Intelligent control method of power steering

Abstract

The present invention relates to the field of automobile technology, and specifically to an intelligent control method for power steering, which aims to enhance the steering accuracy and control stability of a vehicle under various driving environments and with different driver preferences; by combining the driver's preferences, driving mode selection, and vehicle status data (such as vehicle mass, steering wheel angle, hydraulic oil pressure, flow, etc.), the adjustment strategy of the power motor speed is optimized, thereby improving the power steering characteristics of the power steering system, thereby improving driving performance and safety.

Description

Technical Field

[0001] The invention relates to the field of automobile technology, and in particular to an intelligent control method for power steering. Background Art

[0002] Traditional electric hydraulic power steering systems usually rely on fixed power devices, that is, the power provided is fixed, which is difficult to adapt to different driving conditions and driver needs. However, modern adaptive power steering systems should be able to dynamically adjust the power size according to real-time data to provide the best driving experience. In order to achieve this goal, the present invention proposes a method for intelligently controlling power steering speed regulation based on multi-sensor information. Summary of the invention

[0003] In view of the above problems, an embodiment of the present invention provides a power steering intelligent control method.

[0004] An embodiment of the present invention provides a power steering intelligent control method, comprising:

[0005] S1, obtaining road condition information and driving mode information selected by the driver, and selecting a target speed map from a pre-drawn speed map according to the obtained information, wherein the speed map is a three-dimensional coordinate relationship diagram between the vehicle's driving speed, angular acceleration, and the power assist motor speed;

[0006] S2, obtaining the vehicle's driving speed and angular acceleration, and determining the basic speed of the power steering system's power motor in combination with the target speed map;

[0007] S3, obtaining various variables representing the vehicle state and the power steering system state, and using each of the obtained variables to calculate a corrected power steering motor speed;

[0008] S4. Perform weighted fusion on the basic speed and each corrected power-assisting motor speed to obtain the optimal power-assisting motor speed, and use the optimal power-assisting motor speed to adjust the working state of the power-assisting motor.

[0009] Optionally, road condition information is determined based on the GPS positioning and navigation module, including: expressways, urban roads and country roads; driving mode information includes: sports mode, standard mode and comfort mode; each combination of road condition information and each driving mode information corresponds to a speed map.

[0010] Optionally, in S3, various variables representing the vehicle state and the power steering system state are acquired in real time, and a process of calculating a corrected power steering motor speed using each acquired variable includes:

[0011] Get the actual weight W of the vehicle and calculate the power assist F based on the actual weight W z:

[0012] F z =k z W

[0013] Among them, k z is the vehicle weight coefficient;

[0014] Based on the calculation of the actual weight of the power assist, the corrected power assist motor speed n1 is:

[0015]

[0016] Among them, C z Represents the power assistance response coefficient.

[0017] Optionally, in S3, various variables representing the vehicle state and the power steering system state are acquired in real time, and a process of calculating a corrected power steering motor speed using each acquired variable includes:

[0018] Get the actual steering wheel angle δ, and calculate the corrected power-assisted motor speed n2 under the influence of the actual steering wheel angle δ:

[0019] n2=n fo +k f (δ-δ fo ) i

[0020] Among them, n fo Indicates the current power assist motor speed, k f Indicates the influence coefficient of the actual steering wheel angle on the power-assist motor speed, δ fo It represents the reference steering wheel angle and the corresponding steering wheel return position. i is the nonlinear coefficient, which determines the influence of the angle change on the power-assisted motor speed.

[0021] Optionally, in S3, various variables representing the vehicle state and the power steering system state are acquired in real time, and a process of calculating a corrected power steering motor speed using each acquired variable includes:

[0022] Get the pressure and flow rate Q of the hydraulic oil in the power steering system c data;

[0023] During vehicle steering, when the displacement of the hydraulic pump in the power steering system is constant, there is the following relationship between the steering torque generated by the hydraulic cylinder and the power motor speed of the power motor:

[0024]

[0025] Q c =n yy q p

[0026] M zy =ΔP·ALη

[0027]

