Vehicle speed estimation method and device, vehicle and storage medium
By judging the wheel status and combining historical vehicle speed and slope correction, the problem of large speed estimation error of four-wheel drive vehicles under complex road conditions is solved, and the accuracy of speed estimation and stability of vehicle control are improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- GUANGZHOU AUTOMOBILE GROUP CO LTD
- Filing Date
- 2024-06-25
- Publication Date
- 2026-06-02
Smart Images
Figure CN118770246B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of vehicle technology, and in particular to a vehicle speed estimation method, device, vehicle, and storage medium. Background Technology
[0002] Four-wheel drive vehicles propel the vehicle by sending driving force to all four wheels. When the road conditions are good, none of the four wheels will slip, and the vehicle speed can be obtained from the wheel speed sensors. However, in complex road conditions, the state of the four wheels may differ, and one or all wheels may slip. In this case, the vehicle speed calculated from the wheel speed sensors will have a significant error. This speed error will lead to problems with vehicle control, thereby affecting driving safety.
[0003] Related technologies use a vehicle longitudinal dynamics model and vehicle acceleration information as inputs, and employ ordinary Kalman filtering, extended Kalman filtering, or unscented Kalman filtering for vehicle speed estimation. The inputs typically include vehicle driving torque, vehicle rolling resistance, vehicle weight, and gradient information. However, using a vehicle longitudinal dynamics model requires a large amount of input information. During state estimation, Kalman filtering accumulates errors, inevitably leading to some error in the obtained vehicle speed. Furthermore, ordinary Kalman filtering has significant errors for nonlinear systems. Using extended Kalman filtering or unscented Kalman filtering consumes more CPU (Central Processing Unit) resources, resulting in longer program execution times and potentially causing other systemic problems. Summary of the Invention
[0004] This application provides a vehicle speed estimation method, apparatus, vehicle, and storage medium to solve the problems of low accuracy and large estimation error in related technologies.
[0005] The first aspect of this application provides a vehicle speed estimation method, including the following steps: obtaining the state of each wheel at the current moment, wherein the state includes a first state and a second state; if the state of all wheels is the first state, then obtaining the vehicle speed of the vehicle at a historical moment before all wheels enter the second state, and estimating the vehicle speed of the vehicle at the current moment based on the vehicle speed of the vehicle at the historical moment; otherwise, estimating the vehicle speed of the vehicle at the current moment based on the wheel speed of the wheel in the second state.
[0006] Optionally, the vehicle speed at the current moment is estimated based on the vehicle speed at a historical moment, including: inputting the vehicle speed at a historical moment into a first Kalman filter, and outputting the vehicle speed at the current moment from the first Kalman filter.
[0007] Optionally, the calculation formula for the first Kalman filter is:
[0008]
[0009] Where vx is the vehicle's longitudinal velocity, vy is the vehicle's lateral velocity, ax is the vehicle's longitudinal acceleration, ay is the vehicle's lateral acceleration, and sinθ is the sine of the longitudinal pitch angle. denoted as the sine of the lateral roll angle, Δt as the time period, g as the gravitational acceleration, γ as the yaw rate, G as the process noise parameter matrix, w as the process noise, and V as the observation noise.
[0010] Optionally, after estimating the vehicle's current speed, the method further includes: obtaining the gradient of the road segment where the vehicle is currently located; and correcting the vehicle's current speed based on the gradient.
[0011] Optionally, the slope of the road segment where the vehicle is currently located can be obtained, including: obtaining the average slope of the current slope of the vehicle; inputting the average slope into a second Kalman filter, and the second Kalman filter outputting the slope value of the current slope of the vehicle.
[0012] Optionally, the formula for calculating the second Kalman filter is:
[0013]
[0014] Where vx is the vehicle's longitudinal velocity, ax is the vehicle's longitudinal acceleration, sinθ is the sine of the longitudinal pitch angle, Δt is the time period, g is the gravitational acceleration, is the process noise parameter matrix, w is the process noise, and V is the observation noise.
[0015] Optionally, the state of each wheel at the current moment is obtained, including: obtaining the wheel speed of each wheel at the current moment; calculating the wheel-end acceleration of each wheel based on the wheel speed of each wheel, and calculating the wheel speed difference between the wheel speed of each wheel and the wheel speed reference value; and determining the wheel state of each wheel based on the wheel-end acceleration and the wheel speed difference.
[0016] A second aspect of this application provides a vehicle speed estimation device, comprising: an acquisition module for acquiring the state of each wheel at the current moment, wherein the state includes a first state and a second state; a first estimation module for acquiring the vehicle speed of the vehicle at a historical moment before all wheels enter the second state if all wheels are in the first state, and estimating the vehicle speed of the vehicle at the current moment based on the vehicle speed of the vehicle at the historical moment; and a second estimation module for estimating the vehicle speed of the vehicle at the current moment based on the wheel speed of the wheels in the second state otherwise.
[0017] Optionally, the first estimation module is further configured to: input the vehicle speed at a historical moment into a first Kalman filter, and output the vehicle speed at the current moment from the first Kalman filter.
