Smooth transition method for achieving optimal torque through hydraulic pressure and electric drive control

Abstract

The invention relates to the technical field of hydraulic and electric drive cooperative control, and discloses a smooth transition method for achieving the optimal torque through hydraulic pressure and electric drive control, and the method comprises the following steps: S101, collecting basic measurement data; s102, performing digital filtering and conversion processing on the basic measurement data to obtain processed basic measurement data; s103, constructing a hydraulic-electric coupling dynamic model based on the processed basic measurement data, and outputting a predicted torque; s104, carrying out online identification on preset parameters, and comparing the predicted torque with the torque to obtain an identified dynamic model; s105, calculating a control increment by adopting a rolling optimization strategy; and S106, according to the control increment, in combination with the hydraulic pressure at the current moment, the rotating speed of the motor, the preset target hydraulic pressure, the target rotating speed of the motor and the maximum value of the rated torque, updating and adjusting of the hydraulic pressure and the rotating speed of the motor are carried out. According to the invention, optimal torque smooth transition under cooperation of hydraulic pressure and electric drive is realized.

Description

Technical Field

[0001] This invention belongs to the field of hydraulic-electric drive coordinated control technology, specifically involving a method for achieving a smooth transition between hydraulic pressure and electric drive control to achieve optimal torque. Background Technology

[0002] In industrial equipment and hybrid vehicles, hydraulic transmission and electric drive each have their advantages. However, when switching between the two or operating simultaneously, torque output often experiences sudden jumps or fluctuations, leading to unstable equipment operation, reduced efficiency, and impact wear on mechanical components. Traditional control systems often treat hydraulic and electric drive systems as independent units, making it difficult to achieve smooth transitions during switching. Hybrid control schemes based on classic PID or fixed strategies also cannot cope with changes in system parameters or sudden changes in operating conditions, often resulting in response hysteresis or overshoot.

[0003] Furthermore, relying solely on offline calibrated model parameters is insufficient to reflect real-time operating conditions, and accumulated errors can easily lead to safety risks. While existing research proposes improved algorithms in specific areas, it lacks a complete system encompassing data acquisition, dynamic modeling, online identification, predictive control, and real-time feedback. Therefore, there is an urgent need for a comprehensive control scheme that, under the coordinated operation of hydraulic and electric drives, achieves smooth torque transition through rapid and accurate model prediction, staged linear adjustment, and fuzzy PID correction, combined with multi-layered safety protection. This would improve system stability, response speed, and reliability, effectively reduce mechanical shock, and extend equipment life. Summary of the Invention

[0004] This invention provides a method for achieving a smooth transition of optimal torque between hydraulic pressure and electric drive control, solving the technical problems of poor stability and severe wear caused by sudden torque changes and fluctuations during the switching between hydraulic transmission and electric drive in related technologies.

[0005] This invention provides a method for achieving a smooth transition between hydraulic pressure and electric drive control to achieve optimal torque, comprising the following steps:

[0006] S101, collect basic measurement data according to the preset sampling frequency, including: hydraulic pressure signal, motor speed pulse signal and torque signal;

[0007] S102 performs digital filtering and conversion on the basic measurement data to obtain the processed basic measurement data, which includes: hydraulic pressure, motor speed and torque.

[0008] S103, Based on the processed basic measurement data, a dynamic model of hydraulic-electric drive coupling is constructed. This dynamic model takes hydraulic pressure, motor speed and preset parameters as inputs and outputs predicted torque to express the functional relationship between predicted torque and hydraulic pressure and motor speed.

[0009] S104. The least squares method is used to identify the preset parameters online, and the error is compared between the predicted torque output by the model and the torque collected by the sensor. The accuracy of the model is judged according to the preset error standard. When the accuracy of the model meets the preset error standard, the identified dynamic model is obtained.

[0010] S105, in each sampling period, using the identified dynamic model, the current hydraulic pressure, the motor speed and the preset target torque sequence, the control increment is calculated using a rolling optimization strategy according to the preset time period and preset time interval. The control increment includes the increase in hydraulic pressure and the increase in motor speed.