[0028] Among them, n yy is the basic speed corresponding to the steering torque of the hydraulic system, M zy represents the power-assisting torque, K represents the coefficient, A represents the effective working area of ​​the hydraulic cylinder, L represents the effective length of the steering rod acting on the tire knuckle arm, η represents the efficiency of the power-assisting steering system, q represents the displacement of the hydraulic pump, and C q represents the valve clearance flow coefficient, ρ represents the density of the power steering oil, ΔP represents the pressure difference on both sides of the power steering hydraulic cylinder, and S represents the clearance between the valve core and the valve sleeve;

[0029] Set hydraulic oil flow Q c and influence coefficient V fl The relationship is:

[0030] Q c =K fl (V fl -V0)

[0031] Combining the above formulas, we can finally get the influence coefficient V fl The calculation relationship is:

[0032]

[0033] Among them, K fl Indicates the calibration coefficient, V0 indicates the set reference value;

[0034] Using the exponential function to represent the nonlinear relationship, the corrected power-assisted motor speed n3 of the hydraulic pressure flow sensor on the power-assisted motor speed is expressed as:

[0035]

[0036] Among them, k yz is the coefficient. When the pressure of the hydraulic oil changes within a fixed time period, the pressure increases, k yz Taking a negative value, the pressure decreases, k yz Take a positive value.

[0037] Optionally, in S3, various variables representing the vehicle state and the power steering system state are acquired in real time, and a process of calculating a corrected power steering motor speed using each acquired variable includes:

[0038] Get the temperature of the power steering system power motor θ d ;

[0039] Based on the acquired temperature θ d, determine the corrected power-assisted motor speed n4 based on the influence of the power-assisted motor temperature:

[0040]

[0041] Among them, n do Indicates that at the reference temperature θ d0 The reference assist motor speed under d0 Indicates the reference temperature, k dw Indicates the operating safety factor of the power assist motor.

[0042] Optionally, in S3, various variables representing the vehicle state and the power steering system state are acquired in real time, and a process of calculating a corrected power steering motor speed using each acquired variable includes:

[0043] The oil temperature T of the hydraulic oil in the power steering system is obtained. Based on the basic principle that the lower the oil temperature, the higher the speed of the steering oil pump, and the higher the oil temperature, the lower the speed of the steering oil pump, the corrected power steering motor speed n5 under the influence of oil temperature is obtained:

[0044]

[0045] Among them, n 1w >n 2w >n 3w .

[0046] Optionally, in S3, various variables representing the vehicle state and the power steering system state are acquired in real time, and a process of calculating a corrected power steering motor speed using each acquired variable includes:

[0047] In order to more accurately describe the behavior of the power steering system, the voltage and current data are used to affect the speed of the power steering motor, and a compensation factor is introduced to balance the influence of various parameters to obtain the corrected power steering motor speed n6 under the influence of voltage and current data:

[0048] n6=A n e -rP (B n V+C n I)+PD n

[0049] Among them, A n It indicates the speed of the power steering motor when there is no pressure in the power steering system, r indicates the degree coefficient of the influence of pressure on the speed of the power steering motor, P indicates the pressure value in the power steering system, B n The coefficient that indicates the influence of voltage on the speed of the patented motor, C n The coefficient that indicates the influence of current on the speed of the power assist motor, D n Indicates the additional influence coefficient of pressure on the power assist motor speed.

[0050] Optionally, in S4, the process of weighted fusion of the basic speed and each corrected power-assisting motor speed to obtain the optimal power-assisting motor speed includes:

[0051] Get the historical error data for each variable i ;

[0052] Use a sliding window method to estimate the variance of each variable:

[0053]

[0054] Where N represents the size of the sliding window, that is, the number of the most recent N historical error data used to estimate the variance of the variable, e i (tk) represents the error value of the i-th variable at time;

[0055]

[0056] μ i That is, it represents the average value of the most recent N historical error data;

[0057] Based on the calculated variance, the weight of the corrected power assist motor speed corresponding to the variable is updated:

[0058]

[0059] Determine the weight corresponding to the basic speed based on the sum of the various weights being 1;

[0060] The optimal power assist motor speed n is obtained by weighted summing the basic speed, each modified power assist motor speed and the corresponding weights. f .

