Whole vehicle quality identification method, device and equipment and storage medium

Abstract

The invention discloses a whole vehicle quality identification method, device and equipment and a storage medium. The whole vehicle quality identification method comprises the steps that a whole vehicle longitudinal dynamic model is established according to whole vehicle parameters; the whole vehicle parameters comprise engine torque, actual vehicle acceleration and actual vehicle speed; constructing an observation equation based on a Kalman filtering algorithm according to the vehicle longitudinal dynamic model; based on the observation equation, a Kalman filtering algorithm is adopted to carry out parameter identification on the whole vehicle longitudinal dynamic model, and model parameters are obtained; and vehicle quality identification is carried out based on the vehicle longitudinal dynamic model, the observation equation and the model parameters. According to the technical scheme of the embodiment of the invention, the accuracy of vehicle quality identification can be improved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control technology, and in particular to a vehicle mass identification method, device, equipment and storage medium. Background Art

[0002] Medium and heavy trucks have the advantages of large cargo volume, low cost and high economic benefits, and they play an important role in road transportation. Vehicle mass is one of the important factors to be considered when controlling vehicles. How to accurately estimate vehicle mass is of great significance to vehicle control.

[0003] In the prior art, when identifying the mass of a vehicle, many model parameters, such as the drag coefficient, rolling resistance coefficient, and frontal area, are calculated as fixed values. However, during the transportation of medium and heavy trucks, the mass of the vehicle changes drastically due to loading or unloading, and the above-mentioned fixed model parameters will actually change greatly, which leads to a large error between the mass of the vehicle calculated using fixed parameters and the actual mass of the vehicle. Therefore, how to accurately identify the mass of a vehicle is very important for vehicle control. Summary of the invention

[0004] The present invention provides a vehicle mass identification method, device, equipment and storage medium to solve the problem of low vehicle mass identification accuracy.

[0005] According to one aspect of the present invention, a vehicle mass identification method is provided, comprising:

[0006] Establishing a longitudinal dynamics model of the vehicle according to vehicle parameters; the vehicle parameters include engine torque, actual vehicle acceleration and actual vehicle speed;

[0007] According to the longitudinal dynamics model of the whole vehicle, constructing an observation equation based on a Kalman filter algorithm;

[0008] Based on the observation equation, a Kalman filter algorithm is used to perform parameter identification on the longitudinal dynamics model of the whole vehicle to obtain model parameters;

[0009] The vehicle mass is identified based on the vehicle longitudinal dynamics model, the observation equation and the model parameters.

[0010] According to another aspect of the present invention, there is provided a vehicle mass identification device, comprising:

[0011] A model building module, used to build a longitudinal dynamics model of the whole vehicle according to the whole vehicle parameters; the whole vehicle parameters include engine torque, actual vehicle acceleration and actual vehicle speed;

[0012] An observation equation construction module, used to construct an observation equation based on a Kalman filter algorithm according to the longitudinal dynamics model of the whole vehicle;

[0013] A model parameter determination module, used to perform parameter identification on the longitudinal dynamics model of the whole vehicle based on the observation equation and adopt a Kalman filter algorithm to obtain model parameters;

[0014] The vehicle mass identification module is used to identify the vehicle mass based on the vehicle longitudinal dynamics model, the observation equation and the model parameters.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle mass identification method described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle mass identification method described in any embodiment of the present invention when executed.

[0020] According to another aspect of the present invention, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the vehicle mass identification method according to any embodiment of the present disclosure is implemented.

[0021] The technical solution of the embodiment of the present invention establishes a longitudinal dynamics model of the whole vehicle according to the parameters of the whole vehicle, and constructs an observation equation based on the Kalman filter algorithm according to the longitudinal dynamics model of the whole vehicle, and then based on the observation equation, adopts the Kalman filter algorithm to perform parameter identification on the longitudinal dynamics model of the whole vehicle to obtain model parameters, and finally performs vehicle mass identification based on the longitudinal dynamics model of the whole vehicle, the observation equation and the model parameters. Compared with the calculation method of directly taking the model parameters as fixed values, the parameter identification is performed by Kalman filtering, which improves the accuracy of vehicle mass identification when the vehicle mass changes greatly.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 is a flow chart of a vehicle mass identification method provided according to the first embodiment of the present invention;

