Method and system for verifying motor controller output torque

Abstract

The application provides a motor controller output torque verification method and system, comprising: acquiring wheel speed signals collected by a plurality of wheel speed sensors of a target vehicle respectively, and a torque instruction value corresponding to a motor controller of the target vehicle; determining current motion parameters of the target vehicle according to the plurality of wheel speed signals; determining an estimated output torque of a motor controlled by the motor controller based on the current motion parameters through a preset vehicle dynamics model; the preset vehicle dynamics model is used to inversely calculate the actual output torque of the motor according to the wheel speed; and determining an output torque verification result of the motor controller according to a torque deviation between the torque instruction value and the estimated output torque. In the method, the output torque of the motor is estimated based on the wheel speed signal, the output torque of the motor controller can be verified in real time, thereby reducing the verification cost, and further improving the accuracy and applicability of the torque verification under complex driving conditions.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a method and system for verifying the output torque of a motor controller. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the motor controller, as a core component of the vehicle's power system, directly affects the vehicle's power, economy, and driving safety due to the accuracy of its output torque. During vehicle factory testing, fault diagnosis, and operation and maintenance, it is necessary to verify the consistency between the torque command value corresponding to the motor controller and the actual output torque of the motor.

[0003] Existing technologies primarily calculate actual torque by installing high-precision bus current sensors in the high-voltage circuit of the motor, or by conducting offline testing on specialized test benches. However, adding current sensors not only significantly increases the hardware cost per vehicle and the development difficulty of high-voltage integration, but also makes the sensors susceptible to electromagnetic interference and motor parameter drift due to aging, resulting in a severe decrease in calibration accuracy over long-term use. Furthermore, bench testing is not only time-consuming and inefficient, but also cannot replicate the complex operating conditions of a real vehicle, while simplified dynamic model estimation methods suffer from excessive estimation errors due to neglecting dynamic changes in road resistance and load.

[0004] In summary, existing technologies cannot achieve high-precision real-time verification of the output torque of the motor controller under complex driving conditions in real vehicles without increasing hardware costs. Summary of the Invention

[0005] In view of this, the purpose of this application is to provide a method and system for verifying the output torque of a motor controller. By estimating the output torque of the motor based on the wheel speed signal, the output torque of the motor controller can be verified in real time, thereby reducing the verification cost and improving the accuracy and applicability of torque verification under complex driving conditions.

[0006] In a first aspect, the present invention provides a method for verifying the output torque of a motor controller, comprising: The system acquires wheel speed signals collected by multiple wheel speed sensors of the target vehicle, as well as torque command values ​​corresponding to the motor controller of the target vehicle.

[0007] The current motion parameters of the target vehicle are determined based on multiple wheel speed signals.

[0008] Based on the current motion parameters, the estimated output torque of the motor controlled by the motor controller is determined by a preset vehicle dynamics model; the preset vehicle dynamics model is used to inversely calculate the actual output torque of the motor based on the wheel speed.

[0009] The output torque verification result of the motor controller is determined based on the torque deviation between the torque command value and the estimated output torque.

[0010] In an optional implementation, the current motion parameters of the target vehicle are determined based on multiple wheel speed signals, including: Multiple wheel speed signals are preprocessed to obtain the processed wheel speed signal corresponding to each wheel speed signal.

[0011] Multiple processed wheel speed signals are fused to obtain the fused vehicle speed of the target vehicle.

[0012] The target vehicle's current speed and current acceleration are determined based on the merged vehicle speed.

[0013] The current vehicle speed and current vehicle acceleration are determined as the current motion parameters.

[0014] In an optional implementation, multiple wheel speed signals are preprocessed to obtain processed wheel speed signals corresponding to each wheel speed signal, including: For each wheel speed signal, the wheel speed value at the current sampling time and the wheel speed value at the previous sampling time are obtained, and the absolute difference between the wheel speed value at the current sampling time and the wheel speed value at the previous sampling time is determined as the wheel speed change.

[0015] Multiply the wheel speed value at the previous sampling time by the preset wheel speed change ratio to obtain the wheel speed change threshold corresponding to the current sampling time.

[0016] If the wheel speed change exceeds the wheel speed change threshold, the wheel speed value at the current sampling time will be identified as an abnormal wheel speed value.

[0017] Using the current sampling time as the end point of the window, and according to the preset window size, obtain the wheel speed values ​​corresponding to the current sampling time and multiple consecutive sampling times before the current sampling time, and form a sliding window.

[0018] When the wheel speed change exceeds the wheel speed change threshold, the median of multiple wheel speed values ​​within the sliding window is determined, and the abnormal wheel speed value is replaced with the median to obtain the corrected wheel speed signal.

[0019] If the wheel speed change is less than or equal to the wheel speed change threshold, the wheel speed value at the current sampling time is retained to obtain the corrected wheel speed signal.

[0020] The modified wheel speed signal is filtered by a low-pass filter with a preset cutoff frequency to obtain the processed wheel speed signal.

[0021] In an optional implementation, multiple processed wheel speed signals are fused to obtain the fused vehicle speed of the target vehicle, including: Based on the drive type of the wheels corresponding to the multiple wheel speed sensors, the multiple processed wheel speed signals are divided into processed wheel speed signals corresponding to the drive wheels and processed wheel speed signals corresponding to the non-drive wheels.

[0022] The processed wheel speed signals corresponding to each non-drive wheel are averaged to obtain the reference speed.

[0023] For each drive wheel, the value obtained by subtracting the reference speed from the processed wheel speed signal corresponding to that drive wheel is determined as the speed difference, and the ratio between the speed difference and the reference speed is determined as the slip ratio corresponding to that drive wheel.

[0024] Based on the preset slip ratio-fusion weight mapping relationship, the initial fusion weight corresponding to each drive wheel is determined; wherein, the slip ratio-fusion weight mapping relationship is: the larger the absolute value of the slip ratio, the smaller the corresponding fusion weight.

[0025] Obtain the pre-set non-driving wheel weights and determine the initial fusion weights corresponding to each non-driving wheel.

[0026] The initial fusion weights corresponding to each wheel are summed to obtain the weight sum value. The ratio between the initial fusion weight and the weight sum value corresponding to each wheel is determined to obtain the target fusion weight corresponding to each wheel.

[0027] Each processed wheel speed signal is multiplied by its corresponding target fusion weight, and the resulting products are summed to obtain the fused vehicle speed of the target vehicle.

[0028] In an optional implementation, when the target vehicle is turning, after obtaining the fused vehicle speed of the target vehicle, the method further includes: Obtain the yaw rate of the target vehicle, the preset correction coefficient, and the distance between each wheel and the center of the target vehicle.

[0029] For each wheel, the absolute value of the yaw rate, the distance corresponding to the wheel, and the preset correction coefficient are multiplied to obtain the first correction amount for that wheel, and the difference between the value 1 and the first correction amount is determined as the wheel speed correction coefficient for that wheel.

[0030] Each processed wheel speed signal, its corresponding target fusion weight, and its corresponding wheel speed correction coefficient are multiplied together, and the resulting products are summed to obtain the first fusion speed.

[0031] The distance corresponding to each wheel is multiplied by the corresponding target fusion weight, and the resulting products are summed to obtain the weighted distance.

[0032] The weighted distance, the absolute value of the yaw rate, and the preset correction coefficient are multiplied to obtain the second correction amount, and the difference between the value 1 and the second correction amount is determined as the fusion correction coefficient.

[0033] The ratio between the first fusion speed and the fusion correction coefficient is determined as the corrected fusion vehicle speed, and the corrected fusion vehicle speed is used to update the fusion vehicle speed of the target vehicle.

[0034] In an optional implementation, determining the target vehicle's current vehicle speed and current vehicle acceleration based on the fused vehicle speed includes: The fused vehicle speed is determined as the observed vehicle speed of the Kalman filter algorithm, and the vehicle speed and vehicle acceleration are set as the state variables of the Kalman filter algorithm.

[0035] Obtain the vehicle speed, vehicle acceleration, and state covariance corresponding to the previous sampling time.

[0036] Based on the vehicle speed, vehicle acceleration, and sampling time interval at the previous sampling time, the predicted vehicle speed at the current sampling time is determined, and the vehicle acceleration at the previous sampling time is determined as the predicted vehicle acceleration at the current sampling time.

[0037] Based on the state covariance at the previous sampling time, the preset state transition relationship, and the preset process noise covariance, determine the prediction covariance at the current sampling time.

[0038] The observation noise covariance is determined based on the comparison between the absolute value of the predicted vehicle acceleration and the preset acceleration fluctuation threshold. Specifically, when the absolute value of the predicted vehicle acceleration is less than or equal to the preset acceleration fluctuation threshold, the first observation noise covariance is used; when the absolute value of the predicted vehicle acceleration is greater than the preset acceleration fluctuation threshold, the second observation noise covariance, which is greater than the first observation noise covariance, is used.

[0039] The Kalman gain is determined based on the predicted covariance, the pre-defined observation relationship, and the observation noise covariance.

[0040] Based on the speed difference between the observed vehicle speed and the predicted vehicle speed, and the Kalman gain, the predicted vehicle speed and predicted vehicle acceleration are corrected to obtain the current vehicle speed and current vehicle acceleration.

[0041] Based on the Kalman gain, the preset observation relationship, and the predicted covariance, the predicted covariance is updated to obtain the state covariance corresponding to the current sampling time.

[0042] In an optional implementation, determining the target vehicle's current vehicle speed and current vehicle acceleration based on the fused vehicle speed includes: The fused vehicle speed is determined as the current vehicle speed, and the vehicle speed, vehicle acceleration, and sampling time interval corresponding to the previous sampling moment are obtained.

[0043] The value obtained by subtracting the vehicle speed at the previous sampling time from the current vehicle speed is determined as the speed difference, and the ratio between the speed difference and the sampling time interval is determined as the initial vehicle acceleration.

[0044] Obtain the current driving conditions of the target vehicle, and determine the target smoothing coefficient corresponding to the current driving conditions based on the preset correspondence between driving conditions and smoothing coefficients.

[0045] The first acceleration component is obtained by multiplying the initial vehicle acceleration by the target smoothing coefficient.

[0046] The difference between the value 1 and the target smoothing coefficient is multiplied by the vehicle acceleration corresponding to the previous sampling time to obtain the second acceleration component.

[0047] Adding the first acceleration component to the second acceleration component yields the smooth vehicle acceleration.

[0048] If the absolute value of the smoothed vehicle acceleration is less than or equal to a preset acceleration threshold, the smoothed vehicle acceleration is determined as the current vehicle acceleration.

[0049] If the absolute value of the smoothed vehicle acceleration is greater than the preset acceleration threshold, the vehicle acceleration corresponding to the preset number of sampling periods before the current sampling time is averaged, and the resulting average vehicle acceleration is determined as the current vehicle acceleration.

[0050] In an optional implementation, based on current motion parameters, the estimated output torque of the motor controlled by the motor controller is determined using a preset vehicle dynamics model, including: The system acquires the target vehicle's baseline mass, equivalent mass of rotating parts, additional load, standard wheel rolling radius, actual tire pressure, standard tire pressure, road slope angle, current altitude, ambient temperature, drag coefficient, frontal area, motor speed, and preset gravitational acceleration.

[0051] The standard rolling radius of the wheel is corrected based on the change in actual tire pressure relative to standard tire pressure and the additional load, to obtain the current rolling radius of the wheel.

[0052] The rolling resistance coefficient is determined based on the current vehicle speed, and the rolling resistance experienced by the target vehicle is determined based on the vehicle's reference mass, additional load, rolling resistance coefficient, preset gravitational acceleration, and road surface slope angle.

[0053] The air density is determined based on the current altitude and ambient temperature.

[0054] The air resistance experienced by the target vehicle is determined based on air density, drag coefficient, frontal area, and current vehicle speed.

[0055] The slope resistance experienced by the target vehicle is determined based on the vehicle's baseline mass, additional load, preset gravitational acceleration, and road slope angle.

[0056] Based on the vehicle's reference mass, the equivalent mass of rotating components, and the current vehicle acceleration, determine the vehicle inertial force corresponding to the target vehicle.

[0057] The driving force corresponding to the target vehicle is obtained by adding the vehicle's inertial force, rolling resistance, air resistance, and gradient resistance.

[0058] The effective torque of the wheel is obtained by multiplying the driving force by the current rolling radius of the wheel.

[0059] The mechanical loss torque of the transmission system is determined based on the motor speed and the pre-calibrated correspondence between the motor speed and the mechanical loss torque.

[0060] Based on the preset speed range in which the current vehicle speed is located, and the correspondence between the preset speed range and the total efficiency of the transmission system, the total efficiency of the transmission system corresponding to the current vehicle speed is determined.

[0061] The effective torque of the wheel is added to the mechanical loss torque of the transmission system, and the ratio between the sum and the total efficiency of the transmission system is determined as the estimated output torque of the motor controlled by the motor controller.

[0062] In an optional implementation, the output torque verification result of the motor controller is determined based on the torque deviation between the torque command value and the estimated output torque, including: The absolute difference between the torque command value and the estimated output torque is defined as the torque deviation.

[0063] Determine whether the torque deviation is greater than the preset torque deviation threshold.

[0064] If the torque deviation is less than or equal to the preset torque deviation threshold, the output torque verification result is determined to be normal torque output.

[0065] If the torque deviation is greater than the preset torque deviation threshold, determine the duration for which the torque deviation is continuously greater than the preset torque deviation threshold, and obtain the system initialization status flag.

[0066] If the duration reaches the preset duration and the system initialization status flag indicates that the system initialization is complete, the output torque verification result is determined to be abnormal, and the torque monitoring fault status is triggered.

[0067] Secondly, the present invention provides a system for verifying the output torque of a motor controller, comprising: The data acquisition module is used to acquire wheel speed signals collected by multiple wheel speed sensors of the target vehicle, as well as the torque command value corresponding to the motor controller of the target vehicle.

[0068] The motion parameter determination module is used to determine the current motion parameters of the target vehicle based on multiple wheel speed signals.

[0069] The torsion estimation module is used to determine the estimated output torque of the motor controlled by the motor controller based on the current motion parameters and a preset vehicle dynamics model; the preset vehicle dynamics model is used to inversely calculate the actual output torque of the motor based on the wheel speed.