[0061] Optionally, also include:

[0062] Construct a training dataset:

[0063] Each data sample in the training data set includes input data constructed with various variables and basic speeds, and expected data with the optimal assist motor speed as the expected value;

[0064] Construct BP neural network:

[0065] A three-layer network model with two hidden layers and one output layer was constructed. The first hidden layer contained 10 neurons, and the second hidden layer contained 5 neurons. The training functions of the two hidden layers were TRAINLM, the adaptive learning function was LEARNGDM, the performance evaluation function was MSE, and the transfer function was TANSIG.

[0066] BP neural network training:

[0067] Input the input data in the data template into the constructed BP neural network to obtain the output speed of the BP neural network, and use the error between the output speed and the expected data to adjust the parameters of the BP neural network to complete the training process;

[0068] Power steering intelligent control:

[0069] Determine the basic speed and obtain various variables that characterize the vehicle state and the power steering system state, input the basic power-steering speed and various variables into the trained BP neural network to obtain the optimal power-steering motor speed, and use the optimal power-steering motor speed to adjust the working state of the power-steering motor.

[0070] Compared with the prior art, the beneficial effects of the present invention are: it aims to enhance the steering accuracy and control stability of the vehicle in various driving environments and under different driver preferences; by combining the driver's preferences, driving mode selection, and vehicle status data (such as vehicle mass, steering wheel angle, hydraulic oil pressure, flow, etc.), the adjustment strategy of the power-assisted motor speed is optimized, thereby improving the power-assisted characteristics of the power-assisted steering system, thereby improving driving performance and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of the present application, and do not constitute a limitation of the present invention. In the drawings:

[0072] Figure 1 A schematic flow chart of a power steering intelligent control method provided by an embodiment of the present invention;

[0073] Figure 2 A schematic diagram of a speed map provided by an embodiment of the present invention;

[0074] Figure 3 A BP neural network schematic diagram provided by an embodiment of the present invention;

[0075] Figure 4 A BP neural network structure diagram provided by an embodiment of the present invention;

[0076] Figure 5 The fitting result graph of the BP neural network provided in the embodiment of the present invention, wherein the Training subgraph represents the fitting result graph between the output value of the training set and the expected value, the Validation subgraph represents the fitting result graph of the validation set, the Test subgraph represents the fitting result graph of the test set, and the All subgraph represents the fitting result graph of the comprehensive performance of all data. DETAILED DESCRIPTION

[0077] 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 embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0078] See also Figure 1 , an embodiment of the present invention provides a power steering intelligent control method, comprising:

[0079] S1. Obtaining road condition information and driving mode information selected by the driver, and selecting a target speed map from a pre-drawn speed map according to the acquired information, wherein the speed map is a three-dimensional coordinate relationship diagram between vehicle speed, angular acceleration, and power assist motor speed.

[0080] In the implementation, the road condition information is determined according to the GPS positioning navigation module, including: expressways, urban roads and country roads; the driving mode information includes: sports mode, standard mode and comfort mode; each combination of road condition information and each driving mode information corresponds to a speed map, and the specific combination examples are shown in Table 1, and refer to Figure 2 , which is the speed map of the sports mode under highway conditions.

[0081] Table 1. Speed ​​map corresponding to combination mode

[0082]

[0083] In Table 1, light assistance provides the least assistance, and heavy assistance provides the greatest assistance. In addition, in extreme weather conditions, the power steering system provides the minimum assistance to increase driving safety.

[0084] S2. Obtain the vehicle's running speed and angular acceleration, and determine the basic speed of the power steering system's power motor in combination with the target speed map.

[0085] During implementation, the vehicle condition information, road information collection, and the mode selected by the driver can be selected from the set speed map to locate the target map, and the vehicle speed sensor and steering angle sensor can be used to monitor the vehicle's driving speed and steering angle acceleration in real time, and the basic power-assisting speed of the power-assisting motor can be calculated in combination with the target map. In extreme weather conditions, the vehicle system selection mode is turned off, and the appropriate power-assisting parameters can be matched according to the working conditions provided by the positioning and navigation system.