[0025] Figure 2a This is a flow chart of a vehicle mass identification method provided according to the second embodiment of the present invention;

[0026] Figure 2b is a schematic diagram of vehicle forces provided according to the second embodiment of the present invention;

[0027] Figure 3 is a structural schematic diagram of a vehicle mass identification device provided according to Embodiment 3 of the present invention;

[0028] Figure 4 It is a structural schematic diagram of an electronic device for implementing the vehicle mass identification method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] Embodiment 1

[0032] Figure 1 A flowchart of a vehicle mass identification method is provided for the first embodiment of the present invention. This embodiment is applicable to the case where vehicle mass identification is performed based on a Kalman filter algorithm. The method can be performed by a vehicle mass identification device. The vehicle mass identification device can be implemented in the form of hardware and / or software. The vehicle mass identification device can be configured in various general-purpose computing devices. Figure 1 As shown, the method includes:

[0033] S110. Establishing a longitudinal dynamics model of the whole vehicle according to the parameters of the whole vehicle; the parameters of the whole vehicle include engine torque, actual acceleration of the vehicle, and actual speed of the vehicle.

[0034] The vehicle parameters are used to establish the vehicle's longitudinal dynamics model. The vehicle parameters are collected by the vehicle's built-in sensors. The vehicle parameters include the actual vehicle acceleration collected by the acceleration sensor, the actual vehicle speed collected by the speed sensor or the anti-lock braking system, and the engine torque collected by the engine controller.

[0035] In the embodiment of the present invention, the vehicle acceleration equation is listed according to the force conditions during the longitudinal driving process of the vehicle. For example, the vehicle receives the driving force, air resistance (including wind speed), rolling resistance and the component force of gravity in the driving direction during the driving process. Furthermore, according to the calculation formulas of the above-mentioned various forces, the vehicle acceleration equation is further refined. The engine torque, actual vehicle acceleration and actual vehicle speed collected by the vehicle's built-in sensors are substituted into the refined vehicle acceleration equation to obtain the longitudinal dynamics model of the whole vehicle.

[0036] S120. According to the longitudinal dynamics model of the whole vehicle, an observation equation based on the Kalman filter algorithm is constructed.

[0037] In the existing vehicle mass identification method, after the longitudinal dynamics model of the vehicle is established, the drag coefficient and windward area required for calculating air resistance in the longitudinal dynamics model of the vehicle, and the rolling resistance coefficient for calculating rolling resistance are used as fixed values ​​to calculate the vehicle mass. However, on heavy trucks, due to different cargo weights and different trailers used, the above parameters will actually vary greatly, which will cause a large error between the vehicle mass calculated by the existing mass identification scheme and the actual vehicle mass.

[0038] In the embodiment of the present invention, in order to reduce the calculation error and improve the accuracy of vehicle mass identification, after the longitudinal dynamics model of the vehicle is established, the observation equation based on the Kalman filter algorithm is constructed according to the longitudinal dynamics model of the vehicle. Specifically, the design logic of the observation equation is to use the parameters that can be accurately collected by the vehicle-mounted sensors as the known quantities that can be collected, such as the engine torque, the actual acceleration of the vehicle, and the actual speed of the vehicle. Other parameters that cannot be accurately collected are integrated as model parameters.

[0039] S130. Based on the observation equation, a Kalman filter algorithm is used to perform parameter identification on the longitudinal dynamics model of the whole vehicle to obtain model parameters.

[0040] In the embodiment of the present invention, after constructing the observation equation, the Kalman filter algorithm is used based on the observation equation to perform parameter identification on the longitudinal dynamics model of the whole vehicle to obtain model parameters. Specifically, the initial value of the model parameter, the initial value of the error covariance, the expected convergence point, the measurement noise covariance matrix and the transition point are set. Further, the estimation error is calculated, the weighting function is calculated based on the estimation error, and the Kalman gain is calculated based on the weighting function. Further, the state prior estimation is updated based on the Kalman gain, and the covariance matrix is ​​calculated, and finally the updated error covariance is calculated.

