Vehicle center of mass speed estimation method, device, electronic device and storage medium

Abstract

The present application relates to the field of software algorithm technology, and more particularly to a vehicle center of mass speed estimation method, device, electronic device, and storage medium, which includes: obtaining the current driving condition and current operating data of the vehicle to be estimated, determining an observer gain adjustment strategy based on the current driving condition, adjusting a preset state observer based on the observer gain adjustment strategy to obtain a target state observer, and inputting the current operating data into the target state observer to obtain the center of mass speed of the vehicle to be estimated. This solves the problems of large deviations in calculation results of simple vehicle speed estimation methods in related technologies, as well as large modeling errors, difficulty in convergence, difficulty in calibration, difficulty in model calibration, low parameter accuracy, and poor model stability and robustness of accurate vehicle speed estimation methods. By utilizing a powertrain model and combining it with a vehicle kinematic model to construct a low-order linear vehicle model, the stability and robustness of the vehicle center of mass estimation model are improved.

Description

Technical Field

[0001] The present application relates to the field of software algorithm technology, and in particular to a vehicle center of mass and speed estimation method, device, electronic device and storage medium. Background Art

[0002] With the development of new energy vehicles, vehicle control functions are becoming more and more abundant, such as multi-power source energy management, multi-mode / gear switching, multi-objective multi-power source torque collaborative control, etc. Therefore, the dependence on the reference vehicle speed in the field of vehicle control is also increasing. In the field of vehicle control, the vehicle speed estimation problem is one of the key issues in the vehicle motion state estimation problem.

[0003] With the extensive research and application of vehicle information electronic technology and body stability control technology, a variety of body stability controller products have been mass-produced and launched. However, they are generally adapted and installed in the main vehicle models of OEMs in the form of black boxes. The vehicle parameter estimation information is not output to the outside and is limited to its own functional use. In addition, the mass production development of autonomous vehicle stability systems is still in its infancy, with a low overall market share. In addition, the estimation of key parameters such as vehicle driving status, such as center of mass speed, often relies on sensors such as gyroscopes / acceleration, yaw angular velocity, and wheel speed. The localization rate of these sensors is also relatively low. Therefore, it is very necessary to strengthen independent research and product application practice.

[0004] In related technologies, high-order linear or nonlinear models (such as seven-degree-of-freedom or two-degree-of-freedom vehicle models) that combine wheel speed averages, vehicle kinematics, and tire dynamics are commonly used to estimate vehicle speed and related parameters.

[0005] However, the vehicle speed estimation method in the above-mentioned related art has the following problems:

[0006] (1) The reference speed obtained by taking the average wheel speed is the vehicle speed signal released to the vehicle bus by the vehicle stability control system (such as ESP). However, this speed is the average of all the drive wheel speeds and does not represent the actual vehicle speed under conditions such as wheel slip or locking. The calculated result deviates from the actual vehicle speed and is not suitable for feature-rich vehicles.

[0007] (2) High-order linear or nonlinear models that combine vehicle kinematics and tire dynamics often require a large set of vehicle parameters and sensor measurement signals, and rely on meticulous detail processing (such as nonlinear region processing) and gain design to ensure the robustness and accuracy of the algorithm, which increases the challenges and workload of vehicle online applications and needs to be urgently addressed. Summary of the Invention

[0008] The present application provides a vehicle center of mass speed estimation method, device, electronic device and storage medium to solve the problems of large deviation in calculation results of simple vehicle speed estimation methods in related technologies, and large modeling error, difficulty in convergence, difficulty in calibration, difficulty in model calibration, low parameter accuracy and poor model stability and robustness in accurate vehicle speed estimation methods.

[0009] A first embodiment of the present application provides a method for estimating vehicle center of mass speed, comprising the following steps:

[0010] Obtaining the current driving condition and current operating data of the vehicle to be estimated;

[0011] determining an observer gain adjustment strategy based on the current driving condition, and adjusting a preset state observer based on the observer gain adjustment strategy to obtain a target state observer, wherein the preset state observer is obtained by calibrating an initial state observer using driving data of a plurality of target driving conditions;

[0012] The current operating data is input into the target state observer to obtain the center of mass speed of the vehicle to be estimated.

[0013] Optionally, before adjusting the preset state observer according to the observer gain adjustment strategy to obtain the target state observer, the method further includes:

[0014] Determining the plurality of target driving conditions, and collecting a plurality of road driving data under each target driving condition;

[0015] Constructing a discrete-time system state space model, and generating the initial state observer according to the discrete-time system state space model;

[0016] Based on the multiple target driving conditions and the multiple road driving data corresponding to each target driving condition, the initial state observer is calibrated to obtain the preset state observer.

[0017] Optionally, calibrating the initial state observer to obtain the preset state observer based on the multiple target driving conditions and the multiple road driving data corresponding to each target driving condition includes:

[0018] determining a current calibration operating condition from the plurality of target driving operating conditions;

[0019] Determining whether there are multiple rule constraints in the current calibration working condition;

[0020] If the current calibration operating condition has the multiple rule constraints, priorities of the multiple rule constraints are determined, and based on the priorities of the multiple rule constraints, the multiple rule constraints, the current calibration operating condition, and a plurality of road driving data corresponding to the current calibration operating condition, the initial state observer is calibrated; otherwise, the initial state observer is calibrated based on the rule constraints of the current calibration operating condition, the current calibration operating condition, and a plurality of road driving data corresponding to the current calibration operating condition;

[0021] A new calibration condition is determined as the current calibration condition from multiple target driving conditions excluding the current calibration condition, and the step of determining whether multiple rule constraints exist in the current calibration condition is re-executed until all target driving conditions are calibrated to obtain the preset state observer.

[0022] Optionally, the rule constraint includes at least one of a constraint to prevent driving slip, a constraint to prevent braking lock, a constraint to compensate for lateral dynamic error, a constraint to reduce transient error, a constraint to compensate for insufficient dynamic excitation, and a constraint to reduce the impact of low signal-to-noise ratio; wherein,

[0023] The priority of the constraint for preventing drive slip is higher than the priority of the constraint for preventing brake lock, the priority of the constraint for preventing brake lock is higher than the constraint for compensating for lateral dynamic error, the constraint for compensating for lateral dynamic error is equal to the priority of the constraint for reducing transient error, the priority of the constraint for reducing transient error is greater than the priority of the constraint for compensating for insufficient dynamic excitation, the priority of the constraint for compensating for insufficient dynamic excitation is equal to the priority of the constraint for reducing the impact of low signal-to-noise ratio.

