CN115959140B - Vehicle longitudinal resistance obtaining method and device based on Kalman filtering and vehicle - Google Patents
Vehicle longitudinal resistance obtaining method and device based on Kalman filtering and vehicle Download PDFInfo
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Abstract
The invention relates to the technical field of automatic driving, in particular to a vehicle longitudinal resistance obtaining method, device, medium and vehicle based on Kalman filtering, and aims to solve the problem of how to accurately estimate the longitudinal resistance of the whole vehicle of an automatic driving vehicle. For the purpose, the invention constructs the state representation of the longitudinal resistance of the whole vehicle in a prediction stage, constructs a process model according to the dynamics model related to the state representation, and obtains the predicted value of the state representation of the longitudinal resistance of the whole vehicle at the current moment based on the process model; and in the updating stage, according to preset conditions, selectively fusing the predicted value with an observed value expressed by the state of the longitudinal resistance of the whole vehicle at the current moment, updating the predicted value expressed by the state of the longitudinal resistance of the whole vehicle, and obtaining the longitudinal resistance of the whole vehicle at the current moment, thereby providing important reference information of longitudinal dynamics for an auxiliary driving system and assisting decision and control processes of automatic driving.
Description
Technical Field
The invention relates to the technical field of automatic driving, and particularly provides a vehicle longitudinal resistance acquisition method and device based on Kalman filtering, a medium and a vehicle.
Background
Advanced driving assistance functions are receiving more and more attention, and the use scenes are increasing with the progress of sensor and information technology, so that the functional experience is also improving continuously. Along with the expansion of the auxiliary driving coverage scene, more and more longitudinal disturbance such as deceleration strips, pavement pits, power exchange station V grooves, step parking spaces and the like can be encountered on low-speed longitudinal control. The control effect of the auxiliary driving system is directly affected by accurately estimating the longitudinal resistance of the whole vehicle caused by the longitudinal disturbance.
Accordingly, there is a need in the art for a new vehicle longitudinal resistance acquisition scheme for an autonomous vehicle to address the above-described problems.
Disclosure of Invention
The present invention has been made to overcome the above drawbacks, and provides a solution or at least partially solves the problem of how to accurately estimate the longitudinal resistance of the whole vehicle of an autonomous vehicle.
In a first aspect, the present invention provides a method for acquiring longitudinal resistance of a vehicle based on kalman filtering, the method comprising:
prediction stage:
constructing a state representation of the longitudinal resistance of the whole vehicle;
constructing a process model based on the state representation-related dynamics model;
acquiring a predicted value represented by the state of the longitudinal resistance of the whole vehicle at the current moment based on the process model;
updating:
based on preset conditions, selectively fusing the predicted value with the obtained observed value expressed by the state of the longitudinal resistance of the whole vehicle at the current moment, and updating the predicted value expressed by the state of the longitudinal resistance of the whole vehicle to obtain the longitudinal resistance of the whole vehicle at the current moment.
In one technical scheme of the vehicle longitudinal resistance obtaining method based on the Kalman filtering, the state representation comprises the longitudinal resistance of the whole vehicle and the longitudinal speed of the mass center;
the dynamics model is a vehicle longitudinal dynamics model.
In one technical scheme of the vehicle longitudinal resistance obtaining method based on the kalman filtering, the state representation further comprises an output shaft rotating speed of the main speed reducer and a tire longitudinal force;
the dynamics model also includes an electric axis longitudinal dynamics model.
In one aspect of the vehicle longitudinal resistance obtaining method based on kalman filtering, the control variable in the process model is an output shaft torque of a final drive of the vehicle.
In one aspect of the vehicle longitudinal resistance obtaining method based on kalman filtering, the observed value includes an output shaft rotation speed and a centroid longitudinal speed of the final drive.
