A rule-based method and system for optimizing longitudinal ride comfort at the end of vehicle braking

Abstract

This invention relates to a rule-based method and system for optimizing longitudinal ride comfort at the end of vehicle braking. The method comprises: determining the state equation of the vehicle's longitudinal vibration system at the end of braking based on the LuGre tire model; establishing a rule-based vehicle braking end-of-braking controller based on the state equation of the vehicle's longitudinal vibration system at the end of braking; optimizing the control parameters of the vehicle braking end-of-braking controller using a gray wolf particle swarm optimization algorithm to obtain an optimized vehicle braking end-of-braking controller; and embedding the optimized vehicle braking end-of-braking controller into the vehicle brake control unit (BCU) to control changes in vehicle braking pressure. This invention can be widely applied in the field of vehicle braking.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vehicle braking, in particular to a rule-based longitudinal smoothness optimization method and system for vehicle braking at the end of braking. BACKGROUND

[0002] With the increase of the number of automobiles, the congestion of cities is getting worse, and the frequent start-stop working conditions of vehicles can make the comfort of passengers worse. The longitudinal vibration in the process of optimizing vehicle braking can improve the comfort of passengers and reduce the phenomenon of nausea and dizziness. Studies have shown that the human body is more sensitive to horizontal vibration than to vertical vibration. According to the relevant test evaluation results, the smoothness at the end of braking is obviously worse than that at other stages. Therefore, the end of braking is the key stage affecting the smoothness of the braking process, and improving the smoothness at the end of braking is of great significance to improving the smoothness of the entire braking process.

[0003] At present, most of the optimization methods considering longitudinal smoothness adopt the method of limiting the longitudinal acceleration and impact degree in the control process, but the above method does not analyze the vehicle dynamics and tire response characteristics in the braking process in detail, and is not applicable to the control of longitudinal smoothness in non-emergency braking conditions with large acceleration. Therefore, it is a better choice to improve the smoothness of the vehicle in the braking process by analyzing the tire characteristics and longitudinal vibration of the vehicle at the end of braking. SUMMARY

[0004] In view of the above problems, the purpose of the present application is to provide a rule-based longitudinal smoothness optimization method and system for vehicle braking at the end of braking, which can analyze the vehicle dynamics and tire response characteristics in the braking process in detail.

[0005] To achieve the above-mentioned purpose, the present application adopts the following technical solutions: in a first aspect, a rule-based longitudinal smoothness optimization method for vehicle braking at the end of braking is provided, comprising:

[0006] Based on the LuGre tire model, the state equation of the vehicle longitudinal vibration system at the end of braking is determined;

[0007] Based on the state equation of the vehicle longitudinal vibration system at the end of braking, a rule-based vehicle braking controller at the end of braking is established;

[0008] The control parameters of the vehicle braking controller at the end of braking are optimized by using a gray wolf particle swarm hybrid algorithm, and an optimized vehicle braking controller at the end of braking is obtained;

[0009] The optimized vehicle braking controller at the end of braking is embedded into the vehicle braking controller BCU to control the change of the vehicle braking pressure.

[0010] Further, the state equation of the vehicle longitudinal vibration system at the end of braking is:

[0011]

[0012] where x is the state vector; u is the control input; f(x) is the nonlinear term; A, B, D are the corresponding coefficient matrices; wherein:

[0013] x = [x1 x2 x3 x4 x5] T = [v z tf z tr v rf v rr ] T

[0014] u = [T bf T br ] T

[0015] where T bf is the braking torque of the front wheel; T br is the braking torque of the rear wheel; v is the vehicle body mass center speed, z tf is the tire rubber internal friction state of the front wheel; z tr is the tire rubber internal friction state of the rear wheel; v rf is the relative speed of the front wheel, v rr is the relative speed of the rear wheel.

