Method and system for controlling ramp-rushing braking current of vehicle

Abstract

The invention belongs to the technical field of vehicle braking, and particularly relates to a control method and system for a vehicle slope rushing braking current, and the method comprises the steps: when an electric forklift is in a slope rushing braking state, controlling a motor current to reach a first braking current, and obtaining a rising slope of the motor current in a first time period; performing multiple tests under different gradients, collecting test data to establish a first mathematical model, determining a first relationship between the rising slope and the voltage, and calculating a voltage falling control curve under a specific slope based on the first relationship; after the output voltage of the motor controller is zero, the current at zero voltage is obtained, a compensation current control curve is determined, and voltage output is adjusted to reach compensation current; and when the compensation current is reached, the motor controller also predicts the rotating speed of the motor, and when the rotating speed of the motor is reduced to zero, the motor controller closes the electromagnetic brake and stops the current control, so that the slope rushing braking process is completed. According to the invention, safe break-through braking and a current limiting function can be realized.

Description

Technical Field

[0001] The invention belongs to the technical field of vehicle braking, and in particular relates to a method and system for controlling braking current of a vehicle on a ramp. Background Art

[0002] In the application of DC motors in vehicles, in order to control the current of the vehicle during the ramp process, the speed and current of the vehicle need to be obtained. Usually, speed and current sensors need to be installed to use speed and current closed-loop control. However, in order to save costs, DC motors are often not equipped with sensors, but open-loop control is used, which can lead to uncontrollable current.

[0003] A similar prior art Chinese patent application with publication number CN109808507A provides a comprehensive control system for electric forklifts to stop and roll down a slope, and the control steps are as follows: when the speed detection unit detects that the speed of the vehicle is zero, the torque estimation unit calculates the current torque; when the torque is greater than the set threshold, the hill-holding control unit is triggered, and the vehicle is stopped on the slope through the electric braking of the motor; when the vehicle electric braking time exceeds the hill-holding time, the system actively switches to the rolling down control unit; the torque estimation unit calculates the current torque, and when the torque is less than the pre-designed threshold, it is considered that the vehicle is on flat ground, and the system actively exits the rolling down state, the vehicle stops on flat ground and the torque is less than the pre-designed threshold, and returns to the normal working mode. However, this application relies on the preset threshold of the torque to determine whether it is on a slope or rolling down a slope, and does not consider the flexibility of the control strategy.

[0004] Similar prior art also includes a Chinese patent application with publication number CN113085563A, which discloses a slow braking method and device for electric vehicles, the method comprising: S1, the inverter is positively connected to the main motor, the kinetic energy of the electric vehicle is converted into electrical energy through the main motor and the inverter in sequence and stored in the power battery, when the power of the power battery reaches the first preset power, step S2 is implemented; S2, the inverter is reversely connected to the main motor, the main motor consumes the power of the power battery, when the power of the power battery drops to the second preset power, return to step S1. However, this application mainly focuses on energy recovery, without considering the immediacy and response speed of hill braking.

[0005] Therefore, the present invention provides a method and system for controlling the braking current of a vehicle on a ramp. Summary of the invention

[0006] The purpose of the present invention is to provide a new control scheme for vehicle ramp braking current. In the application of ramp braking of a warehouse truck, the current stability and safety of the vehicle during ramp braking are ensured by switching the control strategy without increasing any hardware cost.

[0007] In order to achieve the above-mentioned object of the invention, the present invention provides a method for controlling the braking current of a vehicle on a ramp, which is implemented by executing the following steps: Step S1, monitoring the state of the electric forklift, when the electric forklift is in a slope braking state, the motor controller controls the motor current to reach a first braking current, and also obtains the rising slope of the motor current in a first time period; Step S2, performing multiple tests at different slopes, collecting test data and establishing a first mathematical model based on the test data, determining a first relationship between the rising slope and the voltage based on the first mathematical model, and calculating a voltage drop control curve at a specific slope based on the first relationship; Step S3, after the motor controller output voltage is zero, obtain the current at zero voltage, determine the compensation current control curve, and adjust the voltage output to achieve the compensation current; Step S4, while adjusting the voltage output to reach the compensation current, the motor controller also uses a machine learning algorithm to predict the motor speed based on historical data and real-time measured data. When the motor speed drops to zero, the motor controller turns off the electromagnetic brake, stops current control, and completes the slope braking process.

[0008] As a preferred technical solution of the present invention, the first time period refers to the time interval from the start of slope braking of the electric forklift to the reaching of the first braking current.

