Tanker rollover prevention control method, device, cloud, tanker and control system

Abstract

The application relates to the technical field of automatic driving, in particular to a liquid tank truck rollover prevention control method and device, a cloud terminal, a liquid tank truck and a control system, wherein the method comprises the following steps: acquiring vehicle information sent by the liquid tank truck and liquid filling information obtained according to a sensor; inputting the vehicle information and the liquid filling information into a vehicle fine model and a liquid sloshing fine model respectively, and outputting first parameter calibration results and second parameter calibration results; and delivering the first parameter calibration results and the second parameter calibration results to the liquid tank truck, wherein the liquid tank truck calibrates a vehicle agent model and a liquid agent model by using the first parameter calibration results and the second parameter calibration results respectively, determines a control target of the liquid tank truck by combining vehicle state sensor information observation and liquid related sensor information observation, and performs rollover prevention control on the liquid tank truck based on the control target. Therefore, the problems that real-time calculation control of liquid tank truck rollover prevention cannot be realized in the related art, and a huge amount of calculation cannot be supported are solved.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a method, device, cloud platform, tank truck and control system for preventing rollover of a liquid tanker truck. Background Technology

[0002] Tanker trucks are common vehicles for transporting hazardous chemicals. Because they are only permitted to be partially loaded according to standards, the liquid is prone to sloshing. Combined with their high center of gravity and heavy load, the vehicle's poor stability after liquid coupling makes them susceptible to rollover accidents, leading to hazardous chemical leaks or even explosions. Therefore, in future intelligent vehicle cyber-physical systems, the manual driving of tanker trucks, which is purely for transportation and involves high risks and labor intensity, will be replaced by autonomous driving. For autonomous tanker trucks, predicting liquid sloshing and controlling the vehicle's lateral acceleration accordingly is extremely important.

[0003] The most accurate method for describing liquid sloshing is CFD, but the finite element model involves extremely large computational loads, far from being able to achieve real-time performance. However, a simplified surrogate model can be established based on the vehicle and liquid sloshing mechanism, and an anti-rollover control algorithm considering liquid sloshing can be designed based on the vehicle and liquid models. The measurement or estimation of the state variables required for control can be achieved based on the surrogate model.

[0004] However, the parameters in the proxy model will change with the change of the liquid filling conditions. Each change requires a large number of finite element calculations to calibrate the liquid proxy model, and the computing power of the vehicle cannot support such a large amount of calculation. Summary of the Invention

[0005] This application provides a method, device, cloud platform, tank truck, and control system for preventing tank truck rollover, in order to solve the problems in related technologies such as the inability to achieve real-time calculation and control of tank truck rollover prevention and the inability to support massive computational loads.

[0006] The first aspect of this application provides a method for preventing rollover of a liquid tanker truck. The method is applied in a cloud environment and includes the following steps: acquiring vehicle information sent by the liquid tanker truck and filling information obtained from sensors; inputting the vehicle information and the filling information into a fine vehicle model and a fine liquid sloshing model, respectively, wherein the fine vehicle model outputs a first parameter calibration result, and the fine liquid sloshing model outputs a second parameter calibration result; sending the first parameter calibration result and the second parameter calibration result to the liquid tanker truck, wherein the liquid tanker 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 liquid tanker truck by combining vehicle state sensor information observation and liquid-related sensor information observation, and performs anti-rollover control on the liquid tanker truck based on the control target.

[0007] Optionally, the vehicle fine model is a multibody 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.

[0008] A second aspect of this application provides a method for preventing rollover of a liquid tanker truck. The method includes the following steps: sending vehicle information and filling information obtained from sensors to a cloud platform; the cloud platform inputs the vehicle information and filling information into a fine vehicle model and a fine liquid sloshing model, respectively; the fine vehicle model outputs a first parameter calibration result, and the fine liquid sloshing model outputs a second parameter calibration result; acquiring the first parameter calibration result and the second parameter calibration result from the cloud platform; calibrating the vehicle proxy model and the liquid proxy model using the first parameter calibration result and the second parameter calibration result, respectively; determining the control target of the liquid tanker truck by combining vehicle state sensor information observation and liquid-related sensor information observation; and performing anti-rollover control on the liquid tanker truck based on the control target.

[0009] Optionally, determining the control target of the tanker truck by combining vehicle state sensor information observation and liquid-related sensor information observation includes: estimating a first state quantity of the vehicle agent model by combining vehicle state sensor information observation; estimating a second state quantity of the liquid agent model by combining liquid-related sensor information observation; and determining the control target of the tanker truck based on the first state quantity and the second state quantity.