[0011] S106, based on the control increment, combined with the current hydraulic pressure, motor speed, preset target hydraulic pressure and target motor speed, and the maximum rated torque, sequentially updates and adjusts the hydraulic pressure and motor speed according to a phased linear adjustment strategy.

[0012] Furthermore, the specific steps of S102 include:

[0013] S201, the hydraulic pressure signal is processed by an exponential low-pass filter to obtain the filtered hydraulic pressure;

[0014] S202, the instantaneous original speed is obtained by converting the motor speed pulse signal into the instantaneous original speed based on the time interval between two consecutive effective pulses, and the instantaneous original speed is digitally filtered to obtain the motor speed;

[0015] S203 performs an exponential low-pass filter on the torque signal to obtain the filtered torque.

[0016] Furthermore, the dynamic model is constructed based on linear regression, and the calculation formula for the dynamic model is as follows:

[0017]

[0018] in, Let p(k) represent the predicted torque at the k-th sampling time, p(k) represent the hydraulic pressure at the k-th sampling time, k represent the sampling time index, and n(k) represent the motor speed at the k-th sampling time. p k represents the pressure coefficient in the dynamic model. n represents the rotational speed coefficient in the dynamic model, and b represents the constant term in the dynamic model;

[0019] The preset parameters include: pressure coefficient, speed coefficient, and constant terms in the dynamic model.

[0020] Furthermore, the specific steps of S104 include:

[0021] S301, construct a regression matrix and an output vector. The regression matrix consists of hydraulic pressure, motor speed and constant terms, and the output vector is represented by torque.

[0022] S302, the least squares algorithm is used to calculate the parameter estimate, which includes the pressure coefficient, speed coefficient and constant term, and the obtained parameter estimate is updated to the preset parameter;

[0023] S303 calculates and predicts torque based on updated preset parameters, hydraulic pressure, and motor speed;

[0024] S304 measures the error between the predicted torque and the actual torque, and compares the measurement result with a preset error standard.

[0025] S305, when the error measurement meets the preset error standard, the pressure coefficient, speed coefficient and constant term constitute the identified dynamic model;

[0026] S306, If the error measurement does not meet the preset error standard, repeat S301 to S305 until the error measurement meets the preset error standard.

[0027] Furthermore, the specific steps of S105 include:

[0028] S401, based on the current hydraulic pressure and motor speed, obtains the predicted torque within a preset time period through the identified model;

[0029] S402, construct an objective function based on the deviation between the predicted torque and the preset target torque at the corresponding time;

[0030] S403, under the condition of satisfying the preset constraints, the optimal control increment sequence within the preset time period is obtained through numerical optimization;

[0031] S404, the first step of the optimal control increment sequence is output and used for the current hydraulic pressure and motor speed adjustment.

[0032] Furthermore, the formula for calculating the objective function is as follows:

[0033]

[0034] Where J represents the value of the objective function. T represents the predicted torque calculated by the identified dynamic model at sampling time k+i. ref (k+i) represents the preset target torque at the (k+i)th sampling time, Δu(k+i) represents the control increment at the (k+i)th sampling time, N represents the preset time period, M represents the preset time interval, i represents the sampling time index, and λ represents the first weighting coefficient.

[0035] Furthermore, the preset constraints include:

[0036] The hydraulic pressure shall not exceed the preset maximum hydraulic pressure value, nor be lower than the preset minimum hydraulic pressure value;

[0037] The motor speed shall not exceed the preset maximum motor speed, nor be lower than the preset minimum motor speed;

[0038] The absolute value of the difference between two consecutive control increment steps must not exceed the preset control increment change rate threshold.

[0039] Furthermore, the hydraulic pressure and motor speed are updated and adjusted sequentially according to a phased linear adjustment strategy. The specific steps include:

[0040] S501 divides the number of steps required to reach the target state from the current moment into three stages: stage one, stage two, and stage three, for the first step, the second step, and the third step, respectively;

[0041] S502, based on the number of steps in the preset time period and the preset stage coefficient, calculate the hydraulic pressure increment and motor speed increment for each step in stage one, stage two and stage three respectively.

