A method for controlling dynamic response consistency of a commercial vehicle during acceleration process

Abstract

The application is suitable for the technical field of commercial vehicle driving control, and provides a commercial vehicle acceleration process dynamic response consistency control method, which sets expected dynamic response characteristics through designing a reference model, estimates slow time-varying parameters such as vehicle mass and equivalent resistance coefficient by using an extended Kalman filter, and eliminates static gain difference through feedforward compensation; meanwhile, model reference adaptive control with an L1 filter is adopted to ensure closed-loop dynamic response, and a disturbance observer is used to compensate the influence of transient slope and wind resistance disturbance. The method can keep acceleration dynamic response consistent in the acceleration process under the condition of vehicle mass, slope and wind resistance change, thereby significantly improving driving quality.

Description

Technical Field

[0001] This invention belongs to the field of commercial vehicle drive control technology, and particularly relates to a method for dynamic response consistency control during the acceleration process of commercial vehicles. Background Technology

[0002] In recent years, with the rapid development of automotive technology and the continuous enrichment of vehicle models, commercial vehicle users have paid increasing attention to the dynamic performance of their vehicles. Due to the large load capacity of commercial vehicles and the significant difference between empty and fully loaded conditions, coupled with the influence of factors such as gradients and wind resistance during driving, inconsistent acceleration response often occurs. For example, a vehicle may respond nimbly when lightly loaded, but its response is noticeably sluggish when heavily loaded or climbing hills. This inconsistency in dynamic acceleration response not only affects the driving experience but may also lead to misoperation, thereby endangering driving safety.

[0003] Chinese invention patent CN113147734B, entitled "A Closed-Loop Control Method Based on Driver's Longitudinal Acceleration Intent," collects real-time data on vehicle longitudinal speed, accelerator pedal opening, and actual longitudinal acceleration. It combines this data with stable vehicle speed corresponding to pedal opening and multi-time-domain driving intentions, using a pedal map to construct a nonlinear mapping between the accelerator pedal and acceleration demand. Closed-loop control of the vehicle's longitudinal motion is then achieved through PID control. The main innovation of this method lies in designing the nonlinear mapping relationship between the accelerator pedal and acceleration demand based on the driver's acceleration intention. However, it does not consider the impact of multiple-level changes in unloaded and fully loaded loads, as well as factors such as gradient and wind resistance, on the dynamic response during acceleration.

[0004] Chinese invention patent CN118775539A, entitled "Vehicle Starting Control Method, Device, Equipment, and Storage Medium Based on Acceleration," obtains mass and resistance parameters by identifying vehicle status, establishes a pedal-acceleration mapping based on driver habits, calculates the required traction force, and dynamically adjusts the engagement position according to the clutch torque transmission characteristics to achieve precise control of starting acceleration. Its main innovation lies in using acceleration as the core control target and dynamically calculating traction force to reduce dependence on the clutch torque transmission curve. However, this method does not consider the influence of the vehicle itself and the external environment on the acceleration process and is only applicable to the starting phase. Summary of the Invention

[0005] The purpose of this invention is to provide a dynamic response consistency control method for the acceleration process of commercial vehicles, aiming to solve the problems mentioned in the background art.

[0006] The present invention is implemented as follows: a method for dynamic response consistency control during the acceleration process of a commercial vehicle includes the following steps:

[0007] Step 1: Define the desired dynamic response characteristics. Use a standard second-order system to define the desired acceleration dynamic response characteristics. Obtain the desired dynamic response characteristics by setting the bandwidth and damping.

[0008] Step 2: Based on the extended Kalman filter, slowly time-varying parameters, including vehicle mass and equivalent drag coefficient, are estimated in real time, and the estimation results are applied to feedforward compensation;

[0009] Step 3: Calculate and output the feedforward compensation value based on the vehicle mass and equivalent drag coefficient estimated by the extended Kalman filter;

[0010] Step 4: Estimate the fast time-varying disturbances, including instantaneous slope and sudden change in resistance. When the slope or external resistance changes abruptly, calculate the system's predicted output based on the vehicle driving resistance equation and the dynamic response transfer function of the drive system, and calculate the difference between the predicted output and the actual measured output. After inverting the difference, estimate the transient disturbance force.