[0016] Further, the state equation of the vehicle longitudinal vibration system at the end of braking is established, and a rule-based vehicle braking end controller is established, comprising:

[0017] ① Select a speed threshold as the trigger condition of control, determine whether the speed of the vehicle at the end of braking exceeds the speed threshold, if it exceeds, go to step ②;

[0018] ② Based on the control input u = [k1T bf0 , k2T br0 ] of the controller, the longitudinal vibration control is carried out on the tire at the end of braking, wherein k1, k2 are the braking torque reduction ratios of the front wheel and the rear wheel, T bf0 , T br0 are the front axle and rear axle braking torques before the controller is triggered;

[0019] ③ Determine whether the pressure reduction duration of the vehicle exceeds the pre-set pressure reduction duration threshold, if it exceeds, go to step ⑤; otherwise, go to step ④;

[0020] ④ Use the absolute values of the vibration acceleration and its rate of change of the vehicle to determine whether the vehicle needs to be re-pressurized, if it needs, go to step ⑤; otherwise, go to step ③;

[0021] V. re-pressurizing in the braking system of the vehicle to obtain a control input u = [T bf0 ,T br0 ]] of the controller.

[0022] Further, the grey wolf particle swarm hybrid algorithm is used to optimize the control parameters of the controller at the end of the vehicle braking to obtain an optimized controller at the end of the vehicle braking, comprising:

[0023] a series of particle swarm positions are randomly generated within a value range, the vehicle speed is initialized, and the maximum iteration number of the particle swarm algorithm, the maximum iteration number of the grey wolf algorithm and the population quantity are given;

[0024] the fitness of each particle is calculated;

[0025] the grey wolf particle swarm hybrid algorithm is used to determine the optimal parameter combination according to the fitness of each particle, and the optimized controller at the end of the vehicle braking is obtained based on the optimal parameter combination, wherein the parameter combination comprises the optimal particle value and the optimal population value.

[0026] Further, the grey wolf particle swarm hybrid algorithm is used to determine the optimal parameter combination according to the fitness of each particle, and the optimized controller at the end of the vehicle braking is obtained based on the optimal parameter combination, comprising:

[0027] the optimal particle value and the optimal population value are determined according to the fitness of each particle, that is, the particle position with the minimum fitness in each particle and population, and the particle position is the parameter combination to be optimized;

[0028] the initial positions of the first three particle positions with the minimum fitness are recorded as the initial positions of the alpha wolf, the beta wolf and the delta wolf respectively;

[0029] the positions of the alpha wolf, the beta wolf and the delta wolf are updated, and it is determined whether the maximum iteration number of the grey wolf algorithm is reached, if yes, the final position of the alpha wolf is determined; otherwise, the positions of the alpha wolf, the beta wolf and the delta wolf are updated again;

[0030] the particle positions are recalculated according to the final position of the alpha wolf, and the optimal particle value and the optimal population value are updated;

[0031] if the root mean square error of the prediction result of the particle swarm algorithm is lower than a pre-set threshold value or the maximum iteration number is reached, the optimal parameter combination is determined as the updated optimal particle value and the optimal population value, and the optimized controller at the end of the vehicle braking is obtained based on the optimal parameter combination.

[0032] Further, the updated wolf swarm position is:

[0033]

[0034]

[0035]

[0036]

[0037] wherein X(k) is the position of the wolf group after the kth optimization; B α , B β , B δ are the distances between the alpha wolf, the beta wolf, the delta wolf and the rest of the wolves, respectively; X α (k+1), X β (k+1), X δ (k+1) are the positions of the alpha wolf, the beta wolf, the delta wolf after the k+1th optimization, respectively; A j and D j (j=1, 2, 3) are coefficient vectors, and j is the coefficient number.

[0038] Further, the particle optimal value p besti and the population optimal value g best are updated based on the following formulas:

[0039]

[0040] P i (k+1) = P i (k) + v i (k+1)

[0041] wherein P i (k) and v i (k) are the position and the speed of the particle i after the kth iteration, respectively; c1, c2, c3 are learning rates, respectively, and r1, r2, r3 are random numbers in the range of (0, 1) and do not interfere with each other; w is a nonlinear change strategy.

