Method and device for determining ramp acceleration, vehicle and storage medium

Abstract

The invention relates to a ramp acceleration determination method and device, a vehicle and a storage medium, and the method comprises the steps: obtaining the observation value of each observation variable in an observation variable set of the vehicle at the current moment, the observation variable set comprising the measurement speed of the vehicle and the measurement acceleration of the vehicle; based on a pre-established kinematics model corresponding to the vehicle and a Kalman filter, determining a first predicted value of each state variable in a state variable set of the vehicle at the current moment, the kinematics model being used for indicating a kinematics relationship between the measured acceleration and the measured speed, the jerk of the vehicle and the ramp acceleration of the vehicle; the state variable set comprises a measurement speed, a measurement acceleration, a jerk and a ramp acceleration; and determining a first estimated value of the ramp acceleration of the vehicle at the current moment by using a Kalman filter based on each first predicted value and each observed value. According to the invention, the accuracy of the determined ramp acceleration is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to a method and device for determining ramp acceleration, a vehicle and a storage medium. BACKGROUND

[0002] During vehicle driving, ramp acceleration is a core parameter for realizing power distribution and ramp auxiliary, and the accuracy of ramp acceleration determination directly affects the driving safety and comfort of the vehicle. At present, in order to realize the determination of ramp acceleration, additional hardware is usually added in the vehicle, such as a high-precision slope sensor or a multi-axis accelerometer or a GPS module, so as to convert the data collected by the hardware into ramp acceleration. However, the above-mentioned determination method of ramp acceleration not only increases the production and maintenance cost of the vehicle, but also the hardware signal is easily disturbed by the environment, thereby affecting the accuracy of the determined ramp acceleration. SUMMARY

[0003] One of the purposes of the present application is to provide a method for determining ramp acceleration, which avoids the problems of cost increase and signal interference caused by relying on additional hardware, and by introducing jerk as a state variable into the Kalman filter, the measurement deviation of the measured acceleration caused by the pitching motion of the vehicle can be captured based on the jerk, and the accuracy of the determined ramp acceleration is improved; the second purpose of the present application is to provide a device for determining ramp acceleration; the third purpose of the present application is to provide a vehicle; and the fourth purpose of the present application is to provide a storage medium.

[0004] In order to achieve the above-mentioned purposes, in a first aspect, the present application provides a method for determining ramp acceleration, comprising: obtaining observation values of each observation variable in an observation variable set of a vehicle at a current time, the observation variable set comprising a measured speed of the vehicle and a measured acceleration of the vehicle; determining first prediction values of each state variable in a state variable set of the vehicle at the current time based on a pre-established kinematic model corresponding to the vehicle and a Kalman filter, the kinematic model being used to indicate a kinematic relationship between the measured acceleration and the measured speed, a jerk of the vehicle and a ramp acceleration of the vehicle, the state variable set comprising the measured speed, the measured acceleration, the jerk and the ramp acceleration; determining a first estimated value of the ramp acceleration of the vehicle at the current time by using the Kalman filter based on each of the first prediction values and each of the observation values.

[0005] Optionally, the first predicted values of the state variables in the state variable set of the vehicle at the current time are determined based on the pre-established kinematic model corresponding to the vehicle and a Kalman filter, and the method comprises the following steps of: determining a state equation based on the kinematic model, the state variable set corresponding to the current time and the state variable set corresponding to a first time, the first time being a time preceding the current time; determining a state matrix of the vehicle from the first time to the current time based on the state equation; performing prior estimation by using the Kalman filter based on the state matrix and the second estimated values of the state variables in the state variable set of the vehicle at the first time, so as to obtain the first predicted values of the state variables in the state variable set of the vehicle at the current time.

[0006] Optionally, the first estimated value of the ramp acceleration of the vehicle at the current time is determined by using the Kalman filter based on the first predicted values and the observation values, and the method comprises the following steps of: obtaining process noise covariance of a second time and posterior estimation error covariance of the second time, the second time being a time preceding the first time; determining process noise covariance of the first time based on the process noise covariance of the second time and the posterior estimation error covariance of the second time; determining prior estimation error covariance of the current time based on the process noise covariance of the first time, posterior estimation error covariance of the first time and the state matrix; determining Kalman gain of the current time based on the prior estimation error covariance of the current time and measurement noise covariance; performing posterior estimation by using the Kalman filter based on the Kalman gain, the first predicted values and the observation values, so as to determine the first estimated value of the ramp acceleration of the vehicle at the current time.

[0007] Optionally, the process noise covariance of the first time is determined based on the process noise covariance of the second time and the posterior estimation error covariance of the second time, and the method comprises the following steps of: obtaining a preset performance index and a preset adjustment step, the preset performance index being used to indicate a performance index of H-infinity control, and the preset adjustment step being used to indicate a step of adjusting process noise covariance; determining a first adjustment amount based on the posterior estimation error covariance of the second time, the preset performance index and the preset adjustment step; adjust the process noise covariance of the second time instant by using the first adjustment amount, to determine the process noise covariance of the first time instant.

[0008] Optionally, the determining the Kalman gain of the current time instant based on the prior estimation error covariance of the current time instant and the measurement noise covariance comprises: Optionally, the method further comprises: Optionally, the method further comprises: Optionally, the method further comprises: Optionally, the method further comprises: Optionally, the method further comprises:

[0009] Optionally, the method further comprises: Optionally, the method further comprises: Optionally, the method further comprises: Optionally, the method further comprises: Optionally, the method further comprises:

[0010] Optionally, the method further comprises: Optionally, the method further comprises: determining a first standard deviation between all the first historical estimation values and the first estimation value, and determining a second standard deviation between all the second historical estimation values and the third estimation value; determining a target weight based on the first standard deviation and the second standard deviation; determining a target estimation value of the ramp acceleration of the vehicle at the current time based on the first estimation value, the third estimation value and the target weight.

[0011] To achieve the above object, in a second aspect, the present application provides a ramp acceleration determination device, comprising: an acquisition module, configured to acquire observation values of each observation variable in an observation variable set of a vehicle at a current time, the observation variable set comprising a measured speed of the vehicle and a measured acceleration of the vehicle; a first determination module, configured to determine first prediction values of each state variable in a state variable set of the vehicle at the current time based on a pre-established kinematic model corresponding to the vehicle and a Kalman filter, the kinematic model being used to indicate a kinematic relationship between the measured acceleration and the measured speed, jerk of the vehicle and ramp acceleration of the vehicle, the state variable set comprising the measured speed, the measured acceleration, the jerk and the ramp acceleration; a second determination module, configured to determine a first estimation value of the ramp acceleration of the vehicle at the current time by using the Kalman filter based on each first prediction value and each observation value.

