Active damping control method and system

Abstract

The present application provides an active damping control method and system, the method comprising the steps of: establishing a dynamic model of a transmission system; calibrating state parameters in the dynamic model; calculating state errors of the transmission system according to the calibrated state parameters; and determining active damping control to be output according to the state errors.

Description

Technical Field

[0001] This invention relates to the field of vehicle testing, specifically to the field of automotive calibration testing. The invention discloses an automated testing method and system based on a requirements model, a computer device for implementing the method, and a computer-readable storage medium. Background Technology

[0002] With increasingly stringent fuel consumption and emission regulations, traditional internal combustion engine-powered vehicles are gradually failing to meet market demands, and vehicle electrification has become an industry consensus. However, compared to traditional internal combustion engine vehicles, pure electric vehicles have their motors directly connected to the drivetrain, which lacks sufficient damping components. The motor's torque response is highly sensitive, making it prone to torsional vibration under rapid and significant excitation from driving torque and disturbances. Simultaneously, low-speed torque ripple from the motor exacerbates vibration amplitude, severely impacting overall vehicle ride comfort. Since the engine's shielding effect is absent in pure electric mode, the vibration of the entire drivetrain is more readily perceived by the driver.

[0003] In the prior art, patents with application numbers CN201910707809.0, CN201911112932.4, and CN202010361127.1 provide damping control methods. However, these methods only utilize parameters such as motor torque and speed for active damping control and employ traditional proportional-integral-derivative (PID) control. However, due to the torque coupling relationship between various powertrain components in the vehicle's drivetrain, relying solely on motor parameters and PID control strategies is often insufficient to completely eliminate vehicle-wide vibration. Therefore, a more advanced control algorithm is needed to achieve active damping control of the vehicle at a broader level. Summary of the Invention

[0004] According to a first aspect of the present invention, an active damping control method is provided, the method comprising the following steps: establishing a dynamic model of a transmission system; calibrating state parameters in the dynamic model; calculating the state error of the transmission system based on the calibrated state parameters; and determining the active damping control to be output based on the state error.

[0005] Optionally, according to one or more embodiments of the first aspect, the method further includes: calibrating the state parameters in the dynamic model by optimizing a combination of calibration quantities for selecting the state parameters.

[0006] Optionally, according to one or more embodiments of the first aspect, the method further includes: optimizing the selection of calibration combinations of the state parameters by employing an optimization algorithm to quantify the influence of the calibration of the state parameters.

[0007] Optionally, according to one or more embodiments of the first aspect, the method further includes: the optimization algorithm includes one or more of the following statistical algorithms: least squares method, maximum likelihood estimation method, Bayesian estimation method, linear quadratic optimal control method, and robust control method.

[0008] Optionally, according to one or more embodiments of the first aspect, the method further includes: calculating the state error of the transmission system, including calculating the damping optimization function corresponding to the transmission system under different operating modes.

[0009] Optionally, according to one or more embodiments of the first aspect, the method further includes: calculating the damping optimization function based on the actual value and calibrated value of the state parameter.

[0010] Optionally, according to one or more embodiments of the first aspect, the method further includes: obtaining the actual value of the state parameter by means of sensor measurement or observer observation.

[0011] Optionally, according to one or more embodiments of the first aspect, the method further includes: the state parameters in the dynamic model include one or more of the following parameters: motor speed in the transmission system, engine speed in the transmission system, output wheel speed of the transmission system, output shaft speed of the transmission system, and shaft torque of the transmission system.

[0012] According to a second aspect of the present invention, an active damping control system is provided, the system comprising: a model module configured to establish a dynamic model of a transmission system; a calibration module configured to calibrate state parameters in the dynamic model; a calculation module configured to calculate a state error of the transmission system based on the calibrated state parameters; and a control module configured to determine the active damping control to be output based on the state error.

[0013] Optionally, according to one or more embodiments of the second aspect, the calibration module is configured to optimize the combination of calibration quantities for selecting the state parameters.

[0014] Optionally, according to one or more embodiments of the second aspect, optimizing the selection of the calibration combination of the state parameters includes using an optimization algorithm to quantify the impact of the calibration of the state parameters.

