A method and system for controlling braking current of a vehicle on a ramp

Abstract

The present invention belongs to the field of vehicle braking technology, and specifically relates to 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 an electric forklift is in a ramp braking state, and also obtaining the rising slope of the motor current within 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 voltage, and calculating a voltage drop control curve at a specific slope based on the first relationship; after the motor controller output voltage reaches 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. 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 serve as a current limiting function.

Description

Technical Field

[0001] The present 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 vehicle-mounted DC motor applications, controlling the current during ramping requires measuring the vehicle's speed and current. This typically requires installing speed and current sensors and using closed-loop speed and current control. However, to save costs, DC motors often use open-loop control instead of sensors, which can lead to uncontrolled current.

[0003] A similar prior art Chinese patent application, with publication number CN109808507A, provides a comprehensive control system for holding and rolling down a slope suitable for electric forklifts. The control steps are as follows: when the speed detection unit detects that the vehicle speed is zero, the torque estimation unit calculates the current torque; when the torque is greater than a set threshold, the holding control unit is triggered, and the vehicle is stopped on the slope through the electric braking of the motor; when the vehicle's electric braking time exceeds the holding time, the system actively switches to the rolling down control unit; the torque estimation unit calculates the current torque. When the torque is less than a 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 with the torque less than the pre-designed threshold, returning to normal operating mode. However, this application relies on a preset torque threshold to determine whether the vehicle is holding or rolling down a slope, without considering the flexibility of the control strategy.

[0004] A similar prior art is the Chinese patent application with publication number CN113085563A, which discloses a method and device for slow braking of an electric vehicle. The method includes: S1, the inverter is connected to the main motor in a positive direction, the kinetic energy of the electric vehicle is converted into electrical energy through the main motor and inverter in sequence and stored in the power battery. When the power battery reaches a first preset power level, step S2 is implemented; S2, the inverter is connected to the main motor in a reverse direction, the main motor consumes the power of the power battery, and when the power battery drops to a second preset power level, the process returns to step S1. However, this application focuses primarily on energy recovery and does not consider the immediacy and response speed of hill braking.

[0005] Therefore, the present invention provides a method and system for controlling braking current of a vehicle when running on a slope. 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 warehouse trucks, 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 the following method for controlling the braking current of a vehicle when driving on a slope, which is implemented by performing the following steps:

[0008] 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 within a first time period.

[0009] 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;

[0010] 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;

[0011] Step S4: While adjusting the voltage output to achieve the compensation current, 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.

[0012] 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.

[0013] As a preferred technical solution of the present invention, predicting the speed of a motor based on historical data and real-time measured data includes the following steps:

[0014] Step S41: monitoring back electromotive force, which is the voltage generated when the motor rotates and is proportional to the speed of the motor;

[0015] Step S41: collecting historical data of the motor under different operating conditions, including current, voltage, torque, measured speed, acceleration, and temperature, and obtaining real-time data from the motor's sensors;

[0016] 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;

[0017] Step S43: Calculate the correlation coefficient between each feature data and the rotation speed in the historical data, select first feature data whose correlation coefficient is greater than a preset first threshold, use a dimensionality reduction method to reduce the amount of first feature data, train a prediction model using the training data set, and minimize the prediction error by adjusting the model parameters during the training process;

[0018] Step S44: cross-validate the prediction model using the validation data set, evaluate the prediction model using the test data set, and optimize the model parameters based on the evaluation results to obtain the final prediction model;

[0019] Step S45: normalize the real-time data and input it into the prediction model to obtain the predicted speed.

[0020] 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:

[0021] 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;

[0022] Step S432: Based on the magnitude of the eigenvalues, select the eigenvectors corresponding to the first several largest eigenvalues to form a second eigenmatrix;

[0023] Step S433: Project the first feature matrix onto the second feature matrix to obtain a third feature matrix, and use the feature data corresponding to the third feature matrix as the feature data of the final training prediction model.