[0070] The verification result determination module is used to determine the output torque verification result of the motor controller based on the torque deviation between the torque command value and the estimated output torque.

[0071] This application provides a method and system for verifying the output torque of a motor controller. By utilizing wheel speed signals collected by multiple wheel speed sensors to determine the current motion parameters of the target vehicle, and by using a preset vehicle dynamics model to estimate the actual output torque of the motor, real-time estimation of the motor output torque can be achieved without the need for additional dedicated torque sensors or bus current sensors, reducing hardware costs and detection complexity. Simultaneously, by comparing the torque command value with the estimated output torque to determine the output torque verification result, abnormal torque output of the motor controller can be identified in a timely manner, improving the accuracy, reliability, and efficiency of torque verification, thereby ensuring the vehicle's power performance and driving safety.

[0072] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the application. The objectives and other advantages of this application are realized and obtained through the structures particularly pointed out in the description, claims and drawings.

[0073] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0074] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0075] Figure 1A flowchart illustrating the method for verifying the output torque of a motor controller provided in this application embodiment; Figure 2 A flowchart illustrating the method for determining the current motion parameters of a target vehicle as provided in this application embodiment; Figure 3 A schematic diagram of a system for verifying the output torque of a motor controller is provided for an embodiment of this application.

[0076] Icons: 1-Data acquisition module; 2-Motion parameter determination module; 3-Torsion estimation module; 4-Verification result determination module. Detailed Implementation

[0077] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0078] To help those skilled in the art better understand this application, a brief introduction to its application scenarios and design concepts is provided.

[0079] In new energy vehicles, the motor controller controls the motor's output torque according to corresponding torque commands. The accuracy of the motor's output torque directly affects the vehicle's power performance, energy consumption, and driving safety. Therefore, during vehicle factory testing, fault diagnosis, and operation and maintenance, it is necessary to verify the output torque of the motor controller to determine whether the actual output torque of the motor matches the torque command value.

[0080] Existing technologies typically detect motor output torque directly by adding a torque sensor, or by adding a bus current sensor to collect motor current and then combining this data with the motor's electromagnetic model to estimate the output torque. These methods require dedicated detection hardware and corresponding installation structures, increasing vehicle hardware costs and assembly complexity. Furthermore, the bus current sensor needs to be installed in a high-voltage circuit, placing high demands on installation space, insulation performance, and electromagnetic compatibility. In addition, torque estimation based on motor current is susceptible to interference from electromagnetic interference, motor temperature variations, and aging drift of motor parameters.

[0081] Some existing technologies indirectly estimate motor output torque through vehicle motion, but these typically do not adequately consider factors such as individual wheel slippage, vehicle turning, changes in vehicle load, tire condition changes, rolling resistance, air resistance, and gradient resistance. This leads to a significant discrepancy between the estimated motor output torque and the actual output torque. Furthermore, torque calibration using a dedicated test bench requires the vehicle to be stopped and connected to specialized testing equipment, making it difficult to perform real-time calibration while the vehicle is actually in motion.

[0082] Based on this, this application acquires wheel speed signals collected by multiple wheel speed sensors of the target vehicle, determines the current motion parameters of the target vehicle based on the multiple wheel speed signals, and calculates the actual output torque of the motor by reverse calculation based on the wheel speed using a preset vehicle dynamics model, thereby obtaining the estimated output torque of the motor controlled by the motor controller. Furthermore, this application compares the torque command value with the estimated output torque, and determines the output torque verification result of the motor controller based on the torque deviation between the two.

[0083] Therefore, this application can reuse the existing wheel speed sensors of the target vehicle without adding a dedicated torque sensor or bus current sensor, thereby reducing the hardware cost and implementation complexity of torque verification. At the same time, by comprehensively utilizing multiple wheel speed signals and combining them with the vehicle dynamics model to estimate the motor output torque, the influence of factors such as wheel slippage, vehicle turning, and vehicle driving resistance on the estimation results can be reduced, improving the accuracy and applicability of torque verification under complex driving conditions, and enabling real-time verification during normal vehicle driving.

[0084] To facilitate understanding of this embodiment, the embodiments of this application will be described in detail below.

[0085] This application provides a method for verifying the output torque of a motor controller, referring to... Figure 1 The method for verifying the output torque of a motor controller provided in this application includes: Step S101: Obtain the wheel speed signals collected by the multiple wheel speed sensors of the target vehicle, and the torque command value corresponding to the motor controller of the target vehicle.

[0086] Here, the target vehicle is a new energy vehicle whose motor controller output torque needs to be verified. Multiple wheel speed sensors are respectively installed on multiple wheels of the target vehicle to collect the rotation status of the corresponding wheels according to a preset sampling period.

[0087] Wheel speed signals can be wheel angular velocity, wheel rotational speed, or wheel speed pulse signals, and can also be the equivalent vehicle speed value after linear velocity conversion. When the wheel speed sensor outputs wheel angular velocity, the product of the wheel angular velocity and the current rolling radius of the wheel is determined as the corresponding wheel speed value; when the wheel speed sensor outputs wheel revolutions per minute, the wheel speed per minute, the current rolling radius of the wheel, and twice pi are multiplied, and the result is divided by 60 to obtain the corresponding wheel speed value. The first sampling period uses the standard rolling radius of the wheel for conversion, and subsequent sampling periods use the current rolling radius of the wheel updated in the previous sampling period for conversion. The wheel speed value uniformly represents the converted wheel linear velocity.

[0088] The torque command value is the target torque command value executed by the motor controller at the current sampling time. This target torque command value is sent from the vehicle controller to the motor controller, instructing the motor controller to control the target torque output of the motor. To ensure that the torque command value corresponds to the vehicle's motion response, multiple wheel speed signals and the torque command value are time-aligned according to their sampling timestamps. When the sampling timestamps of multiple wheel speed signals and the torque command value are inconsistent, the torque command value closest to the sampling timestamp of the wheel speed signals is selected within a preset time tolerance range. For example, the sampling time interval for the wheel speed signals is 0.01 seconds.

[0089] Step S102: Determine the current motion parameters of the target vehicle based on multiple wheel speed signals.

[0090] Here, the current motion parameters include the current vehicle speed and current vehicle acceleration. Outlier correction and filtering are performed on multiple wheel speed signals. Fusion weights are determined based on the processed wheel speed signals corresponding to each wheel, and the multiple processed wheel speed signals are fused to obtain the fused vehicle speed. When the target vehicle is turning, turning corrections are applied to the fused vehicle speed based on the yaw rate and the distance between each wheel and the vehicle center.

[0091] Wheel speed anomaly detection employs both a relative change threshold and a minimum wheel speed change threshold. The wheel speed change threshold corresponding to the current sampling time is the larger of the product of the wheel speed value at the previous sampling time and a preset wheel speed change ratio, and the minimum wheel speed change threshold. The minimum wheel speed change threshold is pre-calibrated based on the resolution of the wheel speed sensor and the low-speed test results of the target vehicle to avoid the wheel speed change threshold simultaneously becoming zero when the wheel speed value at the previous sampling time is zero.

[0092] The current vehicle speed and acceleration can be determined simultaneously using the Kalman filter algorithm, or separately using speed difference and smoothing. When using the Kalman filter algorithm, the fused vehicle speed is used as the observed vehicle speed, and the vehicle speed and acceleration are used as state variables. The current vehicle speed and acceleration are obtained through state prediction and observation correction. When using speed difference and smoothing, the fused vehicle speed is determined as the current vehicle speed. The initial vehicle acceleration is determined based on the difference between the current vehicle speed and the vehicle speed at the previous sampling time. Then, the initial vehicle acceleration is smoothed according to the smoothing coefficient corresponding to the current driving condition to obtain the current vehicle acceleration. These two methods of determining motion parameters are alternative implementation methods. Within a calibration cycle, one of the pre-configured motion parameter determination methods is selected and executed.

[0093] To avoid division by zero when calculating slip ratio at zero or low speeds, a minimum reference speed threshold is preset. When the reference speed corresponding to a non-drive wheel is greater than the minimum reference speed threshold, the slip ratio is determined based on the processed wheel speed signal of the drive wheel and the reference speed, and the fusion weight corresponding to the drive wheel is determined based on the slip ratio. When the reference speed is less than or equal to the minimum reference speed threshold, the slip ratio is not calculated, the preset default fusion weight is used to determine the fused vehicle speed, and the current verification status is set to verification disabled.

[0094] Step S103: Based on the current motion parameters, determine the estimated output torque of the motor controlled by the motor controller through a preset vehicle dynamics model; the preset vehicle dynamics model is used to inversely calculate the actual output torque of the motor based on the wheel speed.

[0095] Here, the input data for the preset vehicle dynamics model includes the current vehicle speed, current vehicle acceleration, and vehicle dynamics parameters. These parameters include the vehicle's baseline mass, equivalent mass of rotating components, additional load, current wheel rolling radius, rolling resistance coefficient, road surface slope angle, air density, drag coefficient, frontal area, overall transmission efficiency, overall transmission ratio, and transmission mechanical loss torque.

[0096] The vehicle's inertial force is determined based on its reference mass, the equivalent mass of rotating components, and current vehicle acceleration. Rolling resistance is determined based on the vehicle's reference mass, additional load, rolling resistance coefficient, and road surface slope angle. Air resistance is determined based on air density, drag coefficient, frontal area, and current vehicle speed. Slope resistance is determined based on the vehicle's reference mass, additional load, and road surface slope angle. The inertial force, rolling resistance, air resistance, and slope resistance are then added together to obtain the driving force corresponding to the target vehicle. Finally, the effective wheel torque is determined based on the driving force and the current wheel rolling radius.

[0097] When a reducer, final reducer, or transmission mechanism is installed between the target vehicle's motor and wheels, the effective torque of the wheels is converted to the motor shaft side based on the overall transmission ratio and overall efficiency of the transmission system, and the estimated output torque is determined in conjunction with the mechanical loss torque of the transmission system. If the target vehicle uses a structure where the motor directly drives the wheels, or if the effective torque of the wheels has already been equivalently converted to the motor shaft side, the overall transmission ratio of the transmission system is taken as 1. The mechanical loss torque of the transmission system is determined based on the pre-calibrated relationship between the motor speed and the mechanical loss torque, and the overall efficiency of the transmission system is determined based on the vehicle speed range at the current vehicle speed. The estimated output torque is used to characterize the actual output torque generated by the motor under the current vehicle motion state.

[0098] Step S104: Determine the output torque verification result of the motor controller based on the torque deviation between the torque command value and the estimated output torque.

[0099] Here, the absolute difference between the torque command value and the estimated output torque is defined as the torque deviation, and the torque deviation is compared with a preset torque deviation threshold. When the torque deviation is less than or equal to the preset torque deviation threshold, the output torque verification result is determined to be normal torque output.

[0100] When the torque deviation exceeds a preset torque deviation threshold, the duration for which the torque deviation continuously exceeds the preset threshold is accumulated. When the accumulated duration reaches a preset duration, and the system initialization status indicates that system initialization is complete, the output torque verification result is determined to be an abnormal torque output, and a torque monitoring fault state is triggered. For example, the preset torque deviation threshold is 100 Nm (Newton-meters), and the preset duration is 1 second. If the current verification status is "verification not enabled" or "system initialization not yet complete," the torque output abnormality determination is paused, and the accumulation of the abnormal torque duration is also paused.

[0101] The method for verifying the output torque of a motor controller provided in this application can estimate the actual output torque of the motor using multiple existing wheel speed sensors of the target vehicle without the need to add a dedicated torque sensor or bus current sensor. It can also compare the torque command value with the estimated output torque during normal driving of the target vehicle, thereby reducing the cost of torque verification and improving the accuracy, real-time performance and reliability of torque verification under complex driving conditions.

[0102] In an optional implementation, refer to Figure 2 Step S102 includes the following steps S201-S204.

[0103] Step S201: Preprocess the multiple wheel speed signals to obtain the processed wheel speed signal corresponding to each wheel speed signal.

[0104] Here, preprocessing includes outlier detection and correction, as well as low-pass filtering.

[0105] Each wheel speed signal is preprocessed independently according to the same processing flow, maintaining a one-to-one correspondence between the wheel speed sensor, the wheel, and the processed wheel speed signal during processing. After preprocessing, multiple processed wheel speed signals are obtained, each corresponding to a different wheel speed signal.

[0106] Step S202: Perform fusion processing on multiple processed wheel speed signals to obtain the fused vehicle speed of the target vehicle.

[0107] Here, during the fusion process, the contribution of each wheel to the fusion result is determined based on the reliability of its wheel speed, and the wheel speed differences between different wheels are corrected when the target vehicle is turning. After the fusion process is completed, a fused vehicle speed that can characterize the overall driving speed of the target vehicle is obtained.

[0108] Step S203: Determine the current vehicle speed and current vehicle acceleration of the target vehicle based on the fused vehicle speed.

[0109] Here, the current vehicle speed and current vehicle acceleration can be determined simultaneously through state estimation, or the fused vehicle speed can be used as the current vehicle speed, and the current vehicle acceleration can be determined based on the current vehicle speed and historical vehicle speeds. These two determination methods are alternative implementations; one method is selected for execution during a single verification process, and the two methods do not constitute a sequential execution relationship.

[0110] The fused vehicle speeds are smoothed or their trends analyzed to reduce the impact of wheel speed fluctuations on the current vehicle speed and acceleration. The current vehicle speed characterizes the target vehicle's speed at the current sampling moment, and the current vehicle acceleration characterizes the degree of speed change of the target vehicle at the current sampling moment.

[0111] Step S204: Determine the current vehicle speed and current vehicle acceleration as the current motion parameters.

[0112] Here, the current vehicle speed and current vehicle acceleration at the same sampling moment are combined to obtain the current motion parameters. These current motion parameters carry the corresponding sampling time information and data validity status, ensuring the time correspondence between the current vehicle speed, current vehicle acceleration, and torque command value. When the current motion parameters are valid, they are input into a preset vehicle dynamics model, allowing the model to inversely calculate the estimated output torque of the motor.

[0113] In an optional implementation, step S201 includes the following steps S301-S307.

[0114] Step S301: For each wheel speed signal, obtain the wheel speed value at the current sampling time and the wheel speed value at the previous sampling time, and determine the absolute difference between the wheel speed value at the current sampling time and the wheel speed value at the previous sampling time as the wheel speed change.

