Tank truck rollover prevention control method and apparatus, cloud terminal, tank truck, and control system

Abstract

The present application relates to the technical field of autonomous driving, and in particular relates to a tank truck rollover prevention control method and apparatus, a cloud terminal, a tank truck, and a control system. The method comprises: acquiring vehicle information sent by a tank truck and liquid filling information obtained according to a sensor; inputting the vehicle information and the liquid filling information into a refined vehicle model and a refined liquid sloshing model, respectively, and outputting a first parameter calibration result and a second parameter calibration result, respectively; issuing the first parameter calibration result and the second calibration result to the tank truck, the tank truck using the first parameter calibration result and the second calibration result to calibrate a vehicle surrogate model and a liquid surrogate model, respectively; and determining a tank truck control target in view of observing vehicle state sensor information and observing liquid-related sensor information, and performing rollover prevention control for the tank truck on the basis of the control target. Thus, the problems in the existing technology that rollover prevention control for a tank truck cannot be calculated in real time and that there is no way to support a large calculation volume are resolved.

Description

Liquid tank truck anti-rollover control method, device, cloud, liquid tank truck and control system

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is based on the Chinese patent application with application number 202311247016.8 and application date on September 25, 2023, and the Chinese patent application with application number 202311239283.0 and application date on September 25, 2023, and claims the priority of the Chinese patent application. The entire contents of the Chinese patent application are hereby introduced into this application as a reference. Technical Field

[0003] The present application relates to the field of autonomous driving technology, and in particular to a liquid tank truck anti-rollover control method, device, cloud, liquid tank truck, and control system. Background Art

[0004] Liquid tanker trucks are common vehicles for transporting hazardous chemicals. Standards require them to be partially loaded, which results in liquid sloshing. Furthermore, their high center of mass and heavy load make them less stable after vehicle-to-liquid coupling, making them prone to rollover accidents, leading to hazardous chemical leaks and even explosions. Therefore, in future intelligent automotive cyber-physical systems, the purely transport-oriented, highly dangerous, and labor-intensive manual operation of liquid tanker trucks will be replaced by autonomous driving. For autonomous liquid tanker trucks, predicting liquid sloshing and appropriately controlling the vehicle's lateral acceleration based on this sloshing is crucial.

[0005] The most accurate method for describing liquid sloshing is CFD (Computational Fluid Dynamics), but finite element models are computationally intensive and far from real-time. However, a simplified proxy model based on the vehicle and liquid sloshing mechanisms can be created. Based on this vehicle and liquid model, a rollover prevention control algorithm that accounts for liquid sloshing can be designed. The proxy model can then be used to measure or estimate the state variables required for control.

[0006] However, the parameters in the proxy model will change with the change of filling conditions. Each change requires a large amount of finite element calculations to calibrate the liquid proxy model, but the computing power on the vehicle side cannot support such a large amount of calculations.

[0007] Summary of the Invention

[0008] The present application provides a liquid tank truck anti-rollover control method, device, cloud, liquid tank truck and control system to solve the problems in related technologies such as the inability to achieve real-time calculation and control of liquid tank truck anti-rollover and the inability to support large amounts of calculation.

[0009] A first aspect of the present application provides a method for controlling a liquid tank truck against rollover, which is applied to the cloud, and includes the following steps: obtaining vehicle information sent by the liquid tank truck and liquid filling information obtained based on sensors; inputting the vehicle information and the liquid filling information into a vehicle fine model and a liquid sloshing fine model, respectively, wherein the vehicle fine model outputs a first parameter calibration result, and the liquid sloshing fine model outputs a second parameter calibration result; sending the first parameter calibration result and the second parameter calibration result to the liquid tank truck, wherein the liquid tank truck uses the first parameter calibration result and the second parameter calibration result to calibrate the vehicle proxy model and the liquid proxy model respectively, determines the control target of the liquid tank truck in combination with the vehicle state sensor information observation and the liquid-related sensor information observation, and performs anti-rollover control on the liquid tank truck based on the control target.

[0010] Optionally, the vehicle fine model is a multi-body dynamics model, the liquid sloshing fine model is a finite element model, the vehicle proxy model is a linear simplified dynamics model, and the liquid proxy model is an equivalent pendulum dynamics model.

[0011] Optionally, before inputting the vehicle information and the filling information into the vehicle fine model and the liquid sloshing fine model respectively, it also includes: obtaining a multi-degree-of-freedom equivalent pendulum model of liquid sloshing in the tank of the liquid tank truck; setting the specific configuration and input quantity of the multi-degree-of-freedom equivalent pendulum model, and determining the kinetic energy and potential energy of the multi-degree-of-freedom equivalent pendulum model according to the specific structure and the input quantity; determining the differential dynamics equations of multiple swinging degrees of freedom according to the kinetic energy and potential energy of the multi-degree-of-freedom equivalent pendulum model, and identifying all pending parameters of the differential dynamics equations according to the established liquid sloshing model in the cross section of the liquid tank; and constructing a simplified model of liquid sloshing in the tank of the liquid tank truck according to the differential dynamics equations and all pending parameters.

[0012] Optionally, the multi-degree-of-freedom equivalent pendulum model for obtaining the sloshing of liquid in the tank of a liquid tank truck includes: taking a pendulum rod and a concentrated mass as basic units; hingedly connecting one end of the pendulum rod to a point or a concentrated mass in the cross section of the liquid tank; the motion trajectory of the concentrated mass connected to the pendulum rod without a fixed end is a part of an ellipse; allowing the concentrated mass to be hinged to multiple pendulum rods, and allowing one or more concentrated masses to be fixed to a point in the cross section of the liquid tank without being connected to any pendulum rod, so as to establish the multi-degree-of-freedom equivalent pendulum model.

[0013] Optionally, the specific configuration includes a linear and / or nonlinear combination of at least a plurality of simple pendulums and / or elliptical pendulums.

[0014] Optionally, determining the kinetic energy and potential energy of the multi-degree-of-freedom equivalent pendulum model based on the specific structure and the input quantity includes: setting the point of action and positive direction of the output force and torque of the multi-degree-of-freedom equivalent pendulum model in the cross section of the liquid tank, and establishing a plane rectangular coordinate system with the point of action as the origin and the positive direction as the coordinate axis; expressing the position vector of each concentrated mass of the multi-degree-of-freedom equivalent pendulum model in the plane rectangular coordinate system, and taking the first-order and second-order derivatives of the position vector of each concentrated mass in time to obtain the velocity vector and acceleration vector of each concentrated mass; using the position vector, the velocity vector and the acceleration vector to determine the kinetic energy of the multi-degree-of-freedom equivalent pendulum model, as well as the potential energy of the kinetic energy of the multi-degree-of-freedom equivalent pendulum model at a specified zero potential energy point.

[0015] Optionally, the method of identifying all the undetermined parameters of the differential dynamics equation based on the established liquid sloshing model within the cross section of the liquid tank includes: setting multiple output quantities of the liquid sloshing model and the output mode between the output quantities; obtaining output time series data of the multiple output quantities according to preset working conditions, and constructing a cost function of the undetermined parameters of the differential dynamics equation based on the output mode between the output quantities and the output time series data; optimizing the cost function, and identifying all the undetermined parameters of the differential dynamics equation based on the optimized cost function.

[0016] Optionally, the output includes lateral force, vertical force and rolling moment, the preset working condition includes lateral acceleration step excitation condition and / or lateral acceleration sinusoidal fluctuation condition, and the cost function is the non-negative weighted sum of all error terms.

[0017] A second aspect of the present application provides a method for controlling a liquid tank truck against rollover, which is applied to a liquid tank truck, wherein the method includes the following steps: sending vehicle information and liquid filling information obtained based on sensors to a cloud, wherein the cloud inputs the vehicle information and the liquid filling information into a vehicle fine model and a liquid sloshing fine model, respectively, the vehicle fine model outputs a first parameter calibration result, and the liquid sloshing fine model outputs a second parameter calibration result; obtaining the first parameter calibration result and the second parameter calibration result sent by the cloud, and calibrating the vehicle proxy model and the liquid proxy model using the first parameter calibration result and the second parameter calibration result respectively; determining a control target of the liquid tank truck in combination with vehicle state sensor information observations and liquid-related sensor information observations, and performing anti-rollover control on the liquid tank truck based on the control target.

[0018] Optionally, the method of determining the control target of the liquid tank truck by combining vehicle state sensor information observations and liquid-related sensor information observations includes: estimating a first state quantity of the vehicle agent model by combining vehicle state sensor information observations; estimating a second state quantity of the liquid agent model by combining liquid-related sensor information observations, and determining the control target of the liquid tank truck based on the first state quantity and the second state quantity.

[0019] Optionally, the anti-rollover control of the liquid tank truck based on the control target includes: obtaining a first reference value of the vehicle proxy model and a second reference value of the liquid proxy model; determining a first error weight between the output of the vehicle proxy model and the first reference value, and a second error weight between the output of the liquid proxy model and the second reference value; applying the first error weight to the output of the vehicle proxy model to achieve the control target of trajectory tracking, applying the second error weight to the output of the liquid proxy model to achieve the control target of sway suppression, and soft-constraining the range of part of the output of the liquid proxy model to achieve the control target of anti-rollover.