[0024] Optionally, constructing a discrete-time system state space model includes:

[0025] Establish a system dynamic model of the vehicle;

[0026] constructing a continuous-time system state space model based on the system dynamic model;

[0027] The continuous-time system state-space model is converted into a discrete-time system state-space model.

[0028] According to the vehicle center of mass speed estimation method of the embodiment of the present application, the current driving condition and current operating data of the vehicle to be estimated are obtained, an observer gain adjustment strategy is determined based on the current driving condition, and a preset state observer is adjusted according to the observer gain adjustment strategy to obtain a target state observer. The current operating data is then input into the target state observer to obtain the center of mass speed of the vehicle to be estimated. This method solves the problems of large calculation result deviations in simple vehicle speed estimation methods in the related art, as well as large modeling errors, difficulty in convergence, difficulty in calibration, difficulty in model calibration, low parameter accuracy, and poor model stability and robustness in precise vehicle speed estimation methods. By utilizing the powertrain model and combining it with the vehicle kinematic model to construct a low-order linear vehicle model, the stability and robustness of the vehicle center of mass estimation model are improved.

[0029] A second embodiment of the present application provides a vehicle center of mass speed estimation device, comprising:

[0030] A first acquisition module is used to acquire the current driving condition and current operating data of the vehicle to be estimated;

[0031] an adjustment module, configured to determine an observer gain adjustment strategy based on the current driving condition, and adjust a preset state observer based on the observer gain adjustment strategy to obtain a target state observer, wherein the preset state observer is obtained by calibrating an initial state observer using driving data of a plurality of target driving conditions;

[0032] The second acquisition module is used to input the current operating data into the target state observer to obtain the center of mass speed of the vehicle to be estimated.

[0033] Optionally, before adjusting the preset state observer according to the observer gain adjustment strategy to obtain the target state observer, the adjustment module further includes:

[0034] a determination unit, configured to determine the plurality of target driving conditions and collect a plurality of road driving data under each target driving condition;

[0035] A generating unit, configured to construct a discrete-time system state space model and generate the initial state observer according to the discrete-time system state space model;

[0036] A calibration unit is configured to calibrate the initial state observer to obtain the preset state observer based on the multiple target driving conditions and multiple road driving data corresponding to each target driving condition.

[0037] Optionally, the calibration unit includes:

[0038] a first determining subunit, configured to determine a current calibration operating condition from the plurality of target driving operating conditions;

[0039] A judgment subunit, configured to judge whether the current calibration working condition has multiple rule constraints;

[0040] a calibration subunit, configured to, if the current calibration operating condition has the multiple rule constraints, determine the priorities of the multiple rule constraints, and calibrate the initial state observer based on the priorities of the multiple rule constraints, the multiple rule constraints, the current calibration operating condition, and a plurality of road driving data corresponding to the current calibration operating condition; otherwise, calibrate the initial state observer based on the rule constraints of the current calibration operating condition, the current calibration operating condition, and a plurality of road driving data corresponding to the current calibration operating condition;

[0041] The second determination subunit is used to determine a new calibration condition as the current calibration condition from multiple target driving conditions excluding the current calibration condition, and re-execute the step of determining whether multiple rule constraints exist in the current calibration condition until all target driving conditions are calibrated to obtain the preset state observer.

[0042] Optionally, the rule constraint includes at least one of a constraint to prevent driving slip, a constraint to prevent braking lock, a constraint to compensate for lateral dynamic error, a constraint to reduce transient error, a constraint to compensate for insufficient dynamic excitation, and a constraint to reduce the impact of low signal-to-noise ratio; wherein,

[0043] The priority of the constraint for preventing drive slip is higher than the priority of the constraint for preventing brake lock, the priority of the constraint for preventing brake lock is higher than the constraint for compensating for lateral dynamic error, the constraint for compensating for lateral dynamic error is equal to the priority of the constraint for reducing transient error, the priority of the constraint for reducing transient error is greater than the priority of the constraint for compensating for insufficient dynamic excitation, the priority of the constraint for compensating for insufficient dynamic excitation is equal to the priority of the constraint for reducing the impact of low signal-to-noise ratio.

[0044] Optionally, the generating unit includes:

[0045] A first construction subunit is used to establish a system dynamic model of the vehicle;

[0046] A second construction subunit is used to construct a continuous-time system state space model according to the system dynamic model;

[0047] The conversion subunit is used to convert the continuous-time system state space model into a discrete-time system state space model.

[0048] According to the vehicle center of mass speed estimation device of the embodiment of the present application, the current driving condition and current operating data of the vehicle to be estimated are obtained, an observer gain adjustment strategy is determined based on the current driving condition, and a preset state observer is adjusted according to the observer gain adjustment strategy to obtain a target state observer. The current operating data is then input into the target state observer to obtain the center of mass speed of the vehicle to be estimated. This solves the problems of large calculation result deviations in simple vehicle speed estimation methods in the related art, as well as large modeling errors, difficulty in convergence, difficulty in calibration, difficulty in model calibration, low parameter accuracy, and poor model stability and robustness in precise vehicle speed estimation methods. By utilizing the powertrain model and combining it with the vehicle kinematic model to construct a low-order linear vehicle model, the stability and robustness of the vehicle center of mass estimation model are improved.

[0049] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle center of mass speed estimation method as described in the above embodiment.

[0050] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the vehicle center of mass speed estimation method as described in the above embodiment.

[0051] The fifth aspect of the present application provides a computer program product, including a computer program, which is executed to implement the vehicle center of mass speed estimation method described in the above embodiment.

[0052] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0054] Figure 1 This is a flow chart of a vehicle center of mass speed estimation method provided according to an embodiment of the present application;

[0055] Figure 2 Develop a flow chart for an algorithm according to one embodiment of the present application;

[0056] Figure 3 This is a diagram of the architecture of a four-wheel drive hybrid vehicle according to one embodiment of the present application;

[0057] Figure 4This is a diagram of the interaction architecture of interactive signals involved in an algorithm according to one embodiment of the present application in a vehicle network;

[0058] Figure 5 This is a data diagram of algorithm verification results based on test condition 1 according to one embodiment of the present application;

[0059] Figure 6 This is a data diagram of algorithm verification results based on test condition 2 according to one embodiment of the present application;

[0060] Figure 7 This is a data diagram of algorithm verification results based on test condition 3 according to one embodiment of the present application;

[0061] Figure 8 This is a data diagram of algorithm verification results based on test condition 4 according to one embodiment of the present application;

[0062] Figure 9 1 is a block diagram of an example of a vehicle center of mass speed estimation device according to an embodiment of the present application;

