CN113635879A - Vehicle braking force distribution method - Google Patents
Vehicle braking force distribution method Download PDFInfo
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- CN113635879A CN113635879A CN202111007664.7A CN202111007664A CN113635879A CN 113635879 A CN113635879 A CN 113635879A CN 202111007664 A CN202111007664 A CN 202111007664A CN 113635879 A CN113635879 A CN 113635879A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
- B60T13/00—Transmitting braking action from initiating means to ultimate brake actuator with power assistance or drive; Brake systems incorporating such transmitting means, e.g. air-pressure brake systems
- B60T13/74—Transmitting braking action from initiating means to ultimate brake actuator with power assistance or drive; Brake systems incorporating such transmitting means, e.g. air-pressure brake systems with electrical assistance or drive
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
- B60T13/00—Transmitting braking action from initiating means to ultimate brake actuator with power assistance or drive; Brake systems incorporating such transmitting means, e.g. air-pressure brake systems
- B60T13/74—Transmitting braking action from initiating means to ultimate brake actuator with power assistance or drive; Brake systems incorporating such transmitting means, e.g. air-pressure brake systems with electrical assistance or drive
- B60T13/745—Transmitting braking action from initiating means to ultimate brake actuator with power assistance or drive; Brake systems incorporating such transmitting means, e.g. air-pressure brake systems with electrical assistance or drive acting on a hydraulic system, e.g. a master cylinder
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- Electric Propulsion And Braking For Vehicles (AREA)
- Regulating Braking Force (AREA)
Abstract
The application relates to a vehicle braking force distribution method, which comprises the steps of determining the predicted acceleration of a target vehicle based on the current speed and acceleration of the target vehicle; judging whether the predicted acceleration is a negative value or not, if so, inputting the predicted acceleration and a preset slip rate into a preset fuzzy controller to determine the braking strength of the target vehicle; determining the type of the road surface currently attached by the target vehicle and the corresponding real-time utilization attachment coefficient; determining front and rear axle brake distribution coefficients and single-wheel electro-hydraulic brake distribution coefficients of the target vehicle based on the brake intensity of the target vehicle and by utilizing the adhesion coefficient and a preset brake distribution curve in real time; and correcting the front and rear axle brake distribution coefficients of the target vehicle and the electro-hydraulic brake distribution coefficients of the single wheels according to the energy balance target function, and determining the front and rear axle brake force of the target vehicle, the motor brake force of the single wheels and the hydraulic brake force of the single wheels by combining the target brake torque. The braking device can effectively brake according to different road surfaces and different vehicle conditions.
Description
Technical Field
The application relates to the technical field of new energy automobile braking, in particular to a vehicle braking force distribution method.
Background
Nowadays, energy crisis and environmental pollution have become global problems and challenges, and in order to cope with this situation, countries in the world compete to develop new energy automobile industry. However, due to the defect of the new energy automobile in the aspect of the endurance mileage, the popularization of the new energy automobile is affected. Meanwhile, in order to improve the utilization efficiency of energy, the endurance mileage of the new energy automobile needs to be necessarily prolonged by a regenerative braking technology. At present, most regenerative braking composite strategies have no obvious braking energy feedback effect. The method is embodied in the following aspects: firstly, the traditional regenerative braking strategy mainly focuses on the motor characteristics, the battery characteristics and the rules of the vehicle to research the braking force distribution strategy, and often neglects the influence of factors other than the vehicle on the braking performance, so that the robustness of the established model and the reality is insufficient. Meanwhile, with the development of the auxiliary driving technology and the automatic driving technology, the comfort requirement of people on riding experience is higher and higher. The traditional regenerative braking strategy research mainly focuses on several traditional indexes such as safety, robustness and efficiency, and cannot meet the requirements of new trends of automobile development. In addition, because different drivers have different driving behavior characteristics, the automobile can make different responses according to the instruction of the driver on the premise of meeting the safety under the same working condition. Finally, current driving assistance and automatic driving models are mainly built according to a longitudinal model of the automobile, so that the braking performance of the automobile is greatly influenced and restricted by road conditions in the braking process, and the regenerative braking strategy of the automobile cannot function as expected.
Disclosure of Invention
In view of this, the present application provides a method for distributing braking force of a vehicle, so as to solve the technical problem that the braking effect is low due to the restriction of the road condition and the vehicle working condition in the braking process of the existing vehicle.
In order to solve the above-described problems, the present application provides, in a first aspect, a vehicle braking force distribution method including:
acquiring real-time working condition information of a target vehicle, wherein the real-time working condition information comprises the speed and the acceleration of the target vehicle at the current moment;
determining a predicted acceleration of the target vehicle within a prediction period based on the speed and the acceleration of the target vehicle at the current moment;
judging whether the predicted acceleration is a negative value or not, if so, determining that the target vehicle is braking driving, using the predicted acceleration and a preset slip ratio as characteristic parameters and inputting the characteristic parameters to a preset fuzzy controller so as to determine a driving and braking intention recognition coefficient of the target vehicle, and determining the braking strength of the target vehicle according to the driving and braking intention recognition coefficient of the target vehicle;
determining the type of the road surface currently attached by the target vehicle and the corresponding real-time utilization attachment coefficient based on the dynamic parameters of the target vehicle and the dynamic longitudinal motion equation of the vehicle;
determining a target braking torque of the target vehicle according to the braking intensity of the target vehicle, and determining front and rear axle braking distribution coefficients and single-wheel electro-hydraulic braking distribution coefficients of the target vehicle on the basis of the braking intensity of the target vehicle, real-time utilization of the adhesion coefficient and a preset braking distribution curve;
and correcting the braking force distribution coefficient according to a preset energy balance target function to obtain the corrected front and rear axle braking distribution coefficient of the target vehicle and the electro-hydraulic braking distribution coefficient of the single wheel, and determining the front and rear axle braking force of the target vehicle, the motor braking force of the single wheel and the hydraulic braking force of the single wheel by combining with the target braking torque.
