CN112677771A - Method for controlling regenerative braking of forerunner electric vehicle based on fuzzy control - Google Patents
Method for controlling regenerative braking of forerunner electric vehicle based on fuzzy control Download PDFInfo
- Publication number
- CN112677771A CN112677771A CN202011618813.9A CN202011618813A CN112677771A CN 112677771 A CN112677771 A CN 112677771A CN 202011618813 A CN202011618813 A CN 202011618813A CN 112677771 A CN112677771 A CN 112677771A
- Authority
- CN
- China
- Prior art keywords
- braking
- braking force
- regenerative braking
- strength
- regenerative
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000001172 regenerating effect Effects 0.000 title claims abstract description 91
- 238000000034 method Methods 0.000 title claims abstract description 49
- 230000007246 mechanism Effects 0.000 claims abstract description 12
- 230000008569 process Effects 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 9
- 230000008859 change Effects 0.000 claims description 9
- 239000002243 precursor Substances 0.000 claims 1
- 238000011084 recovery Methods 0.000 abstract description 12
- 238000005265 energy consumption Methods 0.000 abstract description 3
- 230000005484 gravity Effects 0.000 description 6
- 230000003111 delayed effect Effects 0.000 description 3
- 208000019901 Anxiety disease Diseases 0.000 description 2
- 230000036506 anxiety Effects 0.000 description 2
- 238000012938 design process Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000003915 air pollution Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000295 fuel oil Substances 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
Images
Landscapes
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention discloses a regenerative braking control method of a forerunner electric vehicle based on fuzzy control, which comprises the steps of calculating the braking force of front and rear wheels according to a certain rule by judging the braking strength of the whole vehicle during braking; the method comprises the steps that the vehicle speed v of the whole vehicle, the SOC value SOC _ n of a power battery and the brake intensity value z of the whole vehicle, which are detected by a sensor, are used as input variables and input into a fuzzy controller of a brake system, the corresponding output variable is the ratio P of the regenerative braking force of a front wheel to the total braking force of the front wheel, and the distribution relation between the mechanical friction braking force of the front wheel and the regenerative braking force is calculated; the specific braking force of the front and rear wheels and the proportion of the regenerative braking force of the front wheels during braking are obtained, and the braking operation can be carried out through the executing mechanisms of the mechanical braking system and the regenerative braking system. The invention optimizes the proportional relation between the mechanical friction braking force and the regenerative braking force of the front wheel, realizes the maximum braking energy recovery of the whole automobile on the basis of ensuring the braking safety, and reduces the whole energy consumption of the front-wheel-drive electric automobile in running.
Description
Technical Field
The invention belongs to the technical field of electric automobiles, and particularly relates to a regenerative braking control method of a forerunner electric automobile based on fuzzy control.
Background
In recent years, new energy automobiles are vigorously developed in countries around the world to reduce the influence of air pollution and greenhouse effect on the environment. The electric automobile is the most typical one of new energy automobiles, and replaces a vehicle-mounted energy source with traditional fuel oil as a vehicle-mounted power battery, so that no harmful gas is generated during the running of the automobile, zero emission and zero pollution are really realized, and the use of fossil energy is greatly reduced. Under the strong support of policies of various countries, the related technology of electric vehicles becomes a research hotspot in recent years.
However, the development of electric vehicles still has unsolved technical problems, wherein the low energy density of the power battery of the electric vehicle makes the cruising ability of the electric vehicle unsatisfactory is one of the key problems. The regenerative braking system converts the friction heat energy lost in the braking process into electric energy by utilizing the power generation function of the vehicle-mounted driving motor, and the electric energy is stored into the power battery through the battery management system, so that the process improves the energy utilization rate of the whole electric vehicle, and the cruising ability is improved.