[0063] Figure 10 Schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0064] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0065] The following describes a vehicle center of mass speed estimation method, device, electronic device, and storage medium according to an embodiment of the present application with reference to the accompanying drawings. In response to the problems mentioned in the above background technology, such as the large deviation in calculation results of simple vehicle speed estimation methods in the related art, and the large modeling error, difficulty in convergence, difficulty in calibration, difficulty in model calibration, low parameter accuracy, and poor model stability and robustness of accurate vehicle speed estimation methods, the present application provides a vehicle center of mass speed estimation method, in which the current driving condition and current operating data of the vehicle to be estimated are obtained, an observer gain adjustment strategy is determined based on the current driving condition, and a preset state observer is adjusted based on the observer gain adjustment strategy to obtain a target state observer, and the current operating data is input into the target state observer to obtain the center of mass speed of the vehicle to be estimated. As a result, the problems of large deviation in calculation results of simple vehicle speed estimation methods in related technologies, and large modeling errors, difficulty in convergence, difficulty in calibration, difficulty in model calibration, low parameter accuracy, and poor model stability and robustness of accurate vehicle speed estimation methods are solved. By utilizing the powertrain model and combining it with the vehicle kinematic model to construct a low-order linear vehicle model, the stability and robustness of the vehicle center of mass estimation model are improved.

[0066] Specifically, Figure 1 A flow chart of a vehicle center of mass speed estimation method provided in an embodiment of the present application.

[0067] like Figure 1 As shown, the vehicle center of mass speed estimation method includes the following steps:

[0068] In step S101 , the current driving condition and current operating data of the vehicle to be estimated are obtained.

[0069] Specifically, in order to solve the problem of online estimation of the center of mass speed of four-wheel drive vehicles, the embodiment of the present application proposes an independent center of mass speed estimation algorithm that is easy to implement online and apply in mass production, and achieves a high estimation accuracy, thereby solving the problem that the estimation method based on the average wheel speed is seriously inaccurate under conditions such as slipping and locking, and the use of high-order vehicle kinematic models and dynamic models is not conducive to online application of mass-produced models. Among them, the vehicle center of mass speed is the actual movement speed of the vehicle center of mass position.

[0070] Therefore, if Figure 2As shown, the embodiment of the present application is based on an online application of mass-produced models. Taking a hybrid four-wheel drive vehicle as an example, the embodiment of the present application establishes a vehicle dynamics and kinematics model for vehicle speed estimation, including three sub-steps: vehicle system dynamic modeling, construction of a state space estimation model, and discretization of the estimation model. Secondly, a variable gain full-dimensional observer is designed based on the model. Then, the observer algorithm is embedded in the vehicle control software architecture for algorithm implementation. Thirdly, operating data of the actual vehicle on different high and low adhesion road surfaces is collected. Finally, the observer gain is offline parameterized and calibrated based on different vehicle operating condition data. Based on various driving conditions in which the vehicle is located and the operating data corresponding to each driving condition, the center of mass speed of the vehicle to be estimated under different conditions can be determined. For example, the center of mass speed of the vehicle to be estimated is matched based on the current driving condition and current operating data of the vehicle to be estimated. The specific implementation method will be discussed in detail in the following embodiments.

[0071] In step S102, an observer gain adjustment strategy is determined according to the current driving condition, and a preset state observer is adjusted according to the observer gain adjustment strategy to obtain a target state observer, wherein the preset state observer is obtained by calibrating an initial state observer using driving data of multiple target driving conditions.

[0072] Optionally, before adjusting the preset state observer according to the observer gain adjustment strategy to obtain the target state observer, the method further includes: determining multiple target driving conditions and collecting multiple road driving data under each target driving condition; constructing a discrete-time system state space model and generating an initial state observer based on the discrete-time system state space model; and calibrating the initial state observer based on the multiple target driving conditions and the multiple road driving data corresponding to each target driving condition to obtain the preset state observer.

[0073] Optionally, constructing a discrete-time system state space model includes: establishing a system dynamic model of the vehicle; constructing a continuous-time system state space model based on the system dynamic model; and converting the continuous-time system state space model into a discrete-time system state space model.

[0074] The preset state observer can be set by those skilled in the art according to actual computing requirements and is not specifically limited here.

[0075] In order to meet the needs of online installation applications of mass-produced models and improve the accuracy of vehicle center of mass speed estimation, the embodiment of the present application constructs a vehicle system dynamic model, i.e., a low-order (e.g., second-order) linear vehicle model, with the help of a powertrain system model and a vehicle kinematic model. The system dynamic model of the vehicle uses a small number of parameters, and the estimation algorithm designed based on the system dynamic model of the vehicle is easy to calibrate and has strong robustness, which is conducive to online application of real vehicles.

[0076] Specifically, if Figure 2 and Figure 3 As shown, since some variables in the vehicle's system dynamic model are system characteristic parameters and belong to design parameters, they need to be determined in advance, mainly including the vehicle's static rolling resistance coefficient f0, dynamic resistance coefficient f1, wind resistance coefficient f2, front wheel braking coefficient f bf , rear wheel braking coefficient f br , where the static rolling resistance coefficient f0, dynamic resistance coefficient f1, and wind resistance coefficient f2 can be obtained through standard vehicle coasting tests; the front wheel braking coefficient f bf , rear wheel braking coefficient f br Can be obtained from the brake supplier; other parameters such as vehicle mass M, transmission ratio i e 、i m1 、i m2 、i m3 and the tire radius r obtained during the development phase whl , based on the above system characteristic parameters and other vehicle parameters, a second-order linear vehicle model is constructed, which can be shown as follows:

[0077]

[0078] F trc =(T drv -T brk ) / r whl ; (2)

[0079] T drv =T e i e +T m1 i m1 +T m2 i m2 +T m3 i m3 ; (3)

[0080] T brk =f bf (P bfl +P bfr )+f br (P brl +P brr )≈(f bf +f br )P bm ; (4)