[0042] In a second aspect, a rule-based vehicle braking final stage longitudinal smoothness optimization system is provided, comprising:

[0043] a state equation determination module configured to determine a state equation of a vehicle longitudinal vibration system in a braking final stage based on a LuGre tire model;

[0044] a controller establishment module configured to establish a rule-based vehicle braking final stage controller based on the state equation of the vehicle longitudinal vibration system in the braking final stage;

[0045] a parameter optimization module configured to optimize control parameters of the vehicle braking final stage controller by using a grey wolf particle swarm hybrid algorithm to obtain an optimized vehicle braking final stage controller;

[0046] The embedded module is used for embedding the optimized vehicle braking end stage controller into a vehicle brake controller BCU to control the change of vehicle brake pressure.

[0047] In a third aspect, a processing device is provided, comprising computer program instructions, wherein the computer program instructions are used to implement the steps corresponding to the rule-based vehicle braking end stage longitudinal smoothness optimization method when the processing device executes the computer program instructions.

[0048] In a fourth aspect, a computer readable storage medium is provided, wherein the computer readable storage medium stores computer program instructions, and the computer program instructions are used to implement the steps corresponding to the rule-based vehicle braking end stage longitudinal smoothness optimization method when a processor executes the computer program instructions.

[0049] The present application has the following advantages due to the above technical solutions:

[0050] 1. The LuGre tire model is used to obtain the state equation of the vehicle longitudinal vibration system in the braking end stage, a rule-based controller is established, and the key control parameters are optimized through the grey wolf particle swarm hybrid algorithm, so that the smoothness in the braking end stage is improved, and a new way is provided for improving the comfort of passengers.

[0051] 2. The time domain of the longitudinal smoothness optimization method adopted in the present application is the braking end stage which has the greatest impact on longitudinal smoothness, and is not the entire braking process, so it is more targeted and effective, and can reflect the braking intention of the driver to the greatest extent.

[0052] In summary, the present application can be widely applied in the field of vehicle braking. BRIEF DESCRIPTION OF DRAWINGS

[0053] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a description of preferred embodiments, and are not meant to limit the present application. Throughout the drawings, the same reference numerals are used for the same components. In the drawings:

[0054] Figure 1 is a schematic diagram of the overall flow of the method provided by an embodiment of the present application;

[0055] Figure 2 is a schematic diagram of the flow of the grey wolf particle swarm algorithm optimization control parameter provided by an embodiment of the present application. DETAILED DESCRIPTION

[0056] Exemplary embodiments of the present application will be described herein below with reference to the accompanying drawings. While exemplary embodiments of the present application are illustrated, it is to be understood that the present application is not limited to the embodiments described herein, which are presented as examples. Rather, the present application is intended to cover all alternatives, modifications, and equivalents of the embodiments as will be apparent to those skilled in the art upon reading this description. Rather, the present application is intended to cover all alternatives, modifications, and equivalents of the embodiments as will be apparent to those skilled in the art upon reading this description.

[0057] It is to be understood that the terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises", "comprising", "includes", "including" and "has" are inclusive and therefore specify the presence of stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order in which they are described, unless specifically identified as an order dependent step. It is also to be understood that additional or alternative steps can be employed.

[0058] Since the conventional control method cannot solve the problem of the high nonlinearity of the tire and the complex multi-objective optimization control in the braking final stage, the rule-based vehicle braking final stage longitudinal smoothness optimization method and system provided in the embodiments of the present application are mainly proposed for the influence of vehicle longitudinal vibration on the smoothness of the automobile. Based on actual driving experience, a rule-based controller is established in combination with the braking process, and the control parameter optimization is performed through the grey wolf particle swarm algorithm, so as to improve the smoothness in the braking final stage.

[0059] Embodiment 1

[0060] As shown in Figure 1 The present embodiment provides a rule-based vehicle braking final stage longitudinal smoothness optimization method, which comprises the following steps:

[0061] 1) Based on the LuGre tire model, the state equation of the vehicle longitudinal vibration system in the braking final stage is determined.

[0062] Specifically, the braking final stage refers to the process from the vehicle approaching to stop to the relative motion between the tire and the ground being static due to the friction. In the braking final stage, the vehicle body has a certain "rebound" and vibration under the action of the suspension and the tire, although the tire and the ground are relatively static due to the friction. When studying the smoothness of the braking state, the vehicle body cannot be directly connected with the wheel as in the ordinary braking model, and the suspension, tire and vehicle body system need to be remodeled and analyzed based on the vehicle longitudinal vibration mechanism.