In one technical scheme of the vehicle longitudinal resistance obtaining method based on kalman filtering, the method further comprises:
respectively acquiring the change gradients of the output shaft torque, the output shaft rotating speed and the centroid longitudinal speed;
comparing the change gradients of the output shaft torque, the output shaft rotating speed and the centroid longitudinal speed with corresponding gradient thresholds respectively;
when the change gradients of the output shaft torque, the output shaft rotating speed and the centroid longitudinal speed are all larger than the corresponding gradient threshold values, the obtained observed value expressed by the state of the whole vehicle longitudinal resistance at the current moment is subjected to low-pass filtering processing at a lower filtering cut-off frequency so as to reduce the updating speed, and otherwise, the obtained observed value is subjected to low-pass filtering processing at a higher filtering cut-off frequency so as to improve the updating speed.
In one technical scheme of the vehicle longitudinal resistance obtaining method based on kalman filtering, the method selectively fuses the predicted value and the obtained observed value represented by the state of the whole vehicle longitudinal resistance at the current moment based on the preset condition, and updates the predicted value represented by the state of the whole vehicle longitudinal resistance to obtain the whole vehicle longitudinal resistance at the current moment, and includes:
judging whether mechanical braking of the vehicle is interposed;
if yes, using the predicted value updated at the previous moment as the predicted value updated at the current moment to obtain the longitudinal resistance of the whole vehicle at the current moment; and/or
If not, updating the predicted value expressed by the state of the longitudinal resistance of the whole vehicle by using the fused result to obtain the longitudinal resistance of the whole vehicle at the current moment.
In one technical scheme of the vehicle longitudinal resistance obtaining method based on kalman filtering, the judging condition of the mechanical brake intervention is as follows:
the opening degree of a brake pedal of the vehicle is larger than a preset opening degree; or alternatively, the first and second heat exchangers may be,
the braking pressure of the vehicle is larger than a preset pressure value; or alternatively, the first and second heat exchangers may be,
the chassis brake system of the vehicle intervenes automatically.
In one technical scheme of the vehicle longitudinal resistance obtaining method based on kalman filtering, the method further comprises:
acquiring the braking distance after the mechanical braking intervention;
and when the braking distance is greater than a preset distance, resetting the predicted value updated at the current moment.
In one technical scheme of the vehicle longitudinal resistance obtaining method based on kalman filtering, the obtained observed value represented by the state of the whole vehicle longitudinal resistance at the current moment is obtained by performing low-pass filtering on the input observed value represented by the state of the whole vehicle longitudinal resistance so as to align phases of the observed values.
In a second aspect, a control device is provided, the control device comprising at least one processor and at least one storage device, the storage device being adapted to store a plurality of program codes, the program codes being adapted to be loaded and run by the processor to perform the kalman filter based vehicle longitudinal resistance acquisition method according to any one of the technical solutions of the kalman filter based vehicle longitudinal resistance acquisition method.
In a third aspect, a computer readable storage medium is provided, in which a plurality of program codes are stored, the program codes being adapted to be loaded and executed by a processor to perform the kalman filter-based vehicle longitudinal resistance acquisition method according to any one of the technical aspects of the kalman filter-based vehicle longitudinal resistance acquisition method.
In a fourth aspect, a vehicle is provided, which includes a control device according to the control device claim.
The technical scheme provided by the invention has at least one or more of the following beneficial effects:
in the technical scheme of implementing the invention, in a prediction stage, a state representation of the longitudinal resistance of the whole vehicle is constructed, a process model is constructed according to a dynamics model related to the state representation, and a predicted value of the state representation of the longitudinal resistance of the whole vehicle at the current moment is obtained based on the process model; in the updating stage, according to preset conditions, the predicted value and the observed value expressed by the state of the longitudinal resistance of the whole vehicle at the current moment are selectively fused to update the predicted value expressed by the state of the longitudinal resistance of the whole vehicle, so that the longitudinal resistance of the whole vehicle at the current moment is obtained. Through the configuration mode, the invention can accurately estimate the longitudinal resistance of the whole vehicle at the current moment of the vehicle based on Kalman filtering, thereby providing important reference information of longitudinal dynamics for an auxiliary driving system and assisting decision and control process of automatic driving.