[0012] To achieve the above object, in a third aspect, the present application further provides a vehicle, comprising a processor and a memory, the processor being configured to execute a ramp acceleration determination program stored in the memory, so as to implement the ramp acceleration determination method as described above.

[0013] To achieve the above object, in a fourth aspect, the present application further provides a storage medium, the storage medium storing one or more programs, the one or more programs being executable by one or more processors to implement the ramp acceleration determination method as described above.

[0014] The beneficial effects of this application are as follows: This application provides a method for determining ramp acceleration, comprising: acquiring the observed values ​​of each observed variable in the observation variable set of the vehicle at the current moment, the observation variable set including the vehicle's measured speed and measured acceleration; determining the first predicted values ​​of each state variable in the state variable set of the vehicle at the current moment based on a pre-established kinematic model and a Kalman filter, the kinematic model being used to indicate the kinematic relationship between the measured acceleration and the measured speed, the vehicle's jerk and the vehicle's ramp acceleration, the state variable set including the measured speed, measured acceleration, jerk and ramp acceleration; and determining the first estimated value of the ramp acceleration of the vehicle at the current moment using a Kalman filter based on each first predicted value and each observed value. In this embodiment, by utilizing the measured acceleration and speed obtained from existing sensors in the vehicle as observations, a set of state variables including measured speed, measured acceleration, jerk, and ramp acceleration is constructed. A kinematic model is established that includes the relationships between these state variables. Based on this kinematic model, the Kalman filter can determine the first predicted value of each state variable in the preset state set of the vehicle at the current moment. Then, combined with the observations, the first estimated value of the ramp acceleration at the current moment is determined. This fundamentally avoids the increased cost and signal interference caused by adding additional hardware, reducing costs and improving the accuracy of the determined ramp acceleration. At the same time, by introducing jerk as a state variable into the Kalman filter, the measurement deviation of the measured acceleration caused by the vehicle's pitch motion can be captured based on the jerk, further improving the accuracy of the determined ramp acceleration. Attached Figure Description

[0015] Figure 1 This diagram illustrates a flowchart of a method for determining ramp acceleration provided in an embodiment of this application. Figure 2 This diagram illustrates a flowchart of another method for determining ramp acceleration provided in an embodiment of this application. Figure 3 This diagram illustrates the structure of a ramp acceleration determination device provided in an embodiment of this application. Figure 4 This illustration shows a structural diagram of a vehicle provided in an embodiment of this application. Detailed Implementation

[0016] The present application is illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements, and in which: benefits and advantages of the present application can be readily understood by those skilled in the art from the following description, taken in connection with the accompanying drawings, in which: the preferred embodiments of the present application are described herein primarily as they relate to the preferred embodiments of the application. Embodiments of the present application can be implemented or performed in other ways than those specifically described herein without departing from the spirit of the present application. It is therefore contemplated to cover any and all modifications, variations or equivalents that fall within the spirit and scope of the present application. It should be understood that the preferred embodiments are only given by way of illustration and are not used to limit the protective scope of the present application.

[0017] It should be noted that the drawings provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner, and only the components related to the present application are shown in the drawings, not the number, shape and size of the components when actually implemented. The actual implementation of each component may be a random change, and the component layout may be more complex.

[0018] In order to facilitate the understanding of the embodiments of the present application, the following will be further explained and described in specific embodiments in conjunction with the drawings, and the embodiments do not constitute a limitation on the embodiments of the present application.

[0019] Reference Figure 1 , Figure 1 A flowchart of a method for determining a ramp acceleration is provided for the embodiments of the present application. The method for determining a ramp acceleration provided by the embodiments of the present application comprises the following steps: S101: obtaining the observation value of each observation variable in the observation variable set of the vehicle at the current time.

[0020] In the present embodiment, the observation variable set comprises the measured speed of the vehicle and the measured acceleration of the vehicle. The measured speed is used to indicate the speed measured by the speed sensor in the vehicle, and the speed sensor can be a wheel speed sensor. The measured acceleration is used to indicate the acceleration measured by the accelerometer in the vehicle, and the measured acceleration comprises a composite acceleration of multiple physical quantities.

[0021] S102: determining the first predicted value of each state variable in the state variable set of the vehicle at the current time based on the pre-established kinematic model corresponding to the vehicle and the Kalman filter.

[0022] In the present embodiment, the kinematic model is used to indicate the kinematic relationship between the measured acceleration and the measured speed, the jerk of the vehicle and the ramp acceleration of the vehicle, and the state variable set comprises the measured speed, the measured acceleration, the jerk and the ramp acceleration. The jerk is used to indicate the time rate of change of the longitudinal acceleration of the vehicle, and the ramp acceleration is used to indicate the acceleration generated by the component force of gravity along the slope direction when the vehicle is driving on the slope.

[0023] The kinematic model in the above can be represented by the following formula:

[0024] In the above formula, represents the measured acceleration of the vehicle, represents the longitudinal acceleration of the vehicle, represents the ramp acceleration of the vehicle, represents the pitch acceleration of the vehicle, represents the first time derivative of the measured speed of the vehicle, represents the gravitational acceleration, represents the ramp angle, represents the jerk of the vehicle, represents a proportional coefficient calibrated by the suspension stiffness and the height of the center of mass of the vehicle.

[0025] Based on the kinematic model of the vehicle established above, the state variable set may be represented as: After obtaining the kinematic model, a state observation model can be established based on the kinematic model, and the state prediction model includes a state space equation and an observation equation. The state space equation can be represented by the following formula:

[0026] In the above formula, represents the state variable set at time k, and A represents the state matrix, represents the state vector set at time k-1, represents the process noise of each state variable at time k-1.

[0027] The state equation can be determined based on the kinematic model, the state variable set at time k, and the state variable set at time k-1, so as to construct the state space equation by using the state matrix, the state variable set at time k, and the state variable set at time k-1. The state equation can be represented by the following formula:

[0028] In the above formula, represents the measured speed at time k, represents the measured speed at time k-1, represents the measured acceleration at time k-1, represents the ramp acceleration at time k-1, represents the pitch acceleration at time k-1, represents the time interval between time k and time k-1, represents the process noise of the measured speed at time k-1, represents the measured acceleration at time k, jerk at time k-1, process noise of the measured acceleration at time k-1, jerk at time k, process noise of the jerk at time k-1, ramp acceleration at time k, process noise of the ramp acceleration at time k-1.