[0063] Further, since the transient process of the tire needs to be considered when describing the critical state of the vehicle during braking, the LuGre model is introduced to describe the low-frequency resonance state switching of the tire in the critical state of braking. The LuGre model is a dynamic friction model introduced into the interaction between the tire and the ground, and its equation is as follows:

[0064]

[0065]

[0066]

[0067] wherein F x is the friction force on the tire; F z is the vertical load of the tire; z t is the average elastic deformation of the tire; v r is the relative speed of the tire; σ0, σ1, σ2 are the stiffness coefficient, the damping coefficient and the relative viscous damping coefficient of the tire, respectively; θ t is the road friction coefficient; k is the tire ground pressure distribution coefficient; ω is the wheel angular velocity; R is the wheel radius; g(v r ) is the positive slip function; μ c is the Coulomb friction coefficient; μ s is the static friction coefficient; v s is the Stribeck speed; α is the steady-state friction characteristic index; e is the natural constant.

[0068] The state equation of the longitudinal vibration system of the vehicle at the end of braking based on the LuGre tire model is as follows:

[0069]

[0070] wherein x is the state vector; u is the control input; f(x) is the nonlinear term; A, B, D are the corresponding coefficient matrices.

[0071] Specifically, in formula (4):

[0072] x = [x1 x2 x3 x4 x5] T = [v z tf z tr z rf z rr ] T (5)

[0073] u = [T bf T br ] T (6)

[0074] In the formula, x1, x2, x3, x4, and x5 represent parameters v and z, respectively. tf z tr v rf v rr ;T bf T is the braking torque of the front wheels; br v is the braking torque of the rear wheels; v is the velocity of the vehicle's center of gravity; z is the braking torque of the rear wheels. tf This refers to the internal friction state of the front tire rubber; z tr This refers to the internal friction state of the rear tire rubber; v rf v is the relative speed of the front wheels. rr Let be the relative speed of the rear wheels, and:

[0075] v rf =r·w f -v (7)

[0076] v rr =r·w r -v (8)

[0077] In the formula, r is the radius of the wheel; w f w is the wheel speed of the front wheels. r This refers to the wheel speed of the rear wheel.

[0078] Specifically, in formula (4):

[0079]

[0080]

[0081]

[0082]

[0083] In the formula, m is the total vehicle mass; F xf F is the vertical force exerted on the ground by the front wheel. zr J is the vertical force on the ground applied to the rear wheel. wr This is the moment of inertia of the tire.

[0084] 2) such as Figure 1 As shown, a rule-based vehicle braking end-of-braking controller is established based on the state equation of the vehicle longitudinal vibration system at the end of braking to improve the ride comfort at the end of braking.

[0085] 2.1) Select the velocity threshold v re As a trigger condition for control, it is determined whether the vehicle's speed v at the end of braking exceeds a speed threshold v. re If it exceeds the limit, proceed to step 2.2).

[0086] 2.2) Control input u = [k1T] based on the controllerbf0 k2T br0 [This refers to] longitudinal vibration control of the tires at the end of vehicle braking, where k1 and k2 are the reduction ratios of braking torque of the front and rear wheels, respectively, and T... bf0 T br0 These are the front and rear axle braking torques before the controller is triggered.

[0087] Specifically, based on experience and techniques from actual braking processes, at the end of braking, the pressure is reduced, causing the sprung and unsprung masses of the vehicle to separate, increasing the tire's natural frequency and moving it away from low-frequency states, thus moving it away from the human-sensitive area. Furthermore, the tire stiffness-damping system, which has accumulated a certain amount of energy, can release this energy through slight tire rotation, via internal tire friction and potential energy release, as well as friction between the wheel and braking pairs. This significantly reduces the vehicle's longitudinal vibration, thereby improving longitudinal comfort. Therefore, this invention uses this control concept to control the longitudinal vibration of the tires at the end of braking, at which point the braking torque of the front and rear wheels is reduced to k1 and k2 times their original values, respectively.