[0041] After completing the above round of operations, the counter increments and returns to execute the operation of calculating the estimated error until the iterative calculation of the Kalman filter is completed and the model parameters are obtained.

[0042] S140, identifying the vehicle mass based on the vehicle longitudinal dynamics model, observation equations and model parameters.

[0043] In the embodiment of the present invention, the relationship between the model parameters and the vehicle mass is determined based on the vehicle longitudinal dynamics model and the observation equation, and the vehicle mass is calculated according to the model parameter values ​​obtained by Kalman filtering. The above model parameters are parameters after integrating the vehicle longitudinal dynamics model. The vehicle mass can be identified without determining each parameter in the vehicle longitudinal dynamics model one by one, which improves the accuracy of vehicle mass identification without increasing hardware costs.

[0044] The technical solution of the embodiment of the present invention establishes a longitudinal dynamics model of the whole vehicle according to the parameters of the whole vehicle, and constructs an observation equation based on the Kalman filter algorithm according to the longitudinal dynamics model of the whole vehicle, and then based on the observation equation, adopts the Kalman filter algorithm to perform parameter identification on the longitudinal dynamics model of the whole vehicle to obtain model parameters, and finally performs vehicle mass identification based on the longitudinal dynamics model of the whole vehicle, the observation equation and the model parameters. Compared with the calculation method of directly taking the model parameters as fixed values, the parameter identification is performed by Kalman filtering, which improves the accuracy of vehicle mass identification when the vehicle mass changes greatly.

[0045] Embodiment 2

[0046] Figure 2a This is a flow chart of a vehicle mass identification method provided in the second embodiment of the present invention. This embodiment further refines the above embodiment and provides specific steps for vehicle mass identification based on the vehicle longitudinal dynamics model, observation equation and model parameters. Figure 2a As shown, the method includes:

[0047] S210: Establishing a longitudinal dynamics model of the whole vehicle according to the parameters of the whole vehicle; the parameters of the whole vehicle include engine torque, actual acceleration of the vehicle and actual speed of the vehicle.

[0048] Optionally, the vehicle longitudinal dynamics model is specifically:

[0049]

[0050] Among them, a sen is the actual acceleration of the vehicle, T is the engine torque, η is the transmission efficiency, i g is the gear ratio of the gearbox, i0 is the main reduction ratio, r is the tire radius, m is the vehicle mass, C d is the drag coefficient, ρ is the air density, A is the frontal area, v1 is the actual vehicle speed, v0 is the ambient wind speed, g is the acceleration of gravity, and b+kv1 is the rolling resistance coefficient.

[0051] In this optional embodiment, in order to establish a vehicle bus dynamics model, firstly according to Figure 2b The force conditions of the vehicle during longitudinal driving are shown, and the vehicle acceleration equation is listed. In this embodiment, the vehicle braking condition is not considered for the time being, which is as follows:

[0052] F 驱动 -F 风阻 -F 滚阻 -F 重力 =ma

[0053] Among them, F 驱动 is the driving force of the vehicle, F 风阻is the air resistance of the vehicle during driving, F 滚阻 is the rolling resistance of the vehicle during driving, F 重力 is the component of the vehicle's gravity in the direction of travel, m is the mass of the vehicle, and a is the longitudinal acceleration of the vehicle in the direction of travel.

[0054] Furthermore, according to the calculation formulas of various forces in the above vehicle acceleration equation, the vehicle acceleration equation is further refined, wherein the calculation formula of the vehicle driving force is as follows:

[0055]

[0056] Where T is the engine torque, η is the transmission efficiency, i g is the gear ratio of the gearbox, i0 is the main reduction ratio, and r is the tire radius.

[0057] The calculation formula of air resistance during vehicle driving is as follows:

[0058]

[0059] Among them, C d is the drag coefficient, ρ is the air density, A is the frontal area, v is the relative speed of the vehicle, v1 is the actual speed of the vehicle, and v0 is the ambient wind speed in the direction of vehicle travel.

[0060] The calculation formula of rolling resistance during vehicle driving is as follows:

[0061] F 滚阻 =mgμcosα=mg(b+kv1)cosα

[0062] Among them, μ is the rolling resistance coefficient, which can be further decomposed into b+kv1, and α is the ramp angle.