[0081] Among them, F trc is the vehicle traction force, is the longitudinal acceleration of the vehicle's center of mass, v is the longitudinal velocity of the vehicle's center of mass, f0 is the static rolling resistance coefficient of the vehicle, f1 is the dynamic resistance coefficient of the vehicle, f2 is the wind resistance coefficient, α is the road slope, g is the acceleration of gravity, M is the vehicle mass, r whl is the wheel radius, and formula (2) indicates that the vehicle traction torque is F trc r whl , given by the driving torque T drv and braking torque T brk Subtract and we get T e i e is the torque output from the engine to the wheels, i e is the transmission ratio from the engine output shaft to the wheels, T m1 i m1 is the torque output from motor 1 to the wheels, i m1 is the transmission ratio from the output shaft of motor 1 to the wheel, T m2 i m2 is the torque output from motor 2 to the wheels, i m2 is the transmission ratio from the output shaft of motor 2 to the wheels, T m3 i m3 is the torque output from motor 3 to the wheels, i m3 is the transmission ratio from the output shaft of motor 3 to the wheels, P bm is the master cylinder pressure, T brk is the equivalent torque transmitted to the wheels by the braking system, which is the sum of the braking torques provided by the four brake discs. The braking torque provided by the brake disc of each wheel is the wheel cylinder braking pressure (the left front wheel brake pressure P bfl , right front wheel brake pressure P bfr , left rear wheel brake pressure P brl , right rear wheel brake pressure P brr ) and the corresponding braking coefficient (front wheel braking coefficient f bf , rear wheel braking coefficient f br ) are multiplied together to get

[0082] It should be noted that in order to control vehicle costs, mass-produced models are usually only equipped with wheel cylinder brake pressure sensors for each wheel during the development phase for precise adjustment of control parameters. After development is completed, wheel cylinder pressure sensors will no longer be installed, and only the master cylinder pressure (P bm ) sensor is used for real-time control and status detection. Since the brake hydraulic system responds quickly, the four-wheel cylinder brake pressure in formula (4) can be obtained from the master cylinder pressure (P bm ) is approximately expressed.

[0083] Furthermore, if Figure 3As shown, after establishing the system dynamic model of the vehicle, the embodiment of the present application constructs a continuous-time system state space model based on the system dynamic model. By combining the above-mentioned system dynamic models (1)-(4) of the vehicle, the following continuous-time system state space models (5)-(9) can be formally constructed to prepare for the subsequent design of the longitudinal vehicle speed state observer. The specific formula is as follows:

[0084]

[0085] u=[T drv -(f bf +f br )P bm ] / r whl -f0; (7)

[0086]

[0087] Where u is the control input of the continuous-time system state-space model, x is the state vector of the continuous-time system state-space model (including the longitudinal vehicle speed v and its square value), and y is the output vector of the continuous-time system state-space model (including the vehicle longitudinal acceleration a and the longitudinal vehicle speed v). is the derivative of the state vector x, [A, B, C, D] is the parameter matrix of the state space model of the continuous-time system, w is the modeling error, and q is the output signal measurement error.

[0088] It should be noted that the above vehicle system dynamic models (1)-(4) contain v 2 The system becomes a nonlinear system. In order to design the observer, it needs to be linearized. Therefore, the embodiment of the present application chooses to use the square value of the vehicle speed v 2 The whole is taken as a state variable, and the vehicle longitudinal acceleration is derived as the measured value a m The form of exists in the parameter matrix A of the continuous-time system state space model, thereby obtaining the linearized form of the nonlinear dynamic system model for linear observer / estimator design.

[0089] Furthermore, the parameter matrix [A, B, C, D] of the above-mentioned continuous-time system state-space model and the control input u of the continuous-time system state-space model need to be obtained in advance. The state vector x of the continuous-time system state-space model and the state vector y of the continuous-time system state-space model can be calculated by formulas (5)-(9). The modeling error w and the signal measurement error q in the embodiment of this application are mainly required for mathematical derivation and analysis and do not require special calculation.

[0090] That is to say, the parameter matrix [A, B, C, D] of the continuous-time system state space model of the embodiment of the present application can be obtained in advance in the above-mentioned development stage; in the control input u of the continuous-time system state space model, in addition to the aforementioned system characteristic parameters f bf , f br , r whl , f0 needs to be obtained in advance during the development phase, and the remaining parameters T drv The VCU (Vehicle Control Unit) obtains the pressure from the engine controller and motor controller through the vehicle communication CAN network and calculates it according to formula (3); the brake master cylinder pressure P bm It is obtained from the VSC (Vehicle Stability Controller) through the CAN network; w represents the modeling error, and the longitudinal component of the current gravity acceleration gsinα is also included in it and is regarded as the dynamic deviation term caused by the change of road slope. This is because the measured value a of the system acceleration a is used for state feedback estimation in the subsequent observer design. m It will be measured by the acceleration sensor, and this sensor value itself contains the sum of the actual acceleration of the vehicle and the acceleration component caused by the slope. Therefore, the slope component gsinα is used as the dynamic deviation, and the acceleration measurement value a is used in parameter estimation. m Perform closed-loop correction; q represents the measurement error of the output signal y (vehicle longitudinal acceleration a and longitudinal speed v), which is similar to the modeling error w and also includes the slope acceleration component. The measured value y corresponding to the output signal y m =[a m ,v m ] T In the vehicle longitudinal acceleration measurement value a m The longitudinal speed measurement value v is measured from the vehicle acceleration sensor. m The output signal y will be indirectly obtained from the wheel speed sensor and will be used as a state feedback signal in the observer to observe the true longitudinal vehicle speed, obtaining an observation result that is infinitely close to the true value of the vehicle speed. The longitudinal vehicle speed measurement value can be obtained by the vehicle stability system (such as VSC) through the CAN network, or can be approximated by the following calculation method, as shown in the following formula:

[0091]

[0092] Among them, ω i is the wheel speed signal measured by the wheel speed sensor, 3.6 is the unit conversion factor from "meters per second" to "kilometers per hour", r whlis the wheel radius. Formula (10) indicates that the longitudinal speed measurement value is the average of the two non-driven wheel speeds for two-wheel drive (2WD) and the average speed of all four wheels for four-wheel drive (4WD).

[0093] Furthermore, in order for the developed algorithm in the embodiment of the present application to be implemented online in the vehicle embedded controller, a discrete-time algorithm needs to be designed. Therefore, it is necessary to convert the continuous-time system state space model established in the previous step into a discrete-time system state space model. Discretizing the above formulas (5)-(9) can yield the following formula:

[0094]

[0095] C k = C,D k =D,w k =T s w,q k =q; (13)

[0096] Among them, T s is the code execution cycle when the algorithm is to be deployed on a real-time controller (such as a vehicle controller VCU), k is the calculation step size, that is, the kth controller operation cycle, M is the vehicle mass, f1 is the vehicle dynamic drag coefficient, f2 is the wind resistance coefficient, q is the output signal measurement error, w is the modeling error,

[0097] [A, B, C, D] is the parameter matrix of the state space model of the continuous time system, A k is the value of the system matrix A in the kth operating cycle, B k is the value of the input matrix B in the kth operation cycle, w k is the value of the system modeling error w in the kth operating cycle, C k is the value of the output matrix C in the kth operation cycle, D k is the value of the feedforward matrix D in the kth operating cycle, q k is the value of the output signal measurement error q in the kth operating cycle, and I is the unit matrix.