[0088] 2.3) Determine the duration Δt of the vehicle's decompression. low Does it exceed the preset decompression duration threshold t? re If the number exceeds the limit, proceed to step 2.5; otherwise, proceed to step 2.4.

[0089] 2.4) Use the absolute values ​​of the vehicle's vibration acceleration and its rate of change to determine whether the vehicle needs to be recharged. If so, proceed to step 2.5); otherwise, proceed to step 2.3).

[0090] 2.5) Re-pressurize the vehicle's braking system to obtain the controller input u = [T] bf0 ,T br0 This is to restore the vehicle to its pre-decompression pressure level.

[0091] Specifically, during braking, the braking pressure should not remain consistently low. This would cause a sudden sensation for the driver during braking and reduce vehicle safety. Therefore, after longitudinal vibration control, the braking system needs to be repressurized to restore the vehicle to its pre-depressurization pressure level. The pressure recovery condition considers two aspects: firstly, repressurization is possible when the controlled variable, namely longitudinal vibration acceleration, reaches a relatively small value, meaning it has little impact on the human body. This situation is determined using the absolute value of the vibration acceleration and its rate of change; that is, when… If |a x | re Then repressurize, where a x For the longitudinal acceleration of the vehicle,​ is the rate of change of the longitudinal acceleration of the vehicle re is a preset judgment threshold. Through repeated verification, a re = 0.2 m / s 2 On the other hand, it needs to be considered from the safety point of view. In the braking working condition, if the low pressure state of the brake system lasts for too long, it will affect the safety of the vehicle and also interfere with the driver's judgment of the state of the vehicle. Therefore, a time threshold t re is needed. When Δt iow ≥ t re (Δt tow is the duration of pressure reduction), the brake system will automatically re-pressurize.

[0092] 3) A grey wolf particle swarm hybrid algorithm is used to optimize the control parameters of the vehicle brake end controller, so as to obtain an optimized vehicle brake end controller and improve the smoothness of the vehicle at the brake end.

[0093] In the process of optimizing the control of the longitudinal smoothness of the vehicle at the brake end, the speed threshold v re , the brake torque reduction ratios k1 and k2, and the pressure reduction duration threshold t re all need to be set manually, and it is difficult to adjust them to the optimal accuracy through multiple manual parameter adjustments. Therefore, the grey wolf particle swarm hybrid algorithm is used to optimize the control parameters, and the optimal parameters of the controller are obtained through iterative optimization, as shown in FIG. 3, and the specific steps are as follows: Figure 2

[0094] 3.1) Randomly generate a series of particle swarm positions within the value range wherein i is the number of each particle, the initial vehicle speed v i is given; and the maximum iteration number i max of the particle swarm algorithm and the maximum iteration number k max of the grey wolf algorithm, the population number n, the learning rates c1, c2 and c3, and the inertia factors w1 and w2 are given.

[0095] 3.2) Calculate the fitness f(P i ) of each particle.

[0096] Specifically, since the purpose of parameter optimization is to obtain the optimal longitudinal smoothness of the vehicle at the brake end as much as possible, the influence of the body pitch motion on the smoothness also needs to be considered. Therefore, the particle fitness function is set to be the sum of the weighted root mean square value of the longitudinal acceleration a x of the vehicle at the brake end and the weighted root mean square value of the pitch angle acceleration a θ under the control parameters, that is, the particle fitness function f(P i ) is: ​

[0097]

[0098] In the formula, T is the total control time; s is the complex variable in the transfer function; β is the weighting coefficient; W x (s) and W θ (s) represent the longitudinal acceleration a x and pitch acceleration a θ The frequency-weighted filtering function is expressed in the following form:

[0099]

[0100]

[0101] 3.3) Based on the fitness f(P) of each particle i Determine the optimal value p of the particle. besti and the population optimum g best That is, the fitness f(P) of each particle and the population. i The position of the particle with the smallest value is the parameter combination to be optimized, and the fitness f(P) is set accordingly. i The positions of the three smallest particles are denoted as the initial wolf pack positions X for α wolf, β wolf, and δ wolf, respectively. α X β X δ .