[0020] Optionally, the partial output quantity with soft constraints includes at least I rollover ;

[0021] The output of the liquid proxy model is

[0022] Where X is the x-coordinate of the tractor in the world coordinate system, Y is the y-coordinate of the tractor in the world coordinate system, and θ is the swing angle of the equivalent pendulum model. is the swing angular velocity of the equivalent pendulum model, I rollover is the state variable that characterizes the vehicle rollover state, and ψ1 is the heading angle of the tractor.

[0023] Optionally, the state quantity I representing the vehicle rollover state rollover Using the equivalent lateral load transfer rate (LTR) of the suspension force eql :

[0024] Among them, T w is the average wheelbase of each axle, m is the vehicle mass, g is the acceleration of gravity, k r is the roll stiffness, c is the roll damping, φ is the roll angle, is the roll angular velocity, subscript 1 represents the tractor and subscript 2 represents the trailer.

[0025] According to a third aspect of the present application, an anti-rollover control device for a liquid tank truck is provided, which is applied to the cloud, and comprises: an acquisition module for acquiring vehicle information sent by the liquid tank truck and liquid filling information obtained based on sensors; an input module for inputting the vehicle information and the liquid filling information into a vehicle fine model and a liquid sloshing fine model, respectively, wherein the vehicle fine model outputs a first parameter calibration result, and the liquid sloshing fine model outputs a second parameter calibration result; a sending module for sending the first parameter calibration result and the second parameter calibration result to the liquid tank truck, wherein the liquid tank truck calibrates the vehicle proxy model and the liquid proxy model respectively using the first parameter calibration result and the second parameter calibration result, determines the control target of the liquid tank truck in combination with the vehicle state sensor information observation and the liquid-related sensor information observation, and performs anti-rollover control on the liquid tank truck based on the control target.

[0026] Optionally, the vehicle fine model is a multi-body dynamics model, the liquid sloshing fine model is a finite element model, the vehicle proxy model is a linear simplified dynamics model, and the liquid proxy model is an equivalent pendulum dynamics model.

[0027] Optionally, before inputting the vehicle information and the filling information into the vehicle fine model and the liquid sloshing fine model respectively, the input module further includes: an acquisition unit for acquiring a multi-degree-of-freedom equivalent pendulum model of liquid sloshing in the tank of the liquid tanker; a setting unit for setting the specific configuration and input quantity of the multi-degree-of-freedom equivalent pendulum model, and determining the kinetic energy and potential energy of the multi-degree-of-freedom equivalent pendulum model according to the specific structure and the input quantity; a determination unit for determining the differential dynamics equations of multiple swinging degrees of freedom according to the kinetic energy and potential energy of the multi-degree-of-freedom equivalent pendulum model, and identifying all undetermined parameters of the differential dynamics equations according to the established liquid sloshing model in the cross section of the liquid tank; and a construction unit for constructing a simplified model of liquid sloshing in the tank of the liquid tanker according to the differential dynamics equations and all the undetermined parameters.

[0028] Optionally, the acquisition unit is further used to: take a pendulum rod and a concentrated mass as basic units; hinge one end of the pendulum rod to a point or a concentrated mass within the cross section of the liquid tank; the motion trajectory of the concentrated mass connected to the pendulum rod without a fixed end is a part of an ellipse; allow the concentrated mass to be hinged to multiple pendulum rods, and allow one or more concentrated masses to be fixed at a point in the cross section of the liquid tank without being connected to any pendulum rod, so as to establish the multi-degree-of-freedom equivalent pendulum model.

[0029] Optionally, the specific configuration includes a linear and / or nonlinear combination of at least a plurality of simple pendulums and / or elliptical pendulums.

[0030] Optionally, the setting unit is further used to: set the point of action and positive direction of the output force and torque of the multi-degree-of-freedom equivalent pendulum model in the cross section of the liquid tank, and establish a plane rectangular coordinate system with the point of action as the origin and the positive direction as the coordinate axis; represent the position vectors of each concentrated mass of the multi-degree-of-freedom equivalent pendulum model in the plane rectangular coordinate system, and perform first-order and second-order time derivatives of the position vectors of each concentrated mass to obtain the velocity vector and acceleration vector of each concentrated mass; use the position vector, the velocity vector and the acceleration vector to determine the kinetic energy of the multi-degree-of-freedom equivalent pendulum model, as well as the potential energy of the kinetic energy of the multi-degree-of-freedom equivalent pendulum model at a specified zero potential energy point.

[0031] Optionally, the determination unit is further used to: set multiple output quantities of the liquid sloshing model and the output method between the output quantities; obtain output time series data of the multiple output quantities according to preset working conditions, and construct a cost function of the undetermined parameters of the differential dynamics equation based on the output method between the output quantities and the output time series data; optimize the cost function, and identify all undetermined parameters of the differential dynamics equation based on the optimized cost function.

[0032] Optionally, the output includes lateral force, vertical force and rolling moment, the preset working condition includes lateral acceleration step excitation condition and / or lateral acceleration sinusoidal fluctuation condition, and the cost function is the non-negative weighted sum of all error terms.

[0033] The fourth aspect of the present application provides an anti-rollover control device for a liquid tank truck, which is applied to a liquid tank truck, wherein the device includes: a sending module, which is used to send vehicle information and liquid filling information obtained based on sensors to the cloud, wherein the cloud inputs the vehicle information and the liquid filling information into a vehicle fine model and a liquid sloshing fine model respectively, the vehicle fine model outputs a first parameter calibration result, and the liquid sloshing fine model outputs a second parameter calibration result; a calibration module, which is used to obtain the first parameter calibration result and the second parameter calibration result sent from the cloud, and use the first parameter calibration result and the second parameter calibration result to calibrate the vehicle proxy model and the liquid proxy model respectively; a control module, which is used to determine the control target of the liquid tank truck in combination with the vehicle status sensor information observation and the liquid-related sensor information observation, and perform anti-rollover control on the liquid tank truck based on the control target.

[0034] Optionally, the control module is further used to: estimate a first state quantity of the vehicle agent model in combination with vehicle state sensor information observations; estimate a second state quantity of the liquid agent model in combination with liquid-related sensor information observations, and determine the control target of the tank truck based on the first state quantity and the second state quantity.

[0035] Optionally, the control module is further used to: obtain a first reference value of the vehicle proxy model and a second reference value of the liquid proxy model; determine a first error weight between the output of the vehicle proxy model and the first reference value, and a second error weight between the output of the liquid proxy model and the second reference value; apply the first error weight to the output of the vehicle proxy model to achieve the control goal of trajectory tracking, apply the second error weight to the output of the liquid proxy model to achieve the control goal of sway suppression, and perform soft constraints on the range of part of the output of the liquid proxy model to achieve the control goal of anti-rollover.

[0036] Optionally, the partial output quantity with soft constraints includes at least I rollover ;

[0037] The output of the liquid proxy model is

[0038] Where X is the x-coordinate of the tractor in the world coordinate system, Y is the y-coordinate of the tractor in the world coordinate system, and θ is the swing angle of the equivalent pendulum model. is the swing angular velocity of the equivalent pendulum model, I rollover is the state variable that characterizes the vehicle rollover state, and ψ1 is the heading angle of the tractor.

[0039] Optionally, the state quantity I representing the vehicle rollover state rollover Using the equivalent lateral load transfer rate (LTR) of the suspension force eql :

[0040] Among them, T w is the average wheelbase of each axle, m is the vehicle mass, g is the acceleration of gravity, k r is the roll stiffness, c is the roll damping, φ is the roll angle, is the roll angular velocity, subscript 1 represents the tractor and subscript 2 represents the trailer.

[0041] The fifth embodiment of the present application provides a cloud, including: a memory, a processor, and a computer program stored on the memory and runnable on the processor, wherein the processor executes the program to implement the anti-rollover control method for a tank truck as described in the above embodiment.

[0042] The sixth aspect of the present application provides a liquid tank truck, 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 liquid tank truck anti-rollover control method as described in the above embodiment.

[0043] The seventh aspect of the present application provides an anti-rollover control system for a liquid tank truck, comprising: a liquid tank truck, wherein the liquid tank truck includes a sensor, a communication device and a vehicle computing unit, the communication device communicates with the cloud, the vehicle computing unit internally contains an observation and state estimator and a controller, uses sensor data and a proxy model to obtain vehicle and liquid-related state quantities, and uses the proxy model to achieve anti-rollover control of the liquid tank truck; a cloud, wherein the cloud includes a cloud information space, uses a vehicle fine model and a liquid sloshing fine model that are digital twins of the proxy model to provide parameter calibration services for the proxy model.

[0044] An eighth aspect of the present application provides a computer-readable storage medium on which a computer program is stored. The program is executed by a processor to implement the anti-rollover control method for a tank truck as described in the above embodiment.

[0045] Therefore, this application has at least the following beneficial effects:

[0046] The embodiments of the present application can take advantage of the high computing power of the cloud and the high real-time performance of the vehicle (liquid tanker) to establish a relatively accurate model, transmit the data obtained in real time by the vehicle to the cloud, and the cloud calculates and obtains relatively accurate model calibration parameter results based on the wear demand and transmits them to the vehicle to implement model predictive control. Based on the vehicle-cloud collaborative control architecture, a vehicle-cloud collaborative control architecture of cloud finite element calibration + vehicle-side real-time control has been developed to prevent the liquid tanker from entering a dangerous state, realize early prediction of danger, and ensure driving safety and other beneficial effects. As a result, technical problems such as the inability to implement real-time calculation and control of liquid tanker anti-rollover and the inability to support large amounts of calculation in related technologies are solved.

