Brake adjusting method and device based on vehicle load working condition

Abstract

The invention relates to the technical field of vehicle control, and discloses a brake adjusting method and device based on a vehicle load working condition, and the method comprises the steps: obtaining a kinetic parameter of a vehicle under a current load working condition, and estimating a current predicted total mass and a predicted inertia parameter of the vehicle in real time according to the kinetic parameter; updating an initial dynamical model of the vehicle according to the predicted total mass and the predicted inertia parameter to obtain an adjustment parameter; and calculating the distribution proportion of regenerative braking and hydraulic braking in the current driving process of the vehicle according to the adjusting parameters, and adjusting the braking strategy of the vehicle under the current load working condition according to the distribution proportion. The mass and inertia parameters of the vehicle are estimated in real time, the dynamical model is updated according to the mass and inertia parameters, the adaptive capacity of the vehicle to load changes is improved, the distribution proportion of regenerative braking and hydraulic braking is adjusted based on the real-time load working condition, the braking strategy of the vehicle is made to be matched with the actual load working condition all the time, and the braking efficiency is improved. Therefore, the vehicle can obtain stable and efficient braking force under any load.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and more specifically to a braking adjustment method and device based on vehicle load conditions. Background Technology

[0002] New energy heavy-duty trucks, as key equipment in ports, mining areas, and long-haul logistics, are gradually replacing traditional fuel-powered heavy-duty trucks due to their advantages of low carbon emissions, low noise, and high energy efficiency. However, existing autonomous driving systems for new energy heavy-duty trucks have significant shortcomings in both unloaded and fully loaded conditions. In practical applications, the curb weight of a new energy heavy-duty truck is typically around 18 tons, while it can exceed 49 tons when fully loaded, an increase of over 140%, leading to significant changes in its dynamic characteristics. This substantial weight difference not only affects longitudinal acceleration and braking distance but also alters yaw inertia and sideslip characteristics. This results in agile and responsive steering when unloaded, but sluggish steering response when fully loaded, making it prone to understeer or even loss of control during high-speed lane changes.

[0003] Most existing autonomous driving control systems are designed based on vehicle dynamics models with fixed parameters. Their control effects are limited to specific load conditions, and they exhibit many shortcomings in actual logistics scenarios where empty and fully loaded conditions frequently alternate. For example, longitudinal control accuracy decreases, and acceleration and braking responses do not match actual needs; lateral stability is reduced, and understeer or sideslip is likely to occur when cornering at high speeds or during emergency obstacle avoidance; energy management lacks dynamic adjustment, leading to decreased energy efficiency and the risk of motor overload; and the overall control strategy lacks universality and robustness for different loads. Summary of the Invention

[0004] This invention provides a braking adjustment method and device based on vehicle load conditions to solve the problem of how to adaptively adjust the braking strategy of the autonomous driving system of new energy heavy trucks when facing empty and fully loaded conditions.

[0005] In a first aspect, the present invention provides a braking adjustment method based on vehicle load conditions, the method comprising: The vehicle's dynamic parameters under the current load condition are obtained, and the vehicle's current predicted total mass and predicted inertia parameters are estimated in real time based on the dynamic parameters. The initial dynamic model of the vehicle is updated based on the predicted total mass and the predicted inertia parameters to obtain the adjustment parameters; The distribution ratio of regenerative braking and hydraulic braking during the current driving process of the vehicle is calculated based on the adjustment parameters, and the braking strategy of the vehicle under the current load condition is adjusted according to the distribution ratio.

[0006] This invention improves the vehicle's adaptability to load changes by estimating the vehicle's mass and inertia parameters in real time and updating the dynamic model accordingly. It also adjusts the distribution ratio of regenerative braking and hydraulic braking based on real-time load conditions, ensuring that the vehicle's braking strategy always matches the actual load conditions, thereby ensuring that the vehicle can obtain stable and efficient braking force under any load.

[0007] In one optional implementation, the step of acquiring the vehicle's dynamic parameters under the current load condition and estimating the vehicle's current predicted total mass and predicted inertia parameters in real time based on the dynamic parameters includes: Obtain the longitudinal and lateral dynamic parameters of the vehicle under the current load conditions; The longitudinal dynamics equations of the vehicle are generated based on the longitudinal dynamics parameters, and the lateral dynamics equations of the vehicle are generated based on the lateral dynamics parameters. The vehicle's current predicted total mass and predicted inertia parameters are estimated in real time based on the longitudinal dynamic equation and the lateral dynamic equation.

[0008] By utilizing the vehicle's own longitudinal and lateral dynamic equations, a more accurate total mass and moment of inertia under the current operating conditions can be estimated in real time, providing a precise and time-varying model basis, ensuring that the vehicle can maintain optimal handling stability and safety performance under various load conditions.

[0009] In one alternative implementation, the vehicle includes an electric motor; The acquisition of the vehicle's longitudinal and lateral dynamic parameters under the current load conditions includes: The output torque of the motor under the current load condition and the tire radius of the vehicle are obtained, and the driving force generated by the motor is calculated based on the output torque and the tire radius. Obtain the braking pressure and brake caliper force of the vehicle under the current load conditions, and calculate the braking force of the vehicle based on the braking pressure and brake caliper force. Obtain the air density, drag coefficient, frontal area, and longitudinal speed of the vehicle under the current load conditions; calculate the air resistance of the vehicle based on the air density, drag coefficient, frontal area, and longitudinal speed. The vehicle's gravitational acceleration, rolling resistance coefficient, and road slope angle under the current load conditions are obtained. The rolling resistance of the vehicle is calculated based on the gravitational acceleration, the rolling resistance coefficient, and the road slope angle. The slope component force of the vehicle is calculated based on the gravitational acceleration and the road slope angle. The process of generating the longitudinal dynamics equations of the vehicle based on the longitudinal dynamics parameters includes: The longitudinal acceleration of the vehicle under the current load condition is obtained, and the longitudinal dynamic equation of the vehicle under the current load condition is calculated based on the longitudinal acceleration, the driving force, the braking force, the air resistance, the rolling resistance, and the ramp force.

[0010] By integrating all key longitudinal forces, including motor drive force, braking force, air resistance, rolling resistance, and slope component, and based on accurate vehicle sensor data, a high-fidelity longitudinal dynamic equation is constructed. This provides a reliable theoretical model foundation for subsequent real-time and accurate estimation of the vehicle's total mass, overcoming estimation biases caused by neglecting certain resistances in complex driving environments, thereby improving the accuracy of mass estimation and its adaptability to different load conditions and road conditions.

[0011] In one optional implementation, obtaining the longitudinal and lateral dynamic parameters of the vehicle under the current load condition includes: The vehicle's lateral acceleration, longitudinal speed, yaw rate, yaw acceleration, distance between the front and rear axle centers of mass, and lateral force of the front and rear wheels are obtained under the current load conditions. The process of generating the vehicle's lateral dynamics equations based on the lateral dynamics parameters includes: The lateral dynamic equations of the vehicle under the current load condition are generated based on the lateral acceleration, longitudinal vehicle speed, yaw rate, yaw acceleration, distance between the centers of mass of the front and rear axles, and the lateral forces of the front and rear wheels.

[0012] By integrating key dynamic parameters such as lateral acceleration and yaw rate with the lateral forces of the front and rear axles, a dynamic equation that accurately reflects the lateral and yaw motion of the vehicle was constructed. This fully considers the direct impact of load transfer on the lateral forces of the tires, providing a high-fidelity model basis for subsequent real-time and accurate estimation of the vehicle's moment of inertia. It overcomes the errors caused by simplified models that treat the vehicle as a rigid body or ignore tire force changes.