[0010] Optionally, the anti-rollover control of the tanker truck based on the control objective 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 trajectory tracking control objective, applying the second error weight to the output of the liquid proxy model to achieve the sway suppression control objective, and applying soft constraints to the range of a portion of the output of the liquid proxy model to achieve the anti-rollover control objective.

[0011] Optionally, the portion of the output quantity for which the soft constraint is applied includes at least I. rollover ;

[0012] The output of the liquid proxy model is:

[0013] 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. Let I be the angular velocity of the equivalent pendulum model. rollover The state variable representing the vehicle rollover state is ψ1, which is the heading angle of the tractor.

[0014] Optionally, the state quantity I representing the vehicle rollover state rollover Utilizing the lateral load transfer ratio (LTR) equivalent to suspension forces eql :

[0015]

[0016] Among them, T w The average wheelbase is given by m, the vehicle mass is given by g, and the acceleration due to gravity is given by k. r Let φ be the roll stiffness, c be the roll damping, and φ be the roll angle. The value represents the tilt angular velocity. Subscript 1 represents the tractor unit, and subscript 2 represents the trailer unit.

[0017] A third aspect of this application provides a liquid tanker truck anti-rollover control device, which is applied in the cloud. The device includes: an acquisition module for acquiring vehicle information sent by the liquid tanker truck and filling information obtained from sensors; an input module for inputting the vehicle information and the filling information into a fine vehicle model and a fine liquid sloshing model, respectively, wherein the fine vehicle model outputs a first parameter calibration result, and the fine liquid sloshing model outputs a second parameter calibration result; and a distribution module for distributing the first parameter calibration result and the second parameter calibration result to the liquid tanker truck. The liquid tanker 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 liquid tanker truck by combining vehicle state sensor information observation and liquid-related sensor information observation, and performs anti-rollover control on the liquid tanker truck based on the control target.

[0018] Optionally, the vehicle fine model is a multibody 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.

[0019] A fourth aspect of this application provides a tank truck anti-rollover control device. The device is applied to a tank truck and includes: a transmitting module for transmitting vehicle information and filling information obtained from sensors to a cloud, wherein the cloud inputs the vehicle information and the filling information into a fine vehicle model and a fine liquid sloshing model, respectively; the fine vehicle model outputs a first parameter calibration result, and the fine liquid sloshing model outputs a second parameter calibration result; a calibration module for acquiring the first parameter calibration result and the second parameter calibration result sent from 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; and a control module for determining the control target of the tank truck by combining vehicle state sensor information observation and liquid-related sensor information observation, and performing anti-rollover control on the tank truck based on the control target.

[0020] Optionally, the control module is further configured to: estimate a first state quantity of the vehicle proxy model by combining vehicle state sensor information; estimate a second state quantity of the liquid proxy model by combining liquid-related sensor information; and determine the control target of the liquid tanker truck based on the first state quantity and the second state quantity.

[0021] Optionally, the control module is further configured to: acquire 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 objective of trajectory tracking, apply the second error weight to the output of the liquid proxy model to achieve the control objective of sway suppression, and apply soft constraints to the range of a portion of the output of the liquid proxy model to achieve the control objective of rollover prevention.

[0022] Optionally, the portion of the output quantity for which the soft constraint is applied includes at least I. rollover ;

[0023] The output of the liquid proxy model is:

[0024] 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. Let I be the angular velocity of the equivalent pendulum model. rollover The state variable representing the vehicle rollover state is ψ1, which is the heading angle of the tractor.

[0025] Optionally, the state quantity I representing the vehicle rollover state rolloverUtilizing the lateral load transfer ratio (LTR) equivalent to suspension forces eql :

[0026]

[0027] Among them, t w The average wheelbase is given by m, the vehicle mass is given by g, and the acceleration due to gravity is given by k. r Let φ be the roll stiffness, c be the roll damping, and φ be the roll angle. The value represents the tilt angular velocity. Subscript 1 represents the tractor unit, and subscript 2 represents the trailer unit.

[0028] A fifth aspect of this application provides a cloud platform, including: 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 anti-rollover control method for liquid tank trucks as described in the above embodiments.

[0029] A sixth aspect of this application provides a tank truck, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the tank truck anti-rollover control method as described in the above embodiments.

[0030] A seventh aspect of this application provides a liquid tanker anti-rollover control system, comprising: a liquid tanker, wherein the liquid tanker includes sensors, a communication device, and a vehicle computing unit, the communication device communicating with the cloud, the vehicle computing unit containing an observer and state estimator and a controller, acquiring vehicle and liquid-related state quantities using sensor data and a proxy model, and implementing anti-rollover control of the liquid tanker using the proxy model; and a cloud, wherein the cloud includes a cloud information space, providing parameter calibration services for the proxy model using a fine vehicle model and a fine liquid sloshing model digitally twinned with the proxy model.