[0009] As a preferred technical solution of the present invention, predicting the speed of the motor based on historical data and real-time measured data includes the following steps: Step S41, monitoring back electromotive force, which is the voltage generated when the motor rotates and is proportional to the rotation speed of the motor; Step S41, collecting historical data of the motor running under different conditions, including current, voltage, torque, measured speed, acceleration and temperature, and obtaining real-time data from the motor's sensors; Step S42: Delete outliers in the historical data, fill in missing values, standardize the historical data, and divide the historical data into a training data set, a validation data set, and a test data set; Step S43, calculating the correlation coefficient between each feature data and the rotation speed in the historical data, selecting the first feature data whose correlation coefficient is greater than the preset first threshold, using the dimensionality reduction method to reduce the number of the first feature data, using the training data set to train the prediction model, and minimizing the prediction error by adjusting the model parameters during the training process; Step S44: cross-validate the prediction model using the validation data set, evaluate the prediction model using the test data set, and optimize the parameters of the model based on the evaluation results to obtain the final prediction model; Step S45: Standardize the real-time data and input it into the prediction model to obtain the predicted speed. As a preferred technical solution of the present invention, a dimensionality reduction method is used to reduce the amount of feature data, including the following steps: Step S431, performing a second normalization process on the first feature data to generate normalized feature data, generating a corresponding first feature matrix based on the normalized feature data, and calculating the eigenvalues ​​and eigenvectors of the feature matrix; Step S432: Based on the size of the eigenvalues, select the eigenvectors corresponding to the first several largest eigenvalues ​​to form a second eigenmatrix; Step S433: Project the first feature matrix onto the second feature matrix to obtain a third feature matrix, and use feature data corresponding to the third feature matrix as feature data of the final training prediction model.

[0010] As a preferred technical solution of the present invention, performing a second normalization process on the first feature data to generate normalized feature data includes the following steps: For each feature in the first feature data, the corresponding mean and standard deviation are calculated, and for each feature data, the first formula is used for standardization processing, and the first formula is: , where Xi is the i-th feature data, μ is the mean, σ is the standard deviation, and Zi is the value after the i-th feature data is standardized.

[0011] As a preferred technical solution of the present invention, projecting the first characteristic matrix onto the second characteristic matrix to obtain the third characteristic matrix means multiplying the first characteristic matrix by the second characteristic matrix to obtain the third characteristic matrix.

[0012] As a preferred technical solution of the present invention, determining the compensation current control curve includes the following steps: Step S31, before the voltage input drops to zero, the motor controller collects the current data of the motor under different working conditions, the current data including the current value when the voltage is zero; Step S32: Analyze the current characteristics of the motor when the voltage is zero based on the collected current data, where the current characteristics include the magnitude of the current and the change trend of the current; Step S33: Based on the collected current data, current characteristics and physical characteristics of the motor, a second mathematical model is established to describe the current behavior of the motor when the voltage is zero; Step S34: design a control strategy, and determine a compensation current control curve based on the second mathematical model and the control strategy.

[0013] As a preferred technical solution of the present invention, based on the second mathematical model and the control strategy, determining the compensation current control curve includes the following steps: Step S341, establishing a current dynamic model of the motor according to the electrical parameters of the motor; Step S342, maintaining the current within a predetermined safety range as a control target, and designing a control algorithm to adjust the motor current; Step S343, simulating the current dynamics and control algorithm of the motor in the software, and optimizing the control parameters based on the simulation results; Step S344, collecting current data of the motor under different working conditions, adjusting the current dynamic model based on the current data, and determining the compensation current control strategy according to the control target; Step S345: construct a current control curve using a control algorithm and a compensation strategy.

[0014] As a preferred technical solution of the present invention, the current dynamic model is expressed by a third formula, which is: , where L is the inductance in the electrical parameters, R is the resistance, I is the motor current, E is the back electromotive force, and U is the controller output voltage.

[0015] As a preferred technical solution of the present invention, calculating a voltage drop control curve at a specific slope based on the first relationship includes the following steps: Step S21, collecting the current data of the motor in the process of ramp braking in real time, filtering the collected current data, and using a digital signal processor to implement a differential algorithm to calculate the current rising slope; Step S22, establishing an initial mathematical model, determining model parameters of the initial mathematical model according to experimental data and historical operation data, and improving the initial mathematical model based on the model parameters to generate a first mathematical model; Step S23: Based on the first mathematical model, determine the mathematical expression of the descent control curve, substitute the rising slope into the mathematical expression, calculate the corresponding voltage value, and generate the descent control curve of the voltage output; Step S24: obtain verification results through simulation verification, and adjust the descent control curve based on the verification results.

