Vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction

Abstract

This invention belongs to the field of vehicle control, specifically relating to a vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction. This method pre-analyzes the mapping relationship between the coefficient of adhesion and slip ratio during vehicle operation using road test data under different road conditions, and fits the variation of the error between the vehicle's optimal slip ratio and maximum utilization coefficient of adhesion using interpolation with real-time slope and prediction interval. Then, it collects the current vehicle wheel speed information and uses interpolation to predict the current vehicle's optimal slip ratio and maximum utilization coefficient of adhesion; after slope-based correction and prediction region-based correction, and weighted fusion, it combines the designed cost function with the Cuckoo Catfish optimization algorithm to solve for the method that allows the vehicle's acceleration and slip ratio to follow the expected torque increment and executes the control. This invention solves the problems of insufficient accuracy and large delay in optimal slip ratio prediction in existing electric vehicle longitudinal stability control systems.

Description

Technical Field

[0001] This invention belongs to the field of vehicle control, specifically relating to a vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction, and its corresponding model predictive controller (CCO-MPC) and vehicle. Background Technology

[0002] With the continuous advancement of electronic and intelligent technologies, electric vehicle technology is maturing. Compared to gasoline-powered vehicles, electric vehicles have enormous potential in reducing energy consumption, improving driving safety, and enhancing driving performance. Because electric vehicles use an electric motor-based braking system, rather than a hydraulic or pneumatic braking system, they offer superior real-time performance and rapid response, eliminating hydraulic or mechanical delays that could hinder precise control. Therefore, research on the longitudinal stabilization system of electric vehicles is of great significance.

[0003] The design of the braking longitudinal stability system for electric vehicles mainly involves road type identification and prediction, which can be divided into cause-based methods and response-based methods. Cause-based methods require the measurement of external information such as optical, acoustic, temperature, and humidity. This method offers more accurate predictions but is costly, has poor robustness, and struggles to accurately estimate untested roads. Response-based methods primarily calculate the maximum road adhesion coefficient using a dynamic model during vehicle braking. This approach offers more accurate identification but is difficult to control in real-time and exhibits a certain degree of lag.

[0004] The design of a longitudinal braking stability system essentially involves controlling the vehicle to maximize the utilization of the coefficient of friction, thereby achieving maximum longitudinal braking force under conditions of maximum road surface friction. Achieving maximum road surface friction equates to achieving the optimal slip ratio. By controlling the slip ratio during braking to reach this optimal slip ratio, efficient braking performance can be obtained. Causal methods require excessive measurement data and processing time, making it impossible to obtain the optimal slip ratio at the current moment or a specific point in time, thus lacking real-time capability. Response-based methods, such as maximum road surface friction identification, require the vehicle to have reached or be about to reach the critical state of braking instability, often resulting in a sampling time lag. Therefore, predicting the optimal slip ratio before the vehicle reaches the critical state of braking instability, and thus enabling precise vehicle control, has become a pressing technical challenge for those skilled in the art. Summary of the Invention

[0005] To address the issues of insufficient accuracy and significant delay in the prediction of optimal slip ratio in existing longitudinal stability control of electric vehicles, this invention provides a vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction, along with its corresponding model predictive controller and vehicle.

[0006] This invention is achieved using the following technical solution:

[0007] A vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction includes:

[0008] Based on the test data of the current vehicle under different road conditions, the mapping relationship between the adhesion coefficient and the slip ratio during the vehicle driving process is analyzed, and the slip curves opt1 and opt2 under the conditions of minimum road adhesion coefficient and maximum road adhesion coefficient are fitted.

[0009] The error of the current vehicle's optimal slip ratio and maximum utilization adhesion coefficient under the interpolation method is analyzed in advance as a function of the real-time slope, and the corresponding first error curve is fitted.

[0010] The ratio of real-time slip rate to optimal slip rate is used as the prediction interval. D r The error of the optimal slip ratio and maximum utilization adhesion coefficient of the current vehicle under the interpolation method is analyzed in advance as the prediction interval changes, and the corresponding second error curve is fitted.

[0011] Collect current vehicle wheel speed information and combine it with the vehicle's state-space equations to generate the vehicle's adhesion coefficient in real time. m ( t and slip ratio l ( t ); and fit the real-time slip curve of the current vehicle. m ( l ).

[0012] Interpolation is used based on opt1, opt2, and the real-time slip curve. m ( l Predict the optimal slip ratio for the current vehicle. l best and maximum utilization of adhesion coefficient m max .

[0013] The maximum utilization coefficient of adhesion is determined by combining the first error curve and the second error curve respectively. m max and optimal slip ratio l best Slope-based corrections and prediction region-based corrections are performed; then, the two correction results are weighted and fused to obtain the correction value that maximizes the utilization of the adhesion coefficient. Correction value for optimal slip ratio ;

[0014] according to Calculate the target acceleration Follow with real-time slip rate and real-time acceleration and target acceleration The goal is to achieve longitudinal stability control of the vehicle.

[0015] The present invention also includes a model predictive controller, which includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction as described above, and then performs longitudinal stability control on the vehicle in real time based on the collected vehicle wheel speed information and output torque.

