CN115959140A - Kalman filtering-based vehicle longitudinal resistance acquisition method and device and vehicle - Google Patents
Kalman filtering-based vehicle longitudinal resistance acquisition method and device and vehicle Download PDFInfo
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Abstract
The invention relates to the technical field of automatic driving, in particular to a method, a device, a medium and a vehicle for acquiring longitudinal resistance of a 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 method comprises the steps of constructing state representation of the longitudinal resistance of the whole vehicle at a prediction stage, constructing a process model according to a dynamic model related to the state representation, and obtaining a 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, the predicted value and the observed value of the state representation of the longitudinal resistance of the whole vehicle at the current moment are selectively fused, the predicted value of the state representation of the longitudinal resistance of the whole vehicle is updated, the longitudinal resistance of the whole vehicle at the current moment is obtained, important reference information of longitudinal dynamics is provided for an auxiliary driving system, and the decision and control process of automatic driving is assisted.
Description
Technical Field
The invention relates to the technical field of automatic driving, and particularly provides a vehicle longitudinal resistance acquisition method, device, medium and vehicle based on Kalman filtering.
Background
Advanced driving assistance functions are receiving more and more attention, the use scenes of the advanced driving assistance functions are continuously increased along with the progress of sensors and information technology, and the functional experience of the advanced driving assistance functions is continuously improved. Along with the expansion of the coverage scene of the assistant driving, more and more longitudinal disturbances can be encountered on the aspect of low-speed longitudinal control, such as speed bumps, road depressions, power station changing V-shaped grooves, step parking spaces and the like. The accurate estimation of the longitudinal resistance of the whole vehicle caused by the longitudinal disturbance directly influences the control effect of the auxiliary driving system.
Accordingly, there is a need in the art for a new overall longitudinal resistance acquisition solution for autonomous vehicles to address the above-mentioned problems.
Disclosure of Invention
In order to overcome the above drawbacks, the present invention has been developed to provide a solution or at least a partial solution to the problem of how to accurately estimate the overall vehicle longitudinal resistance of an autonomous vehicle.
In a first aspect, the invention provides a vehicle longitudinal resistance acquisition method based on kalman filtering, the method comprising:
a prediction stage:
constructing a state representation of the longitudinal resistance of the whole vehicle;
constructing a process model based on the state representation-related kinetic model;
obtaining a predicted value of the state representation of the longitudinal resistance of the whole vehicle at the current moment based on the process model;
and (3) an updating stage:
and selectively fusing the predicted value and the obtained observed value of the state representation of the longitudinal resistance of the whole vehicle at the current moment based on a preset condition, and updating the predicted value of the state representation of the longitudinal resistance of the whole vehicle to obtain the longitudinal resistance of the whole vehicle at the current moment.
In one technical solution of the vehicle longitudinal resistance obtaining method based on kalman filter, the state representation includes a vehicle longitudinal resistance and a centroid longitudinal speed;
the dynamic model is a vehicle longitudinal dynamic model.
In one technical solution of the vehicle longitudinal resistance obtaining method based on kalman filter, the state representation further includes an output shaft rotation speed of a final drive and a tire longitudinal force;
the kinetic model further comprises an electric-axis longitudinal kinetic model.
In one technical solution of the method for acquiring longitudinal resistance of a vehicle based on kalman filtering, the control variable in the process model is an output shaft torque of a main speed reducer of the vehicle.
In one technical solution 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 main reducer.
In an embodiment of the method for acquiring longitudinal resistance of a vehicle based on kalman filtering, the method further includes:
respectively acquiring the change gradients of the output shaft torque, the output shaft rotating speed and the mass center longitudinal speed;
comparing the change gradients of the output shaft torque, the output shaft rotational speed and the centroid longitudinal speed with corresponding gradient thresholds respectively;
and when the change gradients of the output shaft torque, the output shaft rotating speed and the centroid longitudinal speed are all larger than corresponding gradient threshold values, performing low-pass filtering processing on the obtained observed value represented by the state of the whole vehicle longitudinal resistance at the current moment by using a lower filtering cut-off frequency to reduce the updating speed, or performing low-pass filtering processing by using a higher filtering cut-off frequency to improve the updating speed.
