Slope determination method, and vehicle

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By combining the extended Kalman filter algorithm with pitch angular velocity and fusing longitudinal vehicle speed and gradient change rate, the problem of inaccurate gradient estimation in existing technologies is solved, achieving higher accuracy and faster gradient estimation.

WO2026157656A1PCT designated stage Publication Date: 2026-07-30GREAT WALL MOTOR CO LTD

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Patent Information

Authority / Receiving Office: WO · WO
Patent Type: Applications
Current Assignee / Owner: GREAT WALL MOTOR CO LTD
Filing Date: 2025-12-16
Publication Date: 2026-07-30

AI Technical Summary

Technical Problem

Existing road slope estimation methods based on dynamics and kinematics have limitations, leading to inaccurate slope estimations.

Method used

By employing the extended Kalman filter algorithm combined with pitch angular velocity, and through the calibration of the predicted and observed vectors, longitudinal vehicle speed and gradient change rate are fused to improve the accuracy and robustness of gradient estimation.

Benefits of technology

By integrating the pitch angular velocity and applying the extended Kalman filter algorithm, a faster slope calculation rate and higher estimation accuracy were achieved, improving the tracking effect and robustness of slope estimation.

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Abstract

Provided in the present application are a slope determination method, and a vehicle. The method comprises: on the basis of a first calibrated vector corresponding to the moment previous to the current moment, using an extended Kalman filtering algorithm to calculate a predicted vector corresponding to the current moment, wherein the first calibrated vector comprises a first calibrated longitudinal vehicle speed, a first calibrated slope and a first calibrated slope change rate; and on the basis of the predicted vector corresponding to the current moment and an observation vector corresponding to the current moment, using the extended Kalman filtering algorithm to calculate a calibrated slope corresponding to the current moment, wherein the observation vector comprises a kinematically estimated longitudinal vehicle speed and a pitch rate. During the calibration of the predicted vector, a pitch rate is incorporated, and a slope can be obtained by means of integrating the pitch rate, that is, the slope obtained from the pitch rate is cross-validated with the predicted slope in the predicted vector, such that the accuracy and robustness of slope estimation can be effectively improved. The slope can be obtained from the pitch rate at a faster calculation rate, thereby improving the tracking effect of slope estimation.

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Claims

1. A method for determining slope, wherein, include: Based on the first calibration vector corresponding to the previous time step, the extended Kalman filter algorithm is used to calculate the prediction vector for the current time step. The first calibration vector includes the first calibration longitudinal speed, the first calibration gradient, and the first calibration gradient change rate; the first calibration vector is the calibration vector obtained in the previous moment according to the extended Kalman filter algorithm; Based on the prediction vector and the observation vector at the current moment, the extended Kalman filter algorithm is used to calculate the calibration gradient at the current moment; wherein, the observation vector includes the dynamically estimated longitudinal vehicle speed and pitch angular velocity.

2. The method according to claim 1, wherein, The step of calculating the prediction vector at the current moment using the extended Kalman filter algorithm based on the first calibration vector determined at the previous moment includes: Construct the state vector at the current moment and the state vector at the previous moment; Based on the current state vector, the previous state vector, and the predetermined longitudinal vehicle speed dynamic equation, a state transition equation is constructed. Determine the state transition matrix based on the state transition equation; Based on the state transition matrix and the first calibration vector, the extended Kalman filter algorithm is used to perform prior calculations to determine the prediction vector at the current time.

3. The method according to claim 2, wherein, The longitudinal vehicle speed dynamic equation includes a first dynamic equation and a second dynamic equation; the construction of the state transition equation based on the current state vector, the previous state vector, and the predetermined longitudinal vehicle speed dynamic equation includes: Determine the yaw rate at the current moment; In response to determining that the absolute value of the yaw rate is less than a preset yaw rate threshold, a state transition equation is constructed based on the current state vector, the previous state vector, and the first dynamic equation; wherein, the first dynamic equation characterizes the relationship between the longitudinal vehicle speed change rate, longitudinal acceleration, and gradient. In response to determining that the absolute value of the yaw rate is greater than or equal to a preset yaw rate threshold, a state transition equation is constructed based on the current state vector, the previous state vector, and the second dynamic equation; wherein, the second dynamic equation characterizes the relationship between the longitudinal vehicle speed change rate, longitudinal acceleration, kinematically estimated lateral vehicle speed, yaw rate, and slope.