[0015] Optionally, according to one or more embodiments of the second aspect, the optimization algorithm includes one or more of the following statistical algorithms: least squares method, maximum likelihood estimation method, Bayesian estimation method, linear quadratic optimal control method, and robust control method.

[0016] Optionally, according to one or more embodiments of the second aspect, the calculation module is configured to calculate the damping optimization function corresponding to the transmission system under different operating modes.

[0017] Optionally, according to one or more embodiments of the second aspect, the damping optimization function is calculated based on the actual value and calibrated value of the state parameter.

[0018] Alternatively, according to one or more embodiments of the second aspect, the actual value of the state parameter is obtained by means of sensor measurement or observer observation.

[0019] Optionally, according to one or more embodiments of the second aspect, the state parameters in the dynamic model include one or more of the following parameters: motor speed in the transmission system, engine speed in the transmission system, output wheel speed of the transmission system, output shaft speed of the transmission system, and shaft torque of the transmission system.

[0020] According to a third aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the active damping control method as described in any embodiment of the first aspect of the present invention.

[0021] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, wherein the program, when executed by a processor, implements the active damping control method as described in any embodiment of the first aspect of the present invention.

[0022] The damping control method and system disclosed herein can perform damping control on the entire vehicle drivetrain to improve the driving smoothness of the vehicle.

[0023] By incorporating the figures in this article and subsequently the appendix Figure 1 The specific embodiments used to illustrate certain principles of the invention will make other features and advantages of the methods and systems of the invention clearer or more apparent. Attached Figure Description

[0024] The above and / or other aspects and advantages of the present invention will become clearer and more readily understood from the following description taken in conjunction with the accompanying drawings, in which like or similar elements are denoted by the same reference numerals. The drawings include:

[0025] Figure 1 A flowchart of an active damping control method 100 according to an embodiment of the present invention is shown.

[0026] Figure 2A block diagram of an active damping control system 200 according to an embodiment of the present invention is shown.

[0027] Figure 3 This is a schematic block diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0028] In this specification, the invention is described more fully with reference to the accompanying drawings, which illustrate exemplary embodiments of the invention. However, the invention may be implemented in various forms and should not be construed as being limited to the embodiments given herein. The given embodiments are intended to make the disclosure herein complete and thorough, so as to more fully convey the scope of protection of the invention to those skilled in the art.

[0029] Terms such as "comprising" and "including" indicate that, in addition to the units and steps that are directly and explicitly stated in the specification, the technical solution of the present invention does not exclude the presence of other units and steps that are not directly or explicitly stated. Terms such as "first" and "second" do not indicate the order of the units in terms of time, space, size, etc., but are merely used to distinguish the units.

[0030] The invention is described below with reference to flowchart illustrations, block diagrams, and / or flowcharts of methods and systems according to embodiments of the invention. It will be understood that each block of these flowchart illustrations and / or block diagrams, and combinations thereof, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to constitute a machine, such that these instructions, executable by the processor of the computer or other programmable data processing apparatus, create components for implementing the functions / operations specified in these flowchart illustrations and / or blocks and / or one or more flowchart illustrations.

[0031] To address the vibration issues in the transmission systems of current new energy vehicles, this disclosure proposes an active damping control method and system. The method and system achieve active damping control of the entire vehicle's transmission system, improving the vehicle's driving quality. The active damping control method and system can be applied to different types of vehicles, including pure electric, hybrid, plug-in hybrid, and 48V mild hybrid vehicles. Specifically, the active damping torque provided by the method and system of this disclosure can be applied to one or more motors.

[0032] The method and system disclosed herein employ the concept of multivariable feedback control. By controlling the state error of several parameters in the transmission system, the torque of closed-loop active damping control applied to the motor is obtained, thereby suppressing the vibration of the transmission system.

[0033] Figure 1A flowchart of an active damping control method 100 according to an embodiment of the present invention is shown. Method 100 includes the following steps: step 101, establishing a dynamic model of the transmission system; step 102, calibrating the state parameters in the dynamic model; step 103, calculating the state error of the transmission system based on the calibrated state parameters; and step 104, determining the active damping control to be output based on the state error.