[0016] The present invention also provides a control system for vehicle ramp braking current, comprising the following modules: A first braking current control module is used to monitor the state of the electric forklift. When the electric forklift is in a slope braking state, the motor controller controls the motor current to reach a first braking current, and also obtains a rising slope of the motor current in a first time period; A descent control curve calculation module, used for performing multiple tests at different slopes, collecting test data and establishing a first mathematical model based on the test data, determining a first relationship between the rising slope and the voltage based on the first mathematical model, and calculating a descent control curve of the voltage at a specific slope based on the first relationship; A compensation current control module is used to obtain the current at zero voltage after the motor controller output voltage is zero, determine the compensation current control curve, and adjust the voltage output to achieve the compensation current; The speed estimation module is used to adjust the voltage output to achieve the compensation current. At the same time, the motor controller also uses a machine learning algorithm to predict the motor speed based on historical data and real-time measurement data. When the motor speed drops to zero, the motor controller turns off the electromagnetic brake, stops current control, and completes the slope braking process.

[0017] Compared with the prior art, the beneficial effects of the present invention are at least as follows: In the present invention, when the electric forklift is in slope braking, the motor is first controlled to reach the first braking current, and the current rising slope of the time period is obtained at this time. The descending control curve of the controller voltage output is calculated according to the current rising slope K. After the voltage output is zero, the current value at zero voltage is obtained, the compensation current control curve is determined, the voltage output is adjusted to reach the compensation current, and the motor speed is estimated at the same time. After the speed reaches zero speed, the electromagnetic brake is turned off and the current control is stopped. The present invention can control the first braking current first, reach the zero pressure moment through the voltage output curve, and then determine the compensation current curve without increasing any hardware cost by switching the control strategy. Finally, the shutdown moment is determined by predicting the speed. This method can achieve safe breakthrough braking and play a current limiting function. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of the steps of a method for controlling a braking current of a vehicle on a ramp according to the present invention; Figure 2 The output voltage drop control curve of the motor controller at different slopes of the present invention; Figure 3 Different compensation current control curves of the present invention; Figure 4 The present invention is a structural diagram of the control system of the vehicle ramp braking current. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0021] In the application scenarios of warehouse trucks braking on a slope, the motor controller needs to control the braking current of the travel motor. It is necessary to control the current to prevent damage to the controller due to excessive motor current, and to control the driving comfort of the vehicle.

[0022] Thus, the present invention provides Figure 1 The control method of the braking current of a vehicle on a ramp is implemented by executing the following steps: Step S1, monitoring the state of the electric forklift. When the electric forklift is in a slope braking state, the motor controller controls the motor current to reach a first braking current, and also obtains the rising slope of the motor current in a first time period.

[0023] Specifically, the first time period refers to the time interval from the start of the slope braking of the electric forklift to the reaching of the first braking current. Specifically, it is the time from the start of the slope braking current control to the time when the motor current reaches the predetermined first braking current value. During the first time period, the current change of the motor is detected in real time, and the rising slope of the current is calculated. The rising slope refers to the rate at which the current changes with time. This rising slope is an important parameter for calculating the voltage output drop control curve of the controller in the subsequent steps. It reflects the speed of current rise and is closely related to the slope performance and braking characteristics of the motor. By accurately controlling the rising slope of the current during this time period, it can be ensured that the motor will not be damaged due to excessive current, while also maintaining the driving comfort of the vehicle.

[0024] Step S2, perform multiple tests at different slopes, collect test data and establish a first mathematical model based on the test data, determine a first relationship between the rising slope and the voltage based on the first mathematical model, and calculate a voltage drop control curve at a specific slope based on the first relationship.

[0025] Specifically, the first relationship between the current rising slope and the voltage is determined through experiments and theoretical analysis. The first relationship is obtained based on test data fitting, and describes the voltage value that the motor controller should output under different rising slopes. After determining the first relationship between the rising slope and the voltage, the first relationship is used to calculate the voltage output drop control curve. The drop control curve describes how the motor controller output voltage gradually drops from an initial value to zero or a specific value as time increases during the ramp braking process to control the motor current, such as Figure 2 As shown, the output voltage drop control curve of the motor controller at different slopes. In this way, the motor controller can dynamically adjust the voltage output according to the current rising slope monitored in real time to ensure the stability and safety of the motor current, thereby achieving effective slope braking control.

[0026] Step S3: after the motor controller output voltage is zero, obtain the current at zero voltage, determine the compensation current control curve, and adjust the voltage output to achieve the compensation current.