[0016] The present invention also includes a vehicle that employs the model predictive controller as described above.

[0017] The technical solution provided by this invention has the following beneficial effects:

[0018] This invention first discovers the mapping relationship between the error in predicting the optimal slip ratio and maximum utilization coefficient of adhesion based on interpolation and the real-time slope and prediction interval of the real-time slip curve. It then designs a vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction. This method utilizes a first error curve and a second error curve constructed based on road test data to perform error compensation on the optimal slip ratio and maximum utilization coefficient of adhesion generated by the interpolation method, based on slope and prediction interval respectively. Finally, it uses the two compensation results to obtain a fused, higher-precision correction value.

[0019] This invention utilizes a modified optimal slip ratio and maximum utilization coefficient of adhesion to control the longitudinal stability of a vehicle. In the vehicle control strategy of this invention, a corresponding cost function is designed based on the vehicle's real-time acceleration and slip ratio following the target, and the optimal torque adjustment amount is obtained at each time step using the "Catfish" optimization algorithm. Compared with existing solutions, this invention has higher prediction accuracy for the optimal slip ratio and maximum utilization coefficient of adhesion, higher efficiency in solving adjustment commands, and better real-time performance. It can effectively improve the control effect of vehicle longitudinal stability and contribute to improving the driving stability, driving safety, and ride comfort of electric vehicles. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the steps of the vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction in Embodiment 1 of the present invention.

[0021] Figure 2 This invention is illustrated in Example 1 by the mapping relationship between the slip ratio generated from vehicle road test data under different road conditions and the adhesion coefficient.

[0022] Figure 3 The error fitting curves for the optimal sliding rate and maximum utilization of the adhesion coefficient under the interpolation method.

[0023] Figure 4 The curves show the changes in the error of the optimal slip ratio and the error of the maximum utilization of the adhesion coefficient as a function of the real-time slope of the real-time slip curve.

[0024] Figure 5 The curves represent the variation of the error in the optimal slip ratio and the error in the maximum utilization of the adhesion coefficient with the prediction range of the real-time slip curve.

[0025] Figure 6 This is a schematic diagram of the model prediction controller provided in Embodiment 2 of the present invention.

[0026] Figure 7 The linear velocities of the front and rear wheels and the vehicle speed over time were measured to test the control strategy of this invention applied in the experiment.

[0027] Figure 8 The curves showing the change of various slip rates over time for vehicles using the control strategy of this invention in the test experiment.

[0028] Figure 9 The curves showing the change of various adhesion coefficients over time for vehicles using the control strategy of this invention in the test experiment.

[0029] Figure 10 The curves showing the change of motor drive torque and acceleration over time for a vehicle using the control strategy of this invention in a test experiment. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention.

[0031] Example 1

[0032] This embodiment provides a vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction, such as... Figure 1 As shown, it includes the following steps:

[0033] S1: Based on the test data of the current vehicle under different road conditions, analyze the mapping relationship between the adhesion coefficient and the slip ratio during the vehicle's driving process, and fit the slip curves opt1 and opt2 under the conditions of minimum road adhesion coefficient and maximum road adhesion coefficient.

[0034] When any vehicle travels on different road surfaces, the change in the coefficient of adhesion with the slip ratio varies due to differences in vehicle parameters and road condition parameters such as the coefficient of adhesion. The slip ratio includes the slip rate during vehicle driving and the slip rate during vehicle braking. The coefficient of adhesion and slip ratio, along with their relationship, have a significant impact on subsequent longitudinal stability control of the vehicle. To better analyze the mapping relationship between these two parameters under different road conditions, this embodiment analyzes the mapping relationship between the coefficient of adhesion and the slip ratio based on the massive amount of road test data collected during each vehicle testing phase, and plots the corresponding slip-adjustment curve (i.e., the curve showing the change in the coefficient of adhesion with the slip ratio).

[0035] In practical applications, such as Figure 2 As shown, this embodiment stores, on the one hand, the slip interval obtained under the condition of minimum road surface adhesion coefficient, and uses it as the lower bound curve of the space for the current vehicle to utilize the change of adhesion coefficient with the slip rate, denoted as opt1. On the other hand, it stores the slip interval obtained under the condition of maximum road surface adhesion coefficient, and uses it as the upper bound curve of the space for the current vehicle to utilize the change of adhesion coefficient with the slip rate, denoted as opt2.

[0036] In conventional approaches, technicians typically use interpolation to fit a real-time slip curve during vehicle operation based on real-time acquired slip ratios and the coefficient of adhesion. This allows them to determine the optimal slip ratio and maximum coefficient of adhesion for vehicle control decisions. However, combining... Figure 2 It can be observed that the interpolation method approximates the interpolation of the ordinate by assuming a certain ratio, and that this ratio is similar to the ratio at the optimal slip ratio, and then calculates intermediate data. However, this approximation itself contains a certain degree of error. This error will cause the generated predicted data to deviate from the actual data, and ultimately affect the accuracy of the predicted optimal slip ratio and the maximum utilization adhesion coefficient.