[0102] 3.4) Update the wolf pack position according to the following formula (16) to obtain the updated wolf pack position X(k+1):

[0103]

[0104] In the formula, X(k) represents the wolf pack position after the k-th optimization; B α B β B δ X represents the distances between α wolf, β wolf, δ wolf and the other wolves, respectively; α (k+1)X β (k+1)X δ (k+1) represent the positions of wolf α, wolf β, and wolf δ after the (k+1)th optimization; A j and D j (j = 1, 2, 3) are all coefficient vectors, where j is the coefficient index, and:

[0105] A j =2ar1-a (17)

[0106] D j =2r² (18)

[0107] wherein r1, r2 are random numbers in the range of (0, 1) and do not interfere with each other; is a convergence factor, k max is the maximum number of iterations.

[0108] 3.5) Determine whether the maximum number of iterations k of the grey wolf algorithm is reached max , if yes, determine the final position X of the alpha wolf α , and go to step 3.6); otherwise, go to step 3.4).

[0109] 3.6) Recalculate the particle position and update the particle optimal value p α and the population optimal value g besti based on the following formulas (19) and (20) according to the final position X of the alpha wolf best :

[0110]

[0111] P i (k+1) = P i (k) + v i (k+1) (20)

[0112] wherein P i (k) and v i (k) are the position and velocity of the particle i after iteration k times; c1, c2, c3 are learning rates respectively, r1, r2, r3 are random numbers in the range of (0, 1) and do not interfere with each other; w is a nonlinear variation strategy adopted, the value of w is large in the initial stage and the search ability is strong, and when it approaches the vicinity of the optimal solution, the value of w is reduced and the search of small step is started, and the updating formula of w is:

[0113]

[0114] wherein w1, w2 are the maximum and minimum values of the inertia factor respectively; k is the current iteration coefficient; i max is the maximum number of iterations.

[0115] 3.7) If the root mean square error of the prediction result of the particle swarm algorithm is lower than the pre-set threshold or the maximum number of iterations i max is reached, determine the updated particle optimal value p besti and the population optimal value g best as the optimal particle position, i.e. the optimal parameter combination, and based on the optimal parameter combination, obtain the optimized vehicle braking terminal controller; otherwise, go to step 3.3).

[0116] 4) Embed the optimized vehicle braking terminal controller into the vehicle braking controller BCU to control the change of the vehicle braking pressure.

[0117] Embodiment 2

[0118] The embodiment provides a rule-based vehicle braking end longitudinal smoothness optimization system, comprising:

[0119] A state equation determination module is configured to determine a state equation of a vehicle longitudinal vibration system at a braking end based on a LuGre tire model.

[0120] A controller establishment module is configured to establish a rule-based vehicle braking end controller based on the state equation of the vehicle longitudinal vibration system at the braking end.

[0121] A parameter optimization module is configured to optimize control parameters of the vehicle braking end controller by using a grey wolf particle swarm hybrid algorithm, to obtain an optimized vehicle braking end controller.

[0122] An embedding module is configured to embed the optimized vehicle braking end controller into a vehicle braking controller BCU to control changes in vehicle braking pressure.

[0123] The system provided by the embodiment is used for executing the above-mentioned method embodiments, and specific processes and detailed contents are referred to the above-mentioned embodiments, which will not be described herein.

[0124] Embodiment 3

[0125] The embodiment provides a processing device corresponding to the rule-based vehicle braking end longitudinal smoothness optimization method provided in the embodiment 1, and the processing device can be a processing device for a client, such as a mobile phone, a notebook computer, a tablet computer, a desktop computer, etc., to execute the method of the embodiment 1.

[0126] The processing device comprises a processor, a memory, a communication interface and a bus, the processor, the memory and the communication interface are connected through the bus to complete the communication between each other. The memory stores a computer program capable of running on the processing device, and the processing device runs the computer program to execute the rule-based vehicle braking end longitudinal smoothness optimization method provided in the embodiment 1.

[0127] In some implementations, the memory can be a high-speed random access memory (RAM), and can also include a non-volatile memory, such as at least one disk memory.