[0031] An eighth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the anti-rollover control method for liquid tank trucks as described in the above embodiments.

[0032] Therefore, this application has at least the following beneficial effects:

[0033] This application's embodiments leverage the high computing power of the cloud and the high real-time performance of the vehicle (tanker truck) to establish a relatively accurate model. Real-time data acquired by the vehicle is transmitted to the cloud, where accurate model calibration parameters are calculated based on driving requirements and transmitted back to the vehicle for predictive control. This vehicle-cloud collaborative control architecture, combining cloud-based finite element calibration and real-time vehicle control, prevents tank trucks from entering dangerous states, achieves early prediction of hazards, and ensures driving safety. Therefore, it solves the technical problems in related technologies, such as the inability to achieve real-time computational control to prevent tank truck rollover and the inability to support massive computational loads.

[0034] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0035] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0036] Figure 1 This is a flowchart of a tanker truck anti-rollover control method provided according to an embodiment of this application;

[0037] Figure 2 This is a schematic diagram of a TruckSim vehicle fine dynamics model provided according to an embodiment of this application;

[0038] Figure 3 This is a schematic diagram of the StarCCM+ fine liquid sloshing finite element model provided according to an embodiment of this application;

[0039] Figure 4 This is a flowchart of a tanker truck anti-rollover control method according to another embodiment of this application;

[0040] Figure 5 This is a schematic diagram of the DMTP model of the dual-mass elliptic pendulum provided according to an embodiment of this application;

[0041] Figure 6 This is a schematic diagram of a liquid sloshing state estimation method based on a liquid surface fluctuation sensor provided in an embodiment of this application;

[0042] Figure 7 This is a schematic diagram of a simplified 6-DOF semi-trailer tanker truck model provided according to an embodiment of this application;

[0043] Figure 8 This is a schematic diagram of the trajectory tracking + sway suppression + anti-rollover control algorithm provided according to the embodiments of this application;

[0044] Figure 9This is a schematic diagram illustrating the use of a specific model provided according to a specific embodiment of this application;

[0045] Figure 10 This is a schematic diagram of a tanker truck anti-rollover control method provided according to a specific embodiment of this application;

[0046] Figure 11 A flowchart illustrating the anti-rollover control method for liquid tank trucks provided in a specific embodiment of this application;

[0047] Figure 12 This is a schematic diagram of a tanker truck anti-rollover control device provided according to an embodiment of this application;

[0048] Figure 13 This is a schematic diagram of a tanker truck anti-rollover control device according to another embodiment of this application;

[0049] Figure 14 This is a schematic diagram of a tanker truck anti-rollover control system provided according to an embodiment of this application;

[0050] Figure 15 This is an architecture diagram of the anti-rollover control system for liquid tank trucks provided according to an embodiment of this application. Detailed Implementation

[0051] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0052] The following description, with reference to the accompanying drawings, outlines an embodiment of a liquid tanker truck anti-rollover control method, device, cloud platform, liquid tanker truck, and control system. Addressing the issues mentioned in the background art, such as the extremely high computational load of finite element models, the inability to perform real-time calculations, and the insufficient computing power of the vehicle-side to support the massive computational demands of liquid proxy models, this application provides a liquid tanker truck anti-rollover control method. This method leverages the high computing power of the cloud platform and the high real-time performance of the vehicle-side, developing a vehicle-cloud collaborative control architecture based on cloud-based finite element calibration combined with real-time vehicle-side control. Accurate model parameter calibration results are obtained through cloud-based computation, enabling model predictive control. This solves the problems in related technologies, such as the inability to achieve real-time computational control for liquid tanker truck anti-rollover and the inability to support massive computational demands.

[0053] Specifically, Figure 1 This is a flowchart illustrating a method for preventing tank truck rollover provided in an embodiment of this application.

[0054] like Figure 1As shown, this anti-rollover control method for liquid tank trucks, applied in the cloud, includes the following steps:

[0055] In step S101, the vehicle information sent by the tanker truck and the filling information obtained from the sensors are acquired.

[0056] In step S102, vehicle information and liquid filling information are input 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.

[0057] Among them, the vehicle fine model is a multibody dynamics model, which can be built using the semi-trailer liquid tanker model (empty model without liquid) using 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.

[0058] It is understood that, in this embodiment of the application, vehicle information sent by the tanker truck can be input into a fine vehicle model for parameter calibration, and liquid filling information obtained from sensors and sent by the tanker truck can be input into a fine liquid sloshing model for parameter calibration, thereby obtaining the first parameter calibration result and the second parameter calibration result, respectively. By fully utilizing the high computing power of the cloud, relatively accurate model parameters are obtained through remote computation.