[0013] In one alternative implementation, the vehicle includes a suspension; The real-time estimation of the vehicle's current total mass and inertia parameters based on the longitudinal and lateral dynamic equations includes: The vehicle's current first total mass and first moment of inertia parameters are estimated in real time based on the longitudinal dynamic equation and the lateral dynamic equation. The compression and equivalent stiffness of the suspension under the current load condition and the net weight of the vehicle are obtained, and the current second gross mass of the vehicle is estimated in real time based on the compression, the equivalent stiffness and the net weight of the vehicle. The first total mass and the second total mass are weighted and calculated to obtain the third total mass of the vehicle; The third total mass and the first inertia parameter are subjected to Kalman filtering noise reduction processing to obtain the predicted total mass and predicted inertia parameter.

[0014] By integrating inversion estimation based on dynamic equations and static measurement based on suspension deformation, and utilizing weighted fusion and Kalman filtering, the timeliness of dynamic calculation results is combined with the stability of static measurement data, thereby improving the accuracy and robustness of vehicle total mass and moment of inertia parameter estimation. On the one hand, it effectively smooths the fluctuations caused by single sensor signal noise or instantaneous model errors, and on the other hand, it ensures that the parameter estimation results remain reliable under different load conditions.

[0015] In one alternative implementation, the vehicle is equipped with sensors; The step of performing Kalman filtering noise reduction on the third total mass and the first inertia parameter to obtain the predicted total mass and predicted inertia parameter includes: The state vector of the Kalman filter at the first moment is obtained based on the third total mass and the first inertia parameter; The actual longitudinal acceleration and actual yaw rate of the vehicle are obtained based on the sensors. The third total mass is input into the longitudinal dynamic equation and the lateral dynamic equation to obtain the predicted longitudinal acceleration output by the longitudinal dynamic equation and the predicted yaw rate output by the lateral dynamic equation. The observation vector of the Kalman filter at the first moment is calculated based on the actual longitudinal acceleration, the predicted longitudinal acceleration, the actual yaw rate, and the predicted yaw rate. Based on the state vector and the observation vector, Kalman filtering is applied to the third total mass and the first inertia parameter to reduce noise, thereby obtaining the predicted total mass and the predicted inertia parameter.

[0016] By optimally combining the prior estimate of the state vector with the measured information of the observation vector through the Kalman filter algorithm, noise and instantaneous errors in the third total mass and the first inertia parameters can be dynamically corrected and filtered out, thus ultimately outputting smoother, more stable and reliable predicted total mass and predicted inertia parameters.

[0017] In one optional implementation, updating the initial dynamic model of the vehicle based on the predicted total mass and the predicted inertia parameters to obtain adjustment parameters includes: The vehicle's state transition matrix and input matrix are generated based on the predicted total mass and the predicted inertia parameters; The transverse dynamic equations of the initial dynamic model are updated based on the state transition matrix and the input matrix, and the longitudinal dynamic equations of the initial dynamic model are updated based on the updated transverse dynamic equations. Based on the updated lateral and longitudinal dynamic equations, the vehicle's adjustment parameters under the current load condition are generated.

[0018] By feeding back the real-time identified high-precision predicted total mass and predicted inertia parameters to the initial dynamic model, the state transition matrix and input matrix are dynamically updated, ensuring that the dynamic equations characterizing the vehicle's lateral and longitudinal motion accurately reflect the dynamic characteristics under the current load. The adjustment parameters generated based on this updated dynamic model ensure that the vehicle's braking adjustment system can make optimal decisions according to the actual load conditions, thereby maintaining excellent handling stability and safety performance under various load conditions.

[0019] In one optional implementation, calculating the distribution ratio of regenerative braking to hydraulic braking during the current vehicle driving process based on the adjustment parameters includes: Calculate the energy consumption requirement under the current load condition based on the longitudinal and lateral adjustment parameters. The distribution ratio of regenerative braking and hydraulic braking during the current driving process of the vehicle is calculated based on the energy consumption demand.

[0020] By calculating the actual energy consumption demand under the current dynamic working conditions based on precise longitudinal and lateral adjustment parameters, and calculating the optimal allocation ratio of regenerative braking and hydraulic braking based on the energy consumption demand, kinetic energy can be recovered to the maximum extent and converted into electrical energy while ensuring braking efficiency and stability, thereby effectively improving the comprehensive driving range of new energy vehicles under real load changes.

[0021] In one optional implementation, the step of calculating the energy consumption requirement under the current load condition based on the longitudinal adjustment parameters and the lateral adjustment parameters includes: Obtain the longitudinal deceleration of the vehicle under the current load condition; The braking force required by the vehicle is calculated based on the predicted total mass, the longitudinal deceleration, the air resistance, the rolling resistance, and the slope component. The calculation of the distribution ratio of regenerative braking and hydraulic braking during the current driving process of the vehicle based on the energy consumption demand includes: Obtain the vehicle's braking constraint factor, and allocate the braking demand force according to the braking constraint factor to obtain the first braking demand force corresponding to the regenerative braking and the second braking demand force corresponding to the hydraulic braking.

[0022] By integrating real-time identified vehicle mass, longitudinal deceleration, and various driving resistances, the total braking force required to meet specific braking demands can be accurately calculated. Based on this, a braking constraint factor is introduced to optimize the allocation of regenerative braking and hydraulic braking forces, thereby maximizing energy recovery efficiency while ensuring braking safety and stability, and effectively improving the driving range of new energy vehicles under different loads and road conditions.

[0023] Secondly, the present invention provides a braking adjustment device based on vehicle load conditions, the device comprising: The mass estimation module is used to obtain the dynamic parameters of the vehicle under the current load condition, and to estimate the current predicted total mass and predicted inertia parameters of the vehicle in real time based on the dynamic parameters. The model update module is used to update the initial dynamic model of the vehicle based on the predicted total mass and the predicted inertia parameters to obtain adjustment parameters; The braking adjustment module is used to calculate the distribution ratio of regenerative braking and hydraulic braking during the current driving process of the vehicle based on the adjustment parameters, and to adjust the braking strategy of the vehicle under the current load condition based on the distribution ratio.

[0024] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the braking adjustment method based on vehicle load conditions described in the first aspect or any corresponding embodiment thereof.

[0025] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the braking adjustment method based on vehicle load conditions described in the first aspect or any corresponding embodiment thereof.

[0026] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the braking adjustment method based on vehicle load conditions described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0027] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0028] Figure 1This is a schematic diagram of the architecture of a braking adjustment system according to an embodiment of the present invention; Figure 2 This is a schematic flowchart of a first method for braking adjustment based on vehicle load conditions according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the transverse dynamic model in the initial dynamic model according to an embodiment of the present invention; Figure 4 This is a structural block diagram of a braking adjustment device based on vehicle load conditions according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0031] 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 one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0032] New energy heavy-duty trucks, as key equipment in ports, mining areas, and long-haul logistics, are gradually replacing traditional fuel-powered heavy-duty trucks due to their advantages of low carbon emissions, low noise, and high energy efficiency. However, existing autonomous driving systems for new energy heavy-duty trucks have significant shortcomings in both unloaded and fully loaded conditions. In practical applications, the curb weight of a new energy heavy-duty truck is typically around 18 tons, while it can exceed 49 tons when fully loaded, an increase of over 140%, leading to significant changes in its dynamic characteristics. This substantial weight difference not only affects longitudinal acceleration and braking distance but also alters yaw inertia and sideslip characteristics. This results in agile and responsive steering when unloaded, but sluggish steering response when fully loaded, making it prone to understeer or even loss of control during high-speed lane changes.

[0033] Most autonomous driving control systems in related technologies are designed based on vehicle dynamics models with fixed parameters. Their control effects are limited to specific load conditions and exhibit many shortcomings in actual logistics scenarios where empty and fully loaded conditions frequently alternate. For example, longitudinal control accuracy decreases, and acceleration and braking responses do not match actual needs; lateral stability is reduced, and understeer or sideslip is likely to occur when cornering at high speeds or during emergency obstacle avoidance; energy management lacks dynamic adjustment, leading to decreased energy efficiency and the risk of motor overload; and the overall control strategy lacks universality and robustness for different loads.