[0042] S503, at each sampling time, the increment corresponding to the stage is selected, and the hydraulic pressure and motor speed are iteratively updated through p(k+i)=p(k+i-1)+Δp(i) and n(k+i)=n(k+i-1)+Δn(i), where p(k+i) and n(k+i) represent the hydraulic pressure and motor speed at the (k+i)th sampling time, p(k+i-1) and n(k+i-1) represent the hydraulic pressure and motor speed at the (k+i-1)th sampling time, respectively, and Δp(i) and Δn(i) represent the control increment at the ith sampling time;

[0043] S504 calculates the predicted torque using the identified dynamic model after each iteration and determines whether it exceeds the rated torque limit. If it does, the increment of the current step is reduced.

[0044] Furthermore, the deviation between the current predicted torque and the target torque, as well as the rate of change of the deviation, are monitored in real time. The deviation and the rate of change of the deviation are input into a preset fuzzy control rule base to generate new PID parameters online. The new PID parameters are then applied to incremental PID calculations to generate corrected control increments. The PID parameters include proportional coefficient, integral coefficient, and derivative coefficient.

[0045] Furthermore, the hydraulic pressure, motor speed, and torque obtained in real time during each sampling period are compared with their respective preset safety thresholds. When any data exceeds its corresponding safety threshold, its safety status is set to abnormal, and a preset protection amplitude is subtracted from the current control quantity to generate a protection control quantity so that the hydraulic pressure and motor speed can be restored to their respective safe ranges. If they fail to be restored to their respective safe ranges after a preset number of consecutive attempts, the fault log information is recorded and the system is triggered to stop or alarm.

[0046] The beneficial effects of this invention are as follows: This invention achieves smooth torque transition through coordinated control of hydraulic pressure and electric drive; it adopts a combination of dynamic model and online identification to track system characteristics in real time, ensuring that the error between predicted torque and actual torque is small; based on MPC and a staged linear adjustment strategy, the torque switching time is short and the fluctuation is small, effectively suppressing shock; and it adopts fuzzy PID real-time correction to enhance the response speed and stability under nonlinear conditions. Attached Figure Description

[0047] Figure 1 This is a flowchart of the method for achieving a smooth transition of optimal torque through hydraulic pressure and electric drive control according to the present invention. Detailed Implementation

[0048] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0049] It should be noted that, unless otherwise defined, the technical or scientific terms used in one or more embodiments of the present invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in one or more embodiments of the present invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0050] like Figure 1As shown, the method for achieving a smooth transition to optimal torque through hydraulic pressure and electric drive control includes the following steps:

[0051] S101, collect basic measurement data according to the preset sampling frequency, including: hydraulic pressure signal, motor speed pulse signal and torque signal;

[0052] S102 performs digital filtering and conversion on the basic measurement data to obtain the processed basic measurement data, which includes: hydraulic pressure, motor speed and torque.

[0053] S103, Based on the processed basic measurement data, a dynamic model of hydraulic-electric drive coupling is constructed. This dynamic model takes hydraulic pressure, motor speed and preset parameters as inputs and outputs predicted torque to express the functional relationship between predicted torque and hydraulic pressure and motor speed.

[0054] S104. The least squares method is used to identify the preset parameters online, and the error is compared between the predicted torque output by the model and the torque collected by the sensor. The accuracy of the model is judged according to the preset error standard. When the accuracy of the model meets the preset error standard, the identified dynamic model is obtained.

[0055] S105, in each sampling period, using the identified dynamic model, the current hydraulic pressure, the motor speed and the preset target torque sequence, the control increment is calculated using a rolling optimization strategy according to the preset time period and preset time interval. The control increment includes the increase in hydraulic pressure and the increase in motor speed.

[0056] S106, based on the control increment, combined with the current hydraulic pressure, motor speed, preset target hydraulic pressure and target motor speed, and the maximum rated torque, sequentially updates and adjusts the hydraulic pressure and motor speed according to a phased linear adjustment strategy.