[0047] 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

[0048] 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:

[0049] 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:

[0050] FIG1 is a flow chart of a method for preventing a tank truck from rolling over according to an embodiment of the present application;

[0051] FIG2 is a schematic diagram of a TruckSim vehicle fine dynamics model provided according to an embodiment of the present application;

[0052] FIG3 is a schematic diagram of a StarCCM+ fine liquid sloshing finite element model provided according to an embodiment of the present application;

[0053] FIG4 is a flow chart of a method for modeling liquid sloshing in a tanker truck according to an embodiment of the present application;

[0054] FIG5 is a schematic diagram of a single-degree-of-freedom multi-mass elliptical pendulum according to one embodiment of the present application;

[0055] FIG6 is a schematic diagram of a multi-degree-of-freedom multi-mass elliptical pendulum provided according to one embodiment of the present application;

[0056] FIG7 is a schematic diagram of a linear combination of a simple pendulum and / or an elliptical pendulum according to one embodiment of the present application;

[0057] FIG8 is a schematic diagram of a nonlinear combination of a simple pendulum and / or an elliptical pendulum according to one embodiment of the present application;

[0058] FIG9 is a schematic diagram of a simple pendulum model provided according to one embodiment of the present application;

[0059] FIG10 is a schematic diagram of an elliptical pendulum model provided according to one embodiment of the present application;

[0060] FIG11 is a schematic diagram of the structure and dynamics of a dual-mass elliptical pendulum according to one embodiment of the present application;

[0061] FIG12 is a schematic diagram of the structure and dynamics of a combined double pendulum provided according to one embodiment of the present application;

[0062] FIG13 is a flow chart of a method for preventing a tank truck from rolling over according to another embodiment of the present application;

[0063] FIG14 is a schematic diagram of a dual-mass elliptical pendulum DMTP model provided according to an embodiment of the present application;

[0064] FIG15 is a schematic diagram of a method for estimating liquid sloshing state based on a liquid level fluctuation sensor according to an embodiment of the present application;

[0065] FIG16 is a schematic diagram of a simplified 6-DOF semi-trailer tank truck model provided according to an embodiment of the present application;

[0066] FIG17 is a schematic diagram of a trajectory tracking + sway suppression + rollover prevention control algorithm provided according to an embodiment of the present application;

[0067] FIG18 is a schematic diagram illustrating the use of a specific model provided according to a specific embodiment of the present application;

[0068] FIG19 is a schematic diagram of a method for preventing a tank truck from rolling over according to a specific embodiment of the present application;

[0069] FIG20 is a flowchart showing a method for controlling a tank truck to prevent rollover according to an embodiment of the present application;

[0070] FIG21 is a schematic diagram of a liquid tank truck anti-rollover control device according to an embodiment of the present application;

[0071] FIG22 is a schematic diagram of an anti-rollover control device for a liquid tank truck according to another embodiment of the present application;

[0072] FIG23 is a schematic diagram of a tank truck anti-rollover control system according to an embodiment of the present application;

[0073] Figure 24 is an architectural diagram of the anti-rollover control system for a liquid tank truck provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0074] The following describes in detail embodiments of the present application, examples of which 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.

[0075] The following describes the anti-rollover control method, device, cloud, tanker and control system of the liquid tanker in the embodiment of the present application with reference to the accompanying drawings. In view of the problem mentioned in the above background technology that the finite element model has a huge amount of calculation and cannot be calculated in real time, and the vehicle-side computing power cannot support the huge amount of calculation in the liquid proxy model, the present application provides a liquid tanker anti-rollover control method. In this method, the characteristics of large computing power of the cloud and high real-time of the vehicle side are utilized. Based on the vehicle-cloud collaborative control architecture, a vehicle-cloud collaborative control architecture of cloud-based finite element calibration + vehicle-side real-time control is developed. Cloud computing is used to obtain accurate model parameter calibration results to realize model predictive control. As a result, the problems of the inability to realize real-time calculation and control of liquid tanker anti-rollover and the inability to support huge amounts of calculation in the related technology are solved.

[0076] Specifically, Figure 1 is a flow chart of a liquid tank truck anti-rollover control method provided in an embodiment of the present application.

[0077] As shown in FIG1 , the anti-rollover control method for a liquid tank truck, applied in the cloud, includes the following steps:

[0078] In step S101, vehicle information sent by the tank truck and liquid filling information obtained by the sensor are acquired.

[0079] In step S102 , the vehicle information and the filling liquid information are input into the vehicle fine model and the liquid sloshing fine model respectively, wherein the vehicle fine model outputs a first parameter calibration result, and the liquid sloshing fine model outputs a second parameter calibration result.

[0080] The vehicle fine model is a multibody dynamics model, which can be built using the semi-trailer tanker model (empty model excluding liquid) established in TruckSim. The liquid sloshing fine model is a finite element model, which can be built using StarCCM+ software. The specific model building process is described below.

[0081] It is understood that in this embodiment of the application, vehicle information transmitted by the tanker truck can be input into the vehicle fine-tuning model for parameter calibration, and liquid filling information transmitted by the tanker truck, obtained by sensors, can be input into the liquid sloshing fine-tuning model for parameter calibration, thereby obtaining first and second parameter calibration results, respectively. This leverages the cloud's massive computing power to achieve relatively accurate model parameters through remote computing.

[0082] The TruckSim vehicle dynamics model is shown in Figure 2. The establishment of an accurate vehicle model in TruckSim is divided into the following steps:

[0083] The fixed model parameters are set based on vehicle experiments and measurement data. The trailer's inertia parameters are calculated based on the current fluid filling status. To build a vehicle-fluid coupling co-simulation platform and implement co-simulation between TruckSim and StarCCM+ software, the model's input and output parameters also need to be set. The inputs primarily include the lateral and vertical forces applied to the axle by the outside world, as well as the roll moment applied to the trailer's sprung mass. These forces are derived from the output of the finite element fluid model. The outputs include at least the trailer's lateral acceleration and roll angle, which are used to calculate the acceleration component applied to the finite element model.

[0084] The StarCCM+ liquid sloshing finite element model is shown in Figure 3. The establishment of the StarCCM+ finite element model of liquid sloshing in the tank is divided into the following steps:

[0085] A precise 2D mesh is applied to the tank cross section to ensure that details of the liquid sloshing can be captured. This can also be expanded to a 3D finite element model on the cloud platform. Two Euler phases, liquid and air, and their physical properties are set, along with other model parameters. Inputs and outputs for the co-simulation with TruckSim are configured in the form of reports and plots. The finite element model outputs lateral force, vertical force, and rolling moment to the vehicle model, and the acceleration component of the liquid is continuously updated during the simulation to reflect the vehicle's impact on the liquid.

[0086] In step S103, the first parameter calibration result and the second parameter calibration result are sent to the tank truck, wherein the tank truck uses the first parameter calibration result and the second parameter calibration result to calibrate the vehicle proxy model and the liquid proxy model respectively, combines the vehicle state sensor information observation and the liquid-related sensor information observation to determine the control target of the tank truck, and performs anti-rollover control on the tank truck based on the control target.

[0087] Among them, the vehicle agent model is a linear simplified dynamic model; the liquid agent model is an equivalent pendulum dynamic model; the vehicle state sensors include GPS, IMU, wheel speed sensors, etc.; the liquid-related sensors can be free liquid surface fluctuation sensors, which can ensure real-time measurement of the average inclination angle of the liquid surface and the liquid level under static conditions; the control objectives are trajectory tracking + sway suppression + anti-rollover.

[0088] It can be understood that in the embodiment of the present application, the cloud can send the first parameter calibration result and the second parameter calibration result to the tank truck. The tank truck uses the first parameter calibration result to calibrate the vehicle proxy model and uses the second parameter calibration result to calibrate the liquid proxy model. The control target of the tank truck is determined in combination with the vehicle and vehicle status sensor information observations and liquid-related sensor information observations. The tank truck is controlled to prevent rollover based on the control target, thereby realizing early prediction of the liquid dynamics of the tank truck and preventing the vehicle from entering a dangerous state.

[0089] The following describes a method for modeling liquid sloshing in a tank of a liquid tank truck according to an embodiment of the present application in conjunction with FIG4 .

[0090] As shown in FIG4 , the modeling method for liquid sloshing in the tank of a liquid tank truck includes the following steps:

[0091] In step S401, a multi-degree-of-freedom equivalent pendulum model of liquid sloshing in a tank of a liquid tank truck is obtained.

[0092] In one embodiment of the present application, a multi-degree-of-freedom equivalent pendulum model of liquid sloshing in a tank of a liquid tank truck is obtained, including: taking a pendulum rod and a concentrated mass as basic units; hingedly connecting one end of the pendulum rod to a point or a concentrated mass in the cross section of the liquid tank; the motion trajectory of the concentrated mass connected to the pendulum rod without a fixed end is a part of an ellipse; allowing the concentrated mass to be hinged to multiple pendulum rods, and allowing one or more concentrated masses to be fixed to a point in the cross section of the liquid tank without being connected to any pendulum rod, so as to establish a multi-degree-of-freedom equivalent pendulum model.