Optionally, the determining the predicted acceleration of the target vehicle in the prediction period based on the current speed and acceleration of the target vehicle includes:
and estimating the state transition probability of the target vehicle from the current moment to the next moment by utilizing the maximum likelihood based on the speed and the acceleration of the target vehicle at the current moment, and iteratively solving the predicted acceleration in the whole prediction period based on the Markov acceleration prediction model and the state transition probability.
Optionally, the step of inputting the predicted acceleration and the preset slip ratio as characteristic parameters to a preset fuzzy controller to determine a driving braking intention recognition coefficient of the target vehicle includes:
drawing a membership function of the predicted acceleration, a membership function of the slip rate and a membership function of the brake intention level based on the predicted acceleration and the preset slip rate;
reasoning operation is carried out to obtain result fuzzy quantity based on the membership function of the predicted acceleration, the membership function of the slip ratio and the membership function of the brake intention level and in combination with a preset fuzzy reasoning rule table of the relation between the brake deceleration and the slip ratio;
and performing defuzzification calculation by using a gravity center method based on the result fuzzy quantity to obtain a driving braking intention recognition coefficient of the target vehicle, wherein the gravity center method has the following calculation formula:
wherein u iscenRepresenting the weight obtained by the center-of-gravity method and using the weight as a driving braking intention identification coefficient s; u is the fuzzy domain of the membership function of the braking intention grade; a (U) is a membership function of the brake intention level, U ∈ U.
Optionally, the brake strength of the target vehicle is determined according to the magnitude of the driving brake intention recognition coefficient of the target vehicle, wherein the brake strength includes light braking, moderate braking, heavy braking or emergency braking.
Optionally, the determining the type of the currently attached road surface of the target vehicle and the corresponding real-time utilization attachment coefficient based on the dynamic parameters of the target vehicle and the vehicle dynamic longitudinal motion equation includes:
determining the wheel grounding load and the driving wheel longitudinal force of the target vehicle at the current moment based on the dynamic parameters of the target vehicle and the vehicle longitudinal motion equation;
determining an initial utilization adhesion coefficient and an initial slip rate of the target vehicle based on the wheel grounding load, the longitudinal force of the driving wheel and the acquired rotating speed difference between the driving wheel and the driven wheel of the target vehicle at the current moment;
and determining the type of the road surface currently attached by the target vehicle and the corresponding real-time utilization adhesion coefficient based on the fitted curve of the initial utilization adhesion coefficient and the initial slip ratio of the target vehicle at the current moment.
Optionally, determining the wheel ground contact load, the longitudinal force of the driving wheel, and the rotational speed difference between the driving wheel and the driven wheel at the current moment of the target vehicle based on the dynamic parameters of the target vehicle and the longitudinal motion equation of the vehicle motion, includes:
wherein, FZflA left front wheel ground contact load of the target vehicle; fZfrThe right front wheel ground load of the target vehicle; fZrlA left rear wheel ground contact load of the target vehicle; fZrrA right rear wheel ground load of the target vehicle; m is the mass of the target vehicle; g is the gravity of the target vehicle; b is the distance from the center of mass to the rear axle of the target vehicle, L is the vehicle length of the target vehicle, hgIs the height of the center of mass of the target vehicle; a isx，chIs the inertial acceleration; i is the road gradient value; the road slope value is determined by recurrence two-step multiplication with forgetting;
calculating the longitudinal force of the driving wheel:
wherein, TtIs the driving torque; tb is the braking torque, FzActing on the road surface against normal reaction forces of the wheels, FZ＝FZfl+FZfr+FZrl+FZrr(ii) a f is rolling resistance coefficient, r is rolling radius of wheelJ is the moment of inertia;
is the wheel rotational acceleration.
Optionally, determining an initial utilization adhesion coefficient and an initial slip ratio of the target vehicle based on the wheel contact load of the target vehicle at the current moment, the longitudinal force of the driving wheel, and the obtained rotating speed difference between the driving wheel and the driven wheel, includes:
determining an initial utilization adhesion coefficient of the target vehicle by using a recursive least square method based on the wheel grounding load and the longitudinal force of the driving wheel of the target vehicle;
and estimating the initial slip ratio of the target vehicle by using a Kalman filtering algorithm based on the rotating speed difference of the driving wheel and the driven wheel of the target vehicle.