[0115] Here, each wheel speed signal is processed using the same method. The linear velocity conversion and sampling time alignment of the wheel speed signals are performed as described above, with a sampling time interval Δt of 0.01 seconds. Taking the i-th wheel as an example, the wheel speed change at the k-th sampling time is calculated using the following formula: (1) in, This represents the change in wheel speed of the i-th wheel at the k-th sampling time. This represents the wheel speed value at the current sampling moment. This represents the wheel speed value at the previous sampling time.

[0116] Upon receiving the first valid wheel speed value, this value is written to the wheel speed storage area of ​​the previous sampling time, and the wheel speed change corresponding to the first sample is set to zero. Starting from the second valid sampling time, the wheel speed change is calculated according to the above formula. Each wheel speed signal has its own independent wheel speed storage area to avoid mixing wheel speed values ​​from different wheels.

[0117] Step S302: Multiply the wheel speed value at the previous sampling time by the preset wheel speed change ratio to obtain the wheel speed change threshold corresponding to the current sampling time.

[0118] Here, the wheel speed change threshold for the i-th wheel at the k-th sampling time is calculated according to the following formula: (2) in, This represents the threshold value for the wheel speed change of the i-th wheel at the k-th sampling time. This indicates a preset wheel speed variation ratio. In one specific embodiment, Set the value to 0.15. The preset wheel speed change ratio is calibrated based on the sampling accuracy of the wheel speed sensor, the maximum normal acceleration of the target vehicle, and the sampling time interval, and stored in the calibration parameter area.

[0119] When the wheel speed value at the previous sampling time is zero, the wheel speed change threshold calculated proportionally is also zero. To avoid normal wheel speed changes during vehicle start-up being directly identified as abnormal, after obtaining the wheel speed change threshold using the above formula, the wheel speed change threshold is compared with a preset minimum absolute wheel speed change threshold, and the threshold with the larger value is used as the effective wheel speed change threshold corresponding to the current sampling time. The preset minimum absolute wheel speed change threshold is calibrated based on the resolution of the wheel speed sensor and the results of low-speed vehicle start-up tests. When the wheel speed value at the previous sampling time is greater than the preset minimum wheel speed threshold, the wheel speed change threshold calculated according to the preset wheel speed change ratio is used.

[0120] Step S303: If the wheel speed change is greater than the wheel speed change threshold, the wheel speed value at the current sampling time is determined as an abnormal wheel speed value.

[0121] Here, the anomaly detection condition for the i-th wheel is: (3) When the wheel speed change exceeds the effective wheel speed change threshold at the current sampling time, the wheel speed value at the current sampling time is marked as an abnormal wheel speed value. An abnormal wheel speed value indicates that the current wheel speed value has undergone a sudden change beyond the normal range relative to the previous sampling time. When the wheel speed change is less than or equal to the effective wheel speed change threshold, the wheel speed value at the current sampling time is marked as a normal wheel speed value. Anomaly detection is performed independently for each wheel; if one wheel exhibits an abnormal wheel speed value, the anomaly detection results for the remaining wheels are not changed.

[0122] When the preset wheel speed change ratio is 0.15, formula (3) is expressed as: (4) When formula (3) or formula (4) is satisfied, the wheel speed value at the kth sampling time is determined as the abnormal wheel speed value; when formula (3) or formula (4) is not satisfied, the wheel speed value at the kth sampling time is retained.

[0123] Step S304: Using the current sampling time as the end point of the window, according to the preset window size, obtain the wheel speed values ​​corresponding to the current sampling time and multiple consecutive sampling times before the current sampling time, and form a sliding window.

[0124] Here, the sliding window stores the wheel speed values ​​at consecutive sampling times in a first-in, first-out (FIFO) manner. In one specific embodiment, the preset window size is five sampling points, and the sliding window includes the wheel speed value at the current sampling time as well as the wheel speed values ​​at the four consecutive sampling times preceding the current sampling time. Thus, the wheel speed value at the current sampling time is located at the end of the sliding window and participates in the subsequent median calculation.

[0125] Each time a new wheel speed value is obtained, it is written to the end point of the sliding window, and the wheel speed value furthest from the current sampling time is removed. If a complete sliding window has not yet been formed in the first four sampling times after sampling begins, the missing historical positions are filled with the first valid wheel speed value. Once the sliding window is full, it is updated according to five consecutive sampling points.

[0126] Step S305: When the wheel speed change is greater than the wheel speed change threshold, determine the median of multiple wheel speed values ​​within the sliding window, and replace the abnormal wheel speed value with the median to obtain the corrected wheel speed signal.

[0127] Here, after detecting an abnormal wheel speed value, the five wheel speed values ​​within the sliding window are arranged in ascending order, and the wheel speed value in the third position after arrangement is determined as the median. Subsequently, the abnormal wheel speed value at the current sampling time is replaced with the median, and the median is written into the sliding window position corresponding to the current sampling time and the corrected wheel speed signal.

[0128] Median replacement only corrects abnormal wheel speed values ​​at the current sampling time, without changing wheel speed values ​​already output before the current sampling time. The corrected current wheel speed value is used as the historical wheel speed value for the next sampling time in subsequent processing, preventing the same abnormal wheel speed value from continuously affecting multiple sliding windows. The five-sampling-point sliding window can reduce the interference caused by a single abrupt wheel speed value while preserving the normal wheel speed change trend.

[0129] Step S306: If the wheel speed change is less than or equal to the wheel speed change threshold, retain the wheel speed value at the current sampling time to obtain the corrected wheel speed signal.

[0130] Here, when the wheel speed change is less than or equal to the effective wheel speed change threshold corresponding to the current sampling time, the median replacement is not performed on the wheel speed value at the current sampling time. Instead, the wheel speed value at the current sampling time is directly written into the corrected wheel speed signal and the sliding window.

[0131] Step S307: The corrected wheel speed signal is filtered by a low-pass filter with a preset cutoff frequency to obtain the processed wheel speed signal.

[0132] Here, each corrected wheel speed signal is low-pass filtered to attenuate high-frequency noise that remains after median correction. A second-order Butterworth low-pass filter is used, and its transfer function is: (5) in, Let represent the transfer function of the low-pass filter, and s represent the complex frequency variable. This represents the cutoff angular frequency obtained by converting the cutoff frequency.

[0133] The preset cut-off frequency is dynamically determined according to the current vehicle speed at the previous sampling moment. When the current vehicle speed at the previous sampling moment is in the vehicle speed range of 0-90km / h (kilometers per hour), the preset cut-off frequency is 8Hz (Hertz); when the current vehicle speed at the previous sampling moment is in the vehicle speed range greater than 90km / h, the preset cut-off frequency is 15Hz. When the current vehicle speed is stored in meters per second, the current vehicle speed is converted to km / h first, and then the vehicle speed range corresponding to the current vehicle speed is determined. When the filtering process is performed for the first time and the current vehicle speed at the previous sampling moment has not been obtained, the preset cut-off frequency of 8Hz is adopted.

[0134] The second-order Butterworth low-pass filter is discretized at a sampling time interval of 0.01 seconds, and the corresponding filtering coefficient is determined according to the preset cut-off frequency. In each sampling period, the current filtered output value is calculated according to the current corrected wheel speed value, the historical input value, the historical output value and the filtering coefficient. When the filtering process is performed for the first time, the first valid corrected wheel speed value is written into the historical input position and the historical output position. When the current vehicle speed crosses the boundary of the 90km / h vehicle speed range, the filtering coefficient is updated and the filtering state at the previous sampling moment is retained, so as to avoid sudden change of the processed wheel speed signal caused by the switching of the filtering coefficient. The current filtered output value is the processed wheel speed value corresponding to the current wheel speed signal, and a plurality of consecutive current filtered output values constitute the processed wheel speed signal.

[0135] The acceleration abnormality restriction matched with the wheel speed signal preprocessing is executed after the current vehicle acceleration is obtained. The acceleration abnormality judgment condition matched with the wheel speed signal preprocessing is: (6) wherein, represents the vehicle acceleration corresponding to the current sampling moment t.

[0136] When the current vehicle acceleration satisfies the formula (6), the vehicle accelerations corresponding to the three sampling periods before the current sampling moment are averaged, and the average vehicle acceleration is used to replace the current abnormal vehicle acceleration. The acceleration abnormality restriction acts on the current vehicle acceleration.

[0137] In an optional embodiment, step S202 includes the following steps S401-S407.

[0138] In step S401, according to the drive type of the wheel corresponding to each of the plurality of wheel speed sensors, the plurality of processed wheel speed signals are divided into the processed wheel speed signals corresponding to the driving wheels and the processed wheel speed signals corresponding to the non-driving wheels.

[0139] Here, the vehicle configuration file of the target vehicle pre-records the wheel position and wheel drive type corresponding to each wheel speed sensor. The wheel position includes the left front wheel, right front wheel, left rear wheel, and right rear wheel, and the wheel drive type includes drive wheels and non-drive wheels. Drive wheels are the wheels that receive motor driving force, and non-drive wheels are the wheels that do not receive motor driving force.

[0140] After the vehicle starts, the vehicle configuration file is queried based on the wheel speed sensor identifiers to determine the wheel position and wheel drive type corresponding to each wheel speed sensor. Then, a correspondence is established between the processed wheel speed signals and the wheel drive types, and multiple processed wheel speed signals are divided into processed wheel speed signals corresponding to the drive wheels and processed wheel speed signals corresponding to non-drive wheels.

[0141] Step S402: Average the processed wheel speed signals corresponding to each non-drive wheel to obtain the reference speed.

[0142] Here, since the non-drive wheels do not directly receive the motor's driving force, the probability of them slipping is generally lower than that of the drive wheels. Therefore, the average speed of each non-drive wheel is used as the reference speed. All processed wheel speed signals use the same sampling time and speed unit.

[0143] The reference velocity at the k-th sampling time is calculated according to the following formula: (7) in, This represents the reference velocity at the k-th sampling time; the subscript ref indicates the reference velocity identifier. Indicates the number of non-driving wheels; This represents the processed wheel speed value of the j-th non-driving wheel at the k-th sampling time; the subscript nd indicates the non-driving wheel identifier.

[0144] Step S403: For each drive wheel, the value obtained by subtracting the reference speed from the processed wheel speed signal corresponding to the drive wheel is determined as the speed difference, and the ratio between the speed difference and the reference speed is determined as the slip ratio corresponding to the drive wheel.

[0145] Here, the speed difference of the i-th drive wheel at the k-th sampling time is calculated according to the following formula: (8) in, This represents the speed difference between the i-th drive wheel and the reference speed; This represents the processed wheel speed value corresponding to the i-th drive wheel; the subscript d indicates the drive wheel identifier.

[0146] The slip ratio corresponding to the i-th drive wheel is calculated according to the following formula: (9) in, This represents the slip ratio of the i-th drive wheel at the k-th sampling time. The slip ratio is used as a dimensionless decimal in the fusion weight calculation. When the slip ratio is expressed as a percentage, it is: (10) Specifically, the slip ratio is positive when the processed wheel speed of the drive wheel is greater than the reference speed, and negative when the processed wheel speed of the drive wheel is less than the reference speed. The absolute value of the slip ratio is used to measure the degree to which the drive wheel deviates from the reference speed in the fusion weight determination process.

[0147] When the reference speed is zero, formula (9) cannot complete the division operation. When the reference speed is less than or equal to the preset minimum reference speed threshold, the slip ratio calculation is stopped, and the mapping input corresponding to the slip ratio is set to zero. At this time, the initial fusion weight of the drive wheels adopts the weight corresponding to the slip ratio being zero, and the initial fusion weight of the non-drive wheels adopts the preset non-drive wheel weight. After completing the vehicle speed fusion under low speed conditions, the torque verification status is set to the disabled state. When the reference speed is greater than the preset minimum reference speed threshold and the processed wheel speed signals are valid, the slip ratio calculation is resumed, and the torque verification status is set to the enabled state.

[0148] Step S404: Determine the initial fusion weight corresponding to each drive wheel according to the preset slip ratio-fusion weight mapping relationship; wherein, the slip ratio-fusion weight mapping relationship is: the larger the absolute value of the slip ratio, the smaller the corresponding fusion weight.

[0149] Here, the slip ratio-fusion weight mapping relationship is pre-stored in the calibration parameter area. At each sampling time, the absolute value of the slip ratio corresponding to each drive wheel is obtained, and the slip ratio-fusion weight mapping relationship is queried based on the absolute value of the slip ratio to obtain the initial fusion weight corresponding to each drive wheel. When the absolute value of the slip ratio is small, the processed wheel speed value corresponding to the drive wheel is closer to the reference speed, so a larger initial fusion weight is assigned to the drive wheel; when the absolute value of the slip ratio is large, the drive wheel is more likely to slip, so the initial fusion weight corresponding to the drive wheel is reduced.

[0150] In a specific calibration method, the initial fusion weight of the drive wheels is determined according to the following formula: (11) in, This represents the initial fusion weight corresponding to the i-th drive wheel at the l-th sampling time; This represents the absolute value of the slip ratio corresponding to the i-th drive wheel. Formula (11) is applicable when the absolute value of the slip ratio is within the preset calibration range. When the absolute value of the slip ratio exceeds the preset calibration range, the fusion weight corresponding to the closest boundary slip ratio in the slip ratio-fusion weight mapping relationship is used to avoid obtaining invalid weights through range extrapolation.

[0151] Step S405: Obtain the pre-set non-driving wheel weights and determine the non-driving wheel weights as the initial fusion weights corresponding to each non-driving wheel.

[0152] Here, the weights of the non-driving wheels are pre-calibrated fixed weights stored in the calibration parameter area. In a specific embodiment, the initial fusion weights corresponding to each non-driving wheel are: (12) in, This represents the initial fusion weight corresponding to the j-th non-driving wheel. All non-driving wheels use the same initial fusion weight to ensure that the reference speed source maintains a stable contribution to the fused vehicle speed.

[0153] Step S406: Sum the initial fusion weights corresponding to each wheel to obtain the weight sum value, and determine the ratio between the initial fusion weight and the weight sum value corresponding to each wheel to obtain the target fusion weight corresponding to each wheel.

