This is a flowchart of sub-step S3 of the blower motor energy efficiency control method based on load identification according to an embodiment of this application.

[0014] Figure 5 This is a flowchart of sub-step S4 of the blower motor energy efficiency control method based on load identification according to an embodiment of this application.

[0015] Figure 6 This is a flowchart of sub-step S5 of the blower motor energy efficiency control method based on load identification according to an embodiment of this application.

[0016] Figure 7 This is a block diagram of a blower motor energy efficiency control system based on load identification according to an embodiment of this application. Detailed Implementation

[0017] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0018] To address the problems mentioned above in the background technology, this application proposes a blower motor energy efficiency control method based on load identification. Figure 1 This is a flowchart of a blower motor energy efficiency control method based on load identification according to an embodiment of this application. Figure 2 This is a data flow diagram of a blower motor energy efficiency control method based on load identification according to an embodiment of this application. Figure 1 and Figure 2 As shown, the energy efficiency control method for a blower motor based on load identification includes the following steps: S1, acquiring the motor's three-phase current sampling values, bus voltage, motor electrical angular velocity, motor mechanical angular velocity, and rotor flux reference value; S2, performing basic electromagnetic torque estimation on the motor's three-phase current sampling values ​​and rotor flux reference value to obtain the estimated electromagnetic torque; S3, performing steady-state discrimination and aerodynamic load reference torque calculation based on the motor's mechanical angular velocity to obtain the steady-state flag and aerodynamic load reference torque; S4, based on the steady-state flag, observing the thermal drift parameters of the aerodynamic load reference torque and the estimated electromagnetic torque to obtain the corrected real-time rotor flux; S5, based on the corrected real-time rotor flux, performing energy efficiency optimal control on the target torque command to obtain the optimal dq-axis current command.

[0019] In the aforementioned energy efficiency control method for a blower motor based on load identification, step S1 involves acquiring the sampled values ​​of the motor's three-phase current, bus voltage, motor electrical angular velocity, motor mechanical angular velocity, and rotor flux linkage reference value. It should be understood that due to the large temperature difference in the operating environment of automotive blower motors and the non-linear changes in load caused by the duct condition, rotor flux linkage is prone to thermal drift. Electromagnetic torque estimation, load identification, and energy efficiency control all rely on real-time and accurate operating parameters. The lack of these parameters will lead to torque calculation deviations, inaccurate load identification, and deviations from optimal energy efficiency control. Therefore, this application synchronously acquires the motor's core operating parameters and calls upon the factory calibration reference parameters to provide basic data input for subsequent operations. This ensures that the algorithms in each stage operate based on real-world operating data, providing a prerequisite for eliminating thermal drift interference, improving load identification accuracy, and achieving optimal energy efficiency over a wide temperature range.

[0020] Specifically, in one possible embodiment, step S1 is implemented as follows: The three-phase current sampling value of the motor is acquired through a sampling resistor connected in series on the output side of the inverter or an integrated Hall current sensor, filtered by a signal conditioning circuit, and then transmitted to the microcontroller. The bus voltage is acquired through a high-precision voltage divider resistor network and processed by a differential amplifier circuit to ensure sampling accuracy. The motor electrical angular velocity is obtained by differentiating the rotor electrical angle signal acquired by the rotor position sensor. The motor mechanical angular velocity is obtained by dividing the motor electrical angular velocity by the number of motor pole pairs. The rotor flux linkage reference value is a fixed parameter calibrated at the factory under standard temperature conditions and is pre-stored in the controller's non-volatile memory, which is directly read and retrieved after power-on.

[0021] In the aforementioned energy efficiency control method for a blower motor based on load identification, step S2 involves estimating the electromagnetic torque by taking the sampled values ​​of the three-phase current of the motor and the reference value of the rotor flux linkage. It should be understood that since automotive blower motors need to match the pneumatic load through electromagnetic torque output during operation, and torque cannot be directly measured, and traditional control relies on offline calibrated static parameters without real-time correlation of the dynamic relationship between current and flux linkage, torque estimation errors are easily caused. Therefore, this application further utilizes the collected sampled values ​​of the three-phase current of the motor and the factory-calibrated reference value of the rotor flux linkage to obtain the estimated electromagnetic torque through electromagnetic torque calculation logic. This establishes an initial torque-load correlation reference, providing a reference for subsequent judgment of torque deviations caused by thermal drift. This provides basic torque data for subsequent core steps such as steady-state judgment and thermal drift correction, ensuring the validity of the initial data for load identification and avoiding subsequent control inaccuracies due to the lack of a torque reference.

[0022] In particular, in one specific embodiment, Figure 3 This is a flowchart of sub-step S2 of the blower motor energy efficiency control method based on load identification according to an embodiment of this application. Figure 3 As shown, step S2 includes: S21, based on the rotor electrical angle, performing a three-phase current coordinate system transformation on the sampled values ​​of the three-phase current of the motor to obtain the direct-axis current component and the quadrature-axis current component; S22, performing electromagnetic torque estimation based on reference parameters on the quadrature-axis current component and the rotor flux linkage reference value to obtain the estimated electromagnetic torque.