Drawings
The present disclosure will become more readily understood with reference to the accompanying drawings. As will be readily appreciated by those skilled in the art: the drawings are for illustrative purposes only and are not intended to limit the scope of the present invention. Wherein:
FIG. 1 is a flow chart of the main steps of a Kalman filtering-based vehicle longitudinal resistance acquisition method according to one embodiment of the invention;
fig. 2 is a schematic diagram of the main constituent architecture of a kalman filter-based vehicle longitudinal resistance acquisition method according to an embodiment of the present invention.
Detailed Description
Some embodiments of the invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
In the description of the present invention, a "module," "processor" may include hardware, software, or a combination of both. A module may comprise hardware circuitry, various suitable sensors, communication ports, memory, or software components, such as program code, or a combination of software and hardware. The processor may be a central processor, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and/or signal processing functions. The processor may be implemented in software, hardware, or a combination of both. Non-transitory computer readable storage media include any suitable medium that can store program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, and the like. The term "a and/or B" means all possible combinations of a and B, such as a alone, B alone or a and B. The term "at least one A or B" or "at least one of A and B" has a meaning similar to "A and/or B" and may include A alone, B alone or A and B. The singular forms "a", "an" and "the" include plural referents.
Referring to fig. 1, fig. 1 is a schematic flow chart of main steps of a vehicle longitudinal resistance obtaining method based on kalman filtering according to an embodiment of the present invention. As shown in fig. 1, the vehicle longitudinal resistance acquiring method based on the kalman filter in the embodiment of the invention mainly includes the following steps S101 to S102.
Step S101: prediction stage:
step S1011: a state representation of the longitudinal resistance of the vehicle is constructed.
Step S1012: a process model is constructed based on the state representation-related dynamics model.
Step S1013: and acquiring a predicted value represented by the state of the longitudinal resistance of the whole vehicle at the current moment based on the process model.
In this embodiment, in the prediction stage of the kalman filter, a state representation of the longitudinal resistance of the whole vehicle of the vehicle may be constructed, a process model of the kalman filter may be constructed based on a dynamics model related to the state representation, and a predicted value of the state representation of the longitudinal resistance of the whole vehicle at the current moment may be obtained based on the process model. The state is expressed as a state parameter to be estimated in the Kalman filtering process. The process model is a model that estimates a predicted value at a current time based on a predicted value updated at a previous time.
In one embodiment, the status representation may include the overall vehicle longitudinal resistance and the centroid longitudinal speed; the dynamics model may be a longitudinal dynamics model of the vehicle.
In one embodiment, the status representation may further include an output shaft speed and tire longitudinal force of the final drive; the kinetic model may also include an electric axis longitudinal kinetic model.
In one embodiment, the control variable in the process model may be the output shaft torque of the final drive. Wherein the output shaft torque can be obtained by measuring a torque sensor.
In one embodiment, the final drive may include a front final drive and a rear final drive. The output shaft rotational speed may include a front final drive output shaft rotational speed and a rear final drive output shaft rotational speed. The output shaft torque may include a front final drive output shaft torque and a rear final drive output shaft torque. The tire longitudinal force may include a front tire longitudinal force and a rear axle tire longitudinal force.
In one embodiment, the dynamics model composed of the vehicle longitudinal dynamics model and the electric axis longitudinal dynamics model may be obtained according to the following formulas (1) to (6):
wherein, the liquid crystal display device comprises a liquid crystal display device,
the torque of the output shaft of the front main speed reducer is positive in driving, and the unit is Nm; />
The torque of the output shaft of the rear main speed reducer is positive in driving, and the unit is Nm; />
The unit is radps for the rotation speed of the output shaft of the front main speed reducer; />
The unit is radps for the rotation speed of the output shaft of the rear main speed reducer; />
Mps in units of centroid longitudinal velocity; />
The longitudinal force of the front axle tire is positive forwards, and the unit is N; />
The longitudinal force of the rear axle tire is positive forwards, and the unit is N; />
The longitudinal resistance of the whole vehicle is positive backward, and the unit is N; />
The unit is m, which is the radius of the tire; />
The unit is kg.m for front axle rotation inertia 2 ；
The unit is kg.m for the moment of inertia of the rear axle 2 ；/>
The weight of the whole car is kg.