[0018] Optionally, the calculation formula for the first Kalman filter is:
[0019]
[0020] Where vx is the vehicle's longitudinal velocity, vy is the vehicle's lateral velocity, ax is the vehicle's longitudinal acceleration, ay is the vehicle's lateral acceleration, and sinθ is the sine of the longitudinal pitch angle. denoted as the sine of the lateral roll angle, Δt as the time period, g as the gravitational acceleration, γ as the yaw rate, G as the process noise parameter matrix, w as the process noise, and V as the observation noise.
[0021] Optionally, it also includes: a correction module, used to obtain the slope of the road segment where the vehicle is currently located after estimating the vehicle's current speed; and to correct the vehicle's current speed according to the slope.
[0022] Optionally, the correction module is further configured to: obtain the average slope of the current slope of the vehicle; input the average slope into the second Kalman filter, and the second Kalman filter outputs the slope value of the current slope of the vehicle.
[0023] Optionally, the formula for calculating the second Kalman filter is:
[0024]
[0025] Where vx is the vehicle's longitudinal velocity, ax is the vehicle's longitudinal acceleration, sinθ is the sine of the longitudinal pitch angle, Δt is the time period, g is the gravitational acceleration, is the process noise parameter matrix, w is the process noise, and V is the observation noise.
[0026] Optionally, the acquisition module is further configured to: acquire the wheel speed of each wheel at the current moment; calculate the wheel-end acceleration of each wheel based on the wheel speed of each wheel, and calculate the wheel speed difference between the wheel speed of each wheel and the wheel speed reference value; and determine the wheel state of each wheel based on the wheel-end acceleration and the wheel speed difference.
[0027] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the vehicle speed estimation method as described in the above embodiments.
[0028] A fourth aspect of this application provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, performs the vehicle speed estimation method as described in the above embodiments.
[0029] Therefore, this application has at least the following beneficial effects:
[0030] In this embodiment, the first state and the second state represent different states. The first state represents an abnormal state such as wheel slippage or braking, while the second state represents a normal vehicle state. This embodiment can calculate the vehicle speed using different methods depending on the vehicle's state. When any wheel is in a normal state, the wheel speed accurately represents the vehicle's state, so the vehicle speed can be estimated based on the wheel speed. When all wheels are in an abnormal state, the wheel speed in the abnormal state cannot accurately represent the current vehicle situation. Therefore, this embodiment can estimate the current vehicle speed based on the normal vehicle speed at a historical moment before all wheels entered the abnormal state. Based on this, an accurate estimation of the current vehicle speed can be achieved, thereby reducing the vehicle speed estimation error. This solves the technical problems of low accuracy and large estimation errors in related technologies.
[0031] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description
[0032] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:
[0033] Figure 1 This is a flowchart of the vehicle speed estimation method provided according to an embodiment of this application;
[0034] Figure 2 This is a flowchart of wheel state determination based on acceleration, according to an embodiment of this application.
[0035] Figure 3 This is a flowchart of wheel state determination based on wheel speed difference according to an embodiment of this application;
[0036] Figure 4 This is a flowchart illustrating vehicle speed estimation using Kalman filtering according to an embodiment of this application.
[0037] Figure 5 This is a flowchart illustrating the slope correction calculation method provided in the embodiments of this application.
[0038] Figure 6 This is a flowchart of the wheel state determination based on acceleration in slope estimation according to an embodiment of this application;
[0039] Figure 7 This is a flowchart illustrating the wheel state determination based on wheel speed difference in slope estimation according to an embodiment of this application.
[0040] Figure 8 This is a flowchart of vehicle speed estimation provided according to an embodiment of this application;
[0041] Figure 9 This is an example diagram of a vehicle speed estimation device provided according to an embodiment of this application;
[0042] Figure 10 This is a structural schematic diagram of a vehicle provided according to an embodiment of this application. Detailed Implementation
[0043] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.
[0044] Before describing the vehicle speed estimation method of this application, let's first introduce the vehicle speed estimation methods of the current related technologies.
[0045] Related Technology 1 proposes a longitudinal speed estimation method for all-wheel drive vehicles. The method includes the following steps: First, based on data collected by an accelerometer and a gyroscope, a Kalman filter algorithm is used to fuse multi-sensor data to obtain an estimate of the current road slope of the vehicle; then, based on a kinematic speed estimation method, the longitudinal speed of the vehicle is estimated according to the slope estimation result and the accelerometer data; finally, the integral error caused by noise in the acceleration data is eliminated, and the speed estimation result is corrected according to the wheel speed feedback of each wheel.
[0046] Related technology 2 provides a method and apparatus for estimating vehicle state parameters. The method includes: acquiring vehicle driving state data, a first process state x, and a first process covariance Q; determining a second process covariance Q(k) and a first measurement covariance R(k) based on the vehicle driving state data; acquiring a first measurement value yh from a vehicle sensor; and determining vehicle state parameters based on the first measurement value yh, the first process state x, the first process covariance Q, the second process covariance Q(k), and the first measurement covariance R(k). In the estimation process of vehicle state parameters, the process covariance and measurement covariance are adaptively adjusted based on the driving state data, and then the adjusted process covariance and measurement covariance are used for vehicle state estimation, which can improve the estimation accuracy of vehicle state parameters.