[0029] wherein, , then based on the state equation, the state variable set corresponding to time k and the state variable set corresponding to time k-1, the state matrix can be determined as follows:

[0030] In the above formula, A represents the state matrix, represents the time interval between time k and time k-1, represents a proportional coefficient.

[0031] The observation equation can be represented by the following formula:

[0032] In the above formula, represents the observation variable set at time k, and H represents the observation matrix, represents the measurement noise at time k.

[0033] wherein, , then based on the observation equation, the observation matrix can be determined as follows:

[0034] In the above formula, H represents the observation matrix.

[0035] Specifically, after obtaining the state matrix, the Kalman filter can be used to perform prior estimation based on the state matrix and the second estimated values of each state variable in the state variable set of the vehicle at the first time, to obtain the first predicted values of each state variable of the vehicle at the current time, the first time being the time immediately preceding the current time. It should be noted that the second estimated values of each state variable in the state variable set of the vehicle at the first time are actually obtained by performing posterior estimation using the Kalman filter. The Kalman filter in the above formula can be represented by the following formula:

[0036]

[0037]

[0038]

[0039]

[0040] in the above formula, denotes the predicted value of each state variable in the state variable set at time k, denotes the estimated value of each state variable in the state variable set at time k-1, A denotes a state matrix, denotes the prior estimation error covariance at time k, denotes the posterior estimation error covariance at time k-1, denotes the process noise covariance at time k-1, denotes the Kalman gain, H denotes an observation matrix, and R denotes a measurement noise covariance, denotes the estimated value of each state variable in the state variable set at time k, denotes the observed value of each observation variable in the observation variable set at time k, denotes the posterior estimation error covariance at time k.

[0041] After obtaining the state matrix and the second estimated values of each state variable in the state variable set of the vehicle at the first time, the Kalman filter can use the first formula to make a prior estimation of the predicted value of each state variable in the state variable set of the vehicle at the current time based on the second estimated values and the state matrix, so as to obtain the first predicted value of each state variable in the state variable set of the vehicle at the current time.

[0042] S103: Based on each first predicted value and each observed value, the Kalman filter determines a first estimated value of the ramp acceleration of the vehicle at the current time.

[0043] In the embodiment, after obtaining each first predicted value and each observed value, the Kalman filter determines the first estimated value of each state variable in the state variable set of the vehicle at the current time based on each first predicted value and each observed value by using the second formula to the fourth formula, so as to determine the first estimated value of the ramp acceleration from the determined first estimated values, and then use the first estimated value of the ramp acceleration to realize power distribution and the like during the driving of the vehicle, so as to ensure the driving safety of the vehicle.

[0044] The embodiment provides a determination method of ramp acceleration, which uses measured acceleration and measured speed obtained by using sensors in a vehicle as observation values, constructs a state variable set containing measured speed, measured acceleration, jerk and ramp acceleration, and establishes a kinematic model containing the state variables, so that a Kalman filter can determine first prediction values of each state variable in a preset state set of the vehicle at a current time based on the kinematic model, and then determines a first estimation value of the ramp acceleration at the current time in combination with the observation values, thereby fundamentally avoiding cost increase caused by dependence on additional hardware and signal interference problems, reducing cost and improving the accuracy of the determined ramp acceleration; meanwhile, by introducing the jerk as a state variable into the Kalman filter, the measurement deviation of the measured acceleration caused by vehicle pitching motion can be captured based on the jerk, so as to further improve the accuracy of the determined ramp acceleration.

[0045] Reference Figure 2 , Figure 2 is a flowchart of another determination method of ramp acceleration provided by the embodiment, and the determination method of ramp acceleration provided by the embodiment comprises the following steps. S201: acquiring observation values of each observation variable in an observation variable set of the vehicle at a current time.

[0046] In the embodiment, the S201 step is consistent with the S101 step, and reference can be made to the S101 step.

[0047] S202: determining first prediction values of each state variable in a state variable set of the vehicle at the current time based on a pre-established kinematic model corresponding to the vehicle and a Kalman filter.

[0048] In the embodiment, the S202 step specifically comprises: determining a state equation based on the kinematic model, the state variable set corresponding to the current time and the state variable set corresponding to a first time, the first time being a time preceding the current time; determining a state matrix of the vehicle from the first time to the current time based on the state equation; performing prior estimation by using the Kalman filter based on the state matrix and second estimation values of each state variable in the state variable set of the vehicle at the first time, so as to determine the first prediction values of each state variable of the vehicle at the current time.

[0049] Wherein, the current time can be considered as the k time, the first time can be considered as the k-1 time, thus, the state equation between the state variable set of the vehicle at the current time and the state variable set of the vehicle at the first time can be determined through the state equation, so as to determine the state space model by using the state equation based on the above manner, and then the state space model can be used to determine the state matrix of the vehicle from the first time to the current time (i.e. the state matrix).

[0050] After obtaining the state matrix, the second estimated value of each state variable in the state variable set of the vehicle at the first time is obtained, so that the Kalman filter inputs the state matrix and each second estimated value into the first formula in the Kalman filter for prior estimation, thereby outputting the first prediction value of each state variable in the state variable set of the vehicle at the current time, and then based on each first prediction value and each obtained observation value, the Kalman filter is used for posterior estimation to obtain the final first estimated value of the slope acceleration of the vehicle at the current time. Through the above manner, the kinematic model corresponding to the vehicle is converted into a discrete state equation in this embodiment, the state matrix is determined based on the state equation, and the Kalman filter is driven accordingly to realize the prior estimation of the first prediction value of each state variable of the vehicle at the current time, and then the Kalman filter realizes the accurate posterior estimation of the first estimated value of each state variable in the state variable set of the vehicle at the current time based on each first prediction value.

[0051] S203: determining the first estimated value of the slope acceleration of the vehicle at the current time by using the Kalman filter based on each first prediction value and each observation value.

[0052] In this embodiment, when the Kalman filter performs posterior estimation, if a fixed process noise covariance is used, the filtering of the Kalman filter may be divergent or the estimation accuracy may be not high, therefore, in order to avoid the above problems, this embodiment provides a determination method of the first estimated value, which specifically includes the following steps: obtaining the process noise covariance at the second time and the posterior estimation error covariance at the second time, the second time being the previous time of the first time; determining the process noise covariance at the first time based on the process noise covariance at the second time and the posterior estimation error covariance at the second time; determining the prior estimation error covariance at the current time based on the process noise covariance at the first time, the posterior estimation error covariance at the first time and the state matrix; determining the Kalman gain at the current time based on the prior estimation error covariance at the current time and the measurement noise covariance matrix at the current time; Based on the Kalman gain, each first prediction value and each observation value, a posteriori estimation is performed by using the Kalman filter to determine the first estimation value of the ramp acceleration of the vehicle at the current time.