Step S102: and updating.
Step S1021: based on preset conditions, selectively fusing the predicted value with the obtained observed value expressed by the state of the longitudinal resistance of the whole vehicle at the current moment, and updating the predicted value expressed by the state of the longitudinal resistance of the whole vehicle to obtain the longitudinal resistance of the whole vehicle at the current moment.
In this embodiment, the predicted value and the observed value represented by the state at the current moment may be selectively fused according to a preset condition, so as to update the predicted value represented by the state to obtain the longitudinal resistance of the whole vehicle at the current moment. The observed value is a value obtained by measurement or calculation according to the actual state of the vehicle.
In one embodiment, the observations may include an output shaft rotational speed and a center of mass longitudinal speed of the final drive. The rotation speed of the output shaft can be obtained through measurement of a speed sensor, and the longitudinal speed of the mass center can be obtained through difference of GPS (Global Positioning System ) signals.
In one embodiment, the front axle tire longitudinal force, the rear axle tire longitudinal force and the whole vehicle longitudinal resistance can be calculated through a dynamics model based on the rotation speed of the output shaft of the main speed reduction and the mass center longitudinal speed, and the calculated front axle tire longitudinal force, rear axle tire longitudinal force and whole vehicle longitudinal resistance are also used as the observation values of the state representation at the current moment.
In one embodiment, due to the fact that different observed values have the problems of time delay, signal noise and the like in the measuring process of the sensor, noise can be filtered out by low-pass filtering of the observed values represented by the input longitudinal resistance state of the whole vehicle, and phases of the observed values are aligned so as to facilitate subsequent fusion based on the observed values.
In one embodiment, the Kalman filter equation may be obtained by the following equations (7) to (11):
prediction stage:
updating:
wherein, the liquid crystal display device comprises a liquid crystal display device,
a predicted value represented by a state at time k; />
Is a state transition matrix; />
A predicted value represented by the updated state at time k-1; />
Is an input control matrix; />
Is the control variable at the moment k; />
The covariance matrix at the moment k; />
The covariance matrix after the update at the moment k-1; />
Transpose of the state transition matrix; />
Exciting a noise covariance for the process; />
The Kalman gain at time k; />
Is a state observation matrix; />
The state observation matrix is transposed; />
Is observed noise covariance; />
For the updated state at time kPredicted values of the state representation; />
An observation value represented by a state at time k; />
Is the updated covariance matrix at time k.
Based on the steps S101-S102, in the prediction stage, the embodiment of the invention constructs a state representation of the longitudinal resistance of the whole vehicle, constructs a process model according to a dynamics model related to the state representation, and obtains a predicted value of the state representation of the longitudinal resistance of the whole vehicle at the current moment based on the process model; in the updating stage, according to preset conditions, the predicted value and the observed value expressed by the state of the longitudinal resistance of the whole vehicle at the current moment are selectively fused to update the predicted value expressed by the state of the longitudinal resistance of the whole vehicle, so that the longitudinal resistance of the whole vehicle at the current moment is obtained. Through the configuration mode, the embodiment of the invention can accurately estimate the longitudinal resistance of the whole vehicle at the current moment of the vehicle based on Kalman filtering, thereby providing important reference information of longitudinal dynamics for an auxiliary driving system and assisting decision and control processes of automatic driving.
In one implementation manner of the embodiment of the present invention, the present invention may further include the following steps S103 to S105 in addition to the above step S101 and step S102:
step S103: and respectively acquiring the change gradients of the output shaft torque, the output shaft rotating speed and the centroid longitudinal speed.
Step S104: and comparing the gradients of the output shaft torque, the output shaft rotating speed and the centroid longitudinal speed with corresponding gradient thresholds respectively.
