Vehicle speed estimation method and device and distributed four-wheel-drive configuration vehicle

Abstract

The invention discloses a vehicle speed estimation method and device and a distributed four-wheel-drive configuration vehicle. The vehicle speed estimation accuracy of the distributed four-wheel-drive configuration vehicle can be improved. The vehicle speed estimation method comprises the steps that the front axle speed and the rear axle speed are calculated based on the motor rotating speeds of four motors; acquiring the running state of the whole vehicle and a longitudinal acceleration signal detected by an inertial measurement unit; based on the whole vehicle running state, the front axle speed and the rear axle speed, the axle state is determined; wherein the shaft state comprises a normal shaft and an abnormal shaft; when the abnormal shafts exist, according to the number of the abnormal shafts, determining the vehicle speed measurement value of the whole vehicle and the confidence coefficient of the vehicle speed measurement value of the whole vehicle; wherein the number of the abnormal shafts is inversely proportional to the credibility of the vehicle speed measurement value of the whole vehicle; and calculating the estimated vehicle speed of the whole vehicle based on the vehicle speed measurement value of the whole vehicle, the confidence coefficient of the vehicle speed measurement value of the whole vehicle and the longitudinal acceleration signal.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, specifically to a method and device for estimating vehicle speed and a distributed four-wheel drive vehicle. Background Technology

[0002] With the rapid development of the automotive industry, distributed four-wheel drive vehicles have gradually become a research hotspot and an important direction for future development in the automotive field due to their unique drive method and superior performance advantages. However, since all wheels in a distributed four-wheel drive vehicle can be used as drive wheels, one or more wheels may experience driving or braking slippage under any operating condition. During rapid acceleration or deceleration, all tires may experience severe driving slippage or braking lock-up. Therefore, in the absence of independent non-drive wheels as a reference, especially under complex conditions such as tire slippage, lock-up, or vehicle steering, it is impossible to find a reference wheel that can represent the true vehicle speed. Traditional estimation methods relying on non-drive wheel speeds are not applicable to distributed four-wheel drive vehicles, resulting in significant speed estimation errors. Summary of the Invention

[0003] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a vehicle speed estimation method, apparatus, and a distributed four-wheel drive vehicle, which can improve the accuracy of speed estimation for distributed four-wheel drive vehicles.

[0004] According to a first aspect of this application, a vehicle speed estimation method is provided, applied to a distributed four-wheel drive vehicle, wherein each wheel of the distributed four-wheel drive vehicle is equipped with a corresponding motor. The vehicle speed estimation method includes: calculating the front axle speed and the rear axle speed based on the motor speeds of the four motors; acquiring the overall vehicle operating state and the longitudinal acceleration signal detected by the inertial measurement unit; determining the axle state based on the overall vehicle operating state, the front axle speed, and the rear axle speed; wherein the axle state includes normal axles and abnormal axles; when abnormal axles exist, determining the overall vehicle speed measurement value and the confidence level of the overall vehicle speed measurement value based on the number of abnormal axles; wherein the number of abnormal axles is inversely proportional to the confidence level of the overall vehicle speed measurement value; and calculating the estimated vehicle speed based on the overall vehicle speed measurement value, the confidence level of the overall vehicle speed measurement value, and the longitudinal acceleration signal.

[0005] As one possible implementation, when an abnormal axle exists, the vehicle speed measurement value and its reliability are determined based on the number of abnormal axles. This includes: when the number of abnormal axles indicates a single-axle abnormality, the speed of the normal axle is used as the vehicle speed measurement value; when the number of abnormal axles indicates a dual-axle abnormality, and the vehicle is in a driving state, the minimum value between the front axle speed and the rear axle speed is used as the vehicle speed measurement value; when the number of abnormal axles indicates a dual-axle abnormality, and the vehicle is in a braking state, the maximum value between the front axle speed and the rear axle speed is used as the vehicle speed measurement value; when an abnormal axle exists, and the vehicle is in a non-driving and non-braking state, the average value between the front axle speed and the rear axle speed is used as the vehicle speed measurement value.

[0006] As one possible implementation, the vehicle speed estimation method includes: determining the confidence level of the longitudinal acceleration signal based on the measured vehicle speed and the confidence level of the measured vehicle speed; wherein, calculating the estimated vehicle speed based on the measured vehicle speed, the confidence level of the measured vehicle speed, and the longitudinal acceleration signal includes: constructing a Kalman filter model, taking the measured vehicle speed and the longitudinal acceleration signal as inputs, and using the confidence levels of the measured vehicle speed and the longitudinal acceleration signal as adjustment factors for the covariance matrix; the Kalman filter model outputs the fused vehicle speed; wherein the system error and measurement error of the covariance matrix are determined based on the number of abnormal axes, the system error is inversely proportional to the number of abnormal axes, and the measurement error is directly proportional to the number of abnormal axes.

[0007] As one possible implementation, the vehicle speed estimation method further includes: when there is no abnormal axle, using the average of the front axle speed and the rear axle speed as the vehicle speed measurement value; and using the vehicle speed measurement value as the estimated vehicle speed.

[0008] As one possible implementation, the vehicle speed estimation method includes: calculating the front axle acceleration and the rear axle acceleration based on the front axle speed and the rear axle speed; calculating the front axle slip ratio and the rear axle slip ratio based on the vehicle's operating state, the estimated vehicle speed at the previous moment, the front axle speed, and the rear axle speed; and calculating the front axle speed difference and the rear axle speed difference based on the vehicle's operating state, the estimated vehicle speed at the previous moment, the front axle speed, and the rear axle speed difference; wherein the front axle speed difference represents the difference between the estimated vehicle speed at the previous moment and the front axle speed, and the rear axle speed difference represents the difference between the estimated vehicle speed at the previous moment and the rear axle speed.

[0009] As one possible implementation, the axle state is determined based on the vehicle's operating state, front axle speed, and rear axle speed, including: when the vehicle's operating state is in a driving state, if either the front axle or the rear axle meets any one of the first evaluation conditions, the state of the front axle or the rear axle is determined to be an abnormal axle; wherein, the first evaluation conditions include: axle acceleration greater than or equal to a first preset axle acceleration, slip ratio greater than or equal to a first preset slip ratio, and axle speed difference greater than or equal to a first preset axle speed difference; when either the front axle or the rear axle meets all the second evaluation conditions, the state of the front axle or the rear axle is determined to be a normal axle; wherein, the second evaluation conditions include: axle acceleration less than a second preset axle acceleration, slip ratio less than a second preset slip ratio, and axle speed difference less than a second preset axle speed difference; wherein, the first preset axle acceleration is greater than the second preset axle acceleration, the first preset slip ratio is greater than the second preset slip ratio, and the first preset axle speed difference is greater than the second preset axle speed difference.