[0047] However, the following problems exist:
[0048] 1. When using the longitudinal dynamics model of a vehicle, a lot of input information is required: (1) The driving torque information is usually a signal calculated internally or obtained by a torque sensor. However, there is a certain phase difference between the torque and the acceleration collected by the actual acceleration sensor of the vehicle. Moreover, this phase difference will change with the state of the vehicle. Therefore, there will be a certain error in converting the longitudinal acceleration of the vehicle through torque. (2) The driving resistance of the vehicle is usually obtained by the resistance calculation formula. However, the resistance coefficient is usually a fixed value. Under different working conditions, there will inevitably be a large error. (3) The vehicle weight is usually difficult to obtain without a weight sensor. If it is obtained through vehicle weight state estimation, there will also be a large error in the vehicle weight. In summary, the errors of the above (1)(2)(3) inputs will accumulate errors when performing state estimation, especially continuous state estimation. This will inevitably result in a certain error in the obtained vehicle speed, leading to poor accuracy in vehicle speed estimation.
[0049] 2. When using Kalman filtering for vehicle speed estimation, (1) ordinary Kalman filtering has a large error for nonlinear systems; (2) extended Kalman filtering and unscented Kalman filtering have better estimation capabilities for nonlinear systems, but they require more CPU computing power and occupy more CPU resources, which will lead to longer program running time. In vehicle power control software, the running cycle is usually 10ms, while hardware resources usually need to be kept below a certain load rate. The real-time operation of extended Kalman filtering and unscented Kalman filtering may cause the software to fail to complete within 10ms, which in turn leads to other systemic problems.
[0050] 3. If the speed estimation error is large, it cannot be self-identified and corrected; while using gyroscopes or GPS to estimate or correct the speed would require more vehicle manufacturing costs.
[0051] The following description, with reference to the accompanying drawings, outlines a vehicle speed estimation method, apparatus, vehicle, and storage medium according to embodiments of this application. Addressing the issue mentioned in the background art where vehicle speed estimation is performed using a vehicle longitudinal dynamics model and vehicle acceleration information collected by a vehicle acceleration sensor as input, employing conventional Kalman filtering, extended Kalman filtering, or unscented Kalman filtering. The inputs typically include vehicle driving torque, vehicle driving resistance, vehicle weight, and slope information, etc. The large amount of input information leads to inaccurate speed estimation and errors. This application provides a vehicle speed estimation method. In this method, the state of each wheel can be determined. When all wheels are in a first state, the vehicle speed at the current moment is estimated based on the historical vehicle speeds before all wheels enter the second state. Otherwise, the current vehicle speed is directly estimated based on the wheel speeds of the wheels in the second state. This solves the problems of low accuracy and large estimation errors in related technologies.
[0052] Specifically, Figure 1 This is a schematic flowchart of the vehicle speed estimation method provided in the embodiments of this application.
[0053] like Figure 1 As shown, the vehicle speed estimation method includes the following steps:
[0054] In step S101, the state of each wheel at the current moment is obtained, wherein the state includes a first state and a second state.
[0055] The first state can be an abnormal state, including slippage and braking. Braking means that the vehicle is in a state of braking and slipping. The second state is a normal state, that is, the wheels are not slipping or braking.
[0056] In this embodiment of the application, obtaining the state of each wheel at the current moment includes: obtaining the wheel speed of each wheel at the current moment; calculating the wheel-end acceleration of each wheel based on the wheel speed of each wheel, and calculating the wheel speed difference between the wheel speed of each wheel and the wheel speed reference value; and determining the wheel state of each wheel based on the wheel-end acceleration and the wheel speed difference.
[0057] Wheel speed can be obtained through sensors.
[0058] It is understood that, in the embodiments of this application, the wheel-end acceleration of each wheel can be calculated based on the wheel speed of each wheel, and the wheel speed difference can be calculated based on the wheel speed reference value of each wheel. The wheel state of each wheel can be determined comprehensively based on the wheel-end acceleration and the wheel speed difference.
[0059] The following is a detailed explanation of how to determine the wheel state (hereinafter referred to as wheel state), which is mainly divided into two parts: wheel state determination based on wheel end acceleration (hereinafter referred to as acceleration) and wheel state determination based on wheel speed difference.
[0060] I. Wheel state determination based on acceleration, the process is as follows: Figure 2 As shown.
[0061] 1.1 First, determine whether it is necessary to exit the wheel state. This can be done by taking the following condition as an OR:
[0062] (1) All wheel speeds < first preset value v1 and acceleration < first preset acceleration a1, and the duration is > first preset time t1; v1 and a1 should be close to 0, covering the zero drift of the sensor, indicating that the vehicle is in a stopped state.
[0063] (2) All wheel speeds < v1 and acceleration > a1, duration > t1; v1 should be close to 0, just enough to cover the zero drift of the sensor, a1 should be greater than the zero drift of the acceleration sensor, and the second preset time t2 should be greater than t1. This indicates that the vehicle is in a stopped state, but is stationary on the slope.