[0098] The discrete-time system state-space model represented by the above formulas (11)-(13) provides a recursive initial state observer, namely, an open-loop vehicle speed estimation, that is, the k+1th operating state parameters can be inferred based on the operating state parameters of the kth controller. Since the convergence of the open-loop vehicle speed estimation value is usually difficult to guarantee in the absence of any feedback information, that is, as the vehicle runs, the calculation error of the above discrete-time system state-space model will gradually accumulate and amplify with modeling errors (such as linearization error, discretization error, unmodeled dynamics, etc.), parameter errors, and system noise. Therefore, the embodiment of the present application also needs to design a preset state observer based on this open-loop estimation model, namely, a closed-loop observer, which corrects the open-loop error in real time through key state information, such as multiple target driving conditions of the vehicle and the various road driving data corresponding to each target driving condition (such as vehicle acceleration and speed on ice, snow-cut surfaces, split roads, tile roads, urban roads, etc.).

[0099] Furthermore, the discrete-time system state space model (11)-(13) obtained above can be used to design the following initial state observer (longitudinal vehicle speed estimation algorithm):

[0100]

[0101] in, is the estimated vehicle state parameter vector, including the longitudinal speed estimate and its square value is the output parameter vector of the estimated discrete-time system state-space model, including the estimated vehicle longitudinal acceleration and longitudinal vehicle speed estimate y m is the measured value of the output vector of the discrete-time system state space model, including the measured longitudinal acceleration a m and the measured (and converted according to formula (10)) longitudinal vehicle speed v m , e is the observation error of the output vector y, L is the observer gain vector, including the longitudinal acceleration observation gain L a and longitudinal vehicle speed observation gain L v , respectively used to compare with the longitudinal acceleration observation / estimation error e a and longitudinal speed observation / estimation error e v After multiplication, it is fed back to the initial state observer to form the system state and output Closed-loop observation / estimation of the signal, i.e. the preset state observer.

[0102] Furthermore, in the above estimation algorithms (14)-(16) of the present application, the observer gain L can be equivalently regarded as the open-loop estimation and the measured value y m The closed-loop estimation result is obtained by weighted summation between and When the observer gain L is small, it means that the observation result trusts the open-loop model estimate more, that is, the estimate will be closer to the vehicle speed value calculated by the vehicle system dynamic model (1)-(4) based on the power system torque; when the observer gain value L is large, it means that the observation result trusts the open-loop model estimate more. m The vehicle speed estimation result obtained by closed-loop correction, for example, when L a The larger the value is, the more trust is placed on the longitudinal acceleration measurement signal, that is, the estimated result will be closer to the vehicle speed value obtained by integrating the measured acceleration. v The larger the value, the more trust is placed in the wheel speed measurement signal, that is, the estimated result is closer to the vehicle speed value converted from the wheel speed measurement value.

[0103] Furthermore, if Figure 4 As shown, the embodiment of the present application deploys the algorithm in the vehicle controller, and the input of the algorithm is the longitudinal acceleration measurement value a m , wheel speed measurement value ω i and the brake master cylinder pressure (P bm ) is obtained from the vehicle stability control system through the CAN (Controller Area Network) bus, and the output torque value T of the engine and motor e 、T m1-3 It will also be obtained from the corresponding power component controllers such as the engine controller and motor controller via the CAN bus.

[0104] Optionally, when calibrating the initial state observer to obtain a preset state observer based on multiple target driving conditions and multiple road driving data corresponding to each target driving condition, the method includes: determining the current calibration condition from the multiple target driving conditions; judging whether the current calibration condition has multiple rule constraints; if the current calibration condition has multiple rule constraints, determining the priorities of the multiple rule constraints, and based on the priorities of the multiple rule constraints, calibrating the initial state observer according to the multiple rule constraints, the current calibration condition and the multiple road driving data corresponding to the current calibration condition; otherwise, calibrating the initial state observer based on the rule constraints of the current calibration condition, the current calibration condition and the multiple road driving data corresponding to the current calibration condition; determining a new calibration condition as the current calibration condition from the multiple target driving conditions excluding the current calibration condition, and re-executing the step of judging whether the current calibration condition has multiple rule constraints, until all target driving conditions are calibrated to obtain the preset state observer.

[0105] Optionally, the rule constraints include at least one of a constraint to prevent drive slip, a constraint to prevent brake lock, a constraint to compensate for lateral dynamic errors, a constraint to reduce transient errors, a constraint to compensate for insufficient dynamic excitation, and a constraint to reduce the impact of low signal-to-noise ratio; wherein, the priority of the constraint to prevent drive slip is higher than the priority of the constraint to prevent brake lock, the priority of the constraint to prevent brake lock is higher than the priority of the constraint to compensate for lateral dynamic errors, the constraint to compensate for lateral dynamic errors is equal to the priority of the constraint to reduce transient errors, the priority of the constraint to reduce transient errors is greater than the priority of the constraint to compensate for insufficient dynamic excitation, and the priority of the constraint to compensate for insufficient dynamic excitation is equal to the priority of the constraint to reduce the impact of low signal-to-noise ratio.

[0106] Specifically, the embodiment of the present application first determines multiple target driving conditions, and under each target driving condition, collects multiple road driving data. For example, the multiple target driving conditions are divided according to the control input (throttle, brake, steering) that affects the longitudinal vehicle speed during normal driving by the user, and can be divided into 10 working conditions. The driving data of 5 typical road surfaces, such as urban roads, ice surfaces, snow surfaces, sandy roads, and muddy roads, are collected under the above 10 scenarios respectively. After collecting the driving data of the above 5 typical roads, the above algorithm is verified in 4 test conditions, namely Figure 5 Test condition 1 shown: Sliding after accelerating to 80% throttle on snow surface. Figure 6 The snow surface shown is 100% throttle acceleration-sliding alternating, Figure 7 The snow surface is accelerated by 100% throttle to ice surface, and then decelerated by 100% brake. Figure 8 The algorithm was tested for 40 minutes under actual urban cement road conditions to ensure accuracy. The specific target driving conditions and the corresponding driving data on various road surfaces under each target driving condition are shown in Table 1, and the test speeds on typical roads are shown in Table 2.

[0107] Table 1

[0108]

[0109] Table 2

[0110] Furthermore, the corresponding observer gain adjustment strategies are determined according to the above-mentioned multiple target driving conditions.