Optionally, the determining the road type currently attached to the target vehicle and the corresponding real-time utilization adhesion coefficient based on the fitted curve of the initial utilization adhesion coefficient and the initial slip ratio of the target vehicle at the current time includes:
fitting a vehicle slip rate-utilization adhesion coefficient curve of the target vehicle under various working conditions by using a preset curve fitting equation, and obtaining a real-time utilization adhesion coefficient of the target vehicle, wherein the curve fitting equation is as follows:
wherein, c1、c2、c3Representing an impact factor; u (λ) represents a real-time utilization adhesion coefficient of the target vehicle;
calculating the real-time slip rate of the target vehicle according to the speed and the wheel speed of the target vehicle, wherein the concrete calculation comprises the following steps: center of mass velocity of the target vehicle:
left front wheel slip ratio of the target vehicle:
right front wheel slip ratio of target vehicle
Actual value of real-time slip ratio of target vehicle:
will utilize the adhesion coefficient u in real timei(lambda) and real-time slip rate lambda i are sequentially corresponding to the theoretical value u of the utilization adhesion coefficient of five road surfacesk(lambda) theoretical value of slip ratio lambdakMaking a difference, wherein k is 1, 2. Deltak＝|λi-λk|+|μi(λ)-μk(λ)|；
Take deltakTaking the corresponding road surface type with the minimum difference value as output to obtain the current road surface type attached by the target vehicle, wherein the road surface type comprises a low-attachment road surface, a middle-low attachment road surface, a middle-high attachment road surface and a high-attachment road surface;
optionally, the target braking torque of the target vehicle is determined according to the braking intensity of the target vehicle, and the calculation formula is as follows:
wherein, TbThe braking torque z is the braking intensity of the target vehicle at the current moment; m is the mass of the target vehicle; g is the gravity of the target vehicle; a is the frontal area of the vehicle; cdIs the air resistance coefficient, and rho is the windward coefficient; f is a rolling resistance coefficient, and r is a rolling radius of the wheel;
before determining the front and rear axle brake distribution coefficients of the target vehicle and the electro-hydraulic brake distribution coefficient of a single wheel according to the brake intensity of the target vehicle and by utilizing the adhesion coefficient and the preset brake distribution curve in real time, the method comprises the following steps:
and (4) according to the braking intensity and real-time utilization adhesion coefficient of the target vehicle, combining an ECE (engineering environmental engineering) regulation curve and an ideal braking force I curve, and making a beta braking distribution coefficient curve.
Optionally, the method for correcting the braking force distribution coefficient according to a preset energy balance objective function to obtain a corrected front and rear axle braking distribution coefficient of the target vehicle and an electrohydraulic braking distribution coefficient of a single wheel, so as to obtain front and rear axle braking force of the target vehicle, motor braking force of the single wheel and hydraulic braking force of the single wheel includes:
energy balance objective function:
constraint conditions are as follows:
optimizing by a particle swarm algorithm to obtain the corrected front and rear axle brake distribution coefficients of the target vehicle and the electrohydraulic brake distribution coefficient of a single wheel, wherein the method specifically comprises the following steps:
determining the front and rear axle braking force of the target vehicle, the motor braking force of a single wheel and the hydraulic braking force distribution of the single wheel by combining the following formulas;
wherein x is a control variable, and x is { z, n }; psi is the brake energy feedback rate; omega1，ω2，ω3Three weight coefficients respectively; z is the braking intensity of the target vehicle at the current moment t; t isf-regFeeding back a braking torque for a front wheel;n is the wheel speed; n isfThe working efficiency of the front wheel motor is improved; v, vtThe vehicle speed at the current moment t and the next moment; a is the frontal area of the vehicle; cdIs the air resistance coefficient, and rho is the windward coefficient; t issA system simulation period; pchg_Imax、Pchg_UmaxCharging power corresponding to the maximum charging current and voltage respectively; etabTo the charging efficiency; SOC is battery charge; beta is the front and rear axle braking force distribution coefficient of the target vehicle; alpha is alphafThe electro-hydraulic braking coefficients of the front axle are respectively; t isf、TrFront and rear axle braking forces respectively; t isf-regThe power is generated by a motor of a front axle single wheel; t isf-hydThe hydraulic braking force of the front axle single wheel is adopted.
The beneficial effects of adopting the above embodiment are: in the embodiment, the predicted acceleration of the target vehicle in the prediction period is determined based on the current speed and acceleration of the target vehicle, so that the target vehicle can be automatically recognized as driving or braking driving according to the predicted acceleration, and if the target vehicle is braking driving, the predicted acceleration and the preset slip ratio are used as characteristic parameters and input to the preset fuzzy controller to determine the driving and braking intention recognition coefficient of the target vehicle, so that the braking strength of the target vehicle can be determined, the working condition of the vehicle is considered, and the braking distribution effect is improved; determining the type degree of the road surface currently attached by the target vehicle and the corresponding real-time utilization attachment coefficient based on the dynamic parameters of the target vehicle and the dynamic longitudinal motion equation of the vehicle, thereby considering the braking of different road surface types; in addition, a target braking torque which is required by the target vehicle is determined based on the braking strength of the target vehicle, and then according to the braking strength of the target vehicle, the real-time utilization adhesion coefficient and a preset braking distribution curve, the front and rear axle braking distribution coefficients of the target vehicle, the motor braking distribution coefficient of a single wheel and the hydraulic braking distribution coefficient of the single wheel can be rapidly determined; the three brake distribution coefficients are corrected by using an energy balance function, and the front and rear axle braking force, the single-wheel motor braking force and the single-wheel hydraulic braking force of the target vehicle are determined by combining the target braking torque, so that the braking force distribution coefficients are dynamically adjusted under different working conditions and driving modes to adapt to different road surfaces, and the stability and comfort of the vehicle in the regenerative braking process are improved.
Drawings
FIG. 1 is a flowchart of a method of one embodiment of a method for distributing braking force for a vehicle provided herein;
FIG. 2 is a flowchart of an embodiment of a vehicle braking force distribution method step S103 provided by the present application;
FIG. 3(a) is a schematic representation of membership functions for predicted acceleration as provided herein;
FIG. 3(b) is a graph of membership functions for slip ratios as provided herein;
FIG. 3(c) is a schematic representation of membership functions for the level of braking intent provided herein;
FIG. 4 is a schematic diagram of one embodiment of a fuzzy inference rule table provided herein;
FIG. 5 is a Mamdani graph generated according to a fuzzy rule table as provided herein;
FIG. 6 is a flowchart of an embodiment of a vehicle braking force distribution method provided by the present application, step S104;
fig. 7 is a flowchart of an algorithm for calculating an initial utilization attachment coefficient based on a recursive least square method according to the present application.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the application and together with the description, serve to explain the principles of the application and not to limit the scope of the application.