[0034] In step 101, a dynamic model of the transmission system is established. A dynamic model of the transmission system can be established for a specific object (e.g., a vehicle) based on relevant physics information. Some parameters in the model are unknown or inaccurate; therefore, system identification techniques can be used to identify the model's parameters. Depending on the needs, model identification can be online or offline, and can be performed in the time or frequency domain. Preferably, the identification method can employ a frequency scanning test method. Since some parameters of the vehicle are not measured by sensors, such as axle torsion, an observer design is introduced to estimate the corresponding parameters using an estimator. The methods used by the observer can include commonly used filtering algorithms in the art, including but not limited to Kalman filtering, extended Kalman filtering, unscented Kalman filtering, or particle filtering algorithms.

[0035] In step 102, the state parameters in the dynamic model are calibrated. Specifically, calibrating the state parameters in the dynamic model includes optimizing the combination of calibration values ​​for the state parameters. Optimizing the combination of calibration values ​​for the state parameters may include quantifying the influence of the calibration values ​​for the state parameters using an optimization algorithm. State parameters include, but are not limited to, the motor speed, engine speed, output wheel speed, output shaft speed, and shaft torque in the transmission system.

[0036] Preferably, the calibration parameters can be optimized, specifically including the optimized selection of relevant calibration parameter combinations. This reduces the burden on engineers in processing data and significantly improves the efficiency of the calibration process. Open-loop parameter optimization identifies relevant parameters in the vehicle model, improving the accuracy of the dynamic model established in step 101. Closed-loop parameter optimization optimizes the gain matrix in the optimal control function. Disturbance parameter optimization optimizes the observer correlation coefficient. The optimization algorithm quantifies the influence of relevant calibration parameters, thereby efficiently obtaining relevant optimized parameter combinations and improving the robustness of the calibration process. The optimization algorithm can be a commonly used statistical algorithm, including but not limited to least squares, maximum likelihood estimation, Bayesian estimation, linear quadratic optimal control, robust control, etc.

[0037] Preferably, calibration parameters and their optimization can employ frequency-domain-based data processing. This approach minimizes system disturbance and offers significant advantages in identifying transfer function parameters for steady and unsteady linear dynamic systems. The system's frequency domain characteristics can be correlated with its structure and parameters, facilitating adjustments to the system's dynamic performance by modifying parameters or altering components. The real-vehicle data processing module converts the real-vehicle frequency scan data from the time domain to the frequency domain for processing and analysis, thereby obtaining the Bode plot of the real-vehicle data. The main purpose of the whole-vehicle model processing module is to calculate the open-loop and closed-loop Bode plots of the model.

[0038] Preferably, the calibration parameter optimization module can be based on a weighted least squares optimization strategy to optimize the calibration parameters. First, the frequency axis of the Bode plot is divided into several segments, such as F1Hz-F2Hz, F3Hz-F4Hz, F5Hz-F6Hz, and F7Hz-F8Hz. Different weighting coefficients are assigned to each segment according to actual needs; these weighting coefficients can be imported from an external weighting matrix.

[0039] Preferably, Bode plots can be used for optimization. Bode plots are the most intuitive form of reflecting frequency characteristics such as amplitude and phase frequencies (generally obtained from swept frequency data processing). Acquired data can be imported into the workspace, and the required data can be filtered out. Next, the operating mode and excitation source to be processed are determined, thereby selecting the input and output excitation signals. The input and output data are preprocessed, and then the frequency characteristics of the system are calculated using the effective input and output data through signal analysis. Finally, the system's frequency characteristics are analyzed, and a Bode plot is generated.

[0040] Based on the transmission system dynamics model obtained from the modeling and analysis of the transmission system dynamics established in step 101 (where the model is represented by state-space equations), since the parameter matrix of the equations is known, parameters such as the engine's moment of inertia, the stiffness and damping of the drive shaft, etc., affect the parameter matrix to a certain extent. Importing the parameter matrix for the corresponding case yields the open-loop model of the system and generates Bode plots for all inputs and outputs. Selecting the Bode plot from the generated Bode plots that is excited by actuator one or actuator two yields the Bode plot for the open-loop state. For the construction of the closed-loop system model, the gain matrix for each operating condition needs to be introduced to obtain the closed-loop system model. Selecting the Bode plot from the generated Bode plots that is excited by actuator one or actuator two yields the Bode plot for the closed-loop state.