[0027] Specifically, in the above step S2, the motor controller calculates the voltage drop control curve according to the current rising slope, with the purpose of gradually reducing the output voltage until the voltage drops to zero. This zero voltage point is a preset key moment, marking the end of the first stage of braking current control. When the output voltage of the motor controller drops to zero, the current value of the motor is the key reference point. This current value is the current maintained by the back electromotive force of the motor in the absence of external voltage input. The back electromotive force refers to the voltage generated by the motor itself, such as Figure 3 Different compensation current control curves are shown. According to the current value at zero voltage, the motor controller needs to determine a new control strategy, namely the compensation current control curve. The current control curve is designed based on the current value at zero voltage. The purpose is to compensate for the change in current by adjusting the voltage output after the voltage output is zero to maintain the stability of the motor current. The controller dynamically adjusts the output voltage according to the compensation current control curve to ensure that the motor current is maintained at a safe and stable level. This process involves fine-tuning the voltage output to counteract current fluctuations caused by changes in motor load or speed. The above method can ensure that after the voltage output drops to zero, the motor current can still be effectively controlled to maintain the stability and safety of the vehicle until the vehicle stops completely. This step reflects the intelligent adjustment capability of the motor controller, which can adjust the control strategy according to the actual working state of the motor.

[0028] Step S4, while adjusting the voltage output to reach the compensation current, the motor controller also uses a machine learning algorithm to predict the motor speed based on historical data and real-time measured data. When the motor speed drops to zero, the motor controller turns off the electromagnetic brake, stops current control, and completes the slope braking process.

[0029] Specifically, in order to ensure that the electromagnetic brake is turned off and current control is stopped in time after the vehicle completely stops going uphill, so as to avoid unnecessary energy consumption and potential motor overheating problems, the motor speed is estimated by a machine learning algorithm. By using a machine learning algorithm to predict the motor speed, the motor speed can be accurately predicted, thereby improving the accuracy and efficiency of motor control. When the motor speed drops to zero, it means that the vehicle has stopped going uphill. At this time, the motor controller turns off the electromagnetic brake. The turning off of the electromagnetic brake means the end of the slope planning process. As the electromagnetic brake is turned off, the motor controller stops controlling the motor current. At this time, the motor no longer needs additional current to maintain braking, and the current control process ends. This step reflects the intelligence and adaptability of the control system, and can dynamically adjust the control strategy according to the actual working state of the motor.

[0030] In actual application, the entire breakout braking process is divided into two stages: Stage 1: Start the first braking current control, control the motor to reach the first braking current first, then obtain the current rising slope of the time period, calculate the controller voltage output control curve based on the current rising slope di / dt, and when the controller output voltage is zero, enter Stage 2: Start the second braking current control, determine the compensation current control curve based on the current value at zero voltage, adjust the voltage output to achieve the compensation current, and predict the motor speed at the same time. When the speed reaches zero, turn off the electromagnetic brake and stop the current control. By switching the control strategy, it is ensured that the vehicle can achieve safe breakout braking and play a current limiting function.

[0031] Furthermore, predicting the speed of the motor based on historical data and real-time measured data includes the following steps: Step S41, collecting historical data of the motor running under different conditions, including current, voltage, torque, measured speed, acceleration and temperature, and obtaining real-time data from the motor's sensors; Step S42: Delete outliers in the historical data, fill in missing values, standardize the historical data, and divide the historical data into a training data set, a validation data set, and a test data set; Step S43, calculating the correlation coefficient between each feature data and the rotation speed in the historical data, selecting the first feature data whose correlation coefficient is greater than the preset first threshold, using the dimensionality reduction method to reduce the number of the first feature data, using the training data set to train the prediction model, and minimizing the prediction error by adjusting the model parameters during the training process; Step S44: cross-validate the prediction model using the validation data set, evaluate the prediction model using the test data set, and optimize the parameters of the model based on the evaluation results to obtain the final prediction model; Step S45: normalize the real-time data and input it into the prediction model to obtain the predicted speed.

[0032] Specifically, outliers in historical data are deleted and missing values ​​are filled to ensure data integrity and accuracy. Historical data is standardized to facilitate model processing. The first feature data whose correlation coefficient has a great influence on the rotational speed is calculated, and the number of first feature data is reduced using a dimensionality reduction method. The dimensionality reduction method can effectively reduce the number of features while retaining key information in the data. Therefore, the efficiency of model training can be improved. The generalization ability of the model is evaluated through cross-validation, and overfitting can be identified and avoided, so that the prediction model can make accurate predictions on new data. The prediction model is also evaluated, and the parameters of the model are optimized based on the evaluation results to further improve the accuracy and robustness of the model prediction. The data acquired in real time is standardized and input into the prediction model to obtain the predicted rotational speed.