[0064] (3) The difference between all wheel speeds is less than or equal to the second preset value v2 and the wheel speed is greater than v1, and the duration is greater than the third preset time t3; v2 should be close to 0, indicating that the four wheel speeds are very close during the vehicle's operation, and the probability of slippage or braking is extremely low.
[0065] (4) If the vehicle speed valid flag is 1, it means that the vehicle speed is invalid at this time, so exit the wheel status judgment of all wheels.
[0066] 1.2 Calculate the acceleration of the wheel end speed based on the wheel speed. The acceleration can be calculated by dividing the speed change within the first preset period N1 by the time of N1 periods. The obtained acceleration can be filtered by adding a low-pass filter (optional).
[0067] 1.3 The wheel state is determined based on the acceleration obtained in 1.2. When the acceleration is greater than or equal to the first preset acceleration DA1, the wheel enters the slipping state. When the acceleration is less than or equal to the second preset deceleration DA3, the wheel enters the braking state.
[0068] 1.4 When the wheel is slipping, if the acceleration is less than or equal to the first preset deceleration DA2 and the falling edge triggers, it is determined whether the absolute value of the difference between the wheel speed and the wheel speed reference value is less than the first preset difference DV0. If the condition is met, the slipping state is exited and the normal state is returned.
[0069] 1.5 When the wheel is in braking state, if the acceleration is ≥ the second preset acceleration DA4 falling edge trigger, then it is determined whether the absolute value of the difference between the wheel speed and the wheel speed reference value is < DV0. If the condition is met, the braking state is exited and the normal state is returned.
[0070] 1.6 DA1 and DA4 are positive numbers, representing the wheel-end acceleration thresholds, i.e., the first and second preset accelerations. DA2 and DA3 are negative numbers, representing the wheel-end deceleration thresholds, i.e., the first and second preset decelerations. DA1 can be the maximum actual vehicle acceleration, where DA1 > the absolute value of DA2, meaning the vehicle is more likely to exit a slipping state after entering one. DA3 can be the maximum time-varying vehicle deceleration, where the absolute value of DA3 > DA4, meaning the vehicle is more likely to exit a braking state after entering one. DA1 and DA4 can also be obtained by looking up a table using the acceleration pedal opening signal. DA2 and DA3 can also be obtained by looking up a table using the brake pedal depth, TCS (Traction Control System) status, or the magnitude of the regenerative braking torque.
[0071] 1.7 Update the wheel state based on the above judgment results.
[0072] II. Wheel state determination based on wheel speed difference, the process is as follows: Figure 3 As shown.
[0073] 2.1 Collect all wheel states mentioned above, and extract the set of wheel speeds where the wheel state is normal and the wheel speed signal is fault-free.
[0074] 2.2 Determine whether the vehicle is in a braking state. The determination method is: the brake pedal depressing depth is greater than the pedal depth threshold Pbth, or the TCS function is triggered, or the vehicle's energy recovery torque is greater than the recovery threshold TqRth.
[0075] 2.3 Calculate the reference wheel speed based on the information in 2.1 and 2.2. When the vehicle is braking, the reference wheel speed is the maximum value of the wheel speed set in 2.1. When the vehicle is not braking, the reference wheel speed is the minimum value of the wheel speed set in 2.1.
[0076] 2.4 Calculate the reference wheel speeds of other wheels based on the reference wheel speeds in 2.3. The wheel speed references are calculated based on vehicle structural parameters and vehicle steering angles, etc. The reference wheel speeds are converted to the vehicle's center of gravity speed, and then the speeds of other wheels are calculated using the center of gravity speed. This speed is the wheel speed reference value.
[0077] 2.5 Calculate the difference between each wheel speed and the wheel speed reference value in 2.4.
[0078] 2.6 Determine wheel state based on wheel speed difference value in 2.5: When speed difference > second preset difference value DV1, enter slipping state; when speed difference < fourth preset difference value DV3, enter braking state; when slipping state, if speed difference < third preset difference value DV2, enter exit slipping state; when braking state, if speed difference > fifth preset difference value DV4, enter exit braking state.
[0079] 2.7 Both DV1 and DV2 are positive numbers, and DV1 > DV2. Both DV4 and DV3 are negative numbers, and the absolute value of DV3 > the absolute value of DV4. This threshold needs to be obtained through calibration.
[0080] 2.8 Update the wheel state based on the above judgment results.
[0081] In step S102, if all wheels are in the first state, the vehicle speed at the historical moment before all wheels enter the second state is obtained, and the vehicle speed at the current moment is estimated based on the vehicle speed at the historical moment.
[0082] The historical time can be N2 cycles before all wheels enter the second state. N2 can be obtained through calibration, and the specific historical time can be set according to the specific situation.
[0083] It is understood that, in the embodiments of this application, when all wheels are in the first state, the vehicle speed at a historical time before all wheels enter the second state can be obtained, and the vehicle speed at the current time can be estimated based on the vehicle speed at the historical time. This can estimate the vehicle speed at the current time using less information, and the vehicle speed at a historical time is taken into account in the speed estimation, thereby improving the accuracy of the speed estimation.