[0041] For open-loop systems, for a specific object (such as a vehicle), once the vehicle's hardware, such as the engine, transmission, final drive, and axles, is determined, the actual open-loop performance of the vehicle is fixed. However, for the transmission system model, parameters such as engine moment of inertia and drive shaft stiffness may not be accurate enough, requiring further optimization through open-loop frequency sweep tests. These parameters are modified to make the Bode plot in the model as close as possible to the actual Bode plot of the vehicle. For closed-loop systems, the active damping control system introduces state feedback to address transmission system vibration. Therefore, changes to the gain matrix inevitably lead to changes in the closed-loop model and the actual vehicle's closed-loop frequency sweep performance. Similarly, calibration is modified to make the actual vehicle's performance as close as possible to the model's performance while minimizing the peaks in the Bode plot amplitude, thereby improving the overall vehicle's dynamic response performance.

[0042] To address disturbances, since some state variables in the transmission system model are not observable, a state observer is introduced to design the controller. The coefficients of the state observer have a significant impact on the accuracy of state variable estimation. Therefore, disturbance testing is used to further optimize the coefficients of the state observer. Because changes in the state observer coefficients inevitably affect the disturbance behavior of the entire vehicle, the difference between the Bode plot of the actual vehicle and the Bode plot of the closed-loop model can be used to evaluate the quality of the calibration parameters. The goal is to make the performance of the actual vehicle as close as possible to the closed-loop performance of the model, thereby optimizing the state observer coefficients.

[0043] In step 103, the state error of the transmission system is calculated based on the calibrated state parameters. Specifically, according to different operating modes of the vehicle, the damping optimization function corresponding to the transmission system under different operating modes can be calculated. Specifically, the optimization function J is calculated for each operating mode. The optimization function J is calculated as the sum of the squares of the differences between the actual vehicle value and the model value in each segment. The formula for calculating the optimization function J is as follows:

[0044] (Formula 1)

[0045] Where n is the number of segments, K(i) is the weighting coefficient for each segment, which is imported from the outside by the weighting matrix; Real(i) and Model(i) are the actual value and model value for each segment, respectively.

[0046] Preferably, since modifying the calibration value will also affect other working conditions to some extent, the optimization function J is optimized. sum It can be further defined. The optimization function J sum The calculation formula is as follows:

[0047] (Formula 2)

[0048] Where m represents the number of work scenarios, J represents the optimization function value for each work model, and K(x) is the weighting coefficient for each work scenario, imported from the outside via a weighting matrix. The optimization objective could be to make J... sum Minimum.

[0049] Because multiple operating modes are involved, the calibration parameters need to ensure good performance under all conditions. Therefore, certain trade-offs can be made when setting the relevant calibration parameters. For example, different weights can be assigned to calibration parameters such as motor speed, engine speed, output wheel speed, output shaft speed, and shaft torque in the transmission system.

[0050] Finally, in step 104, the active damping control to be output is determined based on the state error. In step 104, the final error value for all operating modes is calculated. The optimized function values ​​for each operating condition can be organized and stored in a table, and the weighting coefficients for the corresponding operating modes are adjusted based on different platforms. The algorithm first determines whether this part needs to be executed; if not, this part of the program is skipped. If so, the final state error value is calculated based on a weighted least squares algorithm. Then, the active damping control to be output can be determined based on the state error value.

[0051] As mentioned above, for example, the transmission system of a new energy vehicle may have several operating modes, M1, M2, M3, and M4; each operating mode is further divided into three cases: open-loop, closed-loop, and disturbance; each case can be further subdivided into actuator-one excitation or actuator-two excitation; there are a total of several cases. The system and method provided in this disclosure can provide active damping control for various cases.

[0052] A physical model of the transmission system is established, and the parameters in the model are identified using a system identification algorithm to obtain the transmission system model. State observers are established to estimate some vehicle parameters. The state errors of several parameters are obtained by calculating the difference between the expected and estimated (or actual) values ​​of the parameters. The state errors are processed through optimal control to obtain the damping torque applied to the motor for active damping control of the vehicle. Optionally, frequency sweep tests are used to optimize the calibration parameters of the transmission system model, observers, and optimal control.