[0057] In one embodiment of the invention, the hydraulic pressure signal is acquired by a pressure sensor installed at a key location in the hydraulic circuit. The sensor output, after signal conditioning, is connected to a high-speed ADC module to read the original pressure value at a preset sampling frequency. The motor speed pulse signal is output by an encoder installed on the motor shaft. The time interval between consecutive pulses is recorded by a high-speed capture module, converted into instantaneous speed, and sent to subsequent filtering. The torque signal is acquired by a torque sensor at the motor output shaft. The sensor output, after isolation amplification and filtering, is also connected to the ADC for synchronous sampling to obtain the original torque value. All three acquired signals have anti-interference design and accuracy correction, providing reliable data for subsequent digital filtering and control calculations.

[0058] In one embodiment of the present invention, the specific steps of S102 include:

[0059] S201, the hydraulic pressure signal is processed by an exponential low-pass filter to obtain the filtered hydraulic pressure. Specifically, the hydraulic pressure signal is introduced into a first-order exponential low-pass filter for processing to remove high-frequency noise and momentum pulse interference.

[0060] S202, the instantaneous raw speed is obtained by converting the motor speed pulse signal into an instantaneous raw speed based on the time interval between two consecutive valid pulses, and the instantaneous raw speed is digitally filtered to obtain the motor speed. Specifically, this is achieved by... Calculate the motor speed for the k-th pulse, and then calculate the speed for n. raw (k) Applying digital filtering, this embodiment uses an exponential low-pass filter to obtain a smooth motor speed, where n raw (k) represents the motor speed during the k-th pulse, N p t represents the number of pulses generated per unit rotation. k and t k-1 These represent the times of the k-th and (k-1)-th pulses, respectively, in seconds.

[0061] S203 performs an exponential low-pass filter on the torque signal to obtain the filtered torque and remove high-frequency noise.

[0062] In one embodiment of the present invention, the dynamic model is constructed based on linear regression, and the calculation formula of the dynamic model is:

[0063]

[0064] in, Let p(k) represent the predicted torque at the k-th sampling time, and n(k) represent the model's estimate of the actual torque based on the current input. Let p(k) represent the hydraulic pressure at the k-th sampling time, k represent the sampling time index, and n(k) represent the motor speed at the k-th sampling time. p k represents the pressure coefficient in the dynamic model, used to map hydraulic pressure to torque contribution. n represents the speed coefficient in the dynamic model, used to map the motor speed to the torque contribution; b represents the constant term in the dynamic model; and represents the inherent bias of the dynamic model.

[0065] The preset parameters include: pressure coefficient, speed coefficient, and constant terms in the dynamic model.

[0066] In one embodiment of the present invention, the core of step S103 is to map the filtered and converted hydraulic pressure and motor speed into predicted torque through a dynamic model containing preset parameters. By constructing this dynamic model, the current predicted torque of the system can be quickly obtained using only the processed data at the current moment and the preset parameters, thereby providing a theoretical basis for subsequent online model identification, error comparison and model update.

[0067] In one embodiment of the present invention, step S104 specifically includes:

[0068] S301, construct a regression matrix and an output vector. The regression matrix consists of hydraulic pressure, motor speed and constant terms, and the output vector is represented by torque.

[0069] Specifically, the hydraulic pressure and motor speed collected at the k-th sampling time and several times before it are arranged in row-major order. Each row contains the hydraulic pressure, motor speed, and constant 1 corresponding to one sampling time. At the same time, the torque readings corresponding to these times are arranged in order to form a column vector as the output vector. Each row of the regression matrix represents a data point, where the hydraulic pressure and motor speed correspond to the pressure coefficient and speed coefficient to be identified in the model, and the constant term 1 corresponds to the constant term in the model. At this time, the regression matrix and the output vector correspond one-to-one, forming a standard linear regression format.