[0034] Based on this, the present invention provides a braking adjustment method and apparatus based on vehicle load conditions. The method includes: acquiring the vehicle's dynamic parameters under the current load condition, and estimating the vehicle's current predicted total mass and predicted inertia parameters in real time based on the dynamic parameters; updating the vehicle's initial dynamic model based on the predicted total mass and predicted inertia parameters to obtain adjustment parameters; calculating the distribution ratio of regenerative braking and hydraulic braking during the vehicle's current driving process based on the adjustment parameters, and adjusting the vehicle's braking strategy under the current load condition based on the distribution ratio. The present invention estimates the vehicle's mass and inertia parameters in real time and updates the dynamic model accordingly, improving the vehicle's adaptability to load changes. Furthermore, by adjusting the distribution ratio of regenerative braking and hydraulic braking based on real-time load conditions, the present invention ensures that the vehicle's braking strategy always matches the actual load conditions, thereby ensuring that the vehicle obtains stable and efficient braking force under any load.

[0035] The overall architecture of the braking adjustment system of the present invention is as follows: Figure 1 As shown, it mainly includes five functional layers: perception layer, estimation layer, control layer, energy optimization layer, and execution layer. The perception layer is used to collect basic dynamic data of the vehicle's operating state, including longitudinal velocity, lateral velocity, acceleration, suspension compression, steering angle, drive motor torque, and motor braking current. The estimation layer estimates the vehicle's total mass and inertial parameters in real time based on these basic dynamic data. The control layer uses model predictive control methods to dynamically adjust the vehicle's longitudinal and lateral control based on the estimation results of total mass and inertial parameters. The energy optimization layer allocates motor drive and braking energy according to the energy consumption requirements under different loads to maximize energy efficiency. The execution layer includes the steering motor, drive motor, and braking actuator, which are used to receive braking control commands and act on the vehicle body.

[0036] According to an embodiment of the present invention, a braking adjustment method based on vehicle load conditions is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0037] This embodiment provides a braking adjustment method based on vehicle load conditions. Figure 2 This is a flowchart of a braking adjustment method based on vehicle load conditions according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps: Step S201: Obtain the dynamic parameters of the vehicle under the current load condition, and estimate the current predicted total mass and predicted inertia parameters of the vehicle in real time based on the dynamic parameters.

[0038] In a preferred embodiment, the vehicle refers to a new energy heavy-duty truck, and the vehicle's dynamic parameters under the current load condition characterize physical quantities that can be directly or indirectly measured during vehicle operation, reflecting its motion state and force conditions. For example, physical quantities reflecting the vehicle's force and motion state can be continuously collected and read through the vehicle's existing sensor network and controller area network (CAN bus). Dynamic parameters may include: Longitudinal dynamic parameters: such as the drive / braking torque output by the drive motor, and the longitudinal acceleration calculated by wheel speed sensors or inertial measurement units (IMUs) located on the vehicle. For example, real-time motor torque signals are read from the motor controller via the CAN bus, while longitudinal acceleration signals are acquired from the IMU.

[0039] Lateral dynamic parameters: such as the front wheel steering angle obtained by the steering angle sensor, and the yaw rate (i.e. the angular velocity of the vehicle rotating about its vertical axis) and lateral acceleration measured by the inertial measurement unit (IMU).

[0040] Predicted gross vehicle weight (PGW) refers to the real-time estimated total mass of a vehicle, which is the sum of the vehicle's curb weight and the weight of its cargo. For example, if a heavy truck with a curb weight of 18 tons has a predicted GMW of 35 tons, it means that it is currently carrying approximately 17 tons of cargo.

[0041] Predicted inertia parameter refers to the estimated value of yaw moment of inertia, used to characterize how easily a vehicle rotates around its vertical axis. The larger the predicted inertia parameter, the greater the inertia of the vehicle in changing or maintaining its steering state. The predicted inertia parameter is closely related to the vehicle's total mass and its mass distribution. For example, when the same vehicle is fully loaded, due to its increased mass and potentially upward shift of its center of gravity, its yaw moment of inertia will be much greater than its value when unloaded, resulting in a sluggish steering response.

[0042] This embodiment does not call preset fixed values, but utilizes the vehicle's inherent actuators (such as motors) and sensors (such as IMUs) to input the dynamic parameters acquired in real time under the current load condition into the vehicle dynamics model, and then deduce the most likely mass and inertia parameters. This achieves dynamic perception of the drastically changing load state and dynamic response characteristics of new energy heavy trucks.

[0043] Step S202: Update the initial dynamic model of the vehicle based on the predicted total mass and predicted inertia parameters to obtain the adjustment parameters.

[0044] The initial dynamic model of this invention includes a longitudinal dynamic model and a transverse dynamic model, wherein the longitudinal dynamic equation in the longitudinal dynamic model is:

[0045] in, The total mass of the vehicle. For longitudinal acceleration, The driving force generated by the motor, For the braking force of the vehicle. For air resistance, For rolling resistance, The force is the component of the ramp.

[0046] air resistance Calculated using the following formula:

[0047] in, The density of air (unit: kg / m³, preferably 1.225). The drag coefficient is (preferably, a typical drag coefficient value is 0.6~0.8). The vehicle's frontal area (unit: m², typically 7-9 m² for new energy heavy trucks); This refers to the longitudinal vehicle speed (in m / s).

[0048] Rolling resistance Calculated using the following formula:

[0049] in, The total mass of the vehicle. It is the acceleration due to gravity; This is the rolling resistance coefficient (usually taken as 0.01-0.02). This refers to the road slope angle.

[0050] The component of the ramp force is calculated using the following formula:

[0051] in, The total mass of the vehicle. It is the acceleration due to gravity; This refers to the road slope angle.

[0052] As can be seen from the above formula, when the vehicle load changes, the longitudinal dynamics equation... Significant changes will occur, directly affecting acceleration response and braking performance.

[0053] like Figure 3 As shown, in this embodiment of the invention, a single-track model is used as the transverse dynamic model in the initial dynamic model. The transverse dynamic equation of the transverse dynamic model is:

[0054] in, The total mass of the vehicle; This refers to the vehicle's lateral acceleration. The longitudinal speed of the vehicle; The yaw rate of the vehicle; The yaw acceleration of the vehicle; The moment of inertia of the vehicle about its vertical axis, expressed in kg·m², is updated as the load changes. This is the distance between the centers of gravity of the vehicle's front axle; This is the distance between the centers of gravity of the rear axle of the vehicle. This refers to the lateral force on the front wheels of the vehicle. This refers to the lateral force on the rear wheels of the vehicle.

[0055] Among them, the lateral force is linearly related to the tire slip angle, thus the front wheel lateral force... The relationship with the tire slip angle is as follows:

[0056] in, The front wheel lateral stiffness of the vehicle; The steering angle of the front wheels is driven by the steering motor in the vehicle; The lateral speed of the vehicle; The longitudinal speed of the vehicle; This is the distance between the centers of gravity of the vehicle's front axle; Let be the yaw rate of the vehicle.

[0057] Rear wheel lateral force The relationship with the tire slip angle is as follows:

[0058] in, The rear wheel lateral stiffness of the vehicle; The lateral speed of the vehicle; The longitudinal speed of the vehicle; This is the distance between the centers of gravity of the rear axle of the vehicle. Let be the yaw rate of the vehicle.

[0059] As can be seen from the above formula, when the total mass of the vehicle and When changes occur, the vehicle's lateral and yaw response characteristics alter, leading to a decrease in the vehicle's path tracking performance. Therefore, this invention uses real-time estimation... and And use it to modify the longitudinal and lateral dynamic models.