At present, three typical control methods for regenerative braking of an electric vehicle are provided, namely an ideal braking force distribution control method, an optimal braking energy recovery control method and a parallel braking energy recovery control method. The three control methods have advantages and disadvantages, and a unified optimal scheme is not available. In general analysis, the existing regenerative braking control method is in conflict with each other in the aspects of energy recovery maximization and stability and safety during braking, and how to recover more energy as much as possible on the premise of ensuring braking safety is a research focus of current braking recovery control.
Disclosure of Invention
Aiming at the forerunner electric vehicle, the invention reasonably distributes the braking force of front and rear wheels during braking, optimizes the proportional relation between the mechanical friction braking force and the regenerative braking force of the front wheels, realizes the maximum recovery of braking energy on the basis of ensuring the braking safety of the whole vehicle, reduces the integral energy consumption during the running of the forerunner electric vehicle, improves the long-distance running capability of the electric vehicle and relieves the anxiety problem of the mileage of a driver to a certain extent.
The purpose of the invention is realized by the following technical scheme:
a method for controlling the regenerative braking of a forerunner electric vehicle based on fuzzy control includes judging the braking strength of the whole vehicle during braking, calculating the braking force of front and rear wheels according to a certain rule, obtaining the proportional relation between mechanical friction braking force and regenerative braking force according to the braking strength, the SOC of a battery and the vehicle speed value of the whole vehicle through calculation of a fuzzy controller, and generating corresponding braking force through an executing mechanism to enable the whole vehicle to complete braking and energy recovery. The method specifically comprises the following steps:
step one, calculating the distribution of the braking force of front and rear wheels:
s1, judging that the whole automobile is in a braking stage after a vehicle-mounted sensor of the forerunner electric automobile detects information of the increase of the opening degree of a brake pedal;
s2, judging and calculating the braking strength z of the whole vehicle by detecting the change size and the change rate of the opening degree of the brake pedal and the vehicle speed information of the whole vehicle;
and S3, distributing the braking force of the front wheel and the braking force of the rear wheel according to different braking strengths z.
Step two, calculating the distribution of the mechanical friction braking force and the regenerative braking force of the front wheel:
and S4, inputting the vehicle speed v of the whole vehicle, the SOC value SOC _ n of the power battery and the brake intensity value z of the whole vehicle, which are detected by the sensor, into a fuzzy controller of the brake system, wherein the corresponding output variable is the ratio P of the regenerative braking force of the front wheels to the total braking force of the front wheels, and calculating to obtain the distribution relation between the mechanical friction braking force and the regenerative braking force of the front wheels.
Step three, the actuating mechanism carries out braking operation:
according to the first step and the second step, the specific braking force of the front wheel and the specific braking force of the rear wheel during braking and the proportion of the regenerative braking force of the front wheel can be obtained, and the braking operation can be carried out through an actuating mechanism of a mechanical braking system and a regenerative braking system.
Further, in step S3, the distribution rule for distributing the braking forces of the front and rear wheels is:
A. when the braking strength z is less than or equal to 0.15, the braking strength of the interval is small, namely the braking force demand of the electric automobile is small, and at the moment, all the braking force is provided by the front wheels;
B. when the braking strength is more than 0.15 and less than or equal to 0.7, the braking strength in the interval is not large, and in order to enable the regenerative braking force of the front wheels to participate in the braking process as much as possible, the braking forces of the front wheels and the rear wheels are distributed according to an ECE (equal cost effectiveness) rule curve;
C. when the braking strength is more than 0.7 and less than or equal to 0.8, the braking strength in the interval is larger, and in order to improve the safety of the electric automobile during braking, the braking force of the front wheels and the braking force of the rear wheels are distributed according to a braking force distribution curve that the front wheels are firstly locked when the braking strength is 0.8;
D. when the braking strength z is larger than 0.8, the braking strength of the interval is large, and the braking force of the front wheel and the braking force of the rear wheel are distributed according to an I curve.