[0011] Step 5: Update control parameters online to make the system transient performance approximate the reference model, while using filters to limit the output bandwidth;

[0012] Step 6: Adjust the feedforward compensation force Disturbance compensation force and closed-loop adaptive control force The three signals are superimposed to form a comprehensive control signal, which acts on the drive system to control vehicle acceleration. The actual acceleration of the vehicle is compared with the expected acceleration to achieve closed-loop dynamic response control.

[0013] A further technical solution, in step 1, is to use a standard second-order system. The desired acceleration is defined as follows:

[0014] ;

[0015] in, For bandwidth, For damping, For Laplace complex variables;

[0016] Using formula Given a reference acceleration Converted into expected acceleration Output later.

[0017] A further technical solution involves, in step 2, integrating the vehicle's motion state with the parameters to be estimated into a state vector. ,in For vehicle speed, For acceleration, This is the actual driving torque. The angular velocity of the wheel. For the overall vehicle quality, This is the equivalent drag coefficient;

[0018] The state equation is written as ,in, The derivative of the state vector. Represents the state transition function, the input to the state equation. , The expressions for each component of the desired driving torque are as follows:

[0019] ;

[0020] in, This is the stiffness coefficient. The total transmission ratio is... The total transmission efficiency. The radius of the wheel's rolling motion. For rotational inertia, This is the torque response time constant; The derivative of the vehicle speed. The derivative of acceleration, This is the derivative of the actual driving torque. The derivative of the wheel's angular velocity. Let be the derivative of the total vehicle mass. The derivative of the equivalent drag coefficient;

[0021] Determine the observation vector The observation equation is , The observation matrix;

[0022] Initialization is performed, and the initial state estimate and initial error covariance matrix are set based on prior information;

[0023] The state vector is predicted and calculated based on the system's state equation, and the linearized Jacobian matrix is ​​obtained by expanding each component; the predicted state value and the predicted error covariance value are calculated based on the prediction model.

[0024] The Kalman gain is calculated based on real-time observation data, and the predicted state is corrected using the observation information. The vehicle mass, equivalent drag coefficient, and error covariance matrix are updated online.

[0025] Using the posterior state estimate and error covariance at the current moment as the initial value for the next moment, the prediction and update steps are repeated to perform real-time adaptive estimation of vehicle mass and equivalent drag coefficient and output the estimation results.

[0026] In a further technical solution, the formula for calculating the feedforward compensation value in step 3 is as follows:

[0027] ;

[0028] in, This is an estimated value for the vehicle's total weight. This is an estimate of the equivalent drag coefficient. It is a feedforward compensation force.

[0029] A further technical solution involves performing low-pass filtering on the estimated disturbance force in step 4, and applying it to the control channel with the opposite sign to quickly compensate for the transient disturbance and output the result. The calculation formula is as follows:

[0030] ;

[0031] In the above formula, For disturbance compensation force, The nominal model is obtained by simultaneously solving the equations for vehicle driving resistance and the transfer function of the dynamic response of the drive system. It is a low-pass filter. This is the input to the state equation.

[0032] A further technical solution involves defining the tracking error in step 5 based on the model reference adaptive control principle. The calculation formula is as follows:

[0033] ;

[0034] In the above formula, For the desired acceleration;

[0035] The prediction error equation is constructed, and the calculation formula is as follows:

[0036] ;

[0037] In the above formula, For the prediction error of acceleration, This is an estimate of the acceleration. This represents the true value of the acceleration.

[0038] Gradient-based adaptive law is used to update parameter estimates, and the convergence and stability of the parameter update process are adjusted using the learning rate and leakage term.

[0039] The adaptive control input is low-pass filtered, and the filter bandwidth is set to obtain the closed-loop adaptive control force. .

[0040] Another objective of this invention is to provide a dynamic response consistency control system for the acceleration process of a commercial vehicle, based on the above method, comprising: a reference model module, an extended Kalman filter estimation module, a feedforward compensation module, a model reference adaptive control module with an L1 filter, and a disturbance observation and compensation module.

[0041] The reference model module is used to set the desired dynamic response characteristics. A standard second-order system is used to define the desired acceleration dynamic response characteristics. The desired dynamic response characteristics are obtained by setting the bandwidth and damping.

[0042] The extended Kalman filter estimation module is used to estimate slow time-varying parameters such as vehicle mass and equivalent drag coefficient, and the results are applied to feedforward compensation to reduce the burden on closed-loop control.