[0060] Furthermore, after obtaining the real-time estimated total mass and inertia parameters of the vehicle, the corresponding fixed preset parameters in the initial dynamic model are replaced with the real-time estimated total mass and inertia parameters that reflect the current actual load conditions. This results in updated longitudinal and lateral dynamic models, and key variables or coefficients for specific control commands are generated as adjustment parameters within these updated models. For example, in longitudinal control, the adjustment parameters can be represented as weighting coefficients of the cost function in the model predictive controller (MPC), but this invention does not impose specific limitations.

[0061] For example, a vehicle is performing adaptive following at high speed. If the vehicle is currently fully loaded (e.g., the total mass of the vehicle is 49 tons), but the initial model still uses the unloaded mass (e.g., the vehicle is unloaded at 18 tons), the initial unloaded mass is smaller than the actual load condition. Therefore, when achieving the same target acceleration, the braking force calculated by the initial dynamics model will be smaller, resulting in insufficient acceleration and a larger following distance.

[0062] In this embodiment of the invention, the predicted total mass and predicted inertia parameters obtained in real time are updated into the initial longitudinal and lateral dynamic models. Based on the updated dynamic model, the braking adjustment system of this embodiment recalculates the internal parameters of the vehicle controller. For example, the longitudinal controller can output corresponding motor torque commands, thereby accurately achieving the expected acceleration effect and maintaining a stable following distance even under actual full-load conditions.

[0063] Step S203: Calculate the distribution ratio of regenerative braking and hydraulic braking during the current driving process of the vehicle based on the adjustment parameters, and adjust the braking strategy of the vehicle under the current load condition based on the distribution ratio.

[0064] Regenerative braking refers to the process where, during vehicle braking, the drive motor switches to generator mode, using the vehicle's kinetic energy to generate electricity and charge the battery, while simultaneously producing braking force. Regenerative braking can improve a vehicle's range, but the braking torque it provides typically has an upper limit and varies with the battery's state. Hydraulic braking refers to friction braking, specifically using a hydraulic system to push brake pads against the brake disc to generate braking force. It can produce a large and stable braking force, but its energy is dissipated as heat, resulting in waste.

[0065] The allocation ratio represents the proportion of the braking force required by the vehicle, with regenerative braking and hydraulic braking each contributing their respective portions. Based on this allocation ratio, a corresponding braking strategy is generated for the vehicle, coordinating regenerative and hydraulic braking under different load conditions to improve the safety and efficiency of new energy heavy-duty trucks. For example, the vehicle controller receives a braking force demand calculated based on a 49-ton full-load weight, which far exceeds the maximum regenerative braking capacity of the motor. In this case, the maximum value (e.g., 300 Nm) can be allocated to regenerative braking, while hydraulic braking handles the remaining portion after subtracting 300 Nm from the total demand. Further, based on the calculated allocation ratio, the controller adjusts the drive motor controller to output 300 Nm of regenerative braking torque; simultaneously, it adjusts the solenoid valves of the hydraulic braking system to control the brake fluid pressure, generating the required frictional braking force. This ensures stable vehicle speed when the 49-ton heavy-duty vehicle is going downhill, while also maximizing energy recovery efficiency to the maximum value under the current load condition.

[0066] The braking adjustment method based on vehicle load conditions provided in this embodiment improves the vehicle's adaptability to load changes by estimating the vehicle's mass and inertia parameters in real time and updating the dynamic model accordingly. It also adjusts the distribution ratio of regenerative braking and hydraulic braking based on real-time load conditions, ensuring that the vehicle's braking strategy always matches the actual load conditions, thereby ensuring that the vehicle can obtain stable and efficient braking force under any load.

[0067] For step S201, the dynamic parameters of the vehicle under the current load condition are obtained, and the predicted total mass and predicted inertia parameters of the vehicle are estimated in real time based on the dynamic parameters.

[0068] In one specific embodiment, the longitudinal dynamic parameters and lateral dynamic parameters of the vehicle under the current load condition are obtained respectively.

[0069] Specifically, the vehicle includes a motor. In this embodiment, the longitudinal dynamic parameters may include drive and braking parameters, vehicle state and inherent property parameters, and environmental and road condition parameters. Specifically, drive and braking parameters include: drive torque, representing the output shaft torque provided by the motor controller; tire radius, representing the vehicle's tire rolling radius, a fixed mechanical parameter; braking pressure, representing the pressure value in the vehicle's brake hydraulic system; and brake caliper force, representing the actual clamping force acting on the brake disc, converted from braking pressure. Vehicle state and inherent property parameters include: longitudinal speed, representing the vehicle's forward speed relative to the ground; air drag coefficient, representing the inherent coefficient of the vehicle's aerodynamic characteristics; vehicle frontal area, representing the vehicle's projected area in the direction of travel; and rolling resistance coefficient, representing the rolling resistance characteristics between the tire and the road surface. Environmental and road condition parameters include: air density, representing the current atmospheric density, affected by temperature and altitude; road slope angle, representing the angle of inclination between the road and the horizontal plane; and gravitational acceleration.

[0070] After obtaining the longitudinal dynamic parameters of the vehicle under the current load condition, the parameters in the longitudinal dynamic equation are calculated based on the longitudinal dynamic parameters.

[0071] Specifically, the output torque of the motor under the current load condition and the tire radius of the vehicle are obtained, and the driving force generated by the motor is calculated based on the output torque and tire radius:

[0072] in, The driving force generated by the motor; This represents the output torque of the motor under the current load conditions. This refers to the tire radius of the vehicle.

[0073] Obtain the vehicle's braking pressure and caliper force under the current load conditions, and calculate the vehicle's braking force based on the braking pressure and caliper force:

[0074]

[0075] in, For the braking force of the vehicle; The braking pressure of the vehicle under the current load conditions; This refers to the brake caliper clamping force of the vehicle under the current load conditions. This refers to the effective area of ​​the brake wheel cylinder piston. This is the coefficient of friction between the tire and the ground; This is the effective radius of the brake disc.

[0076] Obtain the vehicle's air density, drag coefficient, frontal area, and longitudinal speed under the current load conditions; calculate the vehicle's air resistance based on the air density, drag coefficient, frontal area, and longitudinal speed.

[0077] in: The density of air (unit: kg / m³, preferably 1.225). The drag coefficient is (preferably, a typical drag coefficient value is 0.6~0.8). The vehicle's frontal area (unit: m², typically 7-9 m² for new energy heavy trucks); This refers to the longitudinal vehicle speed (in m / s).

[0078] Obtain the vehicle's gravitational acceleration, rolling resistance coefficient, and road slope angle under the current load conditions, and calculate the vehicle's rolling resistance based on the gravitational acceleration, rolling resistance coefficient, and road slope angle:

[0079] in, The total mass of the vehicle. It is the acceleration due to gravity; This is the rolling resistance coefficient (usually taken as 0.01-0.02). This refers to the road slope angle.

[0080] Calculate the slope component force on the vehicle based on gravitational acceleration and road gradient angle:

[0081] in, The total mass of the vehicle. It is the acceleration due to gravity; This refers to the road slope angle.

[0082] Specifically, the lateral dynamic parameters in this embodiment may include lateral acceleration, longitudinal vehicle speed, yaw rate, yaw acceleration, distance between the center of gravity of the front and rear axles, and lateral forces of the front and rear wheels. Lateral acceleration is the acceleration perpendicular to the longitudinal axis at the vehicle's center of gravity, reflecting the strength of centrifugal force during steering; longitudinal vehicle speed is the speed in the vehicle's forward direction; yaw rate is the angular velocity of the vehicle rotating about its vertical axis; yaw acceleration is the rate of change of the yaw rate; the distance between the center of gravity of the front and rear axles, i.e., the wheelbase, is a fixed geometric parameter of the vehicle, used to characterize the vehicle's geometric relationship and weight distribution during steering; and the lateral forces of the front and rear wheels are the forces generated between the tires and the contact surface, used to provide steering and resist sideslip.