[0093] In the embodiment of the present application, the multi-degree-of-freedom equivalent pendulum model is a pendulum comprising n m lumped masses and n x A specific input system, the rules include the following aspects:

[0094] Rule 1: The pendulum and concentrated mass are used as basic units;

[0095] Rule 2: One end of the pendulum is hinged and fixed to a point in the cross section of the tank, or is not fixed, or is connected to a concentrated mass;

[0096] Rule 3: When a pendulum has no fixed end, the trajectory of the concentrated mass to which it is attached must be a portion of an ellipse;

[0097] Specification 4, centralized mass can articulate multiple rocker arms;

[0098] Rule 5: Allow n fm A concentrated mass is fixed at a certain point on the cross section of the tank without connecting any pendulum, where n fm ≤n m -2.

[0099] Furthermore, when the above-mentioned specific connection rules are met, n m lumped masses and n x The system with specific input quantities is called "multi-degree-of-freedom pendulum model". m +n x -n fm generalized coordinates and n m -n fm degrees of freedom.

[0100] For ease of understanding, Figure 5 of the embodiment of this application shows a single-degree-of-freedom, multi-mass elliptical pendulum. Considering the above rules, this model does not conform to the specific connection method in the specific structure of the above-mentioned model in the embodiment of this application, because multiple concentrated masses are connected to one pendulum rod, violating the above Rule 2. Figures 6, 7, and 8 respectively show situations that conform to the embodiment of this application, with Figure 8 showing a multi-degree-of-freedom, multi-mass elliptical pendulum.

[0101] It should be noted that the embodiments of the present application are not limited to a liquid tank cross-section of a specific shape, and are applicable to most convex cross-section shapes without longitudinal baffles.

[0102] In step S402, a specific configuration and input quantity of the multi-degree-of-freedom equivalent pendulum model are set, and the kinetic energy and potential energy of the multi-degree-of-freedom equivalent pendulum model are determined according to the specific configuration and input quantity.

[0103] It is understandable that the embodiments of the present application may specify a specific configuration of the simplified liquid sloshing model, which includes n m Concentrated masses and their specific connection methods (n m ≥2), and n x A specific input quantity.

[0104] Specific configurations may include linear and / or nonlinear combinations of at least multiple simple pendulums and / or elliptical pendulums. A schematic diagram of a simple pendulum is shown in Figure 9 , and a schematic diagram of an elliptical pendulum is shown in Figure 10 . A linear combination of at least two simple and / or elliptical pendulums (in parallel) is shown in Figure 6 , while a nonlinear combination of at least two simple and / or elliptical pendulums (in series) is shown in Figure 7 .

[0105] In one embodiment of the present application, the kinetic energy and potential energy of a multi-degree-of-freedom equivalent pendulum model are determined according to a specific structure and input quantity, including: setting the point of action and positive direction of the output force and torque of the multi-degree-of-freedom equivalent pendulum model within the cross section of the liquid tank, and establishing a plane rectangular coordinate system with the point of action as the origin and the positive direction as the coordinate axis; expressing the position vector of each concentrated mass of the multi-degree-of-freedom equivalent pendulum model in the plane rectangular coordinate system, and taking the first-order and second-order derivatives of the position vector of each concentrated mass in time to obtain the velocity vector and acceleration vector of each concentrated mass; using the position vector, velocity vector and acceleration vector to determine the kinetic energy of the multi-degree-of-freedom equivalent pendulum model, as well as the potential energy of the kinetic energy of the multi-degree-of-freedom equivalent pendulum model at a specified zero potential energy point.

[0106] The embodiment of the present application determines the kinetic energy and potential energy of the multi-degree-of-freedom equivalent pendulum model according to the specific structure and input quantity, which can be described in detail in conjunction with the following steps:

[0107] Step 1: Specify the points of action of the output force and torque of the liquid sloshing system within the cross section of the tank: the bottom of the tank and the positive direction (right and top, clockwise). Establish a plane rectangular coordinate system within the cross section of the tank with the bottom of the tank as the origin and the right and top positive directions as the coordinate axes.

[0108] Step 2: Express the position vectors of each concentrated mass of the model in the plane rectangular coordinate system in step 1:

[0109] Step 3: Calculate the first-order and second-order derivatives of the position vector of each concentrated mass in step 2 with respect to time to obtain the velocity vector and acceleration vector of each concentrated mass:

[0110] Step 4. Use the position vector in step 2 and the velocity vector and acceleration vector in step 3 to express the system kinetic energy T:

[0111] And the potential energy U of the system at the specified zero potential energy point, when When the plane is a zero potential energy surface, the system potential energy expression is:

[0112] In step S403, differential dynamic equations of multiple swing degrees of freedom are determined based on the kinetic energy and potential energy of the multi-degree-of-freedom equivalent pendulum model, and all undetermined parameters of the differential dynamic equations are identified based on the established liquid sloshing model within the cross section of the liquid tank.

[0113] It can be understood that the embodiment of the present application can establish the differential dynamic equations of all n swinging degrees of freedom of the model by solving the system Lagrange equation, and then use the finite element simulation data of the modeled system under specified standard working conditions to calibrate the unknown parameters in the differential equations.

[0114] In one embodiment of the present application, all the undetermined parameters of the differential dynamics equation are identified based on the established liquid sloshing model within the cross section of the liquid tank, including: setting multiple output quantities of the liquid sloshing model and the output mode between the output quantities; obtaining output time series data of the multiple output quantities according to preset working conditions, and constructing a cost function of the undetermined parameters of the differential dynamics equation based on the output mode between the output quantities and the output time series data; optimizing the cost function, and identifying all the undetermined parameters of the differential dynamics equation based on the optimized cost function.

[0115] The output quantities include lateral force, vertical force, and roll moment. The preset operating conditions include lateral acceleration step excitation conditions and / or lateral acceleration sinusoidal fluctuation conditions, which can also be used under more intense inputs. The cost function is the non-negative weighted sum of all error terms.

[0116] Specifically, the embodiment of the present application can utilize the kinetic energy T and potential energy U in the above embodiment, according to the Lagrange equation:

[0117] Solve the differential dynamics equations of each degree of freedom of the system in sequence, where t is time and θi represents the generalized coordinates corresponding to the ith degree of freedom. Preferably, the following Matlab software function is used: functionalDerivative(L, [θ i ,…])==[0,0]

[0118] You can get the specific solution result expression by adding and are sorted out to form other variables respectively about The second-order differential equation of is solved and the damping term is added at the end.

[0119] Through the above f i Expressions can be used to construct The differential equations of the model are solved by numerical integration method, where the input of the two equations is All other variables are used as state quantities of the differential equation group. After being assigned initial values, the results are saved after each operation. When the next time step needs to be calculated, the state quantities saved in the previous step are taken out as initial values.

[0120] Furthermore, the embodiment of the present application can use StarCCM+ or ANSYS / Fluent software to establish a two-dimensional finite element liquid sloshing model in the cross section of the liquid tank, which includes two Euler phases, the medium is air and the medium is gasoline, and n is specified. y =3 output quantities, namely lateral force F y , vertical force F z and the rolling moment M x This is preferably achieved by setting the desired output report and creating its plot form settings. y When the simulation is finished, all time series data can be exported through the export function of the drawing.

[0121] In the actual implementation process, the embodiment of the present application can specify n cd =5 standard working conditions, namely lateral acceleration a y =1,2,3,4,5m / s 2 The lateral acceleration excitation step condition is simulated for 10s. cd Standard working conditions get n y The total number of outputs is n cd n y =5×3=15 sets of output time series data.

[0122] Furthermore, the embodiment of the present application can construct a cost function for the undetermined parameters of the differential dynamics equation. The cost function is the non-negative weighted sum of all error terms with the same weight, and the specific form is as follows:

[0123] In order to avoid overfitting of parameters, which results in the model only fitting the system response under a specific acceleration used in parameter identification, a is selected. y =1,2,3,4,5m / s 2 The average value of the mean square error of the three-axis forces under the five working conditions is used as the final cost quantity to construct the cost function as shown in formula (8).

[0124] Right now

[0125] in are the time averages of the three-axis forces in the CFD step data.

[0126] Indicates the lateral acceleration a yThe lateral force F output by the model under step excitation y , Indicates the lateral acceleration a y The vertical force F output by the model under step excitation z , Indicates the lateral acceleration a y The roll moment M output by the model under step excitation is x The above three-axis force outputs are all in the form of time series, k represents the output at the kth moment, and there are N+1 moments in total.

[0127] Furthermore, the embodiment of the present application can identify all the unknown parameters in the differential dynamics equation by optimizing the cost equation using an optimal algorithm. The optimization algorithm can be a genetic algorithm, a particle swarm algorithm, an ant colony algorithm, or a simulated annealing algorithm, without specific limitation.

[0128] In step S404, a simplified model of liquid sloshing in the tank of the liquid tank truck is constructed based on the differential dynamics equation and all undetermined parameters.