Step S105: when the change gradients of the output shaft torque, the output shaft rotating speed and the mass center longitudinal speed are all larger than the corresponding gradient threshold values, the obtained observed value expressed by the state of the whole vehicle longitudinal resistance at the current moment is subjected to low-pass filtering processing at a lower filtering cut-off frequency so as to reduce the updating speed, otherwise, the obtained observed value is subjected to low-pass filtering processing at a higher filtering cut-off frequency so as to improve the updating speed.
In this embodiment, filtering processing may be performed on the observed value represented by the state, that is, the gradient of the change of the output shaft torque, the output shaft rotational speed and the centroid longitudinal speed is calculated, and the calculated gradient of the change is compared with the gradient threshold corresponding to the calculated gradient, and if the gradient of the change of the output shaft torque, the gradient of the change of the output shaft rotational speed and the gradient of the centroid longitudinal speed are all greater than the gradient threshold corresponding to the gradient, the observed value represented by the state of the whole vehicle longitudinal resistance at the current moment may be subjected to low-pass filtering processing with a lower filtering cut-off frequency so as to reduce the update speed, or else, low-pass filtering processing is performed with a higher filtering cut-off frequency so as to increase the update speed, that is, the whole vehicle longitudinal resistance outputted by kalman filtering is allowed to be updated rapidly.
In one implementation of the embodiment of the present invention, step S1021 may further include the following steps S10211 to S10213:
step S10211: judging whether mechanical braking of the vehicle is interposed; if yes, go to step S10212; if not, go to step S10213.
Step S10212: and using the predicted value updated at the previous moment as the predicted value updated at the current moment to obtain the longitudinal resistance of the whole vehicle at the current moment.
Step S10213: and updating a predicted value expressed by the state of the longitudinal resistance of the whole vehicle by using the fused result to obtain the longitudinal resistance of the whole vehicle at the current moment.
In this embodiment, since there is an unknown friction resistance moment on the electric shaft after the mechanical brake intervention, the update of the kalman filter should be suspended at this time, and the predicted value updated at the previous time is taken as the predicted value updated at the current time.
In one embodiment, the judging condition of the mechanical brake intervention may include:
the opening degree of a brake pedal of the vehicle is larger than a preset opening degree; or alternatively, the first and second heat exchangers may be,
the braking pressure of the vehicle is greater than a preset pressure value; or alternatively, the first and second heat exchangers may be,
the chassis brake system of the vehicle is automatically interposed.
The preset opening and the preset pressure value can be set by a person skilled in the art according to the actual application requirements.
In one embodiment, after the mechanical braking intervention, the braking distance may be obtained, and when the braking distance is greater than the preset distance, the predicted value updated at the current time may be cleared.
In this embodiment, when the braking distance is greater than the preset distance, the predicted value updated at the current time may be cleared. The preset distance can be set by a person skilled in the art according to the actual application requirements.
In one embodiment, the braking distance may be obtained by integrating the vehicle speed and the sampling time.
In one embodiment, referring to fig. 2, fig. 2 is a schematic diagram of the main component architecture of a vehicle longitudinal resistance obtaining method based on kalman filtering according to an embodiment of the present invention. As shown in fig. 2, the observed value can be subjected to input pre-filtering processing, suspension and update of data fusion of kalman filtering are controlled based on the intervention of mechanical braking, and update speed is controlled through the gradient of change of the observed value so as to realize disturbance observation post-processing, thereby obtaining the longitudinal resistance of the whole vehicle at the current moment. The suspension of data fusion means that the predicted value updated at the previous moment is used as the predicted value updated at the current moment or the predicted value updated at the current moment is cleared; updating refers to updating the predicted value of the state representation of the longitudinal resistance of the whole vehicle using the fused result.
It should be noted that, although the foregoing embodiments describe the steps in a specific order, it will be understood by those skilled in the art that, in order to achieve the effects of the present invention, the steps are not necessarily performed in such an order, and may be performed simultaneously (in parallel) or in other orders, and these variations are within the scope of the present invention.