[0083] After obtaining the longitudinal and lateral dynamic parameters, the longitudinal dynamic equations of the vehicle are generated based on the longitudinal dynamic parameters, and the lateral dynamic equations of the vehicle are generated based on the lateral dynamic parameters.

[0084] Specifically, the longitudinal acceleration of the vehicle under the current load condition is obtained, and the longitudinal dynamic equation of the vehicle under the current load condition is calculated based on the longitudinal acceleration, driving force, braking force, air resistance, rolling resistance, and slope component force.

[0085] in, The total mass of the vehicle. For longitudinal acceleration, The driving force generated by the motor, For the braking force of the vehicle. For air resistance, For rolling resistance, Force component for ramp Based on lateral acceleration, longitudinal vehicle speed, yaw rate, yaw acceleration, distance between the centers of mass of the front and rear axles, and lateral forces of the front and rear wheels, the lateral dynamic equations of the vehicle under the current load condition are generated:

[0086] in, The total mass of the vehicle; This refers to the vehicle's lateral acceleration. The longitudinal speed of the vehicle; The yaw rate of the vehicle; The yaw acceleration of the vehicle; The moment of inertia of the vehicle about its vertical axis, expressed in kg·m², is updated as the load changes. This is the distance between the centers of gravity of the vehicle's front axle; This is the distance between the centers of gravity of the rear axle of the vehicle. This refers to the lateral force on the front wheels of the vehicle. This refers to the lateral force on the rear wheels of the vehicle.

[0087] Among them, the lateral force is linearly related to the tire slip angle, thus the front wheel lateral force... The relationship with the tire slip angle is as follows:

[0088] in, The front wheel lateral stiffness of the vehicle; The steering angle of the front wheels is driven by the steering motor in the vehicle; The lateral speed of the vehicle; The longitudinal speed of the vehicle; This is the distance between the centers of gravity of the vehicle's front axle; Let be the yaw rate of the vehicle.

[0089] Rear wheel lateral force The relationship with the tire slip angle is as follows:

[0090] in, The rear wheel lateral stiffness of the vehicle; The lateral speed of the vehicle; The longitudinal speed of the vehicle; This is the distance between the centers of gravity of the rear axle of the vehicle. Let be the yaw rate of the vehicle.

[0091] Furthermore, the vehicle includes a suspension; based on the longitudinal and lateral dynamic equations, the vehicle's current total mass and inertia parameters are estimated in real time, including: The vehicle's current first gross mass and first moment of inertia parameters are estimated in real time based on the longitudinal and lateral dynamic equations. Specifically, under complex operating conditions, the vehicle's longitudinal and lateral dynamics are significantly coupled; for example, during uphill acceleration, insufficient longitudinal driving force can lead to reduced lateral stability. Therefore, this embodiment couples the longitudinal and lateral dynamic models to obtain:

[0092] in, Let the vehicle's acceleration state vector be... For longitudinal acceleration, For the vehicle's lateral acceleration, The yaw acceleration of the vehicle; The vehicle speed state vector, Let be the lateral speed of the vehicle. Let be the longitudinal speed of the vehicle. The yaw rate of the vehicle; For control input; The predicted total mass of the vehicle; This is the moment of inertia of the vehicle about its vertical axis, expressed in kg·m², and updated as the load changes.

[0093] in, for:

[0094]

[0095] in, The driving force generated by the motor; For the braking force of the vehicle; It was measured by an IMU; air density; This refers to the air drag coefficient; The frontal area of ​​the vehicle; Longitudinal vehicle speed (unit: m / s); For the predicted total mass of the vehicle, It is the acceleration due to gravity; This is the rolling resistance coefficient; This refers to the road slope angle.

[0096] Based on the longitudinal dynamics equation, the formula for estimating the total mass of a vehicle can be obtained as follows:

[0097] in, The first gross mass of the vehicle; The driving force generated by the motor, For the braking force of the vehicle. For air resistance, For rolling resistance, Force component for the ramp; This is the longitudinal acceleration. Therefore, the first total mass of the vehicle can be calculated.

[0098] Furthermore, due to the significant noise from sensors in real-world environments, the first total mass calculated directly using the aforementioned total mass estimation formula will exhibit substantial fluctuations. Therefore, this embodiment utilizes a Kalman filter to smooth the estimated first total mass. Specifically, in new energy heavy-duty trucks, the suspension is typically equipped with a height sensor, which measures the suspension's compression, equivalent stiffness, and the vehicle's net weight under the current load condition. Based on the compression, equivalent stiffness, and net weight, the vehicle's current second total mass is estimated in real time and calculated using the following formula:

[0099] in, The second gross vehicle weight; This represents the equivalent stiffness of the suspension under the current load conditions. It is the acceleration due to gravity; This represents the compression of the suspension under the current load condition. This refers to the vehicle's net weight without any load.

[0100] Furthermore, a weighted calculation is performed on the first and second gross masses to obtain the vehicle's third gross mass:

[0101] in, This is the vehicle's first gross mass. This is the vehicle's second gross mass. It can adaptively adjust according to the vehicle speed and acceleration magnitude. In one embodiment, The value can be obtained using the following method: First, calculate the normalized indices of the vehicle's longitudinal velocity and longitudinal acceleration:

[0102] in, This represents the normalized longitudinal velocity; This represents the normalized longitudinal acceleration; Longitudinal velocity; Maximum longitudinal velocity; It is longitudinal acceleration; For maximum longitudinal acceleration, the preferred option is... The value can be 25 m / s. It can take the value 3.0 m / s.

[0103] The normalized longitudinal velocity and longitudinal acceleration are synthesized:

[0104] in, Indicates the longitudinal composite index; This represents the normalized longitudinal velocity; This represents the normalized longitudinal acceleration; The value is 0.6; It is 0.4.

[0105] A linear mapping of the longitudinal composite index is obtained :

[0106] in, Indicates the longitudinal composite index; It can take the value 0.05; It can take the value 0.95.

[0107] Furthermore, the vehicle is equipped with sensors. Based on the sensor measurements, Kalman filtering is applied to the third total mass and the first inertia parameter for noise reduction, yielding the predicted total mass and predicted inertia parameters. Kalman filtering (EKF) is used to remove fusion noise from the third total mass and the first inertia parameter, where the EKF state vector at time k is set as follows:

[0108] in, Let EKF be the state vector at time k; The third gross vehicle weight. This is the first inertia parameter.

[0109] The observation vector of EKF is generated based on the longitudinal acceleration and yaw rate residuals of the vehicle:

[0110] in, This represents the observation residual vector at time k; This represents the longitudinal acceleration measured by the IMU sensor; This represents the predicted longitudinal acceleration value derived from the updated longitudinal dynamic equations; This represents the yaw rate measured by the IMU sensor. This represents the predicted yaw rate calculated based on the vehicle yaw dynamics model.

[0111] The initial prediction model for Kalman filtering is:

[0112] in, This is the actual value at time k; This is process noise; Let be the predicted value at time (k+1). The observation equation for the Kalman filter is:

[0113] in, The observation value at time k; To measure the observation noise, representing the error between the actual IMU and the suspension height sensor accuracy, the error is converted to a Gaussian distribution. This observation equation is used to construct an observation matrix H, which linearizes the third total mass and the first inertia parameter. Furthermore, the actual sensor observations are obtained. The residual is calculated based on the actual sensor observations and the expected observations calculated based on the predicted state. :

[0114] Substitute the observation equation into the initial prediction model, and based on the residuals... To correct the third total mass:

[0115] in, This is the posterior state prediction, i.e., the state estimate after correction based on the observation at time k; This is the prior state estimate, which is the state estimate corrected based on the previous observation (at time k-1). The previously constructed observation matrix H is a linearized approximation of the third total mass and the first inertia parameter; The Kalman gain determines the correction magnitude; as time progresses, the EKF iteratively adjusts the third total mass and the first inertia parameters, causing the predicted acceleration and yaw rate to gradually approach the measured values. Specifically, the steps include: The state vector of the Kalman filter at the first moment is obtained based on the third total mass and the first inertia parameter.