[0128] In other implementations, the processor can be a central processing unit (CPU), a digital signal processor (DSP) or various types of general-purpose processors, which are not limited herein.

[0129] Embodiment 4

[0130] The embodiment provides a computer program product corresponding to the rule-based vehicle braking end period longitudinal smoothness optimization method provided in the embodiment 1. The computer program product can include a computer readable storage medium, which is loaded with computer readable program instructions for executing the rule-based vehicle braking end period longitudinal smoothness optimization method described in the embodiment 1.

[0131] The computer readable storage medium can be a tangible device that maintains and stores instructions for use by an instruction execution device. The computer readable storage medium can be, for example but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0132] The above-mentioned embodiments are only used for describing the present application, and the structure, connection mode and manufacturing process of each component can be changed. Any equivalent transformation and improvement on the basis of the technical scheme of the present application should not be excluded from the protection scope of the present application.

Claims

1. A rule-based method for optimizing longitudinal ride quality at the end of braking of a vehicle, characterized in that, The application relates to a vehicle braking controller BCU, and relates to a vehicle braking controller BCU based on a grey wolf particle swarm hybrid algorithm. The application comprises the following steps: determining a state equation of a vehicle longitudinal vibration system at a braking end stage based on a LuGre tire model; establishing a rule-based vehicle braking end stage controller based on the state equation of the vehicle longitudinal vibration system at the braking end stage; optimizing control parameters of the vehicle braking end stage controller by using a grey wolf particle swarm hybrid algorithm to obtain an optimized vehicle braking end stage controller; embedding the optimized vehicle braking end stage controller into the vehicle braking controller BCU to control changes in vehicle braking pressure; the step of optimizing the control parameters of the vehicle braking end stage controller by using the grey wolf particle swarm hybrid algorithm to obtain the optimized vehicle braking end stage controller comprises the following steps: Calculating the fitness of each particle is: wherein is the total control time; is a complex variable in the transfer function; is a weighting coefficient; and are frequency weighting filter functions for the longitudinal acceleration and the pitch angle acceleration respectively. randomly generating a series of particle swarm positions within a value range, initializing vehicle speed, and giving a maximum iteration number of the particle swarm algorithm, a maximum iteration number of the grey wolf algorithm and a population quantity; 2. The rule-based vehicle braking end-stage longitudinal smoothness optimization method according to claim 1, characterized in that, determining an optimal parameter combination according to the fitness of each particle by using the grey wolf particle swarm hybrid algorithm, and obtaining the optimized vehicle braking end stage controller based on the optimal parameter combination, wherein the parameter combination comprises a particle optimal value and a population optimal value. wherein is a state vector; is a control input; is a nonlinear term; is a corresponding coefficient matrix; wherein: wherein is the braking torque of the front wheels; is the braking torque of the rear wheels; is the vehicle body center of mass velocity, is the tire rubber internal friction state of the front wheels; is the tire rubber internal friction state of the rear wheels; is the front wheel relative velocity, is the rear wheel relative velocity.

3. A rule-based method for optimizing longitudinal ride comfort at the end of braking of a vehicle according to claim 2, characterized in that, The state equation of the vehicle longitudinal vibration system at the braking end stage is as follows: the step of establishing the rule-based vehicle braking end stage controller based on the state equation of the vehicle longitudinal vibration system at the braking end stage comprises the following steps: (2) controller-based control input Longitudinal vibration control is performed on the tires at the end of braking of the vehicle, wherein , is the reduction ratio of the braking torque of the front wheels and the rear wheels, , is the braking torque of the front axle and the rear axle before the controller is triggered, respectively. ① selecting a speed threshold as a control trigger condition to determine whether the speed of the vehicle at the braking end stage exceeds the speed threshold, and if yes, entering step ②; ③ judging whether the pressure reduction duration of the vehicle exceeds a pre-set pressure reduction duration threshold, and if yes, entering step ⑤; otherwise, entering step ④; • recharging of the pressure in the braking system of the vehicle, obtaining a control input for the controller .