[0086] S3, obtaining various variables representing the vehicle state and the power steering system state, and using each obtained variable to calculate a corrected power steering motor speed.

[0087] In implementation, S3 can be divided into the following situations:

[0088] Case 1: Get the actual weight W of the vehicle and calculate the power F based on the actual weight W z :

[0089] F z =k z W

[0090] Among them, k z is the vehicle weight coefficient;

[0091] Based on the calculation of the actual weight of the power assist, the corrected power assist motor speed n1 is:

[0092]

[0093] Among them, C z Represents the power assistance response coefficient.

[0094] Specifically, for heavier vehicles, such as SUVs, the power steering system needs to provide more power to allow the driver to control the steering wheel more easily. For lighter vehicles, such as small cars, the power demand is lower because the load on the wheels and steering system is relatively small; in implementation, the on-board weighing sensor can be used to obtain the actual weight of the vehicle. The on-board weighing sensor is based on the Wheatstone bridge principle and calculates the output voltage value. The bridge resistance when the bridge is balanced is R1, R2, R3, and R4. When the weighing sensor is affected by the weight of the object, the resistance values ​​of R1 and R3 increase, and the resistance values ​​of R2 and R4 decrease. The bridge resistance value is unbalanced, which will produce a voltage difference, which can be expressed by the formula:

[0095]

[0096] Among them, U d is the bridge output voltage, E is the bridge power supply voltage;

[0097] The bridge output voltage U d Converted to the actual weight W of the vehicle, we get:

[0098]

[0099] Among them, A is the sensitivity of the sensor, and B is the rated output voltage (full scale) of the sensor.

[0100] Case 2: Get the actual steering wheel angle δ and calculate the corrected power-assisted motor speed n2 under the influence of the actual steering wheel angle δ:

[0101] n2=n fo +k f (δ-δ fo ) i

[0102] Among them, n fo Indicates the current power assist motor speed, k f Indicates the influence coefficient of the actual steering wheel angle on the power-assist motor speed, δ fo It represents the reference steering wheel angle and the corresponding steering wheel return position. i is the nonlinear coefficient, which determines the influence of the angle change on the power-assisted motor speed.

[0103] Case 3: The hydraulic pressure flow sensor can be used to obtain the pressure and flow Q of the hydraulic oil in the power steering system c data;

[0104] During vehicle steering, the power steering torque generated by the power steering system is related to the pressure difference on both sides of the hydraulic cylinder, the effective working area of ​​the hydraulic cylinder and other factors. When the displacement of the hydraulic pump in the power steering system is constant, the steering torque generated by the hydraulic cylinder and the power motor speed of the power steering motor have the following relationship:

[0105]

[0106] Q c =n yy q p

[0107] M zy =ΔP·ALη

[0108]

[0109] Among them, n yy is the basic speed corresponding to the steering torque of the hydraulic system, M zy represents the power-assisting torque, K represents the coefficient, A represents the effective working area of ​​the hydraulic cylinder, L represents the effective length of the steering rod acting on the tire knuckle arm, η represents the efficiency of the power-assisting steering system, q represents the displacement of the hydraulic pump, and C q represents the valve clearance flow coefficient, ρ represents the density of the power steering oil, ΔP represents the pressure difference on both sides of the power steering hydraulic cylinder, and S represents the clearance between the valve core and the valve sleeve;

[0110] Set hydraulic oil flow Q c and influence coefficient V fl The relationship is:

[0111] Q c =K fl (V fl -V0)

[0112] Combining the above formulas, we can finally get the influence coefficient V fl The calculation relationship is:

[0113]

[0114] Among them, K fl Indicates the calibration coefficient, V0 indicates the set reference value;

[0115] Using the exponential function to represent the nonlinear relationship, the corrected power-assisted motor speed n3 of the hydraulic pressure flow sensor on the power-assisted motor speed is expressed as:

[0116]

[0117] Among them, k yz is the coefficient. When the pressure of the hydraulic oil changes within a fixed time period, the pressure increases, k yz Taking a negative value, the pressure decreases, k yz Take a positive value, for example, if the pressure of the hydraulic oil increases within 10 minutes, k yz Takes negative value.