Further, in step S4, the design process of the fuzzy controller is as follows:
s41, creating a Mandani fuzzy inference algorithm;
s42, fuzzifying variables, and determining membership functions of input quantity and output quantity;
s43, generating a fuzzy rule control table: obtaining a corresponding fuzzy rule control table according to the membership function;
s44, outputting a fuzzy quantity P: obtaining a corresponding output value P according to the input quantity and the fuzzy rule control table, wherein the output value P is the fuzzy quantity;
s45, defuzzification is carried out on the output quantity P.
Further, the step S42 fuzzifies the variable and determines membership functions of the input quantity and the output quantity, including the following processes:
A. the braking strength z is divided into three subsets (L, M, H), the domain is [0,1], and the membership function adopts a trapmf (trapezoid) type, wherein L represents low braking strength, M represents medium braking strength, and H represents high braking strength. When the braking strength is low or moderate, the ratio P of the regenerative braking force to the total braking force of the front wheels is slightly larger, and when the braking strength is high, particularly in emergency braking (z is more than 0.8), the ratio P is reduced until the ratio P is 0, namely the mechanical friction braking force is fully charged into the braking force of the front wheels;
B. the vehicle speed v of the whole vehicle is also divided into three subsets (L, M, H), the domain of discourse is [0,120], and the membership function adopts a trapmf (trapezoid) type, wherein L represents low vehicle speed, M represents moderate vehicle speed, and H represents high vehicle speed. When the vehicle speed is low or moderate, the braking strength is not large, and the value P can be increased as much as possible; when the vehicle speed is higher, particularly more than 100km/h, the P value is reduced as much as possible until 0, because the vehicle runs at high speed, delayed regenerative braking easily causes safety accidents, and the motor has high rotating speed, small electromagnetic torque and limited recovered energy;
C. the SOC value SOC _ n of the power battery is also divided into three subsets { L, M, H }, the domain of discourse is [0,1], and the membership function adopts gauss2mf (Gaussian 2 type), wherein L represents a low state of charge, M represents a medium state of charge, and H represents a high state of charge. When the state of charge is very high (more than 90%) or very low (less than 15%), the charging has an influence on the normal life of the power battery, so the aforementioned P value should be small, and when the state of charge is moderate, the P value should be increased as much as possible;
D. the ratio P of the regenerative braking force to the total braking force of the front wheels is divided into five subsets { VL, L, M, H, VH }, the domain of argument is [0,1], and the membership function adopts trimf (triangle). Wherein VL and L represent very low and low regenerative braking ratios, respectively, i.e. the motor generates very little regenerative braking force, the front wheels being mainly mechanical brakes; m represents a moderate regenerative braking proportion, namely the regenerative braking force generated by the motor is equivalent to the mechanical braking force; h and VH represent high and very high regenerative braking ratios, respectively, i.e. the motor generates more regenerative braking force, the front wheels being mainly regenerative braking.
Preferably, in the step S45, in the defuzzifying the output quantity P, a gravity center method is used for defuzzifying, the weight in the gravity center method is the membership of each fuzzy quantity, and the calculation formula is:
wherein i is a rule, m is a rule entry, and μ is a membership of a corresponding fuzzy quantity.
The invention has the following beneficial effects:
the regenerative braking control method of the front-wheel-drive electric vehicle based on the fuzzy control, which is developed by the invention, realizes the maximum recovery of braking energy on the basis of ensuring the braking safety of the whole vehicle by reasonably distributing the braking force of the front wheels and the braking force of the rear wheels and the proportion of the regenerative braking force of the front wheels;
the overall energy consumption of the front-wheel-drive electric automobile in running is reduced, the long-distance running capacity of the electric automobile is improved, and the problem of mileage anxiety of a driver is relieved to a certain extent.
Drawings
FIG. 1 is a flowchart illustrating a method for controlling regenerative braking of a front-wheel-drive electric vehicle based on fuzzy control according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and specific embodiments.