[0063] The specific calculation formula for the component force of the vehicle's gravity in the driving direction is as follows:

[0064] F 重力 =mgsinα

[0065] Among them, g is the acceleration due to gravity, m is the mass of the vehicle, and α is the ramp angle.

[0066] The actual acceleration of the vehicle is obtained by using the vehicle's own three-axis acceleration sensor. sen , the horizontal acceleration is as follows:

[0067] a sen =a+gsinα

[0068] Based on the above horizontal acceleration, vehicle acceleration equation, vehicle driving force, air resistance during vehicle driving, rolling resistance during vehicle driving, and the specific calculation method of the component force of the vehicle's gravity in the driving direction, the longitudinal dynamics model of the whole vehicle is determined as follows:

[0069]

[0070] The above vehicle longitudinal dynamics model is applicable to the case where the ramp angle is small and cosα is approximately 1.

[0071] In the above vehicle longitudinal dynamics model, a sen Obtained from the acceleration sensor, T obtained from the engine controller, i g It is obtained from the transmission controller or through vehicle speed calculation, i0 is a fixed parameter of the vehicle, v1 is obtained from the anti-lock braking system or vehicle speed sensor, and other parameters that cannot be obtained are not set as fixed parameters, and the model parameters are identified through the Kalman filter algorithm.

[0072] S220. According to the longitudinal dynamics model of the whole vehicle, an observation equation based on the Kalman filter algorithm is constructed.

[0073] Optionally, the observation equation is specified as:

[0074] Z=HX

[0075] Where Z = a sen is the actual acceleration of the vehicle, H = [T v1 2 v1 1], T is the engine torque, v1 is the actual vehicle speed, x1, x2, x3, x4 are the model parameters of the vehicle longitudinal dynamics model.

[0076] In this optional embodiment, after establishing the longitudinal dynamics model of the whole vehicle, an observation equation based on the Kalman filter algorithm is constructed according to the longitudinal dynamics model of the whole vehicle to realize model parameter identification. The specific observation equation is as follows:

[0077] Z=HX

[0078] Where Z = a sen is the actual acceleration of the vehicle, H = [T v1 2 v1 1], T is the engine torque, v1 is the actual vehicle speed, x1, x2, x3, x4 are model parameters of the longitudinal dynamics model of the whole vehicle. In this optional embodiment, the model parameters contained in the above matrix X are not set as fixed parameters, but the model parameters are continuously identified by the Kalman filter algorithm, which can improve the accuracy of the whole vehicle mass identification compared to the method of directly using the above model parameters as fixed values.

[0079] S230. Based on the observation equation, a Kalman filter algorithm is used to perform parameter identification on the longitudinal dynamics model of the whole vehicle to obtain model parameters.

[0080] Optionally, the expression equation of the Kalman filter algorithm is specifically:

[0081] Calculate the estimated error e k :

[0082] e k =Z k -HX k-1

[0083] Calculate the weighting function q k :

[0084]

[0085] Calculate the Kalman gain K k :

[0086] K k =q k P k H T (R+q k HP k-1 H T ) -1 ;

[0087] Calculate the updated state prior estimate X k :

[0088] X k =X k-1 +K k e k

[0089] Calculate the covariance matrix Q k :

[0090] Q k =q k P d H T HP d (R+q k HP d H T ) -1

[0091] Calculate the updated error covariance P k :

[0092] P k =(IK k H)P k-1 +Q k ;

[0093] Among them, Z k is the actual acceleration of the vehicle at time k, HX k-1 is the estimated vehicle acceleration at time k, ξ is the switching point, H = [T v1 2 v1 1], T is the engine torque, v1 is the actual vehicle speed, R is the measurement noise covariance matrix, X k is the model parameter at time k, P d is the expected convergence point.

[0094] In this optional embodiment, an expression equation of the Kalman filter algorithm is provided to perform model parameter identification. In the parameter identification process, the initial value of the state estimation X0, the initial value of the error covariance P0, and the expected convergence point P d , measurement noise covariance matrix R, transition point ξ, k=1.