[0111]

[0112] And the corresponding constraint rules, for example, constraint rule a is to prevent drive slip, constraint rule b is to prevent brake locking, constraint rule c is to compensate for lateral dynamic error (the influence of lateral motion on longitudinal speed), constraint rule d is to reduce transient error, constraint rule e is to compensate for insufficient dynamic excitation, and constraint rule f is to reduce the impact of low signal-to-noise ratio. Among them, the priority of the constraint to prevent drive slip is higher than the priority of the constraint to prevent brake locking, the priority of the constraint to prevent brake locking is higher than the constraint to compensate for lateral dynamic error, the priority of the constraint to compensate for lateral dynamic error is equal to the priority of the constraint to reduce transient error, the priority of the constraint to reduce transient error is greater than the priority of the constraint to compensate for insufficient dynamic excitation, and the priority of the constraint to compensate for insufficient dynamic excitation is equal to the priority of the constraint to reduce the impact of low signal-to-noise ratio.

[0113] Specifically, first, the current calibration condition is determined in multiple target driving conditions. For example, the current calibration condition is scenario 1 in Table 1: high throttle condition. Then, the corresponding observer gain adjustment strategy is matched according to this condition. In the process of matching the corresponding observer gain adjustment strategy, it is necessary to consider whether there are multiple rule constraints in the current calibration condition, and the priorities of multiple rule constraints are determined when there are multiple rule constraints. Based on the judgment, if there are multiple rule constraints in the rule constraints in the high throttle condition, and it is determined through judgment that the priority of the multiple rule constraints is constraint rule a to prevent drive slippage, the current calibration condition, that is, the high throttle condition, can be used. The initial state observer is calibrated based on the multiple rule constraints of the current calibration condition, the current calibration condition, and the multiple road driving data corresponding to the current calibration condition; if the current calibration condition does not contain multiple rule constraints, the initial state observer is calibrated based on the rule constraints of the current calibration condition, such as constraint rule a preventing drive slippage, the current calibration condition, and the multiple road driving data corresponding to the current calibration condition; at the same time, a new calibration condition is determined as the current calibration condition from the multiple target driving conditions excluding the current calibration condition, and the step of determining whether the current calibration condition has multiple rule constraints is re-executed until all target driving conditions are calibrated to obtain a preset state observer.

[0114] For example, based on the above-mentioned multiple observer gain adjustment constraint rules af, constraint rule a prevents the influence of driving slip and requires reducing the longitudinal vehicle speed observation gain L when the throttle is large. v This is because the wheels tend to slip more when driving with a large throttle than with a small throttle. When slipping, the speed calculated based on the wheel speed can no longer represent the actual speed. At this time, a small L should be used. v To reduce the trust in the vehicle speed / wheel speed measurement value, let the vehicle speed estimation result be more estimated by the estimation model (11)-(13) and the longitudinal acceleration measurement value a m The closed-loop feedback component The two take the lead.

[0115] Constraint rule b prevents the effect of brake lock by reducing the longitudinal speed observation gain L during heavy braking. v This is because the wheels are more likely to lock in heavy braking than in light braking. When the wheels are locked, the speed calculated based on the wheel speed can no longer represent the actual speed. In this case, a small L v To reduce the trust in the vehicle speed / wheel speed measurement, let the vehicle speed estimation result be more influenced by the estimation model and the longitudinal acceleration measurement a m Closed-loop feedback is the main factor.

[0116] Constraint rule c compensates for lateral dynamic error (the effect of lateral motion on longitudinal velocity) by increasing the observation gain vector L (including L a and L v ), this is because the vehicle will be accompanied by lateral movement and yaw movement when turning, and the two will have a certain amount of intervention or superposition effect on the longitudinal movement. The open-loop model (1)-(4) or (5)-(9) or (11)-(13) does not model the system dynamics of this part of the lateral and longitudinal correlation, which will cause the longitudinal vehicle speed calculated by the open-loop model during the turning process to deviate from the true longitudinal vehicle speed that is superimposed with the lateral and yaw movements. Therefore, when making a large turn, it is necessary to increase the closed-loop observation gain vector L to reduce the trust in the open-loop estimation model, so that the vehicle speed estimation result is more dominated by the acceleration and vehicle speed measurement values.

[0117] Constraint rule d reduces transient errors by increasing the observation gain vector L (including L a and L v ), this is because when the accelerator or brake is operated quickly, the torque characteristics of the vehicle engine and other power systems and the hydraulic characteristics of the brake system have transient delays and nonlinearities, making the driving and braking torque signals T in the open-loop model (2) drv 、T brk (estimated by the drive and brake system controllers based on their own operating characteristics and then broadcast to the vehicle CAN bus) will deviate from its corresponding true value under sudden acceleration or deceleration transients, reducing the transient credibility of the open-loop model. At this time, it is also necessary to increase the closed-loop observation gain vector L to reduce the trust in the open-loop estimation model, allowing the vehicle speed estimation results to be more dominated by the acceleration and speed measurement values.

[0118] Constraint rule e compensates for insufficient dynamic excitation and requires appropriately increasing the observation gain vector L (including L a and L v), this is because when the throttle or brake is small, the driving and braking torques in the open-loop model (2) as the excitation inputs of the dynamic system are small, and the state and output calculation results of the entire model will also be small, and may even be comparable to the error magnitude of the model itself. In this case, the system error is amplified. Therefore, when the system is small in excitation, it is necessary to reduce the trust in the open-loop model and increase the trust in the measured value, that is, to use a large closed-loop feedback gain L.

[0119] Constraint rule f reduces the impact of low signal-to-noise ratio by appropriately reducing the longitudinal acceleration observation gain L when the throttle opening and brake travel are small. a This is because the acceleration measurement value a is smaller when the throttle and brake strokes are smaller. m The amplitude is small, even comparable to the noise amplitude, that is, the signal-to-noise ratio (SNR) is too low, which means that the credibility of the acceleration measurement signal is reduced, and its corresponding gain needs to be reduced to reduce its credibility. At this time, the estimation result will be more influenced by the open-loop model and the vehicle speed measurement signal v m The closed-loop feedback component The two take the lead.

[0120] Therefore, the embodiment of the present application can obtain the target state observer by adjusting the observation gain vector L according to the above-mentioned multiple target driving conditions and the multiple road driving data corresponding to each target driving condition, and according to multiple rule constraints, adjusting the preset state observer according to the observation gain vector L. Among them, the above-mentioned adjustment takes the convergence speed and stability of the observation results as the comprehensive goals, and will be able to quickly select the vehicle speed estimation algorithm gain value of the corresponding vehicle model under different conditions.

[0121] In step S103 , the current operating data is input into the target state observer to obtain the center of mass speed of the vehicle to be estimated.