[0154] Here, the weight sum corresponding to the k-th sampling time is calculated according to the following formula: (13) in, This represents the weights and values; N represents the number of wheels participating in the fusion. This represents the initial fusion weight corresponding to the l-th wheel.

[0155] The target fusion weight corresponding to the l-th wheel is calculated according to the following formula: (14) in, This represents the target fusion weight corresponding to the l-th wheel. After normalization, the sum of all target fusion weights is 1. Normalization eliminates the influence of the number of wheels participating in fusion and the initial range of fusion weight values ​​on the dimension of the fused vehicle speed.

[0156] In step S407, each processed wheel speed signal is multiplied by the corresponding target fusion weight, and the resulting products are summed to obtain the fused vehicle speed of the target vehicle.

[0157] Here, the fused vehicle speed at the k-th sampling time is calculated according to the following formula: (15) in, This represents the fused vehicle speed at the k-th sampling time. This represents the processed wheel speed value corresponding to the l-th wheel; This represents the target fusion weight corresponding to the l-th wheel.

[0158] When the target vehicle is traveling in a straight line, the fused vehicle speed obtained by formula (15) is used to determine the current vehicle speed and current vehicle acceleration. When the target vehicle is turning, the fused vehicle speed obtained by formula (15) is used as the fused vehicle speed before turning correction, and the fused vehicle speed is updated according to the yaw rate, the distance between the wheel and the center of the vehicle, and the preset correction coefficient.

[0159] In an optional implementation, the target vehicle is determined to be in a turning state when the absolute value of its yaw rate is greater than a preset turning threshold. When the target vehicle is in a turning state, after obtaining the fused vehicle speed, the method further includes the following steps S501-S506.

[0160] Step S501: Obtain the yaw rate of the target vehicle, the preset correction coefficient, and the distance between each wheel and the center of the target vehicle.

[0161] Here, the yaw rate is acquired from the target vehicle's existing yaw rate sensor. The yaw rate characterizes the speed at which the target vehicle rotates around its vertical axis. The yaw rate is obtained at a time matching the sampling moment of the currently processed wheel speed signal, ensuring that the yaw rate and multiple processed wheel speed signals correspond to the same vehicle motion state. The unit of yaw rate is rad / s (radians per second).

[0162] The distance between each wheel and the vehicle center is a pre-measured vehicle structural parameter stored in the vehicle configuration file. The vehicle center is the origin of the vehicle coordinate system, and the distance corresponding to each wheel is the distance between the wheel center and the vehicle center. Each wheel corresponds to a separate distance parameter, with the unit of the distance parameter being meters.

[0163] The preset correction coefficient is used to adjust the degree to which yaw rate and wheel position correct for the processed wheel speed value. To avoid confusion between the preset correction coefficient and the sampling time number k, subsequent formulas use... This indicates the preset correction factor.

[0164] The preset correction coefficients are obtained through actual vehicle calibration. During the calibration process, the target vehicle is controlled to travel at different vehicle speeds and turning radii, and the wheel speed, yaw rate, and reference vehicle speed measured by external speed measuring equipment for each wheel are collected. For multiple candidate correction coefficients, the corrected fused vehicle speed is calculated, and the error between the corrected fused vehicle speed and the reference vehicle speed is compared. The candidate correction coefficient with the smallest average error is determined as the preset correction coefficient and stored in the calibration parameter area.

[0165] Step S502: For each wheel, multiply the absolute value of the yaw rate, the distance corresponding to the wheel, and the preset correction coefficient to obtain the first correction amount corresponding to the wheel, and determine the difference between the value 1 and the first correction amount as the wheel speed correction coefficient corresponding to the wheel.

[0166] Here, the first correction value corresponding to the i-th wheel at the k-th sampling time is calculated according to the following formula: (16) in, This represents the first correction amount corresponding to the i-th wheel; Indicates the preset correction factor; This represents the yaw rate at the k-th sampling time. Represents the absolute value of the yaw rate; This represents the distance between the i-th wheel and the center of the vehicle.

[0167] The absolute value of the yaw rate is used for calculation, ensuring that left-turn and right-turn states are treated with the same correction rule. The wheel speed correction coefficient for the i-th wheel is calculated using the following formula: (17) in, This represents the wheel speed correction coefficient for the i-th wheel. The greater the distance between the wheel and the vehicle center, the greater the impact of yaw motion on the processed wheel speed value, and the smaller the wheel speed correction coefficient.

[0168] The preset correction coefficient and the applicable range of the turning correction are calibrated to ensure that the first correction amount is less than 1. When the calculated first correction amount is greater than or equal to 1, the turning correction state at the current sampling time is marked as invalid, and the current first correction amount is no longer used to update the fused vehicle speed.

[0169] Step S503: Multiply each processed wheel speed signal, the corresponding target fusion weight, and the corresponding wheel speed correction coefficient, and sum the results of each product to obtain the first fusion speed.

[0170] Here, the target fusion weights obtained in step S406 are used.

[0171] The first fusion velocity at the k-th sampling time is calculated according to the following formula: (18) in, This represents the first fusion speed at the k-th sampling time; N represents the number of wheels participating in the fusion. This represents the processed wheel speed value corresponding to the i-th wheel; This represents the target fusion weight corresponding to the i-th wheel; This represents the wheel speed correction coefficient corresponding to the i-th wheel.

[0172] The first fusion speed is the weighted sum of the processed wheel speed values ​​after adjustment by the target fusion weight and the wheel speed correction coefficient. The wheel speed correction coefficient is used to reduce the impact of wheel position differences on the fusion result during turning, and the target fusion weight is used to maintain the reliability of the wheel speeds corresponding to different wheels.

[0173] In step S504, the distance corresponding to each wheel is multiplied by the corresponding target fusion weight, and the resulting products are summed to obtain the weighted distance.

[0174] Here, the weighted distance at the i-th sampling time is calculated according to the following formula: (19) in, Indicates the weighted distance; the subscript w indicates the weighting identifier; This represents the target fusion weight corresponding to the i-th wheel; This represents the distance between the i-th wheel and the center of the vehicle.

[0175] The weighted distance comprehensively reflects the distance between each wheel participating in the fusion and the vehicle center. Wheels with higher target fusion weights contribute more to the weighted distance, while wheels with lower target fusion weights contribute less.

[0176] Step S505: Multiply the absolute values ​​of the weighted distance and yaw rate by the preset correction coefficient to obtain the second correction amount, and determine the difference between the value 1 and the second correction amount as the fusion correction coefficient.

[0177] Here, the second correction at the k-th sampling time is calculated according to the following formula: (20) in, This indicates the second correction amount; the subscript f indicates the fusion identifier.

[0178] The fusion correction coefficient at the k-th sampling time is calculated according to the following formula: (twenty one) in, This represents the fusion correction factor. The fusion correction factor is used to normalize the overall speed scaling caused by each wheel speed correction factor.

[0179] Before calculating the corrected fused vehicle speed, the absolute value of the fusion correction coefficient is compared with a preset minimum denominator threshold. If the absolute value of the fusion correction coefficient is less than or equal to the preset minimum denominator threshold, no division operation is performed, the fused vehicle speed before the turn correction is retained, and the turn correction status at the current sampling time is marked as invalid.

[0180] Step S506: The ratio between the first fusion speed and the fusion correction coefficient is determined as the corrected fusion vehicle speed, and the fusion vehicle speed of the target vehicle is updated using the corrected fusion vehicle speed.

[0181] Here, the corrected fused vehicle speed at the k-th sampling time is calculated according to the following formula: (twenty two) in, This represents the corrected fused vehicle speed. Substituting equations (18) to (21) into equation (22), we get: (twenty two) Formula (23) simultaneously considers the processed wheel speed value, target fusion weight, distance between the wheel and the vehicle center, and yaw rate of the target vehicle for each wheel. After completing the calculation of formula (22) or formula (23), the fusion vehicle speed obtained in step S407 is replaced with the corrected fusion vehicle speed. The current vehicle speed and current vehicle acceleration are subsequently determined based on the updated fusion vehicle speed.

[0182] In an optional implementation, step S203 includes steps S601-S608. This implementation simultaneously determines the current vehicle speed and current vehicle acceleration using a Kalman filter algorithm. This implementation is an alternative to the implementation that determines the current vehicle acceleration based on speed difference and smoothing processing; the two implementations are not sequentially executed.

[0183] Step S601: Determine the fused vehicle speed as the observed vehicle speed of the Kalman filter algorithm, and set the vehicle speed and vehicle acceleration as the state variables of the Kalman filter algorithm.

[0184] Here, when the target vehicle is traveling in a straight line, the fused vehicle speed obtained in step S407 is used as the observed vehicle speed; when the target vehicle is turning, the fused vehicle speed updated in step S506 is used as the observed vehicle speed.

[0185] The state vector at the kth sampling time is: (twenty four) in, This represents the state vector at the k-th sampling time. Represents the vehicle speed state variable; This represents the vehicle's acceleration state variable.

[0186] The observed vehicle speed at the k-th sampling time is: (25) in, This represents the observed vehicle speed at the k-th sampling time. This represents the fused vehicle speed at the k-th sampling time.

[0187] The state transition relationship between vehicle speed and vehicle acceleration is as follows: (26) The state transition matrix is: (27) in, Represents the state transition matrix; This represents the sampling time interval; in a specific embodiment, it is set to 0.01 seconds. This represents the process noise at the k-th sampling time.

[0188] The observation relationship between the observed vehicle speed and the state vector is as follows: (28) The observation matrix is: (29) in, Represents the observation matrix; This represents the observation noise at the k-th sampling time. The observation matrix indicates that the fused vehicle speed is used to observe the vehicle speed state variable, while the vehicle acceleration state variable does not have independent direct observation values.

[0189] Step S602: Obtain the vehicle speed, vehicle acceleration, and state covariance corresponding to the previous sampling time.

[0190] Here, the vehicle speed and acceleration corresponding to the previous sampling time are the vehicle speed and acceleration obtained by the Kalman filter algorithm after completing the state correction at the previous sampling time. The state covariance corresponding to the previous sampling time is the state covariance obtained by the Kalman filter algorithm after completing the covariance update at the previous sampling time.

[0191] State covariance is used to characterize the vehicle speed estimation error, the vehicle acceleration estimation error, and the correlation between the two estimation errors. After each state correction and covariance update at the current sampling time, the current vehicle speed, the current vehicle acceleration, and the state covariance corresponding to the current sampling time are written to the historical state storage area for retrieval at the next sampling time.

[0192] When the Kalman filter algorithm is first started, the historical state storage area does not contain state data from the previous sampling time. At this time, the first valid fused vehicle speed is set as the initial vehicle speed, the initial vehicle acceleration is set to zero, and the identity matrix is ​​set as the initial state covariance. After completing the initial state setting, state prediction and state correction are performed starting from the next sampling time.

[0193] Step S603: Based on the vehicle speed, vehicle acceleration, and sampling time interval corresponding to the previous sampling time, determine the predicted vehicle speed corresponding to the current sampling time, and determine the vehicle acceleration corresponding to the previous sampling time as the predicted vehicle acceleration corresponding to the current sampling time.

[0194] Here, this implementation adopts a state transition relationship where the vehicle acceleration remains constant between two adjacent sampling times. The predicted vehicle speed corresponding to the current sampling time is calculated according to the following formula: (30) The predicted vehicle acceleration at the current sampling time is calculated using the following formula: (31) in, This represents the vehicle speed at the k-th sampling time, predicted based on the state at the (k-1)-th sampling time. This represents the vehicle acceleration at the k-th sampling time, predicted based on the state at the (k-1)-th sampling time. This indicates the vehicle speed after the state correction was completed at the previous sampling time. This represents the vehicle acceleration after the state correction was completed at the previous sampling time.

[0195] This means using data up to the (k-1)th sampling time to predict the kth sampling time. This indicates that the correction result for the k-th sampling time is obtained using the data up to the k-th sampling time.

[0196] Step S604: Determine the prediction covariance corresponding to the current sampling time based on the state covariance corresponding to the previous sampling time, the preset state transition relationship, and the preset process noise covariance.

[0197] In one specific embodiment, the preset process noise covariance is: (32) in, This represents the preset process noise covariance. The process noise variance corresponding to vehicle speed is set to 0.01, and the process noise variance corresponding to vehicle acceleration is set to 0.1.

[0198] The prediction covariance at the current sampling time is calculated using the following formula: (33) in, This represents the prediction covariance at the current sampling time. This represents the state covariance after the update was completed at the previous sampling time. This represents the transpose of the state transition matrix.

[0199] The prediction covariance combines the state estimation error at the previous sampling time with the process noise in the current sampling period, and is used to characterize the uncertainty of predicting vehicle speed and vehicle acceleration.

[0200] Step S605: Determine the observation noise covariance based on the comparison between the absolute value of the predicted vehicle acceleration and the preset acceleration fluctuation threshold; wherein, when the absolute value of the predicted vehicle acceleration is less than or equal to the preset acceleration fluctuation threshold, the first observation noise covariance is used; when the absolute value of the predicted vehicle acceleration is greater than the preset acceleration fluctuation threshold, the second observation noise covariance, which is greater than the first observation noise covariance, is used.

[0201] In one specific embodiment, the preset acceleration fluctuation threshold is set to 0.8 m / s². 2 The first observation noise covariance is taken as 0.5, and the second observation noise covariance is taken as 5.0. The observation noise covariance is determined according to the following formula: (34) in, This represents the observation noise covariance used at the k-th sampling time.

[0202] When the absolute value of the predicted vehicle acceleration is less than or equal to the preset acceleration fluctuation threshold, the target vehicle corresponds to a smooth driving state, and the instantaneous disturbance to the fused vehicle speed is relatively small. Therefore, a smaller first observation noise covariance is used. When the absolute value of the predicted vehicle acceleration is greater than the preset acceleration fluctuation threshold, the target vehicle corresponds to a bumpy driving state or a state with large acceleration and deceleration changes. The possibility of instantaneous fluctuations in the fused vehicle speed increases, so a larger second observation noise covariance is used.

[0203] Step S606: Determine the Kalman gain based on the predicted covariance, the preset observation relationship, and the observation noise covariance.

[0204] Here, the Kalman gain is calculated according to the following formula: (35) in, This represents the Kalman gain at the k-th sampling time. This represents the transpose of the observation matrix; the superscript -1 indicates matrix inversion.