[0095] Among them, the above-mentioned state estimation initial value X0 can be calculated based on the conventional data of the vehicle, that is, the empirical value; the initial value of the error covariance P0 can be set to the unit matrix, and then the covariance P0 can be collected through multiple experiments. k The final value at the time of convergence is used as the initial value P0 of the error covariance; the measurement noise covariance matrix R is related to the acceleration sensor used. The error of the sensor is positively distributed during measurement. The acceleration sensor used is measured and tested, and the data is collected to calculate the variance. The obtained variance is used to measure the noise covariance matrix R.

[0096] Furthermore, based on the above initial values, the estimated error is calculated:

[0097] e k =Z k -HX k-1

[0098] Based on the estimation error, the weighting function q is calculated k :

[0099]

[0100] Based on the weighting function, the measurement noise covariance matrix and the initial value of the error covariance, the Kalman gain K is calculated. k :

[0101] K k =q k P k H T (R+q k HP k-1 H T ) -1 ;

[0102] Based on the Kalman gain and the estimation error, calculate the updated state prior estimate X k :

[0103] X k =X k-1 +K k e k

[0104] Based on the weighting function, the measurement noise covariance matrix and the initial value of the error covariance, the covariance matrix Q is calculated. k :

[0105] Q k =q k P d H T HP d (R+q k HP d H T ) -1

[0106] Based on the covariance matrix, calculate the updated error covariance P k :

[0107] P k =(IK k H)P k-1 +Q k ;

[0108] After completing a round of calculation, the counter k is incremented and returned to execute the operation of calculating the estimation error, realizing the iterative calculation of the Kalman filter and obtaining the model parameter X required for the longitudinal dynamics model of the whole vehicle.

[0109] S240. Determine the relationship expression between the model parameters and the vehicle mass based on the vehicle longitudinal dynamics model and the observation equation.

[0110] In the embodiment of the present invention, after obtaining the model parameters of the longitudinal dynamics model of the whole vehicle, the longitudinal dynamics model of the whole vehicle is combined with the observation equation to obtain the relationship expression between the model parameters and the mass of the whole vehicle, which is specifically as follows:

[0111]

[0112] Among them, η is the transmission efficiency, i g is the gear ratio of the gearbox, i0 is the main reduction ratio, r is the tire radius, m is the vehicle mass, and v1 is the actual speed of the vehicle.

[0113] S250: Identify the vehicle mass based on the relationship expression and model parameters.

[0114] In the embodiment of the present invention, after obtaining the relationship expression between the model parameters and the vehicle mass, the vehicle mass identification is further performed based on the relationship expression and the numerical value of the model parameters. Specifically, when the model parameter X converges, the current value of X is stored, and all model parameters in the Kalman filter algorithm are restored to the initial value. At this time, the model parameter X can be considered to be a parameter value that conforms to the current vehicle state, where: At this time, x1 is a constant that has been calculated, and we can get

[0115] Optionally, the execution conditions for vehicle mass identification include:

[0116] There is no fault in the communication of the anti-lock brake system or the vehicle speed sensor;

[0117] The actual speed of the vehicle is greater than the speed threshold;

[0118] Communicate with the engine controller;

[0119] The acceleration sensor is not faulty;

[0120] The engine torque is greater than the torque limit.

[0121] In this optional embodiment, before performing the vehicle mass identification operation, in order to ensure accurate execution of the operation, the following conditions of the vehicle may be tested:

[0122] 1. The anti-lock braking system communication is normal or the vehicle speed sensor is fault-free to ensure that the actual vehicle speed can be accurately collected;

[0123] 2. The actual speed of the vehicle is greater than the speed threshold to ensure that the vehicle is in a driving state;

[0124] 3. Communicate with the engine controller to ensure that the engine torque can be accurately obtained;

[0125] 4. The acceleration sensor is fault-free to ensure that the actual acceleration of the vehicle can be accurately obtained;

[0126] 5. The engine torque is greater than the torque limit.

[0127] After the vehicle satisfies the above five judgment conditions at the same time, the vehicle mass identification solution in the embodiment of the present invention can be executed. The vehicle mass identification solution provided by the embodiment of the present invention only needs to obtain the actual vehicle speed, actual vehicle acceleration and engine torque through sensors. These sensors have low acquisition difficulty and high acquisition accuracy, thereby improving the quality estimation accuracy without increasing any hardware cost.