[0053] Figure 2A block diagram of an active damping control system 200 according to an embodiment of the present invention is shown. System 200 includes the following modules: a model module 201 configured to establish a dynamic model of a transmission system; a calibration module 202 configured to calibrate state parameters in the dynamic model; a calculation module 203 configured to calculate the state error of the transmission system based on the calibrated state parameters; and a control module 204 configured to determine the active damping control to be output based on the state error.

[0054] Model module 201 is configured to establish a dynamic model of the transmission system. A dynamic model of the transmission system can be established for a specific object (e.g., a vehicle) based on relevant physics information. Some parameters in the model are unknown or inaccurate; therefore, system identification techniques can be used to identify the model's parameters. Depending on the needs, model identification can be online or offline, and can be performed in the time or frequency domain. Preferably, the identification method can employ a frequency scanning test method. Since some parameters of the vehicle are not measured by sensors, such as axle torsion, an observer design is introduced to estimate the corresponding parameters using an estimator. The methods used by the observer can include commonly used filtering algorithms in the art, including but not limited to Kalman filtering, extended Kalman filtering, unscented Kalman filtering, or particle filtering algorithms.

[0055] The calibration module 202 is configured to calibrate the state parameters in the dynamic model. Specifically, calibrating the state parameters in the dynamic model includes optimizing the selection of a combination of calibration values ​​for the state parameters. Optimizing the selection of the calibration combination of the state parameters may include quantifying the influence of the calibration values ​​of the state parameters using an optimization algorithm. State parameters include, but are not limited to, the motor speed, engine speed, output wheel speed, output shaft speed, and shaft torque in the transmission system.

[0056] Preferably, the calibration parameters can be optimized, specifically including the optimized selection of relevant calibration parameter combinations. This reduces the burden on engineers in processing data and significantly improves the efficiency of the calibration process. Open-loop parameter optimization identifies relevant parameters in the vehicle model, improving the accuracy of the dynamic model established in model module 201. Closed-loop parameter optimization optimizes the gain matrix in the optimal control function. Perturbation parameter optimization optimizes the observer correlation coefficient. The optimization algorithm quantifies the influence of relevant calibration parameters, thereby efficiently obtaining relevant optimized parameter combinations and improving the robustness of the calibration process. The optimization algorithm can be a commonly used statistical algorithm, including but not limited to least squares, maximum likelihood estimation, Bayesian estimation, linear quadratic optimal control, robust control, etc.

[0057] Preferably, calibration parameters and their optimization can employ frequency-domain-based data processing. This approach minimizes system disturbance and offers significant advantages in identifying transfer function parameters for steady and unsteady linear dynamic systems. The system's frequency domain characteristics can be correlated with its structure and parameters, facilitating adjustments to the system's dynamic performance by modifying parameters or altering components. The real-vehicle data processing module converts the real-vehicle frequency scan data from the time domain to the frequency domain for processing and analysis, thereby obtaining the Bode plot of the real-vehicle data. The main purpose of the whole-vehicle model processing module is to calculate the open-loop and closed-loop Bode plots of the model.

[0058] Preferably, the calibration parameter optimization module can be based on a weighted least squares optimization strategy to optimize the calibration parameters. First, the frequency axis of the Bode plot is divided into several segments, such as F1Hz-F2Hz, F3Hz-F4Hz, F5Hz-F6Hz, and F7Hz-F8Hz. Different weighting coefficients are assigned to each segment according to actual needs; these weighting coefficients can be imported from an external weighting matrix.

[0059] Preferably, Bode plots can be used for optimization. Bode plots are the most intuitive form of reflecting frequency characteristics such as amplitude and phase frequencies (generally obtained from swept frequency data processing). Acquired data can be imported into the workspace, and the required data can be filtered out. Next, the operating mode and excitation source to be processed are determined, thereby selecting the input and output excitation signals. The input and output data are preprocessed, and then the frequency characteristics of the system are calculated using the effective input and output data through signal analysis. Finally, the system's frequency characteristics are analyzed, and a Bode plot is generated.