[0070] S302, the least squares algorithm is used to calculate the parameter estimate, which includes the pressure coefficient, speed coefficient and constant term, and the obtained parameter estimate is updated to the preset parameter;

[0071] Specifically, the least squares algorithm calculates the set of ternary parameter values ​​that minimizes the sum of squared errors between the regression matrix and the output vector; these ternary parameters are the parameter estimates obtained in this iteration. After the solution is completed, the obtained pressure coefficient, speed coefficient, and constant term are immediately written back into the model to replace the original preset parameters, ensuring that the model parameters used for subsequent predictions are always up-to-date.

[0072] S303, calculate the predicted torque based on the updated preset parameters, hydraulic pressure and motor speed, and input the above data into the calculation formula of the dynamic model to calculate the predicted torque;

[0073] S304 measures the error between the predicted torque and the actual torque, and compares the measurement result with the preset error standard to determine whether the accuracy of the current dynamic model meets the design requirements.

[0074] S305, when the error metric meets the preset error standard, the pressure coefficient, speed coefficient and constant term constitute the identified dynamic model, which is the current optimal model;

[0075] S306, If the error measurement does not meet the preset error standard, repeat S301 to S305 until the error measurement meets the preset error standard.

[0076] In one embodiment of the present invention, step S105 specifically includes:

[0077] S401, based on the current hydraulic pressure and motor speed, obtains the predicted torque within a preset time period through the identified model;

[0078] S402, construct an objective function based on the deviation between the predicted torque and the preset target torque at the corresponding time;

[0079] S403, Under the condition of satisfying the preset constraints, the optimal control increment sequence within the preset time period is obtained through numerical optimization. The numerical optimization can be carried out by using a quadratic programming algorithm to solve for the multi-step control increment sequence that minimizes the objective function, i.e. the optimal control increment sequence.

[0080] S404, the first step of the optimal control increment sequence is output and used for the adjustment of hydraulic pressure and motor speed in the current period. Specifically, the optimal control increment sequence obtained in S403 is analyzed. In order to ensure real-time response to system disturbances and reduce the amount of calculation, only the first step of the sequence is taken and used as the actual control output of the kth sampling period to adjust the hydraulic pressure or motor speed.

[0081] The above steps rely on the identified dynamic model and numerical optimization algorithm to generate the optimal control increment within a preset time period in the future, and apply the first step increment in each sampling cycle, thereby realizing the joint smooth adjustment of hydraulic pressure and motor speed, effectively improving torque tracking accuracy and ensuring control stability.

[0082] In one embodiment of the present invention, the formula for calculating the objective function is:

[0083]

[0084] Where J represents the value of the objective function. T represents the predicted torque calculated by the identified dynamic model at sampling time k+i. ref (k+i) represents the preset target torque at the (k+i)th sampling time, Δu(k+i) represents the control increment at the (k+i)th sampling time, N represents the preset time period, M represents the preset time interval, i represents the sampling time index, and λ represents the first weighting coefficient. This is used to measure the tracking error between the predicted torque and the target torque of the dynamic model. By minimizing this, the predicted torque can be made as close as possible to the corresponding target torque. This represents the sum of squares of the control increment changes, used to limit the adjustment of hydraulic pressure or motor speed from being too drastic. By incorporating the square of the control increment into the objective function, the optimization result tends to be a smoother Δu(k+i). This objective function ensures that the target can be tracked as accurately as possible throughout the preset time period by accumulating the squares of the error between the predicted torque and the target torque over the next N steps. By accumulating the squares of the control increment over the next M steps, it ensures that the adjustment of hydraulic pressure and motor speed will not have abrupt changes, thereby avoiding system shock and mechanical wear.

[0085] In one embodiment of the present invention, the preset constraint conditions include:

[0086] The hydraulic pressure shall not exceed the preset maximum hydraulic pressure value, nor be lower than the preset minimum hydraulic pressure value;

[0087] The motor speed shall not exceed the preset maximum motor speed, nor be lower than the preset minimum motor speed;

[0088] The absolute value of the difference between two consecutive control increment steps must not exceed the preset control increment change rate threshold.