[0023] Specifically, in step S21, based on the rotor electrical angle, the sampled values ​​of the three-phase current of the motor are transformed into a three-phase current coordinate system to obtain the direct-axis current component and the quadrature-axis current component. It should be understood that since the three-phase current of the motor is an alternating current quantity that changes periodically with time, directly using it for electromagnetic torque calculation requires handling complex alternating phase relationships and cannot effectively distinguish between the current component affecting torque output and the current component affecting magnetic field strength, resulting in low torque calculation accuracy and delayed control response. Therefore, this application further combines the real-time acquired rotor electrical angle to transform the sampled values ​​of the three-phase current of the motor into a three-phase current coordinate system to obtain the direct-axis current component and the quadrature-axis current component. This converts the alternating three-phase current into a DC component in a synchronous rotating coordinate system, achieving accurate separation of the torque-related current component and the magnetic field-related current component. This significantly simplifies the subsequent electromagnetic torque calculation logic, improves the direct correlation between the current component and the torque output, lays a data foundation for accurate estimation of electromagnetic torque, and accelerates the controller's dynamic control response speed to torque output, adapting to the real-time control requirements of automotive blower motors.

[0024] Specifically, in one possible embodiment, step S21 is implemented as follows: First, the controller acquires real-time rotor electrical angle signals from a rotor position sensor (such as a Hall effect position sensor), and performs jitter removal and phase compensation processing on the signals using software algorithms to ensure the accuracy of the angle data. Then, a Clark transform is performed on the collected three-phase current sample values ​​to convert the current signals in a three-phase stationary coordinate system (abc coordinate system) into current signals in a two-phase stationary coordinate system (αβ coordinate system), completing dimensionality reduction. Next, a Park transform is performed in conjunction with the processed rotor electrical angles to convert the current signals in the two-phase stationary coordinate system into direct-axis and quadrature-axis current components in a two-phase rotating coordinate system (dq coordinate system) that rotates synchronously with the rotor. Finally, the two current components are transmitted to the electromagnetic torque estimation module and simultaneously stored in the controller's dedicated data register to provide stable current component data for subsequent steps.

[0025] Specifically, step S22 involves estimating the electromagnetic torque based on reference parameters using the quadrature-axis current component and the rotor flux linkage reference value to obtain the estimated electromagnetic torque. It should be understood that, in a synchronous rotating coordinate system, the direct-axis current component primarily functions to regulate the motor's magnetic field strength, while the quadrature-axis current component directly determines the output magnitude of the motor's electromagnetic torque. Furthermore, the rotor flux linkage reference value is a core parameter reflecting the motor's magnetic field strength. Ignoring the direct correlation between the quadrature-axis current component and the rotor flux linkage reference value will lead to significant deviations in the electromagnetic torque estimation. Therefore, this application further utilizes the quadrature-axis current component obtained through coordinate system transformation, combined with the factory-calibrated rotor flux linkage reference value and inherent motor parameters, such as the number of pole pairs, to obtain the estimated electromagnetic torque based on reference parameters through dedicated calculation logic. This focuses on the core current component directly related to torque output, eliminating the interference of the direct-axis current component on torque estimation. In a specific example of this application, step S22 includes: estimating the electromagnetic torque based on reference parameters using the following formula:

[0026] in, This is the reference value for rotor flux linkage. This represents the number of pole pairs of the motor. For the quadrature-axis current component, This is the estimated electromagnetic torque. That is, based on field-oriented control theory, the torque is quantitatively calculated through parameter correlation. Since the aforementioned coordinate system transformation uses a constant amplitude transformation principle, meaning the amplitude of the transformed current component is equal to the amplitude of the three-phase current, the mathematical power in the dq-axis coordinate system is not directly equivalent to the total physical power of the three-phase system. Therefore, a coefficient needs to be introduced into the formula. Power equivalence correction is performed to restore the actual electromagnetic torque output by the motor. Based on this correction factor, the magnitude of the electromagnetic torque is strictly proportional to the rotor flux linkage reference value and the quadrature-axis current component, while the number of motor pole pairs, as a mechanical structural parameter, further determines the final torque level generated per unit current. In this way, without complex magnetic field integration calculations, the estimated electromagnetic torque can be quickly calculated using simple algebraic multiplication. This meets the requirements of the vehicle controller for millisecond-level high-frequency control response and ensures that the initial estimated torque is consistent with the motor's design output characteristics. It provides accurate benchmark data for subsequent comparison with aerodynamic load reference torque and for identifying deviations caused by flux linkage thermal drift, ensuring the accuracy of core data for load identification.