[0084] In this embodiment of the application, estimating the speed of a vehicle at the current moment based on the vehicle speed at a historical moment includes: inputting the vehicle speed at a historical moment into a first Kalman filter, and outputting the vehicle speed at the current moment from the first Kalman filter.
[0085] It is understood that in this embodiment of the application, the vehicle speed at a historical moment can be input into the first Kalman filter, and the first Kalman filter outputs the vehicle speed at the current moment. The specific calculation formula of the first Kalman filter is as follows.
[0086] The formula for calculating the first Kalman filter is:
[0087]
[0088] Where vx is the vehicle's longitudinal velocity, vy is the vehicle's lateral velocity, ax is the vehicle's longitudinal acceleration, ay is the vehicle's lateral acceleration, and sinθ is the sine of the longitudinal pitch angle. denoted as the sine of the lateral roll angle, Δt as the time period, g as the gravitational acceleration, γ as the yaw rate, G as the process noise parameter matrix, w as the process noise, and V as the observation noise.
[0089] In step S103, otherwise the vehicle speed at the current moment is estimated based on the wheel speed of the wheel in the second state.
[0090] It is understood that in the embodiments of this application, when any one of the wheels is in the second state, the vehicle speed at the current moment is estimated based on the wheel speed of the wheel in the second state, so as to improve the accuracy of the vehicle speed estimation.
[0091] In this embodiment of the application, after estimating the vehicle speed at the current moment, the method further includes: obtaining the slope of the road segment where the vehicle is currently located; and correcting the vehicle speed at the current moment based on the slope.
[0092] It is understood that the embodiments of this application take into account the influence of the slope on the estimation result during the vehicle speed estimation process. Therefore, the embodiments of this application can correct the vehicle speed at the current moment according to the current slope of the vehicle to improve the accuracy of the vehicle speed estimation.
[0093] Specifically, the process for determining whether to use Kalman filtering for vehicle speed estimation in this application embodiment is as follows: Figure 4 As shown.
[0094] 1. Obtain the final wheel state result and determine whether there is a wheel in a normal state among the four wheels. If there is, proceed to slope correction calculation. If there is no wheel, it indicates that all wheels are slipping or braking. Then reset the Kalman filter and proceed to speed estimation.
[0095] 2. When resetting the Kalman filter, it is necessary to add the vehicle speed and gradient correction under normal conditions.
[0096] (1) Normal state vehicle speed: The normal state vehicle speed is the vehicle speed in the first N2 cycles when all wheels enter the abnormal state. N2 can be obtained through calibration.
[0097] (2) The slope correction calculation process is as follows.
[0098] In this embodiment of the application, obtaining the slope of the road segment where the vehicle is currently located includes: obtaining the average slope of the current slope of the vehicle; inputting the average slope into a second Kalman filter, and the second Kalman filter outputting the slope value of the current slope of the vehicle.
[0099] It is understood that the embodiments of this application can obtain the average slope of the current slope of the vehicle, input the average slope into the second Kalman filter, and the second Kalman filter outputs the slope value of the current slope of the vehicle. The specific calculation formula of the second Kalman filter is as follows.
[0100] The formula for calculating the second Kalman filter is:
[0101]
[0102] Where vx is the vehicle's longitudinal velocity, ax is the vehicle's longitudinal acceleration, sinθ is the sine of the longitudinal pitch angle, Δt is the time period, s is the gravitational acceleration, is the process noise parameter matrix, w is the process noise, and V is the observation noise.
[0103] Specifically, in this application embodiment, the slope correction is taken as the input of the vehicle speed estimate when all wheels of the vehicle enter an abnormal state, and the calculation process is as follows: Figure 5 As shown.
[0104] 1. Average Slope Calculation: The average slope is calculated using the average theoretical slope over the tslope time period. The formula for calculating the theoretical slope is:
[0105] Sinθ = (ax - avx) / ;
[0106] In the formula, Sinθ is the sine of the slope, ax is the longitudinal acceleration collected by the vehicle sensor, and avx is the longitudinal acceleration calculated from the vehicle speed.
[0107] The average slope is used as the initial input parameter for slope estimation.
[0108] 2. Whether the slope estimation conditions are met.
[0109] Since gradient is used as input for vehicle speed estimation, it needs to be estimated before speed estimation. Therefore, it's necessary to determine if any wheels of the vehicle are prone to slipping or braking. The method for determining slipping or braking trends is the same as the wheel state determination method described above, but the threshold parameters are different (e.g., ...). Figure 6 and Figure 7 (As shown). When all wheels are in an abnormal state, it indicates that the vehicle is prone to slipping or braking, and the slope Kalman filter estimation is initiated.
[0110] Specific threshold parameter requirements:
[0111] DAT1 and DAT4 are positive numbers, representing the wheel-end acceleration thresholds, specifically the third and fourth preset accelerations, respectively. DAT2 and DAT3 are negative numbers, representing the wheel-end deceleration thresholds, specifically the third and fourth preset decelerations, respectively. DAT1 can be the maximum actual vehicle acceleration, where DAT1 > the absolute value of DAT2, meaning the vehicle is more likely to exit a slipping state after entering one. DAT3 can be the maximum time-varying vehicle deceleration, where the absolute value of DAT3 > DAT4, meaning the vehicle is more likely to exit a braking state after entering one. DAT1 and DAT4 can also be obtained by looking up a table using the acceleration pedal opening signal. DAT2 and DAT3 can also be obtained by looking up a table using the brake pedal depth, TCS status, or kinetic energy recovery torque.