[0205] The Kalman gain is used to adjust the contribution of the predicted state and the observed vehicle speed to the state correction result. When the observation noise covariance increases, the influence of the observed vehicle speed on the state correction result decreases; when the observation noise covariance decreases, the influence of the observed vehicle speed on the state correction result increases.

[0206] Step S607: Based on the speed difference between the observed vehicle speed and the predicted vehicle speed and the Kalman gain, the predicted vehicle speed and predicted vehicle acceleration are corrected to obtain the current vehicle speed and current vehicle acceleration.

[0207] Here, the speed difference between the observed vehicle speed and the predicted vehicle speed is calculated using the following formula: (36) in, This represents the velocity difference at the k-th sampling time. This represents the predicted state vector at the current sampling time. Since the observation matrix is... The speed difference is equal to the difference between the observed vehicle speed and the predicted vehicle speed.

[0208] The state vector at the current sampling moment is corrected according to the following formula: (37) in, This represents the corrected state vector at the current sampling time. The vehicle velocity component in the state vector is determined as the current vehicle velocity, and the vehicle acceleration component is determined as the current vehicle acceleration, i.e.: (38) in, Indicates the current vehicle speed; This indicates the current vehicle acceleration.

[0209] Step S608: Update the predicted covariance based on the Kalman gain, the preset observation relationship, and the predicted covariance to obtain the state covariance corresponding to the current sampling time.

[0210] Here, the state covariance corresponding to the current sampling time is updated according to the following formula: (39) in, This represents the updated state covariance at the current sampling time. Represents the identity matrix.

[0211] After updating the state covariance, the current vehicle speed, current vehicle acceleration, and the state covariance corresponding to the current sampling time are written into the historical state storage area for state prediction and covariance prediction at the next sampling time. This forms a Kalman filter process that runs continuously according to the sampling period.

[0212] The embodiments of this application can dynamically adjust the influence of the vehicle speed in the state correction process according to the vehicle's motion state. In a smooth driving state, it can track vehicle speed changes in a timely manner, and in a bumpy driving state or a state with large acceleration and deceleration changes, it can suppress instantaneous wheel speed disturbances, thereby improving the stability of the current vehicle speed and current vehicle acceleration.

[0213] In an optional implementation, step S203 further includes the following steps S701-S708.

[0214] Step S701: Determine the fused vehicle speed as the current vehicle speed, and obtain the vehicle speed, vehicle acceleration, and sampling time interval corresponding to the previous sampling moment.

[0215] Here, when the target vehicle is traveling in a straight line, the fused vehicle speed obtained in step S407 is determined as the current vehicle speed; when the target vehicle is turning, the fused vehicle speed updated in step S506 is determined as the current vehicle speed.

[0216] The vehicle speed corresponding to the previous sampling time is the current vehicle speed determined at the previous sampling time, and the vehicle acceleration corresponding to the previous sampling time is the current vehicle acceleration obtained after smoothing or averaging at the previous sampling time. After determining the current vehicle speed and current vehicle acceleration each time, the current vehicle speed and current vehicle acceleration are written to the historical state storage area for reading at the next sampling time.

[0217] The sampling time interval is the time interval between the current sampling moment and the previous sampling moment. In one specific embodiment, the sampling time interval... Take 0.01 seconds. When a valid fused vehicle speed is obtained for the first time, the obtained fused vehicle speed is written to the historical state storage area, and the initial vehicle acceleration is set to zero. No speed difference is performed at the first sampling time; the initial vehicle acceleration is calculated starting from the second valid sampling time.

[0218] Step S702: The value obtained by subtracting the vehicle speed corresponding to the previous sampling time from the current vehicle speed is determined as the speed difference, and the ratio between the speed difference and the sampling time interval is determined as the initial vehicle acceleration.

[0219] Here, the velocity difference corresponding to the current sampling time t is calculated according to the following formula: (40) in, This represents the velocity difference between the current sampling time and the previous sampling time. Indicates the current vehicle speed; This indicates the vehicle speed at the previous sampling time.

[0220] The initial vehicle acceleration at the current sampling time is calculated using the following formula: (41) in, This represents the initial vehicle acceleration at the current sampling time; the subscript 0 indicates the initial value before smoothing. The initial vehicle acceleration is obtained by differentiating the vehicle speeds of two adjacent sampling times. Small fluctuations in the wheel speed signal are easily amplified after differencing, so smoothing is still required.

[0221] Step S703: Obtain the current driving condition corresponding to the target vehicle, and determine the target smoothing coefficient corresponding to the current driving condition based on the preset correspondence between driving conditions and smoothing coefficients.

[0222] Here, the current driving condition is determined by the driving condition identifier output by the vehicle controller. The driving condition identifier and the fused vehicle speed are matched using a timestamp to ensure that the current driving condition and the current vehicle speed correspond to the same sampling time. The preset correspondence between driving conditions and smoothing coefficients is stored in the calibration parameter area.

[0223] In one specific embodiment, the target smoothing coefficient The value ranges from 0.3 to 0.5, and the subscript "smooth" indicates smoothness. When the current driving condition is bumpy road conditions or abrupt acceleration and deceleration, the target smoothness coefficient is 0.3; when the current driving condition is normal urban road conditions, the target smoothness coefficient is 0.4; when the current driving condition is high-speed stable cruising conditions, the target smoothness coefficient is 0.5.

[0224] The smaller the target smoothing coefficient, the greater the proportion of the vehicle acceleration corresponding to the previous sampling time in the smoothing result, and the stronger the smoothing effect; the larger the target smoothing coefficient, the greater the proportion of the initial vehicle acceleration in the smoothing result, and the faster the vehicle acceleration responds to the current vehicle speed change.

[0225] Step S704: Multiply the initial vehicle acceleration by the target smoothing coefficient to obtain the first acceleration component.

[0226] Here, the first acceleration component is calculated according to the following formula: (42) in, This represents the first acceleration component corresponding to the current sampling time; Indicates the initial vehicle acceleration; This represents the target smoothing coefficient. The first acceleration component characterizes the contribution of the current vehicle speed change to the smoothing result.

[0227] Step S705: Multiply the difference between the value 1 and the target smoothing coefficient by the vehicle acceleration corresponding to the previous sampling time to obtain the second acceleration component.

[0228] Here, the second acceleration component is calculated according to the following formula: (43) in, This represents the second acceleration component corresponding to the current sampling time; The first acceleration component represents the vehicle acceleration at the previous sampling time. The second acceleration component is used to characterize the contribution of historical vehicle acceleration to the smoothing result.

[0229] Step S706: Add the first acceleration component and the second acceleration component to obtain the smooth vehicle acceleration.

[0230] Here, the smooth vehicle acceleration is calculated according to the following formula: (44) Substituting formulas (41) to (43) into formula (44), we get: (45) in, This represents the smoothed vehicle acceleration corresponding to the current sampling time. Formula (45) weights the initial vehicle acceleration and the vehicle acceleration corresponding to the previous sampling time to keep the vehicle acceleration continuous and reduce the amplification effect of high-frequency fluctuations in the wheel speed signal after passing through the speed difference.

[0231] Step S707: If the absolute value of the smoothed vehicle acceleration is less than or equal to a preset acceleration threshold, the smoothed vehicle acceleration is determined as the current vehicle acceleration.

[0232] In one specific embodiment, the preset acceleration threshold is set to 12 m / s². 2 When the smoothed vehicle acceleration satisfies the following formula, the smoothed vehicle acceleration is determined as the current vehicle acceleration: (46) The current vehicle acceleration is: (47) in, This represents the current vehicle acceleration determined at the current sampling time. The absolute value of the smoothed vehicle acceleration does not exceed the preset acceleration threshold, indicating that the smoothed vehicle acceleration is within the normal range of allowable variation for the target vehicle.

[0233] Step S708: If the absolute value of the smoothed vehicle acceleration is greater than the preset acceleration threshold, the vehicle acceleration corresponding to the preset number of sampling periods before the current sampling time is averaged, and the resulting average vehicle acceleration is determined as the current vehicle acceleration.

[0234] In one specific embodiment, the preset number is three. Historical vehicle acceleration averaging is initiated when the smoothed vehicle acceleration satisfies the following formula: (48) Read the vehicle acceleration corresponding to the three sampling periods prior to the current sampling time, and calculate the average vehicle acceleration according to the following formula: (49) in, This represents the average vehicle acceleration at the current sampling time. , and These represent the vehicle accelerations that were finally determined in the three sampling cycles prior to the current sampling time.

[0235] The average vehicle acceleration is determined as the current vehicle acceleration, that is: (50) When the verification method is first started, the three historical vehicle acceleration storage locations are initialized to zero. Each time the current vehicle acceleration is obtained, the three historical vehicle acceleration storage locations are updated in chronological order. If valid vehicle accelerations have not yet been written to all three historical vehicle acceleration storage locations, the torque verification status is set to disabled; once valid vehicle accelerations have been written to all three historical vehicle acceleration storage locations, the torque verification status is set to enabled.

[0236] In this embodiment, the current vehicle speed is directly derived from the fused vehicle speed, and the current vehicle acceleration is obtained through speed differential, adaptive smoothing under operating conditions, and abnormal acceleration limitation. This processing can reduce the impact of wheel speed signal fluctuations on the vehicle acceleration calculation results, and improve the continuity and stability of the current vehicle acceleration while maintaining the vehicle acceleration response speed.

[0237] In an optional implementation, step S103 includes the following steps S801-S812.

[0238] Step S801: Obtain the target vehicle's reference mass, equivalent mass of rotating parts, additional load, standard wheel rolling radius, actual tire pressure, standard tire pressure, road slope angle, current altitude, ambient temperature, drag coefficient, frontal area, motor speed, and preset gravitational acceleration.

[0239] Here, the vehicle's baseline mass, standard wheel rolling radius, standard tire pressure, drag coefficient, and frontal area are the vehicle structural parameters corresponding to the target model. These parameters are pre-stored in the vehicle configuration file. The drag coefficient is calibrated through wind tunnel testing, and the frontal area is determined based on the target vehicle's body dimensions.

[0240] The equivalent mass of rotating components is used to represent the rotational inertia generated by rotating components such as wheels, drive shafts, and motor rotors as equivalent to the vehicle's translational mass. In one specific embodiment, the equivalent mass of rotating components is taken as 8% of the vehicle's baseline mass, i.e.: (51) in, The equivalent mass of the rotating component is indicated by eq; the equivalent identifier is indicated by m; and the reference mass of the vehicle is indicated by m.

[0241] Additional load represents the mass increase of the target vehicle's current occupants, cargo, and auxiliary equipment relative to the vehicle's baseline mass. The additional load is output by the vehicle load estimation module and matched with current motion parameters using a timestamp. Actual tire pressure is collected by the tire pressure monitoring system, road slope angle is obtained by the target vehicle's attitude detection system, altitude is obtained by the positioning device, ambient temperature is collected by the ambient temperature sensor, and motor speed is obtained by the motor controller. The preset gravitational acceleration is 9.81 m / s². 2 .

[0242] The forward direction is taken as the positive direction of vehicle movement. The road slope angle is taken as positive when going uphill and negative when going downhill. The unit of the current vehicle speed is uniformly m / s, the unit of the current vehicle acceleration is uniformly m / s2, the unit of each mass parameter is uniformly kg, and the unit of each length parameter is uniformly m.

[0243] Step S802: Based on the change in actual tire pressure relative to standard tire pressure and additional load, the standard rolling radius of the wheel is corrected to obtain the current rolling radius of the wheel.

[0244] Here, the current rolling radius of the wheel is affected by tire pressure and vehicle load. Changes in tire pressure alter the degree of tire deformation, and changes in load also change the actual distance between the wheel center and the road surface. The current rolling radius of the wheel is calculated using the following formula: (52) Where r represents the current rolling radius of the wheel; Indicates the standard rolling radius of the wheel; P represents the actual tire pressure; Indicates standard tire pressure; This indicates the change in actual tire pressure relative to standard tire pressure; Indicates additional load; This indicates the tire pressure correction factor; This represents the load correction factor.

[0245] Tire pressure correction factors and load correction factors are calibrated through wheel rolling radius tests under different tire pressures and loads, and stored in the calibration parameter area. Actual tire pressure and standard tire pressure use the same pressure unit, while additional load uses a mass unit that matches the load correction factor.

[0246] When performing torque estimation for the first time and before obtaining effective actual tire pressure or additional load, the standard rolling radius of the wheel is used to calculate the current sampling time, and the current torque estimation result is marked as pending correction. After obtaining effective actual tire pressure and additional load, the current rolling radius of the wheel is updated according to formula (52).

[0247] Step S803: Determine the rolling resistance coefficient based on the current vehicle speed, and determine the rolling resistance experienced by the target vehicle based on the vehicle's reference mass, additional load, rolling resistance coefficient, preset gravitational acceleration, and road surface slope angle.

[0248] Here, when driving on asphalt roads, the rolling resistance coefficient is typically between 0.012 and 0.018. In one specific embodiment, the rolling resistance coefficient is dynamically adjusted according to the current vehicle speed: (53) in, Indicates the rolling resistance coefficient; This indicates the current vehicle speed, which is expressed in m / s.

[0249] The rolling resistance experienced by the target vehicle is calculated using the following formula: (54) in, This indicates the rolling resistance experienced by the target vehicle; Indicates the road surface slope angle.

[0250] Step S804: Determine the air density based on the current altitude and ambient temperature.

[0251] Here, air density decreases with increasing altitude and varies with ambient temperature. A two-dimensional correspondence table between altitude, ambient temperature, and air density is pre-established and stored in the calibration parameter area. The two-dimensional correspondence table includes multiple altitude calibration points, multiple temperature calibration points, and the air density corresponding to each set of altitude and temperature calibration points.

[0252] After obtaining the current altitude and ambient temperature, determine two adjacent altitude calibration points and two adjacent temperature calibration points. Read the air density corresponding to the four sets of calibration point combinations and interpolate the four air densities to obtain the air density corresponding to the current altitude and ambient temperature. If the current altitude or ambient temperature coincides with a calibration point in the two-dimensional correspondence table, directly read the air density of the corresponding calibration point.