[0027] In the aforementioned blower motor energy efficiency control method based on load identification, step S3 involves performing steady-state discrimination and pneumatic load reference torque calculation based on the motor's mechanical angular velocity to obtain the steady-state flag and pneumatic load reference torque. It should be understood that during the operation of an automotive blower motor, transient conditions such as damper switching and changes in internal / external circulation modes occur. In these situations, the motor's mechanical angular velocity fluctuates drastically, and the load torque and electromagnetic torque do not match. Directly calculating the pneumatic load torque would introduce significant errors. Therefore, this application further performs steady-state discrimination and pneumatic load reference torque calculation based on the motor's mechanical angular velocity to obtain the steady-state flag and pneumatic load reference torque. This allows for the initial screening of stable load conditions, followed by the calculation of the actual pneumatic load torque based on stable angular velocity data. This ensures that subsequent thermal drift parameter observations are only performed when the load is stable, avoiding load judgment deviations caused by transient interference. Simultaneously, it provides an accurate pneumatic load benchmark for comparative estimation of electromagnetic torque, ensuring the precision of the direction and magnitude of thermal drift correction and improving overall control reliability.

[0028] In particular, in one specific embodiment, Figure 4 This is a flowchart of sub-step S3 of the blower motor energy efficiency control method based on load identification according to an embodiment of this application. Figure 4 As shown, step S3 includes: S31, performing steady-state determination of motion state based on the motor mechanical angular velocity of the current control cycle and the motor mechanical angular velocity of the previous control cycle to obtain a steady-state flag; S32, calculating the aerodynamic load reference torque based on the motor mechanical angular velocity of the current control cycle.

[0029] Specifically, step S31 involves performing a steady-state determination of the motion state based on the motor's mechanical angular velocity in the current control cycle and the motor's mechanical angular velocity in the previous control cycle to obtain a steady-state flag. It should be understood that, due to transient conditions, such as when a user suddenly switches to defrost mode, the automotive blower motor needs to accelerate or decelerate to adapt to the new load. In this case, the mechanical angular velocity changes significantly, and there is a difference between the load torque and the electromagnetic torque, making it impossible to accurately reflect the actual pneumatic load through torque relationships. Therefore, this application further performs a steady-state determination of the motion state based on the motor's mechanical angular velocity in the current and previous control cycles to obtain a steady-state flag. This allows for the selection of stable load conditions by analyzing the changes in angular velocity between adjacent cycles, clarifying the effective execution conditions for subsequent steps. This allows for defining a stable operating condition range for pneumatic load reference torque calculation and thermal drift parameter observation, avoiding load calculation and parameter correction under transient conditions, ensuring that subsequent data comes from stable load scenarios, and improving the accuracy of load identification and parameter correction.

[0030] Specifically, in one possible embodiment, step S31 is implemented as follows: First, the controller retrieves the motor mechanical angular velocity data of the current control cycle (k) and the previous control cycle (k-1) from the data register and performs de-jitter processing on the two sets of data. Then, the absolute difference between the two sets of angular velocities, i.e., the rate of change, is calculated and compared with a preset steady-state discrimination threshold, such as 3 rad / s. Next, if the rate of change is less than the threshold, the steady-state duration counter is incremented by 1; if it is greater than the threshold, the counter is reset to zero. Finally, when the counter value reaches a preset duration, such as 5 to 10 sampling cycles, a steady-state flag (value 1) is generated; otherwise, a non-steady-state flag (value 0) is generated, and the flag is transmitted in real time to the aerodynamic torque calculation unit and the thermal drift parameter observation module.

[0031] Specifically, step S32 calculates the pneumatic load reference torque based on the motor's mechanical angular velocity in the current control cycle. It should be understood that since the pneumatic load torque of an automotive blower motor has a fixed physical relationship with the square of the mechanical angular velocity, and frictional torque such as bearing friction and wind resistance losses exists during operation, the total torque reflecting the actual load cannot be directly obtained from the angular velocity alone; it needs to be corrected using a friction model. Therefore, this application further calculates the pneumatic load reference torque based on the motor's mechanical angular velocity in the current control cycle, thereby first calculating the basic pneumatic torque through the angular velocity, and then superimposing the frictional torque to obtain the total reference torque including all load losses. In a specific example of this application, step S32 includes: calculating the pneumatic load reference torque using the following formula:

[0032]