[0060] Based on the transmission system dynamics model obtained from the modeling and analysis of the transmission system dynamics established in model module 201 (where the model is represented by state-space equations), since the parameter matrix of the equations is known, parameters such as the engine's moment of inertia, the stiffness and damping of the drive shaft, etc., affect the parameter matrix to a certain extent. Importing the parameter matrix for the corresponding case yields the open-loop model of the system and generates Bode plots for all inputs and outputs. Selecting the Bode plot excited by actuator one or actuator two from the generated Bode plots yields the Bode plot for the open-loop state. For the construction of the closed-loop system model, the gain matrix for each operating condition needs to be introduced to obtain the closed-loop system model. Selecting the Bode plot excited by actuator one or actuator two from the generated Bode plots yields the Bode plot for the closed-loop state.

[0061] For open-loop systems, for a specific object (such as a vehicle), once the vehicle's hardware, such as the engine, transmission, final drive, and axles, is determined, the actual open-loop performance of the vehicle is fixed. However, for the transmission system model, parameters such as engine moment of inertia and drive shaft stiffness may not be accurate enough, requiring further optimization through open-loop frequency sweep tests. These parameters are modified to make the Bode plot in the model as close as possible to the actual Bode plot of the vehicle. For closed-loop systems, the active damping control system introduces state feedback to address transmission system vibration. Therefore, changes to the gain matrix inevitably lead to changes in the closed-loop model and the actual vehicle's closed-loop frequency sweep performance. Similarly, calibration is modified to make the actual vehicle's performance as close as possible to the model's performance while minimizing the peaks in the Bode plot amplitude, thereby improving the overall vehicle's dynamic response performance.

[0062] To address disturbances, since some state variables in the transmission system model are not observable, a state observer is introduced to design the controller. The coefficients of the state observer have a significant impact on the accuracy of state variable estimation. Therefore, disturbance testing is used to further optimize the coefficients of the state observer. Because changes in the state observer coefficients inevitably affect the disturbance behavior of the entire vehicle, the difference between the Bode plot of the actual vehicle and the Bode plot of the closed-loop model can be used to evaluate the quality of the calibration parameters. The goal is to make the performance of the actual vehicle as close as possible to the closed-loop performance of the model, thereby optimizing the state observer coefficients.

[0063] The calculation module 203 is configured to calculate the state error of the transmission system based on the calibrated state parameters. Specifically, according to different operating modes of the vehicle, the damping optimization function corresponding to the transmission system under different operating modes can be calculated. Specifically, the optimization function J is calculated for each operating mode. The optimization function J calculates the sum of squares of the differences between the actual vehicle value and the model value in each segment. The formula for calculating the optimization function J is as follows:

[0064] (Formula 3)

[0065] Where n is the number of segments, K(i) is the weighting coefficient for each segment, which is imported from the outside by the weighting matrix; Real(i) and Model(i) are the actual value and model value for each segment, respectively.

[0066] Preferably, since modifying the calibration value will also affect other working conditions to some extent, the optimization function J is optimized. sum It can be further defined. The optimization function J sum The calculation formula is as follows:

[0067] (Formula 4)

[0068] Where m represents the number of work scenarios, J represents the optimization function value for each work model, and K(x) is the weighting coefficient for each work scenario, imported from the outside via a weighting matrix. The optimization objective could be to make J... sum Minimum.

[0069] Because multiple operating modes are involved, the calibration parameters need to ensure good performance under all conditions. Therefore, certain trade-offs can be made when setting the relevant calibration parameters. For example, different weights can be assigned to calibration parameters such as motor speed, engine speed, output wheel speed, output shaft speed, and shaft torque in the transmission system.

[0070] Finally, the control module 204 is configured to determine the required active damping control output based on the state error. The control module 204 is configured to calculate the final error value under all operating modes. The optimized function values ​​for each operating condition can be organized and stored in a table, with the weighting coefficients for the corresponding operating modes adjusted based on different platforms. The algorithm first determines whether this part needs to be executed; if not, this part of the program is skipped. If so, the final state error value is calculated based on a weighted least squares algorithm. Then, the required active damping control output can be determined based on the state error value.

[0071] As described above, for example, a vehicle drivetrain may have several operating modes, M1, M2, M3, and M4; each operating mode is further divided into three cases: open-loop, closed-loop, and disturbance; each case can be further subdivided into actuator-one excitation or actuator-two excitation; there are a total of several cases. The system and method provided in this disclosure can provide active damping control for various cases.