[0010] As one possible implementation, the axle state is determined based on the vehicle's operating state, front axle speed, and rear axle speed, including: when the vehicle's operating state is braking, if either the front axle or the rear axle meets any one of the third evaluation conditions, the state of the front axle or the rear axle is determined to be an abnormal axle; wherein, the third evaluation conditions include: axle acceleration less than a third preset axle acceleration, slip ratio greater than or equal to a third preset slip ratio, and axle speed difference greater than or equal to a third preset axle speed difference; when either the front axle or the rear axle meets all the fourth evaluation conditions, the state of the front axle or the rear axle is determined to be a normal axle; wherein, the fourth evaluation conditions include: axle acceleration greater than or equal to a fourth preset axle acceleration, slip ratio less than a fourth preset slip ratio, and axle speed difference less than a fourth preset axle speed difference; wherein, the third preset axle acceleration is less than the fourth preset axle acceleration, the third preset slip ratio is greater than the fourth preset slip ratio, and the third preset axle speed difference is greater than the fourth preset axle speed difference.

[0011] One possible implementation involves calculating the front axle speed and the rear axle speed based on the motor speeds of the four motors, including: calculating the sum of the motor speeds of the two front axle motors; calculating the average speed of the front axle motors based on the sum of the motor speeds of the two front axle motors; taking the absolute value of the average speed of the front axle motors as the front axle speed; and calculating the sum of the motor speeds of the two rear axle motors; calculating the average speed of the rear axle motors based on the sum of the motor speeds of the two rear axle motors; taking the absolute value of the average speed of the rear axle motors as the rear axle speed.

[0012] According to a second aspect of this application, a vehicle speed estimation device is provided, applied to a distributed four-wheel drive vehicle, wherein each wheel of the distributed four-wheel drive vehicle is equipped with a corresponding motor. The vehicle speed estimation device includes: a first calculation module for calculating the front axle speed and the rear axle speed based on the motor speeds of the four motors; an acquisition module for acquiring the overall vehicle operating state and the longitudinal acceleration signal detected by the inertial measurement unit; a first determination module for determining the axle state based on the overall vehicle operating state, the front axle speed, and the rear axle speed; wherein the axle state includes normal axles and abnormal axles; a second determination module for determining the overall vehicle speed measurement value and the confidence level of the overall vehicle speed measurement value based on the number of abnormal axles when abnormal axles exist; wherein the number of abnormal axles is inversely proportional to the confidence level of the overall vehicle speed measurement value; and a second calculation module for calculating the estimated overall vehicle speed based on the overall vehicle speed measurement value, the confidence level of the overall vehicle speed measurement value, and the longitudinal acceleration signal.

[0013] According to a third aspect of this application, a distributed four-wheel drive vehicle is provided, comprising: four wheels; wherein each wheel is equipped with a corresponding motor, with two motors on the front axle serving as front axle motors and two motors on the rear axle serving as rear axle motors; and a vehicle speed estimation device as described in the second aspect or any implementation thereof, wherein the vehicle speed estimation device is communicatively connected to the front axle motors and the rear axle motors.

[0014] This application provides a vehicle speed estimation method, device, and distributed four-wheel drive vehicle. The distributed four-wheel drive vehicle installs drive motors on or near each wheel, enabling independent control and torque distribution to each wheel. By acquiring the rotational speeds of the four motors, the front and rear axle speeds are calculated. Vehicle speed is then measured using these front and rear axle speeds, which reduces speed fluctuations and provides more stable results compared to calculating wheel speeds individually. Based on the vehicle's operating status, front and rear axle speeds, axle status is determined. The presence of abnormal axles reduces the reliability of the overall vehicle speed measurement, mitigating the negative impact of abnormal axles on speed estimation and improving estimation accuracy. Finally, the overall vehicle speed measurement and the longitudinal acceleration signal detected by the inertial measurement unit are combined to calculate the estimated vehicle speed. This estimation, incorporating confidence levels, overcomes the limitations of a single data source, obtaining more accurate and reliable speed estimation results and improving the speed estimation accuracy of the distributed four-wheel drive vehicle. Attached Figure Description

[0015] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1 This is a schematic flowchart of a vehicle speed estimation method provided in an exemplary embodiment of this application.

[0017] Figure 2 This is a schematic diagram of the process for determining the operating status of a vehicle provided in an exemplary embodiment of this application.

[0018] Figure 3 This is a schematic diagram of a distributed four-wheel drive vehicle speed estimation process provided as an exemplary embodiment of this application.

[0019] Figure 4 This is a schematic diagram of the vehicle speed estimation device provided in an exemplary embodiment of this application.

[0020] Figure 5 This is a structural diagram of an electronic device provided in an exemplary embodiment of this application. Detailed Implementation

[0021] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0022] Currently, for traditional two-wheel drive vehicles, the estimation of their reference speed typically relies on wheel speed sensors on the non-drive wheels. The basic principle is that under ideal driving conditions (i.e., no tire slippage / skid), the wheel speed of the non-drive wheels can accurately reflect the vehicle's actual longitudinal speed. Therefore, by monitoring the wheel speed of the non-drive wheels and combining this with a vehicle model and filtering algorithms, a relatively reliable reference speed can be estimated under most operating conditions. In contrast to traditional two-wheel drive vehicles, distributed four-wheel drive vehicles place the drive motors at or near each wheel, achieving independent control and precise torque distribution to each wheel, significantly improving the vehicle's power, handling stability, and off-road capability.