[0116] The actual longitudinal acceleration and actual yaw rate of the vehicle are obtained based on sensors.

[0117] By inputting the third total mass into the longitudinal and lateral dynamic equations, the predicted longitudinal acceleration output by the longitudinal dynamic equation and the predicted yaw rate output by the lateral dynamic equation are obtained.

[0118] The observation vector of the Kalman filter at the first moment is calculated based on the actual longitudinal acceleration, the predicted longitudinal acceleration, the actual yaw rate, and the predicted yaw rate.

[0119] Kalman filtering is applied to the third total mass and the first inertia parameter based on the state vector and the observation vector to reduce noise, thereby obtaining the predicted total mass and predicted inertia parameter.

[0120] The braking adjustment method based on vehicle load conditions provided in this embodiment effectively filters out noise in the sensor signals by comparing theoretical predictions based on a dynamic model with actual sensor measurements and applying Kalman filtering for noise reduction. This results in predicted total mass and predicted inertia parameters that are closer to the true values. Furthermore, since the vehicle's total mass (e.g., load variations) and moment of inertia are not constant during actual operation, this embodiment enables online real-time estimation of these time-varying parameters, allowing the braking adjustment system to dynamically track changes in vehicle state. These filtered and optimized accurate parameters can be fed back to the vehicle's control system, laying the foundation for more precise decision-making and control by the control system.

[0121] For step S202, the initial dynamic model of the vehicle is updated based on the predicted total mass and predicted inertia parameters to obtain the adjustment parameters.

[0122] In one embodiment, the vehicle's MPC controller ensures that the vehicle can stably track the reference path and maintain the desired longitudinal speed under different load conditions. Specifically, the MPC controller includes a cost function.

[0123] The MPC controller contains a predictive model, in which... The prediction time domain length of the prediction model, i.e., the length of the prediction window, is... Number of discrete sampling steps The sampling period is determined by the sampling period, which is generally 0.05s. For autonomous driving of heavy trucks, a longer path needs to be anticipated, requiring coverage of 2s. Np Take 40; The desired output, such as the desired position deviation, vehicle speed and other dynamic parameters; The predicted output of the prediction model; For control inputs to the predictive model, such as dynamic parameters like steering angle, driving force, and braking force; The preset error weight matrix, ,in This represents the longitudinal velocity error weight, in units of 1 / (m / s). 2 The initial value is 10; The weight for lateral deviation error, in units of 1 / m 2 The initial value is 1000; Weight for heading error, in units of 1 / rad 2 The initial value is 200, which can be adjusted in actual operation; To control the input weight matrix, ,in The initial weight for the corner is set to 1.0 to penalize excessively large corners and changes in corner angle. The initial value for the driving force weight is 0.01, which is used to penalize large driving forces. The initial value for braking force weight is 0.5, which is used to penalize large braking forces to prevent braking shocks. It can be adjusted in actual operation.

[0124] Furthermore, in each control cycle, the MPC controller calls upon the results of the load estimation layer to dynamically update the state transition matrix and input matrix of the prediction model in the MPC controller:

[0125]

[0126] in:

[0127] The state transition matrix varies with mass and inertia; The input matrix varies with mass and inertia; Let k be the actual state matrix at time k; Let be the predicted state matrix at time (k+1); Let be the input matrix at time (k+1); This is the output matrix; The vehicle's lateral speed, in m / s; Yaw rate (rad / s); The longitudinal speed of the vehicle is expressed in m / s. The lateral offset between the vehicle's center of gravity and the centerline of the planned path; The difference between the vehicle heading angle and the path tangent angle measured by the IMU.

[0128] in,

[0129]

[0130] in, ; ; ; ; ; ; ; .

[0131] in, The total mass of the vehicle; For air resistance; For rolling resistance; The longitudinal speed of the vehicle; air density; This refers to the air drag coefficient; The frontal area of ​​the vehicle; It is the acceleration due to gravity; This is the rolling resistance coefficient; The road slope angle; The yaw rate of the vehicle; The vehicle's lateral speed; The front wheel lateral stiffness of the vehicle; The rear wheel lateral stiffness of the vehicle; This is the distance between the centers of gravity of the vehicle's front axle; This is the distance between the centers of gravity of the rear axle of the vehicle. Let be the moment of inertia of the vehicle about its vertical axis.

[0132] Specifically, the steps include the following: The state transition matrix and input matrix of the vehicle are generated based on the predicted total mass and predicted inertia parameters. The transverse dynamic equations of the initial dynamic model are updated based on the state transition matrix and the input matrix, and the longitudinal dynamic equations of the initial dynamic model are updated based on the updated transverse dynamic equations. Based on the updated lateral and longitudinal dynamic equations, the vehicle's adjustment parameters under the current load condition are generated.

[0133] Furthermore, to ensure vehicle safety, this embodiment can introduce constraints in the MPC controller, including tire lateral force constraints, motor torque constraints, and braking pressure constraints. These constraints are used to ensure that the vehicle will not become unstable due to excessive operation under full load conditions.

[0134] Tire lateral force constraint manifests as follows:

[0135] in, The road surface adhesion coefficient; This refers to the lateral force on the front axle. For front axle normal load; This is the lateral force on the rear axle; This is the normal load on the rear axle.

[0136] The motor torque constraint is manifested as follows:

[0137] in, This refers to the motor torque; This is the minimum constraint value for motor torque; This represents the maximum constraint value for motor torque.

[0138] Braking pressure constraint manifests as follows:

[0139] in, Braking pressure; This represents the maximum constraint value for braking pressure.

[0140] For step S203, the distribution ratio of regenerative braking and hydraulic braking during the current driving process of the vehicle is calculated based on the adjustment parameters, and the braking strategy of the vehicle under the current load condition is adjusted according to the distribution ratio.

[0141] In one specific embodiment, the longitudinal deceleration of the vehicle under the current load condition is obtained, and the braking force required by the vehicle is calculated based on the predicted total mass, longitudinal deceleration, air resistance, rolling resistance, and slope component.

[0142] The distribution ratio of regenerative braking and hydraulic braking during the current driving process of the vehicle is calculated based on energy consumption demand. Specifically, the braking constraint factor of the vehicle is obtained, and the braking demand force is distributed according to the braking constraint factor to obtain the first braking demand force corresponding to regenerative braking and the second braking demand force corresponding to hydraulic braking.

[0143] Specifically, when adaptively generating the braking strategy, a preset error weight matrix is ​​used. and control input weight matrix The weights in the formula are set as the product of the initial value and the adaptive factor, where the adaptive factor is determined by... and Decision, among which For the predicted total mass of the vehicle, This represents the longitudinal speed of the vehicle.

[0144] When a vehicle has a large load mass, it will cause vehicle dynamic lag, requiring stricter tracking of speed indicators to ensure transportation efficiency and safety. Therefore, the longitudinal speed weight is calculated using the following formula. :

[0145] in, The predicted total mass of the vehicle; The unloaded reference mass of the vehicle; This is the initial value for the longitudinal velocity weight; The sensitivity coefficient is set to 1.

[0146] A large vehicle load can result in a slower lateral response. Excessive lateral weighting can lead to aggressive control commands or unattainable lateral performance. Therefore, the lateral weighting is calculated using the following formula. and heading weight :

[0147] The sensitivity coefficient is set to 0.2. These are the initial values ​​for the weights; The unloaded reference mass of the vehicle; This represents the predicted total mass of the vehicle.

[0148] When the vehicle is unloaded, it allows for agile steering, thus reducing the weighting of the steering angle input. When the vehicle is fully loaded, the weight of the steering angle input is increased. :

[0149] in, The sensitivity coefficient is 0.5. Input initial values ​​for the weights at the corners; The unloaded reference mass of the vehicle; This represents the predicted total mass of the vehicle.