[0033] in, The mechanical angular velocity of the motor in the current control cycle. , and These are the coefficients of the friction torque model. For frictional torque, This refers to the system drag coefficient from the most recent update. This is the reference torque for the pneumatic load. Specifically, Static friction corresponding to a rotational speed of 0 Corresponding to viscous friction, which has a linear relationship with rotational speed, This corresponds to the nonlinear friction loss under high-speed operating conditions. This method can cover the friction conditions across the entire speed range of the blower motor, avoiding estimation errors caused by a single friction model at low or high speeds. This allows for accurate quantification of the friction loss during motor operation, eliminating interference for subsequent separation of pneumatic load torque and friction torque. Based on this, the pneumatic load reference torque formula... Following the fundamental law of fluid mechanics that aerodynamic drag is proportional to the square of the flow velocity, The aerodynamic drag torque is quantified, and the total load torque of the motor is fully modeled after adding the friction torque. As a system resistance coefficient, it can be adaptively updated to reflect actual operating condition changes such as duct resistance and filter element status. Based on the real-time motor mechanical angular velocity, the true load reference torque under the current operating condition can be calculated, providing a benchmark for subsequent comparison of electromagnetic torque and identification of thermal drift. In this way, a reference torque that perfectly matches the actual load can be obtained, providing an accurate benchmark for subsequent comparison and estimation of electromagnetic torque and identification of torque deviation caused by flux thermal drift. This ensures that thermal drift correction can accurately compensate for parameter attenuation and avoid correction deviations caused by inaccurate load torque calculation.

[0034] In the aforementioned energy efficiency control method for a blower motor based on load identification, step S4 involves observing the thermal drift parameters of the pneumatic load reference torque and the estimated electromagnetic torque based on a steady-state flag to obtain a corrected real-time rotor flux linkage. It should be understood that due to the large temperature fluctuations in the operating environment of automotive blower motors, the rotor flux linkage decays as the temperature rises, causing a deviation between the electromagnetic torque estimated based on the factory baseline and the actual pneumatic load torque. Furthermore, the deviation under transient conditions is caused by load changes, not thermal drift. Therefore, this application further observes the thermal drift parameters of the pneumatic load reference torque and the estimated electromagnetic torque based on a steady-state flag to obtain a corrected real-time rotor flux linkage. This allows for the filtering of effective torque deviations only under steady-state conditions, focusing on correcting flux linkage decay caused by thermal drift. This ensures that flux linkage correction only addresses parameter changes caused by temperature, eliminating transient load interference and enabling the rotor flux linkage to match the actual operating conditions in real time. This provides accurate core parameters for subsequent optimal energy efficiency control, avoiding load identification errors and energy loss due to flux linkage inaccuracies.

[0035] In particular, in one specific embodiment, Figure 5 This is a flowchart of sub-step S4 of the blower motor energy efficiency control method based on load identification according to an embodiment of this application. Figure 5 As shown, step S4 includes: S41, in response to the steady-state flag being 1, calculating the effective torque deviation between the aerodynamic load reference torque and the estimated electromagnetic torque; S42, performing online correction of the effective torque deviation based on the PI observer to obtain the corrected real-time rotor flux.

[0036] Specifically, in step S41, in response to the steady-state flag being 1, the effective torque deviation between the pneumatic load reference torque and the estimated electromagnetic torque is calculated. It should be understood that, since the motor electromagnetic torque and pneumatic load torque are theoretically equal under steady-state conditions, the deviation is solely due to rotor flux thermal drift. However, the deviation under transient conditions originates from the dynamic torque during acceleration or deceleration and cannot reflect thermal drift. Therefore, this application further calculates the effective torque deviation between the pneumatic load reference torque and the estimated electromagnetic torque when the steady-state flag is 1, thereby extracting the torque difference caused solely by thermal drift and eliminating interference from the dynamic process. This provides deviation data directly related to flux thermal drift, offering accurate input for subsequent flux correction, avoiding errors in correction direction or amplitude due to transient deviations, and ensuring the effectiveness of thermal drift correction.

[0037] Specifically, in one possible embodiment, step S41 is implemented as follows: First, the controller monitors the steady-state flag in real time. When the flag is detected to be 1, the deviation calculation logic is triggered. Then, the pneumatic load reference torque and the estimated electromagnetic torque data are read from the controller buffer, and noise filtering is performed on both sets of data to eliminate high-frequency interference. Next, the difference between the two sets of torques is obtained through subtraction, which is the effective torque deviation. Then, the deviation value is checked for reasonableness to confirm that it is within the preset thermal drift deviation range, such as ±0.5 N·m, and abnormal data is excluded. Finally, the verified effective torque deviation is stored in the deviation data buffer for use by the thermal drift parameter observation module.

[0038] Preferably, when calculating the effective torque deviation between the pneumatic load reference torque and the estimated electromagnetic torque, the precise difference between the net pneumatic load reference torque and the net estimated electromagnetic torque is calculated. That is, It includes frictional torque The total load reference, and This is the total electromagnetic torque; therefore, from the estimated electromagnetic torque... Subtract the more deterministic frictional torque component that is unrelated to aerodynamic loads. The estimated net aerodynamic torque can then be obtained. Furthermore, this estimated net aerodynamic torque... Torque compared to a purely aerodynamic load model By performing differential calculations, the improved effective torque deviation can be obtained. .