[0089] In one embodiment of the present invention, the hydraulic pressure and motor speed are updated and adjusted sequentially according to a phased linear adjustment strategy. Specific steps include:

[0090] S501 divides the number of steps required to reach the target state from the current moment into three stages: stage one, stage two, and stage three, which correspond to the first step, the second step, and the third step, respectively. Stage one corresponds to the initial stage, where the increase in hydraulic pressure and motor speed is small; stage two corresponds to the middle stage, where the increase is moderate; and stage three corresponds to the later stage, where the increase is large.

[0091] S502, based on the number of steps in the preset time period and the preset stage coefficient, calculate the hydraulic pressure increment and motor speed increment for each step in stage one, stage two and stage three respectively. Specifically, for each stage, the increment for each step is obtained by dividing the difference between the hydraulic pressure and the target and the difference between the motor speed and the target by the product of the total number of steps and the stage coefficient.

[0092] S503, at each sampling time, the increment corresponding to the stage is selected, and the hydraulic pressure and motor speed are iteratively updated through p(k+i)=p(k+i-1)+Δp(i) and n(k+i)=n(k+i-1)+Δn(i), where p(k+i) and n(k+i) represent the hydraulic pressure and motor speed at the (k+i)th sampling time, p(k+i-1) and n(k+i-1) represent the hydraulic pressure and motor speed at the (k+i-1)th sampling time, respectively, and Δp(i) and Δn(i) represent the control increment at the ith sampling time;

[0093] S504 calculates the predicted torque using the identified dynamic model after each iteration and determines whether it exceeds the rated torque limit. If it does, the increment of the current step is reduced.

[0094] By dividing the transition process into three stages—initial, intermediate, and final—and setting different increment coefficients for each stage, the minimum step adjustment is used in the initial stage, the step size is gradually increased in the intermediate stage, and the maximum step size is used to approach the target in the final stage. This effectively avoids torque shocks caused by sudden changes in hydraulic pressure or motor speed. After each iteration, the identified dynamic model is immediately called to calculate the predicted torque and compare it with the preset rated torque upper limit. If an over-limit is detected, the current step increment is reduced in time or subsequent large adjustments are stopped in advance. This achieves dynamic constraint on the system torque, ensuring both the transition speed and that the torque is always kept within a safe range, thus improving the robustness and reliability of the system.

[0095] In one embodiment of the present invention, the deviation between the current predicted torque and the target torque and the rate of change of the deviation are monitored in real time. The deviation and the rate of change of the deviation are input into a preset fuzzy control rule base to generate new PID parameters online. The new PID parameters are then applied to incremental PID calculation to generate a corrected control increment. The PID parameters include: proportional coefficient, integral coefficient and derivative coefficient.

[0096] Specifically, the fuzzy control rule base is used to infer the deviation and the rate of change of deviation, convert them into corresponding fuzzy subsets, dynamically generate corresponding PID parameter increments, update the PID parameters based on the PID parameter increments, and input the updated PID parameters into the incremental PID algorithm to generate the corrected control increment at the current moment; the calculation formula for the incremental PID is:

[0097] Δu(k)=K p (k)[e(k)-e(k-1)]+K i (k)e(k)+K d (k)

[0098] [e(k)-2e(k-1)+e(k-2)];

[0099] Where Δu(k) represents the corrected control increment at this moment, and K p (k) represents the updated scaling factor, K i (k) represents the updated integral coefficient, K d (k) represents the updated differential coefficient, and e(k), e(k-1) and e(k-2) represent the torque deviation at sampling times k, k-1 and k-2. By applying the corrected control increment, the output torque of the system can approach the target torque more quickly and accurately, eliminating the tracking error caused by nonlinearity or disturbance.

[0100] In one embodiment of the present invention, the hydraulic pressure, motor speed, and torque obtained in real time during each sampling period are compared with their respective preset safety thresholds. When any data exceeds its corresponding safety threshold, its safety status is set to abnormal, and a preset protection amplitude is subtracted from the current control quantity to generate a protection control quantity so that the hydraulic pressure and motor speed can be restored to their respective safe ranges. If they fail to be restored to their respective safe ranges after a preset number of consecutive times, fault log information is recorded and the system is triggered to stop or alarm.