In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
Referring to fig. 1, a method flowchart of an embodiment of a vehicle braking force distribution method provided by the present application is provided, and the vehicle braking force distribution method includes the following steps:
s101, acquiring real-time working condition information of a target vehicle, wherein the real-time working condition information comprises the speed and the acceleration of the target vehicle at the current moment;
s102, determining the predicted acceleration of the target vehicle in a prediction period based on the current speed and acceleration of the target vehicle;
s103, judging whether the predicted acceleration is a negative value or not, if so, determining that the target vehicle is in braking driving, using the predicted acceleration and a preset slip ratio as characteristic parameters and inputting the characteristic parameters to a preset fuzzy controller so as to determine a driving braking intention recognition coefficient of the target vehicle, and determining the braking strength of the target vehicle according to the driving braking intention recognition coefficient of the target vehicle;
s104, determining the type of the road surface currently attached by the target vehicle and a corresponding real-time utilization attachment coefficient based on the dynamic parameters of the target vehicle and the dynamic longitudinal motion equation of the vehicle;
s105, determining a target braking torque of the target vehicle according to the braking intensity of the target vehicle, and determining front and rear axle braking distribution coefficients and single-wheel electro-hydraulic braking distribution coefficients of the target vehicle on the basis of the braking intensity of the target vehicle, real-time utilization of the adhesion coefficient and a preset braking distribution curve;
s106, according to a preset energy balance target function, the braking force distribution coefficient is corrected to obtain the corrected front and rear axle braking distribution coefficient of the target vehicle and the electro-hydraulic braking distribution coefficient of the single wheel, and the front and rear axle braking force of the target vehicle, the motor braking force of the single wheel and the hydraulic braking force of the single wheel are determined by combining the target braking torque.
In the embodiment, the predicted acceleration of the target vehicle in the prediction period is determined based on the current speed and acceleration of the target vehicle, so that the target vehicle can be automatically recognized as driving or braking driving according to the predicted acceleration, and if the target vehicle is braking driving, the predicted acceleration and the preset slip ratio are used as characteristic parameters and input to the preset fuzzy controller to determine the driving and braking intention recognition coefficient of the target vehicle, so that the braking strength of the target vehicle can be determined, the working condition of the vehicle is considered, and the braking distribution effect is improved; determining the type degree of the road surface currently attached by the target vehicle and the corresponding real-time utilization attachment coefficient based on the dynamic parameters of the target vehicle and the dynamic longitudinal motion equation of the vehicle, thereby considering the braking of different road surface types; in addition, a target braking torque which is required by the target vehicle is determined based on the braking strength of the target vehicle, and then according to the braking strength of the target vehicle, the real-time utilization adhesion coefficient and a preset braking distribution curve, the front and rear axle braking distribution coefficients of the target vehicle, the motor braking distribution coefficient of a single wheel and the hydraulic braking distribution coefficient of the single wheel can be rapidly determined; the three brake distribution coefficients are corrected by using an energy balance function, and the front and rear axle braking force, the single-wheel motor braking force and the single-wheel hydraulic braking force of the target vehicle are determined by combining the target braking torque, so that the braking force distribution coefficients are dynamically adjusted under different working conditions and driving modes to adapt to different road surfaces, and the stability and comfort of the vehicle in the regenerative braking process are improved.
In the present embodiment, the real-time condition information includes acceleration and deceleration pedal signals and characteristic information of displacement, acceleration, speed, four-wheel speed, etc. of the target vehicle.
In one embodiment, in step S102, determining the predicted acceleration of the target vehicle within the prediction period based on the speed and the acceleration of the target vehicle at the current time includes:
estimating the state transition probability of the target vehicle from the current moment to the next moment by utilizing the maximum likelihood based on the speed and the acceleration of the target vehicle at the current moment, and iteratively solving the predicted acceleration in the whole prediction period based on the Markov acceleration prediction model and the state transition probability;
it should be noted that, in order to obtain complete data, interpolation compensation and singular value elimination need to be performed on the speed and acceleration of the target vehicle at the current moment; in order to count the velocity and acceleration distribution, the data needs to be discretized by dividing different intervals (16 speed intervals and 26 acceleration intervals), which is specifically as follows:
wherein v represents the speed of the target vehicle at the present time;
an acceleration representing a current time of the target vehicle;
based on Markov theory, estimating the probability of state transition from the acceleration at the time k to the acceleration at the time k +1 by using a maximum likelihood method according to the speed and the acceleration at the time k,
further obtaining a probability transition matrix as the following formula, and establishing a Markov acceleration prediction model;
during on-line prediction, at the moment k, the vehicle speed sensor inputs the current vehicle speed into the Markov acceleration prediction model and judges the working condition of the vehicle speed at the current moment, and the acceleration one-step state transition probability matrix under the corresponding working condition is obtained. Inputting the current acceleration, and taking the acceleration value corresponding to the maximum state transition probability of the Markov acceleration prediction model as the acceleration value at the moment of k + 1. According to the relation between the speed and the acceleration, the predicted speed at the moment of k +1 is obtained on the basis; after the velocity value and the acceleration value at the moment k +1 are obtained, the velocity value and the acceleration value at the moment k +2 can be obtained by inputting the velocity value and the acceleration value into the Markov acceleration prediction model again; by iterating the above steps, the predicted acceleration and predicted vehicle speed over the entire prediction period can be obtained.