[0253] Step S805: Determine the air resistance experienced by the target vehicle based on air density, drag coefficient, frontal area, and current vehicle speed.

[0254] Here, the air resistance experienced by the target vehicle is calculated using the following formula: (55) in, This indicates the air resistance experienced by the target vehicle; Indicates air density; A represents the drag coefficient; A represents the frontal area; v represents the current vehicle speed.

[0255] Air resistance increases with the square of the current vehicle speed. When the current vehicle speed is zero, air resistance is zero. This implementation calculates the magnitude of air resistance according to the vehicle's forward direction and includes air resistance as a resistance term opposite to the vehicle's forward direction in the driving force calculation.

[0256] Step S806: Determine the slope resistance experienced by the target vehicle based on the vehicle's reference mass, additional load, preset gravitational acceleration, and road slope angle.

[0257] Here, the gradient resistance experienced by the target vehicle is calculated using the following formula: (56) in, This indicates the gradient resistance experienced by the target vehicle.

[0258] When the target vehicle is going uphill, the road slope angle is positive, and the slope resistance is positive; when the target vehicle is going downhill, the road slope angle is negative, and the slope resistance is negative. By retaining the positive and negative signs of the road slope angle, the slope resistance can be increased or decreased to adjust the driving force required by the target vehicle in uphill and downhill conditions, respectively.

[0259] Step S807: Determine the vehicle inertial force corresponding to the target vehicle based on the vehicle's reference mass, the equivalent mass of the rotating component, and the current vehicle acceleration.

[0260] Here, the vehicle inertial force corresponding to the target vehicle is calculated according to the following formula: (57) in, This indicates the vehicle's inertial force corresponding to the target vehicle; inertia indicates the vehicle's inertial force identifier. This indicates the current vehicle acceleration.

[0261] When the current vehicle acceleration is positive, the vehicle's inertial force is also positive; when the current vehicle acceleration is negative, the vehicle's inertial force is also negative. The equivalent mass of the rotating components allows the calculation of the vehicle's inertial force to simultaneously reflect the influence of both the vehicle's translational and rotating components on the acceleration process.

[0262] Step S808: Add the vehicle's inertial force, rolling resistance, air resistance, and slope resistance to obtain the driving force corresponding to the target vehicle.

[0263] Here, the driving force corresponding to the target vehicle is calculated according to the following formula: (58) in, This indicates the driving force corresponding to the target vehicle; "drive" indicates the driving force identifier.

[0264] Formula (58) comprehensively considers the effects of vehicle acceleration and deceleration, tire-road contact, air action, and road slope on the longitudinal motion of the vehicle. When the driving force is positive, the target vehicle needs the motor to provide positive driving torque; when the driving force is negative, the target vehicle is in a deceleration or energy recovery state.

[0265] Step S809: Multiply the driving force by the current rolling radius of the wheel to obtain the effective torque of the wheel.

[0266] Here, the effective torque of the wheel is calculated according to the following formula: (59) in, This indicates the effective torque of the wheel; wheel represents the wheel identifier; r represents the current rolling radius of the wheel. Using the current rolling radius of the wheel, corrected for tire pressure and additional load, can reduce the error caused by a fixed rolling radius in the calculation of the effective torque of the wheel.

[0267] Step S810: Determine the mechanical loss torque of the transmission system based on the motor speed and the pre-calibrated correspondence between the motor speed and the mechanical loss torque.

[0268] Here, the mechanical loss torque of the transmission system includes mechanical losses generated by gear meshing, bearing friction, and seal friction. The mechanical loss torque is measured at multiple motor speed calibration points through bench testing to establish the correspondence between motor speed and mechanical loss torque, and this correspondence is stored in the calibration parameter area.

[0269] The mechanical loss torque of the transmission system is expressed as a function of the motor speed: (60) in, This indicates the mechanical loss torque of the transmission system; loss indicates the mechanical loss indicator; n indicates the motor speed. This indicates the correspondence between the pre-calibrated motor speed and the mechanical loss torque.

[0270] When the current motor speed coincides with the calibrated speed, the corresponding transmission system mechanical loss torque is directly read. When the current motor speed is between two adjacent calibrated speeds, interpolation is performed based on the mechanical loss torques corresponding to the two adjacent calibrated speeds to obtain the transmission system mechanical loss torque corresponding to the current motor speed. During the calibration process, the transmission system mechanical loss torque is uniformly converted to the wheel side so as to be added to the effective wheel torque.

[0271] Step S811: Determine the total efficiency of the transmission system corresponding to the current vehicle speed based on the preset speed range in which the current vehicle speed is located and the correspondence between the preset speed range and the total efficiency of the transmission system.

[0272] Here, the relationship between the preset vehicle speed range and the overall efficiency of the transmission system is as follows: (61) in, This indicates the overall efficiency of the transmission system; "total" indicates the overall efficiency indicator; and "v" indicates the current vehicle speed.

[0273] When the current vehicle speed is less than 30 km / h, the target vehicle is in a low-speed, high-torque operating condition. The gear and bearing loss ratio is set at 15%, and the overall transmission system efficiency is taken as 0.85. When the current vehicle speed is between 30-90 km / h, the target vehicle is in a medium-speed, rated operating condition. The gear and bearing loss ratio is set at 10%, and the overall transmission system efficiency is taken as 0.90. When the current vehicle speed is greater than 90 km / h, the target vehicle is in a high-speed, light-load operating condition. The gear and bearing loss ratio is set at 5%, and the overall transmission system efficiency is taken as 0.95.

[0274] When the current vehicle speed is stored in meters per second, first convert the current vehicle speed to kilometers per hour, and then determine the preset speed range corresponding to the current vehicle speed.

[0275] Step S812: Add the effective torque of the wheel to the mechanical loss torque of the transmission system, and determine the ratio between the sum and the total efficiency of the transmission system as the estimated output torque of the motor controlled by the motor controller.

[0276] Here, the relationship between the estimated output torque of the motor, the effective torque of the wheels, the mechanical loss torque of the transmission system, and the overall efficiency of the transmission system is as follows: (62) The estimated output torque of the motor is calculated using the following formula: (63) in, This indicates the estimated output torque of the motor; est represents the estimated value.

[0277] Substituting formulas (52) to (60) into formula (63), we obtain the complete formula for calculating the estimated output torque of the motor: (64) Formula (64) calculates the driving force required for the longitudinal movement of the vehicle based on the current vehicle speed and current vehicle acceleration, and then calculates the estimated output torque of the motor based on the current rolling radius of the wheel, mechanical loss torque and the total efficiency of the transmission system.

[0278] When a direct drive structure is used between the motor and the wheel, the total transmission ratio of the transmission system can be set to 1, and the estimated output torque of the motor can be calculated according to formula (63); when a reducer or main reducer is installed between the motor and the wheel, the total transmission ratio of the transmission system can also be obtained, and the estimated output torque of the motor can be calculated according to the following formula: (65) in, This represents the total transmission ratio of the transmission system. The total transmission ratio of the transmission system is determined based on the transmission ratio of the reducer, the transmission ratio of the final reducer, and the transmission ratio corresponding to the current gear. When formula (65) is used, the mechanical loss torque of the transmission system is uniformly converted to the wheel side. When the total transmission ratio of the transmission system is already included in the equivalent parameters of the effective torque of the wheel or the calibration results of the mechanical loss, the total transmission ratio of the transmission system is treated as 1.

[0279] In this embodiment, the preset vehicle dynamics model comprehensively considers the influence of vehicle mass, additional load, tire condition, vehicle acceleration, vehicle speed, road slope, air condition and transmission system loss on the motor output torque, and can reverse calculate the estimated output torque of the motor based on the actual driving state of the target vehicle.

[0280] In an optional implementation, step S104 includes the following steps S901-S905.

[0281] Step S901: The absolute difference between the torque command value and the estimated output torque is determined as the torque deviation.

[0282] Here, the torque command value is the target output torque of the motor controller at the current sampling moment, and the estimated output torque is the motor output torque calculated by the preset vehicle dynamics model based on the current motion parameters. The torque command value and the estimated output torque are matched according to the sampling timestamp, and only the torque command value and the estimated output torque corresponding to the same sampling moment participate in the torque deviation calculation.

[0283] The torque deviation at the current sampling time t is calculated using the following formula: (66) in, This indicates the torque deviation at the current sampling time; cmd represents the command identifier. Indicates the torque command value; est indicates the estimation identifier; This indicates the estimated output torque. The unit for torque deviation is Nm.

[0284] The torque deviation is calculated using the absolute difference, so that both cases where the estimated output torque is higher than the torque command value and the estimated output torque is lower than the torque command value are verified according to the magnitude of the deviation.

[0285] Step S902: Determine whether the torque deviation is greater than the preset torque deviation threshold.

[0286] Here, the preset torque deviation threshold is calibrated based on the allowable torque control error of the target vehicle model, the estimation error of the vehicle dynamics model, and the measurement error of the wheel speed sensor, and stored in the calibration parameter area. In a specific embodiment, the preset torque deviation threshold is: (67) in, This indicates the preset torque deviation threshold; thres indicates the threshold identifier.

[0287] In each sampling period, the current torque deviation is compared with a preset torque deviation threshold. The comparison results include torque deviation less than or equal to the preset torque deviation threshold, and torque deviation greater than the preset torque deviation threshold.

[0288] Step S903: If the torque deviation is less than or equal to the preset torque deviation threshold, determine that the output torque verification result is normal.

[0289] Here, the condition for determining normal torque output is: (68) When formula (68) is satisfied, the output torque verification result corresponding to the current sampling time is determined to be normal torque output, and the cumulative number of cycles and duration of the torque deviation exceeding the preset torque deviation threshold are cleared to zero.

[0290] When a torque monitoring fault condition has not yet been triggered, the normal judgment at the current sampling time is used to maintain the normal monitoring state. When a torque monitoring fault condition has been triggered, the normal judgment at the current sampling time only indicates that the current torque deviation has recovered to the allowable range, and the method for clearing the torque monitoring fault condition is determined by the pre-set fault recovery rules.

[0291] Step S904: If the torque deviation is greater than the preset torque deviation threshold, determine the duration for which the torque deviation is continuously greater than the preset torque deviation threshold, and obtain the system initialization status identifier.

[0292] Here, when the torque deviation exceeds a preset torque deviation threshold, the current sampling time is recorded as an over-threshold sampling time. When the torque deviation at the previous sampling time is less than or equal to the preset torque deviation threshold, the current sampling time is taken as the start time of the continuous over-threshold process, and the cumulative cycle count is set to 1. When the torque deviation at the previous sampling time is also greater than the preset torque deviation threshold, the cumulative cycle count is increased by 1.

[0293] The duration for which the torque deviation continuously exceeds a preset torque deviation threshold is determined based on the cumulative number of cycles and the sampling time interval corresponding to the continuous threshold exceeding process. In one specific embodiment, the sampling time interval is 0.01 seconds, and the duration reaches 1 second when the torque deviation is greater than the preset torque deviation threshold for 100 consecutive sampling cycles. When the torque deviation in any sampling cycle is less than or equal to the preset torque deviation threshold, the continuous threshold exceeding process ends, and the cumulative number of cycles and the duration are cleared to zero.

[0294] Duration is denoted as 'fault' indicates a fault indicator. In one specific embodiment, the preset duration is: .in, Indicates the preset duration.

[0295] The system initialization status flag indicates whether the data and operating status required for torque verification have been initialized. The system initialization status flag is represented as follows: (69) in, This indicates the system initialization status; init indicates the initialization flag.

[0296] When multiple wheel speed signals are valid, the historical data required for wheel speed preprocessing has been written, the current motion parameters are valid, and the vehicle parameters required for the preset vehicle dynamics model have been obtained, the system initialization status flag is set to 1. If any initialization condition is not met, the system initialization status flag is set to 0.

[0297] When the system initialization status flag is 0, the duration for which the torque deviation continuously exceeds the preset torque deviation threshold will not be accumulated, and the already accumulated duration will be cleared to avoid triggering a torque monitoring fault state due to invalid estimation results generated during the initialization process.

[0298] Step S905: If the duration reaches the preset duration and the system initialization status flag indicates that the system initialization is complete, determine that the output torque verification result is abnormal and trigger the torque monitoring fault status.

[0299] Here, triggering a torque monitoring fault condition requires the following conditions to be met simultaneously: (70) in, Indicates the current torque deviation Greater than the preset torque deviation threshold ; Indicates the duration of the continuous threshold exceedance process. Reaching the preset duration ; This indicates that system initialization is complete.

[0300] When all three conditions are met simultaneously, the output torque verification result is determined to be an abnormal torque output, the torque monitoring fault state is set to the triggered state, and a torque monitoring fault signal is output to the vehicle fault management module. If the torque deviation is greater than the preset torque deviation threshold but the duration does not reach the preset duration, continuous over-threshold monitoring is maintained, and the torque monitoring fault state is not triggered. If the duration reaches the preset duration but the system initialization status flag is 0, the torque monitoring fault state is not triggered.

[0301] The fault handling strategy triggered by a torque monitoring fault is configured based on the vehicle application scenario and fault classification rules. The fault handling strategy does not affect the determination of the output torque verification result.

[0302] In one specific embodiment, the verification method is applied to the real-vehicle driving monitoring of a front-wheel-drive new energy vehicle. The left and right front wheels of the target vehicle are drive wheels, and the left and right rear wheels are non-drive wheels. The sampling time interval for the wheel speed signals is 0.01 seconds. The target vehicle has completed parameter initialization, and the system initialization status flag is 1.

[0303] The target vehicle enters the left turn lane at approximately 60 km / h. The current sampling time shows a left front wheel speed of 24.80 m / s. The wheel speeds of the left front wheel at the four consecutive sampling times prior to this sampling time are 16.16 m / s, 16.18 m / s, 16.20 m / s, and 16.21 m / s, respectively. The change in the current left front wheel speed exceeds the corresponding wheel speed change threshold; therefore, 24.80 m / s is identified as an abnormal wheel speed value. A sliding window is formed using these five wheel speed values. The median of the five wheel speed values ​​within the sliding window is 16.20 m / s; therefore, 16.20 m / s is used to replace the abnormal wheel speed value. After low-pass filtering, the processed wheel speed value for the left front wheel is 16.20 m / s. After undergoing the same treatment, the front right wheel, left rear wheel, and right rear wheel have corresponding wheel speed values ​​of 17.10 m / s, 16.10 m / s, and 17.00 m / s, respectively.