[0059] TruckSim vehicle detailed dynamics model, such as Figure 2 As shown, the creation of an accurate vehicle model in TruckSim involves the following steps:

[0060] The model's fixed parameters are set based on vehicle experimental 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 realize co-simulation between TruckSim and StarCCM+ software, the model's input and output parameters also need to be set. The inputs mainly include the lateral and vertical forces applied to the axle by external forces, as well as the roll moment applied to the trailer's sprung mass. These forces come from the output of the finite element fluid model; the outputs include at least the trailer's lateral acceleration and roll angle, used to calculate the acceleration components applied to the finite element model.

[0061] StarCCM+ liquid sloshing finite element model, such as Figure 3 As shown, the establishment of the finite element model of the sloshing liquid inside the StarCCM+ tank involves the following steps:

[0062] A precise two-dimensional mesh is created for the liquid tank cross-section to capture the details of liquid sloshing. The model can also be extended to a three-dimensional finite element model on the cloud platform. Two Eulerian phases, liquid and air, and their physical properties are set, along with other model parameters. Input and output parameters for co-simulation with TruckSim are configured in report and plot formats. The finite element model outputs lateral force, vertical force, and roll moment to the vehicle model, and the vehicle's influence on the liquid is reflected by continuously updating the acceleration components acting on the liquid during simulation.

[0063] In step S103, the calibration results of the first parameter and the calibration results of the second parameter are sent to the tank truck. The tank truck uses the calibration results of the first parameter and the calibration results of the second parameter to calibrate the vehicle agent model and the liquid agent model respectively. The control target of the tank truck is determined by combining the observation of vehicle state sensor information and the observation of liquid related sensor information. Based on the control target, the tank truck is subjected to anti-rollover control.

[0064] Among them, the vehicle proxy model is a linear simplified dynamic model; the liquid proxy model is an equivalent pendulum dynamic model; the vehicle status 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 tilt angle of the liquid surface and the liquid level under static conditions; the control objectives are trajectory tracking + sway suppression + rollover prevention.

[0065] It is understood that, in this embodiment of the application, the cloud can send the calibration results of the first parameter and the calibration results of the second parameter to the tank truck. The tank truck uses the calibration results of the first parameter to calibrate the vehicle agent model and the calibration results of the second parameter to calibrate the liquid agent model. It also combines the observation of vehicle and vehicle state sensor information and the observation of liquid-related sensor information to determine the control target of the tank truck. Based on the control target, the tank truck is controlled to prevent rollover, thereby realizing the early prediction of the liquid dynamics of the tank truck and avoiding the vehicle from entering a dangerous state.

[0066] According to the anti-rollover control method for liquid tank trucks proposed in the embodiments of this application, the high computing power of the cloud can be used to obtain more accurate model parameter calibration results. The model parameter calibration results are transmitted to the vehicle (liquid tank truck) end to realize model predictive control, which can predict the dynamics of the vehicle and liquid in advance, thereby avoiding the vehicle from entering a dangerous state.

[0067] The anti-rollover control method for liquid tank trucks described in the above embodiments is applied to the cloud, and the anti-rollover control method for liquid tank trucks described in the following embodiments is applied to liquid tank trucks.

[0068] Figure 4 This is a flowchart illustrating a method for preventing tank truck rollover provided in an embodiment of this application.

[0069] like Figure 4As shown, this anti-rollover control method for liquid tank trucks, applied to liquid tank trucks, includes the following steps:

[0070] In step S201, vehicle information and liquid filling information obtained from sensors are sent to the cloud. The cloud inputs the vehicle information and liquid 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.

[0071] It is understood that the liquid tanker truck in this application embodiment can send vehicle information and filling information obtained from sensors to the cloud in real time, making full use of the high real-time advantage of the vehicle end. The cloud then performs parameter calibration calculations on the vehicle information and filling information. The cloud inputs the vehicle information and 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.

[0072] In step S202, the first parameter calibration result and the second parameter calibration result sent from the cloud are obtained, and the vehicle agent model and the liquid agent model are calibrated respectively using the first parameter calibration result and the second parameter calibration result.

[0073] It is understood that, in the embodiments of this application, the liquid tanker 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 agent model, and use the second parameter calibration result to calibrate the liquid agent model.

[0074] In step S203, the control target of the liquid tanker is determined by combining the observation of vehicle status sensor information and liquid-related sensor information, and the anti-rollover control of the liquid tanker is performed based on the control target.

[0075] It is understood that the embodiments of this application can combine vehicle status sensor observations to determine the control target of the tanker truck, and further perform anti-rollover control of the tanker truck based on the control target, thereby realizing the early prediction of the liquid dynamics of the tanker truck and avoiding the vehicle from entering a dangerous state.