[0129] Based on the above examples, the present invention can construct a simplified liquid sloshing model by combining the identified undetermined parameters with the differential dynamics equations. Given an initial state and input, the model state at any moment can be solved. Given the state at any moment, the lateral force, vertical force, and rolling moment output at that moment can be calculated.

[0130] Therefore, by setting the specific configuration and input of the multi-degree-of-freedom equivalent pendulum model, the kinetic energy and potential energy of the multi-degree-of-freedom equivalent pendulum model are determined, and the differential dynamics equations of multiple swing degrees of freedom are determined by the kinetic energy and potential energy of the multi-degree-of-freedom equivalent pendulum model. Then, the finite element simulation data of the modeled system under specified standard working conditions are used to calibrate the undetermined parameters in the differential equations, and a simplified liquid sloshing model is constructed based on the identified undetermined parameters and differential dynamics equations. Compared with the existing single-degree-of-freedom equivalent pendulum model, the multi-degree-of-freedom equivalent pendulum model greatly improves the fitting accuracy of the liquid tank dynamics system. It has high versatility and is not limited to a specific shape of liquid tank cross-section. It is applicable to most convex cross-section shapes without longitudinal baffles. It also greatly improves the accuracy of liquid sloshing modeling without increasing the amount of calculation, solving the problems of the limited applicability of the model in related technologies and the low accuracy of liquid sloshing modeling.

[0131] The following embodiments of the present application can be combined with specific embodiments 1 and 2 to describe in detail the steps of the above-mentioned method for modeling liquid sloshing in the tank of a liquid tank truck.

[0132] 1. Specific embodiment 1

[0133] As shown in Figure 8, a two-degree-of-freedom, two-mass elliptical pendulum model was used to establish the liquid sloshing model for an elliptical cross-section tank (major axis a = 1.24 m, minor axis b = 0.9 m, tank length 11 m, and tank fill ratio 50%). The two-mass elliptical pendulum model is essentially a nonlinear combination of a simple pendulum model and an elliptical pendulum model. The following describes only the differences from the general embodiment described above.

[0134] In the embodiment of the present application, the concentrated mass n m = 3, including 1 fixed mass and 2 swinging masses. The two swinging masses are connected in series, the trajectory of the first swinging mass is part of the ellipse, and the specified input is the lateral acceleration vertical acceleration and roll angular acceleration That is n x =3.

[0135] In the embodiment of the present application, the position vector in step 2 is:

[0136] In the embodiment of the present application, the velocity vector in step 3 and the acceleration vector for

[0137] In the embodiment of the present application, the identified undetermined coefficients of the dynamic equation can be found in Table 1, where Table 1 is an independent parameter table of the two-mass elliptical pendulum model. For parameters not specified in Table 1, please refer to the structure and dynamics diagram of the two-mass elliptical pendulum shown in Figure 11.

[0138] Table 1

[0139] 2. Specific embodiment 2

[0140] As shown in Figure 12, a 2-DOF combined double pendulum model was used to establish a liquid sloshing model for an elliptical cross-section liquid tank (major axis a = 1.24 m, minor axis b = 0.9 m, tank length 11 m, and tank fill ratio 50%). The combined double pendulum model is essentially a linear combination of a simple pendulum model and an elliptical pendulum model. The following describes only the differences from the aforementioned Specific Example 1.

[0141] In the embodiment of the present application, the concentrated mass n m = 3, including 1 fixed mass and 2 swinging masses. The two swinging masses are connected in series, the trajectory of the first swinging mass is part of the ellipse, and the specified input is the lateral acceleration vertical acceleration and roll angular acceleration That is n x =3.

[0142] In the embodiment of the present application, the position vector of step 2 is:

[0143] In the embodiment of the present application, the velocity vector of step 3 is and the acceleration vector for

[0144] In the embodiment of the present application, the undetermined coefficients of the dynamic equation identified are shown in Table 2, which is a table of independent parameters of the combined double pendulum elliptical pendulum model. Parameters not described in Table 2 can be referred to the structural and dynamic schematic diagram of the combined double pendulum shown in Figure 12.

[0145] Table 2

[0146] According to the anti-rollover control method for a liquid tanker truck proposed in the embodiment of the present application, the computing advantage of the high computing power of the cloud can be utilized to obtain more accurate model parameter calibration results, and the model parameter calibration parameter results are transmitted to the vehicle (liquid tanker) end for realizing model predictive control, which can predict the vehicle and liquid dynamics in advance, thereby preventing the vehicle from entering a dangerous state.

[0147] The anti-rollover control method for a liquid tank truck described in the above embodiment is applied to the cloud, and the anti-rollover control method for a liquid tank truck described in the following embodiment is applied to a liquid tank truck.

[0148] FIG13 is a flow chart of a method for preventing a tank truck from rolling over provided in an embodiment of the present application.

[0149] As shown in FIG13 , the liquid tank truck anti-rollover control method, applied to the liquid tank truck, includes the following steps:

[0150] In step S1301, the vehicle information and the filling information obtained by the sensor are sent to the cloud, wherein the cloud inputs the vehicle information and the filling information into the vehicle fine model and the liquid sloshing fine model respectively, the vehicle fine model outputs the first parameter calibration result, and the liquid sloshing fine model outputs the second parameter calibration result.

[0151] It can be understood that the liquid tanker in the embodiment of the present application can send vehicle information and liquid filling information obtained based on sensors to the cloud in real time, making full use of the high real-time advantage of the vehicle side, and further perform parameter calibration calculation on the vehicle information and liquid filling information on the cloud. The cloud inputs the vehicle information and liquid filling information into the vehicle fine model and the liquid sloshing fine model respectively for calibration, and obtains the first parameter calibration result and the second parameter calibration result.

[0152] In step S1302, the first parameter calibration result and the second parameter calibration result sent from the cloud are obtained, and the vehicle proxy model and the liquid proxy model are calibrated respectively using the first parameter calibration result and the second parameter calibration result.

[0153] It can be understood that the liquid tanker in the embodiment of the present application can obtain the first parameter calibration result and the second parameter calibration result sent from the cloud, use the first parameter calibration result to calibrate the vehicle proxy model, and use the second parameter calibration result to calibrate the liquid proxy model.

[0154] In step S1303, the control target of the tank truck is determined by combining the vehicle state sensor information observation and the liquid-related sensor information observation, and the tank truck is subjected to anti-rollover control based on the control target.

[0155] It can be understood that the embodiments of the present application can combine vehicle status sensor observations to determine the control target of the tank truck, and further perform anti-rollover control on the tank truck based on the control target, thereby achieving early prediction of the liquid dynamics of the tank truck and preventing the vehicle from entering a dangerous state.

[0156] It should be noted that, in the embodiment of the present application, the tank truck end can perform anti-rollover control on the tank truck based on the control target through a controller, and the controller can be a multi-constraint MPC controller; in the embodiment of the present application, a double-mass elliptical pendulum (Double Mass Trammel Pendulum, DMTP) model can be used as a more accurate nonlinear liquid sloshing proxy model to construct the motion equations in the observer.

[0157] The two-degree-of-freedom (DOF) dual-mass elliptical pendulum (DMTP) model, shown in Figure 14, consists of three lumped masses: two oscillating masses and one fixed mass. These masses can be excited by lateral, vertical, and roll accelerations from the vehicle model. The derivation uses the Lagrange method. Once the position, velocity, and acceleration vectors of the three lumped masses are expressed, the Lagrange equations can be solved to obtain the differential dynamic equations for the two oscillating degrees of freedom. Given a given input, the three-dimensional forces can be calculated at any given moment. While the DMTP model is more accurate but highly nonlinear, it is not currently suitable for use in vehicle controllers. However, it can be used to design the equations of motion for observers.

[0158] In an embodiment of the present application, the control target of the liquid tank truck is determined in combination with vehicle state sensor information observations and liquid-related sensor information observations, including: estimating a first state quantity of the vehicle agent model in combination with the vehicle state sensor information observations; estimating a second state quantity of the liquid agent model in combination with the liquid-related sensor information observations, and determining the control target of the liquid tank truck based on the first state quantity and the second state quantity.

[0159] It can be understood that the embodiment of the present application can estimate the first state quantity of the vehicle proxy model required for control based on the calibration results obtained in the above steps combined with the vehicle state sensor observation, and estimate the second state quantity of the liquid proxy model required for control combined with the liquid-related sensor information observation, and determine the control target of the tank truck based on the first state quantity and the second state quantity.

[0160] Among them, the liquid-related sensor can be a free liquid surface fluctuation sensor. The liquid sloshing state estimation method using the liquid surface fluctuation sensor in the embodiment of the present application is shown in Figure 15. The average liquid surface inclination data obtained by the liquid surface fluctuation sensor is smoothed by the Luenberger observer, and the smoothed angle is then input into the unscented Kalman filter that uses the DMTP model to build the motion equation, and finally the estimated equivalent pendulum angle corresponding to the liquid sloshing state is output.

[0161] It should be noted that the embodiment of the present application can use a 5DOF simplified semi-trailer model to estimate the state quantity required for vehicle model control, and use a 6DOF simplified semi-trailer tank truck model as a prediction model used for model predictive control.