[0053] Wherein, if the current time is regarded as k time, the first time is regarded as k-1 time, and the second time is regarded as k-2 time. The a posteriori estimation can be understood as a process that the Kalman filter corrects the prediction value of the a priori estimation based on the observation value and the Kalman gain to obtain the optimal estimation value.

[0054] Specifically, when the a posteriori estimation is needed, the process noise covariance at the second time and the a posteriori estimation error covariance at the second time are obtained, so as to dynamically obtain the process noise covariance at the first time by using the process noise covariance at the second time and the a posteriori estimation error covariance at the second time, so as to match the dynamic change of the running state of the vehicle, for example, from flat road to ramp or from uniform speed to acceleration. After obtaining the process noise covariance at the first time, the a priori estimation error covariance at the current time can be determined based on the second formula in the Kalman filter by using the process noise covariance at the first time, the a priori estimation error covariance at the first time and the state matrix A.

[0055] After obtaining the a priori estimation error covariance at the current time, the a priori estimation error covariance at the current time, the observation matrix H and the measurement noise covariance R are input into the third formula of the Kalman filter, so as to output the Kalman gain at the current time.

[0056] After obtaining the Kalman gain at the current time, the Kalman gain at the current time, each first prediction value , each observation value and the observation matrix H are input into the fourth formula in the Kalman filter, so as to output the estimation value of each state variable in the state variable set of the vehicle at the current time , and the obtained The first estimation value of the slope acceleration of the vehicle at the current time is obtained. Through the above method, the embodiment adjusts the process noise covariance through a dynamic recursive process to adapt to different driving conditions of the vehicle, accurately calculates the dynamic Kalman gain in combination with the prior estimation error covariance, effectively balances the interference of the error of the Kalman filter and the sensor noise, and can improve the estimation accuracy of the slope acceleration.

[0057] In the above, the process noise covariance at the first time is determined based on the process noise covariance at the second time and the posteriori estimation error covariance at the second time, and the process noise covariance at the first time comprises: obtaining a preset performance index and a preset adjustment step; determining a first adjustment amount based on the posteriori estimation error covariance at the second time, the preset performance index and the preset adjustment step; using the first adjustment amount to adjust the process noise covariance at the second time to obtain the process noise covariance at the first time.

[0058] The preset performance index is used to indicate the performance index of H infinity control, and the preset adjustment step is used to indicate the step of adjusting the process noise covariance. The preset performance index can be understood as an index based on H infinity control to constrain the filtering robustness and estimation accuracy of the Kalman filter, and its function is to ensure that the dynamically adjusted process noise covariance meets different complex conditions. The preset performance index can be set according to actual needs, for example, the preset performance index may be 1.2. The preset adjustment step is a quantitative parameter for controlling the adjustment range of the process noise covariance, and the preset adjustment step can be set according to actual needs, for example, the preset adjustment step may be 0.001-0.01.

[0059] After obtaining the posteriori estimation error covariance at the second time, the preset performance index and the preset adjustment step, the posteriori estimation error covariance at the second time, the preset performance index and the preset adjustment step are input into the following formula to output a first adjustment amount, which is used to indicate the increment of the process noise covariance at the second time.

[0060]

[0061] In the above formula, represents the first adjustment amount, represents the preset adjustment step, represents the preset performance index, represents the posteriori estimation error covariance at the second time, represents a unit matrix.

[0062] After obtaining the first adjustment amount , the first adjustment amount and the process noise covariance at the second moment are input into the following formula, that is, the sum value between the first adjustment amount and the process noise covariance at the second moment is determined to obtain the process noise covariance at the second moment .

[0063]

[0064] In the above formula, represents the process noise covariance at the first moment, represents the process noise covariance at the second moment, represents the first adjustment amount.

[0065] It should be noted that when is greater than 0, increases, when is less than 0, decreases, and when is equal to 0, it indicates that the performance exactly meets the robustness requirement.

[0066] In the above manner, the embodiment introduces the preset performance index of H-infinity control, provides robustness constraints for dynamic adjustment of the process noise covariance, ensures the robustness of the Kalman filter under complex working conditions, and avoids sudden changes in the process noise covariance by presetting the adjustment step, thereby ensuring the smoothness of the filtering process. On this basis, the first adjustment amount of the process noise covariance is quantified by combining the posterior estimation error covariance, so as to realize accurate updating of the process noise covariance.

[0067] In the above, the Kalman gain at the current moment is determined based on the prior estimation error covariance at the current moment and the measurement noise covariance matrix at the current moment, including: obtaining a set of historical observation values corresponding to the measured acceleration in a first sliding time window; performing Fourier transform on the set of historical observation values and the observation value corresponding to the measured acceleration of the vehicle at the current moment to obtain the total energy and the high-frequency energy in the frequency domain corresponding to the measured acceleration; determining a second adjustment amount based on the total energy and the high-frequency energy in the frequency domain; adjusting the measurement noise covariance at the first moment by using the second adjustment amount to obtain an adjusted measurement noise covariance; determining the Kalman gain at the current moment based on the prior estimation error covariance at the current moment and the adjusted measurement noise covariance.

[0068] The first sliding time window is located before the current time and continuous with the current time in time. The first sliding time window can be set according to actual needs, and the first sliding time window includes historical observation values of the measured acceleration at a plurality of historical times to obtain a historical observation value set. Each historical time and the current time are sampling times determined based on a sampling frequency of the accelerometer. The Fourier transform refers to converting a time-domain signal into a frequency-domain signal to separate low-frequency useful energy and high-frequency interference energy (i.e., high-frequency energy) from the signal. The low-frequency useful energy refers to the total energy of the signal in the low-frequency range, and the high-frequency interference energy refers to the total energy of the signal in the high-frequency range. The frequency-domain total energy is the total energy between the low-frequency useful energy and the high-frequency interference energy, and the high-frequency energy is actually the high-frequency interference energy. The second adjustment amount is used to indicate an adjustment amount of the measurement noise covariance.