[0033] Furthermore, the dimension reduction method is used to reduce the amount of feature data, including the following steps: Step S431, performing a second normalization process on the first feature data to generate normalized feature data, generating a corresponding first feature matrix based on the normalized feature data, and calculating the eigenvalues ​​and eigenvectors of the feature matrix; Step S432: Based on the size of the eigenvalues, select the eigenvectors corresponding to the first several largest eigenvalues ​​to form a second eigenmatrix; Step S433: Project the first feature matrix onto the second feature matrix to obtain a third feature matrix, and use feature data corresponding to the third feature matrix as feature data of the final training prediction model.

[0034] Specifically, assuming that the first feature data includes five data features of current, voltage, torque, acceleration and temperature, the first feature data is subjected to a second standardization process to generate standardized feature data. The specific method of the second standardization process will be explained in detail later. After obtaining the standardized feature data, the first feature matrix is ​​calculated based on the standardized feature data. Assuming that the data matrix generated by the standardized feature data is X, each row in X represents a group of data, and each column represents a feature. X T is the transposed matrix of X, n is the number of samples, then the calculation formula of the first characteristic matrix is , and then calculate the eigenvalues ​​and eigenvectors of the first feature matrix. According to the size of the eigenvalues, select the eigenvectors corresponding to the first several largest eigenvalues ​​as the principal components. These principal components can explain most of the variance in the data. The selected eigenvectors, then project the first feature matrix onto the second feature matrix to obtain the third feature matrix, and use the feature data corresponding to the third feature matrix as the feature data of the final training prediction model. The specific projection method will be explained in detail later. Through the above method, we can effectively reduce the number of features and improve the efficiency of model training while retaining the key information in the data.

[0035] Furthermore, performing a second normalization process on the first feature data to generate normalized feature data comprises the following steps: For each feature in the first feature data, the corresponding mean and standard deviation are calculated, and for each feature data, the first formula is used for standardization processing, and the first formula is: , where Xi is the i-th feature data, μ is the mean, σ is the standard deviation, and Zi is the value after the i-th feature data is standardized.

[0036] Specifically, different features may have different dimensions, such as current, voltage, and temperature. Standardization can eliminate the impact of dimensions and make different features on the same scale, which is convenient for comparison and processing. Therefore, each feature data is standardized through the above method so that the mean of each feature is close to 0 and the standard deviation is close to 1. For machine learning algorithms based on gradient descent, such as linear regression and logistic regression, standardization can improve the convergence speed of the algorithm. When the scales of features are inconsistent, the path of gradient descent may be very tortuous, resulting in slow convergence. After standardization, the path of gradient descent is more direct, thereby accelerating the convergence speed.

[0037] Furthermore, projecting the first characteristic matrix onto the second characteristic matrix to obtain the third characteristic matrix means multiplying the first characteristic matrix by the second characteristic matrix to obtain the third characteristic matrix.

[0038] Specifically, the first characteristic matrix is ​​X, the second characteristic matrix is ​​C, and the third characteristic matrix is ​​Xnew=X⋅C. Since the second characteristic matrix is ​​a matrix composed of eigenvectors corresponding to the first several largest eigenvalues, the dimension of the second characteristic matrix is ​​smaller than that of the first characteristic matrix. Therefore, the third characteristic matrix is ​​obtained by projecting the first characteristic matrix onto the second characteristic matrix to achieve the purpose of dimensionality reduction.

[0039] Further, determining the compensation current control curve includes the following steps: Step S31, before the voltage output drops to zero, the motor controller collects current data of the motor under different working conditions, the current data including the current value when the voltage is zero; Step S32: Analyze the current characteristics of the motor when the voltage is zero based on the collected current data, where the current characteristics include the magnitude of the current and the change trend of the current; Step S33: Based on the collected current data, current characteristics and physical characteristics of the motor, a second mathematical model is established to describe the current behavior of the motor when the voltage is zero; Step S34: design a control strategy, and determine a compensation current control curve based on the second mathematical model and the control strategy.