In one embodiment, referring to fig. 2, in step S103, the step of inputting the predicted acceleration and the preset slip ratio as characteristic parameters to a preset fuzzy controller to determine the driving braking intention recognition coefficient of the target vehicle includes:
s201, drawing a membership function of the predicted acceleration, a membership function of the slip rate and a membership function of the brake intention level based on the predicted acceleration and the preset slip rate; it should be noted that, the predicted acceleration and the preset slip ratio are used as input characteristic parameters of the fuzzy controller, and a membership function of the predicted acceleration, a membership function of the slip ratio and a membership function of the braking intention level are formulated, which are respectively shown in fig. 3(a), fig. 3(b) and fig. 3 (c); the domains are [ -10,0], [0,0.8] and [0,1] respectively;
s202, carrying out reasoning operation to obtain a result fuzzy quantity based on a membership function of the predicted acceleration, a membership function of the slip ratio and a membership function of the brake intention level and combining a preset fuzzy reasoning rule table of the relation between the brake deceleration and the slip ratio; wherein the fuzzy inference rule table is shown in fig. 4; it should be noted that the resulting fuzzy quantity includes different fuzzy sets, respectively fuzzy sets of predicted acceleration, slip rate and brake intention level. As shown in fig. 5, the Mamdani graph generated according to the fuzzy rule table may be combined with the Mamdani graph inference operation to obtain the result fuzzy amount.
S203, based on the result fuzzy quantity, performing defuzzification calculation by using a gravity center method to obtain a driving braking intention recognition coefficient of the target vehicle, wherein the gravity center method has the following calculation formula:
wherein u iscenRepresenting the weight obtained by the center-of-gravity method and using the weight as a driving braking intention identification coefficient s; u is the fuzzy domain of the membership function of the braking intention grade; a (U) is a membership function of the brake intention level, U ∈ U.
In one embodiment, in step S103, the braking strength of the target vehicle is determined according to the magnitude of the driving braking intention recognition coefficient of the target vehicle, wherein the driving braking intention recognition coefficient includes a light braking strength, a moderate braking strength, a heavy braking strength and an emergency braking strength. It should be noted that the brake intention recognition of the embodiment gets rid of the conventional scheme of judging the brake intention by using the pedal and the throttle opening and the opening change rate thereof, and improves the applicability of the brake intention recognition in different driving modes. And the change of the slip rate is considered in the subsequent scheme, so that the safety of vehicle driving and braking is improved.
Optionally, in step S103, after determining whether the predicted acceleration is a negative value, the vehicle braking force distribution method of the embodiment further includes: if the predicted acceleration is a positive value, the driving of the target vehicle is determined, and a predicted acceleration signal is transmitted to a Vehicle Control Unit (VCU).
In one embodiment, referring to fig. 6, the step S104 of determining the type of the road surface to which the target vehicle is currently attached based on the dynamic parameters of the target vehicle and the vehicle dynamic longitudinal motion equation includes:
s601, determining the wheel grounding load and the driving wheel longitudinal force of the target vehicle at the current moment based on the dynamic parameters of the target vehicle and the vehicle motion longitudinal motion equation;
in one embodiment, the specific calculation is as follows:
wherein, FZflA left front wheel ground contact load of the target vehicle; fZfrThe right front wheel ground load of the target vehicle; fZrlA left rear wheel ground contact load of the target vehicle; fZrrA right rear wheel ground load of the target vehicle; m is the mass of the target vehicle; g is the gravity of the target vehicle; b is the distance from the center of mass to the rear axle of the target vehicle, L is the vehicle length of the target vehicle, hgIs the height of the center of mass of the target vehicle; a isx，chIs the inertial acceleration; i is the road gradient value; the road slope value is determined by recurrence two-step multiplication with forgetting; it should be noted that the dynamic parameters of the target vehicle include a mass m of the target vehicle, a gravity g of the target vehicle, a centroid-to-rear axle distance b of the target vehicle, a vehicle length L of the target vehicle, and a centroid height h of the target vehiclegAnd the inertial acceleration a of the target vehiclex，ch。
Calculating the longitudinal force of the driving wheel:
wherein, TtIs the driving torque; tb is the braking torque, FzActing on the road surface against normal reaction forces of the wheels, FZ＝FZfl+FZfr+FZrl+FZrr(ii) a f is a rolling resistance coefficient, r is a rolling radius of the wheel, and j is rotational inertia;
is the wheel rotational acceleration.
S602, determining an initial utilization adhesion coefficient and an initial slip rate of the target vehicle based on the wheel grounding load, the longitudinal force of the driving wheel and the acquired rotating speed difference between the driving wheel and the driven wheel of the target vehicle at the current moment; it should be noted that the difference in the rotational speeds of the drive wheels and the driven wheels of the target vehicle may be determined by an on-board sensor of the target vehicle.
In an embodiment, step S302 specifically includes: determining an initial utilization adhesion coefficient of the target vehicle by utilizing a recursive least square method based on the wheel grounding load and the longitudinal force of the driving wheel of the target vehicle; FIG. 7 is a flow chart of an algorithm for initially utilizing the sticking coefficient, where θ (t) is the parameter estimator, i.e., the sticking coefficient is utilized;
is an input regression vector; e (t) is the system identification error; y (t) ═ Fx is the drive wheel longitudinal force;
is the wheel load; the input quantities θ (0), P (0) are constants; delta is a forgetting factor, and the value range is 0.95-0.995.
Further, the initial slip ratio of the target vehicle is estimated by using a Kalman filtering algorithm based on the rotating speed difference between the driving wheel and the driven wheel of the target vehicle.
And S603, determining the type of the road surface currently attached by the target vehicle based on the fitting curve of the initial utilization attachment coefficient and the initial slip ratio of the target vehicle at the current moment.