[0304] The averaged wheel speed values ​​for the left and right rear wheels yielded a reference speed of 16.55 m / s. The slip ratio for the left front wheel was approximately -2.11%, and for the right front wheel, it was approximately 3.32%. Based on the absolute values ​​of the slip ratios, the initial fusion weights for the left and right front wheels were determined to be approximately 0.496 and 0.493, respectively, while the initial fusion weights for the left and right rear wheels were both set to 0.4. After weight normalization, the target fusion weights for the left front wheel, right front wheel, left rear wheel, and right rear wheel were approximately 0.277, 0.276, 0.224, and 0.224, respectively. The weighted sum of the four processed wheel speed values ​​yielded a fused vehicle speed of approximately 16.60 m / s before cornering correction.

[0305] The current yaw rate is 0.30 rad / s, the preset correction coefficient is 0.30 s / m, the distances between the left and right front wheels and the vehicle center are both 1.80 meters, and the distances between the left and right rear wheels and the vehicle center are both 1.40 meters. After turning correction based on the yaw rate, preset correction coefficient, distances to each wheel, and target fusion weight, the corrected fused vehicle speed is approximately 16.60 m / s. The corrected fused vehicle speed is used to update the target vehicle's fused vehicle speed.

[0306] This embodiment uses velocity difference and smoothing to determine the current vehicle acceleration. The vehicle velocity at the previous sampling time was 16.59 m / s, and the vehicle acceleration at the previous sampling time was 0.30 m / s². 2 The current driving condition is a typical urban road condition, and the target smoothing coefficient is set to 0.4. The speed difference between the current vehicle speed and the vehicle speed at the previous sampling time is 0.01 m / s, and the initial vehicle acceleration is 1.00 m / s². 2 After smoothing, the smoothed vehicle acceleration is 0.58 m / s². 2The absolute value of the smooth vehicle acceleration did not exceed 12 m / s². 2 Therefore, 0.58m / s 2 This is determined to be the current vehicle acceleration.

[0307] The target vehicle has a base mass of 1600 kg, an equivalent mass of 128 kg for rotating parts, an additional load of 120 kg, a standard wheel rolling radius of 0.31 m, an actual tire pressure of 240 kPa, a standard tire pressure of 250 kPa, a road surface slope angle of 1°, an altitude of 500 m, an ambient temperature of 20 degrees Celsius, a drag coefficient of 0.29, and a frontal area of ​​2.2 m². 2 The preset gravitational acceleration is 9.81 m / s². 2 After correction based on pre-calibrated tire pressure and load correction factors, the current wheel rolling radius is approximately 0.309m. Using the altitude and ambient temperature as references, the air density is found to be approximately 1.167 kg / m³. 3 .

[0308] Based on the current vehicle speed, the rolling resistance coefficient is determined to be approximately 0.01266. Based on the above vehicle parameters, the calculated vehicle inertial force is approximately 1002.2 N, rolling resistance is approximately 213.7 N, air resistance is approximately 102.6 N, and gradient resistance is approximately 294.5 N. Adding these four forces together, the corresponding driving force for the target vehicle is approximately 1613.0 N. Multiplying the driving force by the current rolling radius of the wheel, the effective wheel torque is approximately 499.0 Nm.

[0309] The current motor speed is 3200 r / min. Based on the pre-calibrated relationship between motor speed and mechanical loss torque, the mechanical loss torque of the transmission system is determined to be 25 Nm. The current vehicle speed is between 30 and 90 km / h, therefore the overall transmission system efficiency is set to 0.90. The target vehicle's gear ratio has been converted into the corresponding torque calibration parameters, and the overall transmission ratio of the transmission system is treated as 1. Therefore, the estimated output torque of the motor is approximately 582.2 Nm.

[0310] In the first monitoring phase, the torque command value corresponding to the motor controller was 560 Nm, and the torque deviation between the torque command value and the estimated output torque was approximately 22.2 Nm. Since the torque deviation is less than the preset torque deviation threshold of 100 Nm, the output torque verification result is determined to be normal.

[0311] During the subsequent monitoring phase, the torque command value corresponding to the motor controller increased to 720 Nm, but the actual motion state of the target vehicle did not change accordingly. The estimated output torque calculated based on the wheel speed signal remained around 582 Nm. At this time, the torque deviation was approximately 138 Nm. After the torque deviation continuously exceeded the preset torque deviation threshold for 100 sampling periods, the duration reached 1 second. Since the system initialization status flag was 1, the output torque verification result was determined to be an abnormal torque output, triggering a torque monitoring fault state.

[0312] The verification method provided in this application can estimate the motor output torque using existing wheel speed signals during the normal driving process of the target vehicle, and identify abnormal situations where the actual output torque of the motor does not reach the torque command value by torque deviation and continuous over-threshold time.

[0313] Based on this, embodiments of this application provide a system for verifying the output torque of a motor controller, referring to... Figure 3 The motor controller output torque verification system provided in this application includes: Data acquisition module 1 is used to acquire wheel speed signals collected by multiple wheel speed sensors of the target vehicle, as well as torque command values ​​corresponding to the motor controller of the target vehicle.

[0314] The motion parameter determination module 2 is used to determine the current motion parameters of the target vehicle based on multiple wheel speed signals.

[0315] Torque estimation module 3 is used to determine the estimated output torque of the motor controlled by the motor controller based on the current motion parameters and through a preset vehicle dynamics model; the preset vehicle dynamics model is used to inversely calculate the actual output torque of the motor based on the wheel speed.

[0316] The verification result determination module 4 is used to determine the output torque verification result of the motor controller based on the torque deviation between the torque command value and the estimated output torque.

[0317] The motor controller output torque verification system provided in this application acquires multiple wheel speed signals and torque command values, determines the current motion parameters based on the multiple wheel speed signals, and uses a preset vehicle dynamics model to reverse calculate the estimated output torque of the motor. It can determine whether the motor output torque is abnormal in real time based on the torque deviation between the torque command value and the estimated output torque, thereby achieving low-cost, real-time and reliable output torque verification without the need to add a torque sensor or bus current sensor.

[0318] In an optional implementation, the motion parameter determination module 2 is further configured to: Multiple wheel speed signals are preprocessed to obtain the processed wheel speed signal corresponding to each wheel speed signal.

[0319] Multiple processed wheel speed signals are fused to obtain the fused vehicle speed of the target vehicle.

[0320] The target vehicle's current speed and current acceleration are determined based on the merged vehicle speed.

[0321] The current vehicle speed and current vehicle acceleration are determined as the current motion parameters.

[0322] In an optional implementation, the motion parameter determination module 2 is further configured to: For each wheel speed signal, the wheel speed value at the current sampling time and the wheel speed value at the previous sampling time are obtained, and the absolute difference between the wheel speed value at the current sampling time and the wheel speed value at the previous sampling time is determined as the wheel speed change.

[0323] Multiply the wheel speed value at the previous sampling time by the preset wheel speed change ratio to obtain the wheel speed change threshold corresponding to the current sampling time.

[0324] If the wheel speed change exceeds the wheel speed change threshold, the wheel speed value at the current sampling time will be identified as an abnormal wheel speed value.

[0325] Using the current sampling time as the end point of the window, and according to the preset window size, obtain the wheel speed values ​​corresponding to the current sampling time and multiple consecutive sampling times before the current sampling time, and form a sliding window.

[0326] When the wheel speed change exceeds the wheel speed change threshold, the median of multiple wheel speed values ​​within the sliding window is determined, and the abnormal wheel speed value is replaced with the median to obtain the corrected wheel speed signal.

[0327] If the wheel speed change is less than or equal to the wheel speed change threshold, the wheel speed value at the current sampling time is retained to obtain the corrected wheel speed signal.

[0328] The modified wheel speed signal is filtered by a low-pass filter with a preset cutoff frequency to obtain the processed wheel speed signal.

[0329] In an optional implementation, the motion parameter determination module 2 is further configured to: Based on the drive type of the wheels corresponding to the multiple wheel speed sensors, the multiple processed wheel speed signals are divided into processed wheel speed signals corresponding to the drive wheels and processed wheel speed signals corresponding to the non-drive wheels.

[0330] The processed wheel speed signals corresponding to each non-drive wheel are averaged to obtain the reference speed.

[0331] For each drive wheel, the value obtained by subtracting the reference speed from the processed wheel speed signal corresponding to that drive wheel is determined as the speed difference, and the ratio between the speed difference and the reference speed is determined as the slip ratio corresponding to that drive wheel.

[0332] Based on the preset slip ratio-fusion weight mapping relationship, the initial fusion weight corresponding to each drive wheel is determined; wherein, the slip ratio-fusion weight mapping relationship is: the larger the absolute value of the slip ratio, the smaller the corresponding fusion weight.

[0333] Obtain the pre-set non-driving wheel weights and determine the initial fusion weights corresponding to each non-driving wheel.

[0334] The initial fusion weights corresponding to each wheel are summed to obtain the weight sum value. The ratio between the initial fusion weight and the weight sum value corresponding to each wheel is determined to obtain the target fusion weight corresponding to each wheel.

[0335] Each processed wheel speed signal is multiplied by its corresponding target fusion weight, and the resulting products are summed to obtain the fused vehicle speed of the target vehicle.

[0336] In an optional implementation, the motion parameter determination module 2 is further configured to: Obtain the yaw rate of the target vehicle, the preset correction coefficient, and the distance between each wheel and the center of the target vehicle.

[0337] For each wheel, the absolute value of the yaw rate, the distance corresponding to the wheel, and the preset correction coefficient are multiplied to obtain the first correction amount for that wheel, and the difference between the value 1 and the first correction amount is determined as the wheel speed correction coefficient for that wheel.

[0338] Each processed wheel speed signal, its corresponding target fusion weight, and its corresponding wheel speed correction coefficient are multiplied together, and the resulting products are summed to obtain the first fusion speed.

[0339] The distance corresponding to each wheel is multiplied by the corresponding target fusion weight, and the resulting products are summed to obtain the weighted distance.

[0340] The weighted distance, the absolute value of the yaw rate, and the preset correction coefficient are multiplied to obtain the second correction amount, and the difference between the value 1 and the second correction amount is determined as the fusion correction coefficient.

[0341] The ratio between the first fusion speed and the fusion correction coefficient is determined as the corrected fusion vehicle speed, and the corrected fusion vehicle speed is used to update the fusion vehicle speed of the target vehicle.

[0342] In an optional implementation, the motion parameter determination module 2 is further configured to: The fused vehicle speed is determined as the observed vehicle speed of the Kalman filter algorithm, and the vehicle speed and vehicle acceleration are set as the state variables of the Kalman filter algorithm.

[0343] Obtain the vehicle speed, vehicle acceleration, and state covariance corresponding to the previous sampling time.

[0344] Based on the vehicle speed, vehicle acceleration, and sampling time interval at the previous sampling time, the predicted vehicle speed at the current sampling time is determined, and the vehicle acceleration at the previous sampling time is determined as the predicted vehicle acceleration at the current sampling time.

[0345] Based on the state covariance at the previous sampling time, the preset state transition relationship, and the preset process noise covariance, determine the prediction covariance at the current sampling time.

[0346] The observation noise covariance is determined based on the comparison between the absolute value of the predicted vehicle acceleration and the preset acceleration fluctuation threshold. Specifically, when the absolute value of the predicted vehicle acceleration is less than or equal to the preset acceleration fluctuation threshold, the first observation noise covariance is used; when the absolute value of the predicted vehicle acceleration is greater than the preset acceleration fluctuation threshold, the second observation noise covariance, which is greater than the first observation noise covariance, is used.

[0347] The Kalman gain is determined based on the predicted covariance, the pre-defined observation relationship, and the observation noise covariance.

[0348] Based on the speed difference between the observed vehicle speed and the predicted vehicle speed, and the Kalman gain, the predicted vehicle speed and predicted vehicle acceleration are corrected to obtain the current vehicle speed and current vehicle acceleration.

[0349] Based on the Kalman gain, the preset observation relationship, and the predicted covariance, the predicted covariance is updated to obtain the state covariance corresponding to the current sampling time.

[0350] In an optional implementation, the motion parameter determination module 2 is further configured to: The fused vehicle speed is determined as the current vehicle speed, and the vehicle speed, vehicle acceleration, and sampling time interval corresponding to the previous sampling moment are obtained.

[0351] The value obtained by subtracting the vehicle speed at the previous sampling time from the current vehicle speed is determined as the speed difference, and the ratio between the speed difference and the sampling time interval is determined as the initial vehicle acceleration.

[0352] Obtain the current driving conditions of the target vehicle, and determine the target smoothing coefficient corresponding to the current driving conditions based on the preset correspondence between driving conditions and smoothing coefficients.

[0353] The first acceleration component is obtained by multiplying the initial vehicle acceleration by the target smoothing coefficient.

[0354] The difference between the value 1 and the target smoothing coefficient is multiplied by the vehicle acceleration corresponding to the previous sampling time to obtain the second acceleration component.

[0355] Adding the first acceleration component to the second acceleration component yields the smooth vehicle acceleration.

[0356] If the absolute value of the smoothed vehicle acceleration is less than or equal to a preset acceleration threshold, the smoothed vehicle acceleration is determined as the current vehicle acceleration.

[0357] If the absolute value of the smoothed vehicle acceleration is greater than the preset acceleration threshold, the vehicle acceleration corresponding to the preset number of sampling periods before the current sampling time is averaged, and the resulting average vehicle acceleration is determined as the current vehicle acceleration.

[0358] In an optional implementation, the torsion estimation module 3 is further configured to: The system acquires the target vehicle's baseline mass, equivalent mass of rotating parts, additional load, standard wheel rolling radius, actual tire pressure, standard tire pressure, road slope angle, current altitude, ambient temperature, drag coefficient, frontal area, motor speed, and preset gravitational acceleration.

[0359] The standard rolling radius of the wheel is corrected based on the change in actual tire pressure relative to standard tire pressure and the additional load, to obtain the current rolling radius of the wheel.