In one technical solution of the method for acquiring a longitudinal resistance of a vehicle based on kalman filtering, the selectively fusing the predicted value and the observed value of the state representation of the longitudinal resistance of the entire vehicle at the current time based on a preset condition, and updating the predicted value of the state representation of the longitudinal resistance of the entire vehicle to obtain the longitudinal resistance of the entire vehicle at the current time includes:
judging whether mechanical braking of the vehicle intervenes;
if so, using the updated predicted value at the last moment as the updated predicted value at the current moment to obtain the longitudinal resistance of the whole vehicle at the current moment; and/or
And if not, updating the predicted value of the state representation of the longitudinal resistance of the whole vehicle by using the fused result so as to obtain the longitudinal resistance of the whole vehicle at the current moment.
In one technical solution of the method for acquiring longitudinal resistance of a vehicle based on kalman filtering, the determination condition of mechanical braking intervention is as follows:
the opening degree of a brake pedal of the vehicle is greater than a preset opening degree; or the like, or, alternatively,
the brake pressure of the vehicle is greater than a preset pressure value; or the like, or, alternatively,
the chassis braking system of the vehicle intervenes automatically.
In an embodiment of the method for acquiring longitudinal resistance of a vehicle based on kalman filtering, the method further includes:
obtaining the braking distance after the mechanical braking intervention;
and when the braking distance is greater than a preset distance, resetting the updated predicted value at the current moment.
In one technical solution of the vehicle longitudinal resistance acquiring method based on kalman filtering, the acquired observation value represented by the state of the vehicle longitudinal resistance at the current time is obtained by low-pass filtering the input observation value represented by the state of the vehicle longitudinal resistance so as to align the phases of the observation values.
In a second aspect, a control device is provided, comprising at least one processor and at least one memory device 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 above-mentioned aspects of the kalman filter based vehicle longitudinal resistance acquisition method.
In a third aspect, a computer readable storage medium is provided, having stored therein a plurality of program codes adapted to be loaded and run by a processor to execute the kalman filter based vehicle longitudinal resistance acquisition method according to any one of the above-mentioned aspects of the kalman filter based vehicle longitudinal resistance acquisition method.
In a fourth aspect, a vehicle is provided that includes the control device of the control device aspect.
One or more technical schemes of the invention at least have one or more of the following beneficial effects:
in the technical scheme of implementing the method, in the prediction stage, the state representation of the longitudinal resistance of the whole vehicle is constructed, a process model is constructed according to a dynamic model related to the state representation, and the 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; and in the updating stage, selectively fusing the predicted value and the observed value of the state representation of the longitudinal resistance of the whole vehicle at the current moment according to preset conditions to update the predicted value of the state representation of the longitudinal resistance of the whole vehicle, so as to obtain the longitudinal resistance of the whole vehicle at the current moment. Through the configuration mode, the method 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 the decision and control process of automatic driving.
Drawings
The disclosure of the present invention will become more readily understood with reference to the accompanying drawings. As is readily understood by those skilled in the art: these drawings are for illustrative purposes only and are not intended to constitute a limitation on the scope of the present invention. Wherein:
FIG. 1 is a flow chart illustrating 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 a main component 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 only for explaining the technical principle 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" or "processor" may include hardware, software, or a combination of both. A module may comprise hardware circuitry, various suitable sensors, communication ports, memory, may comprise software components such as program code, and may be a combination of software and hardware. The processor may be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and/or signal processing functionality. The processor may be implemented in software, hardware, or a combination thereof. 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" denotes 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" means similar to "A and/or B" and may include only A, only B, or both A and B. The singular forms "a", "an" and "the" may include the plural forms as well.