In an embodiment, step S303 specifically includes the following steps:
fitting a vehicle slip rate-utilization adhesion coefficient curve of the target vehicle under various working conditions by using a preset curve fitting equation, and obtaining an actual value of the real-time utilization adhesion coefficient of the target vehicle, wherein the curve fitting equation is as follows:
wherein, c1、c2、c3Representing an impact factor; u (λ) represents a real-time utilization adhesion coefficient of the target vehicle;
calculating an actual value of a real-time slip ratio of the target vehicle according to the speed and the wheel speed of the target vehicle, wherein the concrete calculation comprises the following steps: center of mass velocity of the target vehicle:
left front wheel slip ratio of the target vehicle:
right front wheel slip ratio of target vehicle
Actual value of real-time slip ratio of target vehicle:
the actual value u of the adhesion coefficient will be utilized in real timei(lambda) and the actual value of slip ratio lambda i are sequentially associated with the theoretical value u of the coefficient of adhesion by use of five road surfacesk(λ)、λkMaking a difference, wherein k is 1, 2. Deltak＝|λi-λk|+|μi(λ)-μk(λ)|；
Take deltakThe corresponding road surface type when the difference value is minimum is used as output, so that the current road surface type attached by the target vehicle can be obtained, and the road surface types comprise a low-attachment road surface, a medium-low attachment road surface, a medium-high attachment road surface and a high-attachment road surface; the embodiment helps to select different brake distributors by identifying the current road surface type of the target vehicleFormula (II) is shown.
It should be noted that, on the premise of ensuring the driving safety of the vehicle, in order to maintain the stability of the vehicle in the braking process and improve the comfort, when the braking force distribution ratio coefficient is selected, the influence of the road adhesion condition of the current driving road of the electric vehicle and the braking strength required by different driving behavior characteristics should be comprehensively considered, and the regulation and the motor and battery characteristic constraints are combined.
In one embodiment, in step S105, a target braking torque of the target vehicle is determined according to the braking intensity of the target vehicle, and the calculation formula is as follows:
wherein, TbThe braking torque z is the braking intensity of the target vehicle at the current moment; m is the mass of the target vehicle; g is the gravity of the target vehicle; a is the frontal area of the vehicle; cdIs the air resistance coefficient, and rho is the windward coefficient; f is the rolling resistance coefficient, and r is the rolling radius of the wheel.
In an embodiment, before determining the front-rear axle brake distribution coefficient and the single-wheel electro-hydraulic brake distribution coefficient of the target vehicle according to the brake intensity of the target vehicle, the real-time adhesion coefficient and the preset brake distribution curve in step S105, the vehicle brake force distribution method of this embodiment further includes:
and (4) according to the braking intensity and real-time utilization adhesion coefficient of the target vehicle, combining an ECE (engineering environmental engineering) regulation curve and an ideal braking force I curve, and making a beta braking distribution coefficient curve. In one embodiment, the beta brake distribution coefficient curve satisfies the following condition:
wherein z is the braking intensity of the target vehicle at the current moment; b is the distance from the center of mass to the rear axle of the target vehicle, L is the vehicle length of the target vehicle, hgIs the height of the center of mass of the target vehicle.
In one embodiment, in step S106, determining to correct the braking force distribution coefficient according to a preset energy balance objective function, so as to obtain the corrected front and rear axle braking distribution coefficient of the determined target vehicle and the electrohydraulic braking distribution coefficient of the single wheel, so as to obtain the front and rear axle braking force of the target vehicle, the motor braking force of the single wheel, and the hydraulic braking force of the single wheel, including:
energy balance objective function:
constraint conditions are as follows:
it should be noted that, optimization is performed through a particle swarm algorithm to obtain the corrected front and rear axle brake distribution coefficients of the target vehicle and the electrohydraulic brake distribution coefficient of a single wheel, which is specifically as follows:
determining the front and rear axle braking force of the target vehicle, the motor braking force of a single wheel and the hydraulic braking force distribution of the single wheel by combining the following formulas;
wherein x is a control variable, and x is { z, n }; psi is the brake energy feedback rate; omega1，ω2，ω3Three weight coefficients respectively; z is the braking intensity of the target vehicle at the current moment t; t isf-regFor feeding back braking torque to front wheel(ii) a n is the wheel speed; n isfThe working efficiency of the front wheel motor is improved; v, vtThe vehicle speed at the current moment t and the next moment; a is the frontal area of the vehicle; cdIs the air resistance coefficient, and rho is the windward coefficient; t issA system simulation period; pchg_Imax、Pchg-UmaxCharging power corresponding to the maximum charging current and voltage respectively; etabTo the charging efficiency; SOC is battery charge; beta is the front and rear axle braking force distribution coefficient of the target vehicle; alpha is alphafThe electro-hydraulic braking coefficients of the front axle are respectively; t isf、TrFront and rear axle braking forces respectively; t isf-regThe power is generated by a motor of a front axle single wheel; t isf-hydThe hydraulic braking force of the front axle single wheel is adopted. Note that the motor braking force of the rear axle single wheel and the hydraulic braking force of the rear axle single wheel may be determined by conversion of the above equations.
The embodiment converts each finally obtained braking force into an electric signal and transmits the electric signal to the ECB controller and the motor controller of the target vehicle, the ECB controller and the motor controller calculate and output the required torque according to the transmitted signal, the safety, the stability and the comfort of the vehicle in the braking process are ensured, and the energy generated by the final regenerative braking flows into the battery BMS.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Different from the prior art, the embodiment determines the predicted acceleration of the target vehicle in the prediction period based on the current speed and acceleration of the target vehicle, so that the target vehicle can be automatically recognized as driving or braking driving according to the predicted acceleration, and if the target vehicle is braking driving, the predicted acceleration and the preset slip ratio are used as characteristic parameters and input to the preset fuzzy controller to determine the driving and braking intention recognition coefficient of the target vehicle, so that the braking intensity of the target vehicle can be determined, the working condition of the vehicle is considered, and the braking distribution effect is improved; determining the type degree of the road surface currently attached by the target vehicle and the corresponding real-time utilization attachment coefficient based on the dynamic parameters of the target vehicle and the dynamic longitudinal motion equation of the vehicle, thereby considering the braking of different road surface types; in addition, a target braking torque which is required by the target vehicle is determined based on the braking strength of the target vehicle, and then according to the braking strength of the target vehicle, the real-time utilization adhesion coefficient and a preset braking distribution curve, the front and rear axle braking distribution coefficients of the target vehicle, the motor braking distribution coefficient of a single wheel and the hydraulic braking distribution coefficient of the single wheel can be rapidly determined; the three brake distribution coefficients are corrected by using an energy balance function, and the front and rear axle braking force, the single-wheel motor braking force and the single-wheel hydraulic braking force of the target vehicle are determined by combining the target braking torque, so that the braking force distribution coefficients are dynamically adjusted under different working conditions and driving modes to adapt to different road surfaces, and the stability and comfort of the vehicle in the regenerative braking process are improved.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application.