[0150] d. Braking force input weight To encourage greater use of regenerative braking under full load, the braking penalty can be appropriately reduced: ,

[0151] in, The sensitivity coefficient is 0.2. The initial value for the weight of the braking force is input; The unloaded reference mass of the vehicle; This represents the predicted total mass of the vehicle.

[0152] When the vehicle's predicted total mass When the value is increased, the driving force demand rises, but to avoid emergency overload of the motor, the penalty should be increased to limit large fluctuations in driving force.

[0153] in, The sensitivity coefficient is 0.6. Input the initial values ​​of the weights for the driving force.

[0154] The braking adjustment method based on vehicle load conditions provided in this embodiment calculates the actual braking demand after overcoming gradients and resistance by combining real-time estimated predicted total mass and current longitudinal deceleration parameters, providing a reliable data foundation for efficient braking. Secondly, it dynamically adjusts the distribution ratio of regenerative braking and hydraulic braking according to energy consumption demand and braking constraint factors, ensuring that as much braking energy as possible is recovered under constraint conditions and converted into electrical energy for storage, directly improving the vehicle's driving range. While ensuring safety, it also considers energy saving and smoothness, maximizing the use of regenerative braking to save energy while ensuring timely replenishment of braking force by hydraulic braking, thus guaranteeing braking performance and driving experience.

[0155] This embodiment also provides a braking adjustment device based on vehicle load conditions. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0156] This embodiment provides a braking adjustment device based on vehicle load conditions, such as... Figure 4 As shown, it includes: The mass estimation module 401 is used to obtain the dynamic parameters of the vehicle under the current load condition, and to estimate the current predicted total mass and predicted inertia parameters of the vehicle in real time based on the dynamic parameters.

[0157] The model update module 402 is used to update the initial dynamic model of the vehicle based on the predicted total mass and predicted inertia parameters to obtain adjustment parameters.

[0158] The braking adjustment module 403 is used to calculate the distribution ratio of regenerative braking and hydraulic braking during the current driving process of the vehicle based on the adjustment parameters, and adjust the braking strategy of the vehicle under the current load condition based on the distribution ratio.

[0159] In some alternative implementations, the quality estimation module 401 includes: The parameter acquisition unit is used to acquire the longitudinal and lateral dynamic parameters of the vehicle under the current load conditions.

[0160] The equation building unit is used to generate the longitudinal dynamic equations of the vehicle based on the longitudinal dynamic parameters, and to generate the lateral dynamic equations of the vehicle based on the lateral dynamic parameters.

[0161] The mass estimation unit is used to estimate the vehicle's current predicted total mass and predicted inertia parameters in real time based on the longitudinal and lateral dynamic equations.

[0162] The vehicle includes a motor; the parameter acquisition unit includes: The first parameter acquisition unit is used to acquire the output torque of the motor under the current load condition and the tire radius of the vehicle, and to calculate the driving force generated by the motor based on the output torque and tire radius.

[0163] The second parameter acquisition unit is used to acquire the braking pressure and brake caliper force of the vehicle under the current load conditions, and to calculate the braking force of the vehicle based on the braking pressure and brake caliper force.

[0164] The third parameter acquisition unit is used to acquire the air density, air drag coefficient, vehicle frontal area, and longitudinal speed of the vehicle under the current load conditions; and to calculate the air drag of the vehicle based on the air density, air drag coefficient, vehicle frontal area, and longitudinal speed.

[0165] The fourth parameter acquisition unit is used to acquire the vehicle's gravitational acceleration, rolling resistance coefficient, and road slope angle under the current load conditions, calculate the vehicle's rolling resistance based on the gravitational acceleration, rolling resistance coefficient, and road slope angle, and calculate the vehicle's slope component force based on the gravitational acceleration and road slope angle.

[0166] Equation building units include: The longitudinal equation construction unit is used to obtain the longitudinal acceleration of the vehicle under the current load condition, and calculate the longitudinal dynamic equation of the vehicle under the current load condition based on the longitudinal acceleration, driving force, braking force, air resistance, rolling resistance and slope component force.

[0167] The parameter acquisition unit also includes: The fifth parameter acquisition unit is used to acquire the vehicle's lateral acceleration, longitudinal speed, yaw rate, yaw acceleration, distance between the center of gravity of the front and rear axles, and lateral force of the front and rear wheels under the current load conditions. The equation building unit also includes: The lateral equation building unit is used to generate the lateral dynamic equations of the vehicle under the current load condition based on lateral acceleration, longitudinal vehicle speed, yaw rate, yaw acceleration, distance between the center of gravity of the front and rear axles, and the lateral forces of the front and rear wheels.

[0168] The vehicle includes a suspension system; a mass estimation unit, including: The first mass estimation unit is used to estimate the vehicle's current first total mass and first inertia parameters in real time based on the longitudinal dynamic equation and the lateral dynamic equation.

[0169] The second mass estimation unit is used to obtain the compression, equivalent stiffness of the suspension under the current load condition, and the net weight of the vehicle, and to estimate the current second total mass of the vehicle in real time based on the compression, equivalent stiffness, and net weight of the vehicle.

[0170] The third mass estimation unit is used to perform a weighted calculation on the first gross mass and the second gross mass to obtain the third gross mass of the vehicle. The predicted mass estimation unit is used to perform Kalman filtering noise reduction on the third total mass and the first inertia parameter to obtain the predicted total mass and predicted inertia parameter.

[0171] The vehicle is equipped with sensors; a predictive quality estimation unit includes: The state vector acquisition sub-unit is used to obtain the state vector of the Kalman filter at the first moment based on the third total mass and the first inertia parameter.

[0172] The sensor parameter acquisition subunit is used to acquire the vehicle's actual longitudinal acceleration and actual yaw rate based on the sensors.

[0173] The prediction parameter acquisition sub-unit is used to input the third total mass into the longitudinal dynamic equation and the transverse dynamic equation to obtain the predicted longitudinal acceleration output by the longitudinal dynamic equation and the predicted yaw rate output by the transverse dynamic equation.

[0174] The observation vector acquisition sub-unit is used to calculate the observation vector of the Kalman filter at the first moment based on the actual longitudinal acceleration, predicted longitudinal acceleration, actual yaw rate, and predicted yaw rate.

[0175] The predicted mass estimation subunit is used to perform Kalman filtering noise reduction on the third total mass and the first inertia parameter based on the state vector and the observation vector to obtain the predicted total mass and predicted inertia parameter.

[0176] Model update module 402 includes: The matrix generation unit is used to generate the vehicle's state transition matrix and input matrix based on the predicted total mass and predicted inertia parameters.

[0177] The equation update unit is used to update the transverse dynamic equation of the initial dynamic model based on the state transition matrix and the input matrix, and to update the longitudinal dynamic equation of the initial dynamic model based on the updated transverse dynamic equation. The parameter generation unit is used to generate the vehicle's adjustment parameters under the current load condition based on the updated lateral and longitudinal dynamic equations.

[0178] Brake adjustment module 403 includes: The energy consumption demand calculation unit is used to calculate the energy consumption demand under the current load condition based on the longitudinal adjustment parameters and the lateral adjustment parameters.

[0179] The allocation ratio calculation unit is used to calculate the allocation ratio of regenerative braking and hydraulic braking during the current driving process of the vehicle based on energy consumption demand.

[0180] The energy demand calculation unit includes: The parameter acquisition subunit is used to acquire the longitudinal deceleration of the vehicle under the current load condition.

[0181] The parameter calculation subunit is used to calculate the vehicle's braking force requirement based on the predicted total mass, longitudinal deceleration, air resistance, rolling resistance, and slope component.

[0182] The allocation ratio calculation unit includes: The allocation subunit is used to obtain the vehicle's braking constraint factor, allocate the braking demand force according to the braking constraint factor, and obtain the first braking demand force corresponding to regenerative braking and the second braking demand force corresponding to hydraulic braking.