[0043] The feedforward compensation module calculates and outputs the feedforward compensation value based on the vehicle mass and equivalent drag coefficient estimated by the extended Kalman filter.

[0044] The disturbance observation and compensation module is used to estimate fast time-varying disturbances such as instantaneous slope and sudden resistance. When the slope or external resistance changes abruptly, the system predicts the output based on the vehicle driving resistance equation and the dynamic response transfer function of the drive system, and calculates the difference between the predicted output and the actual measured output. The transient disturbance force is then estimated by taking the inverse of the difference.

[0045] The model reference adaptive control module with L1 filter is used to update control parameters online, so that the transient performance of the system approximates the reference model, while using the filter to limit the output bandwidth.

[0046] This invention provides a method for consistent dynamic response control during the acceleration process of commercial vehicles. This method designs a reference model to set the desired dynamic response characteristics, uses an extended Kalman filter to estimate slow time-varying parameters such as vehicle mass and equivalent drag coefficient, and eliminates static gain differences through feedforward compensation. Simultaneously, model reference adaptive control with an L1 filter ensures the closed-loop dynamic response, and a disturbance observer compensates for the effects of transient gradient and wind resistance disturbances. This method can maintain consistent acceleration dynamic response during acceleration under varying vehicle mass, gradient, and wind resistance conditions, thereby significantly improving driving quality. Attached Figure Description

[0047] Figure 1 A diagram illustrating the overall control architecture of a dynamic response consistency control method for the acceleration process of a commercial vehicle, provided in an embodiment of the present invention.

[0048] Figure 2 A diagram illustrating the effect of dynamic response consistency control in accelerating the process. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0050] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0051] like Figure 1 As shown, an embodiment of the present invention provides a method for dynamic response consistency control during the acceleration process of a commercial vehicle, comprising the following steps:

[0052] Step 1: Define the desired dynamic response characteristics. Use a standard second-order system to define the desired acceleration dynamic response characteristics. Obtain the desired dynamic response characteristics by setting the bandwidth and damping.

[0053] Through the standard second-order system The desired acceleration is defined as follows:

[0054] ;

[0055] in, For bandwidth, For damping, It is a Laplace complex variable.

[0056] Using formula Given a reference acceleration Converted into expected acceleration Output later.

[0057] Step 2: Based on the extended Kalman filter, slowly time-varying parameters, including vehicle mass and equivalent drag coefficient, are estimated in real time, and the estimation results are applied to feedforward compensation to reduce the burden on closed-loop control.

[0058] The vehicle's motion state and the parameters to be estimated are integrated into a state vector. ,in For vehicle speed, For acceleration, This is the actual driving torque. The angular velocity of the wheel. For the overall vehicle quality, This is the equivalent drag coefficient.

[0059] The state equation is written as ,in, The derivative of the state vector. Represents the state transition function, the input to the state equation. , The expressions for each component of the desired driving torque are as follows:

[0060] ;

[0061] in, This is the stiffness coefficient. The total transmission ratio is... The total transmission efficiency. The radius of the wheel's rolling motion. For rotational inertia, This is the torque response time constant; The derivative of the vehicle speed. The derivative of acceleration, This is the derivative of the actual driving torque. The derivative of the wheel's angular velocity. Let be the derivative of the total vehicle mass. This is the derivative of the equivalent drag coefficient.

[0062] Determine the observation vector The observation equation is , This is the observation matrix.

[0063] Initialization is performed by setting the initial state estimate and the initial error covariance matrix based on prior information.

[0064] The state vector is predicted and calculated based on the system's state equation, and the linearized Jacobian matrix is ​​obtained by expanding each component; the predicted state value and the predicted error covariance value are calculated based on the prediction model.

[0065] The Kalman gain is calculated based on real-time observation data, and the predicted state is corrected using the observation information. The vehicle mass, equivalent drag coefficient, and error covariance matrix are updated online.

[0066] Using the posterior state estimate and error covariance at the current moment as the initial value for the next moment, the prediction and update steps are repeated to perform real-time adaptive estimation of vehicle mass and equivalent drag coefficient and output the estimation results.

[0067] Step 3: Calculate and output the feedforward compensation value based on the vehicle mass and equivalent drag coefficient estimated by the extended Kalman filter. The formula for calculating the feedforward compensation value is as follows:

[0068] ;

[0069] in, This is an estimated value for the vehicle's total weight. This is an estimate of the equivalent drag coefficient. It is a feedforward compensation force.