Referring to fig. 1, fig. 1 is a flow chart illustrating main steps of a kalman filter-based vehicle longitudinal resistance acquisition method according to an embodiment of the present invention. As shown in fig. 1, the kalman filter-based vehicle longitudinal resistance obtaining method in the embodiment of the present invention mainly includes the following steps S101 to S102.
Step S101: a prediction stage:
step S1011: and constructing a state representation of the longitudinal resistance of the whole vehicle of the vehicle.
Step S1012: a process model is constructed based on the state representation-related kinetic model.
Step S1013: and obtaining 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 this embodiment, in a prediction stage of kalman filtering, a state representation of the entire vehicle longitudinal resistance of the vehicle may be constructed, a process model of the kalman filtering may be constructed based on a dynamic model related to the state representation, and a predicted value of the state representation of the entire vehicle longitudinal resistance of the vehicle at the current time may be obtained based on the process model. Wherein, the state is represented as a state parameter needing to be estimated in the Kalman filtering process. The process model is a model for estimating the predicted value at the current time based on the predicted value updated at the previous time.
In one embodiment, the state representation may include vehicle longitudinal drag and center of mass longitudinal velocity; the dynamical model may be a longitudinal dynamical model of the vehicle.
In one embodiment, the state representation may further include an output shaft speed of the final drive and a tire longitudinal force; the kinetic model may also include an electric-axis longitudinal kinetic model.
In one embodiment, the control variable in the process model may be an output shaft torque of the final drive. Wherein the output shaft torque may be measured by a torque sensor.
In one embodiment, the final drive may include a front final drive and a rear final drive. The output shaft speed may include a front final drive output shaft speed and a rear final drive output shaft 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 dynamic model composed of the vehicle longitudinal dynamic model and the electric axle longitudinal dynamic model may be obtained according to the following equations (1) to (6):
wherein the content of the first and second substances,
the torque of an output shaft of the front main speed reducer is positive, and the unit is Nm; />
The torque of an output shaft of the rear main reducer is positive, and the unit is Nm; />
The unit is radps for the rotating speed of the output shaft of the front main reducer; />
The unit is radps for the rotating speed of the output shaft of the rear main reducer; />
Is the centroid longitudinal velocity, in mps; />
Is the longitudinal force of the front axle tire, the forward direction is positive, and the unit is N; />
Is the longitudinal force of the rear axle tire, positive forwards, and the unit is N; />
The longitudinal resistance of the whole vehicle is positive backwards, and the unit is N; />
Is the tire radius in m; />
Is the rotary inertia of the front axle in kg.m 2 ；
Is the rotational inertia of the rear axle in kg.m 2 ；/>
Is the quality of the whole vehicleThe amount is in kg.
Step S102: and (5) an updating stage.
Step S1021: and based on preset conditions, selectively fusing the predicted value and the obtained observed value of the state representation of the longitudinal resistance of the whole vehicle at the current moment, and updating the predicted value of the state representation 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 time may be selectively fused according to a preset condition, so as to update the predicted value represented by the state, and obtain the longitudinal resistance of the whole vehicle at the current time. The observation value is a value measured or calculated from an actual state of the vehicle.
In one embodiment, the observed values may include an output shaft speed and a center of mass longitudinal speed of the final drive. The rotational speed of the output shaft can be obtained by measuring with a speed sensor, and the longitudinal speed of the center of mass can be obtained by differentiating a GPS (Global Positioning System) signal.
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 dynamic model based on the output shaft rotating speed and the mass center longitudinal speed of the main speed reduction, and the front axle tire longitudinal force, the rear axle tire longitudinal force and the whole vehicle longitudinal resistance obtained through calculation are also used as observed values of the state representation at the current moment.
In one embodiment, because different observed values have the problems of time delay, signal noise and the like in the process of measurement through the sensor, the observed values expressed by the input longitudinal resistance state of the whole vehicle can be subjected to low-pass filtering, so that the noise is filtered, and the phases of the observed values are aligned, so that the subsequent fusion based on the observed values is facilitated.