[0023] In distributed four-wheel drive vehicles, the accuracy of speed estimation is even more critical because each wheel can be driven and braked independently. For example, when a vehicle is turning, differential steering can be achieved by properly distributing torque to the four wheels, reducing the turning radius and improving vehicle maneuverability. However, if the speed estimation is inaccurate, the control system cannot correctly determine the vehicle's driving state, leading to unreasonable torque distribution and affecting the vehicle's handling stability and safety. However, all wheels in a distributed four-wheel drive vehicle can act as drive wheels and may also become braking wheels during energy recovery. This means that under any operating condition, one or more wheels may experience driving or braking slippage. Therefore, it is impossible to find a reference wheel that represents the true vehicle speed, and traditional estimation methods relying on the speed of non-drive wheels are not applicable to distributed four-wheel drive vehicles. Furthermore, during rapid acceleration or deceleration, all tires may experience severe driving slippage or braking lock-up. At this time, the speed signals of all four wheels are severely distorted, resulting in a significant deviation from the true vehicle speed. Relying solely on wheel speed sensors, the control system cannot distinguish whether this deviation is caused by changes in vehicle speed or tire slip rate, which can easily lead to incorrect vehicle speed estimation, and consequently cause the control system to malfunction or fail.

[0024] To address this challenge, existing technologies typically involve introducing additional sensors, such as inertial measurement units (IMUs), global positioning systems (GPS), or optical radar sensors. While IMUs (providing longitudinal / lateral acceleration and yaw rate) offer wheel-independent motion information, they suffer from integral drift errors, leading to decreased accuracy over extended periods or at low speeds. GPS signals are easily lost or interfered with in environments like tunnels and urban canyons, and their update frequency is often low, failing to meet the stringent requirements of real-time control. Furthermore, adding these sensors increases the system's hardware cost and complexity. Therefore, solutions relying on additional sensors suffer from shortcomings in terms of cost, reliability, and signal continuity.

[0025] To address the problem of accurately calculating the vehicle speed of vehicles with distributed four-wheel drive configurations, this application proposes a method for estimating vehicle speed. Figure 1 This is a schematic flowchart illustrating a vehicle speed estimation method provided in an exemplary embodiment of this application. Figure 1 For example, firstly, based on the motor speeds of the four motors, calculate the front axle speed and the rear axle speed (see...). Figure 1 (S110). Acquire the vehicle's operating status and the longitudinal acceleration signal detected by the inertial measurement unit (see S110). Figure 1 (S120). Based on the overall vehicle operating status, front axle speed, and rear axle speed, determine the axle status (see S120). Figure 1S130), where axle status includes normal axles and abnormal axles. When abnormal axles are present, the vehicle speed measurement value and the confidence level of the vehicle speed measurement value are determined based on the number of abnormal axles (see S130). Figure 1 (S140), where the number of abnormal axles is inversely proportional to the confidence level of the vehicle speed measurement. Based on the vehicle speed measurement, the confidence level of the vehicle speed measurement, and the longitudinal acceleration signal, the estimated vehicle speed is calculated (see S140). Figure 1 (S150).

[0026] The following text combines Figure 1 The vehicle speed estimation method provided in the embodiments of this application will be described in more detail.

[0027] In S110, the front axle speed and rear axle speed are calculated based on the motor speeds of the four motors.

[0028] In some embodiments, the motor speeds of the four motors can first be subjected to mean filtering and PT1 filtering. Mean filtering is used to smooth data and suppress noise. Based on the idea of ​​neighborhood averaging, it replaces the original value of a pixel with the average value of the pixels in its neighborhood, thereby smoothing the image and removing noise. The calculation process of mean filtering is relatively simple and easy to implement. PT1 filtering is a first-order low-pass filter, commonly used in signal processing to remove high-frequency noise while preserving the main trend of the signal. Its name comes from "Proportional-T1" (proportional-first-order hysteresis). Its core principle is to smooth the input signal using a first-order differential equation.

[0029] In other embodiments, in addition to the mean filtering and PT1 filtering exemplified above, other noise and smoothing methods can be used to filter the motor speeds of the four motors.

[0030] In some embodiments, since the motor speed is measured in rpm and the shaft speed is a speed signal measured in m / s, the average of the sum of the front axle motor speeds can be converted into the front axle speed, and the average of the sum of the rear axle motor speeds can be converted into the rear axle speed. For example, the sum of the motor speeds of the two front axle motors is calculated, and the average speed of the front axle motors is calculated based on this sum. The absolute value of this average speed is then taken as the front axle speed. Similarly, the sum of the motor speeds of the two rear axle motors is calculated, and the average speed of the rear axle motors is calculated based on this sum. The absolute value of this average speed is then taken as the rear axle speed.

[0031] As one possible implementation, the method for converting motor speed to shaft speed can be as follows: First, the relationship between wheel speed (linear velocity) and motor speed is: v = ω × r, where v is wheel speed (m / s), ω is wheel angular velocity (rad / s), and r is tire radius (m). Second, the motor speed n (RPM) is converted to angular velocity ω: 0.104719753×n (rad / s). Next, considering the effect of the main reducer, the motor speed is reduced and then transmitted to the wheels. The reduction ratio is i (dimensionless). Therefore, the wheel angular velocity is: Then, substitute the wheel angular velocity into the linear velocity formula: If we define B = v (wheel speed) and ignore the motor speed n (assuming the formula is used to calculate wheel speed per unit speed), then the formula simplifies to: Abs indicates taking the absolute value, ensuring the result is positive and avoiding negative speeds caused by occasional negative values ​​in sensor signals or calculation errors, thus guaranteeing the stability of subsequent logic control. Using a simplified formula, inputting the tire radius r (m) and reduction ratio i, the wheel speed coefficient B (m / s per RPM) can be obtained, allowing for rapid estimation of wheel speed. After converting the motor speed into wheel speed, the average of the two front wheel speeds is equivalent to the front axle speed, and the average of the two rear wheel speeds is equivalent to the rear axle speed.

[0032] In S120, the vehicle's operating status and the longitudinal acceleration signal detected by the inertial measurement unit are acquired.

[0033] In some embodiments, the vehicle's operating state includes driving state, braking state, and other states. An Inertial Measurement Unit (IMU) is a sensor device that measures the triaxial linear acceleration and angular velocity of an object using accelerometers and gyroscopes. It can sense the motion state of the vehicle (such as attitude, direction, and velocity changes) in real time, and therefore, the longitudinal acceleration signal of the vehicle can be detected through the IMU.