[0037] For example, the relationship between the optimal slip ratio points is scaled along the horizontal axis. The conventional interpolation method calculates the vertical coordinates of the two curves by unifying the horizontal axis, and then calculates the result using the ratio of the vertical coordinates; the corresponding approximate relationship is as follows:

[0038] ;

[0039] In the above formula, l opt1 , l opt2 These are the optimal slip ratio values ​​predicted based on opt1 and opt2, respectively. l opt0 The value of the slip ratio is the optimal slip ratio predicted based on the currently fitted real-time slip curve. , Each represents the current time. t The adhesion coefficients corresponding to opt1 and opt2 are as follows; For the current moment t The calculated adhesion coefficient (corresponding to the ordinate value of the real-time slip curve at the current moment) is used.

[0040] Furthermore, based on the above formula, the predicted optimal slip ratio value can be obtained. l opt0 for:

[0041] ;

[0042] However, due to actual geometric characteristics, the point that approximates the optimal slip ratio at real-time should be compensated for on the horizontal axis. This length can be reflected on the corrected vertical axis, and the relationship is as follows:

[0043] ;

[0044] In the above formula, , These are the corrections to the ordinate on opt1 and opt2, respectively; This represents the optimal slip ratio obtained using the modified method.

[0045] In summary, the corrected optimal slip ratio can be obtained. Represented as:

[0046] ;

[0047] contrast l opt0 and The expression clearly shows that there are also differences in the maximum utilization adhesion coefficient, which should be corrected.

[0048] Similarly, the modified maximum utilization coefficient of adhesion It can be represented as:

[0049] ;

[0050] In the above formula, m max1 , m max2 These are the maximum utilization adhesion coefficients predicted based on opt1 and opt2, respectively.

[0051] S2: Pre-analyze the error of the current vehicle's optimal slip ratio and maximum utilization of adhesion coefficient under the interpolation method as a function of the real-time slope, and fit the corresponding first error curve.

[0052] As mentioned earlier, the optimal slip ratio and maximum effective slip ratio predicted using interpolation methods based on real-time updates of the adhesion coefficient and slip ratio contain errors. In practical applications, it is necessary to correct these errors. However, since real-time slip curves are always incomplete in practical applications, the strategy of using the error analysis results described earlier for reverse error correction is not feasible in practice.

[0053] To address this issue, the engineers in this embodiment discovered a correlation between the real-time slope of each point in the updated real-time slip curve and the errors in the maximum utilization coefficient of adhesion and the optimal slip rate predicted by interpolation. Therefore, by using massive amounts of test data, the relationship between the errors in the optimal slip rate and the maximum utilization coefficient of adhesion under the interpolation method and the real-time slope can be established in advance, and a corresponding first error curve can be fitted. Then, the errors in the optimal slip rate and the maximum utilization coefficient of adhesion predicted by the interpolation method are corrected based on this error curve. This novel error correction approach can achieve high-precision error correction based on the real-time slope of the dynamically updated real-time slip curve, rather than the complete curve, overcoming the shortcomings of the original error correction strategy.

[0054] Specifically, in practical applications, the error fitting curves of the optimal sliding ratio and the maximum utilization adhesion coefficient under the interpolation method can be obtained through simulation experiments, as shown in the figure. Figure 3 As shown in the figure. Observation shows that there is a strong correlation between the real-time slope and the error, thus the corresponding first error curve can be fitted. The first error curve finally fitted in this embodiment (the curve of the change of the error of the optimal slip ratio / error of the maximum utilization of the adhesion coefficient as a function of the real-time slope of the real-time slip curve) is shown in the figure. Figure 4 As shown, its expression is as follows:

[0055] ;

[0056] In the above formula, The slope of the slip curve is... k The first error of the optimal slip ratio; The slope of the slip curve is... k The first error that maximizes the utilization of the adhesion coefficient.

[0057] S3: Use the ratio of real-time slippage rate to optimal slippage rate as the prediction interval. D r The error of the optimal slip ratio and maximum utilization adhesion coefficient of the current vehicle under the interpolation method is analyzed in advance as the prediction interval changes, and the corresponding second error curve is fitted.

[0058] In addition to the slope-based error correction strategy described above, those skilled in the art in this embodiment combine... Figure 2It was also found that as the real-time slip rate gets closer to the optimal slip rate, its trend is similar to the slope, and this value can be used to correct the predicted value of the optimal slip rate. Therefore, this embodiment uses the real-time slip rate... l With optimal slip ratio l best The ratio is denoted as the prediction interval. D r ,Right now:

[0059] .

[0060] Building upon this, this embodiment further utilizes massive amounts of vehicle road test data to analyze and determine the prediction interval based on the real-time slip curve. D r There is also a strong correlation between the error of the optimal slip ratio and the error of the maximum utilization of the adhesion coefficient, and this can be used to fit the corresponding second slip curve. Specifically, the second error curve finally fitted in this embodiment (the curve showing the change of the error of the optimal slip ratio or the error of the maximum utilization of the adhesion coefficient as a function of the prediction range of the real-time slip curve) is as follows: Figure 5 As shown, its expression is as follows:

[0061] ;

[0062] In the above formula, The prediction interval for the slip curve is: D r The second error of the optimal slip ratio; Indicates the prediction interval as D r The second error that maximizes the utilization of the adhesion coefficient.