In one embodiment, the kalman filter equation may be obtained by the following equations (7) to (11):
a prediction stage:
and (3) an updating stage:
wherein the content of the first and second substances,
a predicted value for state representation at time k; />
Is a state transition matrix; />
A predicted value of the updated state representation at the time of k-1; />
Inputting a control matrix; />
Is a control variable at the moment k; />
Is a covariance matrix at the k moment; />
The covariance matrix is updated at the time of k-1; />
Is a transpose of the state transition matrix; />
Exciting the noise covariance for the process; />
Kalman gain at time k; />
A state observation matrix; />
Is a transpose of the state observation matrix; />
To observe the noise covariance; />
A predicted value for the updated state representation at time k; />
An observed value represented by a state at time k; />
Is the updated covariance matrix at time k.
Based on the steps S101 to S102, in the prediction stage, the state representation of the longitudinal resistance of the whole vehicle is constructed, a process model is constructed according to a dynamic model related to the state representation, and the 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; and in the updating stage, selectively fusing the predicted value and the observed value of the state representation of the longitudinal resistance of the whole vehicle at the current moment according to preset conditions to update the predicted value of the state representation of the longitudinal resistance of the whole vehicle, so as to obtain the longitudinal resistance of the whole vehicle at the current moment. 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 the decision and control process of automatic driving.
In one implementation of the embodiment of the present invention, in addition to the above step S101 and step S102, the present invention may further include the following steps S103 to S105:
step S103: and respectively obtaining the change gradients of the torque of the output shaft, the rotating speed of the output shaft and the longitudinal speed of the mass center.
Step S104: and respectively comparing the change gradients of the output shaft torque, the output shaft rotating speed and the centroid longitudinal speed with corresponding gradient threshold values.
Step S105: and when the change gradients of the torque of the output shaft, the rotating speed of the output shaft and the longitudinal speed of the mass center are all larger than corresponding gradient threshold values, performing low-pass filtering processing on the observed value represented by the state of the longitudinal resistance of the whole vehicle at the current moment by using a lower filtering cut-off frequency to reduce the updating speed, or performing low-pass filtering processing by using a higher filtering cut-off frequency to improve the updating speed.
In this embodiment, the observed values represented by the states may be subjected to filtering processing, that is, variation gradients of the output shaft torque, the output shaft rotational speed, and the centroid longitudinal speed are calculated, and the calculated variation gradients are respectively compared with the gradient thresholds corresponding thereto, and if the variation gradients of the output shaft torque, the output shaft rotational speed, and the centroid longitudinal speed are all greater than the gradient thresholds corresponding thereto, the observed values represented by the states of the entire vehicle longitudinal resistance at the current time may be subjected to low-pass filtering processing at a lower filtering cut-off frequency to reduce the updating speed, otherwise, the observed values represented by the states of the entire vehicle longitudinal resistance at the current time may be subjected to low-pass filtering processing at a higher filtering cut-off frequency to increase the updating speed, that is, the entire vehicle longitudinal resistance output by the kalman filtering is allowed to be updated quickly.
In one implementation of the embodiment of the present invention, the step S1021 may further include the following steps S10211 to S10213:
step S10211: judging whether mechanical braking of the vehicle intervenes; 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 the predicted value of the state representation of the longitudinal resistance of the whole vehicle by using the fused result so as to obtain the longitudinal resistance of the whole vehicle at the current moment.
In the present embodiment, since there is an unknown frictional resistance torque on the electric axis after the intervention of the mechanical brake, the update of the kalman filter should be suspended, and the predicted value updated at the previous time is used as the predicted value updated at the current time.
In one embodiment, the determination condition of the mechanical brake intervention may include:
the opening degree of a brake pedal of the vehicle is greater than a preset opening degree; or the like, or, alternatively,
the brake pressure of the vehicle is greater than a preset pressure value; or the like, or, alternatively,
the chassis brake system of the vehicle intervenes automatically.
The preset opening and the preset pressure value can be set by those skilled in the art according to the requirements of practical application.
In one embodiment, after the mechanical braking is involved, 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 those skilled in the art according to the requirements of practical application.