[0034] As one possible implementation method, Figure 2 This is a schematic diagram of the process for determining the operating status of a vehicle according to an exemplary embodiment of this application, such as... Figure 2 As shown, the process for determining the overall vehicle operating status can be as follows: input of signals such as motor speed, longitudinal acceleration, throttle opening, brake pedal opening, and motor torque (see...). Figure 2 S21) determines whether the following conditions are met: accelerator is pressed, brake is not pressed, vehicle speed is below a very small threshold (which can be calibrated), and the front axle is not in an abnormal (slippage or lock-up) state (see S21). Figure 2 If it is S22), it can be determined to be in a driving state (see S22). Figure 2(S23). If not, continue to determine whether the following conditions are met: braking or braking power is greater than a certain threshold (calibrable), front axle speed is lower than a very small threshold (calibrable), and the front axle is not in an abnormal (slippage or lock-up) state (see S23). Figure 2 If so, it is determined to be in a braking state (see S24). Figure 2 If not (S25), determine it as another state (see S25). Figure 2 (S26). In other words, if it is neither a braking state nor a driving state, it is determined to be another state.

[0035] In S130, the axle status is determined based on the overall vehicle operating status, front axle speed, and rear axle speed. The axle status includes normal axles and abnormal axles.

[0036] In some embodiments, if the vehicle is neither in a driving state nor a braking state, and in other states the driving power is less than or equal to a certain threshold (which can be calibrated), and the braking power is less than or equal to a certain threshold (which can be calibrated), then all four wheels are considered equivalent to driven wheels, and the wheel speed can represent the vehicle speed. There is no need to determine the abnormal axle; the average of the front axle speed and the rear axle speed is taken as the measured vehicle speed. This measured vehicle speed is also the final estimated vehicle speed. In other words, in other states, it can be considered that there is no abnormal axle. When there is no abnormal axle, the average of the front axle speed and the rear axle speed is used as the measured vehicle speed, and this measured vehicle speed is used as the estimated vehicle speed.

[0037] In other implementations, during both driving and braking states, an abnormal axle determination is required before further calculating the vehicle's speed. The abnormal axle determination process can be as follows:

[0038] (i) Perform axis acceleration calculations. Based on the front axis velocity and the rear axis velocity, calculate the front axis acceleration and the rear axis acceleration. For example, take several step sizes (which can be calibrated) to calculate the axis acceleration. Front axis acceleration = (front axis velocity at time T - front axis velocity at the initial time) / time length, where the time length consists of several step sizes. Rear axis acceleration = (rear axis velocity at time T - rear axis velocity at the initial time) / time length.

[0039] (II) Slip ratio calculation: Based on the vehicle's operating state, the estimated vehicle speed, front axle speed, and rear axle speed at the previous moment, calculate the front axle slip ratio and rear axle slip ratio. Under braking conditions, the front axle slip ratio is... The rear axle slip ratio In driving mode, the slip ratio of the front axle is The rear axle slip ratio Where v veh It is the estimated speed of the entire vehicle a moment before, v faxisV represents the front axle speed. raxis This indicates the rear axle speed, with a slip ratio ranging from 0% to 100%.

[0040] (III) Calculate the axle speed difference. Based on the vehicle's operating state, the estimated vehicle speed at the previous moment, the front axle speed, and the rear axle speed, calculate the front axle speed difference and the rear axle speed difference. The front axle speed difference represents the difference between the estimated vehicle speed at the previous moment and the front axle speed, and the rear axle speed difference represents the difference between the estimated vehicle speed at the previous moment and the rear axle speed. Under braking conditions, the front axle speed difference v... veh ―v faxis Rear axle speed difference v veh ―v raxis In drive mode, the front axle speed difference v faxis ―v veh Rear axle speed difference v raxis ―v veh , where v veh It is the estimated speed of the entire vehicle a moment before, v faxis V represents the front axle speed. raxis This indicates the rear axle speed, with a speed difference range limited to >0.

[0041] In some embodiments, after completing the preliminary calculations according to steps (i) to (iii) above, when the vehicle is in a driving state, if either the front axle or the rear axle meets any one of the first evaluation conditions, the state of the front axle or the rear axle is determined to be an abnormal axle; wherein, the first evaluation conditions include: axle acceleration greater than or equal to a first preset axle acceleration, slip ratio greater than or equal to a first preset slip ratio, and axle speed difference greater than or equal to a first preset axle speed difference. When either the front axle or the rear axle meets all the second evaluation conditions, the state of the front axle or the rear axle is determined to be a normal axle; wherein, the second evaluation conditions include: axle acceleration less than a second preset axle acceleration, slip ratio less than a second preset slip ratio, and axle speed difference less than a second preset axle speed difference; wherein, the first preset axle acceleration is greater than the second preset axle acceleration, the first preset slip ratio is greater than the second preset slip ratio, and the first preset axle speed difference is greater than the second preset axle speed difference.

[0042] For example, in driving mode, if the axle acceleration is greater than A_ThdDrv (first preset axle acceleration), the slip ratio is higher than Slip_ThdDrv (first preset slip ratio), and the difference between the vehicle speed and the axle speed is higher than VDiff_ThdDrv (first preset axle speed difference), then the axle is considered to have slipped and is identified as an abnormal axle. If all three conditions are met—axle acceleration less than A_ThdDrvMin (second preset axle acceleration), slip ratio less than Slip_ThdDrvMin (second preset slip ratio), and the difference between the vehicle speed and the axle speed less than VDiff_ThdDrvMin (second preset axle speed difference)—the axle is considered to have returned to normal. The acceleration thresholds for A_ThdDrv and A_ThdDrvMin can be calibrated, with A_ThdDrv > A_ThdDrvMin > 0. Slip_ThdDrv and Slip_ThdDrvMin are obtained by looking up the CUR based on axle speed, with Slip_ThdDrv > Slip_ThdDrvMin. When the axle speed is low, the slip ratio threshold is set higher to prevent false positives at low speeds. When the axle speed is high, the slip ratio threshold is set lower to promptly eliminate slipping axles. VDiff_ThdDrv and VDiff_ThdDrvMin are obtained by looking up the CUR based on vehicle speed, with VDiff_ThdDrv > VDiff_ThdDrvMin. When the vehicle speed is low, the axle speed difference threshold is set lower to promptly eliminate abnormal axles while also preventing false positives. When the vehicle speed is high, the axle speed difference threshold is set slightly higher to ensure that the driving force is maximized while also promptly eliminating abnormal axles.