[0118] Case 4: The temperature sensor can be used to obtain the temperature of the power steering system power motor θ d ;

[0119] The power steering system can adjust the power steering motor speed according to the actual temperature of the power steering motor to ensure the best performance of the power steering motor under different temperature conditions. When the motor temperature exceeds the safety threshold, the power steering system will automatically reduce the motor speed to prevent overheating. Based on this, the temperature θ d , determine the corrected power-assisted motor speed n4 based on the influence of the power-assisted motor temperature:

[0120]

[0121] Among them, n do Indicates that at the reference temperature θ d0 The reference assist motor speed under d0 Indicates the reference temperature, k dw Indicates the operating safety factor of the power assist motor.

[0122] Case 5: The oil temperature sensor can be used to obtain the oil temperature T of the hydraulic oil in the power steering system. Based on the basic principle that the lower the oil temperature, the higher the speed of the steering oil pump, and the higher the oil temperature, the lower the speed of the steering oil pump, the corrected power steering motor speed n5 under the influence of oil temperature is obtained:

[0123]

[0124] Among them, n 1w >n 2w >n 3w .

[0125] Case 6: The greater the pressure of the power steering system, the lower the speed of the power steering motor is expected to be to reduce the power output. Therefore, in order to more accurately describe the behavior of the power steering system, the voltage and current data are used to affect the speed of the power steering motor, and a compensation factor is introduced to balance the influence of various parameters to obtain the corrected power steering motor speed n6 under the influence of voltage and current data:

[0126] n6=A n e -rP (B n V+C n I)+PD n

[0127] Among them, A n It indicates the speed of the power steering motor when there is no pressure in the power steering system, r indicates the degree coefficient of the influence of pressure on the speed of the power steering motor, P indicates the pressure value in the power steering system, B n The coefficient that indicates the influence of voltage on the speed of the patented motor, C n The coefficient that indicates the influence of current on the speed of the power assist motor, D n Indicates the additional influence coefficient of pressure on the power assist motor speed.

[0128] It should be noted here that the type and number of variables used to determine the correction of the power-assisted motor speed can be replaced or optimized according to the calculation requirements. For example, the driver status feedback can be optimized. The driver status feedback can be determined based on the driving time and can be divided into five levels, namely extremely poor status, ordinary status, good status, very good status, and excellent status, and the corresponding correction of the power-assisted motor speed increases in sequence.

[0129] S4. Perform weighted fusion on the basic speed and each corrected power-assisting motor speed to obtain the optimal power-assisting motor speed, and use the optimal power-assisting motor speed to adjust the working state of the power-assisting motor.

[0130] In practice, in order to dynamically adjust the weights, it is necessary to regularly evaluate the performance of the variables collected by each sensor, so the historical error data of each variable can be obtained. i ;

[0131] e i =n i -n true

[0132] Among them, n i Represents the historical data of the variable, n true Indicates the true value corresponding to the historical data of the variable;

[0133] Use a sliding window method to estimate the variance of each variable:

[0134]

[0135] Where N represents the size of the sliding window, that is, the number of the most recent N historical error data used to estimate the variance of the variable, e i (tk) represents the error value of the i-th variable at time;

[0136]

[0137] μ i That is, it represents the average value of the most recent N historical error data;

[0138] Based on the calculated variance, the weight of the corrected power assist motor speed corresponding to the i-th variable is updated:

[0139]

[0140] Determine the weight corresponding to the basic speed based on the sum of the various weights being 1;

[0141] The optimal power assist motor speed n is obtained by weighted summing the basic speed, each modified power assist motor speed and the corresponding weights. f .

[0142] During implementation, in order to accurately and quickly control the speed of the power-assisted motor, improve efficiency, and meet the real-time and accuracy of the system, the BP neural network can be used to complete multi-sensor data fusion to obtain the optimal power-assisted motor speed.

[0143] Specifically, they include:

[0144] Construct a training dataset:

[0145] Each data sample in the training data set includes input data constructed with various variables and basic speeds, and expected data with the optimal assist motor speed as the expected value;

[0146] The input signal of the BP neural network is set to 7 sensor signals, and the output is a single output of the desired speed, expressed as:

[0147]

[0148] in, They represent the actual weight, actual turning angle, pressure flow, temperature, voltage and current, driver status feedback and basic power-assisted speed under the kth group of data samples respectively; g k It represents the optimal power assist motor speed for k groups of expected outputs.