[0122] Specifically, after obtaining the target state observer, the embodiment of the present application inputs the current operating data into the target state observer, thereby completing offline parameter adjustment and calibration of the observer gain according to the vehicle operating data under different working conditions, and obtaining the center of mass speed of the vehicle to be estimated.

[0123] In summary, through the detailed discussion of the above embodiments of the present application, the following beneficial effects can be achieved:

[0124] (1) The model used in the embodiments of the present application is low-order (second order) and linear, and thus simple to implement online, robust, and easy to calibrate in engineering (only two parameters need to be calibrated). It is suitable for mass-produced models and no longer relies on the vehicle stability control system.

[0125] (2) The vehicle center of mass speed estimation method of the embodiment of the present application is also applicable to two-wheel drive vehicles such as front-wheel drive and rear-wheel drive, and can overcome the problem that the true vehicle speed of a two-wheel drive vehicle under the condition of non-drive wheel locking cannot be calculated based on the wheel speed;

[0126] (3) The embodiment of the present application designs an online state observer based on the vehicle dynamic model, forming a feedforward closed-loop architecture of "model feedforward estimation + sensor feedback correction", and performs variable gain confidence design according to the operating conditions of the entire vehicle. It also performs joint estimation for related unknown parameters such as ramps, thereby ensuring the accuracy of the four-wheel drive reference speed estimation under all operating conditions and different road adhesion conditions, such as ice, snow, split roads, sand, ramps, etc., and the overall estimation accuracy reaches 95%.

[0127] (4) The comprehensive estimation accuracy of the embodiment of the present application on high and low adhesion coefficient roads (conventional roads and icy and snowy roads, etc.) can reach an average estimation error of less than 5.5% and a root mean square error of 3 km / h.

[0128] According to the vehicle center of mass speed estimation method of the embodiment of the present application, the current driving condition and current operating data of the vehicle to be estimated are obtained, an observer gain adjustment strategy is determined based on the current driving condition, and a preset state observer is adjusted according to the observer gain adjustment strategy to obtain a target state observer. The current operating data is then input into the target state observer to obtain the center of mass speed of the vehicle to be estimated. This method solves the problems of large calculation result deviations in simple vehicle speed estimation methods in the related art, as well as large modeling errors, difficulty in convergence, difficulty in calibration, difficulty in model calibration, low parameter accuracy, and poor model stability and robustness in precise vehicle speed estimation methods. By utilizing the powertrain model and combining it with the vehicle kinematic model to construct a low-order linear vehicle model, the stability and robustness of the vehicle center of mass estimation model are improved.

[0129] Next, a vehicle center of mass speed estimation device proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0130] Figure 9 4 is a block diagram of a vehicle center of mass speed estimation device according to an embodiment of the present application.

[0131] like Figure 9 As shown, the vehicle center of mass speed estimation device 10 includes: a first acquisition module 100, an adjustment module 200 and a second acquisition module 300.

[0132] The first acquisition module 100 is used to acquire the current driving condition and current operating data of the vehicle to be estimated;

[0133] An adjustment module 200 is configured to determine an observer gain adjustment strategy based on the current driving condition and adjust a preset state observer based on the observer gain adjustment strategy to obtain a target state observer, wherein the preset state observer is obtained by calibrating an initial state observer using driving data from multiple target driving conditions;

[0134] The second acquisition module 300 is used to input the current operating data into the target state observer to obtain the center of mass speed of the vehicle to be estimated.

[0135] Optionally, before adjusting the preset state observer according to the observer gain adjustment strategy to obtain the target state observer, the adjustment module 200 further includes:

[0136] a determination unit, configured to determine a plurality of target driving conditions and collect a plurality of road driving data under each target driving condition;

[0137] A generation unit, used for constructing a discrete-time system state space model and generating an initial state observer according to the discrete-time system state space model;

[0138] The calibration unit is used to calibrate the initial state observer to obtain a preset state observer based on multiple target driving conditions and multiple road driving data corresponding to each target driving condition.

[0139] Optionally, the calibration unit includes:

[0140] A first determining subunit, configured to determine a current calibration operating condition from a plurality of target driving operating conditions;

[0141] A judgment subunit, used to judge whether there are multiple rule constraints in the current calibration working condition;

[0142] a calibration subunit for determining priorities of the multiple rule constraints if the current calibration condition exists, and calibrating the initial state observer based on the priorities of the multiple rule constraints, the multiple rule constraints, the current calibration condition, and a plurality of road driving data corresponding to the current calibration condition; otherwise, calibrating the initial state observer based on the rule constraints of the current calibration condition, the current calibration condition, and a plurality of road driving data corresponding to the current calibration condition;

[0143] The second determination subunit is used to determine a new calibration condition as the current calibration condition from multiple target driving conditions excluding the current calibration condition, and re-execute the step of determining whether multiple rule constraints exist in the current calibration condition until all target driving conditions are calibrated and a preset state observer is obtained.

[0144] Optionally, the rule constraint includes at least one of a constraint to prevent driving slip, a constraint to prevent braking lock, a constraint to compensate for lateral dynamic error, a constraint to reduce transient error, a constraint to compensate for insufficient dynamic excitation, and a constraint to reduce the impact of low signal-to-noise ratio; wherein,

[0145] The priority of the constraint to prevent drive slip is higher than the priority of the constraint to prevent brake lock, the priority of the constraint to prevent brake lock is higher than the priority of the constraint to compensate for lateral dynamic error, the priority of the constraint to compensate for lateral dynamic error is equal to the priority of the constraint to reduce transient error, the priority of the constraint to reduce transient error is higher than the priority of the constraint to compensate for insufficient dynamic excitation, and the priority of the constraint to compensate for insufficient dynamic excitation is equal to the priority of the constraint to reduce the impact of low signal-to-noise ratio.

[0146] Optionally, the generating unit includes:

[0147] A first construction subunit is used to establish a system dynamic model of the vehicle;

[0148] A second construction subunit is used to construct a continuous-time system state space model based on the system dynamic model;

[0149] The conversion subunit is used to convert the continuous-time system state space model into the discrete-time system state space model.

[0150] According to the vehicle center of mass speed estimation device of the embodiment of the present application, the current driving condition and current operating data of the vehicle to be estimated are obtained, an observer gain adjustment strategy is determined based on the current driving condition, and a preset state observer is adjusted according to the observer gain adjustment strategy to obtain a target state observer. The current operating data is then input into the target state observer to obtain the center of mass speed of the vehicle to be estimated. This solves the problems of large calculation result deviations in simple vehicle speed estimation methods in the related art, as well as large modeling errors, difficulty in convergence, difficulty in calibration, difficulty in model calibration, low parameter accuracy, and poor model stability and robustness in precise vehicle speed estimation methods. By utilizing the powertrain model and combining it with the vehicle kinematic model to construct a low-order linear vehicle model, the stability and robustness of the vehicle center of mass estimation model are improved.