[0072] A physical model of the transmission system is established, and the parameters in the model are identified using a system identification algorithm to obtain the transmission system model. State observers are established to estimate some vehicle parameters. The state errors of several parameters are obtained by calculating the difference between the expected and estimated (or actual) values ​​of the parameters. The state errors are processed through optimal control to obtain the damping torque applied to the motor for active damping control of the vehicle. Optionally, frequency sweep tests are used to optimize the calibration parameters of the transmission system model, observers, and optimal control.

[0073] Figure 3 This is a schematic block diagram of a computer device according to another embodiment of the present invention. The computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the above-described active damping control method.

[0074] According to another aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, can implement the above-described active damping control method.

[0075] The embodiments and examples presented herein are provided to best illustrate embodiments according to the present technology and its particular applications, thereby enabling those skilled in the art to practice and use the invention. However, those skilled in the art will understand that the above description and examples are provided merely for ease of illustration and example. The descriptions presented are not intended to cover all aspects of the invention or to limit the invention to the precise forms disclosed.

Claims

1. An active damping control method, characterized in that, The method includes the following steps: Establish a dynamic model of the transmission system; Calibrate the state parameters in the dynamic model; Calculate the state error of the transmission system based on the calibrated state parameters; and The required active damping control output is determined based on the aforementioned state error. The calculation of the state error of the transmission system includes calculating the damping optimization function corresponding to the transmission system under different operating modes. The damping optimization function is a weighted sum of the optimization function values ​​under each operating model, and the optimization function value is the sum of the squares of the differences between the actual vehicle value and the model value. The calibration of the state parameters in the dynamic model includes assigning different weights to the motor speed, engine speed, output wheel speed, output shaft speed, and shaft torque in the transmission system.

2. The method according to claim 1, wherein, Calibrating the state parameters in the dynamic model includes optimizing the combination of calibration parameters.

3. The method according to claim 2, wherein, Optimizing the selection of the calibration combination of the state parameters includes using an optimization algorithm to quantify the impact of the calibration of the state parameters.

4. The method according to claim 3, wherein, The optimization algorithm includes one or more of the following statistical algorithms: least squares method, maximum likelihood estimation method, Bayesian estimation method, linear quadratic optimal control method, and robust control method.

5. The method according to claim 1, wherein, The damping optimization function is calculated based on the actual value and calibrated value of the state parameters.

6. The method according to claim 5, wherein, The actual values ​​of the state parameters are obtained through sensor measurement or observation by an observer.

7. An active damping control system, characterized in that, The system includes: Model module, configured to establish a dynamic model of the transmission system; A calibration module, configured to calibrate the state parameters in the dynamic model; A calculation module is configured to calculate the state error of the transmission system based on calibrated state parameters; as well as The control module is configured to determine the required active damping control output based on the state error. The calculation module is further configured to calculate the damping optimization function corresponding to the transmission system under different operating modes. The damping optimization function is a weighted sum of the optimization function values ​​under each operating model. The optimization function value is the sum of the squares of the differences between the actual vehicle value and the model value. The calibration module is further configured to assign different weights to the motor speed, engine speed, output wheel speed, output shaft speed, and shaft torque in the transmission system.

8. The system according to claim 7, wherein, The calibration module is configured to optimize the combination of calibration values ​​for the state parameters.

9. The system according to claim 8, wherein, Optimizing the selection of the calibration combination of the state parameters includes using an optimization algorithm to quantify the impact of the calibration of the state parameters.

10. The system according to claim 9, wherein, The optimization algorithm includes one or more of the following statistical algorithms: least squares method, maximum likelihood estimation method, Bayesian estimation method, linear quadratic optimal control method, and robust control method.

11. The system according to claim 7, wherein, The damping optimization function is calculated based on the actual value and calibrated value of the state parameters.

12. The system according to claim 11, wherein, The actual values ​​of the state parameters are obtained through sensor measurement or observation by an observer.

13. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to achieve: The active damping control method as described in any one of claims 1-6.

14. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program can be implemented when executed by a processor: The active damping control method as described in any one of claims 1-6.

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