[0162] Among them, the 6-DOF simplified semi-trailer tanker model used for the tanker-side model predictive controller is shown in Figure 16. It is a combination of a 5-DOF semi-trailer model and a linearized simple pendulum agent model. The two interact and influence each other through force and acceleration. After integrating them and expanding the relevant state quantities according to the controller requirements, a 6-DOF semi-trailer tanker agent model can be obtained.

[0163] In an embodiment of the present application, anti-rollover control is performed on a tank truck based on a control objective, including: obtaining a first reference value of a vehicle proxy model and a second reference value of a liquid proxy model; determining a first error weight between an output of the vehicle proxy model and the first reference value, and a second error weight between an output of the liquid proxy model and the second reference value; applying a first error weight to the output of the vehicle proxy model to achieve a control objective of trajectory tracking, applying a second error weight to the output of the liquid proxy model to achieve a control objective of sway suppression, and performing soft constraints on the range of part of the output of the liquid proxy model to achieve the control objective of anti-rollover.

[0164] The first reference value and the second reference value can be set according to specific circumstances and are not specifically limited thereto.

[0165] It can be understood that the embodiment of the present application achieves the control goals of trajectory tracking and sway suppression by applying error weights to the outputs of the vehicle proxy model and the liquid proxy model relative to their corresponding reference values, and achieves the control goal of anti-rollover by softly constraining the range of some outputs.

[0166] Specifically, the embodiment of the present application can determine the first error weight between the output of the vehicle proxy model and the first reference value, determine the second error weight between the output of the liquid proxy model and the second reference value, apply the first error weight to the output of the vehicle proxy model to achieve the control goal of trajectory tracking, apply the second error weight to the output of the liquid proxy model to achieve the control goal of sway suppression, and achieve the control goal of anti-rollover by softly constraining the range of part of the output of the liquid proxy model, wherein the following formula is used to achieve the trajectory tracking + sway suppression + anti-rollover control goals, and the schematic diagram of the trajectory tracking + sway suppression + anti-rollover control algorithm is shown in Figure 17.

[0167] In the embodiment of the present application, the output quantity of the soft constraint is at least I rollover ;

[0168] The output of the liquid proxy model is

[0169] Where X is the x-coordinate of the tractor in the world coordinate system, Y is the y-coordinate of the tractor in the world coordinate system, and θ is the swing angle of the equivalent pendulum model. is the swing angular velocity of the equivalent pendulum model, I rollover is the state variable that characterizes the vehicle rollover state, and ψ1 is the heading angle of the tractor.

[0170] In the embodiment of the present application, the state quantity I representing the vehicle rollover state rollover Using the equivalent lateral load transfer rate (LTR) of the suspension force eql :

[0171] Among them, T w is the average wheelbase of each axle, m is the vehicle mass, g is the acceleration of gravity, k r is the roll stiffness, c is the roll damping, φ is the roll angle, is the roll angular velocity, subscript 1 represents the tractor and subscript 2 represents the trailer.

[0172] It should be noted that in the above formula of the lateral load transfer rate, subscript 1 represents the tractor and subscript 2 represents the trailer. For an integral liquid tank truck without a trailer, the value of subscript 1 can be substituted into the value of subscript 2.

[0173] According to the anti-rollover control method for a liquid tank truck proposed in an embodiment of the present application, the high real-time performance of the vehicle side is utilized to send the vehicle information and liquid filling information of the liquid tank truck to the cloud in real time, further control the model processing data on the cloud side, receive the model parameter calibration results sent from the cloud side, and utilize the model predictive control on the vehicle side to achieve early prediction of the dynamics of the liquid tank truck and the liquid, thereby preventing the liquid tank truck from entering a dangerous state.

[0174] In combination with the anti-rollover control method for liquid tank trucks applied to the cloud and liquid tank trucks respectively described in the above embodiments, the anti-rollover control method for liquid tank trucks of the present application is described below through a specific embodiment, wherein the specific use of the model used in this embodiment is shown in Figure 18, including the cloud-side precise model (TruckSim vehicle fine dynamics model, StarCCM+ liquid fine finite element model), and the vehicle-side proxy model (5DOF simplified semi-trailer, linear simple pendulum LSP, 6DOF simplified semi-trailer, dual-mass elliptical pendulum DMTP).

[0175] The following will describe the anti-rollover control method for a tank truck according to this specific embodiment with reference to FIG19 . The specific expansion diagram is shown in FIG20 , and includes the following steps:

[0176] Step 1: The cloud adjusts the vehicle and liquid sloshing models established on the cloud based on the vehicle information sent by the vehicle and the liquid filling information obtained by the sensors. Based on the calculation results of the fine models, the cloud performs the required calibration calculations for the vehicle and liquid proxy models, and sends the calibration results to the vehicle. Preferably, the vehicle fine model is a multibody dynamics model, preferably built using TruckSim software; preferably, the liquid fine model is a finite element model, preferably built using StarCCM+ software; preferably, the vehicle proxy model is a linear simplified dynamics model; and preferably, the liquid proxy model is an equivalent pendulum dynamics model.

[0177] Step 2: The vehicle-side observation and state estimator observes and estimates the vehicle proxy model state quantities required for control based on the vehicle state sensor information, and observes and estimates the liquid proxy model state quantities required for control based on the liquid-related sensor information. Preferably, the vehicle state sensors include GPS, IMU, and wheel speed sensors; preferably, the liquid state sensor is a free surface fluctuation sensor capable of real-time measurement of the average liquid surface inclination and static liquid level.

[0178] Step 3: The vehicle-side controller performs control according to the control target based on the state information in step 2. Preferably, the controller is a multi-constrained MPC controller, and preferably, the control target is trajectory tracking + sway suppression + rollover prevention.

[0179] Step 4: Repeat steps 2-3 and simultaneously perform other vehicle-cloud collaborative control tasks, such as global path planning and cloud-controlled predictive cruise control.

[0180] In summary, the anti-rollover control method for liquid tank trucks of the present application obtains accurate model parameters by establishing a relatively accurate model and utilizing cloud computing, which can be used to implement model predictive control. Compared with the model-free adaptive control MFAC, it can predict the dynamics of the vehicle and liquid in advance, thereby preventing the vehicle from entering a dangerous state, rather than rescuing the vehicle after it has entered a dangerous state; it makes full use of the large computing power of the cloud and the high real-time characteristics of the vehicle side, and develops a vehicle-cloud collaborative control architecture based on the vehicle-cloud collaborative control architecture of cloud-based finite element calibration + vehicle-side real-time control.

[0181] Next, the anti-rollover control device for a tank truck proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0182] Figure 21 is a block diagram of the anti-rollover control device for a liquid tank truck according to an embodiment of the present application.

[0183] As shown in FIG21 , the liquid tank truck anti-rollover control device 10 is applied in the cloud, and includes: an acquisition module 101 , an input module 102 and a sending module 103 .

[0184] Among them, the acquisition module 101 is used to obtain the vehicle information sent by the tank truck and the liquid filling information obtained based on the sensor; the input module 102 is used to input the vehicle information and the liquid filling information into the vehicle fine model and the liquid sloshing fine model respectively, wherein the vehicle fine model outputs the first parameter calibration result, and the liquid sloshing fine model outputs the second parameter calibration result; the sending module 103 is used to send the first parameter calibration result and the second parameter calibration result to the tank truck, wherein the tank truck uses the first parameter calibration result and the second parameter calibration result to calibrate the vehicle proxy model and the liquid proxy model respectively, and determines the control target of the tank truck in combination with the vehicle state sensor information observation and the liquid-related sensor information observation, and performs anti-rollover control on the tank truck based on the control target.

[0185] In the embodiment of the present application, the vehicle fine model is a multi-body dynamics model, the liquid sloshing fine model is a finite element model, the vehicle proxy model is a linear simplified dynamics model, and the liquid proxy model is an equivalent pendulum dynamics model.

[0186] In an embodiment of the present application, before the vehicle information and the filling information are respectively input into the vehicle fine model and the liquid sloshing fine model, the input module 102 also includes: an acquisition unit for acquiring a multi-degree-of-freedom equivalent pendulum model of liquid sloshing in the tank of the liquid tanker; a setting unit for setting the specific configuration and input quantity of the multi-degree-of-freedom equivalent pendulum model, and determining the kinetic energy and potential energy of the multi-degree-of-freedom equivalent pendulum model according to the specific structure and input quantity; a determination unit for determining the differential dynamics equations of multiple swinging degrees of freedom according to the kinetic energy and potential energy of the multi-degree-of-freedom equivalent pendulum model, and identifying all pending parameters of the differential dynamics equations according to the established liquid sloshing model in the cross section of the liquid tank; and a construction unit for constructing a simplified model of liquid sloshing in the tank of the liquid tanker according to the differential dynamics equations and all pending parameters.

[0187] In an embodiment of the present application, the acquisition unit is further used to: take a pendulum rod and a concentrated mass as basic units; hinge one end of the pendulum rod to a point or a concentrated mass within the cross section of the liquid tank; the motion trajectory of the concentrated mass connected to the pendulum rod without a fixed end is a part of an ellipse; allow the concentrated mass to be hinged to multiple pendulum rods, and allow one or more concentrated masses to be fixed at a point in the cross section of the liquid tank without being connected to any pendulum rod, so as to establish a multi-degree-of-freedom equivalent pendulum model.