[0024] 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:

[0025] For each feature in the first feature data, the corresponding mean and standard deviation are calculated, and each feature data is standardized using the first formula, which is: , where Xi is the i-th feature data, μ is the mean, σ is the standard deviation, and Zi is the value after standardization of the i-th feature data.

[0026] 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.

[0027] As a preferred technical solution of the present invention, determining the compensation current control curve includes the following steps:

[0028] Step S31: Before the voltage input drops to zero, the motor controller collects current data of the motor under different working states, where the current data includes the current value when the voltage is zero;

[0029] 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;

[0030] 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;

[0031] Step S34: designing a control strategy, and determining a compensation current control curve based on the second mathematical model and the control strategy.

[0032] 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:

[0033] Step S341: establishing a current dynamic model of the motor according to the electrical parameters of the motor;

[0034] Step S342: Maintaining the current within a predetermined safety range is used as a control target, and a control algorithm is designed to adjust the motor current;

[0035] Step S343: simulating the current dynamics and control algorithm of the motor in software, and optimizing the control parameters based on the simulation results;

[0036] Step S344: collecting current data of the motor under different working conditions, adjusting the current dynamic model based on the current data, and determining a compensation current control strategy according to the control target;

[0037] Step S345: construct a current control curve using a control algorithm and a compensation strategy.

[0038] As a preferred technical solution of the present invention, 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.

[0039] 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:

[0040] Step S21: collecting the current data of the motor in the process of braking on the slope in real time, filtering the collected current data, and calculating the current rising slope by using a differential algorithm implemented by a digital signal processor;

[0041] Step S22: establishing an initial mathematical model, determining model parameters of the initial mathematical model based on experimental data and historical operation data, and improving the initial mathematical model based on the model parameters to generate a first mathematical model;

[0042] Step S23: Determine a mathematical expression of a descent control curve based on the first mathematical model, substitute the rising slope into the mathematical expression, calculate the corresponding voltage value, and generate a descent control curve for the voltage output;

[0043] Step S24: obtain verification results through simulation verification, and adjust the descent control curve based on the verification results.

[0044] The present invention also provides a control system for vehicle ramp braking current, comprising the following modules:

[0045] 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 obtains the rising slope of the motor current within a first time period;

[0046] a descent control curve calculation module, configured to perform multiple tests at different slopes, collect test data, 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 descent control curve of the voltage at a specific slope based on the first relationship;

[0047] The 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;

[0048] 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.

[0049] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0050] 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 controller voltage output descending control curve 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 achieve 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 by switching the control strategy, and then reach the zero pressure moment through the voltage output curve, and then determine the compensation current curve. 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

[0051] Figure 1 This is a flowchart of the steps of a method for controlling braking current of a vehicle on a ramp according to the present invention;

[0052] Figure 2 The output voltage drop control curve of the motor controller at different slopes of the present invention is shown in FIG.

[0053] Figure 3 are different compensation current control curves of the present invention;

[0054] Figure 4 The figure is a structural diagram of a control system for braking current of a vehicle on a slope according to the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0056] It is understood that the terms "first," "second," etc., used herein may be used 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, 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 without departing from the scope of this application.

[0057] In applications where warehouse trucks require ramp braking, the motor controller needs to control the braking current of the travel motor. This requires both controlling the ramp current to prevent damage to the controller due to excessive motor current and ensuring driving comfort.

[0058] Thus, the present invention provides Figure 1 The method for controlling the braking current of a vehicle on a ramp is implemented by executing the following steps:

[0059] Step S1: 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 the rising slope of the motor current in a first time period.

[0060] Specifically, the first time period refers to the time interval from the start of the electric forklift's slope braking 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 motor current changes are 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 controller voltage output drop control curve in subsequent steps. It reflects the speed of current rise and is closely related to the motor's slope performance and braking characteristics. By precisely 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 comfort of vehicle driving.

[0061] 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.

[0062] 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, which 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 the initial value to zero or a specific value as time increases during the slope braking process to control the motor current, such as Figure 2 As shown in the figure, the output voltage drop control curve of the motor controller at different slopes is displayed. 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.

[0063] 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.

[0064] 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 3Different 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 current change 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.