[0076] It should be noted that, in this embodiment, the tanker truck end can use a controller to perform anti-rollover control on the tanker truck based on the control target. The controller can be a multi-constraint MPC controller. In this embodiment, the double mass elliptical pendulum (DMTP) model can be used as a more accurate nonlinear liquid sloshing proxy model to construct the motion equations in the observer.

[0077] The DMTP model of a two-mass elliptic pendulum with two degrees of freedom is as follows: Figure 5As shown, the system comprises 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. After representing the position, velocity, and acceleration vectors of the three lumped masses, the Lagrange equations can be solved to obtain the differential dynamic equations for the two oscillating degrees of freedom. Furthermore, given the input, the triaxial forces at any given time can be calculated. While the DMTP model, as a more accurate but highly nonlinear model, is currently unsuitable for use in onboard controllers, it can be used to design the motion equations of observers.

[0078] In this embodiment of the application, the control target of the liquid tanker is determined by combining vehicle state sensor information observation and liquid-related sensor information observation, including: estimating a first state quantity of the vehicle agent model by combining vehicle state sensor information observation; estimating a second state quantity of the liquid agent model by combining liquid-related sensor information observation; and determining the control target of the liquid tanker based on the first state quantity and the second state quantity.

[0079] It is understood that, based on the calibration results obtained in the above steps, the first state quantity of the vehicle agent model required for control can be estimated by combining the vehicle state sensor observations, and the second state quantity of the liquid agent model required for control can be estimated by combining the liquid-related sensor information observations, and the control target of the liquid tanker can be determined according to the first state quantity and the second state quantity.

[0080] The liquid-related sensor can be a free liquid surface fluctuation sensor. In this embodiment, the liquid sloshing state estimation method using the liquid surface fluctuation sensor is as follows: Figure 6 As shown, the average tilt angle data of the liquid surface obtained by the liquid surface sway sensor is smoothed by the Luenberger observer. The smoothed angle is then input into the unscented Kalman filter that uses the DMTP model to build the motion equation, and finally outputs the estimated equivalent sway angle corresponding to the liquid sloshing state.

[0081] It should be noted that, in the embodiments of this application, a 5DOF simplified semi-trailer model can be used to estimate the state variables required for vehicle model control, and a 6DOF simplified semi-trailer liquid tanker model can be used as the predictive model for model predictive control.

[0082] Among them, the 6-DOF simplified semi-trailer tanker model used for the tanker-side model predictive controller is as follows: Figure 7 As shown, it is a combination of a 5-DOF semi-trailer model and a linearized pendulum proxy model. The two interact and influence each other through force and acceleration. After integrating them and expanding the relevant state variables according to the controller requirements, a 6-DOF semi-trailer liquid tanker proxy model can be obtained.

[0083] In this embodiment, anti-rollover control of a liquid tanker truck based on a control objective includes: acquiring 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 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 objective of trajectory tracking, applying the second error weight to the output of the liquid proxy model to achieve the control objective of sway suppression, and applying soft constraints to the range of a portion of the output of the liquid proxy model to achieve the anti-rollover control objective.

[0084] The first and second reference values ​​can be set according to specific circumstances, and no specific restrictions are imposed on them.

[0085] It is understood that the embodiments of this application achieve the control objectives of trajectory tracking and sway suppression by applying error weights to the outputs of the vehicle agent model and the liquid agent model and their corresponding reference values, and achieve the control objective of anti-rollover by applying soft constraints to the range of some outputs.

[0086] Specifically, the embodiments of this application can determine a first error weight between the output of the vehicle proxy model and a first reference value, determine a second error weight between the output of the liquid proxy model and a second reference value, apply the first error weight to the output of the vehicle proxy model to achieve the control objective of trajectory tracking, apply the second error weight to the output of the liquid proxy model to achieve the control objective of sway suppression, and achieve the control objective of rollover prevention by applying soft constraints to the range of a portion of the output of the liquid proxy model. The trajectory tracking + sway suppression + rollover prevention control objective is achieved using the following formula, and a schematic diagram of the trajectory tracking + sway suppression + rollover prevention control algorithm is shown below. Figure 8 As shown.

[0087] In this embodiment of the application, the output quantity for which soft constraints are applied includes at least I. rollover ;

[0088] The output of the liquid proxy model is

[0089] 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. I is the angular velocity of the equivalent pendulum model. rollover The state variable representing the vehicle rollover state is ψ1, which is the heading angle of the tractor.