[0040] Specifically, determining the compensation current control curve means that after the output voltage of the motor controller drops to zero, in order to maintain the stability of the motor current, a new current control strategy, namely the compensation current control curve, needs to be determined. Before the voltage output drops to zero, the controller will collect the current data of the motor under different working conditions, including the current value at zero voltage. After determining the compensation current control curve by the above method, the current characteristics of the motor at zero voltage are analyzed, including the current size, change trend and possible load influence. According to the collected data and the physical characteristics of the motor, a mathematical model is established to describe the current behavior of the motor at zero voltage, and a control strategy is designed. This strategy can adjust the output of the controller according to the difference between the actual current and the target current of the motor to compensate for the change of the current. The compensation current control curve is determined by using the above mathematical model and control strategy. This curve describes how to maintain the stability of the motor current by adjusting the output of the controller after the voltage output is zero. The compensation current control curve can be determined by the above method to ensure that the current of the motor is still effectively controlled after the voltage output drops to zero, so as to maintain the stable operation of the motor and protect the motor from damage.

[0041] Further, based on the second mathematical model and the control strategy, determining the compensation current control curve includes the following steps: Step S341, establishing a current dynamic model of the motor according to the electrical parameters of the motor; Step S342, maintaining the current within a predetermined safety range as a control target, and designing a control algorithm to adjust the motor current; Step S343, simulating the current dynamics and control algorithm of the motor in the software, and optimizing the control parameters based on the simulation results; Step S344, collecting current data of the motor under different working conditions, adjusting the current dynamic model based on the current data, and determining the compensation current control strategy according to the control target; Step S345: construct a current control curve using a control algorithm and a compensation strategy.

[0042] Specifically, the current dynamic model of the motor is established according to the electrical parameters of the motor (resistance R, inductance L). Usually, this model can be expressed as a differential equation, which will be expressed using the third formula below. Maintaining the current within a predetermined safe range is the control target. A control algorithm is designed to adjust the motor current. The control algorithm (such as a PID controller) is used to adjust the motor current controller. The output voltage of the motor controller is adjusted according to the difference between the actual current and the reference current. The current dynamics of the motor and the control algorithm are simulated in a software environment (such as MATLAB / SimUlink) to verify the effectiveness of the control strategy. The control parameters are optimized according to the simulation results. Experiments are conducted on the actual motor to collect current data under different working conditions. These data are used to adjust and verify the accuracy of the mathematical model. According to the control target and the dynamic characteristics of the motor, the compensation current control strategy is determined, which may include static compensation (such as current cutoff value) and dynamic compensation (such as current reference value that changes over time). The control algorithm and compensation strategy are used to construct a compensation current control curve. This curve describes how the controller should adjust its output to maintain the stability of the current at different time points or conditions.

[0043] Furthermore, the current dynamic model is expressed by the third formula, which is: , where L is the inductance in the electrical parameters, R is the resistance, I is the motor current, E is the back electromotive force, and U is the controller output voltage.

[0044] Specifically, the current dynamic model is represented by the third formula above. The current dynamic model is also the second mathematical model above. This model describes the dynamic behavior of the motor current over time. It is usually a differential equation that takes into account the electrical parameters of the motor (such as resistance and inductance) and the influence of back electromotive force. The current dynamic model is the basis for determining the compensation current control curve because it provides a mathematical description of how the motor current responds to changes in the controller output voltage.

[0045] Further, calculating a voltage drop control curve at a specific slope based on the first relationship includes the following steps: Step S21, collecting the current data of the motor in the process of ramp braking in real time, filtering the collected current data, and using a digital signal processor to implement a differential algorithm to calculate the current rising slope; Step S22, establishing an initial mathematical model, determining model parameters of the initial mathematical model according to experimental data and historical operation data, and improving the initial mathematical model based on the model parameters to generate a first mathematical model; Step S23: Based on the first mathematical model, determine the mathematical expression of the descent control curve, substitute the rising slope into the mathematical expression, calculate the corresponding voltage value, and generate the descent control curve of the voltage output; Step S24: obtain verification results through simulation verification, and adjust the descent control curve based on the verification results.

[0046] Specifically, a high-precision current sensor is used to collect the current value of the motor in the process of ramp braking in real time, and the collected current data is filtered to eliminate noise and interference to ensure the accuracy of the data. A differential algorithm is implemented using a digital signal processor (DSP), or a microcontroller (MCV) is used to implement the differential algorithm to calculate the current rising slope, and a suitable mathematical model is selected or developed, which can predict the value of voltage U based on the slope K. Common models include linear regression models, polynomial models, or nonlinear models based on experience. The parameters of the model are determined based on experimental data and historical operation data, and the mathematical expression of the voltage output drop control curve is determined based on the relationship model between the slope K and the voltage U. For example, if the model is linear, the control curve can be expressed as U = aK + b, where a and b are model parameters. The value of the slope K is substituted into the mathematical expression of the control curve, and the corresponding voltage U value is calculated to generate a decreasing control curve for the voltage output. The effectiveness of the control curve is verified through simulation or small-scale experiments to ensure that it can meet the actual needs of slope braking. According to the verification results, the control curve is fine-tuned to optimize the control effect and ensure that the motor current is controlled within a safe range. Through the above steps, the decreasing control curve of the controller voltage output can be calculated according to the current rising slope, thereby achieving effective control of the vehicle's slope braking current and ensuring the safety of the vehicle and driving comfort.