[0112] The sixth preset difference DVT1 and the seventh preset difference DVT2 are both positive, and DVT1>DVT2. The eighth preset difference DVT3 and the ninth preset difference DVT4 are both negative, and the absolute value of DVT3>DVT4. This threshold needs to be obtained through calibration.
[0113] The absolute values of DAT1-DAT4 are less than the absolute values of DA1-DA4 respectively; the absolute values of DVT0-DVT4 are less than the absolute values of DV0-DV4 respectively;
[0114] 3. Slope Kalman filter estimation.
[0115] The state equations and observation equations for slope Kalman filter estimation are as follows:
[0116]
[0117] Where vx is the vehicle's longitudinal velocity, ax is the vehicle's longitudinal acceleration, sin is the sine of the longitudinal pitch angle, Δt is the time period, g is the gravitational acceleration, G is the process noise parameter matrix, w is the process noise, V is the observation noise, and ax is obtained through the vehicle acceleration sensor.
[0118] Kalman filtering process:
[0119]
[0120]
[0121] (1) is the state transition equation (prediction equation). Here, F is the predicted value (prior estimate), B is the state transition matrix, G is the process noise parameter matrix, w is the process noise, and Q is the covariance matrix of the process noise (Q = E[ww]). T ]);
[0122] (2) K is the Kalman gain;
[0123] (3) For the estimated value (posterior estimate), through The estimated value is corrected, where H is the observation transformation matrix, D is the observation input parameter matrix, v is the observation noise, and R is the covariance matrix of the observation noise (R = E[vv]). T ]);
[0124] (4)P t To estimate the error The covariance matrix, (5) For prediction error The covariance matrix, x t The actual value;
[0125] 4.4 Slope Limitation and Slope Correction Update: The slope is set to a maximum of 100% for both uphill and downhill sections. The estimated slope is then placed within this range. If the slope exceeds the range, the boundary value of the range is taken. Finally, the slope correction is input into the vehicle speed Kalman filter estimation.
[0126] Kalman filtering is used to estimate or calculate vehicle speed, such as... Figure 4 As shown.
[0127] 1. When there are wheels in normal condition, convert the wheel speed of the wheels in normal condition to the vehicle's center of gravity speed and output the average value.
[0128] 2. When vehicle speed Kalman filter estimation is required, classical Kalman filter estimation is used, and the state equation and observation equation are as follows:
[0129]
[0130] Where vx is the vehicle's longitudinal velocity, vy is the vehicle's lateral velocity, ax is the vehicle's longitudinal acceleration, ay is the vehicle's lateral acceleration, and sinθ is the sine of the longitudinal pitch angle. denoted as the sine of the lateral roll angle, Δt as the time period, g as the gravitational acceleration, γ as the yaw rate, G as the process noise parameter matrix, w as the process noise, and V as the observation noise.
[0131] This application embodiment also determines whether the vehicle speed estimation result is valid after the vehicle speed estimation is completed.
[0132] Determine vehicle speed status and update vehicle speed and valid speed flag.
[0133] 1. The following conditions are used to determine if the vehicle speed estimation is invalid.
[0134] 1.1 If the difference between all wheel speeds is less than or equal to the third preset value v3 and the wheel speed is greater than v1, and the difference between the vehicle's center of gravity speed calculated from the wheel speed and the current vehicle speed is greater than the fourth preset value v4; and the duration is greater than the fourth preset time t4, v4 should be greater than v3, it indicates that the four wheel speeds are very close during the vehicle's operation, but the current vehicle speed differs significantly from the values of the four wheel speeds, indicating that there may be a very large error in the vehicle speed at this time.
[0135] 1.2 If the vehicle speed increases or decreases and the time it remains at the limit of the increase or decrease exceeds the fifth preset time t5, it indicates that the vehicle speed is increasing or decreasing too rapidly, suggesting a potentially large error in the vehicle speed reading. The speed increase limit is defined as the maximum value of the change in speed and the maximum value of the vehicle speed during acceleration. Specifically, the vehicle speed during acceleration should be less than the maximum value, and the change in speed should be less than the maximum change in speed. The speed decrease limit is defined as the maximum value of the change in speed and the minimum value of the vehicle speed during deceleration. Specifically, the vehicle speed during deceleration should be greater than the minimum value, and the change in speed should be less than the maximum change in speed.
[0136] 1.3 Accelerometer sensor signal failure.
[0137] 2. Based on the above results, determine whether the vehicle speed estimation is valid. If the vehicle speed is valid, update the vehicle speed and set the vehicle speed valid flag to 1. If the vehicle speed is invalid, use the valid value of the vehicle speed at the previous moment of the wheel speed until the vehicle speed is valid and then update it. At the same time, set the vehicle speed valid flag to 0.