[0043] In some embodiments, after completing the preliminary calculations according to steps (i) to (iii) above, when the vehicle is in a braking state, if either the front axle or the rear axle meets any one of the third evaluation conditions, the state of the front axle or the rear axle is determined to be an abnormal axle; wherein, the third evaluation conditions include: axle acceleration less than a third preset axle acceleration, slip ratio greater than or equal to a third preset slip ratio, and axle speed difference greater than or equal to a third preset axle speed difference; when either the front axle or the rear axle meets all the fourth evaluation conditions, the state of the front axle or the rear axle is determined to be a normal axle; wherein, the fourth evaluation conditions include: axle acceleration greater than or equal to a fourth preset axle acceleration, slip ratio less than a fourth preset slip ratio, and axle speed difference less than a fourth preset axle speed difference; wherein, the third preset axle acceleration is less than the fourth preset axle acceleration, the third preset slip ratio is greater than the fourth preset slip ratio, and the third preset axle speed difference is greater than the fourth preset axle speed difference.

[0044] For example, during braking, if the axle acceleration is less than A_ThdBrk (the third preset axle acceleration), the slip ratio is higher than Slip_ThdBrk (the third preset slip ratio), and the difference between the vehicle speed and the axle speed is higher than VDiff_ThdBrk (the third preset axle speed difference), then the axle is considered to have locked up and is identified as an abnormal axle. If all three conditions are met—axle acceleration greater than A_ThdBrkMin (the fourth preset axle acceleration), slip ratio lower than Slip_ThdBrkMin (the fourth preset slip ratio), and the difference between the vehicle speed and the axle speed lower than VDiff_ThdBrkMin (the fourth preset axle speed difference)—the axle is considered to have returned to normal. The acceleration thresholds A_ThdBrk and A_ThdBrkMin can be calibrated, with A_ThdBrk < A_ThdBrkMin < 0. Slip_ThdBrk and Slip_ThdBrkMin are obtained by looking up the CUR based on vehicle speed. Slip_ThdBrk > Slip_ThdBrkMin. When the vehicle speed is low, the slip ratio threshold is set higher to prevent false alarms at low speeds. When the vehicle speed is high, the slip ratio is set lower to promptly eliminate locked axles. VDiff_ThdBrk and VDiff_ThdBrkMin are also obtained by looking up the CUR based on vehicle speed. VDiff_ThdBrk > VDiff_ThdBrkMin. When the vehicle speed is low, the axle speed difference threshold is set lower to promptly eliminate abnormal axles while also preventing false alarms. When the vehicle speed is high, the axle speed difference threshold is set slightly higher to ensure that the driving force is maximized while also promptly eliminating abnormal axles.

[0045] In some embodiments, among shaft acceleration, slip ratio, and shaft speed difference, shaft acceleration is a necessary condition. Slip ratio and speed difference are based on the same principle and can both be used as criteria. Alternatively, only one of slip ratio and shaft speed difference can be chosen as the criterion for determining an abnormal shaft. For example, whether an abnormal shaft is determined by shaft acceleration and slip ratio, or by shaft acceleration and shaft speed difference, can be used to determine whether an abnormal shaft is present.

[0046] In S140, when abnormal axles are present, the vehicle speed measurement value and its confidence level are determined based on the number of abnormal axles. The number of abnormal axles is inversely proportional to the confidence level of the vehicle speed measurement value.

[0047] In some embodiments, after determining the abnormal axle, the number of abnormal axles is determined. When the number of abnormal axles indicates a single axle abnormality, the speed of the normal axle (without slippage or lock-up) is used as the vehicle speed measurement value; when the number of abnormal axles indicates a dual-axle abnormality, and the vehicle is in driving mode, the minimum value between the front axle speed and the rear axle speed is used as the vehicle speed measurement value; when the number of abnormal axles indicates a dual-axle abnormality, and the vehicle is in braking mode, the maximum value between the front axle speed and the rear axle speed is used as the vehicle speed measurement value; if no abnormal axle is detected (no slippage or lock-up occurs), or if an abnormal axle exists but the vehicle is in non-driving and non-braking mode, the average value between the front axle speed and the rear axle speed is used as the vehicle speed measurement value.

[0048] In some embodiments, the number of abnormal axles can be used to determine the confidence level of the vehicle speed measurement. This confidence level can then serve as reference data for subsequent fusion calculations, improving the accuracy of the final estimated vehicle speed. For example, when using a Kalman filter model for fusion calculations, a higher number of abnormal axles results in lower confidence levels for the vehicle speed measurement, while a lower number of abnormal axles results in higher confidence levels. This allows for the elimination of the influence of abnormal axles during skidding, steering, or wheel lock-up, effectively improving the accuracy of speed estimation.

[0049] In S150, the estimated vehicle speed is calculated based on the vehicle speed measurement value, the confidence level of the vehicle speed measurement value, and the longitudinal acceleration signal.

[0050] In some embodiments, a Kalman filter can be used for filtered signal fusion processing to fuse the vehicle speed measurement value with the longitudinal acceleration signal. The confidence level of the vehicle speed measurement value and the longitudinal acceleration signal can be adjusted under different vehicle operating conditions (normal, slip, lock-up, etc.) to achieve a more accurate estimation of the vehicle speed.

[0051] First, the system can be modeled based on the vehicle's equations of motion, which are as follows:

[0052] v t =v t―1 +(a―gsinθ)Δt

[0053] In the vehicle's equation of motion, v t Indicates the vehicle speed at the current moment, v t―1 denoted as the vehicle speed at the previous moment, 'a' represents the acceleration measured by the accelerometer with a slope component, 'g' represents the gravitational acceleration, 'sinθ' represents the sine of the slope, and 'Δt' represents the sampling step size.

[0054] Secondly, based on the vehicle's equation of motion, the state equation is as follows:

[0055]

[0056] In the state equations, the system state variables v k Indicates the vehicle's measured speed, sinθ k The sine value representing the slope; state matrix control quantity u k =a k a k The control matrix represents the longitudinal acceleration value with a slope component measured by the accelerometer. y k =v k The output matrix is ​​C = [1 0].

[0057] Finally, the state equation can be simplified to the following form:

[0058]

[0059] In the simplified state equations, A represents the state matrix, B represents the control matrix, C represents the output matrix, and x... k Let x represent the system state at time K. k+1 Let y represent the system state at time K+1. k The output signal of the velocity model, u k This indicates the control quantity.