[0360] The rolling resistance coefficient is determined based on the current vehicle speed, and the rolling resistance experienced by the target vehicle is determined based on the vehicle's reference mass, additional load, rolling resistance coefficient, preset gravitational acceleration, and road surface slope angle.

[0361] The air density is determined based on the current altitude and ambient temperature.

[0362] The air resistance experienced by the target vehicle is determined based on air density, drag coefficient, frontal area, and current vehicle speed.

[0363] The slope resistance experienced by the target vehicle is determined based on the vehicle's baseline mass, additional load, preset gravitational acceleration, and road slope angle.

[0364] Based on the vehicle's reference mass, the equivalent mass of rotating components, and the current vehicle acceleration, determine the vehicle inertial force corresponding to the target vehicle.

[0365] The driving force corresponding to the target vehicle is obtained by adding the vehicle's inertial force, rolling resistance, air resistance, and gradient resistance.

[0366] The effective torque of the wheel is obtained by multiplying the driving force by the current rolling radius of the wheel.

[0367] The mechanical loss torque of the transmission system is determined based on the motor speed and the pre-calibrated correspondence between the motor speed and the mechanical loss torque.

[0368] Based on the preset speed range in which the current vehicle speed is located, and the correspondence between the preset speed range and the total efficiency of the transmission system, the total efficiency of the transmission system corresponding to the current vehicle speed is determined.

[0369] The effective torque of the wheel is added to the mechanical loss torque of the transmission system, and the ratio between the sum and the total efficiency of the transmission system is determined as the estimated output torque of the motor controlled by the motor controller.

[0370] In an optional implementation, the verification result determination module 4 is further configured to: The absolute difference between the torque command value and the estimated output torque is defined as the torque deviation.

[0371] Determine whether the torque deviation is greater than the preset torque deviation threshold.

[0372] If the torque deviation is less than or equal to the preset torque deviation threshold, the output torque verification result is determined to be normal torque output.

[0373] If the torque deviation is greater than the preset torque deviation threshold, determine the duration for which the torque deviation is continuously greater than the preset torque deviation threshold, and obtain the system initialization status flag.

[0374] If the duration reaches the preset duration and the system initialization status flag indicates that the system initialization is complete, the output torque verification result is determined to be abnormal, and the torque monitoring fault status is triggered.

[0375] The computer program product provided in this application includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0376] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0377] Furthermore, in the description of the embodiments of this application, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0378] In the description of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0379] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims.

Claims

1. A method for verifying the output torque of a motor controller, characterized in that, include: The wheel speed signals collected by multiple wheel speed sensors of the target vehicle and the torque command value corresponding to the motor controller of the target vehicle are obtained respectively. The current motion parameters of the target vehicle are determined based on multiple wheel speed signals. Based on the current motion parameters, the estimated output torque of the motor controlled by the motor controller is determined through a preset vehicle dynamics model. The preset vehicle dynamics model is used to inversely calculate the actual output torque of the motor based on the wheel speed. The output torque verification result of the motor controller is determined based on the torque deviation between the torque command value and the estimated output torque.

2. The method for verifying the output torque of a motor controller according to claim 1, characterized in that, Determining the current motion parameters of the target vehicle based on multiple wheel speed signals includes: Each of the multiple wheel speed signals is preprocessed to obtain a processed wheel speed signal corresponding to each wheel speed signal. The multiple processed wheel speed signals are fused to obtain the fused vehicle speed of the target vehicle; The current vehicle speed and current vehicle acceleration of the target vehicle are determined based on the fused vehicle speed; The current vehicle speed and the current vehicle acceleration are determined as the current motion parameters.

3. The method for verifying the output torque of a motor controller according to claim 2, characterized in that, The step of preprocessing the multiple wheel speed signals to obtain the processed wheel speed signal corresponding to each wheel speed signal includes: For each wheel speed signal, the wheel speed value at the current sampling time and the wheel speed value at the previous sampling time are obtained, and the absolute difference between the wheel speed value at the current sampling time and the wheel speed value at the previous sampling time is determined as the wheel speed change. Multiply the wheel speed value at the previous sampling time by the preset wheel speed change ratio to obtain the wheel speed change threshold corresponding to the current sampling time; If the wheel speed change exceeds the wheel speed change threshold, the wheel speed value at the current sampling time is determined as an abnormal wheel speed value. Using the current sampling time as the end point of the window, and according to a preset window size, obtain the wheel speed values ​​corresponding to the current sampling time and multiple consecutive sampling times before the current sampling time, respectively, to form a sliding window; When the wheel speed change is greater than the wheel speed change threshold, the median of the multiple wheel speed values ​​within the sliding window is determined, and the abnormal wheel speed value is replaced by the median to obtain the corrected wheel speed signal; If the wheel speed change is less than or equal to the wheel speed change threshold, the wheel speed value at the current sampling time is retained to obtain the corrected wheel speed signal; The modified wheel speed signal is obtained by filtering the modified wheel speed signal using a low-pass filter with a preset cutoff frequency.

4. The method for verifying the output torque of a motor controller according to claim 2, characterized in that, The step of fusing multiple processed wheel speed signals to obtain the fused vehicle speed of the target vehicle includes: Based on the drive type of the wheels corresponding to the multiple wheel speed sensors, the multiple processed wheel speed signals are divided into processed wheel speed signals corresponding to the drive wheels and processed wheel speed signals corresponding to non-drive wheels. The processed wheel speed signals corresponding to each of the non-driving wheels are averaged to obtain a reference speed. For each drive wheel, the value obtained by subtracting the reference speed from the processed wheel speed signal corresponding to the drive wheel is determined as the speed difference, and the ratio between the speed difference and the reference speed is determined as the slip ratio corresponding to the drive wheel. Based on the preset slip ratio-fusion weight mapping relationship, the initial fusion weight corresponding to each drive wheel is determined; wherein, the slip ratio-fusion weight mapping relationship is: the larger the absolute value of the slip ratio, the smaller the corresponding fusion weight; Obtain the pre-set weights of the non-driving wheels, and determine the weights of the non-driving wheels as the initial fusion weights corresponding to each of the non-driving wheels; The initial fusion weights corresponding to each wheel are summed to obtain the weight sum value, and the ratio between the initial fusion weight corresponding to each wheel and the weight sum value is determined to obtain the target fusion weight corresponding to each wheel. Each processed wheel speed signal is multiplied by its corresponding target fusion weight, and the resulting products are summed to obtain the fused vehicle speed of the target vehicle.

5. The method for verifying the output torque of a motor controller according to claim 4, characterized in that, When the target vehicle is turning, after obtaining the merged vehicle speed of the target vehicle, the method further includes: The yaw rate of the target vehicle, the preset correction coefficient, and the distance between each wheel and the center of the target vehicle are obtained. For each wheel, the absolute value of the yaw rate, the distance corresponding to the wheel, and the preset correction coefficient are multiplied to obtain the first correction amount corresponding to the wheel, and the difference between the value 1 and the first correction amount is determined as the wheel speed correction coefficient corresponding to the wheel. Each processed wheel speed signal, its corresponding target fusion weight, and its corresponding wheel speed correction coefficient are multiplied together, and the resulting products are summed to obtain the first fusion speed. The distance corresponding to each wheel is multiplied by the corresponding target fusion weight, and the resulting products are summed to obtain the weighted distance. Multiply the weighted distance, the absolute value of the yaw rate, and the preset correction coefficient to obtain the second correction amount, and determine the difference between the value 1 and the second correction amount as the fusion correction coefficient; The ratio between the first fusion speed and the fusion correction coefficient is determined as the corrected fusion vehicle speed, and the corrected fusion vehicle speed is used to update the fusion vehicle speed of the target vehicle.

6. The method for verifying the output torque of a motor controller according to claim 4 or 5, characterized in that, Determining the current vehicle speed and current vehicle acceleration of the target vehicle based on the fused vehicle speed includes: The fused vehicle speed is determined as the observed vehicle speed of the Kalman filter algorithm, and the vehicle speed and vehicle acceleration are set as the state variables of the Kalman filter algorithm. Obtain the vehicle speed, vehicle acceleration, and state covariance at the previous sampling time. Based on the vehicle speed, vehicle acceleration, and sampling time interval corresponding to the previous sampling time, the predicted vehicle speed corresponding to the current sampling time is determined, and the vehicle acceleration corresponding to the previous sampling time is determined as the predicted vehicle acceleration corresponding to the current sampling time. Based on the state covariance corresponding to the previous sampling time, the preset state transition relationship, and the preset process noise covariance, determine the prediction covariance corresponding to the current sampling time; The observation noise covariance is determined based on the comparison between the absolute value of the predicted vehicle acceleration and the preset acceleration fluctuation threshold; wherein, when the absolute value of the predicted vehicle acceleration is less than or equal to the preset acceleration fluctuation threshold, a first observation noise covariance is used; when the absolute value of the predicted vehicle acceleration is greater than the preset acceleration fluctuation threshold, a second observation noise covariance greater than the first observation noise covariance is used. The Kalman gain is determined based on the predicted covariance, the preset observation relationship, and the observation noise covariance. Based on the speed difference between the observed vehicle speed and the predicted vehicle speed and the Kalman gain, the predicted vehicle speed and the predicted vehicle acceleration are corrected to obtain the current vehicle speed and the current vehicle acceleration; Based on the Kalman gain, the preset observation relationship, and the prediction covariance, the prediction covariance is updated to obtain the state covariance corresponding to the current sampling time.

7. The method for verifying the output torque of a motor controller according to claim 4 or 5, characterized in that, Determining the current vehicle speed and current vehicle acceleration of the target vehicle based on the fused vehicle speed includes: The fused vehicle speed is determined as the current vehicle speed, and the vehicle speed, vehicle acceleration, and sampling time interval corresponding to the previous sampling moment are obtained; The value obtained by subtracting the vehicle speed corresponding to the previous sampling time from the current vehicle speed is determined as the speed difference, and the ratio between the speed difference and the sampling time interval is determined as the initial vehicle acceleration; Obtain the current driving condition of the target vehicle, and determine the target smoothing coefficient corresponding to the current driving condition based on the preset correspondence between driving conditions and smoothing coefficients. Multiply the initial vehicle acceleration by the target smoothing coefficient to obtain the first acceleration component; The difference between the value 1 and the target smoothing coefficient is multiplied by the vehicle acceleration corresponding to the previous sampling time to obtain the second acceleration component; Add the first acceleration component to the second acceleration component to obtain the smooth vehicle acceleration; If the absolute value of the smooth vehicle acceleration is less than or equal to a preset acceleration threshold, the smooth vehicle acceleration is determined as the current vehicle acceleration. If the absolute value of the smoothed vehicle acceleration is greater than the preset acceleration threshold, the vehicle acceleration corresponding to a preset number of sampling periods before the current sampling time is averaged, and the resulting average vehicle acceleration is determined as the current vehicle acceleration.

8. The method for verifying the output torque of a motor controller according to claim 2, characterized in that, The step of determining the estimated output torque of the motor controlled by the motor controller based on the current motion parameters and using a preset vehicle dynamics model includes: The target vehicle's reference mass, equivalent mass of rotating parts, additional load, standard wheel rolling radius, actual tire pressure, standard tire pressure, road slope angle, current altitude, ambient temperature, drag coefficient, frontal area, motor speed, and preset gravitational acceleration are obtained. Based on the change in actual tire pressure relative to standard tire pressure and the additional load, the standard rolling radius of the wheel is corrected to obtain the current rolling radius of the wheel; The rolling resistance coefficient is determined based on the current vehicle speed, and the rolling resistance experienced by the target vehicle is determined based on the vehicle reference mass, the additional load, the rolling resistance coefficient, the preset gravitational acceleration, and the road surface slope angle. The air density is determined based on the altitude of the current location and the ambient temperature. The air resistance experienced by the target vehicle is determined based on the air density, the drag coefficient, the frontal area, and the current vehicle speed. The slope resistance experienced by the target vehicle is determined based on the vehicle's baseline mass, the additional load, the preset gravitational acceleration, and the road surface slope angle. The vehicle inertial force corresponding to the target vehicle is determined based on the vehicle reference mass, the equivalent mass of the rotating component, and the current vehicle acceleration. The driving force corresponding to the target vehicle is obtained by adding the vehicle inertial force, the rolling resistance, the air resistance, and the slope resistance. Multiplying the driving force by the current rolling radius of the wheel yields the effective torque of the wheel; Based on the motor speed and the pre-calibrated correspondence between the motor speed and the mechanical loss torque, the mechanical loss torque of the transmission system is determined; Based on the preset speed range in which the current vehicle speed is located, and the correspondence between the preset speed range and the total efficiency of the transmission system, the total efficiency of the transmission system corresponding to the current vehicle speed is determined. The effective torque of the wheel is added to the mechanical loss torque of the transmission system, and the ratio between the sum and the total efficiency of the transmission system is determined as the estimated output torque of the motor controlled by the motor controller.

9. The method for verifying the output torque of a motor controller according to claim 1, characterized in that, The step of determining the output torque verification result of the motor controller based on the torque deviation between the torque command value and the estimated output torque includes: The absolute difference between the torque command value and the estimated output torque is defined as the torque deviation. Determine whether the torque deviation is greater than a preset torque deviation threshold; If the torque deviation is less than or equal to the preset torque deviation threshold, the output torque verification result is determined to be normal torque output. If the torque deviation is greater than the preset torque deviation threshold, determine the duration for which the torque deviation is continuously greater than the preset torque deviation threshold, and obtain the system initialization status identifier; If the duration reaches the preset duration and the system initialization status indicator indicates that the system initialization is complete, the output torque verification result is determined to be an abnormal torque output, and a torque monitoring fault status is triggered.

10. A system for verifying the output torque of a motor controller, characterized in that, include: The data acquisition module is used to acquire wheel speed signals collected by multiple wheel speed sensors of the target vehicle, as well as the torque command value corresponding to the motor controller of the target vehicle. The motion parameter determination module is used to determine the current motion parameters of the target vehicle based on multiple wheel speed signals; The torsion estimation module is used to determine the estimated output torque of the motor controlled by the motor controller based on the current motion parameters and a preset vehicle dynamics model. The preset vehicle dynamics model is used to inversely calculate the actual output torque of the motor based on the wheel speed. The verification result determination module is used to determine the output torque verification result of the motor controller based on the torque deviation between the torque command value and the estimated output torque.

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