[0138] In summary, compared with existing technologies, the vehicle speed estimation method of this application requires less input information during the speed estimation process, effectively avoiding errors in speed estimation caused by errors in input information such as driving torque, driving resistance, and vehicle weight. Furthermore, the use of time-segmented Kalman filtering reduces the cumulative estimation error, improving the efficiency and accuracy of speed estimation. The use of time-segmented Kalman filtering for speed estimation also reduces the CPU's computing power requirements, resulting in lower system costs, faster software operation, higher efficiency, and greater system stability. Finally, it can perform real-time validity assessment of speed estimation and self-identify and correct potential estimation errors.
[0139] The following specific embodiment illustrates the vehicle speed estimation process of this application, such as... Figure 8 As shown, it includes:
[0140] 1. Wheel state determination based on acceleration.
[0141] The acceleration threshold for determining the wheel state is determined by acceleration, and the threshold parameters for speed, acceleration, and time for determining the exit wheel state are also determined.
[0142] 2. Wheel state judgment based on wheel speed difference.
[0143] 3. Whether to perform Kalman filtering to estimate vehicle speed.
[0144] 4. Kalman filtering to estimate vehicle speed or calculate vehicle speed.
[0145] Incorporate gradient correction into vehicle speed estimation.
[0146] 5. Determine vehicle speed status and update vehicle speed and valid speed flag.
[0147] According to the vehicle speed estimation method proposed in the embodiments of this application, the first state and the second state represent different states. The first state represents an abnormal state such as wheel slippage or braking, while the second state represents a normal state of the vehicle. Different methods can be used to calculate the vehicle speed in combination with the different states of the vehicle. When any wheel is in a normal state, the wheel speed can accurately represent the vehicle state, so the vehicle speed can be estimated based on the wheel speed. When all wheels are in an abnormal state, the wheel speed in the abnormal state cannot accurately represent the current vehicle situation. Therefore, the vehicle speed at the current moment can be estimated based on the vehicle speed in the normal state at a historical moment before all wheels entered the abnormal state. On this basis, the accurate estimation of the vehicle at the current moment can be achieved, thereby reducing the vehicle speed estimation error.
[0148] Next, the vehicle speed estimation device proposed according to the embodiments of this application is described with reference to the accompanying drawings.
[0149] Figure 9 This is a block diagram of a vehicle speed estimation device according to an embodiment of this application.
[0150] like Figure 9 As shown, the vehicle speed estimation device 10 includes: an acquisition module 100, a first estimation module 200, and a second estimation module 300.
[0151] The acquisition module 100 is used to acquire the state of each wheel at the current moment, wherein the state includes a first state and a second state; the first estimation module 200 is used to acquire the vehicle speed at the historical moment before all wheels enter the second state if the state of all wheels is the first state, and estimate the vehicle speed at the current moment based on the vehicle speed at the historical moment; the second estimation module 300 is used to estimate the vehicle speed at the current moment based on the wheel speed of the wheel in the second state otherwise.
[0152] In this embodiment of the application, the first estimation module 300 is further configured to: input the vehicle speed at a historical time into a first Kalman filter, and output the vehicle speed at the current time from the first Kalman filter.
[0153] In this embodiment, the calculation formula for the first Kalman filter is:
[0154]
[0155] Where vx is the vehicle's longitudinal velocity, vy is the vehicle's lateral velocity, ax is the vehicle's longitudinal acceleration, ay is the vehicle's lateral acceleration, and sinθ is the sine of the longitudinal pitch angle. denoted as the sine of the lateral roll angle, Δt as the time period, g as the gravitational acceleration, γ as the yaw rate, G as the process noise parameter matrix, w as the process noise, and V as the observation noise.
[0156] In this embodiment of the application, the apparatus 10 further includes a correction module.
[0157] The correction module is used to estimate the vehicle's current speed and then obtain the slope of the road segment where the vehicle is currently located; and correct the vehicle's current speed based on the slope.
[0158] In this embodiment, the correction module is further configured to: obtain the average slope of the current slope of the vehicle; input the average slope into the second Kalman filter, and the second Kalman filter outputs the slope value of the current slope of the vehicle.
[0159] In this embodiment, the calculation formula for the second Kalman filter is:
[0160]
[0161] Where vx is the vehicle's longitudinal velocity, ax is the vehicle's longitudinal acceleration, sinθ is the sine of the longitudinal pitch angle, Δt is the time period, g is the gravitational acceleration, is the process noise parameter matrix, w is the process noise, and V is the observation noise.
[0162] In this embodiment of the application, the acquisition module 100 is further configured to: acquire the wheel speed of each wheel at the current moment; calculate the wheel-end acceleration of each wheel based on the wheel speed of each wheel, and calculate the wheel speed difference between the wheel speed of each wheel and the wheel speed reference value; and determine the wheel state of each wheel based on the wheel-end acceleration and the wheel speed difference.
[0163] It should be noted that the foregoing explanation of the vehicle speed estimation method embodiment also applies to the vehicle speed estimation device of this embodiment, and will not be repeated here.