[0065] Step S4: While adjusting the voltage output to achieve the compensation current, 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.

[0066] 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 the 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.

[0067] In actual application, the entire breakaway braking process is divided into two stages. Stage 1: Initiating the first braking current control, the motor is controlled to reach the first braking current. At this time, the current rise slope during this time period is obtained. Based on the current rise slope di / dt, the controller voltage output control curve is calculated. When the controller output voltage reaches zero, the second stage begins: initiating the second braking current control. Based on the current value at zero voltage, the compensation current control curve is determined, and the voltage output is adjusted to achieve the compensation current. The motor speed is also predicted. Once the speed reaches zero, the electromagnetic brake is turned off, and current control is stopped. By switching the control strategy, the vehicle can achieve safe breakaway braking and the current limiting function is realized.

[0068] Furthermore, the motor speed is predicted based on historical data and real-time measured data, including the following steps:

[0069] Step S41: collecting historical data of the motor under different operating conditions, including current, voltage, torque, measured speed, acceleration, and temperature, and obtaining real-time data from the motor's sensors;

[0070] 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;

[0071] Step S43: Calculate the correlation coefficient between each feature data and the rotation speed in the historical data, select first feature data whose correlation coefficient is greater than a preset first threshold, use a dimensionality reduction method to reduce the amount of first feature data, train a prediction model using the training data set, and minimize the prediction error by adjusting the model parameters during the training process;

[0072] Step S44: cross-validate the prediction model using the validation data set, evaluate the prediction model using the test data set, and optimize the model parameters based on the evaluation results to obtain the final prediction model;

[0073] Step S45: normalize the real-time data and input it into the prediction model to obtain the predicted speed.

[0074] Specifically, by deleting outliers in historical data and filling missing values to ensure the integrity and accuracy of the data, the historical data is standardized to facilitate model processing, and the first feature data with a large impact on the rotational speed is calculated by calculating the correlation coefficient, 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, it can improve the efficiency of model training, and evaluate the generalization ability of the model through cross-validation. It can identify and avoid overfitting, 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.

[0075] Furthermore, the dimensionality reduction method is used to reduce the amount of feature data, including the following steps:

[0076] 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;

[0077] Step S432: Based on the magnitude of the eigenvalues, select the eigenvectors corresponding to the first several largest eigenvalues to form a second eigenmatrix;

[0078] Step S433: Project the first feature matrix onto the second feature matrix to obtain a third feature matrix, and use the feature data corresponding to the third feature matrix as the feature data of the final training prediction model.

[0079] Specifically, assuming that the first feature data includes five data features: current, voltage, torque, acceleration, and temperature, the first feature data is subjected to a second normalization process to generate standardized feature data. The specific method of the second normalization 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 , then calculate the eigenvalues and eigenvectors of the first feature matrix, and select the eigenvectors corresponding to the first several largest eigenvalues as principal components according to the size of the eigenvalues. These principal components can explain most of the variance in the data, and use 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, improve the efficiency of model training, and retain the key information in the data.

[0080] Furthermore, performing a second normalization process on the first feature data to generate normalized feature data includes the following steps:

[0081] For each feature in the first feature data, the corresponding mean and standard deviation are calculated, and each feature data is standardized using the first formula, which is: , where Xi is the i-th feature data, μ is the mean, σ is the standard deviation, and Zi is the value after standardization of the i-th feature data.

[0082] Specifically, different features may have different dimensions, such as current, voltage, and temperature. Standardization can eliminate the impact of dimension, placing different features on the same scale for easier comparison and processing. Therefore, the above method standardizes each feature data so that its mean approaches 0 and its standard deviation approaches 1. For machine learning algorithms based on gradient descent, such as linear regression and logistic regression, standardization can improve the convergence rate of the algorithm. When the scales of features are inconsistent, the gradient descent path can be very tortuous, resulting in slow convergence. After standardization, the gradient descent path becomes more direct, thereby accelerating convergence.

[0083] 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.

[0084] 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 the 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.