[0151] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0152] A memory 1001 , a processor 1002 , and a computer program stored in the memory 1001 and executable on the processor 1002 .

[0153] When the processor 1002 executes the program, the vehicle center of mass speed estimation method provided in the above embodiment is implemented.

[0154] Furthermore, the electronic device further includes:

[0155] The communication interface 1003 is used for communication between the memory 1001 and the processor 1002 .

[0156] The memory 1001 is used to store computer programs that can be run on the processor 1002 .

[0157] The memory 1001 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0158] If the memory 1001, the processor 1002, and the communication interface 1003 are implemented independently, the communication interface 1003, the memory 1001, and the processor 1002 can be connected to each other via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 10 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0159] Optionally, in a specific implementation, if the memory 1001, the processor 1002 and the communication interface 1003 are integrated on a chip, the memory 1001, the processor 1002 and the communication interface 1003 can communicate with each other through an internal interface.

[0160] The processor 1002 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0161] This embodiment also provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the vehicle center of mass and vehicle speed estimation method described above is implemented.

[0162] This embodiment further provides a computer program product, including a computer program, which is executed to implement the vehicle center of mass speed estimation method of the above embodiment.

[0163] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0164] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0165] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0166] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory.

[0167] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0168] Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above-mentioned embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0169] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0170] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for estimating vehicle center of mass speed, characterized in that: The following steps are involved: Obtaining the current driving condition and current operating data of the vehicle to be estimated; determining an observer gain adjustment strategy based on the current driving condition, and adjusting a preset state observer based on the observer gain adjustment strategy to obtain a target state observer, wherein the preset state observer is obtained by calibrating an initial state observer using driving data of a plurality of target driving conditions; Inputting the current operating data into the target state observer to obtain the center of mass speed of the vehicle to be estimated; Before adjusting the preset state observer according to the observer gain adjustment strategy to obtain the target state observer, the method further includes: determining the multiple target driving conditions and collecting a variety of road driving data under each target driving condition; constructing a discrete-time system state space model and generating the initial state observer based on the discrete-time system state space model; and calibrating the initial state observer based on the multiple target driving conditions and the variety of road driving data corresponding to each target driving condition to obtain the preset state observer. The method of calibrating the initial state observer to obtain the preset state observer based on the multiple target driving conditions and the multiple road driving data corresponding to each target driving condition includes: determining a current calibration condition from the multiple target driving conditions; determining whether the current calibration condition has multiple rule constraints; if the current calibration condition has multiple rule constraints, determining the priorities of the multiple rule constraints, and calibrating the initial state observer based on the priorities of the multiple rule constraints, the multiple rule constraints, the current calibration condition, and the multiple road driving data corresponding to the current calibration condition; otherwise, calibrating the initial state observer based on the rule constraints of the current calibration condition, the current calibration condition, and the multiple road driving data corresponding to the current calibration condition; determining a new calibration condition from the multiple target driving conditions excluding the current calibration condition as the current calibration condition, and re-performing the step of determining whether the current calibration condition has multiple rule constraints, until all target driving conditions are calibrated to obtain the preset state observer.

2. The method according to claim 1, characterized in that The rule constraint includes at least one of a constraint to prevent driving slip, a constraint to prevent braking lock, a constraint to compensate for lateral dynamic errors, a constraint to reduce transient errors, a constraint to compensate for insufficient dynamic excitation, and a constraint to reduce the impact of low signal-to-noise ratio; wherein, The priority of the constraint for preventing drive slip is higher than the priority of the constraint for preventing brake lock, the priority of the constraint for preventing brake lock is higher than the constraint for compensating for lateral dynamic error, the constraint for compensating for lateral dynamic error is equal to the priority of the constraint for reducing transient error, the priority of the constraint for reducing transient error is greater than the priority of the constraint for compensating for insufficient dynamic excitation, the priority of the constraint for compensating for insufficient dynamic excitation is equal to the priority of the constraint for reducing the impact of low signal-to-noise ratio.

3. The method according to claim 1, characterized in that The constructing of the discrete-time system state space model includes: Establish a system dynamic model of the vehicle; constructing a continuous-time system state space model based on the system dynamic model; The continuous-time system state-space model is converted into a discrete-time system state-space model.

4. A vehicle center of mass speed estimation device, characterized in that: include: A first acquisition module is used to acquire the current driving condition and current operating data of the vehicle to be estimated; an adjustment module, configured to determine an observer gain adjustment strategy based on the current driving condition, and adjust a preset state observer based on the observer gain adjustment strategy to obtain a target state observer, wherein the preset state observer is obtained by calibrating an initial state observer using driving data of a plurality of target driving conditions; A second acquisition module is used to input the current operating data into the target state observer to obtain the center of mass speed of the vehicle to be estimated; Before adjusting the preset state observer according to the observer gain adjustment strategy to obtain the target state observer, the adjustment module further includes: a determination unit for determining the multiple target driving conditions and collecting a variety of road driving data under each target driving condition; a generation unit for constructing a discrete-time system state space model and generating the initial state observer based on the discrete-time system state space model; and a calibration unit for calibrating the initial state observer based on the multiple target driving conditions and a variety of road driving data corresponding to each target driving condition to obtain the preset state observer. The calibration unit includes: a first determination subunit, configured to determine a current calibration condition from the multiple target driving conditions; a judgment subunit, configured to determine whether the current calibration condition has multiple rule constraints; a calibration subunit, configured to determine the priorities of the multiple rule constraints if the current calibration condition has the multiple rule constraints, and based on the priorities of the multiple rule constraints, calibrate the initial state observer according to the multiple rule constraints, the current calibration condition, and the multiple road driving data corresponding to the current calibration condition; otherwise, calibrate the initial state observer based on the rule constraints of the current calibration condition, the current calibration condition, and the multiple road driving data corresponding to the current calibration condition; a second determination subunit, configured to determine a new calibration condition from the multiple target driving conditions excluding the current calibration condition as the current calibration condition, and re-execute the step of determining whether the current calibration condition has multiple rule constraints, until all target driving conditions are calibrated to obtain the preset state observer.

5. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle center of mass speed estimation method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the vehicle center of mass speed estimation method according to any one of claims 1 to 3.

7. A computer program product comprising a computer program, characterized in that The computer program is executed to implement the vehicle center of mass speed estimation method according to any one of claims 1 to 3.

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