[0060] One possible implementation involves determining the confidence level of the longitudinal acceleration signal based on the vehicle speed measurement and its confidence level. In the Kalman filter model, the confidence levels of the vehicle speed measurement and the longitudinal acceleration signal are dynamic and mutually influential, ultimately determining the calculated vehicle speed. Confidence levels can be quantified as uncertainty. In Kalman filtering, uncertainty is represented by a covariance matrix. By using the confidence levels of the vehicle speed measurement and the longitudinal acceleration signal as adjustment factors for the covariance matrix, the Kalman filter model can balance internal model and external sensor information, thus yielding the optimal estimate.

[0061] One possible implementation involves constructing a Kalman filter model. The vehicle speed measurement and longitudinal acceleration signal are taken as inputs, and the confidence levels of the vehicle speed measurement and longitudinal acceleration signal are used as adjustment factors for the covariance matrix. The Kalman filter model outputs the fused vehicle speed. The systematic error and measurement error of the covariance matrix are determined based on the number of outlier axes. The systematic error is inversely proportional to the number of outlier axes, while the measurement error is directly proportional. For example, in adjusting the input covariance matrix of the Kalman filter model, Q represents the systematic error and R represents the measurement error. The values ​​of Q and R are obtained by looking up the CUR (Continuous Urpometry) based on the number of outlier axes. The more outlier axes there are, the larger R is and the smaller Q is, indicating that the measurement error covariance is increasing, and the system no longer trusts the measured values ​​but relies more on the acceleration signal from the IMU (Integrated Measurement Unit).

[0062] In some embodiments, the vehicle speed measurement value and the longitudinal acceleration signal from the IMU sensor are input into a Kalman filter after mean filtering and PT1 filtering. Based on the established system equations, the vehicle speed can be estimated. This vehicle speed integrates wheel speed and acceleration sensor signals, resulting in higher accuracy.

[0063] In some embodiments, when an IMU sensor is available, the Kalman filter fusion algorithm may not be used. In non-full slip conditions, the vehicle speed measured by the whole vehicle can be directly used as the final estimated vehicle speed. In full slip conditions, the vehicle speed can be calculated by integrating the acceleration signal from the IMU sensor.

[0064] In some embodiments, if there is no IMU sensor, except in cases where both axles are abnormal, the vehicle speed measured by the whole vehicle can be used directly as the final estimated vehicle speed. When both axles are abnormal, the integral of the acceleration estimated at the previous moment is used as the estimated vehicle speed.

[0065] In some embodiments, a distributed four-wheel drive vehicle includes four wheels; each wheel is equipped with a corresponding motor, with the two motors on the front axle serving as front axle motors and the two motors on the rear axle serving as rear axle motors. To address the problem of low speed estimation accuracy in distributed four-wheel drive vehicles, a vehicle speed estimation device is provided that is communicatively connected to the front and rear axle motors. This device implements a vehicle speed estimation method, thereby improving the accuracy of speed estimation. Figure 3 This is a schematic diagram of a distributed four-wheel drive vehicle speed estimation process provided as an exemplary embodiment of this application. Figure 3 For example, signals such as motor speed, longitudinal acceleration, throttle opening, brake pedal opening, and motor torque are collected. Figure 2 S30), filters the motor speed, and calculates the front axle speed and rear axle speed based on the motor speed. Figure 2 S31), to determine the abnormal axis ( Figure 2If all axles are normal and there are no abnormal axles, then the average axle speed of the front and rear axles is used as the estimated vehicle speed. Figure 2 If all values ​​are abnormal (S33), the smaller axle speed is used for driving and the larger axle speed is used for braking, which is then used to estimate the vehicle speed. Figure 2 (S34). If there is a partial abnormality, the abnormal shaft will be removed, and the normal shaft speed will be used. Figure 2 (S35). Wheel speed is determined based on the normal axle speed, and the vehicle speed is obtained based on the wheel speed. Figure 2 The S36 of the vehicle will input the measured vehicle speed into the Kalman filter. Figure 2 In S37), the longitudinal acceleration signal is filtered ( Figure 2 S38), input Kalman filter ( Figure 2 In S37), the final calculation is performed using a Kalman filter to obtain the estimated vehicle speed. Figure 2 (S39).

[0066] Figure 4 This is a schematic diagram of the vehicle speed estimation device provided in an exemplary embodiment of this application, as shown below. Figure 4 As shown, the vehicle speed estimation device 4 is applied to a distributed four-wheel drive vehicle. In the distributed four-wheel drive vehicle, each wheel is equipped with a corresponding motor. The vehicle speed estimation device 4 includes: a first calculation module 41, used to calculate the front axle speed and rear axle speed based on the motor speeds of the four motors; an acquisition module 42, used to acquire the overall vehicle operating state and the longitudinal acceleration signal detected by the inertial measurement unit; a first determination module 43, used to determine the axle state based on the overall vehicle operating state, the front axle speed, and the rear axle speed; wherein, the axle state includes normal axles and abnormal axles; a second determination module 44, used to determine the overall vehicle speed measurement value and the confidence level of the overall vehicle speed measurement value based on the number of abnormal axles when abnormal axles exist; wherein, the number of abnormal axles is inversely proportional to the confidence level of the overall vehicle speed measurement value; and a second calculation module 45, used to calculate the estimated vehicle speed based on the overall vehicle speed measurement value, the confidence level of the overall vehicle speed measurement value, and the longitudinal acceleration signal.

[0067] As one possible implementation, the second determining module 44 can be configured as follows: when the number of abnormal axles indicates a single axle abnormality, the speed of the normal axle is used as the vehicle speed measurement value; when the number of abnormal axles indicates a dual axle abnormality and the vehicle is in driving mode, the minimum value between the front axle speed and the rear axle speed is used as the vehicle speed measurement value; when the number of abnormal axles indicates a dual axle abnormality and the vehicle is in braking mode, the maximum value between the front axle speed and the rear axle speed is used as the vehicle speed measurement value; when there is an abnormal axle and the vehicle is in non-driving and non-braking mode, the average value between the front axle speed and the rear axle speed is used as the vehicle speed measurement value.

[0068] As one possible implementation, the vehicle speed estimation device 4 can be configured to: determine the confidence level of the longitudinal acceleration signal based on the vehicle speed measurement value and the confidence level of the vehicle speed measurement value; wherein, the second calculation module 45 can be configured to: construct a Kalman filter model, take the vehicle speed measurement value and the longitudinal acceleration signal as input, take the confidence level of the vehicle speed measurement value and the confidence level of the longitudinal acceleration signal as adjustment factors of the covariance matrix, and output the fused vehicle speed from the Kalman filter model; wherein, the system error and measurement error of the covariance matrix are determined according to the number of abnormal axes, the system error is inversely proportional to the number of abnormal axes, and the measurement error is directly proportional to the number of abnormal axes.