[0069] After obtaining the historical observation value set and the observation value of the measured acceleration of the vehicle at the current time, the historical observation value set and the observation value are spliced to obtain spliced data in the time domain. The spliced data is subjected to Fourier transform to convert the spliced data in the time domain into spliced data in the frequency domain, so as to obtain energy corresponding to each frequency. The sum of all energies is obtained, that is, the frequency-domain total energy, and the sum of the energies in the high-frequency range is obtained, that is, the high-frequency energy. The frequency-domain total energy and the high-frequency interference energy can be determined by the following formula.

[0070]

[0071] In the above formula, represents the high-frequency energy, represents the frequency-domain total energy, represents the historical observation value or the observation value of the measured acceleration, and STFT represents the Fourier transform, represents the cutoff frequency, represents the maximum frequency.

[0072] After obtaining the high-frequency energy and the frequency-domain total energy , the ratio between and can be determined. When the ratio is less than a first preset threshold, the second adjustment amount is determined to be 0, and the first preset threshold can be 15%. When the ratio is greater than or equal to the first preset threshold, the second adjustment amount is determined to be , represents the high-frequency noise gain coefficient, represents the unit matrix. The second adjustment amount can be determined by the following formula.

[0073]

[0074] In the above formula, represents the high-frequency energy, denotes the total energy in the frequency domain, denotes a high-frequency noise gain coefficient, denotes a unit matrix, is the measurement noise covariance before adjustment, which is pre-calibrated.

[0075] S204: Obtain the target weight of the vehicle, the wheel-end torque of the vehicle, the air resistance, the rolling resistance, the longitudinal acceleration of the vehicle at the current time, and the pre-established dynamics model corresponding to the vehicle.

[0076] S205: Based on the target weight, the wheel-end torque of the vehicle, the air resistance, the rolling resistance, and the longitudinal acceleration, determine a third estimated value of the slope acceleration of the vehicle at the current time by using the dynamics model.

[0077] S206: Based on the first estimated value and the third estimated value, determine a target estimated value corresponding to the slope acceleration of the vehicle at the current time.

[0078] For the steps S204 to S206 described above, the target weight is the actual total weight of the vehicle at the current time, which can be obtained by a weight sensor arranged in the vehicle. The wheel-end torque of the vehicle is the torque output by the driving wheel of the vehicle, which can be calculated by the transmission ratio. The air resistance refers to the resistance of the vehicle body during driving, which can be calculated by the existing air resistance model. The rolling resistance refers to the resistance generated by the contact between the tire and the road surface during driving, which can be calculated by the existing rolling resistance model. The longitudinal acceleration is the actual acceleration during driving, which can be obtained by differentiating the vehicle speed collected by the speed sensor in the vehicle. The dynamics model is actually the dynamic relationship between the wheel-end torque of the vehicle and the acceleration resistance, air resistance, rolling resistance, and slope resistance of the vehicle. Since the vehicle acceleration resistance is equal to the product of the target weight and the longitudinal acceleration, and the vehicle slope resistance is equal to the product of the target weight and the slope acceleration, the estimated value of the slope acceleration can be determined by using the dynamics model when the target weight, the wheel-end torque of the vehicle, the air resistance, the rolling resistance, and the longitudinal acceleration are known. The running dynamics model is represented by the following formula.

[0079]

[0080] In the above formula, denotes the wheel-end torque of the vehicle, denotes the vehicle acceleration resistance, , denotes the target weight, denotes the longitudinal acceleration, denotes the air resistance, denotes the rolling resistance, denotes the slope resistance, , represents the ramp acceleration.

[0081] Based on the above kinetic model, a determination formula of the estimated value of the ramp acceleration can be obtained, which is specifically as follows.

[0082]

[0083] The represented meanings of each parameter in the above formula are consistent with the above kinetic model, and this embodiment will not be described here.

[0084] On the basis of obtaining the target weight of the vehicle, the wheel end torque of the vehicle, the air resistance, the rolling resistance and the longitudinal acceleration of the vehicle at the current time, each parameter is input into the determination formula of the estimated value of the ramp acceleration, so as to output the third estimated value of the ramp acceleration of the vehicle at the current time, and the first estimated value and the third estimated value are fused to determine the target estimated value of the ramp acceleration of the vehicle at the current time, so as to improve the accuracy of the estimation of the ramp acceleration.

[0085] When the first estimated value and the third estimated value are fused, the deviation between the third estimated value and the first estimated value and the target weight corresponding to the deviation are determined, the product between the deviation and the target weight is determined, and the sum value between the first estimated value and the product is determined as the target estimated value. In the above manner, the third estimated value of the ramp acceleration is determined by introducing the kinetic model corresponding to the vehicle, and the first estimated value of the ramp acceleration based on the Kalman filter is fused to obtain the target estimated value corresponding to the final ramp acceleration, which avoids the inaccuracy problem of the single model estimation, realizes the complementary advantages in all working conditions through double-source information fusion, and improves the accuracy of the estimation of the ramp acceleration.

[0086] Specifically, when the target estimated value is determined, the current working condition corresponding to the vehicle can be determined based on the measured speed at the current time, so as to determine the first preset weight corresponding to the first estimated value and the second preset weight corresponding to the second estimated value based on the working condition. The historical target estimated values corresponding to a plurality of historical time points within the third sliding time window are obtained, the historical target estimated value is the target estimated value determined at the historical time point, the mean value between all the obtained historical target estimated values is determined to obtain the fusion estimation mean value, the first absolute deviation between the first estimated value and the fusion estimation mean value and the second absolute deviation between the third estimated value and the fusion estimation mean value are determined. The first preset weight is corrected by using the first absolute deviation, and the second preset weight is corrected by using the second absolute deviation, so as to determine the weighted sum by using the first estimated value, the corrected first preset weight, the third estimated value and the corrected second preset weight, so as to obtain the target estimated value.

[0087] The first preset weight and the second preset weight can be obtained according to a preset mapping table between a working condition and a weight. When the first preset weight and the second preset weight are corrected, a correction coefficient of the first preset weight and the second preset weight can be determined according to a preset mapping table between a weight and a correction coefficient, and the first preset weight and the second preset weight are corrected by using the respective correction coefficients.

[0088] The S206 specifically includes: obtaining a first historical estimation value and a second historical estimation value corresponding to each historical moment in the second sliding time window; determining a first standard deviation between all the first historical estimation values and the first estimation value, and determining a second standard deviation between all the second historical estimation values and the second estimation value; determining a target weight based on the first standard deviation and the second standard deviation; determining a target estimation value of the slope acceleration of the vehicle at the current moment based on the first estimation value, the third estimation value and the target weight.