[0085] Furthermore, determining the compensation current control curve includes the following steps:

[0086] Step S31: Before the voltage output drops to zero, the motor controller collects current data of the motor under different working states, where the current data includes the current value when the voltage is zero;

[0087] 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;

[0088] 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;

[0089] Step S34: designing a control strategy, and determining a compensation current control curve based on the second mathematical model and the control strategy.

[0090] Specifically, determining the compensation current control curve involves determining a new current control strategy, namely the compensation current control curve, to maintain the stability of the motor current after the output voltage of the motor controller drops to zero. Before the voltage output drops to zero, the controller collects current data of the motor under different operating conditions, including the current value at zero voltage. After determining the compensation current control curve using the above method, the current characteristics of the motor at zero voltage are analyzed, including the current magnitude, change trend, and possible load influence. Based on 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 controller output according to the difference between the actual motor current and the target current to compensate for the current change. Using the above mathematical model and control strategy, the compensation current control curve is determined. This curve describes how to maintain the stability of the motor current by adjusting the controller output after the voltage output is zero. The above method can determine the compensation current control curve to ensure that the motor current is still effectively controlled after the voltage output drops to zero, thereby maintaining stable operation of the motor and protecting the motor from damage.

[0091] Furthermore, based on the second mathematical model and the control strategy, a compensation current control curve is determined, including the following steps:

[0092] Step S341: establishing a current dynamic model of the motor according to the electrical parameters of the motor;

[0093] Step S342: Maintaining the current within a predetermined safety range is used as a control target, and a control algorithm is designed to adjust the motor current;

[0094] Step S343: simulating the current dynamics and control algorithm of the motor in software, and optimizing the control parameters based on the simulation results;

[0095] Step S344: collecting current data of the motor under different working conditions, adjusting the current dynamic model based on the current data, and determining a compensation current control strategy according to the control target;

[0096] Step S345: construct a current control curve using a control algorithm and a compensation strategy.

[0097] Specifically, a dynamic model of the motor current is established based on the motor's electrical parameters (resistance R, inductance L). This model can typically be expressed as a differential equation, as shown in the third formula below. Maintaining the current within a predetermined safe range is the control objective. A control algorithm is designed to regulate the motor current. This control algorithm (such as a PID controller) adjusts the motor controller's output voltage based on the difference between the actual current and a reference current. The motor current dynamics and control algorithm are simulated in a software environment (such as MATLAB / SimULink) to verify the effectiveness of the control strategy. Control parameters are optimized based on the simulation results. Experiments are conducted on actual motors to collect current data under different operating conditions. This data is used to adjust and verify the accuracy of the mathematical model. Based on the control objective and the dynamic characteristics of the motor, a compensation current control strategy is determined. This may include static compensation (such as a current cutoff value) and dynamic compensation (such as a time-varying current reference value). Using the control algorithm and compensation strategy, a compensation current control curve is constructed. This curve describes how the controller should adjust its output to maintain current stability at different time points or under different conditions.

[0098] 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.

[0099] Specifically, the current dynamic model, expressed as the third equation above, is also the second mathematical model. This model describes the dynamic behavior of the motor current over time. It is typically a differential equation that accounts for the effects of the motor's electrical parameters (such as resistance and inductance) and back EMF. The current dynamic model is fundamental to determining the compensated current control curve because it provides a mathematical description of how the motor current responds to changes in the controller's output voltage.

[0100] Furthermore, calculating a voltage drop control curve at a specific slope based on the first relationship includes the following steps:

[0101] Step S21: collecting the current data of the motor in the process of braking on the slope in real time, filtering the collected current data, and calculating the current rising slope by using a differential algorithm implemented by a digital signal processor;

[0102] Step S22: establishing an initial mathematical model, determining model parameters of the initial mathematical model based on experimental data and historical operation data, and improving the initial mathematical model based on the model parameters to generate a first mathematical model;

[0103] Step S23: Determine a mathematical expression of a descent control curve based on the first mathematical model, substitute the rising slope into the mathematical expression, calculate the corresponding voltage value, and generate a descent control curve for the voltage output;

[0104] Step S24: obtain verification results through simulation verification, and adjust the descent control curve based on the verification results.