[0149] Construct BP neural network:

[0150] A three-layer network model with two hidden layers and one output layer was constructed. The first hidden layer contained 10 neurons, and the second hidden layer contained 5 neurons. The training functions of the two hidden layers were TRAINLM, the adaptive learning function was LEARNGDM, the performance evaluation function was MSE, and the transfer function was TANSIG.

[0151] At the same time, set the input S of the hidden layer of the BP neural network k and output B k :

[0152]

[0153] Among them, q is the number of neurons in the hidden layer.

[0154] Set the input L of the output layer of the BP neural network k And the actual speed output Y k They are:

[0155]

[0156] Set the network weights W and V between the input layer and the hidden layer, and between the output layer and the hidden layer as:

[0157]

[0158] Set the thresholds of the hidden layer and output layer to θ and γ respectively:

[0159]

[0160] BP neural network training:

[0161] Input the input data in the data template into the constructed BP neural network to obtain the output speed of the BP neural network, and use the error between the output speed and the expected data to adjust the parameters of the BP neural network to complete the training process;

[0162] See also Figure 3 ,The training of three-layer BP neural network includes two processes: pattern forward propagation and error back propagation process;

[0163] 1) Pattern forward propagation process:

[0164] Including calculating the input s of the hidden layer j and output b j :

[0165]

[0166] b j =f1(s j )

[0167] Calculate the input l and output y of the output layer:

[0168]

[0169] y=f2(l)

[0170] 2) Error back propagation process

[0171] According to the given expected optimal assist motor speed, calculate the correction error d of the output layer:

[0172] d=(gy)f2'(l)

[0173] Calculate the correction error e of the hidden layer j :

[0174] e j =v j df1'(s j )

[0175] Then, according to whether the expected optimal boost motor speed output signal mean square error setting condition is met, that is, E≤ε, it is judged whether the accuracy of the BP neural network meets the actual demand, where:

[0176]

[0177] Finally, the gradient descent method is used to calibrate the network weights and thresholds of the output layer and hidden layer respectively:

[0178]

[0179] Among them, a and β are learning rates, 0.01<α, β<0.8.

[0180] Power steering intelligent control:

[0181] Determine the basic speed and obtain various variables that characterize the vehicle state and the power steering system state, input the basic power-steering speed and various variables into the trained BP neural network to obtain the optimal power-steering motor speed, and use the optimal power-steering motor speed to adjust the working state of the power-steering motor.

[0182] Example

[0183] The network model was established using MATLAB software. Figure 4 As shown, the network model contains a three-layer network model with two hidden layers and one output layer. The first hidden layer contains 10 neurons, the second hidden layer contains 5 neurons, the training functions of the two hidden layers are TRAINLM, the adaptive learning function is LEARNGDM, the performance evaluation function is MSE, and the transfer function is TANSIG;

[0184] The orthogonal design scheme is used to establish the sample data in the training data set. As shown in Table 2, each variable can be divided into three levels, and the orthogonal results are shown in Table 3;

[0185] Table 2. Variables and levels of orthogonal experiment

[0186] level 1 2 3 Factor A Vehicle weight (kg) 1000 2000 3000 Factor B Steering wheel position angle (°) -540 0 540 Factor C Controller input current (A) 10 60 110 Factor D Pressure sensor(Mpa) 2 6 10 Factor E Maximum temperature of electric oil pump (℃) -20 40 100 Factor F Fatigue Physiological Identification 1 (very bad condition) 3 (Good condition) 5 (Excellent condition) Factor G Motor speed in the original map 1000 3000 5000

[0187] Table 3. Design and structure of orthogonal experiment

[0188]

[0189]

[0190] The orthogonal test data includes 27 groups of data. Because the dimensions of each variable are not uniform, the maximum and minimum method can be used for dimensionless processing before training. The dimensionless processed data is input into the BP neural network model for training. The training results are as follows: Figure 5 shown; according to Figure 5 It can be seen that the training results basically coincide with the 45-degree line, the output value is basically consistent with the target value, and the R value is 0.99655, indicating that the network training effect is good; in addition, as shown in the verification data in Table 4, the maximum error between the training result and the actual result is 9.88, which once again proves that the network accuracy is high after training.