[0047] According to another aspect of the embodiment of the present invention, Figure 4 As shown, a control system for a vehicle ramp braking current is also provided, including a first braking current control module, a descent control curve calculation module, a compensation current control module and a speed estimation module, which are used to implement a vehicle ramp braking current control method as described above, and the specific functions of each module are as follows: A first braking current control module is used to monitor the state of the electric forklift. When the electric forklift is in a slope braking state, the motor controller controls the motor current to reach a first braking current, and also obtains a rising slope of the motor current in a first time period; A descent control curve calculation module, used for performing multiple tests at different slopes, collecting test data and establishing a first mathematical model based on the test data, determining a first relationship between the rising slope and the voltage based on the first mathematical model, and calculating a descent control curve of the voltage at a specific slope based on the first relationship; A compensation current control module is used to obtain the current at zero voltage after the motor controller output voltage is zero, determine the compensation current control curve, and adjust the voltage output to achieve the compensation current; The speed estimation module is used to adjust the voltage output to achieve the compensation current. At the same time, the motor controller also uses a machine learning algorithm to predict the motor speed based on historical data and real-time measurement data. When the motor speed drops to zero, the motor controller turns off the electromagnetic brake, stops current control, and completes the slope braking process.

[0048] In summary, the present invention proposes a method and system for controlling the braking current of a vehicle on a ramp, the method includes controlling the motor current to reach a first braking current when the electric forklift is in a ramp braking state, and also obtaining the rising slope of the motor current in a first time period; performing multiple tests at different slopes, collecting test data to establish a first mathematical model, determining a first relationship between the rising slope and the voltage, and calculating a voltage drop control curve at a specific slope based on the first relationship; after the motor controller outputs a voltage of zero, obtaining the current at zero voltage, determining a compensation current control curve, and adjusting the voltage output to achieve the compensation current; while achieving the compensation current, the motor controller also predicts the motor speed, and when the motor speed drops to zero, the motor controller turns off the electromagnetic brake, stops current control, and completes the ramp braking process. The present invention can achieve safe braking and play a current limiting function.

[0049] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0050] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (RambUs) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0051] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0052] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0053] The above are only preferred embodiments of the present invention and are 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 in the protection scope of the present invention.

Claims

1. A method for controlling braking current of a vehicle on a ramp, characterized in that: The steps include: Step S1, monitoring the state of the electric forklift, when the electric forklift is in a slope braking state, the motor controller controls the motor current to reach a first braking current, and also obtains the rising slope of the motor current in a first time period, the first time period refers to the time interval from the start of the slope braking of the electric forklift to the reaching of the first braking current; Step S2, performing multiple tests at different slopes, collecting test data and establishing a first mathematical model based on the test data, determining a first relationship between the rising slope and the voltage based on the first mathematical model, and calculating a voltage drop control curve at a specific slope based on the first relationship; Step S3, after the motor controller output voltage is zero, obtain the current at zero voltage, determine the compensation current control curve, and adjust the voltage output to achieve the compensation current; Step S4, while adjusting the voltage output to reach the compensation current, the motor controller also uses a machine learning algorithm to predict the motor speed based on historical data and real-time measured data. When the motor speed drops to zero, the motor controller turns off the electromagnetic brake, stops current control, and completes the slope braking process.

2. The method according to claim 1, characterized in that Predicting the motor speed based on historical data and real-time measured data includes the following steps: Step S41, collecting historical data of the motor running under different conditions, including current, voltage, torque, measured speed, acceleration and temperature, and obtaining real-time data from the motor's sensors; Step S42: Delete outliers in the historical data, fill in missing values, standardize the historical data, and divide the historical data into a training data set, a validation data set, and a test data set; Step S43, calculating the correlation coefficient between each feature data and the rotation speed in the historical data, selecting the first feature data whose correlation coefficient is greater than the preset first threshold, using the dimensionality reduction method to reduce the number of the first feature data, using the training data set to train the prediction model, and minimizing the prediction error by adjusting the model parameters during the training process; Step S44: cross-validate the prediction model using the validation data set, evaluate the prediction model using the test data set, and optimize the parameters of the model based on the evaluation results to obtain the final prediction model; Step S45: normalize the real-time data and input it into the prediction model to obtain the predicted speed.