[0090] In this embodiment of the application, the state variable I characterizing the vehicle rollover state is... rollover Utilizing the lateral load transfer ratio (LTR) equivalent to suspension forces eql :

[0091]

[0092] Among them, T w The average wheelbase is given by m, the vehicle mass is given by g, and the acceleration due to gravity is given by k. r Let φ be the roll stiffness, c be the roll damping, and φ be the roll angle. The value represents the tilt angular velocity. Subscript 1 represents the tractor unit, and subscript 2 represents the trailer unit.

[0093] It should be noted that in the above formula for lateral load transfer rate, subscript 1 represents the tractor and subscript 2 represents the trailer. For integral tank trucks without trailers, the value of subscript 1 can be substituted into the value of subscript 2.

[0094] According to the anti-rollover control method for liquid tank trucks proposed in this application, the vehicle information and liquid filling information of the liquid tank truck are sent to the cloud in real time, taking advantage of the high real-time performance of the vehicle end. The cloud model processes the data, receives the model parameter calibration results sent from the cloud, and uses the model prediction control on the vehicle end to predict the dynamics of the liquid tank truck and the liquid in advance, thereby avoiding the liquid tank truck from entering a dangerous state.

[0095] Based on the above embodiments describing the anti-rollover control methods for liquid tank trucks applied to both cloud-based and liquid tank truck systems, the following specific embodiment illustrates the anti-rollover control method for liquid tank trucks of this application. The specific uses of the model used in this embodiment are as follows: Figure 9 As shown, it includes cloud-based precise models (TruckSim vehicle fine dynamics model, StarCCM+ liquid fine finite element model) and vehicle-side proxy models (5DOF simplified semi-trailer, linear single pendulum LSP, 6DOF simplified semi-trailer, dual-mass elliptic pendulum DMTP).

[0096] The following will combine Figure 10 This section describes the anti-rollover control method for liquid tank trucks in this specific embodiment, as shown in the detailed schematic diagram below. Figure 11 As shown, it includes the following steps:

[0097] Step 1: Based on the vehicle information sent from the vehicle terminal and the fluid filling information obtained from sensors, the cloud-based system adjusts the fine vehicle model and the fine fluid sloshing model established in the cloud. Based on the calculation results of the fine models, the cloud-based system performs parameter calibration calculations for the vehicle and fluid proxy models required by the vehicle terminal, and sends the parameter calibration results to the vehicle terminal. Preferably, the fine vehicle model is a multibody dynamics model, preferably established using TruckSim software; preferably, the fine fluid model is a finite element model, preferably established using StarCCM+ software; preferably, the vehicle proxy model is a linear simplified dynamics model; preferably, the fluid proxy model is an equivalent pendulum dynamics model.

[0098] Step 2: The vehicle-side observation and state estimator observes and estimates the vehicle proxy model state quantities required for control based on vehicle state sensor information, and observes and estimates the liquid proxy model state quantities required for control based on 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, which can ensure real-time measurement of the average liquid surface tilt angle and the liquid level under static conditions.

[0099] Step 3: The vehicle-side controller performs control according to the control objective based on the state information from Step 2. Preferably, the controller is a multi-constraint MPC controller, and preferably, the control objective is trajectory tracking + sway suppression + rollover prevention.

[0100] 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.

[0101] In summary, the anti-rollover control method for liquid tank trucks in this application establishes a relatively accurate model and obtains accurate model parameters through cloud computing, which can be used to realize model predictive control. Compared with model-free adaptive control (MFAC), it can predict the dynamics of the vehicle and liquid in advance, thereby avoiding the vehicle from entering a dangerous state, rather than trying to save the vehicle after it has already entered a dangerous state. It makes full use of the high computing power of the cloud and the high real-time performance of the vehicle, and develops a vehicle-cloud collaborative control architecture based on the vehicle-cloud collaborative control architecture, which combines cloud finite element calibration with real-time vehicle control.

[0102] Next, the anti-rollover control device for liquid tank trucks according to embodiments of this application is described with reference to the accompanying drawings.

[0103] Figure 12 This is a block diagram of a tanker truck anti-rollover control device according to an embodiment of this application.

[0104] like Figure 12 As shown, the anti-rollover control device 10 for liquid tank trucks is applied to the cloud and includes: an acquisition module 101, an input module 102, and a distribution module 103.

[0105] The acquisition module 101 is used to acquire vehicle information sent by the tanker truck and filling information obtained from sensors; the input module 102 is used to input the vehicle information and 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 tanker truck, wherein the tanker truck uses the first parameter calibration result and the second parameter calibration result to calibrate the vehicle agent model and the liquid agent model respectively, and determines the control target of the tanker truck by combining the observation of vehicle state sensor information and liquid related sensor information, and performs anti-rollover control of the tanker truck based on the control target.

[0106] In this embodiment, the vehicle fine model is a multibody 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.