[0164] According to the vehicle speed estimation device proposed in the embodiments of this application, the first state and the second state represent different states. The first state represents an abnormal state such as wheel slippage or braking, while the second state represents a normal state of the vehicle. Different methods can be used to calculate the vehicle speed in combination with the different states of the vehicle. When any wheel is in a normal state, the wheel speed can accurately represent the vehicle state, so the vehicle speed can be estimated based on the wheel speed. When all wheels are in an abnormal state, the wheel speed in the abnormal state cannot accurately represent the current vehicle situation. Therefore, the vehicle speed at the current moment can be estimated based on the vehicle speed in the normal state at a historical moment before all wheels entered the abnormal state. On this basis, the accurate estimation of the vehicle at the current moment can be achieved, thereby reducing the vehicle speed estimation error.
[0165] Figure 10 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include:
[0166] The memory 1001, the processor 1002, and the computer program stored on the memory 1001 and capable of running on the processor 1002.
[0167] When the processor 1002 executes the program, it implements the vehicle speed estimation method provided in the above embodiments.
[0168] Furthermore, the vehicle also includes:
[0169] Communication interface 1003 is used for communication between memory 1001 and processor 1002.
[0170] The memory 1001 is used to store computer programs that can run on the processor 1002.
[0171] The memory 1001 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.
[0172] If the memory 1001, processor 1002, and communication interface 1003 are implemented independently, then the communication interface 1003, memory 1001, and processor 1002 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 10The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.
[0173] Optionally, in a specific implementation, if the memory 1001, processor 1002, and communication interface 1003 are integrated on a single chip, then the memory 1001, processor 1002, and communication interface 1003 can communicate with each other through an internal interface.
[0174] The processor 1002 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.
[0175] This application also provides a computer-readable storage medium storing a computer program or instructions thereon, which, when executed by a processor, implements the vehicle speed estimation method described above.
[0176] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
[0177] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.
[0178] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.
[0179] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.
[0180] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
Claims
1. A vehicle speed estimation method characterized by comprising: Includes the following steps: Obtain the current state of each wheel, wherein the state includes a first state and a second state; If all wheels are in the first state, then obtain the vehicle speed at the historical moment before all wheels enter the second state, and estimate the vehicle speed at the current moment based on the vehicle speed at the historical moment. Otherwise, estimate the vehicle speed at the current moment based on the wheel speed of the wheel in the second state; The step of estimating the vehicle speed at the current moment based on the vehicle speed at the historical moment includes: The vehicle speed at the historical moment is input into the first Kalman filter, and the first Kalman filter outputs the vehicle speed at the current moment. The calculation formula for the first Kalman filter is: ； ； wherein is a vehicle longitudinal speed, is a vehicle lateral speed, is a vehicle longitudinal acceleration, is a vehicle lateral acceleration, is a sine of a longitudinal pitch angle, is a sine of a lateral roll angle, is a time period, is a gravitational acceleration, is a yaw rate, is a process noise parameter matrix, is a process noise, is an observation noise.
2. The vehicle speed estimation method according to claim 1, characterized by, After estimating the vehicle's speed at the current moment, it also includes: Obtain the slope of the road segment where the vehicle is currently located; The vehicle speed at the current moment is adjusted based on the slope.
3. The vehicle speed estimation method according to claim 2, characterized by, The step of obtaining the slope of the road segment where the vehicle is currently located includes: Obtain the average slope of the current slope of the vehicle; The average slope is input into a second Kalman filter, and the second Kalman filter outputs the slope value of the current slope of the vehicle.
4. The vehicle speed estimation method according to claim 3, characterized by, The calculation formula for the second Kalman filter is as follows: ； ； wherein, is the vehicle longitudinal speed, is the vehicle longitudinal acceleration, is the sine of the longitudinal pitch angle, is the time period, is the gravitational acceleration, is the process noise parameter matrix, is the process noise, is the observation noise.
5. The vehicle speed estimation method according to claim 1, characterized by, The process of obtaining the current state of each wheel includes: Obtain the wheel speed of each wheel at the current moment; Calculate the wheel-end acceleration of each wheel based on the wheel speed of each wheel, and calculate the wheel speed difference between the wheel speed of each wheel and the wheel speed reference value; The wheel state of each wheel is determined based on the wheel-end acceleration and the wheel speed difference.
6. A vehicle speed estimation device for executing the vehicle speed estimation method according to any one of claims 1 to 5, characterized by include: The acquisition module is used to acquire the state of each wheel at the current moment, wherein the state includes a first state and a second state; The first estimation module is used to obtain the vehicle speed at a historical moment before all wheels enter the second state if all wheels are in the first state, and to estimate the vehicle speed at the current moment based on the vehicle speed at the historical moment. The second estimation module is used to estimate the vehicle speed at the current moment based on the wheel speed of the wheel in the second state.
7. A vehicle characterized by comprising: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the vehicle speed estimation method as described in any one of claims 1-5.
8. A computer readable storage medium having stored thereon a computer program or instructions, characterized in that, When the computer program or instructions are executed by the processor, they are used to implement the vehicle speed estimation method as described in any one of claims 1-5.