[0089] The second sliding time window is located before the current moment and is continuous with the current moment in time, and the first sliding time window includes a plurality of historical moments, each historical moment corresponding to a first historical estimation value and a second historical estimation value of the slope acceleration of the vehicle at the historical moment. The first historical estimation value is used to indicate an estimation value of the slope acceleration of the vehicle at the historical moment determined by using a Kalman filter, that is, determined by the S201 to S203 steps. The second historical estimation value is used to indicate an estimation value of the slope acceleration of the vehicle at the historical moment determined by using a dynamics model, that is, determined by the S204 and S205 steps. The first standard deviation is a quantitative index of the dispersion degree between all the first historical estimation values and the first estimation value of the current moment. The second standard deviation is a quantitative index of the dispersion degree between all the second historical estimation values and the third estimation value of the current moment.

[0090] The second sliding time window can be determined by determining an acceleration change rate of the vehicle at the current moment, the acceleration change rate being equal to a second-order time derivative of the vehicle speed collected by a wheel speed sensor in the vehicle. The second sliding time window is determined based on the acceleration change rate. The relationship between the second sliding time window and the acceleration change rate can be represented by the following formula.

[0091]

[0092] In the above formula, denotes the second sliding time window, denotes a calibration coefficient, denotes the acceleration change rate.

[0093] After obtaining each first historical estimation value and the first estimation value, a first standard deviation between all the first historical estimation values and the first estimation value is determined by using a standard deviation formula, and after obtaining each second historical estimation value and the third estimation value, a second standard deviation between all the second historical estimation values and the third estimation value is determined by using the standard deviation formula. The first standard deviation and the second standard deviation are input into the following formula to obtain a target weight.

[0094]

[0095] In the above formula, represents the target weight corresponding to the current time, represents the second standard deviation corresponding to the current time, represents the first standard deviation corresponding to the current time. It should be noted that when the acceleration change rate of the vehicle is greater than 2 m / s 2 , the following condition is forcibly limited ≤ 0.1 m / s 2 to suppress the divergence of the Kalman filter.

[0096] After obtaining the target weight, the target weight, the second estimation value and the third estimation value are input into the following formula to obtain the target estimation value of the ramp acceleration of the vehicle at the first time.

[0097]

[0098] In the above formula, represents the target estimation value of the ramp acceleration of the vehicle at the current time, represents the target weight corresponding to the current time, represents the first estimation value of the ramp acceleration of the vehicle at the current time, represents the third estimation value of the ramp acceleration of the vehicle at the current time.

[0099] By the above method, the embodiment provides a target estimation value determination method of the ramp acceleration of the vehicle. The standard deviation of the ramp acceleration estimated by the Kalman filter and the standard deviation of the ramp acceleration estimated by the dynamic model are determined based on the second sliding time window, and the weights of the first estimation value and the third estimation value are dynamically allocated based on the standard deviations, so that the target estimation value of the ramp acceleration is determined, and the target estimation value determined under any working condition is automatically inclined to the estimation value of the ramp acceleration which is more stable and more reliable in recent period, and the estimation accuracy of the ramp acceleration is further significantly improved.

[0100] The embodiment provides a method for determining ramp acceleration, which comprises the following steps: obtaining observation values of each observation variable in an observation variable set of a vehicle at a current time, wherein the observation variable set comprises a measured speed of the vehicle and a measured acceleration of the vehicle; determining first prediction values of each state variable in a state variable set of the vehicle at the current time based on a previously established kinematic model corresponding to the vehicle and a Kalman filter, wherein the kinematic model is used for indicating a kinematic relationship among the measured acceleration, the measured speed, jerk of the vehicle and ramp acceleration of the vehicle, and the state variable set comprises the measured speed, the measured acceleration, the jerk and the ramp acceleration; and determining a first estimation value of the ramp acceleration of the vehicle at the current time by using the Kalman filter based on each first prediction value and each observation value.

[0101] Reference Figure 3 , Figure 3 A structural schematic diagram of a device for determining ramp acceleration is provided in the embodiment. The device for determining ramp acceleration comprises an acquisition module 10, a first determination module 20 and a second determination module 30. The acquisition module 10 is used for obtaining observation values of each observation variable in an observation variable set of a vehicle at a current time, wherein the observation variable set comprises a measured speed of the vehicle and a measured acceleration of the vehicle. The first determination module 20 is used for determining first prediction values of each state variable in a state variable set of the vehicle at the current time based on a previously established kinematic model corresponding to the vehicle and a Kalman filter, wherein the kinematic model is used for indicating a kinematic relationship among the measured acceleration, the measured speed, jerk of the vehicle and ramp acceleration of the vehicle, and the state variable set comprises the measured speed, the measured acceleration, the jerk and the ramp acceleration. The second determination module 30 is used for determining a first estimation value of the ramp acceleration of the vehicle at the current time by using the Kalman filter based on each first prediction value and each observation value.

[0102] In the embodiment, the first determination module 20 is further used for: determining a state equation based on the kinematic model, the state variable set corresponding to the current time and the state variable set corresponding to a first time, wherein the first time is a last time of the current time; determining a state matrix of the vehicle from the first time to the current time based on the state equation; performing prior estimation by using the Kalman filter based on the state matrix and a second estimation value of each state variable in the state variable set of the vehicle at the first time, so as to determine the first prediction values of each state variable in the state variable set of the vehicle at the current time.

[0103] In this embodiment, the second determining module 30 is further configured to: obtain the process noise covariance at the second time and the posteriori estimation error covariance at the second time, the second time being a time prior to the first time; determine the process noise covariance at the first time based on the process noise covariance at the second time and the posteriori estimation error covariance at the second time; determine the priori estimation error covariance at the current time based on the process noise covariance at the first time, the posteriori estimation error covariance at the first time and the state matrix; determine the Kalman gain at the current time based on the priori estimation error covariance at the current time and the measurement noise covariance; perform a posteriori estimation based on the Kalman gain, each of the first prediction values and each of the observation values by using the Kalman filter to determine the first estimation value of the hill start acceleration of the vehicle at the current time.

[0104] In this embodiment, the second determining module 30 is further configured to: obtain a preset performance index and a preset adjustment step, the preset performance index being used to indicate a performance index of H-infinity control, and the preset adjustment step being used to indicate a step of adjusting the process noise covariance; determine a first adjustment amount based on the posteriori estimation error covariance at the second time, the preset performance index and the preset adjustment step; adjust the process noise covariance at the second time by using the first adjustment amount to determine the process noise covariance at the first time.