[0107] It should be noted that the foregoing explanation of the embodiment of the anti-rollover control method for liquid tank trucks also applies to the anti-rollover control device for liquid tank trucks in this embodiment, and will not be repeated here.

[0108] The anti-rollover control device for liquid tank trucks proposed in the embodiments of this application can utilize the high computing power of the cloud to obtain more accurate model parameter calibration results. The model parameter calibration results are then transmitted to the vehicle to achieve model predictive control, which can predict the dynamics of the vehicle and the liquid in advance, thereby preventing the vehicle from entering a dangerous state.

[0109] like Figure 13 As shown, the anti-rollover control device 10 for liquid tank trucks is applied to liquid tank trucks and includes: a sending module 201, a calibration module 202, and a control module 203.

[0110] The sending module 201 is used to send vehicle information and liquid filling information obtained from sensors to the cloud. The cloud inputs the vehicle information and liquid 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 from the cloud, and calibrates the vehicle proxy model and the liquid proxy model respectively using the first parameter calibration result and the second parameter calibration result. The control module 203 is used to determine the control target of the liquid tanker by combining the observation of vehicle status sensor information and liquid related sensor information, and to perform anti-rollover control of the liquid tanker based on the control target.

[0111] In this embodiment, the control module 300 is further configured to: estimate a first state quantity of the vehicle agent model by combining vehicle state sensor information; estimate a second state quantity of the liquid agent model by combining liquid-related sensor information; and determine the control target of the tanker truck based on the first and second state quantities.

[0112] In this embodiment, the control module 300 is further configured to: acquire 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 objective of trajectory tracking, apply the second error weight to the output of the liquid proxy model to achieve the control objective of sway suppression, and apply soft constraints to the range of a portion of the output of the liquid proxy model to achieve the control objective of rollover prevention.

[0113] In this embodiment of the application, the output quantity for which soft constraints are applied includes at least I. rollover ;

[0114] The output of the liquid proxy model is

[0115] 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. I is the angular velocity of the equivalent pendulum model. rollover The state variable representing the vehicle rollover state is ψ1, which is the heading angle of the tractor.

[0116] In this embodiment of the application, the state variable I characterizing the vehicle rollover state is... rollover Utilizing the lateral load transfer ratio (LTR) equivalent to suspension forces eql :

[0117]

[0118] Among them, T w The average wheelbase is given by m, the vehicle mass is given by g, and the acceleration due to gravity is given by k. r Let φ be the roll stiffness, c be the roll damping, and φ be the roll angle. The value represents the tilt angular velocity. Subscript 1 represents the tractor unit, and subscript 2 represents the trailer unit.

[0119] It should be noted that the foregoing explanation of the embodiment of the anti-rollover control method for liquid tank trucks also applies to the anti-rollover control device for liquid tank trucks in this embodiment, and will not be repeated here.

[0120] According to the embodiment of this application, the anti-rollover control device for liquid tank trucks utilizes the high real-time performance of the vehicle end to send the vehicle information and liquid filling information of the liquid tank truck to the cloud in real time. It further controls the model processing data in the cloud, receives the model parameter calibration results sent from the cloud, and uses the model prediction control at the vehicle end to predict the dynamics of the liquid tank truck and the liquid in advance, thereby preventing the liquid tank truck from entering a dangerous state.

[0121] This application also provides a cloud platform, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the anti-rollover control method for liquid tank trucks as described in the above embodiments.

[0122] This application also provides a liquid tanker truck, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the liquid tanker truck anti-rollover control method as described in the above embodiments.

[0123] The following describes an embodiment of a tanker truck anti-rollover control system provided in this application, with reference to the accompanying drawings.

[0124] like Figure 14 As shown, the anti-rollover control system 30 for liquid tank trucks includes: a liquid tank truck 31 and a cloud platform 32.

[0125] The liquid tanker includes sensors, communication devices, and a vehicle computing unit. The communication devices communicate with the cloud. The vehicle computing unit contains an observer and state estimator and a controller. It uses sensor data and a surrogate model to acquire the relevant state quantities of the vehicle and the liquid. The surrogate model is used to realize the anti-rollover control of the liquid tanker. The cloud includes a cloud information space. It uses a fine vehicle model and a fine liquid sloshing model that are digital twins of the surrogate model to provide parameter calibration services for the surrogate model.

[0126] It is understood that in this embodiment of the application, the tanker truck communicates with the cloud through a communication device, uses sensors and a proxy model to acquire vehicle and liquid-related state quantities, and uses the proxy model to realize anti-rollover control of the tanker truck. The cloud includes a cloud information space, and uses a vehicle fine model and a liquid sloshing fine model that are digital twins with the proxy model to provide parameter calibration services for the proxy model and obtain more accurate model parameters. The controlled tanker truck, actuators, sensors, communication devices and other hardware in the physical world are used to perform transportation tasks, measure the state of the vehicle and liquid, perceive the surrounding environment, and communicate with the cloud and surrounding vehicles.