[0183] The braking adjustment device based on vehicle load conditions provided in this embodiment of the invention can execute the braking adjustment method based on vehicle load conditions provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0184] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0185] The following is a detailed reference. Figure 5This diagram illustrates a suitable structural schematic for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from memory 508 into random access memory (RAM) 503. RAM 503 also stores various programs and data required for the operation of the electronic device. The processor 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0186] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0187] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a memory 508, or installed from a ROM 502. When the computer program is executed by the processor 501, it performs the functions defined in the braking adjustment method based on vehicle load conditions according to embodiments of the present invention.

[0188] Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0189] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the braking adjustment method based on vehicle load conditions shown in the above embodiments is implemented.

[0190] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0191] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A braking adjustment method based on vehicle load conditions, characterized in that, The method includes: The vehicle's dynamic parameters under the current load condition are obtained, and the vehicle's current predicted total mass and predicted inertia parameters are estimated in real time based on the dynamic parameters. The initial dynamic model of the vehicle is updated based on the predicted total mass and the predicted inertia parameters to obtain the adjustment parameters; The distribution ratio of regenerative braking and hydraulic braking during the current driving process of the vehicle is calculated based on the adjustment parameters, and the braking strategy of the vehicle under the current load condition is adjusted according to the distribution ratio.

2. The method according to claim 1, characterized in that, The process of acquiring the vehicle's dynamic parameters under the current load condition and estimating the vehicle's current predicted total mass and predicted inertia parameters in real time based on the dynamic parameters includes: Obtain the longitudinal and lateral dynamic parameters of the vehicle under the current load conditions; The longitudinal dynamics equations of the vehicle are generated based on the longitudinal dynamics parameters, and the lateral dynamics equations of the vehicle are generated based on the lateral dynamics parameters. The vehicle's current predicted total mass and predicted inertia parameters are estimated in real time based on the longitudinal dynamic equation and the lateral dynamic equation.

3. The method according to claim 2, characterized in that, The vehicle includes a motor; The acquisition of the vehicle's longitudinal and lateral dynamic parameters under the current load conditions includes: The output torque of the motor under the current load condition and the tire radius of the vehicle are obtained, and the driving force generated by the motor is calculated based on the output torque and the tire radius. Obtain the braking pressure and brake caliper force of the vehicle under the current load conditions, and calculate the braking force of the vehicle based on the braking pressure and brake caliper force. Obtain the air density, drag coefficient, frontal area, and longitudinal speed of the vehicle under the current load conditions; calculate the air resistance of the vehicle based on the air density, drag coefficient, frontal area, and longitudinal speed. The vehicle's gravitational acceleration, rolling resistance coefficient, and road slope angle under the current load conditions are obtained. The rolling resistance of the vehicle is calculated based on the gravitational acceleration, the rolling resistance coefficient, and the road slope angle. The slope component force of the vehicle is calculated based on the gravitational acceleration and the road slope angle. The process of generating the longitudinal dynamics equations of the vehicle based on the longitudinal dynamics parameters includes: The longitudinal acceleration of the vehicle under the current load condition is obtained, and the longitudinal dynamic equation of the vehicle under the current load condition is calculated based on the longitudinal acceleration, the driving force, the braking force, the air resistance, the rolling resistance, and the ramp force.

4. The method according to claim 2, characterized in that, The acquisition of the vehicle's longitudinal and lateral dynamic parameters under the current load conditions includes: The vehicle's lateral acceleration, longitudinal speed, yaw rate, yaw acceleration, distance between the front and rear axle centers of mass, and lateral force of the front and rear wheels are obtained under the current load conditions. The process of generating the vehicle's lateral dynamics equations based on the lateral dynamics parameters includes: The lateral dynamic equations of the vehicle under the current load condition are generated based on the lateral acceleration, longitudinal vehicle speed, yaw rate, yaw acceleration, distance between the centers of mass of the front and rear axles, and the lateral forces of the front and rear wheels.

5. The method according to claim 2, characterized in that, The vehicle includes a suspension; The real-time estimation of the vehicle's current total mass and inertia parameters based on the longitudinal and lateral dynamic equations includes: The vehicle's current first total mass and first moment of inertia parameters are estimated in real time based on the longitudinal dynamic equation and the lateral dynamic equation. The compression and equivalent stiffness of the suspension under the current load condition and the net weight of the vehicle are obtained, and the current second gross mass of the vehicle is estimated in real time based on the compression, the equivalent stiffness and the net weight of the vehicle. The first total mass and the second total mass are weighted and calculated to obtain the third total mass of the vehicle; The third total mass and the first inertia parameter are subjected to Kalman filtering noise reduction processing to obtain the predicted total mass and predicted inertia parameter.

6. The method according to claim 5, characterized in that, The vehicle is equipped with sensors; The step of performing Kalman filtering noise reduction on the third total mass and the first inertia parameter to obtain the predicted total mass and predicted inertia parameter includes: The state vector of the Kalman filter at the first moment is obtained based on the third total mass and the first inertia parameter; The actual longitudinal acceleration and actual yaw rate of the vehicle are obtained based on the sensors. The third total mass is input into the longitudinal dynamic equation and the lateral dynamic equation to obtain the predicted longitudinal acceleration output by the longitudinal dynamic equation and the predicted yaw rate output by the lateral dynamic equation. The observation vector of the Kalman filter at the first moment is calculated based on the actual longitudinal acceleration, the predicted longitudinal acceleration, the actual yaw rate, and the predicted yaw rate. Based on the state vector and the observation vector, Kalman filtering is applied to the third total mass and the first inertia parameter to reduce noise, thereby obtaining the predicted total mass and the predicted inertia parameter.

7. The method according to claim 6, characterized in that, The initial dynamic model of the vehicle is updated based on the predicted total mass and the predicted inertia parameters to obtain adjustment parameters, including: The vehicle's state transition matrix and input matrix are generated based on the predicted total mass and the predicted inertia parameters; The transverse dynamic equations of the initial dynamic model are updated based on the state transition matrix and the input matrix, and the longitudinal dynamic equations of the initial dynamic model are updated based on the updated transverse dynamic equations. Based on the updated lateral and longitudinal dynamic equations, the vehicle's adjustment parameters under the current load condition are generated.

8. The method according to claim 3, characterized in that, The calculation of the distribution ratio of regenerative braking and hydraulic braking during the current driving process of the vehicle based on the adjustment parameters includes: Calculate the energy consumption requirement under the current load condition based on the longitudinal and lateral adjustment parameters. The distribution ratio of regenerative braking and hydraulic braking during the current driving process of the vehicle is calculated based on the energy consumption demand.

9. The method according to claim 3, characterized in that, The calculation of energy consumption demand under the current load condition based on the longitudinal and lateral adjustment parameters includes: Obtain the longitudinal deceleration of the vehicle under the current load condition; The braking force required by the vehicle is calculated based on the predicted total mass, the longitudinal deceleration, the air resistance, the rolling resistance, and the slope component. The calculation of the distribution ratio of regenerative braking and hydraulic braking during the current driving process of the vehicle based on the energy consumption demand includes: Obtain the vehicle's braking constraint factor, and allocate the braking demand force according to the braking constraint factor to obtain the first braking demand force corresponding to the regenerative braking and the second braking demand force corresponding to the hydraulic braking.

10. A braking adjustment device based on vehicle load conditions, characterized in that, The device includes: The mass estimation module is used to obtain the dynamic parameters of the vehicle under the current load condition, and to estimate the current predicted total mass and predicted inertia parameters of the vehicle in real time based on the dynamic parameters. The model update module is used to update the initial dynamic model of the vehicle based on the predicted total mass and the predicted inertia parameters to obtain adjustment parameters; The braking adjustment module is used to calculate the distribution ratio of regenerative braking and hydraulic braking during the current driving process of the vehicle based on the adjustment parameters, and to adjust the braking strategy of the vehicle under the current load condition based on the distribution ratio.

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