[0128] Optionally, the technical solution of the embodiment of the present invention further includes:

[0129] During the vehicle quality identification process, the vehicle driving status is collected in real time;

[0130] When the vehicle is in a braking state or a turning state, the vehicle mass identification operation is suspended and the intermediate parameters in the Kalman filter algorithm are frozen;

[0131] When the vehicle is stationary and the engine of the vehicle is running, timing is performed by a timer, and when the timing exceeds a time threshold, the intermediate parameters in the Kalman filter algorithm are reset to initial parameter values.

[0132] In this optional embodiment, the vehicle mass identification is started by inputting the real-time vehicle data into the Kalman filter algorithm. During the vehicle mass identification process, the vehicle driving status is collected in real time. When the vehicle driving state is a braking state, the force on the vehicle will increase the braking force. When the vehicle driving state is a turning state, the vehicle force condition will also change. At this time, it is necessary to suspend the vehicle mass identification operation and freeze the intermediate parameters in the Kalman filter algorithm. When the vehicle resumes straight driving and there is no braking operation, continue to use the above-mentioned frozen intermediate parameters and the real-time collected vehicle state parameters to identify the vehicle mass.

[0133] In addition, when the vehicle is stationary and the engine is running, the timer is used to count the time. When the time exceeds the time threshold, the vehicle may have undergone operations such as loading / unloading that significantly changed the vehicle mass. At this time, in order to ensure the accuracy of vehicle mass identification, it is necessary to reset the intermediate parameters in the Kalman filter algorithm to the initial parameter values ​​and recalculate.

[0134] The technical solution of the embodiment of the present invention establishes a longitudinal dynamics model of the whole vehicle according to the parameters of the whole vehicle, and constructs an observation equation based on the longitudinal dynamics model of the whole vehicle, and then uses the Kalman filter algorithm based on the observation equation to perform parameter identification on the longitudinal dynamics model of the whole vehicle to obtain model parameters, and determines the relationship expression between the model parameters and the whole vehicle mass according to the longitudinal dynamics model of the whole vehicle and the observation equation, and performs vehicle mass identification according to the relationship expression and the model parameters. Parameter identification is performed by means of Kalman filtering, which improves the accuracy of vehicle mass identification when the vehicle mass changes significantly.

[0135] Embodiment 3

[0136] Figure 3 This is a schematic diagram of the structure of a vehicle mass identification device provided by the third embodiment of the present invention. Figure 3 As shown, the device comprises:

[0137] The model building module 310 is used to build a longitudinal dynamics model of the whole vehicle according to the whole vehicle parameters; the whole vehicle parameters include engine torque, actual vehicle acceleration and actual vehicle speed;

[0138] An observation equation construction module 320 is used to construct an observation equation based on a Kalman filter algorithm according to the longitudinal dynamics model of the whole vehicle;

[0139] A model parameter determination module 330 is used to perform parameter identification on the longitudinal dynamics model of the vehicle based on the observation equation and adopt a Kalman filter algorithm to obtain model parameters;

[0140] The vehicle mass identification module 340 is used to identify the vehicle mass based on the vehicle longitudinal dynamics model, the observation equation and the model parameters.

[0141] The technical solution of the embodiment of the present invention establishes a longitudinal dynamics model of the whole vehicle according to the parameters of the whole vehicle, and constructs an observation equation based on the Kalman filter algorithm according to the longitudinal dynamics model of the whole vehicle, and then based on the observation equation, adopts the Kalman filter algorithm to perform parameter identification on the longitudinal dynamics model of the whole vehicle to obtain model parameters, and finally performs vehicle mass identification based on the longitudinal dynamics model of the whole vehicle, the observation equation and the model parameters. Compared with the calculation method of directly taking the model parameters as fixed values, the parameter identification is performed by Kalman filtering, which improves the accuracy of vehicle mass identification when the vehicle mass changes greatly.