Claims (10)
1. A vehicle braking force distribution method, characterized by comprising:
acquiring real-time working condition information of a target vehicle, wherein the real-time working condition information comprises the speed and the acceleration of the target vehicle at the current moment;
determining a predicted acceleration of the target vehicle within a prediction period based on the speed and the acceleration of the target vehicle at the current moment;
judging whether the predicted acceleration is a negative value or not, if so, determining that the target vehicle is braking driving, using the predicted acceleration and a preset slip ratio as characteristic parameters and inputting the characteristic parameters to a preset fuzzy controller so as to determine a driving and braking intention recognition coefficient of the target vehicle, and determining the braking strength of the target vehicle according to the driving and braking intention recognition coefficient of the target vehicle;
determining the type of the road surface currently attached by the target vehicle and the corresponding real-time utilization attachment coefficient based on the dynamic parameters of the target vehicle and the dynamic longitudinal motion equation of the vehicle;
determining a target braking torque of the target vehicle according to the braking intensity of the target vehicle, and determining front and rear axle braking distribution coefficients and single-wheel electro-hydraulic braking distribution coefficients of the target vehicle on the basis of the braking intensity of the target vehicle, real-time utilization of the adhesion coefficient and a preset braking distribution curve;
and correcting the braking force distribution coefficient according to a preset energy balance target function to obtain the corrected front and rear axle braking distribution coefficient of the target vehicle and the electro-hydraulic braking distribution coefficient of the single wheel, and determining the front and rear axle braking force of the target vehicle, the motor braking force of the single wheel and the hydraulic braking force of the single wheel by combining with the target braking torque.
2. The vehicle braking force distribution method according to claim 1, wherein the determining a predicted acceleration of the target vehicle within a prediction period based on the speed and acceleration of the target vehicle at the present time includes:
and estimating the state transition probability of the target vehicle from the current moment to the next moment by utilizing the maximum likelihood based on the speed and the acceleration of the target vehicle at the current moment, and iteratively solving the predicted acceleration in the whole prediction period based on the Markov acceleration prediction model and the state transition probability.
3. The vehicle braking force distribution method according to claim 1, characterized in that the predicted acceleration, a preset slip ratio are input as characteristic parameters to a preset fuzzy controller to determine a driving braking intention recognition coefficient of a target vehicle, including:
drawing a membership function of the predicted acceleration, a membership function of the slip rate and a membership function of the brake intention level based on the predicted acceleration and the preset slip rate;
reasoning operation is carried out to obtain result fuzzy quantity based on the membership function of the predicted acceleration, the membership function of the slip ratio and the membership function of the brake intention level and in combination with a preset fuzzy reasoning rule table of the relation between the brake deceleration and the slip ratio;
and performing defuzzification calculation by using a gravity center method based on the result fuzzy quantity to obtain a driving braking intention recognition coefficient of the target vehicle, wherein the gravity center method has the following calculation formula:
wherein u iscenRepresenting the weight obtained by the center-of-gravity method and using the weight as a driving braking intention identification coefficient s; u is the fuzzy domain of the membership function of the braking intention grade; a (U) is a membership function of the brake intention level, U ∈ U.
4. The vehicle braking force distribution method according to claim 1, wherein the braking intensity of the target vehicle is determined according to the magnitude of the driving braking intention recognition coefficient of the target vehicle, wherein the braking intensity includes light braking, moderate braking, heavy braking or emergency braking.
5. The vehicle brake force distribution method according to claim 1, wherein the determining of the type of road surface to which the target vehicle is currently attached and the corresponding real-time utilization attachment coefficient based on the dynamic parameters of the target vehicle and the vehicle dynamic longitudinal motion equation comprises:
determining the wheel grounding load and the driving wheel longitudinal force of the target vehicle at the current moment based on the dynamic parameters of the target vehicle and the vehicle longitudinal motion equation;
determining an initial utilization adhesion coefficient and an initial slip rate of the target vehicle based on the wheel grounding load, the longitudinal force of the driving wheel and the acquired rotating speed difference between the driving wheel and the driven wheel of the target vehicle at the current moment;
and determining the type of the road surface currently attached by the target vehicle and the corresponding real-time utilization adhesion coefficient based on the fitted curve of the initial utilization adhesion coefficient and the initial slip ratio of the target vehicle at the current moment.
6. The vehicle braking force distribution method according to claim 5, characterized in that the wheel contact load, the driving wheel longitudinal force, and the driving wheel and driven wheel rotation speed difference at the current moment of the target vehicle are determined based on the dynamic parameters of the target vehicle and the vehicle motion longitudinal motion equation, and are specifically calculated as follows:
wherein, FZflA left front wheel ground contact load of the target vehicle; fZfrThe right front wheel ground load of the target vehicle; fZrlA left rear wheel ground contact load of the target vehicle; fZrrA right rear wheel ground load of the target vehicle; m is the mass of the target vehicle; g is the gravity of the target vehicle; b is the distance from the center of mass to the rear axle of the target vehicle, L is the vehicle length of the target vehicle, hgIs the height of the center of mass of the target vehicle; a isx，chIs the inertial acceleration; i is the road gradient value; the road slope value is determined by recurrence two-step multiplication with forgetting;
calculating the longitudinal force of the driving wheel:
wherein, TtIs the driving torque; t isbFor braking torque, FzActing on the road surface against normal reaction forces of the wheels, FZ＝FZfl+FZfr+FZrl+FZrr(ii) a f is the rolling resistance coefficient, r is the rolling radius of the wheel, j isMoment of inertia;
is the wheel rotational acceleration.