[0188] In the embodiment of the present application, the specific configuration includes a linear and / or nonlinear combination of at least a plurality of simple pendulums and / or elliptical pendulums.

[0189] In an embodiment of the present application, a setting unit is further used to: set the point of action and positive direction of the output force and torque of the multi-degree-of-freedom equivalent pendulum model within the cross section of the liquid tank, and establish a plane rectangular coordinate system with the point of action as the origin and the positive direction as the coordinate axis; represent the position vectors of each concentrated mass of the multi-degree-of-freedom equivalent pendulum model in the plane rectangular coordinate system, and perform first-order and second-order time derivatives of the position vector of each concentrated mass to obtain the velocity vector and acceleration vector of each concentrated mass; use the position vector, velocity vector and acceleration vector to determine the kinetic energy of the multi-degree-of-freedom equivalent pendulum model, as well as the potential energy of the kinetic energy of the multi-degree-of-freedom equivalent pendulum model at a specified zero potential energy point.

[0190] In an embodiment of the present application, multiple output quantities of a liquid sloshing model and an output method between the output quantities are set; output time series data of the multiple output quantities are obtained according to preset working conditions, and a cost function of the undetermined parameters of the differential dynamics equation is constructed based on the output method between the output quantities and the output time series data; the cost function is optimized, and all the undetermined parameters of the differential dynamics equation are identified based on the optimized cost function.

[0191] In an embodiment of the present application, the output quantities include lateral force, vertical force and roll moment, the preset working conditions include lateral acceleration step excitation conditions and / or lateral acceleration sinusoidal fluctuation conditions, and the cost function is the non-negative weighted sum of all error terms.

[0192] It should be noted that the above explanation of the embodiment of the liquid tank truck anti-rollover control method is also applicable to the liquid tank truck anti-rollover control device of this embodiment, and will not be repeated here.

[0193] According to the anti-rollover control device for a liquid tanker truck proposed in the embodiment of the present application, the computing advantage of the high computing power of the cloud can be utilized to obtain more accurate model parameter calibration results, and the model parameter calibration parameter results are transmitted to the vehicle side for realizing model predictive control, which can predict the vehicle and liquid dynamics in advance, thereby preventing the vehicle from entering a dangerous state.

[0194] As shown in FIG22 , the liquid tank truck anti-rollover control device 10 is applied to a liquid tank truck and includes: a sending module 201 , a calibration module 202 and a control module 203 .

[0195] Among them, the sending module 201 is used to send vehicle information and filling information obtained based on the sensor to the cloud, wherein the cloud inputs the vehicle information and filling information into the vehicle fine model and the liquid sloshing fine model respectively, the vehicle fine model outputs the first parameter calibration result, and the liquid sloshing fine model outputs the second parameter calibration result; the calibration module 202 is used to obtain the first parameter calibration result and the second parameter calibration result sent by the cloud, and use the first parameter calibration result and the second parameter calibration result to calibrate the vehicle proxy model and the liquid proxy model respectively; the control module 203 is used to determine the control target of the tank truck in combination with the vehicle status sensor information observation and the liquid-related sensor information observation, and perform anti-rollover control on the tank truck based on the control target.

[0196] In an embodiment of the present application, the control module 300 is further used to: estimate the first state quantity of the vehicle agent model in combination with the vehicle state sensor information observation; estimate the second state quantity of the liquid agent model in combination with the liquid-related sensor information observation, and determine the control target of the tanker truck based on the first state quantity and the second state quantity.

[0197] In an embodiment of the present application, the control module 300 is further used to: obtain a first reference value of the vehicle proxy model and a second reference value of the liquid proxy model; determine a first error weight between the output of the vehicle proxy model and the first reference value, and a second error weight between the output of the liquid proxy model and the second reference value; apply the first error weight to the output of the vehicle proxy model to achieve the control goal of trajectory tracking, apply the second error weight to the output of the liquid proxy model to achieve the control goal of sway suppression, and soft-constrain the range of part of the output of the liquid proxy model to achieve the control goal of anti-rollover.

[0198] In the embodiment of the present application, the output quantity of the soft constraint is at least I rollover ;

[0199] The output of the liquid proxy model is

[0200] Where X is the x-coordinate of the tractor in the world coordinate system, Y is the y-coordinate of the tractor in the world coordinate system, and θ is the swing angle of the equivalent pendulum model. is the swing angular velocity of the equivalent pendulum model, I rollover is the state variable that characterizes the vehicle rollover state, and ψ1 is the heading angle of the tractor.

[0201] In the embodiment of the present application, the state quantity I representing the vehicle rollover state rollover Using the equivalent lateral load transfer rate (LTR) of the suspension force eql :

[0202] Among them, T w is the average wheelbase of each axle, m is the vehicle mass, g is the acceleration of gravity, k r is the roll stiffness, c is the roll damping, φ is the roll angle, is the roll angular velocity, subscript 1 represents the tractor and subscript 2 represents the trailer.

[0203] It should be noted that the above explanation of the embodiment of the liquid tank truck anti-rollover control method is also applicable to the liquid tank truck anti-rollover control device of this embodiment, and will not be repeated here.

[0204] According to the liquid tank truck anti-rollover control device proposed in the embodiment of the present application, the high real-time advantage of the vehicle side is utilized to send the vehicle information and liquid filling information of the liquid tank truck to the cloud in real time, further control the model processing data on the cloud, receive the model parameter calibration results sent from the cloud, and use the model predictive control on the vehicle side to achieve early prediction of the liquid tank truck and liquid dynamics, thereby preventing the liquid tank truck from entering a dangerous state.

[0205] An embodiment of the present application also provides a cloud, including: a memory, a processor, and a computer program stored in the memory and runnable on the processor, the processor executes the program to implement the liquid tank truck anti-rollover control method as described in the above embodiment.

[0206] An embodiment of the present application also provides a liquid tank truck, comprising: a memory, a processor, and a computer program stored in the memory and runnable on the processor. The processor executes the program to implement the liquid tank truck anti-rollover control method as described in the above embodiment.

[0207] The following describes a tank truck anti-rollover control system provided by an embodiment of the present application with reference to the accompanying drawings.

[0208] As shown in FIG23 , the liquid tank truck anti-rollover control system 30 includes: a liquid tank truck 31 and a cloud 32 .

[0209] Among them, the liquid tanker includes sensors, communication devices and vehicle computing units. The communication devices communicate with the cloud. The vehicle computing unit contains observation and state estimators and controllers. It uses sensor data and proxy models to obtain vehicle and liquid-related state quantities, and uses proxy models to achieve anti-rollover control of the liquid tanker; the cloud includes cloud information space, which uses the vehicle fine model and liquid sloshing fine model that are digital twins of the proxy model to provide parameter calibration services for the proxy model.

[0210] It can be understood that in the embodiment of the present application, the tank truck communicates with the cloud through a communication device, uses sensors and proxy models to obtain vehicle and liquid-related state quantities, and uses the proxy model to realize anti-rollover control of the tank truck. The cloud includes a cloud information space, and uses the vehicle fine model and liquid sloshing fine model that are digital twins of the proxy model to provide parameter calibration services for the proxy model to obtain more accurate model parameters. Among them, the controlled tank truck, actuator, sensor, communication device and other hardware in the physical world are used to perform transportation tasks, measure vehicle and liquid status, perceive the surrounding environment, and communicate with the cloud and surrounding vehicles.

[0211] Specifically, as shown in Figure 24, the tank truck anti-rollover control system of the embodiment of the present application is a collaborative control architecture based on cloud-based finite element calibration + vehicle-side agent model real-time control.

[0212] The architecture consists of a cyberspace and a physical space. The physical space contains the controlled tanker truck and other traffic participants. The cyberspace consists of two layers: an information mapping layer and a fusion application layer, both distributed on the vehicle side and in the cloud. The information mapping layer includes a simple digital twin of the vehicle, consisting of a vehicle and liquid proxy model, on the vehicle side. The cloud side contains a detailed digital twin model of the tanker truck and other traffic participants. The fusion application layer is distributed across the vehicle-side controller and cloud servers. The vehicle-side controller performs model predictive control based on data from the digital twin model of the simple proxy model of the vehicle.

[0213] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned tank truck anti-rollover control method.

[0214] 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.

[0215] 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.

[0216] 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 N executable instructions for implementing a custom logical function or process step, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a 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 pertain.

[0217] 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: 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, a field programmable gate array, etc.

[0218] Those skilled in the art will understand that all or part of the steps in the method of the above 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.

[0219] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A liquid tank truck anti-rollover control method, characterized in that: The method is applied in the cloud, wherein the method comprises the following steps: Obtain vehicle information sent by the tank truck and filling information obtained by sensors; Inputting the vehicle information and the filling liquid information into a vehicle fine model and a liquid sloshing fine model respectively, wherein the vehicle fine model outputs a first parameter calibration result, and the liquid sloshing fine model outputs a second parameter calibration result; The first parameter calibration result and the second parameter calibration result are sent to the tank truck, wherein the tank truck calibrates the vehicle proxy model and the liquid proxy model respectively using the first parameter calibration result and the second parameter calibration result, determines the control target of the tank truck in combination with the vehicle state sensor information observation and the liquid-related sensor information observation, and performs anti-rollover control on the tank truck based on the control target.