[0142] Optionally, the vehicle longitudinal dynamics model is specifically:

[0143]

[0144] Among them, a sen is the actual acceleration of the vehicle, T is the engine torque, η is the transmission efficiency, i g is the gear ratio of the gearbox, i0 is the main reduction ratio, r is the tire radius, m is the vehicle mass, C d is the drag coefficient, ρ is the air density, A is the frontal area, v1 is the actual vehicle speed, v0 is the ambient wind speed, g is the acceleration of gravity, and b+kv1 is the rolling resistance coefficient.

[0145] Optionally, the observation equation is specifically:

[0146] Z=HX

[0147] Where Z = a sen is the actual acceleration of the vehicle, H = [T v1 2 v1 1], T is the engine torque, v1 is the actual vehicle speed, x1, x2, x3, x4 are model parameters of the vehicle longitudinal dynamics model.

[0148] Optionally, the expression equation of the Kalman filter algorithm is specifically:

[0149] Calculate the estimated error e k :

[0150] e k =Z k -HX k-1

[0151] Calculate the weighting function q k :

[0152]

[0153] Calculate the Kalman gain K k :

[0154] K k =q k P k H T (R+q k HP k-1 H T ) -1 ;

[0155] Calculate the updated state prior estimate X k :

[0156] X k =X k-1 +K k e k

[0157] Calculate the covariance matrix Q k :

[0158] Q k =q k P d H T HP d (R+q k HP d H T ) -1

[0159] Calculate the updated error covariance P k :

[0160] P k =(IK k H)P k-1 +Q k ;

[0161] Among them, Z kis the actual acceleration of the vehicle at time k, HX k-1 is the estimated vehicle acceleration at time k, ξ is the switching point, H = [T v1 2 v1 1], T is the engine torque, v1 is the actual vehicle speed, R is the measurement noise covariance matrix, X k is the model parameter at time k, P d is the expected convergence point.

[0162] Optionally, the vehicle mass identification module 340 is specifically used for:

[0163] Determining, based on the vehicle longitudinal dynamics model and the observation equation, a relationship expression between model parameters and vehicle mass;

[0164] The vehicle mass is identified based on the relationship expression and model parameters.

[0165] Optionally, the execution conditions for vehicle mass identification include:

[0166] There is no fault in the communication of the anti-lock brake system or the vehicle speed sensor;

[0167] The actual speed of the vehicle is greater than the speed threshold;

[0168] Communicate with the engine controller;

[0169] The acceleration sensor is not faulty;

[0170] The engine torque is greater than the torque limit.

[0171] Optionally, the vehicle mass identification device further includes:

[0172] A parameter freezing module is used to suspend the vehicle mass identification operation and freeze the intermediate parameters in the Kalman filter algorithm when the vehicle is in a braking state or a turning state;

[0173] The parameter resetting module is used to count the time through a timer when the vehicle is in a stationary state and the engine of the vehicle is in a running state. When the timing exceeds a time threshold, the intermediate parameters in the Kalman filter algorithm are reset to initial parameter values.

[0174] The vehicle mass identification device provided in the embodiment of the present invention can execute the vehicle mass identification method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0175] In the technical solution of the present invention, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0176] Embodiment 4

[0177] According to an embodiment of the present invention, the present invention also provides an electronic device, a readable storage medium and a computer program product.

[0178] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, applicators, blade applicators, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0179] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0180] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0181] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the vehicle mass identification method.

[0182] In some embodiments, the vehicle mass identification method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the vehicle mass identification method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the vehicle mass identification method in any other appropriate manner (e.g., by means of firmware).

[0183] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0184] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or application.

[0185] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0186] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0187] The systems and techniques described herein may be implemented in a computing system that includes a backend component (e.g., as a data applier), or a computing system that includes a middleware component (e.g., an application applier), or a computing system that includes a frontend component (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0188] A computing system may include a client and an application server. The client and the application server are generally remote from each other and usually interact through a communication network. The relationship between the client and the application server is generated by computer programs running on corresponding computers and having a client-application server relationship with each other. The application server may be a cloud application server, also known as a cloud computing application server or a cloud host, which is a host product in a cloud computing application system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS applications.