[0063] S4: Collect the current wheel speed information of the vehicle and combine it with the vehicle's state-space equations to generate the vehicle's adhesion coefficient in real time. m ( t and slip ratio l ( t ); and fit the real-time slip curve of the current vehicle. m ( l ).

[0064] The solution provided in this embodiment is an enhanced response-based method. Therefore, this method does not require monitoring a large amount of environmental information that affects the adhesion coefficient and slip rate. Instead, it only needs to acquire vehicle state data such as wheel speed, and then combine the vehicle's state-space equation to determine the vehicle's real-time adhesion coefficient and real-time slip rate.

[0065] Specifically, in this embodiment, a semi-rear-wheel-drive electric vehicle model is selected as the control objective, considering only longitudinal dynamics. The method for establishing the vehicle's state-space equations is as follows:

[0066] Force analysis of the front and rear wheels:

[0067] ;

[0068] In the above formula, I The moment of inertia of the wheel; , These are the angular accelerations of the front and rear wheels, respectively. T r R is the rear wheel torque; r is the wheel radius; F xf , F xr These are the longitudinal forces acting on the front and rear wheels, respectively.

[0069] Vertical force on the rear wheel F zr and utilizing the adhesion coefficient m r It can be represented as:

[0070] ;

[0071] In the above formula, G For vehicle weight; l 1 represents the front wheelbase; m For vehicle quality; h The height of the vehicle's center of gravity; Indicates vehicle acceleration; L This refers to the total length of the vehicle's wheelbase.

[0072] An overall analysis of the vehicle yields the following results:

[0073] ;

[0074] Known slip ratio l The calculation formula is as follows:

[0075] ;

[0076] In the above formula, v Indicates vehicle speed; This is the angular velocity of the rear wheel.

[0077] Because this model is a rear-wheel drive electric vehicle, the front wheels can be considered as driven wheels to reflect vehicle speed. v :

[0078] ;

[0079] In the above formula, This represents the angular velocity of the front wheel.

[0080] Then, slip ratio lThe formula can be rewritten as:

[0081] ;

[0082] Differentiating both sides of the above equation, we obtain the following formula:

[0083] ;

[0084] In the above formula, express l The first derivative.

[0085] Substituting the overall analytical expression, we can obtain:

[0086] ;

[0087] Taking the derivative of the slip ratio formula twice consecutively, we get:

[0088] ;

[0089] In the above formula, express l The second derivative; express The second derivative; express The second derivative of .

[0090] Therefore, under discrete conditions, the current time t The second derivative of the slip ratio It can be represented by the slip ratio values ​​at three consecutive moments as follows:

[0091] ;

[0092] In the above formula, , They are respectively t , t The first derivative of the slip ratio at time -1; , , They are respectively t , t -1、 t Slip rate at time -2; T s The time interval for discrete sampling.

[0093] Combining the two equations, we can obtain:

[0094] ;

[0095] In summary, the state-space equation for the vehicle can be obtained as follows:

[0096]

[0097] In the above formula, a 1. a 2. a 3 represents the three coefficients in the slip ratio term; b 1. b 2. b 3 are the three coefficients in the acceleration term, and they satisfy:

[0098] ;

[0099] ;

[0100] ;

[0101] ;

[0102] ;

[0103] Based on the constructed state-space equations, it can be seen that the adhesion coefficient is calculated using the vehicle's wheel speed information. m The method is as follows:

[0104] ;

[0105] In the above formula, I The moment of inertia of the wheel; This represents the angular acceleration of the rear wheel; r The radius of the wheel; G For vehicle weight; l 1 represents the front wheelbase; m For vehicle quality; h The height of the vehicle's center of gravity; Indicates vehicle acceleration; L This refers to the total length of the vehicle's wheelbase. F zr This indicates the vertical force acting on the rear wheel; F xr This indicates the longitudinal force acting on the rear wheel.

[0106] Calculate the slip ratio based on the vehicle's wheel speed information. l The method is as follows:

[0107] ;

[0108] In the above formula, v Indicates vehicle speed; This indicates the angular velocity of the rear wheel.

[0109] Therefore, this embodiment can update the vehicle's coefficient of adhesion in real time based on the collected wheel speed information. m ( t and slip ratio l ( t Based on this, by using the values ​​of the two parameters as the x-coordinate and y-coordinate respectively, a series of points can be obtained. m , l By fitting these discrete data points, the real-time slip curve of the current vehicle can be obtained. m ( l ).

[0110] S5: Employ interpolation based on opt1, opt2, and the real-time slip curve. m ( l Predict the optimal slip ratio for the current vehicle. l best and maximum utilization of adhesion coefficient m max .

[0111] Calculate the optimal slip ratio of the vehicle using interpolation. l best and maximum utilization of adhesion coefficient m max The method has been introduced above. Specifically, the calculation formulas for both are as follows:

[0112] ;

[0113] In the above formula, m 0 ( t This indicates that the slip curve is based on real-time slip curves. m ( l The adhesion coefficient is determined at the current moment; m 1 ( t () represents the current adhesion coefficient determined according to opt1; m 2 ( t () represents the current adhesion coefficient determined according to opt2; l opt1 and l opt2 These represent the optimal sliding ratios for the lower and upper bound curves, respectively.