[0039] First, from the perspective of improving the signal-to-noise ratio, the signal is the part that we really want to observe, affected by the thermal drift of the magnetic flux linkage, namely the permanent magnet torque, which directly acts on the aerodynamic and frictional loads, while the noise / interference is caused by the frictional torque. This indicates that it is a relatively independent physical process mainly related to rotational speed and grease temperature, and its model ( , and The parameters are pre-calibrated and inherently contain some model error. Therefore, if this model error introduces an effective torque deviation... In the calculation, this common-mode interference, such as friction torque, becomes a distractor. Therefore, if this interference is removed from both (or at least one) of the components before the differential calculation, the deviation can be minimized. It more purely reflects the permanent magnet torque error caused by inaccurate flux estimation, thereby improving the signal quality used for parameter observation.

[0040] Furthermore, from the perspective of model decoupling, since the motor system contains two main physical sub-models, the electromagnetic model (related to current and flux linkage) and the mechanical load model (related to aerodynamics and friction), the output of the electromagnetic model ( Subtract the friction model ( The contribution of ) can be projected onto the pure aerodynamic load domain, and then compared with the pure aerodynamic load model ( By comparing the two, the source of the deviation becomes clearer, directly pointing to the uncertainty parameter (i.e., flux linkage) in the electromagnetic model, thus avoiding the contamination of flux linkage observation by friction model errors. Specifically, step S41 includes: calculating the friction torque based on the motor mechanical angular velocity and friction torque model coefficients; subtracting the friction torque from the estimated electromagnetic torque to obtain the estimated net aerodynamic torque; and determining the difference between the estimated net aerodynamic torque and the modeled pure aerodynamic torque as the effective torque deviation.

[0041] Specifically, steady-state net aerodynamic torque deviation is calculated for the estimated electromagnetic torque based on reference parameters. Real-time mechanical angular velocity of motor And the most recently updated system drag coefficient Friction torque model coefficients , and The steady-state flag is checked, and subsequent deviation calculations are performed only if it equals one. Then, with the steady-state flag equal to one, the friction torque is calculated based on the motor's mechanical angular velocity and the friction torque model coefficients. Then, subtract the friction torque from the estimated electromagnetic torque to obtain the estimated net aerodynamic torque, i.e. The estimated net aerodynamic torque is obtained Furthermore, the modeled pure aerodynamic torque It is the torque that is theoretically generated purely by aerodynamic load.

[0042] Furthermore, in response to the steady-state flag being equal to one, the difference between the estimated net aerodynamic torque and the modeled pure aerodynamic torque is determined as the effective torque deviation, to more accurately reflect the error caused by inaccurate flux linkage parameters:

[0043] in This represents the effective torque deviation. By eliminating the direct interference of friction model errors, the input signal to the flux linkage observer is cleaner, making the corrected flux linkage closer to the true value and achieving higher observation accuracy. Furthermore, since the system is less sensitive to calibration errors in the friction coefficient, even minor deviations in the friction model calibration will not be directly transmitted to the flux linkage correction, improving the robustness of the entire adaptive algorithm. Moreover, the cleaner error signal helps the PI observer converge to a steady state more quickly, reducing unnecessary oscillations and improving the dynamic performance of parameter adaptation.

[0044] Specifically, step S42 involves online correction of the effective torque deviation using a PI observer to obtain the corrected real-time rotor flux linkage. It should be understood that since the effective torque deviation only reflects the degree of thermal drift, it cannot be directly used for flux linkage correction. Furthermore, simple proportional correction is prone to overshoot, and simple integral correction has a lag response, making it difficult to adapt to the dynamic temperature changes of automotive blower motors. Therefore, this application further utilizes a PI observer to process the effective torque deviation, achieving online correction of the rotor flux linkage and obtaining the corrected real-time rotor flux linkage. This combines the fast response of proportional control with the steady-state error-free characteristics of integral control to dynamically adjust the flux linkage value. In a specific example of this application, step S42 includes: online correction of the effective torque deviation using a PI observer based on the following formula:

[0045]

[0046]