[0070] Step 4: Estimate instantaneous slope, sudden change in resistance and other rapidly changing disturbances. When the slope or external resistance changes abruptly, calculate the system's predicted output based on the vehicle's driving resistance equation and the dynamic response transfer function of the drive system, and calculate the difference between the predicted output and the actual measured output. After inverting the difference, estimate the transient disturbance force.

[0071] The estimated disturbance force is low-pass filtered and applied to the control channel with the opposite sign to quickly compensate for the transient disturbance and output the result. The calculation formula is as follows:

[0072] ;

[0073] In the above formula, For disturbance compensation force, The nominal model is obtained by simultaneously solving the equations for vehicle driving resistance and the transfer function of the dynamic response of the drive system. It is a low-pass filter. This is the input to the state equation.

[0074] Step 5: Update the control parameters online to make the system transient performance approximate the reference model, while using a filter to limit the output bandwidth.

[0075] Based on the principle of model reference adaptive control, the tracking error is defined. The calculation formula is as follows:

[0076] ;

[0077] In the above formula, The desired acceleration.

[0078] The prediction error equation is constructed, and the calculation formula is as follows:

[0079] ;

[0080] In the above formula, For the prediction error of acceleration, This is an estimate of the acceleration. This represents the true value of the acceleration.

[0081] The parameter estimates are updated using a gradient-based adaptive law, and the convergence and stability of the parameter update process are adjusted by using the learning rate and leakage term.

[0082] The adaptive control input is low-pass filtered, and the filter bandwidth is set to ensure the accuracy of the low- and mid-frequency adaptive response and suppress high-frequency interference, thus obtaining the closed-loop adaptive control force. .

[0083] Step 6: [The sentence is incomplete and requires more context to be translated accurately.] , and The three signals are superimposed to form a comprehensive control signal, which acts on the drive system to control vehicle acceleration. The actual acceleration of the vehicle is compared with the expected acceleration to achieve closed-loop dynamic response control.

[0084] like Figure 1 As shown, another embodiment of the present invention provides a dynamic response consistency control system for the acceleration process of a commercial vehicle, which is based on the above method and includes: a reference model module, an extended Kalman filter estimation module, a feedforward compensation module, a model reference adaptive control module with an L1 filter, and a disturbance observation and compensation module.

[0085] The reference model module is used to set the desired dynamic response characteristics. A standard second-order system is used to define the desired acceleration dynamic response characteristics. The desired dynamic response characteristics are obtained by setting the bandwidth and damping.

[0086] The extended Kalman filter estimation module is used to estimate slow time-varying parameters such as vehicle mass and equivalent drag coefficient, and the results are applied to feedforward compensation to reduce the burden on closed-loop control.

[0087] The feedforward compensation module calculates and outputs the feedforward compensation value based on the vehicle mass and equivalent drag coefficient estimated by the extended Kalman filter.

[0088] The disturbance observation and compensation module is used to estimate fast time-varying disturbances such as instantaneous slope and sudden resistance. When the slope or external resistance changes abruptly, the system predicts the output based on the vehicle driving resistance equation and the dynamic response transfer function of the drive system, and calculates the difference between the predicted output and the actual measured output. The transient disturbance force is then estimated by taking the inverse of the difference.

[0089] The model reference adaptive control module with L1 filter is used to update control parameters online, so that the transient performance of the system approximates the reference model, while using the filter to limit the output bandwidth.