[0114] The optimal slip ratio was calculated. l best Then, in the real-time slip curve m ( l In ), the optimal slip ratio l best The corresponding function value is the maximum utilization adhesion coefficient. m max .

[0115] S6: Combine the first and second error curves respectively to determine the maximum utilization of the adhesion coefficient. m max and optimal slip ratio l best Slope-based corrections and prediction region-based corrections are performed; then, the two correction results are weighted and fused to obtain the correction value that maximizes the utilization of the adhesion coefficient. Correction value for optimal slip ratio .

[0116] As mentioned earlier, in this embodiment, the technicians have analyzed the errors in the maximum utilization coefficient of adhesion and the optimal slip ratio obtained by the interpolation method based on massive amounts of road test data, and compared them with the real-time slope of the real-time adhesion-slip curve. k and prediction interval D r The mapping relationship between them was established; and corresponding first and second error functions were pre-constructed. Based on this, the maximum utilization of the adhesion coefficient was determined by combining the first error curve. m max and optimal slip ratio l best The method for slope-based correction is as follows:

[0117] ;

[0118] In the above formula, Indicates based on slope l best The first correction value; Indicates based on slope m max The first correction value.

[0119] Combining the second error curve with the maximum utilization of the adhesion coefficient m max and optimal slip ratio l best The method for making corrections based on the prediction interval is as follows:

[0120] ;

[0121] In the above formula, Indicates based on prediction interval l best The second correction value; Indicates based on prediction interval m max The second correction value.

[0122] Finally, the two correction results are merged to maximize the utilization of the correction value of the adhesion coefficient. Correction value for optimal slip ratio The calculation formula is as follows:

[0123] ;

[0124] In the above formula, h 1 and h 2 represents the weights of the two correction methods in the slip ratio; h 3 and h 4 represents the weights of the two correction methods used in the adhesion coefficient.

[0125] S7: According to Calculate the target acceleration And follow with real-time slip rate and real-time acceleration and Design a cost function for the objective J Find the set of torque increments that minimize the cost function.

[0126] In this embodiment, the correction value for the maximum utilization of the adhesion coefficient can be... This is converted into maximum acceleration and used as the target value for acceleration control. When the maximum adhesion coefficient is reached, the target acceleration is also achieved in this state. (That is, the maximum acceleration), which satisfies the following formula:

[0127] ;

[0128] In the above formula, F x2 This represents the longitudinal force on the rear wheel at maximum acceleration. F z2 This represents the vertical force on the rear wheel corresponding to the maximum acceleration.

[0129] After sorting, we can obtain:

[0130] .

[0131] In this embodiment, the state-space equation can be further rewritten as:

[0132]

[0133] In the above formula, x Indicates the state item. ; u Indicates control items, u = T r ; A , B , C These are the coefficient matrices for the state term, control term, and constant term, respectively. ; ; .

[0134] Discretizing the state-space equations yields:

[0135]

[0136] In the above formula, ; ; .

[0137] Set the prediction step size to P t Control step size set to m t Then design the cost function J The expression is as follows:

[0138] ;

[0139] In the above formula, express t Time prediction t + j Real-time acceleration at any given moment; express t + j The target acceleration at any given moment; express t Time prediction t + j Real-time scroll rate at any given moment; express t + j The optimal slip ratio at any given time; express t + j The torque increment at time t; This represents the weighting coefficient corresponding to the control acceleration term; This represents the weighting coefficient corresponding to the control slip ratio term; This represents the weighting coefficient corresponding to the control torque increment term.

[0140] When the cost function J The optimal control term when the minimum value is obtained. for:

[0141] ;

[0142] In the above formula, the optimal control term is... The set of control variables obtained by solving; this set consists of t time, t +1 time, ..., t + m Increment of control variables at time -1 Composition. Among them, due to... u = T r Therefore, the increment of the control variable in this embodiment is also the change in torque.

[0143] S8: Combine the motor torque of the previous moment with the dynamically updated set of torque increments to generate the motor torque of the next moment, and control the motor output of the vehicle accordingly to achieve longitudinal stability control of the vehicle.

[0144] In practical applications, the control step size in this embodiment is set to 2. At each time step, the torque change for the next time step is calculated, and corresponding control commands are issued to the vehicle control system. The above operation logic is then repeated at the next time step, ensuring that the vehicle's real-time slip rate and real-time acceleration always follow the target slip rate and target acceleration, thereby achieving longitudinal stability of the vehicle. Specifically, the motor torque is generated based on the torque increment set. T tq The expression is as follows:

[0145] ;

[0146] In the above formula, This represents the motor torque at the previous moment; This represents the torque increment at the current moment, obtained by optimizing the algorithm using the cuckoo catfish algorithm. The transmission ratio; or For mechanical efficiency.