[0101] It should be noted that the interval and threshold sizes are set for ease of comparison. The size of the threshold depends on the amount of sample data and the base number set by those skilled in the art for each set of sample data, as long as it does not affect the proportional relationship between the parameter and the quantized value. Furthermore, the above formulas are all dimensionless calculations, and the formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0102] The embodiments of the present invention have been described above. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of the present embodiments, all of which are within the protection scope of the present embodiments.

Claims

1. A method for achieving a smooth transition of optimal torque through hydraulic pressure and electric drive control, characterized in that, Includes the following steps: S101, collect basic measurement data according to the preset sampling frequency, including: hydraulic pressure signal, motor speed pulse signal and torque signal; S102 performs digital filtering and conversion on the basic measurement data to obtain the processed basic measurement data, which includes: hydraulic pressure, motor speed and torque. S103, Based on the processed basic measurement data, a dynamic model of hydraulic-electric drive coupling is constructed. This dynamic model takes hydraulic pressure, motor speed and preset parameters as inputs and outputs predicted torque to express the functional relationship between predicted torque and hydraulic pressure and motor speed. S104. The least squares method is used to identify the preset parameters online, and the error is compared between the predicted torque output by the model and the torque collected by the sensor. The accuracy of the model is judged according to the preset error standard. When the accuracy of the model meets the preset error standard, the identified dynamic model is obtained. S105, in each sampling period, using the identified dynamic model, the current hydraulic pressure, the motor speed and the preset target torque sequence, the control increment is calculated using a rolling optimization strategy according to the preset time period and preset time interval. The control increment includes the increase in hydraulic pressure and the increase in motor speed. S106, based on the control increment, combined with the current hydraulic pressure, motor speed, preset target hydraulic pressure and target motor speed, and the maximum rated torque, sequentially updates and adjusts the hydraulic pressure and motor speed according to a phased linear adjustment strategy.

2. The method for achieving smooth transition of optimal torque through hydraulic pressure and electric drive control according to claim 1, characterized in that, The specific steps in S102 include: S201, the hydraulic pressure signal is processed by an exponential low-pass filter to obtain the filtered hydraulic pressure; S202, the instantaneous original speed is obtained by converting the motor speed pulse signal into the instantaneous original speed based on the time interval between two consecutive effective pulses, and the instantaneous original speed is digitally filtered to obtain the motor speed; S203 performs an exponential low-pass filter on the torque signal to obtain the filtered torque.

3. The method for achieving smooth transition of optimal torque through hydraulic pressure and electric drive control according to claim 2, characterized in that, The dynamic model is constructed based on linear regression, and the calculation formula for the dynamic model is as follows: in, Let p(k) represent the predicted torque at the k-th sampling time, p(k) represent the hydraulic pressure at the k-th sampling time, k represent the sampling time index, and n(k) represent the motor speed at the k-th sampling time. p k represents the pressure coefficient in the dynamic model. n represents the rotational speed coefficient in the dynamic model, and b represents the constant term in the dynamic model; The preset parameters include: pressure coefficient, speed coefficient, and constant terms in the dynamic model.

4. The method for achieving smooth transition of optimal torque through hydraulic pressure and electric drive control according to claim 2, characterized in that, The specific steps in S104 include: S301, construct a regression matrix and an output vector. The regression matrix consists of hydraulic pressure, motor speed and constant terms, and the output vector is represented by torque. S302, the least squares algorithm is used to calculate the parameter estimate, which includes the pressure coefficient, speed coefficient and constant term, and the obtained parameter estimate is updated to the preset parameter; S303 calculates and predicts torque based on updated preset parameters, hydraulic pressure, and motor speed; S304 measures the error between the predicted torque and the actual torque, and compares the measurement result with a preset error standard. S305, when the error measurement meets the preset error standard, the pressure coefficient, speed coefficient and constant term constitute the identified dynamic model; S306, If the error measurement does not meet the preset error standard, repeat S301 to S305 until the error measurement meets the preset error standard.