[0069] As one possible implementation, the vehicle speed estimation device 4 can also be configured to: when there is no abnormal axle, use the average of the front axle speed and the rear axle speed as the vehicle speed measurement value; and use the vehicle speed measurement value as the vehicle speed estimation value.

[0070] As one possible implementation, the vehicle speed estimation device 4 can be configured to: calculate the front axle acceleration and the rear axle acceleration based on the front axle speed and the rear axle speed; calculate the front axle slip ratio and the rear axle slip ratio based on the overall vehicle operating state, the estimated overall vehicle speed at the previous moment, the front axle speed, and the rear axle speed; and calculate the front axle speed difference and the rear axle speed difference based on the overall vehicle operating state, the estimated overall vehicle speed at the previous moment, the front axle speed, and the rear axle speed difference. The front axle speed difference represents the difference between the estimated overall vehicle speed at the previous moment and the front axle speed, and the rear axle speed difference represents the difference between the estimated overall vehicle speed at the previous moment and the rear axle speed.

[0071] As one possible implementation, the first determining module 43 can be configured as follows: when the vehicle is in a driving state, if either the front axle or the rear axle meets any one of the first evaluation conditions, the state of the front axle or the rear axle is determined to be an abnormal axle; wherein, the first evaluation conditions include: axle acceleration greater than or equal to a first preset axle acceleration, slip ratio greater than or equal to a first preset slip ratio, and axle speed difference greater than or equal to a first preset axle speed difference; when either the front axle or the rear axle meets all the second evaluation conditions, the state of the front axle or the rear axle is determined to be a normal axle; wherein, the second evaluation conditions include: axle acceleration less than a second preset axle acceleration, slip ratio less than a second preset slip ratio, and axle speed difference less than a second preset axle speed difference; wherein, the first preset axle acceleration is greater than the second preset axle acceleration, the first preset slip ratio is greater than the second preset slip ratio, and the first preset axle speed difference is greater than the second preset axle speed difference.

[0072] As one possible implementation, the first determining module 43 can be configured as follows: when the vehicle is in braking mode, if either the front axle or the rear axle meets any one of the third evaluation conditions, the front axle or the rear axle is determined to be an abnormal axle; wherein, the third evaluation conditions include: axle acceleration less than a third preset axle acceleration, slip ratio greater than or equal to a third preset slip ratio, and axle speed difference greater than or equal to a third preset axle speed difference; when either the front axle or the rear axle meets all the fourth evaluation conditions, the front axle or the rear axle is determined to be a normal axle; wherein, the fourth evaluation conditions include: axle acceleration greater than or equal to a fourth preset axle acceleration, slip ratio less than a fourth preset slip ratio, and axle speed difference less than a fourth preset axle speed difference; wherein, the third preset axle acceleration is less than the fourth preset axle acceleration, the third preset slip ratio is greater than the fourth preset slip ratio, and the third preset axle speed difference is greater than the fourth preset axle speed difference.

[0073] As one possible implementation, the first calculation module 41 can be configured to: calculate the sum of the motor speeds of the two motors on the front axle; calculate the average speed of the front axle motors based on the sum of the motor speeds of the two motors on the front axle; take the absolute value of the average speed of the front axle motors as the front axle speed; and calculate the sum of the motor speeds of the two motors on the rear axle; calculate the average speed of the rear axle motor based on the sum of the motor speeds of the two motors on the rear axle; take the absolute value of the average speed of the rear axle motors as the rear axle speed.

[0074] An electronic device includes: a processor; a memory for storing processor-executable instructions; and a processor for executing the vehicle speed estimation method described in the embodiments of this application.

[0075] Below, for reference Figure 5 This application describes an electronic device according to embodiments thereof. The electronic device may be either or both of a first device and a second device, or a standalone device independent of them, which may communicate with the first device and the second device to receive acquired input signals from them.

[0076] Figure 5 A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0077] like Figure 5 As shown, the electronic device 50 includes one or more processors 51 and memory 52.

[0078] The processor 51 may be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and may control other components in the electronic device 50 to perform desired functions.

[0079] The memory 52 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 51 may execute the program instructions to implement the vehicle speed estimation methods of the various embodiments of this application described above and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.

[0080] In one example, the electronic device 50 may also include an input device 53 and an output device 54, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0081] When the electronic device is a standalone device, the input device 53 can be a communication network connector for receiving the collected input signals from the first device and the second device.

[0082] In addition, the input device 53 may also include, for example, a keyboard, a mouse, etc.

[0083] The output device 54 can output various information to the outside, including determined distance information, direction information, etc. The output device 54 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0084] Of course, for the sake of simplicity, Figure 5 Only some of the components of the electronic device 50 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device 50 may include any other suitable components depending on the specific application.

[0085] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0086] A computer-readable storage medium stores a computer program for executing the vehicle speed estimation method described in the embodiments provided in this application.

[0087] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0088] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for estimating vehicle speed, characterized in that, Applied to distributed four-wheel drive vehicles, where each wheel is equipped with a corresponding motor, the vehicle speed estimation method includes: Calculate the front axle speed and rear axle speed based on the motor speeds of the four motors; Acquire the vehicle's operating status and the longitudinal acceleration signal detected by the inertial measurement unit; Based on the overall vehicle operating status, front axle speed, and rear axle speed, the axle status is determined; wherein, the axle status includes normal axles and abnormal axles; When abnormal axles are present, the vehicle speed measurement value and the confidence level of the vehicle speed measurement value are determined based on the number of abnormal axles; wherein, the number of abnormal axles is inversely proportional to the confidence level of the vehicle speed measurement value. The estimated vehicle speed is calculated based on the vehicle speed measurement value, the confidence level of the vehicle speed measurement value, and the longitudinal acceleration signal.