[0090] In a preferred embodiment of the present invention, using the above method, a commercial vehicle was selected as the research object. Two different operating conditions were chosen to compare the control effect of the consistency of acceleration dynamic response. Operating condition one was a vehicle mass of 40,000 kg, an equivalent drag coefficient of 300 Ns / m, and an initial vehicle speed of 20 m / s; operating condition two was a vehicle mass of 30,000 kg, an equivalent drag coefficient of 500 Ns / m, and a vehicle speed of 10 m / s. Figure 2 It can be seen that this method has a good control effect.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for consistent dynamic response control during the acceleration process of a commercial vehicle, characterized in that, Includes the following steps: Step 1: Define the desired dynamic response characteristics. Use a standard second-order system to define the desired acceleration dynamic response characteristics. Obtain the desired dynamic response characteristics by setting the bandwidth and damping. Step 2: Based on the extended Kalman filter, slowly time-varying parameters, including vehicle mass and equivalent drag coefficient, are estimated in real time, and the estimation results are applied to feedforward compensation; Step 3: Calculate and output the feedforward compensation value based on the vehicle mass and equivalent drag coefficient estimated by the extended Kalman filter; Step 4: Estimate the fast time-varying disturbances, including instantaneous slope and sudden change in resistance. When the slope or external resistance changes abruptly, calculate the system's predicted output based on the vehicle driving resistance equation and the dynamic response transfer function of the drive system, and calculate the difference between the predicted output and the actual measured output. After inverting the difference, estimate the transient disturbance force. Step 5: Update control parameters online to make the system transient performance approximate the reference model, while using filters to limit the output bandwidth; Step 6: Adjust the feedforward compensation force Disturbance compensation force and closed-loop adaptive control force The three signals are superimposed to form a comprehensive control signal, which acts on the drive system to control vehicle acceleration. The actual acceleration of the vehicle is compared with the expected acceleration to achieve closed-loop dynamic response control.

2. The method for consistent dynamic response control during acceleration of commercial vehicles according to claim 1, characterized in that, Through the standard second-order system The desired acceleration is defined as follows: ； in, For bandwidth, For damping, For Laplace complex variables; Using formula Given a reference acceleration Converted into expected acceleration Output later.

3. The method for consistent dynamic response control during acceleration of commercial vehicles according to claim 2, characterized in that, In step 2, the vehicle motion state and the parameters to be estimated are integrated into a state vector. ,in For vehicle speed, For acceleration, This is the actual driving torque. The angular velocity of the wheel. For the overall vehicle quality, This is the equivalent drag coefficient; The state equation is written as ,in, The derivative of the state vector. Represents the state transition function, the input to the state equation. , The expressions for each component of the desired driving torque are as follows: ； in, This is the stiffness coefficient. The total transmission ratio is... The total transmission efficiency. The radius of the wheel's rolling motion. For rotational inertia, This is the torque response time constant; The derivative of the vehicle speed. The derivative of acceleration, This is the derivative of the actual driving torque. The derivative of the wheel's angular velocity. Let be the derivative of the total vehicle mass. The derivative of the equivalent drag coefficient; Determine the observation vector The observation equation is , The observation matrix; Initialization is performed, and the initial state estimate and initial error covariance matrix are set based on prior information; The state vector is predicted and calculated based on the system's state equation, and the linearized Jacobian matrix is ​​obtained by expanding each component; the predicted state value and the predicted error covariance value are calculated based on the prediction model. The Kalman gain is calculated based on real-time observation data, and the predicted state is corrected using the observation information. The vehicle mass, equivalent drag coefficient, and error covariance matrix are updated online. Using the posterior state estimate and error covariance at the current moment as the initial value for the next moment, the prediction and update steps are repeated to perform real-time adaptive estimation of vehicle mass and equivalent drag coefficient and output the estimation results.

4. The method for consistent dynamic response control during acceleration of commercial vehicles according to claim 3, characterized in that, In step 3, the formula for calculating the feedforward compensation value is as follows: ； in, This is an estimated value for the vehicle's total weight. This is an estimate of the equivalent drag coefficient. It is a feedforward compensation force.

5. The method for consistent dynamic response control during acceleration of commercial vehicles according to claim 4, characterized in that, In step 4, the estimated disturbance force is low-pass filtered and applied to the control channel with the opposite sign to quickly compensate for the transient disturbance and output the result. The calculation formula is as follows: ； In the above formula, For disturbance compensation force, The nominal model is obtained by simultaneously solving the equations for vehicle driving resistance and the transfer function of the dynamic response of the drive system. It is a low-pass filter. This is the input to the state equation.

6. The method for consistent dynamic response control during acceleration of commercial vehicles according to claim 5, characterized in that, In step 5, the tracking error is defined according to the principle of model reference adaptive control. The calculation formula is as follows: ； In the above formula, For the desired acceleration; The prediction error equation is constructed, and the calculation formula is as follows: ； In the above formula, For the prediction error of acceleration, This is an estimate of the acceleration. This represents the true value of the acceleration. Gradient-based adaptive law is used to update parameter estimates, and the convergence and stability of the parameter update process are adjusted using the learning rate and leakage term. The adaptive control input is low-pass filtered, and the filter bandwidth is set to obtain the closed-loop adaptive control force. .

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