[0047] in, This is the cumulative value of the integral term from the previous period. This is the cumulative value of the integral term for the current period. This is the total flux linkage correction calculated for the current period. and For the PI parameters of the PI observer, The sampling period is This is the reference value for magnetic flux. For effective torque deviation, This is the corrected real-time rotor flux linkage. That is, it is first calculated by accumulating the integral value from the previous control cycle. Combined with integral coefficients Torque deviation (i.e., the difference between the pneumatic load reference torque and the estimated electromagnetic torque) and sampling period This process gradually eliminates the steady-state component of the torque deviation, ensuring the correction has zero steady-state error and preventing long-term mismatch of flux linkage parameters caused by slow temperature drift. Based on this, a discretized form of a typical PI controller is adopted, where the proportional term... It can quickly respond to instantaneous torque deviations, enabling rapid correction of flux linkage, and the integral term... It continuously compensates for steady-state deviations, and the combination of both takes into account the response speed and accuracy of the correction, while also addressing torque deviations. By directly linking the difference between the actual load torque and the estimated electromagnetic torque, the rotor flux attenuation caused by temperature changes is transformed into a quantifiable correction. Ultimately, by superimposing the rotor flux reference value and the real-time correction, dynamic updates of the flux parameters are achieved. Specifically, when temperature increases causing physical attenuation of the rotor flux, the stator current required to maintain the same motor speed and load increases, leading to an overestimation of the estimated electromagnetic torque calculated based on the unattenuated reference flux. This results in a negative effective torque deviation and a negative flux correction. This negative correction is superimposed on the reference value, reducing the calculated real-time rotor flux value and accurately reflecting the thermal attenuation characteristics of the flux. As temperature changes, the correction adaptively adjusts to ensure the flux parameters remain consistent with the actual motor conditions. This allows for real-time and stable compensation for rotor flux thermal attenuation, preventing flux fluctuations during the correction process from causing motor instability. It ensures the corrected flux always matches the actual temperature conditions, providing high-precision parameter support for subsequent energy efficiency control.

[0048] In the aforementioned blower motor energy efficiency control method based on load identification, step S5 involves performing energy-optimal control on the target torque command based on the corrected real-time rotor flux linkage to obtain the optimal dq-axis current command. It should be understood that since the target torque command (from the vehicle HVAC controller) needs to be achieved through current drive, rotor flux linkage thermal drift can cause a mismatch between the original current command and the actual torque demand. Furthermore, uncorrected flux linkage can cause energy efficiency control to deviate from the optimal range of maximum torque-to-current ratio (MTPA), increasing energy loss. Therefore, this application further utilizes the corrected real-time rotor flux linkage to perform energy-optimal control on the target torque command to obtain the optimal dq-axis current command, thereby adapting the current distribution to the current flux linkage state and ensuring that current loss is minimized while meeting torque requirements. This avoids overcurrent or insufficient torque caused by flux linkage misalignment, reduces motor copper losses and the burden on the vehicle's electrical grid, and ensures stable blower motor airflow to meet the comfort requirements of automotive air conditioning.

[0049] In particular, in one specific embodiment, Figure 6This is a flowchart of sub-step S5 of the blower motor energy efficiency control method based on load identification according to an embodiment of this application. Figure 6 As shown, step S5 includes: S51, determining the optimal total current amplitude and the optimal current vector angle based on the corrected real-time rotor flux linkage and target torque command; S52, generating the optimal dq axis current command based on the optimal total current amplitude and the optimal current vector angle.

[0050] Specifically, in step S51, the optimal total current amplitude and optimal current vector angle are determined based on the corrected real-time rotor flux linkage and the target torque command. It should be understood that since the target torque command needs to be converted into specific current parameters before execution, and the total current amplitude determines the torque output intensity, while the current vector angle determines the dq-axis current distribution ratio, these two parameters not calculated based on the corrected flux linkage will lead to current redundancy or insufficient torque. Therefore, this application further combines the corrected real-time rotor flux linkage and the target torque command to determine the optimal total current amplitude and optimal current vector angle, thereby ensuring that the current parameters accurately match the current flux linkage state, ensuring that the current amplitude is minimized and the vector angle is optimal while meeting the torque requirements. This avoids additional losses caused by improper current distribution, allows the motor to operate in the optimal MTPA range, reduces motor heating, extends motor life, and adapts to the long-term operating requirements of automotive blower motors.

[0051] Specifically, in one possible embodiment, step S51 is implemented as follows: First, the controller retrieves the corrected real-time rotor flux linkage and target torque command, while simultaneously reading inherent parameters such as the number of motor pole pairs. Then, based on the correlation between flux linkage and torque, the minimum total current amplitude required to satisfy the target torque is calculated. Next, the vector angle optimization logic is activated, and the optimal current vector angle that minimizes current loss is determined by combining the MTPA curve (pre-stored in the controller). Then, the total current amplitude is verified to its rated value, and the vector angle is verified to its range, ensuring it is within the 0-90° range. Finally, the verified optimal total current amplitude and optimal current vector angle are stored in a dedicated data area for subsequent current command generation steps.

[0052] Specifically, in step S52, the optimal dq-axis current command is generated based on the optimal total current amplitude and the optimal current vector angle. It should be understood that since the optimal total current amplitude and vector angle are abstract parameters, they cannot directly drive the inverter, and the dq-axis current is the direct control object of the inverter; failure to convert it into this command will cause the drive logic to fail to execute. Therefore, this application further generates the optimal dq-axis current command based on the optimal total current amplitude and the optimal current vector angle, thereby converting the abstract current parameters into a specific control signal that the inverter can recognize, achieving precise current allocation. This ensures that the inverter outputs according to the most energy-efficient current scheme, driving the motor to operate in the high-efficiency zone while meeting torque requirements, avoiding drive disturbances caused by missing commands, ensuring stable operation of the blower motor, and adapting to the dynamic load requirements of automotive air conditioning.