2. The vehicle speed estimation method according to claim 1, characterized in that, When abnormal axles are present, the vehicle speed measurement value and its reliability are determined based on the number of abnormal axles, including: When the number of abnormal shafts indicates a single shaft abnormality, the speed of the normal shaft is used as the vehicle speed measurement value. When the number of abnormal axles indicates a dual-axle abnormality and the vehicle is in driving condition, the minimum value between the front axle speed and the rear axle speed is taken as the vehicle speed measurement value. When the number of abnormal axles indicates a dual-axle abnormality and the vehicle is in braking condition, the maximum value between the front axle speed and the rear axle speed shall be used as the vehicle speed measurement value. When an abnormal axle exists, and the vehicle is in a non-driving and non-braking operating state, the average of the front axle speed and the rear axle speed is used as the vehicle speed measurement value.

3. The vehicle speed estimation method according to claim 1, characterized in that, Methods for estimating vehicle speed include: The confidence level of the longitudinal acceleration signal is determined based on the vehicle speed measurement value and the confidence level of the vehicle speed measurement value. The calculation of the estimated vehicle speed, based on the vehicle speed measurement value, the confidence level of the vehicle speed measurement value, and the longitudinal acceleration signal, includes: A Kalman filter model is constructed, taking the vehicle speed measurement and the longitudinal acceleration signal as inputs, and using the confidence levels of the vehicle speed measurement and the longitudinal acceleration signal as adjustment factors for the covariance matrix. The Kalman filter model outputs the fused vehicle speed. The system error and measurement error of the covariance matrix are determined based on the number of abnormal axes. The system error is inversely proportional to the number of abnormal axes, and the measurement error is directly proportional to the number of abnormal axes.

4. The vehicle speed estimation method according to claim 1, characterized in that, Vehicle speed estimation methods also include: When there is no abnormal axle, the average of the front axle speed and the rear axle speed is used as the vehicle speed measurement value. The vehicle speed measurement value is used as the estimated vehicle speed.

5. The vehicle speed estimation method according to claim 1, characterized in that, Methods for estimating vehicle speed include: Calculate the front axle acceleration and the rear axle acceleration based on the front axle speed and the rear axle speed; Based on the vehicle's operating status, the estimated vehicle speed, front axle speed, and rear axle speed at the previous moment, calculate the front axle slip ratio and rear axle slip ratio. Based on the vehicle's operating status, the estimated vehicle speed, front axle speed, and rear axle speed at the previous moment, calculate the front axle speed difference and the rear axle speed difference; wherein, the front axle speed difference represents the difference between the estimated vehicle speed and the front axle speed at the previous moment, and the rear axle speed difference represents the difference between the estimated vehicle speed and the rear axle speed at the previous moment.

6. The vehicle speed estimation method according to claim 5, characterized in that, Based on the overall vehicle operating status, front axle speed, and rear axle speed, the axle status is determined, including: When the vehicle is in driving mode, if either the front axle or the rear axle meets any one of the first evaluation conditions, the front axle or the rear axle is determined to be an abnormal axle. The first evaluation conditions include: axle acceleration greater than or equal to a first preset axle acceleration, slip ratio greater than or equal to a first preset slip ratio, and axle speed difference greater than or equal to a first preset axle speed difference. When the front or rear axle meets all the second evaluation conditions, the front or rear axle is determined to be a normal axle; wherein, the second evaluation conditions include: axle acceleration is less than a second preset axle acceleration, slip ratio is less than a second preset slip ratio, and axle speed difference is less than a second preset axle speed difference; Wherein, the first preset axis acceleration is greater than the second preset axis acceleration, the first preset slip ratio is greater than the second preset slip ratio, and the first preset axis velocity difference is greater than the second preset axis velocity difference.

7. The vehicle speed estimation method according to claim 5, characterized in that, Based on the overall vehicle operating status, front axle speed, and rear axle speed, the axle status is determined, including: When the vehicle is in braking state, if either the front axle or the rear axle meets any of the third evaluation conditions, the front axle or the rear axle is determined to be an abnormal axle. The third evaluation conditions include: axle acceleration less than a third preset axle acceleration, slip ratio greater than or equal to a third preset slip ratio, and axle speed difference greater than or equal to a third preset axle speed difference. When the front or rear axle meets all the fourth evaluation conditions, the front or rear axle is determined to be a normal axle; wherein, the fourth evaluation conditions include: the axle acceleration is greater than or equal to the fourth preset axle acceleration, the slip ratio is less than the fourth preset slip ratio, and the axle speed difference is less than the fourth preset axle speed difference; Wherein, the acceleration of the third preset axis is less than the acceleration of the fourth preset axis, the slip ratio of the third preset axis is greater than the slip ratio of the fourth preset axis, and the velocity difference of the third preset axis is greater than the velocity difference of the fourth preset axis.

8. The vehicle speed estimation method according to claim 1, characterized in that, Based on the motor speeds of the four motors, calculate the front axle speed and the rear axle speed, including: Calculate the sum of the motor speeds of the two motors on the front axle; Calculate the average speed of the front axle motors based on the sum of their speeds. The absolute value of the average rotational speed of the front axle motor is taken as the front axle speed; and Calculate the sum of the motor speeds of the two motors on the rear axle; Calculate the average speed of the rear axle motors based on the sum of their speeds. The absolute value of the average rotational speed of the rear axle motor is taken as the rear axle speed.

9. A vehicle speed estimation device, characterized in that, Applied to distributed four-wheel drive vehicles, where each wheel is equipped with a corresponding motor, the vehicle speed estimation device includes: The first calculation module is used to calculate the front axle speed and the rear axle speed based on the motor speeds of the four motors; The acquisition module is used to acquire the vehicle's operating status and the longitudinal acceleration signal detected by the inertial measurement unit; The first determining module is used to determine the axle status based on the vehicle's operating status, front axle speed, and rear axle speed; wherein the axle status includes normal axles and abnormal axles; The second determining module is used to determine the vehicle speed measurement value and the confidence level of the vehicle speed measurement value based on the number of abnormal axles when abnormal axles are present; wherein, the number of abnormal axles is inversely proportional to the confidence level of the vehicle speed measurement value; The second calculation module is used to calculate the estimated vehicle speed based on the vehicle speed measurement value, the confidence level of the vehicle speed measurement value, and the longitudinal acceleration signal.

10. A distributed four-wheel drive vehicle, characterized in that, include: Four wheels; each wheel is equipped with a corresponding motor, with the two motors on the front axle serving as the front axle motors and the two motors on the rear axle serving as the rear axle motors; The vehicle speed estimation device as described in claim 9 is communicatively connected to the front axle motor and the rear axle motor.

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