7. The vehicle braking force distribution method according to claim 6, wherein determining an initial utilization adhesion coefficient and an initial slip ratio of the target vehicle based on the wheel contact-with-ground load, the driving wheel longitudinal force, and the acquired driving wheel and driven wheel rotation speed difference at the current time of the target vehicle includes:
determining an initial utilization adhesion coefficient of the target vehicle by using a recursive least square method based on the wheel grounding load and the longitudinal force of the driving wheel of the target vehicle;
and estimating the initial slip ratio of the target vehicle by using a Kalman filtering algorithm based on the rotating speed difference of the driving wheel and the driven wheel of the target vehicle.
8. The vehicle braking force distribution method according to claim 7, wherein the determining of the road surface type currently attached to the target vehicle and the corresponding real-time utilization adhesion coefficient based on the fitted curve of the initial utilization adhesion coefficient and the initial slip ratio at the current time of the target vehicle comprises:
fitting a vehicle slip rate-utilization adhesion coefficient curve of the target vehicle under various working conditions by using a preset curve fitting equation, and obtaining a real-time utilization adhesion coefficient of the target vehicle, wherein the curve fitting equation is as follows:
wherein, c1、c2、c3Representing an impact factor; u (λ) represents a real-time utilization adhesion coefficient of the target vehicle;
calculating the real-time slip rate of the target vehicle according to the speed and the wheel speed of the target vehicle, wherein the concrete calculation comprises the following steps: center of mass velocity of the target vehicle:
left front wheel slip ratio of the target vehicle:
right front wheel slip ratio of target vehicle
Actual value of real-time slip ratio of target vehicle:
will utilize the adhesion coefficient u in real timei(lambda) and real-time slip rate lambda i are sequentially corresponding to the theoretical value u of the utilization adhesion coefficient of five road surfacesk(lambda) theoretical value of slip ratio lambdakMaking a difference, wherein k is 1, 2. Deltak＝|λi-λk|+|μi(λ)-μk(λ)|；
Take deltakAnd taking the corresponding road surface type when the difference value is minimum as an output to obtain the current road surface type attached by the target vehicle, wherein the road surface type comprises a low-attachment road surface, a medium-low attachment road surface, a medium-high attachment road surface and a high-attachment road surface.
9. The vehicle braking force distribution method according to claim 1, wherein the target braking torque of the target vehicle is determined according to the braking intensity of the target vehicle, and the calculation formula is as follows:
wherein, TbThe braking torque z is the braking intensity of the target vehicle at the current moment; m is the mass of the target vehicle; g is the gravity of the target vehicle; a is the frontal area of the vehicle; cdIs the air resistance coefficient, and rho is the windward coefficient; f is rolling resistance coefficient, r is wheel rolling halfDiameter;
before determining the front and rear axle brake distribution coefficients of the target vehicle and the electro-hydraulic brake distribution coefficient of a single wheel according to the brake intensity of the target vehicle and by utilizing the adhesion coefficient and the preset brake distribution curve in real time, the method comprises the following steps:
and (4) according to the brake intensity of the target vehicle and the real-time utilization adhesion coefficient, combining an ECE (engineering environmental engineering) regulation curve and an ideal brake force I curve, making a beta brake distribution coefficient curve and using the beta brake distribution coefficient curve as a brake distribution curve.
10. The vehicle braking force distribution method according to claim 1, wherein the braking force distribution coefficient is corrected according to a preset energy balance objective function to obtain a corrected front and rear axle braking distribution coefficient of the target vehicle and an electro-hydraulic braking distribution coefficient of a single wheel so as to obtain front and rear axle braking force of the target vehicle, motor braking force of the single wheel and hydraulic braking force of the single wheel, and the method comprises the following steps:
energy balance objective function:
constraint conditions are as follows:
optimizing by a particle swarm algorithm to obtain the corrected front and rear axle brake distribution coefficients of the target vehicle and the electrohydraulic brake distribution coefficient of a single wheel, wherein the method specifically comprises the following steps:
determining the front and rear axle braking force of the target vehicle, the motor braking force of a single wheel and the hydraulic braking force distribution of the single wheel by combining the following formulas;
wherein x is a control variable, and x is { z, n }; psi is the brake energy feedback rate; omega1，ω2，ω3Three weight coefficients respectively; z is the braking intensity of the target vehicle at the current moment t; t isf-regFeeding back a braking torque for a front wheel; n is the wheel speed; n isfThe working efficiency of the front wheel motor is improved; v, vtThe vehicle speed at the current moment t and the next moment; a is the frontal area of the vehicle; cdIs the air resistance coefficient, and rho is the windward coefficient; t issA system simulation period; pchg_Imax、Pchg_UmaxCharging power corresponding to the maximum charging current and voltage respectively; etabTo the charging efficiency; SOC is battery charge; beta is the front and rear axle braking force distribution coefficient of the target vehicle; alpha is alphafThe electro-hydraulic braking coefficients of the front axle are respectively; t isf、TrFront and rear axle braking forces respectively; t isf-regThe power is generated by a motor of a front axle single wheel; t isf-hydThe hydraulic braking force of the front axle single wheel is adopted.
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