3. The method according to claim 2, characterized in that Using dimensionality reduction methods to reduce the number of feature data includes the following steps: Step S431, performing a second normalization process on the first feature data to generate normalized feature data, generating a corresponding first feature matrix based on the normalized feature data, and calculating the eigenvalues ​​and eigenvectors of the feature matrix; Step S432: Based on the size of the eigenvalues, select the eigenvectors corresponding to the first several largest eigenvalues ​​to form a second eigenmatrix; Step S433: Project the first feature matrix onto the second feature matrix to obtain a third feature matrix, and use feature data corresponding to the third feature matrix as feature data of the final training prediction model.

4. The method according to claim 3, characterized in that Performing a second normalization process on the first feature data to generate normalized feature data includes the following steps: For each feature in the first feature data, the corresponding mean and standard deviation are calculated, and for each feature data, the first formula is used for standardization processing, and the first formula is: , where Xi is the i-th feature data, μ is the mean, σ is the standard deviation, and Zi is the value after the i-th feature data is standardized.

5. The method according to claim 3, characterized in that: Projecting the first characteristic matrix onto the second characteristic matrix to obtain the third characteristic matrix means multiplying the first characteristic matrix by the second characteristic matrix to obtain the third characteristic matrix.

6. The method according to claim 1, characterized in that Determining the compensation current control curve includes the following steps: Step S31, before the voltage output drops to zero, the motor controller collects current data of the motor under different working conditions, the current data including the current value when the voltage is zero; Step S32: Analyze the current characteristics of the motor when the voltage is zero based on the collected current data, where the current characteristics include the magnitude of the current and the change trend of the current; Step S33: Based on the collected current data, current characteristics and physical characteristics of the motor, a second mathematical model is established to describe the current behavior of the motor when the voltage is zero; Step S34: design a control strategy, and determine a compensation current control curve based on the second mathematical model and the control strategy.

7. The method according to claim 6, characterized in that Based on the second mathematical model and the control strategy, determining the compensation current control curve includes the following steps: Step S341, establishing a current dynamic model of the motor according to the electrical parameters of the motor; Step S342, maintaining the current within a predetermined safety range as a control target, and designing a control algorithm to adjust the motor current; Step S343, simulating the current dynamics and control algorithm of the motor in the software, and optimizing the control parameters based on the simulation results; Step S344, collecting current data of the motor under different working conditions, adjusting the current dynamic model based on the current data, and determining the compensation current control strategy according to the control target; Step S345: construct a current control curve using a control algorithm and a compensation strategy.

8. The method according to claim 6, characterized in that The current dynamic model is expressed by the third formula, which is: , where L is the inductance in the electrical parameters, R is the resistance, I is the motor current, E is the back electromotive force, and U is the controller output voltage.

9. The method according to claim 1, characterized in that: Calculating a voltage drop control curve at a specific slope based on the first relationship includes the following steps: Step S21, collecting the current data of the motor in the process of ramp braking in real time, filtering the collected current data, and using a digital signal processor to implement a differential algorithm to calculate the current rising slope; Step S22, establishing an initial mathematical model, determining model parameters of the initial mathematical model according to experimental data and historical operation data, and improving the initial mathematical model based on the model parameters to generate a first mathematical model; Step S23, based on the first mathematical model, determine the mathematical expression of the descent control curve, substitute the rising slope into the mathematical expression, calculate the corresponding voltage value, and generate the descent control curve of the voltage output; Step S24: obtain verification results through simulation verification, and adjust the descent control curve based on the verification results.

10. A control system for a vehicle ramp braking current, used to implement a vehicle ramp braking current control method as claimed in any one of claims 1 to 9, characterized in that: Includes the following modules: A first braking current control module is used to monitor the state of the electric forklift. When the electric forklift is in a slope braking state, the motor controller controls the motor current to reach a first braking current, and also obtains a rising slope of the motor current in a first time period; A descent control curve calculation module, used for performing multiple tests at different slopes, collecting test data and establishing a first mathematical model based on the test data, determining a first relationship between the rising slope and the voltage based on the first mathematical model, and calculating a descent control curve of the voltage at a specific slope based on the first relationship; A compensation current control module is used to obtain the current at zero voltage after the motor controller output voltage is zero, determine the compensation current control curve, and adjust the voltage output to achieve the compensation current; The speed estimation module is used to adjust the voltage output to achieve the compensation current. At the same time, the motor controller also uses a machine learning algorithm to predict the motor speed based on historical data and real-time measurement data. When the motor speed drops to zero, the motor controller turns off the electromagnetic brake, stops current control, and completes the slope braking process.

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