[0105] In this embodiment, the second determining module 30 is further configured to: obtain a set of historical observation values corresponding to the measured acceleration in a first sliding time window, the first sliding time window being located before the current time and being continuous in time with the current time; perform Fourier transform on the set of historical observation values and the observation value of the measured acceleration of the vehicle at the current time to obtain a total energy in frequency domain and a high frequency energy corresponding to the measured acceleration; determine a second adjustment amount based on the total energy in frequency domain and the high frequency energy; adjust the measurement noise covariance by using the second adjustment amount to obtain an adjusted measurement noise covariance; determine the Kalman gain at the current time based on the priori estimation error covariance at the current time and the adjusted measurement noise covariance.

[0106] The slope acceleration determination device provided in the embodiment further comprises a third determination module, which is configured to: In the process of determining the first estimated value, the target weight of the vehicle, the vehicle wheel end torque, the air resistance, the rolling resistance, the longitudinal acceleration of the vehicle at the current time, and a pre-established dynamic model corresponding to the vehicle are obtained; Based on the target weight, the vehicle wheel end torque, the air resistance, the rolling resistance, and the longitudinal acceleration, the third estimated value of the slope acceleration of the vehicle at the current time is determined by using the dynamic model; Based on the first estimated value and the third estimated value, the target estimated value of the slope acceleration of the vehicle at the current time is determined.

[0107] In the embodiment, the third determination module is further configured to: The first historical estimated value and the second historical estimated value corresponding to each historical time in a second sliding time window are obtained, the second sliding time window is located before the current time and is continuous in time with the current time, the first historical estimated value is used to indicate the estimated value of the slope acceleration of the vehicle at the historical time determined by using the Kalman filter, and the second historical estimated value is used to indicate the estimated value of the slope acceleration of the vehicle at the historical time determined by using the dynamic model; The first standard deviation between all the first historical estimated values and the first estimated value is determined, and the second standard deviation between all the second historical estimated values and the third estimated value is determined; Based on the first standard deviation and the second standard deviation, a target weight is determined; Based on the first estimated value, the third estimated value, and the target weight, the target estimated value of the slope acceleration of the vehicle at the current time is determined.

[0108] The embodiment provides a determination device of ramp acceleration. The determination device uses measured acceleration and measured speed obtained by using sensors in a vehicle as observation values, constructs a state variable set containing the measured speed, the measured acceleration, the jerk and the ramp acceleration, and establishes a kinematic model containing the state variables, so that a Kalman filter can determine first prediction values of each state variable in a preset state set of the vehicle at a current time based on the kinematic model, and then determines a first estimation value of the ramp acceleration at the current time in combination with the observation values. The determination device fundamentally avoids cost increase caused by dependence on additional hardware and signal interference problems, reduces cost and improves the accuracy of the determined ramp acceleration. Meanwhile, the determination device introduces the jerk as a state variable into the Kalman filter, so that the measurement deviation of the measured acceleration caused by pitching motion of the vehicle can be captured based on the jerk, and the accuracy of the determined ramp acceleration is further improved.

[0109] Reference Figure 4 As shown in the figure, Figure 4 A vehicle structure schematic diagram is provided for the embodiment of the application. The vehicle 400 in the embodiment can include at least one processor 401, a vehicle memory 402, at least one network interface 404 and other user interfaces 403. The various components in the vehicle 400 are coupled together through a bus system 405. It can be understood that the bus system 405 is used to realize the connection communication between the components. The bus system 405 includes a data bus, a power bus, a control bus and a state signal bus. However, for the purpose of clear illustration, various buses are marked as the bus system 405.

[0110] The user interface 403 can include a display, a keyboard or a click vehicle (for example, a mouse, a trackball, a touchpad or a touch screen, etc.).

[0111] It is to be understood that the memory 402 in the embodiments of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. The non-volatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a Random Access Memory (RAM) used as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 402 described herein is intended to include, without being limited to, these and any other suitable types of memory.

[0112] In some embodiments, the memory 402 stores the following elements, executable units or data structures, or a subset of them, or an extended set of them: an operating system 4021 and application programs 4022.

[0113] The operating system 4021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application programs 4022 include various application programs, such as a Media Player, a Browser, etc., for implementing various application services. The programs for implementing the methods of the embodiments of the present application can be included in the application programs 4022.

[0114] In the embodiments of the present application, the processor 401 is configured to execute the methods provided by the various method embodiments by invoking the programs or instructions stored in the memory 402, specifically, the programs or instructions stored in the application programs 4022.

[0115] The method disclosed by the embodiments of the present application can be applied to the processor 401 or implemented by the processor 401. The processor 401 can be an integrated circuit chip having a signal processing capability. In the implementation process, each step of the above method can be completed by an integrated logic circuit or an instruction in the form of software in the processor 401. The processor 401 described above can be a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. Each method, step and logic block diagram disclosed in the embodiments of the present application can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as a hardware code processor for execution, or a combination of hardware and software units in the code processor for execution. The software unit can be located in a random access memory, a flash memory, a read only memory, a programmable read only memory or an electrically erasable programmable memory, a register or other mature storage medium in the art. The storage medium is located in the memory 402, and the processor 401 reads the information in the memory 402 and combines the hardware to complete the above method.

[0116] It can be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or at least one application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described in the embodiments of the present application, or a combination thereof.

[0117] For software implementation, the technology described herein can be implemented by units performing the functions described herein. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0118] The embodiment of the present application further provides a storage medium (computer readable storage medium). The storage medium stores one or at least one program. The storage medium can include a volatile memory, such as a random access memory; the storage medium can also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk or a solid state disk; and the storage medium can also include a combination of the above-mentioned memories.

[0119] When the one or at least one program stored in the storage medium is executed by the one or at least one processor. When the storage medium is applied to a vehicle, the above-mentioned method executed in the vehicle can be implemented. The processor is configured to execute the vehicle program stored in the memory, so as to implement the above-mentioned method executed in the vehicle.

[0120] Those skilled in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be realized in electronic hardware, computer software, or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been described in general terms in the above description. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0121] It should be noted that the terms "one embodiment", "an embodiment", "exemplary embodiment", "some embodiments", etc. in the specification mean that the described embodiments can include a particular feature, structure or characteristic, but not necessarily every embodiment. In addition, such phrases do not necessarily refer to the same embodiment. In addition, when a particular feature, structure or characteristic is described in conjunction with an embodiment, it is within the knowledge of those skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments described explicitly or implicitly.