[0105] Specifically, a high-precision current sensor is used to collect the motor's current value in real time during the slope braking process. The collected current data is filtered to eliminate noise and interference to ensure data accuracy. A differential algorithm is implemented using a digital signal processor (DSP), or a microcontroller (MCV). The differential algorithm is calculated using the current rising slope. A suitable mathematical model is selected or developed that can predict the value of voltage U based on the slope K. Common models include linear regression models, polynomial models, or empirically based nonlinear models. The model parameters are determined based on experimental data and historical operating data. Based on the relationship model between slope K and voltage U, the mathematical expression for the voltage output drop control curve is determined. 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 to calculate the corresponding voltage U value, thereby generating 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 hill braking. Based on 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 hill braking current and ensuring vehicle safety and driving comfort.

[0106] According to another aspect of the embodiment of the present invention, Figure 4 As shown, a control system for vehicle braking current when approaching a slope is also provided, comprising a first braking current control module, a descent control curve calculation module, a compensation current control module, and a speed estimation module, for implementing a vehicle braking current control method when approaching a slope as described above. The specific functions of each module are as follows:

[0107] 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 obtains the rising slope of the motor current within a first time period;

[0108] a descent control curve calculation module, configured to perform multiple tests at different slopes, collect test data, 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 descent control curve of the voltage at a specific slope based on the first relationship;

[0109] The 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;

[0110] 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.

[0111] 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 zero voltage, 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. 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 serve as a current limiting function.

[0112] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, 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.

[0113] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The above-described program can be stored in a non-volatile computer-readable storage medium. When executed, the program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein 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. By way of 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 DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0114] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above 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.

[0115] The above embodiments merely represent several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the scope of the present invention, and these modifications and improvements fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be based on the appended claims.

[0116] 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 scope of protection 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 status of the electric forklift. When the electric forklift is in a hill-braking state, the motor controller controls the motor current to reach a first braking current and obtains the rising slope of the motor current within a first time period. The first time period refers to the time interval from the start of the hill-braking state 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 achieve the compensation current, 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; 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 states, where the current data includes 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, designing a control strategy, and determining a compensation current control curve based on the second mathematical model and the control strategy, including: 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 is used as a control target, and a control algorithm is designed to adjust the motor current; Step S343: simulating the current dynamics and control algorithm of the motor in 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 a compensation current control strategy according to the control target; Step S345: construct a current control curve using a control algorithm and a compensation strategy.

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 under different operating 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: Calculate the correlation coefficient between each feature data and the rotation speed in the historical data, select first feature data whose correlation coefficient is greater than a preset first threshold, use a dimensionality reduction method to reduce the amount of first feature data, train a prediction model using the training data set, and minimize 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 model parameters 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 Use dimensionality reduction methods to reduce the number 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 magnitude 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 the feature data corresponding to the third feature matrix as the 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 each feature data is standardized using the first formula, which is: , where Xi is the i-th feature data, μ is the mean, σ is the standard deviation, and Zi is the value after standardization of the i-th feature data.

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 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.

7. 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 braking on the slope in real time, filtering the collected current data, and calculating the current rising slope by using a differential algorithm implemented by a digital signal processor; Step S22: establishing an initial mathematical model, determining model parameters of the initial mathematical model based on 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: Determine a mathematical expression of a descent control curve based on the first mathematical model, substitute the rising slope into the mathematical expression, calculate the corresponding voltage value, and generate a descent control curve for the voltage output; Step S24: obtain verification results through simulation verification, and adjust the descent control curve based on the verification results.

8. A control system for a vehicle's braking current when approaching a slope, used to implement a method for controlling a vehicle's braking current when approaching a slope as claimed in any one of claims 1 to 7, 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 obtains the rising slope of the motor current within a first time period; a descent control curve calculation module, configured to perform multiple tests at different slopes, collect test data, 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 descent control curve of the voltage at a specific slope based on the first relationship; The 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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