4. The method according to claim 2, wherein, The state vector includes longitudinal vehicle speed, gradient, and rate of change of gradient; The process of constructing a state transition equation based on the current state vector, the previous state vector, and a pre-determined longitudinal vehicle speed dynamic equation includes: Based on the longitudinal vehicle speed of the previous moment, the solution cycle of the extended Kalman filter algorithm, and the longitudinal vehicle speed dynamic equation, establish the first transformation relationship between the longitudinal vehicle speed of the current moment and the longitudinal vehicle speed of the previous moment. Based on the slope at the previous moment, the solution period, and the slope change rate at the previous moment, a second conversion relationship is established between the slope at the current moment and the slope at the previous moment. Based on the slope change rate at the previous moment, the solution period, and the pre-built slope model, a third transformation relationship is established between the slope change rate at the current moment and the slope change rate at the previous moment. State transition equations are constructed based on the first, second, and third transformation relations.

5. The method according to claim 4, wherein, The step of establishing a first transformation relationship between the longitudinal vehicle speed at the current moment and the longitudinal vehicle speed at the previous moment, based on the longitudinal vehicle speed at the previous moment, the solution period of the extended Kalman filter algorithm, and the dynamic equation of the longitudinal vehicle speed, includes: Determine the product of the solution period of the extended Kalman filter algorithm and the longitudinal vehicle speed change rate at the previous moment; The sum of the longitudinal speed at the previous moment and the product value is used as the longitudinal speed at the current moment.

6. The method according to claim 4, wherein, The step of establishing a second conversion relationship between the slope at the current moment and the slope at the previous moment based on the slope at the previous moment, the solution period, and the slope change rate at the previous moment includes: Determine the product of the solution period of the extended Kalman filter algorithm and the slope change rate at the previous moment; The sum of the slope at the previous moment and the product is used as the slope at the current moment.

7. The method according to claim 4, wherein, The step of establishing a third transformation relationship between the slope change rate at the current moment and the slope change rate at the previous moment based on the slope change rate at the previous moment, the solution period, and the pre-constructed slope model includes: Determine the product of the solution period of the extended Kalman filter algorithm and the derivative of the slope change rate at the previous moment; The sum of the slope change rate at the previous moment and the product of the slope change rate and the slope change rate at the current moment is used as the slope change rate at the current moment.

8. The method according to claim 4, wherein, State transition equations are constructed based on the first, second, and third transformation relations, including: The third transformation relationship is transformed using a pre-built slope model; The state transition equation is constructed based on the first transformation relation, the second transformation relation, and the transformed third transformation relation.

9. The method according to claim 1, wherein, Based on the prediction vector and the observation vector at the current moment, the calibration slope at the current moment is calculated using the extended Kalman filter algorithm, including: The Kalman gain at the current time step is calculated using the extended Kalman filter algorithm; Based on the predicted vector, the Kalman gain at the current time, and the observation vector at the current time, the extended Kalman filter algorithm is used to perform calibration calculations to determine the second calibration vector corresponding to the current time. The second calibration vector includes the calibration slope at the current time.

10. The method according to claim 9, wherein, The calculation of the Kalman gain at the current moment using the extended Kalman filter algorithm includes: Based on the Jacobian matrix, error covariance matrix, and process covariance matrix of the previous time step, the error covariance matrix of the current time step is calculated. Based on the error covariance matrix, measurement matrix, and measurement covariance matrix at the current moment, the Kalman gain at the current moment is calculated.

11. The method according to claim 10, wherein, The method further includes: In response to the difference between the first calibration slope and the calibration slope at the current time exceeding a preset slope threshold, the second calibration vector is replaced according to the first calibration vector and the observation vector at the previous time, and the value corresponding to the longitudinal vehicle speed in the process covariance matrix and the value corresponding to the longitudinal vehicle speed in the measurement covariance matrix are increased.

12. The method according to claim 10, wherein, Before calculating the calibration slope at the current moment using the extended Kalman filter algorithm based on the prediction vector and the observation vector at the current moment, the method further includes: In response to the current vehicle operating condition being a preset condition, the value corresponding to the longitudinal vehicle speed in the measurement covariance matrix at the current moment is increased.

13. The method according to claim 10, wherein, The method further includes: In response to the difference between the rate of change of calibration slope in the second calibration vector and the rate of change of calibration slope exceeding a preset rate of change threshold, the value corresponding to the longitudinal vehicle speed in the measurement covariance matrix at the current moment is increased. Based on the prediction vector and the observation vector at the current moment, the calibration slope at the current moment is recalculated using the extended Kalman filter algorithm.

14. The method according to claim 1, wherein, The method further includes: In response to the previous time being the initial time, a first calibration vector for the initial time is randomly determined.

15. A vehicle, wherein, The vehicles include: Memory, used to store executable program code; A processor for calling and running the executable program code from the memory, causing the vehicle to perform the method as described in any one of claims 1 to 14.