[0053] Specifically, in one possible embodiment, step S52 is implemented as follows: First, the controller retrieves the optimal total current amplitude and optimal current vector angle from the dedicated data area. Then, it executes polar coordinate to Cartesian coordinate transformation logic, converting the total current amplitude and vector angle into corresponding d-axis and q-axis current components. Next, the transformed current components are filtered to eliminate computational noise. Then, the current components are checked for upper and lower limits to ensure they do not exceed the motor's rated d / q-axis current. Finally, the verified d-axis and q-axis current values ​​are encapsulated into an optimal dq-axis current command, stored in the controller output register, and synchronously transmitted to the inverter's current loop control unit to drive the motor.

[0054] In summary, the energy efficiency control method for a blower motor based on load identification, as described in this application, is explained. First, it collects multi-dimensional operating parameters of the blower motor in real time and estimates the basic electromagnetic torque based on the rotor flux linkage reference value. Simultaneously, by analyzing the variation characteristics of the motor's mechanical angular velocity, a steady-state discrimination mechanism and an aerodynamic load reference model are constructed, thereby accurately determining the current aerodynamic load reference torque during stable operation. Furthermore, utilizing the difference between the aerodynamic load reference torque and the estimated basic torque, a thermal drift parameter observer is constructed to dynamically capture and correct rotor flux linkage attenuation caused by temperature changes in real time. Finally, based on the corrected real-time flux linkage parameters, an energy efficiency optimal control algorithm is executed to calculate the optimal shaft current command. This effectively eliminates the interference of temperature changes on the load identification accuracy, thereby achieving accurate load matching and optimal energy efficiency operation of the blower motor under wide temperature ranges and complex operating conditions.

[0055] Figure 7 This is a block diagram of a blower motor energy efficiency control system based on load identification according to an embodiment of this application. Figure 7As shown, the blower motor energy efficiency control system 100 based on load identification according to an embodiment of this application includes: a multi-parameter synchronous acquisition module 110, used to acquire the motor three-phase current sampling value, bus voltage, motor electrical angular velocity, motor mechanical angular velocity, and rotor flux reference value; an electromagnetic torque estimation module 120, used to perform basic electromagnetic torque estimation on the motor three-phase current sampling value and rotor flux reference value to obtain the estimated electromagnetic torque; a steady-state discrimination and torque calculation module 130, used to perform steady-state discrimination and aerodynamic load reference torque calculation based on the motor mechanical angular velocity to obtain a steady-state flag and aerodynamic load reference torque; a thermal drift parameter observation module 140, used to observe thermal drift parameters on the aerodynamic load reference torque and the estimated electromagnetic torque based on the steady-state flag to obtain the corrected real-time rotor flux; and an energy efficiency optimal control module 150, used to perform energy efficiency optimal control on the target torque command based on the corrected real-time rotor flux to obtain the optimal dq axis current command.

[0056] As described above, the load-identification-based blower energy efficiency control system 100 according to the embodiments of this application can be implemented in various wireless terminals, such as servers with load-identification-based blower energy efficiency control algorithms. In one possible implementation, the load-identification-based blower energy efficiency control system 100 according to the embodiments of this application can be integrated into the wireless terminal as a software module and / or a hardware module. For example, the load-identification-based blower energy efficiency control system 100 can be a software module in the operating system of the wireless terminal, or it can be an application developed for the wireless terminal; of course, the load-identification-based blower energy efficiency control system 100 can also be one of many hardware modules of the wireless terminal.

[0057] Alternatively, in another example, the load-based blower energy efficiency control system 100 and the wireless terminal can also be separate devices, and the load-based blower energy efficiency control system 100 can be connected to the wireless terminal via wired and / or wireless networks, and transmit interactive information in accordance with an agreed data format.

[0058] Here, those skilled in the art will understand that the specific operations of each step in the above-described load-identification-based blower motor energy efficiency control system have been referenced above. Figures 1 to 6 The description of the energy efficiency control method for blower motors based on load identification has been detailed, and therefore, its repeated description will be omitted.

Claims

1. A load identification based blower motor efficiency control method, characterized in that, The method comprises the following steps: obtaining motor three-phase current sample values, bus voltage, motor electrical angular velocity, motor mechanical angular velocity and rotor flux reference value; performing basic electromagnetic torque estimation on the motor three-phase current sample values and the rotor flux reference value to obtain an estimated electromagnetic torque; performing steady state discrimination and aerodynamic load reference torque calculation based on the motor mechanical angular velocity to obtain a steady state flag and an aerodynamic load reference torque; performing thermal drift parameter observation on the aerodynamic load reference torque and the estimated electromagnetic torque based on the steady state flag to obtain a corrected real-time rotor flux; performing energy efficiency optimal control on a target torque instruction based on the corrected real-time rotor flux to obtain optimal d-q axis current instructions.