In one embodiment, the braking distance may be obtained by integrating the vehicle speed and the sampling time.
In one implementation, referring to fig. 2, fig. 2 is a schematic diagram of a main component architecture of a kalman filtering-based vehicle longitudinal resistance acquisition method according to an implementation of an embodiment of the present invention. As shown in fig. 2, the observation value may be subjected to an input filtering pre-processing, based on whether mechanical braking is involved, suspension and update of data fusion of kalman filtering are controlled, and an update speed is controlled by a change gradient of the observation value to realize disturbance observation post-processing, so as to obtain a longitudinal resistance of the entire vehicle at the current time. The suspension of the data fusion refers to using the predicted value updated at the last moment as the updated predicted value at the current moment or clearing the updated predicted value at the current moment; and updating means updating the predicted value of the state representation of the longitudinal resistance of the whole vehicle by using the fused result.
It should be noted that, although the foregoing embodiments describe each step in a specific sequence, those skilled in the art can understand that, in order to achieve the effect of the present invention, different steps do not have to be executed in such a sequence, and they may be executed simultaneously (in parallel) or in other sequences, and these changes are all within the scope of the present invention.
It will be understood by those skilled in the art that all or part of the flow of the method according to the above-described embodiment may be implemented by a computer program, which may be stored in a computer-readable storage medium and used to implement the steps of the above-described embodiments of the method when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying said computer program code, medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunications signal, software distribution medium, or the like. It should be noted that the computer readable storage medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable storage media that does not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
Furthermore, the invention also provides a control device. In an embodiment of the control device according to the present invention, the control device comprises 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 method embodiment, and the processor may be configured to execute a program in the storage device, the program including but not limited to the program for executing the kalman filter based vehicle longitudinal resistance acquisition method of the above method embodiment. For convenience of explanation, only the parts related to the embodiments of the present invention are shown, and details of the specific techniques are not disclosed.
The control device in the embodiment of the present invention may be a control device apparatus formed including various electronic apparatuses. In some possible embodiments, the control device may include a plurality of storage devices and a plurality of processors. The program for executing the kalman filtering based vehicle longitudinal resistance obtaining method according to the foregoing method embodiment may be divided into a plurality of sub-programs, and each sub-program may be loaded and run by a processor to execute different steps of the kalman filtering based vehicle longitudinal resistance obtaining method according to the foregoing method embodiment. Specifically, each piece of subroutine may be stored in a different storage device, and each processor may be configured to execute one or more programs in the storage devices to jointly implement the kalman filter based vehicle longitudinal resistance acquisition method according to the above method embodiment, that is, each processor executes different steps of the kalman filter based vehicle longitudinal resistance acquisition method according to the above method embodiment to jointly implement the kalman filter based vehicle longitudinal resistance acquisition method according to the above method embodiment.
The plurality of processors may be processors disposed on the same device, for example, the control apparatus may be a high-performance device composed of a plurality of processors, and the plurality of processors may be processors configured on the high-performance device. The plurality of processors may be processors disposed on different devices, for example, the control device 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 computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium may be configured to store a program for executing the kalman filter based vehicle longitudinal resistance acquisition method of the above method embodiment, and the program may be loaded and executed by a processor to implement the kalman filter based vehicle longitudinal resistance acquisition method. For convenience of explanation, only the parts related to the embodiments of the present invention are shown, and details of the specific techniques are not disclosed. The computer readable storage medium may be a storage device formed by including various electronic devices, and optionally, the computer readable storage medium is a non-transitory computer readable storage medium in the embodiment of the present invention.
Further, the invention also provides a vehicle. In one vehicle embodiment according to the invention, the vehicle may comprise the control device of the control device embodiment.
Further, it should be understood that, since the modules are only configured to illustrate the functional units of the apparatus of the present invention, the corresponding physical devices of the modules may be the processor itself, or a part of software, a part of hardware, or a part of a combination of software and hardware in the processor. Thus, 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 solutions to deviate from the principle of the present invention, and therefore, the technical solutions after splitting or combining will fall within the protection scope of the present invention.