5. The method for achieving smooth transition of optimal torque through hydraulic pressure and electric drive control according to claim 4, characterized in that, The specific steps in S105 include: S401, based on the current hydraulic pressure and motor speed, obtains the predicted torque within a preset time period through the identified model; S402, construct an objective function based on the deviation between the predicted torque and the preset target torque at the corresponding time; S403, under the condition of satisfying the preset constraints, the optimal control increment sequence within the preset time period is obtained through numerical optimization; S404, the first step of the optimal control increment sequence is output and used for the current hydraulic pressure and motor speed adjustment.

6. The method for achieving smooth transition of optimal torque through hydraulic pressure and electric drive control according to claim 5, characterized in that, The formula for calculating the objective function is as follows: Where J represents the value of the objective function. T represents the predicted torque calculated by the identified dynamic model at sampling time k+i. ref (k+i) represents the preset target torque at the (k+i)th sampling time, Δu(k+i) represents the control increment at the (k+i)th sampling time, N represents the preset time period, M represents the preset time interval, i represents the sampling time index, and λ represents the first weighting coefficient.

7. The method for achieving smooth transition of optimal torque through hydraulic pressure and electric drive control according to claim 5, characterized in that, The preset constraints include: The hydraulic pressure shall not exceed the preset maximum hydraulic pressure value, nor be lower than the preset minimum hydraulic pressure value; The motor speed shall not exceed the preset maximum motor speed, nor be lower than the preset minimum motor speed; The absolute value of the difference between two consecutive control increment steps must not exceed the preset control increment change rate threshold.

8. The method for achieving smooth transition of optimal torque through hydraulic pressure and electric drive control according to claim 1, characterized in that, The hydraulic pressure and motor speed are updated and adjusted sequentially according to a phased linear adjustment strategy. The specific steps include: S501 divides the number of steps required to reach the target state from the current moment into three stages: stage one, stage two, and stage three, for the first step, the second step, and the third step, respectively; S502, based on the number of steps in the preset time period and the preset stage coefficient, calculate the hydraulic pressure increment and motor speed increment for each step in stage one, stage two and stage three respectively. S503, at each sampling time, the increment corresponding to the stage is selected, and the hydraulic pressure and motor speed are iteratively updated through p(k+i)=p(k+i-1)+Δp(i) and n(k+i)=n(k+i-1)+Δn(i), where p(k+i) and n(k+i) represent the hydraulic pressure and motor speed at the (k+i)th sampling time, p(k+i-1) and n(k+i-1) represent the hydraulic pressure and motor speed at the (k+i-1)th sampling time, respectively, and Δp(i) and Δn(i) represent the control increment at the ith sampling time; S504 calculates the predicted torque using the identified dynamic model after each iteration and determines whether it exceeds the rated torque limit. If it does, the increment of the current step is reduced.

9. The method for achieving smooth transition of optimal torque through hydraulic pressure and electric drive control according to claim 1, characterized in that, The deviation between the current predicted torque and the target torque, as well as the rate of change of the deviation, are monitored in real time. The deviation and the rate of change of the deviation are input into a preset fuzzy control rule base to generate new PID parameters online. The new PID parameters are then applied to incremental PID calculations to generate corrected control increments. The PID parameters include proportional coefficient, integral coefficient, and derivative coefficient.

10. The method for achieving smooth transition of optimal torque through hydraulic pressure and electric drive control according to claim 1, characterized in that, The hydraulic pressure, motor speed, and torque collected and processed in real time during each sampling period are compared with their respective preset safety thresholds. When any data exceeds its corresponding safety threshold, its safety status is set to abnormal, and a preset protection amplitude is subtracted from the current control quantity to generate a protection control quantity so that the hydraulic pressure and motor speed are restored to their respective safe ranges. If the system fails to recover to its respective safe range after a preset number of consecutive attempts, the fault log information will be recorded and the system will be triggered to stop or alarm.

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