4. The rule-based vehicle braking end-stage longitudinal smoothness optimization method of claim 1, wherein, ④ judging whether the vehicle needs to be re-pressurized by using the absolute value of the vibration acceleration and the change rate of the vehicle, and if yes, entering step ⑤; otherwise, entering step ③; the step of determining the optimal parameter combination according to the fitness of each particle by using the grey wolf particle swarm hybrid algorithm and obtaining the optimized vehicle braking end stage controller based on the optimal parameter combination comprises the following steps: The first three particle positions with the minimum fitness are recorded as wolf, wolf, wolf's initial position; right Wolf, Wolf, The wolf's position is updated, and it is determined whether the maximum number of iterations of the Gray Wolf Algorithm has been reached. If so, then the algorithm is confirmed. The wolf's final location; otherwise, re-evaluate. Wolf, Wolf, The wolf's location has been updated; According to The final position of the wolf, recalculate the particle position, and update the particle optimal value and population optimal value; determining the particle optimal value and the population optimal value according to the fitness of each particle, that is, the particle position with the minimum fitness in each particle and the population, and the particle position is the parameter combination to be optimized; 5. A rule-based method for optimizing longitudinal ride comfort at the end of braking of a vehicle as claimed in claim 4, characterized in that, if the root mean square error of the prediction result of the particle swarm algorithm is lower than a pre-set threshold or the maximum iteration number is reached, then the updated particle optimal value and the population optimal value are determined as the optimal parameter combination, and the optimized vehicle braking end stage controller is obtained based on the optimal parameter combination. wherein, is the position of the wolf pack after the first optimization; is the position of the wolf after the first optimization; is the position of the wolf after the first optimization; is the distance between the wolf and the rest of the wolf pack after the first optimization; is the position of the wolf after the first optimization; is the position of the wolf after the first optimization; is the position of the wolf after the first optimization; is the coefficient vector, is the coefficient number.

6. The rule-based vehicle braking end-stage longitudinal smoothness optimization method according to claim 4, characterized in that, The particle optimum value is updated based on the following equation and the population optimum value : wherein, and are the position and velocity of the particle after iteration ; are the position and velocity of the particle after iteration ; are the position and velocity of the particle after iteration ; are random numbers within the range [0, 1] that do not interfere with each other; is the adopted nonlinear variation strategy.

7. A rule-based vehicle braking end-of-braking longitudinal ride comfort optimization system, characterized by, The updated wolf swarm position is as follows: The application comprises the following steps: a state equation determining module for determining a state equation of a vehicle longitudinal vibration system at a braking end stage based on a LuGre tire model; a controller establishing module for establishing a rule-based vehicle braking end stage controller based on the state equation of the vehicle longitudinal vibration system at the braking end stage; a parameter optimizing module for optimizing control parameters of the vehicle braking end stage controller by using a grey wolf particle swarm hybrid algorithm to obtain an optimized vehicle braking end stage controller; an embedding module for embedding the optimized vehicle braking end stage controller into the vehicle braking controller BCU to control changes in vehicle braking pressure; The grey wolf particle swarm hybrid algorithm is used to optimize the control parameters of the vehicle braking end controller, and an optimized vehicle braking end controller is obtained, comprising: A series of particle swarm positions are randomly generated within a value range, vehicle speed is initialized, and the maximum iteration number of the particle swarm algorithm, the maximum iteration number of the grey wolf algorithm, and the population quantity are given; Calculating the fitness of each particle is: wherein is the total control time; is a complex variable in the transfer function; is a weighting factor; and are frequency weighting filter functions for the longitudinal acceleration and the pitch angle acceleration respectively. The grey wolf particle swarm hybrid algorithm is used to determine the optimal parameter combination according to the fitness of each particle, and the optimized vehicle braking end controller is obtained based on the optimal parameter combination, wherein the parameter combination includes the optimal value of the particle and the optimal value of the population.

8. A processing device, characterized by Computer program instructions are included, wherein the computer program instructions are executed by a processing device to implement the steps corresponding to the rule-based vehicle braking end longitudinal smoothness optimization method in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer program instructions are stored on the computer readable storage medium, wherein the computer program instructions are executed by the processor to implement the steps corresponding to the rule-based vehicle braking end longitudinal smoothness optimization method in any one of claims 1-6.

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