It will be appreciated by those skilled in the art that the present invention may implement all or part of the above-described methods according to the above-described embodiments, or may be implemented by means of a computer program for instructing relevant hardware, where the computer program may be stored in a computer readable storage medium, and where the computer program may implement the steps of the above-described embodiments of the method when executed by a processor. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable storage medium may include: any entity or device, medium, usb disk, removable hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier wave signals, telecommunications signals, software distribution media, and the like capable of carrying the computer program code. It should be noted that the computer readable storage medium may include content that is subject to appropriate increases and decreases as required by jurisdictions and by jurisdictions in which such computer readable storage medium does not include electrical carrier signals and telecommunications signals.
Further, the invention also provides a control device. In one control device embodiment according to the present invention, the control device includes a processor and a storage device, the storage device may be configured to store a program for executing the kalman filter-based vehicle longitudinal resistance acquisition method of the above-described method embodiment, and the processor may be configured to execute the program in the storage device, including, but not limited to, the program for executing the kalman filter-based vehicle longitudinal resistance acquisition method of the above-described method embodiment. For convenience of explanation, only those portions of the embodiments of the present invention that are relevant to the embodiments of the present invention are shown, and specific technical details are not disclosed, please refer to the method portions of the embodiments of the present invention.
The control device in the embodiment of the invention can be a control device formed by various electronic devices. In some possible embodiments, the control device may include a plurality of memory devices and a plurality of processors. While the program for performing the kalman filter based vehicle longitudinal resistance obtaining method of the above method embodiment may be divided into a plurality of sub-programs, each of which may be loaded and executed by a processor to perform the different steps of the kalman filter based vehicle longitudinal resistance obtaining method of the above method embodiment, respectively. Specifically, each of the subroutines may be respectively stored in different storage devices, and each of the processors may be configured to execute the programs in one or more storage devices to collectively implement the kalman filter-based vehicle longitudinal resistance acquisition method of the above method embodiment, that is, each of the processors executes different steps of the kalman filter-based vehicle longitudinal resistance acquisition method of the above method embodiment to collectively implement the kalman filter-based vehicle longitudinal resistance acquisition method of the above method embodiment.
The plurality of processors may be processors disposed on the same device, and for example, the control means may be a high-performance device composed of a plurality of processors, and the plurality of processors may be processors disposed on the high-performance device. In addition, the plurality of processors may be processors disposed on different devices, for example, the control apparatus may be a server cluster, and the plurality of processors may be processors on different servers in the server cluster.
Further, the invention also provides a computer readable storage medium. In one embodiment of the computer-readable storage medium according to the present invention, the computer-readable storage medium may be configured to store a program for performing the kalman filter-based vehicle longitudinal resistance acquisition method of the above-described method embodiment, which may be loaded and executed by a processor to implement the kalman filter-based vehicle longitudinal resistance acquisition method. For convenience of explanation, only those portions of the embodiments of the present invention that are relevant to the embodiments of the present invention are shown, and specific technical details are not disclosed, please refer to the method portions of the embodiments of the present invention. The computer readable storage medium may be a storage device including various electronic devices, and optionally, the computer readable storage medium in the embodiments of the present invention is a non-transitory computer readable storage medium.
Further, the invention also provides a vehicle. In one vehicle embodiment according to the invention, the vehicle may comprise control means in an embodiment of the control means.
Further, it should be understood that, since the respective modules are merely set to illustrate the functional units of the apparatus of the present invention, the physical devices corresponding to the modules may be the processor itself, or a part of software in the processor, a part of hardware, or a part of a combination of software and hardware. Accordingly, the number of individual modules in the figures is merely illustrative.
Those skilled in the art will appreciate that the various modules in the apparatus may be adaptively split or combined. Such splitting or combining of specific modules does not cause the technical solution to deviate from the principle of the present invention, and therefore, the technical solution after splitting or combining falls within the protection scope of the present invention.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will fall within the scope of the present invention.