2. The load identification based blower motor efficiency control method of claim 1, wherein, The method of performing basic electromagnetic torque estimation on the motor three-phase current sample values and the rotor flux reference value to obtain an estimated electromagnetic torque comprises the following steps: performing three-phase current coordinate system transformation on the motor three-phase current sample values based on the rotor electrical angle to obtain direct-axis current components and quadrature-axis current components; performing electromagnetic torque estimation based on the reference parameters on the quadrature-axis current components and the rotor flux reference value to obtain the estimated electromagnetic torque.

3. The load identification based blower motor efficiency control method of claim 2, wherein, The method of performing electromagnetic torque estimation based on the reference parameters on the quadrature-axis current components and the rotor flux reference value to obtain the estimated electromagnetic torque comprises the following steps: performing electromagnetic torque estimation based on the reference parameters on the quadrature-axis current components and the rotor flux reference value by using the following formula: ; wherein is a rotor flux reference value, is a number of motor pole pairs, is a quadrature axis current component, is an estimated electromagnetic torque.

4. The load identification based blower motor efficiency control method of claim 1, wherein, The method of performing steady state discrimination and aerodynamic load reference torque calculation based on the motor mechanical angular velocity to obtain a steady state flag and an aerodynamic load reference torque comprises the following steps: performing motion state steady state discrimination based on the motor mechanical angular velocity of the current control period and the motor mechanical angular velocity of the previous control period to obtain the steady state flag; calculating the aerodynamic load reference torque based on the motor mechanical angular velocity of the current control period by using the following formula: ; wherein, is the motor mechanical angular velocity of the current control period, , and is a friction torque model coefficient, is the friction torque, is the system drag coefficient updated most recently, is the aerodynamic load reference torque.

5. The load identification based blower motor efficiency control method of claim 1, wherein, The method of performing thermal drift parameter observation on the aerodynamic load reference torque and the estimated electromagnetic torque based on the steady state flag to obtain a corrected real-time rotor flux comprises the following steps: in response to the steady state flag being 1, calculating an effective torque deviation between the aerodynamic load reference torque and the estimated electromagnetic torque; performing rotor flux online correction based on the PI observer on the effective torque deviation to obtain the corrected real-time rotor flux.

6. The load identification based blower motor efficiency control method according to claim 5, characterized in that, The method of calculating an effective torque deviation between the aerodynamic load reference torque and the estimated electromagnetic torque comprises the following steps: calculating a friction torque based on the motor mechanical angular velocity and the friction torque model coefficient; subtracting the friction torque from the estimated electromagnetic torque to obtain an estimated net aerodynamic torque; determining the difference between the estimated net aerodynamic torque and the modeled pure aerodynamic torque as the effective torque deviation.

7. The load identification based blower motor efficiency control method of claim 5, wherein, The method of performing rotor flux online correction based on the PI observer on the effective torque deviation to obtain the corrected real-time rotor flux comprises the following steps: performing rotor flux online correction based on the PI observer on the effective torque deviation by using the following formula: 。 8. wherein, is the accumulated value of the integral term of the previous cycle, is the accumulated value of the integral term of the current cycle, is the total flux linkage correction calculated in the current cycle, and is the PI parameter of the PI observer, is the sampling period, is the flux reference value, is the effective torque deviation, is the modified real-time rotor flux linkage.

9. The load identification based blower motor efficiency control method of claim 1, wherein, The method of performing energy efficiency optimal control on a target torque instruction based on the corrected real-time rotor flux to obtain optimal d-q axis current instructions comprises the following steps: determining an optimal total current amplitude and an optimal current vector angle based on the corrected real-time rotor flux and the target torque instruction; generating the optimal d-q axis current instructions based on the optimal total current amplitude and the optimal current vector angle.

10. A load recognition based blower motor efficiency control system, comprising: The method comprises the following steps: The multi-parameter synchronous acquisition module is configured to acquire motor three-phase current sampling values, bus voltage, motor electrical angular velocity, motor mechanical angular velocity, and rotor flux reference values. The electromagnetic torque estimation module is configured to perform basic electromagnetic torque estimation on the motor three-phase current sampling values and the rotor flux reference values to obtain an estimated electromagnetic torque. The steady-state discrimination and torque calculation module is configured to perform steady-state discrimination and aerodynamic load reference torque calculation based on the motor mechanical angular velocity to obtain a steady-state flag and an aerodynamic load reference torque. The thermal drift parameter observation module is configured to perform thermal drift parameter observation on the aerodynamic load reference torque and the estimated electromagnetic torque based on the steady-state flag to obtain a corrected real-time rotor flux. The energy efficiency optimal control module is configured to perform energy efficiency optimal control on a target torque instruction based on the corrected real-time rotor flux to obtain optimal d-q axis current instructions.

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