[0147] It is important to emphasize that the problem of finding the set of torque increments that minimizes the cost function in step S7 can be solved through iterative optimization. In practical applications, this embodiment uses the Cuckoocatfish optimizer (CCO). The Cuckoocatfish optimizer is a novel metaheuristic algorithm inspired by the search, predation, and parasitism behaviors of cuckoo catfish. This algorithm solves numerical optimization problems by simulating the survival strategies of cuckoo catfish in nature. In the early iterations, the algorithm primarily employs multidimensional surround search and space compression strategies to effectively limit the escape space of the prey (cittafish), ensuring extensive exploration of the solution space. In the middle of the iterations, the algorithm adopts a transition strategy to facilitate a smooth transition from exploration to exploitation. In the later stages of the iterations, the algorithm uses a chaotic predation mechanism to generate perturbations around the optimal solution to enhance the development of the optimal solution. Throughout the optimization process, individual guidance, parasitism, and death mechanisms are also incorporated, allowing individuals to adjust their positions in real time, improving the overall convergence accuracy.

[0148] In this embodiment, the algorithm logic for solving the torque increment set using the cuckoo catfish optimization algorithm is as follows:

[0149] (1) Initialization

[0150] Generate by N An initial population consisting of individuals, individuals in D Uniformly distributed in 3D space. The location of each individual. First, initialize as follows:

[0151]

[0152] In the above formula, For the first i The individual in the first d Positional components in the dimension; Ub d , Lb d The first d Upper and lower bounds of decision variables; rand It is a random number between 0 and 1.

[0153] (2) Exploration stage

[0154] The algorithm updates the position of the cuckoo catfish using a space compression strategy and a surround search strategy.

[0155] In the space-compression strategy, cuckoo catfish populations cooperate to compress the search area in order to more easily capture prey. In the early stages of iteration, individuals collaborate with multiple random individuals in the population to compress the prey's movement space; the position update formula is:

[0156]

[0157] In the above formula, It is the first i The updated position of each individual; X best This is the globally optimal solution; X i For the current individual; X r1 , X r2 , X r3 An individual randomly selected from the population; rd | is a random number that follows a standard normal distribution and is used to describe the strength of cooperation among populations.

[0158] As iterations proceed, the population acquires certain spatial information, at which point more precise compression can be performed by reducing the number of random individuals:

[0159]

[0160] In the above formula, Z2 is a D-dimensional random vector of 0 to 1, used to simulate unpredictable random resistance in space; For the first i The position of each individual after compression.

[0161] The circling search strategy simulates the behavior of organisms circling their prey in spiral and spherical patterns. The spiral circling search strategy uses spiral motion to expand the spatial search range. Position updates are guided by an attraction vector `step`, combined with a spiral coefficient, and the corresponding update formula is as follows:

[0162]

[0163] In the above formula, X e To guide individuals; F Direction factor; R 1 is a D-dimensional random vector; T It is a contraction factor; n The contraction efficiency index; V It is a velocity vector, the calculation method of which depends on the individual aggregation degree. J i With initial degree of aggregation J in Comparison; It This represents the current iteration number. s and c Here is a set of helical coefficients, which are calculated as follows:

[0164]

[0165] In the above formula, a b1 and b1 are constants that control the shape of the spiral; i It is a spiral angle that varies with the individual index.

[0166] Spherical orbit search updates the position by searching around a pre-selected sphere center. (Sphere center) RotX r Randomly select from the top three best individuals and the population mean, and update the formula as follows:

[0167]

[0168] In the above formula, w For adaptive adjustment of the wrapping radius; Rt 1 and Rt 2 represents the rotation angle in D-dimensional space; X q It is a randomly selected operator.

[0169] To achieve a smooth transition between exploration and exploitation, CCO introduces a transition strategy. During this phase, the population is divided into two parts: one part moves towards the global optimum. X best One group moves to enhance utilization, while another moves in the opposite direction to continue exploration. Its update formula incorporates the Lévy flight distribution:

[0170]

[0171] In the above formula, The This is a parameter that limits the distance the vehicle can move. steps 2 is the attraction vector; lev(D) is the D-dimensional Lévy distribution function; v 1 is a random number that follows a normal distribution; β =1.5; R 2 and R 3 is a D-dimensional random vector; r 1. r 2. r 3 represents a sequence of random individuals.

[0172] (3) Utilization stage

[0173] The population employs a chaotic predation strategy during the exploitation phase, and the corresponding position updates are as follows:

[0174]

[0175] In the above formula, Cy and Gs These are the chaos coefficients that simulate the small-scale and large-scale movements of cichlids, respectively. E It is the kinetic energy factor; S The swimming vector is used; this mechanism allows the cuckoo catfish to generate perturbations of varying intensities around its prey as it approaches, thereby improving its ability to develop the optimal solution.

[0176] The death and parasitism strategy employed in the algorithm simulates the laws of natural selection. When an individual fails to find a better food source (i.e., a better solution) in multiple iterations, its risk of death increases. The algorithm determines whether an individual is "dead" based on a decreasing death probability, die. After death, an individual will "regenerate" in one of two ways: either by parasitically generating new individuals near the optimal solution, or by randomly regenerating throughout the search space, thus introducing new diversity into the population and helping the algorithm escape local optima.

[0177] The optimal individual is obtained through the algorithm, and the corresponding solution is the desired solution. That is:

[0178]

[0179] The motor torque is represented by the torque on the wheel as follows:

[0180]

[0181] In the above formula, T tq This refers to the motor torque; The transmission ratio; or For mechanical efficiency.