Claims (12)
1. A method for acquiring longitudinal resistance of a vehicle based on kalman filtering, the method comprising:
prediction stage:
constructing a state representation of the longitudinal resistance of the whole vehicle;
constructing a process model based on the state representation-related dynamics model;
acquiring a predicted value represented by the state of the longitudinal resistance of the whole vehicle at the current moment based on the process model;
updating:
based on preset conditions, selectively fusing the predicted value with the obtained observed value expressed by the state of the longitudinal resistance of the whole vehicle at the current moment, and updating the predicted value expressed by the state of the longitudinal resistance of the whole vehicle to obtain the longitudinal resistance of the whole vehicle at the current moment;
based on preset conditions, selectively fusing the predicted value with the obtained observed value represented by the state of the longitudinal resistance of the whole vehicle at the current moment, and updating the predicted value represented by the state of the longitudinal resistance of the whole vehicle to obtain the longitudinal resistance of the whole vehicle at the current moment, wherein the method comprises the following steps:
judging whether mechanical braking of the vehicle is interposed;
if yes, using the predicted value updated at the previous moment as the predicted value updated at the current moment to obtain the longitudinal resistance of the whole vehicle at the current moment;
if not, updating the predicted value expressed by the state of the longitudinal resistance of the whole vehicle by using the fused result to obtain the longitudinal resistance of the whole vehicle at the current moment.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the state representation comprises the longitudinal resistance and the longitudinal speed of the mass center of the whole vehicle;
the dynamics model is a vehicle longitudinal dynamics model.
3. The method of claim 2, wherein the step of determining the position of the substrate comprises,
the state representation further includes an output shaft speed and a tire longitudinal force of the final drive;
the dynamics model also includes an electric axis longitudinal dynamics model.
4. The method of claim 3, wherein the step of,
the control variable in the process model is the output shaft torque of the final drive of the vehicle.
5. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the observations include an output shaft rotational speed and a centroid longitudinal speed of the final drive.
6. The method of claim 5, wherein the method further comprises:
respectively acquiring the change gradients of the output shaft torque, the output shaft rotating speed and the centroid longitudinal speed of the main speed reducer;
comparing the change gradients of the output shaft torque, the output shaft rotating speed and the centroid longitudinal speed with corresponding gradient thresholds respectively;
when the change gradients of the output shaft torque, the output shaft rotating speed and the centroid longitudinal speed are all larger than the corresponding gradient threshold values, the obtained observed value expressed by the state of the whole vehicle longitudinal resistance at the current moment is subjected to low-pass filtering processing at a lower filtering cut-off frequency so as to reduce the updating speed, and otherwise, the obtained observed value is subjected to low-pass filtering processing at a higher filtering cut-off frequency so as to improve the updating speed.
7. The method according to claim 1, wherein the judging condition of the mechanical brake intervention is:
the opening degree of a brake pedal of the vehicle is larger than a preset opening degree; or alternatively, the first and second heat exchangers may be,
the braking pressure of the vehicle is larger than a preset pressure value; or alternatively, the first and second heat exchangers may be,
the chassis brake system of the vehicle intervenes automatically.
8. The method according to claim 1 or 7, characterized in that the method further comprises:
acquiring the braking distance after the mechanical braking intervention;
and when the braking distance is greater than a preset distance, resetting the predicted value updated at the current moment.
9. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the obtained observation value represented by the state of the longitudinal resistance of the whole vehicle at the current moment is obtained by carrying out low-pass filtering on the input observation value represented by the state of the longitudinal resistance of the whole vehicle so as to align phases of the observation values.
10. A control device comprising at least one processor and at least one storage device, the storage device being adapted to store a plurality of program code, characterized in that the program code is adapted to be loaded and executed by the processor to perform the method of any one of claims 1 to 9.
11. A computer readable storage medium having stored therein a plurality of program codes, characterized in that the program codes are adapted to be loaded and executed by a processor to perform the method of any one of claims 1 to 9.
12. A vehicle, characterized in that the vehicle comprises the control device of claim 10.
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