2. The liquid tank truck anti-rollover control method according to claim 1, characterized in that: The vehicle fine model is a multi-body dynamics model, the liquid sloshing fine model is a finite element model, the vehicle proxy model is a linear simplified dynamics model, and the liquid proxy model is an equivalent pendulum dynamics model.

3. The liquid tank truck anti-rollover control method according to claim 1 or 2, characterized in that: Before the vehicle information and the filling information are input into the vehicle fine model and the liquid sloshing fine model respectively, the method further includes: Obtain a multi-degree-of-freedom equivalent pendulum model for the sloshing of liquid in a tank truck; Setting a specific configuration and input amount of the multi-degree-of-freedom equivalent pendulum model, and determining the kinetic energy and potential energy of the multi-degree-of-freedom equivalent pendulum model according to the specific structure and the input amount; Determining differential dynamic equations of multiple swinging degrees of freedom according to the kinetic energy and potential energy of the multi-degree-of-freedom equivalent pendulum model, and identifying all undetermined parameters of the differential dynamic equations according to the established liquid sloshing model in the cross section of the liquid tank; A simplified model of liquid sloshing in the tank of the liquid tank truck is constructed according to the differential dynamics equation and all the unknown parameters.

4. The liquid tank truck anti-rollover control method according to claim 3, characterized in that: The method for obtaining a multi-degree-of-freedom equivalent pendulum model for liquid sloshing in a tank of a liquid tank truck comprises: The basic unit is a rocker and concentrated mass; Hinging one end of the pendulum rod to a point or a concentrated mass in the cross section of the liquid tank; The motion trajectory of a concentrated mass connected to a pendulum without a fixed end is a portion of an ellipse; The concentrated mass is allowed to hinge multiple pendulum rods, and one or more concentrated masses are allowed to be fixed at a point on the cross section of the liquid tank without being connected to any pendulum rod, so as to establish the multi-degree-of-freedom equivalent pendulum model.

5. The liquid tank truck anti-rollover control method according to claim 4, characterized in that: The specific configuration includes a linear and / or nonlinear combination of at least a plurality of simple pendulums and / or elliptical pendulums.

6. The liquid tank truck rollover prevention control method according to claim 3, characterized in that: The step of determining the kinetic energy and potential energy of the multi-degree-of-freedom equivalent pendulum model according to the specific structure and the input quantity includes: Setting the action point and positive direction of the output force and torque of the multi-degree-of-freedom equivalent pendulum model in the cross section of the liquid tank, and establishing a plane rectangular coordinate system with the action point as the origin and the positive direction as the coordinate axis; The position vectors of the concentrated masses of the multi-degree-of-freedom equivalent pendulum model are expressed in the plane rectangular coordinate system, and the first-order and second-order time derivatives of the position vectors of the concentrated masses are respectively calculated to obtain the velocity vectors and acceleration vectors of the concentrated masses; The position vector, the velocity vector and the acceleration vector are used to determine the kinetic energy of the multi-degree-of-freedom equivalent pendulum model and the potential energy of the kinetic energy of the multi-degree-of-freedom equivalent pendulum model at a specified zero potential energy point.

7. The liquid tank truck anti-rollover control method according to claim 3, characterized in that: The method of identifying all the undetermined parameters of the differential dynamics equation according to the established liquid sloshing model in the cross section of the liquid tank includes: Setting a plurality of output quantities of the liquid sloshing model and output modes between the output quantities; Acquire output time series data of the plurality of outputs according to a preset working condition, and construct a cost function of undetermined parameters of the differential dynamics equation based on an output mode between the outputs and the output time series data; The cost function is optimized, and all undetermined parameters of the differential dynamics equation are identified based on the optimized cost function.

8. The liquid tank truck anti-rollover control method according to claim 7, characterized in that: The output comprises lateral force, vertical force and rolling moment, the preset working condition comprises lateral acceleration step excitation working condition and / or lateral acceleration sinusoidal fluctuation working condition, and the cost function is the non-negative weighted sum of all error terms.

9. A liquid tank truck anti-rollover control method, characterized in that: The method is applied to a liquid tank truck, wherein the method comprises the following steps: Sending vehicle information and liquid filling information obtained according to the sensor to the cloud, wherein the cloud inputs the vehicle information and the liquid filling information into a vehicle fine model and a liquid sloshing fine model respectively, the vehicle fine model outputs a first parameter calibration result, and the liquid sloshing fine model outputs a second parameter calibration result; Obtaining the first parameter calibration result and the second parameter calibration result sent from the cloud, and using the first parameter calibration result and the second parameter calibration result to calibrate the vehicle proxy model and the liquid proxy model respectively; The control target of the liquid tank truck is determined by combining the observation of vehicle state sensor information and the observation of liquid-related sensor information, and the liquid tank truck is subjected to anti-rollover control based on the control target.

10. The liquid tank truck anti-rollover control method according to claim 9, characterized in that: The step of determining the control target of the tanker truck by combining the vehicle state sensor information observation and the liquid related sensor information observation includes: estimating a first state quantity of the vehicle agent model in combination with vehicle state sensor information observation; The second state quantity of the liquid proxy model is estimated by combining the liquid-related sensor information observation, A control target of the liquid tank truck is determined according to the first state quantity and the second state quantity.

11. The liquid tank truck anti-rollover control method according to claim 9, characterized in that: The anti-rollover control of the tank truck based on the control target includes: Acquire a first reference value of the vehicle proxy model and a second reference value of the liquid proxy model; Determining a first error weight between an output of the vehicle proxy model and the first reference value, and a second error weight between an output of the liquid proxy model and the second reference value; The first error weight is applied to the output of the vehicle proxy model to achieve the control goal of trajectory tracking, the second error weight is applied to the output of the liquid proxy model to achieve the control goal of sway suppression, and soft constraints are imposed on the range of part of the output of the liquid proxy model to achieve the control goal of anti-rollover.

12. The liquid tank truck anti-rollover control method according to claim 11, characterized in that: The partial output quantity with soft constraints at least includes I rollover ; The output of the liquid proxy model is Where X is the x-coordinate of the tractor in the world coordinate system, Y is the y-coordinate of the tractor in the world coordinate system, and θ is the swing angle of the equivalent pendulum model. is the swing angular velocity of the equivalent pendulum model, I rollover is the state quantity that characterizes the vehicle rollover state, and ψ1 is the heading angle of the tractor.

13. The liquid tank truck anti-rollover control method according to claim 12, characterized in that: The state quantity I representing the vehicle rollover state rollover Using the lateral load transfer rate LTR equivalent to the suspension force eql : Among them, T w is the average wheelbase of each axle, m is the vehicle mass, g is the acceleration of gravity, k r is the roll stiffness, c is the roll damping, φ is the roll angle, is the roll angular velocity, subscript 1 represents the tractor and subscript 2 represents the trailer.

14. A liquid tank truck anti-rollover control device, characterized in that: The device is applied in the cloud, wherein the device comprises: An acquisition module, used to acquire vehicle information sent by the tank truck and liquid filling information obtained by the sensor; An input module, used to input the vehicle information and the filling liquid information into a vehicle fine model and a liquid sloshing fine model respectively, wherein the vehicle fine model outputs a first parameter calibration result, and the liquid sloshing fine model outputs a second parameter calibration result; A sending module is used to send the first parameter calibration result and the second parameter calibration result to the tank truck, wherein the tank truck uses the first parameter calibration result and the second parameter calibration result to calibrate the vehicle proxy model and the liquid proxy model respectively, combines the vehicle state sensor information observation and the liquid-related sensor information observation to determine the control target of the tank truck, and performs anti-rollover control on the tank truck based on the control target.

15. A liquid tank truck anti-rollover control device, characterized in that: The device is applied to a liquid tank truck, wherein the device comprises: A sending module, used for sending vehicle information and filling liquid information obtained according to the sensor to the cloud, wherein the cloud inputs the vehicle information and the filling liquid information into a vehicle fine model and a liquid sloshing fine model respectively, the vehicle fine model outputs a first parameter calibration result, and the liquid sloshing fine model outputs a second parameter calibration result; A calibration module, used to obtain the first parameter calibration result and the second parameter calibration result sent from the cloud, and use the first parameter calibration result and the second parameter calibration result to calibrate the vehicle proxy model and the liquid proxy model respectively; The control module is used to determine the control target of the tank truck in combination with the vehicle state sensor information observation and the liquid related sensor information observation, and to perform anti-rollover control on the tank truck based on the control target.

16. A cloud, 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 tank truck anti-rollover control method as described in any one of claims 1 to 8.

17. A liquid tank truck, 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 tank truck anti-rollover control method as described in any one of claims 9 to 13.

18. A liquid tank truck rollover prevention control system, characterized in that: include: A liquid tank truck, wherein the liquid tank truck comprises a sensor, a communication device and a vehicle computing unit, the communication device communicates with the cloud, the vehicle computing unit internally comprises an observation and state estimator and a controller, uses sensor data and a proxy model to obtain vehicle and liquid related state quantities, and uses the proxy model to implement anti-rollover control of the liquid tank truck; The cloud includes a cloud information space, and provides parameter calibration services for the proxy model by using a vehicle fine model and a liquid sloshing fine model that are digital twins of the proxy model.

19. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the tank truck anti-rollover control method as described in any one of claims 1-13.

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