[0127] Specifically, such as Figure 15 As shown, the anti-rollover control system for liquid tank trucks in this application embodiment is a collaborative control architecture based on cloud-based finite element calibration and real-time control using a vehicle-side agent model.

[0128] The architecture comprises an information space and a physical space. The physical space includes the controlled liquid tanker truck and other traffic participants. The information space consists of two layers: an information mapping layer and a fusion application layer, both distributed on both the vehicle and cloud sides. In the information mapping layer, the vehicle side contains a simple digital twin of the vehicle, consisting of a vehicle and liquid agent model. The cloud side contains a more detailed digital twin of the liquid tanker truck and digital twins of other traffic participants. The fusion application layer is distributed across the vehicle controller and the cloud server. In the vehicle controller, the controller performs predictive control based on the digital twin data of the simple agent model of the vehicle.

[0129] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described anti-rollover control method for liquid tank trucks.

[0130] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0131] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0132] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0133] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0134] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0135] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for preventing tank truck rollover, characterized in that, The method is applied to tank trucks, and the method includes the following steps: The vehicle information and the fluid filling information obtained from the sensors are sent to the cloud. The cloud inputs the vehicle information and the fluid filling information into the vehicle fine model and the fluid sloshing fine model, respectively. The vehicle fine model outputs the first parameter calibration result, and the fluid sloshing fine model outputs the second parameter calibration result. 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 agent model and the liquid agent model respectively; The control target of the liquid tanker is determined by combining the observation of vehicle status sensor information and liquid-related sensor information, and the anti-rollover control of the liquid tanker is performed based on the control target; The anti-rollover control of the tanker truck based on the control target includes: Obtain the first reference value of the vehicle proxy model and the 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; The first error weight is applied to the output of the vehicle proxy model to achieve the control objective of trajectory tracking, the second error weight is applied to the output of the liquid proxy model to achieve the control objective of sway suppression, and the range of a portion of the output of the liquid proxy model is subject to soft constraints to achieve the control objective of rollover prevention.

2. The method for preventing tank truck rollover as described in claim 1, characterized in that, The process of determining the control target of the tanker truck by combining vehicle status sensor information observation and liquid-related sensor information observation includes: The first state variable of the vehicle agent model is estimated by combining vehicle state sensor information observations. The second state variable of the liquid surrogate model is estimated by combining information from liquid-related sensors. The control target of the tanker truck is determined based on the first state quantity and the second state quantity.

3. The anti-rollover control method for liquid tank trucks according to claim 1, characterized in that, The portion of the output that applies soft constraints includes at least the following: ; The output of the liquid proxy model is: , in, Let x be the x-coordinate of the tractor in the world coordinate system. Let y be the y-coordinate of the tractor in the world coordinate system. The pendulum angle is the equivalent pendulum model. The angular velocity of the equivalent pendulum model is given by [the value of the pendulum]. State variables that characterize the rollover state of a vehicle. This is the heading angle of the tractor unit.

4. The method for preventing tank truck rollover according to claim 3, characterized in that, The state quantity characterizing the vehicle rollover state Utilizing the lateral load transfer rate equivalent to suspension force : in, The average wheelbase of each axle. For vehicle quality, It is the acceleration due to gravity. For roll stiffness, For roll damping, The roll angle is... The value represents the tilt angular velocity. Subscript 1 represents the tractor unit, and subscript 2 represents the trailer unit.

5. A tanker truck anti-rollover control device, characterized in that, The device is applied to a tank truck, wherein the device includes: The sending module is used to send vehicle information and liquid filling information obtained from sensors to the cloud. The cloud inputs the vehicle information and the liquid 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 is used to obtain the calibration results of the first parameter and the second parameter from the cloud, and to calibrate the vehicle agent model and the liquid agent model respectively using the calibration results of the first parameter and the second parameter. The control module is used to determine the control target of the tanker truck by combining vehicle status sensor information observation and liquid-related sensor information observation, and to perform anti-rollover control on the tanker truck based on the control target; wherein, The anti-rollover control of the tanker truck based on the control target includes: Obtain the first reference value of the vehicle proxy model and the 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; The first error weight is applied to the output of the vehicle proxy model to achieve the control objective of trajectory tracking, the second error weight is applied to the output of the liquid proxy model to achieve the control objective of sway suppression, and the range of a portion of the output of the liquid proxy model is subject to soft constraints to achieve the control objective of rollover prevention.

6. A liquid tanker truck, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the anti-rollover control method for liquid tank trucks as described in any one of claims 1-4.

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