[0191] Table 4. Verification data

[0192]

[0193]

[0194] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A power steering intelligent control method, characterized in that: include: S1, obtaining road condition information and driving mode information selected by the driver, and selecting a target speed map from a pre-drawn speed map according to the obtained information, wherein the speed map is a three-dimensional coordinate relationship diagram between the vehicle's driving speed, angular acceleration, and the power assist motor speed; S2, obtaining the vehicle's driving speed and angular acceleration, and determining the basic speed of the power steering system's power motor in combination with the target speed map; S3, obtaining various variables representing the vehicle state and the power steering system state, and using each of the obtained variables to calculate a corrected power steering motor speed; S4. Perform weighted fusion on the basic speed and each corrected power-assisting motor speed to obtain the optimal power-assisting motor speed, and use the optimal power-assisting motor speed to adjust the working state of the power-assisting motor.

2. The power steering intelligent control method according to claim 1, characterized in that: The road condition information is determined according to the GPS positioning and navigation module, including: expressways, urban roads and country roads; the driving mode information includes: sports mode, standard mode and comfort mode; each combination of road condition information and each driving mode information corresponds to a speed map.

3. The power steering intelligent control method according to claim 1, characterized in that: In S3, various variables representing the vehicle state and the power steering system state are obtained in real time, and a process of correcting the power steering motor speed is calculated using each variable obtained, including: Get the actual weight W of the vehicle and calculate the power assist F based on the actual weight W z : F z =k z W Among them, k z is the vehicle weight coefficient; Based on the calculation of the actual weight of the power assist, the corrected power assist motor speed n1 is: Among them, C z Represents the power assistance response coefficient.

4. The power steering intelligent control method according to claim 1, characterized in that: In S3, various variables representing the vehicle state and the power steering system state are obtained in real time, and a process of correcting the power steering motor speed is calculated using each variable obtained, including: Get the actual steering wheel angle δ, and calculate the corrected power-assisted motor speed n2 under the influence of the actual steering wheel angle δ: n2=n fo +k f (d-d) fo ) i Among them, n fo Indicates the current power assist motor speed, k f Indicates the influence coefficient of the actual steering wheel angle on the power-assist motor speed, δ fo It represents the reference steering wheel angle and the corresponding steering wheel return position. i is the nonlinear coefficient, which determines the influence of the angle change on the power-assisted motor speed.

5. The power steering intelligent control method according to claim 1, characterized in that: In S3, various variables representing the vehicle state and the power steering system state are obtained in real time, and a process of correcting the power steering motor speed is calculated using each variable obtained, including: Get the pressure and flow rate Q of the hydraulic oil in the power steering system c data; During vehicle steering, when the displacement of the hydraulic pump in the power steering system is constant, there is the following relationship between the steering torque generated by the hydraulic cylinder and the power motor speed of the power motor: Q c =n yy q p M zy =ΔP·ALη Among them, n yy is the basic speed corresponding to the steering torque of the hydraulic system, M zy represents the power-assisting torque, K represents the coefficient, A represents the effective working area of ​​the hydraulic cylinder, L represents the effective length of the steering rod acting on the tire knuckle arm, η represents the efficiency of the power-assisting steering system, q represents the displacement of the hydraulic pump, and C q represents the valve clearance flow coefficient, ρ represents the density of the power steering oil, ΔP represents the pressure difference on both sides of the power steering hydraulic cylinder, and S represents the clearance between the valve core and the valve sleeve; Set hydraulic oil flow Q c and influence coefficient V fl The relationship is: Q c =K fl (V fl -V0) Combining the above formulas, we can finally get the influence coefficient V fl The calculation relationship is: Among them, K fl Indicates the calibration coefficient, V0 indicates the set reference value; Using the exponential function to represent the nonlinear relationship, the corrected power-assisted motor speed n3 of the hydraulic pressure flow sensor on the power-assisted motor speed is expressed as: Among them, k yz is the coefficient. When the pressure of the hydraulic oil changes within a fixed time period, the pressure increases, k yz Taking a negative value, the pressure decreases, k yz Take a positive value.