So far, the technical solutions of the present invention have 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 the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
Claims (13)
1. A Kalman filtering-based vehicle longitudinal resistance acquisition method is characterized by comprising the following steps:
a prediction stage:
constructing a state representation of the overall longitudinal resistance of the vehicle;
constructing a process model based on the state representation-related kinetic model;
obtaining a predicted value of the state representation of the longitudinal resistance of the whole vehicle at the current moment based on the process model;
and (3) an updating stage:
and selectively fusing the predicted value and the obtained observed value of the state representation of the longitudinal resistance of the whole vehicle at the current moment based on a preset condition, and updating the predicted value of the state representation of the longitudinal resistance of the whole vehicle to obtain the longitudinal resistance of the whole vehicle at the current moment.
2. The method of claim 1,
the state representation comprises the longitudinal resistance and the mass center longitudinal speed of the whole vehicle;
the dynamic model is a vehicle longitudinal dynamic model.
3. The method of claim 2,
the state representation further comprises an output shaft rotation speed of a main speed reducer and a tire longitudinal force;
the kinetic model further comprises an electric-axis longitudinal kinetic model.
4. The method of claim 3,
the control variable in the process model is an output shaft torque of a final drive of the vehicle.
5. The method of claim 1,
the observed values include the output shaft speed and the centroid longitudinal speed of the main reducer.
6. The method of claim 5, further comprising:
respectively acquiring the change gradients of the torque of an output shaft, the rotating speed of the output shaft and the longitudinal speed of the mass center;
comparing the change gradients of the output shaft torque, the output shaft rotational speed and the centroid longitudinal speed with corresponding gradient thresholds respectively;
and when the change gradients of the output shaft torque, the output shaft rotating speed and the centroid longitudinal speed are all larger than corresponding gradient threshold values, performing low-pass filtering processing on the obtained observed value represented by the state of the whole vehicle longitudinal resistance at the current moment by using a lower filtering cut-off frequency to reduce the updating speed, or performing low-pass filtering processing by using a higher filtering cut-off frequency to improve the updating speed.
7. The method according to any one of claims 1-6, wherein the selectively fusing the predicted value and the obtained observed value of the state representation of the longitudinal resistance of the whole vehicle at the current moment based on the preset condition to update the predicted value of the state representation of the longitudinal resistance of the whole vehicle to obtain the longitudinal resistance of the whole vehicle at the current moment comprises:
judging whether mechanical braking of the vehicle intervenes;
if so, using the updated predicted value at the last moment as the updated predicted value at the current moment to obtain the longitudinal resistance of the whole vehicle at the current moment; and/or
And if not, updating the predicted value of the state representation of the longitudinal resistance of the whole vehicle by using the fused result so as to obtain the longitudinal resistance of the whole vehicle at the current moment.
8. The method according to claim 7, wherein the determination condition of the mechanical brake intervention is:
the opening degree of a brake pedal of the vehicle is greater than a preset opening degree; or the like, or, alternatively,
the brake pressure of the vehicle is greater than a preset pressure value; or the like, or, alternatively,
the chassis braking system of the vehicle intervenes automatically.
9. The method of claim 8, further comprising:
obtaining the braking distance after the mechanical braking intervention;
and when the braking distance is greater than a preset distance, clearing the updated predicted value at the current moment.
10. The method of claim 1,
and the obtained observed value represented by the state of the longitudinal resistance of the whole vehicle at the current moment is obtained by low-pass filtering the input observed value represented by the state of the longitudinal resistance of the whole vehicle so as to align the phases of the observed values.
11. A control device comprising at least one processor and at least one memory device, said memory device being adapted to store a plurality of program codes, characterized in that said program codes are adapted to be loaded and run by said processor to perform the method according to any of claims 1 to 10.
12. 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 run by a processor to perform the method of any of claims 1 to 10.
13. A vehicle characterized by comprising the control apparatus of claim 11.
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