[0182] Choosing both the prediction step size and the control step size to be 2, and using the first term with the minimum cost function as the control variable adjustment value, the optimized motor torque can be expressed as:

[0183] .

[0184] Example 2

[0185] The present invention also includes a model predictive controller, which includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction as in Embodiment 2, and then performs longitudinal stability control on the vehicle in real time based on the collected vehicle wheel speed information and output torque.

[0186] Specifically, such as Figure 6 As shown, the model prediction controller provided in this embodiment includes a data acquisition unit, an interpolation prediction unit, a slope correction unit, a prediction interval correction unit, a data fusion unit, and a cuckoo catfish solving unit.

[0187] The data acquisition unit acquires wheel speed information in real time and processes the data to obtain the vehicle's current slip ratio and coefficient of adhesion. The interpolation prediction unit uses interpolation to calculate preliminary predictions of the vehicle's optimal slip ratio and maximum coefficient of adhesion based on pre-stored upper and lower bound curves.

[0188] The slope correction unit is used to correct the initial predicted values ​​of the optimal slip ratio and the maximum utilization adhesion coefficient based on the slope using a pre-stored first error curve. The prediction interval correction unit is used to correct the initial predicted values ​​of the optimal slip ratio and the maximum utilization adhesion coefficient based on the prediction interval using a pre-stored second error curve.

[0189] The data fusion unit is used to weight and fuse the results of the slope correction unit and the prediction interval correction unit to obtain the correction values ​​of the optimal slip ratio and the maximum utilization adhesion coefficient.

[0190] The Cuckoo Catfish solver unit is used to calculate the target torque output of the motor to achieve vehicle torque regulation. It first calculates the corresponding target acceleration based on the optimal slip ratio; then, it constructs a state-space equation from the dynamic model, converting it into the variables required for model predictive control; next, it imports angular velocity and angular acceleration values ​​collected by the data acquisition unit and substitutes them into the equation; finally, it uses the Cuckoo Catfish optimization algorithm to minimize the cost function, solving for the motor's target torque, and outputting it to the motor to achieve control.

[0191] This embodiment also provides a vehicle that employs the model predictive controller as described above.

[0192] Simulation Experiment

[0193] To verify the performance of the vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction provided by this invention, technicians conducted simulations and tests on the relevant schemes.

[0194] The simulation experiment was set to the following conditions: the vehicle was brought to a stop by emergency braking from an initial speed of 50 km / h, and the adhesion coefficient was set to 0.8 to 1 and then back to 0.8 for two docking processes.

[0195] Under these conditions, the linear velocities of the front and rear wheels and the vehicle speed over time of a vehicle using the control strategy of this invention are shown in the following curves. Figure 7 As shown in the figure. The curves showing the changes in various slip rates of the vehicle over time are as follows. Figure 8 As shown in the figure, the curves of the vehicle's various adhesion coefficients changing over time are as follows: Figure 9 As shown in the figure, the curves of the vehicle's motor driving torque and acceleration changing over time are as follows: Figure 10 As shown.

[0196] Based on the data in the figure, it can be calculated that under the control strategy of the present invention, the root mean square error (RMSE) of the vehicle's predicted slip rate and optimal slip rate is 0.005385; the mean absolute percentage error (MAPE) is 1.1859%; the root mean square error (RMSE) of the predicted utilization coefficient of adhesion and the maximum utilization coefficient of adhesion is 0.023914; and the mean absolute percentage error (MAPE) is 2.7289%.

[0197] Therefore, the solution of the present invention can achieve more accurate slip rate prediction. Based on this, and using the predicted slip rate and the adhesion coefficient, the present invention also achieves more stable longitudinal control of the vehicle, thereby improving the comfort and safety of the vehicle driving process.

[0198] The above description is only a preferred embodiment of the present invention and is 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 within the protection scope of the present invention.

Claims

1. A vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction, characterized in that, It includes: Based on the test data of the current vehicle under different road conditions, the mapping relationship between the adhesion coefficient and the slip ratio during the vehicle driving process is analyzed, and the slip curves opt1 and opt2 under the conditions of minimum road adhesion coefficient and maximum road adhesion coefficient are fitted. The error of the current vehicle’s optimal slip ratio and maximum utilization of adhesion coefficient under the interpolation method is analyzed in advance, and the first error curve is fitted. The ratio of real-time slip rate to optimal slip rate is used as the prediction interval. D r The error of the current vehicle's optimal slip ratio and maximum utilization adhesion coefficient under the interpolation method is analyzed in advance and the second error curve is fitted. Collect current vehicle wheel speed information and combine it with the vehicle's state-space equations to generate the vehicle's adhesion coefficient in real time. μ ( t ) and slip ratio λ ( t Fit the real-time slip curve of the current vehicle. μ ( λ ); Interpolation is used based on opt1, opt2, and μ ( λ Predict the optimal slip ratio for the current vehicle. λ best and maximum utilization of adhesion coefficient μ max ; The maximum utilization coefficient of adhesion is determined by combining the first error curve and the second error curve respectively. μ max and optimal slip ratio λ best Slope-based corrections and prediction region-based corrections are performed; then, the two correction results are weighted and fused to obtain the correction value that maximizes the utilization of the adhesion coefficient. Correction value for optimal slip ratio ; according to Calculate the target acceleration Follow with real-time slip rate and real-time acceleration and target acceleration The goal is to achieve longitudinal stability control of the vehicle.