6. The power steering intelligent control method according to claim 1, characterized in that: In S3, various variables representing the vehicle state and the power steering system state are obtained in real time, and a process of correcting the power steering motor speed is calculated using each variable obtained, including: Get the temperature of the power steering system power motor θ d ; Based on the acquired temperature θ d , determine the corrected power-assisted motor speed n4 based on the influence of the power-assisted motor temperature: Among them, n do Indicates that at the reference temperature θ d0 The reference assist motor speed under d0 Indicates the reference temperature, k dw Indicates the operating safety factor of the power assist motor.

7. The power steering intelligent control method according to claim 1, characterized in that: In S3, various variables representing the vehicle state and the power steering system state are obtained in real time, and a process of correcting the power steering motor speed is calculated using each variable obtained, including: The oil temperature T of the hydraulic oil in the power steering system is obtained. Based on the basic principle that the lower the oil temperature, the higher the speed of the steering oil pump, and the higher the oil temperature, the lower the speed of the steering oil pump, the corrected power steering motor speed n5 under the influence of oil temperature is obtained: Among them, n 1w >n 2w >n 3w .

8. The power steering intelligent control method according to claim 1, characterized in that: In S3, various variables representing the vehicle state and the power steering system state are obtained in real time, and a process of correcting the power steering motor speed is calculated using each variable obtained, including: In order to more accurately describe the behavior of the power steering system, the voltage and current data are used to affect the speed of the power steering motor, and a compensation factor is introduced to balance the influence of various parameters to obtain the corrected power steering motor speed n6 under the influence of voltage and current data: n6=A n And -rP (B n V+C n I)+PD n Among them, A n It indicates the speed of the power-assisted motor when there is no pressure in the power-assisted steering system. r indicates the degree of influence of pressure on the speed of the power-assisted motor. P indicates the pressure value in the power-assisted steering system. B n The coefficient that indicates the influence of voltage on the speed of the patented motor, C n The coefficient that indicates the influence of current on the speed of the power assist motor, D n Indicates the additional influence coefficient of pressure on the power assist motor speed.

9. The power steering intelligent control method according to claim 1, characterized in that: In S4, the process of weighted fusion of the basic speed and each corrected power-assisting motor speed to obtain the optimal power-assisting motor speed includes: Get the historical error data for each variable i ; Use a sliding window method to estimate the variance of each variable: Where N represents the size of the sliding window, that is, the number of the most recent N historical error data used to estimate the variance of the variable, e i (tk) represents the error value of the i-th variable at time; μ i That is, it represents the average value of the most recent N historical error data; Based on the calculated variance, the weight of the corrected power assist motor speed corresponding to the variable is updated: Determine the weight corresponding to the basic speed based on the sum of the various weights being 1; The optimal power assist motor speed n is obtained by weighted summing the basic speed, each modified power assist motor speed and the corresponding weights. f .

10. The power steering intelligent control method according to claim 1, characterized in that: Also includes: Construct a training dataset: Each data sample in the training data set includes input data constructed with various variables and basic speeds, and expected data with the optimal assist motor speed as the expected value; Construct BP neural network: A three-layer network model with two hidden layers and one output layer was constructed. The first hidden layer contained 10 neurons, and the second hidden layer contained 5 neurons. The training functions of the two hidden layers were TRAINLM, the adaptive learning function was LEARNGDM, the performance evaluation function was MSE, and the transfer function was TANSIG. BP neural network training: Input the input data in the data template into the constructed BP neural network to obtain the output speed of the BP neural network, and use the error between the output speed and the expected data to adjust the parameters of the BP neural network to complete the training process; Power steering intelligent control: Determine the basic speed and obtain various variables that characterize the vehicle state and the power steering system state, input the basic power-steering speed and various variables into the trained BP neural network to obtain the optimal power-steering motor speed, and use the optimal power-steering motor speed to adjust the working state of the power-steering motor.

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