[0189] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0190] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A vehicle mass identification method, characterized in that: include: Establishing a longitudinal dynamics model of the vehicle according to vehicle parameters; the vehicle parameters include engine torque, actual vehicle acceleration and actual vehicle speed; According to the longitudinal dynamics model of the whole vehicle, constructing an observation equation based on a Kalman filter algorithm; Based on the observation equation, a Kalman filter algorithm is used to perform parameter identification on the longitudinal dynamics model of the whole vehicle to obtain model parameters; The vehicle mass is identified based on the vehicle longitudinal dynamics model, the observation equation and the model parameters.

2. The method according to claim 1, characterized in that The vehicle longitudinal dynamics model is specifically: Among them, a sen is the actual acceleration of the vehicle, T is the engine torque, η is the transmission efficiency, i g is the gear ratio of the gearbox, i0 is the main reduction ratio, r is the tire radius, m is the vehicle mass, C d is the drag coefficient, ρ is the air density, A is the frontal area, v1 is the actual vehicle speed, v0 is the ambient wind speed, g is the acceleration of gravity, and b+kv1 is the rolling resistance coefficient.

3. The method according to claim 1, characterized in that The observation equation is specifically: Z=HX Where Z = a sen is the actual acceleration of the vehicle, H = [T v1 2 v1 1], T is the engine torque, v1 is the actual vehicle speed, x1, x2, x3, x4 are model parameters of the vehicle longitudinal dynamics model.

4. The method according to claim 3, characterized in that The expression equation of the Kalman filter algorithm is specifically: Calculate the estimated error e k : yes k =Z k -HX k-1 Calculate the weighting function q k : Calculate the Kalman gain K k : K k =q k P k H T (R+q k HP k-1 H T ) -1 ; Calculate the updated state prior estimate X k : X k =X k-1 +K k e k Calculate the covariance matrix Q k : Q k =q k P d H T HP d (R+q k HP d H T ) -1 Calculate the updated error covariance P k : P k =(I-K k H)P k-1 +Q k ; Among them, Z k is the actual acceleration of the vehicle at time k, HX k-1 is the estimated vehicle acceleration at time k, ξ is the switching point, H = [T v1 2 v1 1], T is the engine torque, v1 is the actual vehicle speed, R is the measurement noise covariance matrix, X k is the model parameter at time k, P d is the expected convergence point.

5. The method according to claim 3, characterized in that: Based on the vehicle longitudinal dynamics model, the observation equation and the model parameters, the vehicle mass identification is performed, including: Determining, based on the vehicle longitudinal dynamics model and the observation equation, a relationship expression between model parameters and vehicle mass; The vehicle mass is identified based on the relationship expression and model parameters.

6. The method according to claim 1, characterized in that The execution conditions of vehicle quality identification include: There is no fault in the communication of the anti-lock brake system or the vehicle speed sensor; The actual speed of the vehicle is greater than the speed threshold; Communicate with the engine controller; The acceleration sensor is not faulty; The engine torque is greater than the torque limit.

7. The method according to claim 1, characterized in that Also includes: During the vehicle quality identification process, the vehicle driving status is collected in real time; When the vehicle is in a braking state or a turning state, the vehicle mass identification operation is suspended and the intermediate parameters in the Kalman filter algorithm are frozen; When the vehicle is stationary and the engine of the vehicle is running, timing is performed by a timer, and when the timing exceeds a time threshold, the intermediate parameters in the Kalman filter algorithm are reset to initial parameter values.

8. A vehicle mass identification device, characterized in that: include: A model building module, used to build a longitudinal dynamics model of the whole vehicle according to the whole vehicle parameters; the whole vehicle parameters include engine torque, actual vehicle acceleration and actual vehicle speed; An observation equation construction module, used to construct an observation equation based on a Kalman filter algorithm according to the longitudinal dynamics model of the whole vehicle; A model parameter determination module, used to perform parameter identification on the longitudinal dynamics model of the whole vehicle based on the observation equation and adopt a Kalman filter algorithm to obtain model parameters; The vehicle mass identification module is used to identify the vehicle mass based on the vehicle longitudinal dynamics model, the observation equation and the model parameters.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle mass identification method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle mass identification method according to any one of claims 1 to 7 when executed.

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