2. The vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction as described in claim 1, characterized in that, The expression for the first error curve is as follows: ； In the above formula, The slope of the slip curve is... k The first error of the optimal slip ratio; The slope of the slip curve is... k The first error that maximizes the utilization of the adhesion coefficient; The expression for the second error curve is as follows: ； In the above formula, The prediction interval for the slip curve is: D r The second error of the optimal slip ratio; Indicates the prediction interval as D r The second error that maximizes the utilization of the adhesion coefficient.

3. The vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction as described in claim 2, characterized in that, The coefficient of adhesion is calculated based on the vehicle's wheel speed information. μ The method is as follows: ； In the above formula, T r This refers to the torque of the rear wheels; I The moment of inertia of the wheel; This represents the angular acceleration of the rear wheel; r The radius of the wheel; G For vehicle weight; l 1 represents the front wheelbase; m For vehicle quality; h The height of the vehicle's center of gravity; Indicates vehicle acceleration; L This refers to the total length of the vehicle's wheelbase. F zr This indicates the vertical force acting on the rear wheel; F xr This indicates the longitudinal force acting on the rear wheel; Calculate the slip ratio based on the vehicle's wheel speed information. λ The method is as follows: ； In the above formula, v Indicates vehicle speed; This indicates the angular velocity of the rear wheel.

4. The vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction as described in claim 3, characterized in that, Based on interpolation, using opt1, opt2, and the real-time slip curve μ ( λ Predict the optimal slip ratio for the current vehicle. λ best The calculation formula is: ； In the above formula, μ 0 ( t This indicates that the slip curve is based on real-time slip curves. μ ( λ The adhesion coefficient is determined at the current moment; μ 1 ( t () represents the current adhesion coefficient determined according to opt1; μ 2 ( t () represents the current adhesion coefficient determined according to opt2; λ opt1 and λ opt2 These represent the optimal slip ratios for the lower bound curve opt1 and the upper bound curve opt2, respectively. Real-time slip curve μ ( λ In ), the optimal slip ratio λ best The corresponding function value is the maximum utilization adhesion coefficient. μ max .

5. The vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction as described in claim 4, characterized in that, Combining the first error curve with the maximum utilization of the adhesion coefficient μ max and optimal slip ratio λ best The method for slope-based correction is as follows: ； In the above formula, Indicates based on slope λ best The first correction value; Indicates based on slope μ max The first correction value; Combining the second error curve with the maximum utilization of the adhesion coefficient μ max and optimal slip ratio λ best The method for making corrections based on the prediction interval is as follows: ； In the above formula, Indicates based on prediction interval λ best The second correction value; Indicates based on prediction interval μ max The second correction value; Correction value for maximum utilization of adhesion coefficient Correction value for optimal slip ratio The calculation formula is as follows: ； In the above formula, h 1 and h 2 represents the weights of the two correction methods in the slip ratio; h 3 and h 4 represents the weights of the two correction methods used in the adhesion coefficient.

6. The vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction as described in claim 5, characterized in that, according to Calculate the target acceleration of the vehicle The method is as follows: ； Follow with real-time slip rate and real-time acceleration and target acceleration To achieve vehicle longitudinal stability control, the methods include: Follow with real-time slip rate and real-time acceleration and Design a cost function for the objective J The set of torque increments that minimizes the cost function is obtained by using the cuckoo catfish optimization algorithm. The motor torque at the next moment is generated by combining the motor torque at the previous moment with the dynamically updated set of torque increments, and the motor output of the vehicle is controlled accordingly to achieve longitudinal stability control of the vehicle.

7. The vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction as described in claim 6, characterized in that, The cost function J The expression is as follows: ； In the above formula, express t Time prediction t + j Real-time acceleration at any given moment; express t + j The target acceleration at any given moment; express t Time prediction t + j Real-time scroll rate at any given moment; express t + j The optimal slip ratio at any given time; express t + j The torque increment at time t; P t Indicates the observation step size; m t Indicates the control step size; This represents the weighting coefficient corresponding to the control acceleration term; This represents the weighting coefficient corresponding to the control slip ratio term; This represents the weighting coefficient corresponding to the control torque increment term.

8. The vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction as described in claim 5, characterized in that, Generating motor torque based on torque increment set T tq The expression is as follows: ； In the above formula, This represents the motor torque at the previous moment; This represents the torque increment at the current moment, obtained by optimizing the algorithm using the cuckoo catfish algorithm. The transmission ratio; η For mechanical efficiency.

9. A model prediction controller, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the vehicle longitudinal stability control method based on optimal slip ratio fusion compensation prediction as described in any one of claims 1-8, and then performs longitudinal stability control on the vehicle in real time based on the collected vehicle wheel speed information and output torque.

10. A vehicle, characterized in that: It employs the model prediction controller as described in claim 9.

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