[0122] It should be noted that in this document, relational terms such as "first" and "second", and the like can only be used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method or article including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method or article. Without more limitations, the element defined by the phrase "comprising a" does not exclude the presence of additional identical elements in the process, method, article or vehicle including the element.

[0123] The above embodiments are only preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Any equivalent replacement or transformation of the present application made by those skilled in the art based on the present application is within the protection scope of the present application.

Claims

1. A method of determining ramp acceleration, characterized by, The method comprises: obtaining observation values of each observation variable in an observation variable set of a vehicle at a current time, the observation variable set comprising a measured speed of the vehicle and a measured acceleration of the vehicle; determining first prediction values of each state variable in a state variable set of the vehicle at the current time based on a pre-established kinematic model corresponding to the vehicle and a Kalman filter, the kinematic model being used to indicate a kinematic relationship between the measured acceleration and the measured speed, jerk of the vehicle and slope acceleration of the vehicle, and the state variable set comprising the measured speed, the measured acceleration, the jerk and the slope acceleration; determining a first estimation value of the slope acceleration of the vehicle at the current time by using the Kalman filter based on each of the first prediction values and each of the observation values.

2. The method of determining ramp-up acceleration according to claim 1, wherein The method further comprises: determining a state equation based on the kinematic model, the state variable set corresponding to the current time and the state variable set corresponding to a first time, the first time being a previous time of the current time; determining a state matrix of the vehicle from the first time to the current time based on the state equation; determining the first prediction values of each state variable in the state variable set of the vehicle at the current time by using the Kalman filter to perform prior estimation based on the state matrix and second estimation values of each state variable in the state variable set of the vehicle at the first time.

3. The method of determining ramp-up acceleration according to claim 2, wherein The method further comprises: obtaining a process noise covariance at a second time and a posteriori estimation error covariance at the second time, the second time being a previous time of the first time; determining a process noise covariance at the first time based on the process noise covariance at the second time and the a posteriori estimation error covariance at the second time; determining a priori estimation error covariance at the current time based on the process noise covariance at the first time, a posteriori estimation error covariance at the first time and the state matrix; determining a Kalman gain at the current time based on the a priori estimation error covariance at the current time and a measurement noise covariance; determining the first estimation value of the slope acceleration of the vehicle at the current time by using the Kalman filter to perform a posteriori estimation based on the Kalman gain, each of the first prediction values and each of the observation values.

4. The method of determining ramp-up acceleration according to claim 3, wherein The method further comprises: obtaining a preset performance index and a preset adjustment step, the preset performance index being used to indicate a performance index of H-infinity control, and the preset adjustment step being used to indicate a step of adjusting the process noise covariance; determine a first adjustment amount based on the posterior estimation error covariance at the second time, the preset performance index, and the preset adjustment step; adjust the process noise covariance at the second time by using the first adjustment amount to determine a process noise covariance at the first time.

5. The method of determining ramp-up acceleration according to claim 3, wherein The determination of the Kalman gain at the current time based on the prior estimation error covariance at the current time and the measurement noise covariance comprises: obtain a set of historical observation values corresponding to the measured acceleration in a first sliding time window, the first sliding time window being located before the current time and being continuous in time with the current time; perform Fourier transform on the set of historical observation values and the observation value of the measured acceleration of the vehicle at the current time to obtain a total energy and a high frequency energy in a frequency domain corresponding to the measured acceleration; determine a second adjustment amount based on the total energy in the frequency domain and the high frequency energy; adjust the measurement noise covariance by using the second adjustment amount to obtain an adjusted measurement noise covariance; determine the Kalman gain at the current time based on the prior estimation error covariance at the current time and the adjusted measurement noise covariance.

6. The method of determining ramp-up acceleration according to claim 1, wherein The method further comprises: In the process of determining the first estimated value, obtain the target weight of the vehicle, the vehicle wheel end torque, the air resistance, the rolling resistance, and the longitudinal acceleration of the vehicle at the current time, and a pre-established dynamic model corresponding to the vehicle; determine a third estimated value of the slope acceleration of the vehicle at the current time by using the dynamic model based on the target weight, the vehicle wheel end torque, the air resistance, the rolling resistance, and the longitudinal acceleration; determine a target estimated value of the slope acceleration of the vehicle at the current time based on the first estimated value and the third estimated value.

7. The method of determining ramp-up acceleration according to claim 6, wherein The determination of the target estimated value of the slope acceleration of the vehicle at the current time based on the first estimated value and the third estimated value comprises: obtain first historical estimated values and second historical estimated values corresponding to each historical time in a second sliding time window, the second sliding time window being located before the current time and being continuous in time with the current time, the first historical estimated value being used to indicate an estimated value of the slope acceleration of the vehicle at the historical time determined by using the Kalman filter, and the second historical estimated value being used to indicate an estimated value of the slope acceleration of the vehicle at the historical time determined by using the dynamic model; determine a first standard deviation between all the first historical estimated values and the first estimated value, and determine a second standard deviation between all the second historical estimated values and the third estimated value; determine a target weight based on the first standard deviation and the second standard deviation; determine the target estimated value of the slope acceleration of the vehicle at the current time based on the first estimated value, the third estimated value, and the target weight.

8. A device for determining ramp-up acceleration, characterized in that comprise: An acquisition module is configured to acquire an observation value of each observation variable in an observation variable set of a vehicle at a current time, the observation variable set including a measured speed of the vehicle and a measured acceleration of the vehicle; A first determination module is configured to determine a first predicted value of each state variable in a state variable set of the vehicle at the current time based on a pre-established kinematic model corresponding to the vehicle and a Kalman filter, the kinematic model being used to indicate a kinematic relationship between the measured acceleration and the measured speed, a jerk of the vehicle, and a slope acceleration of the vehicle, the state variable set including the measured speed, the measured acceleration, the jerk, and the slope acceleration; A second determination module is configured to determine a first estimated value of the slope acceleration of the vehicle at the current time by using the Kalman filter based on each of the first predicted values and each of the observation values.

9. A vehicle characterized by comprising: The method comprises: A processor and a memory, the processor being configured to execute a determination program of a slope acceleration stored in the memory, so as to implement the determination method of the slope acceleration according to any one of claims 1-7.

10. A storage medium, characterized by The storage medium stores one or more programs, and the one or more programs are executable by one or more processors to implement the determination method of the slope acceleration according to any one of claims 1-7.

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