A method for controlling the regenerative braking of a forerunner electric vehicle based on fuzzy control includes judging the braking strength of the whole vehicle during braking, calculating the braking force of front and rear wheels according to a certain rule, obtaining the proportional relation between mechanical friction braking force and regenerative braking force according to the braking strength, the SOC of a battery and the vehicle speed value of the whole vehicle through calculation of a fuzzy controller, and generating corresponding braking force through an executing mechanism to enable the whole vehicle to complete braking and energy recovery. The method specifically comprises the following steps:
step one, calculating the distribution of the braking force of front and rear wheels:
s1, judging that the whole automobile is in a braking stage after a vehicle-mounted sensor of the forerunner electric automobile detects information of the increase of the opening degree of a brake pedal;
s2, judging and calculating the braking strength z of the whole vehicle by detecting the change size and the change rate of the opening degree of the brake pedal and the vehicle speed information of the whole vehicle;
and S3, distributing the braking force of the front wheel and the braking force of the rear wheel according to different braking strengths z.
Step two, calculating the distribution of the mechanical friction braking force and the regenerative braking force of the front wheel:
and S4, inputting the vehicle speed v of the whole vehicle, the SOC value SOC _ n of the power battery and the brake intensity value z of the whole vehicle, which are detected by the sensor, into a fuzzy controller of the brake system, wherein the corresponding output variable is the ratio P of the regenerative braking force of the front wheels to the total braking force of the front wheels, and calculating to obtain the distribution relation between the mechanical friction braking force and the regenerative braking force of the front wheels.
Step three, the actuating mechanism carries out braking operation:
according to the first step and the second step, the specific braking force of the front wheel and the specific braking force of the rear wheel during braking and the proportion of the regenerative braking force of the front wheel can be obtained, and the braking operation can be carried out through an actuating mechanism of a mechanical braking system and a regenerative braking system.
Further, in step S3, the distribution rule for distributing the braking forces of the front and rear wheels is:
A. when the braking strength z is less than or equal to 0.15, the braking strength of the interval is small, namely the braking force demand of the electric automobile is small, and at the moment, all the braking force is provided by the front wheels;
B. when the braking strength is more than 0.15 and less than or equal to 0.7, the braking strength in the interval is not large, and in order to enable the regenerative braking force of the front wheels to participate in the braking process as much as possible, the braking forces of the front wheels and the rear wheels are distributed according to an ECE (equal cost effectiveness) rule curve;
C. when the braking strength is more than 0.7 and less than or equal to 0.8, the braking strength in the interval is larger, and in order to improve the safety of the electric automobile during braking, the braking force of the front wheels and the braking force of the rear wheels are distributed according to a braking force distribution curve that the front wheels are firstly locked when the braking strength is 0.8;
D. when the braking strength z is larger than 0.8, the braking strength of the interval is large, and the braking force of the front wheel and the braking force of the rear wheel are distributed according to an I curve.
Further, in step S4, the design process of the fuzzy controller is as follows:
s41, creating a Mandani fuzzy inference algorithm;
s42, fuzzifying variables, and determining membership functions of input quantity and output quantity;
s43, generating a fuzzy rule control table: obtaining a corresponding fuzzy rule control table according to the membership function;
s44, outputting a fuzzy quantity P: obtaining a corresponding output value P according to the input quantity and the fuzzy rule control table, wherein the output value P is the fuzzy quantity;
s45, defuzzification is carried out on the output quantity P.
Further, the step S42 fuzzifies the variable and determines membership functions of the input quantity and the output quantity, including the following processes:
A. the braking strength z is divided into three subsets (L, M, H), the domain is [0,1], and the membership function adopts a trapmf (trapezoid) type, wherein L represents low braking strength, M represents medium braking strength, and H represents high braking strength. When the braking strength is low or moderate, the ratio P of the regenerative braking force to the total braking force of the front wheels is slightly larger, and when the braking strength is high, particularly in emergency braking (z is more than 0.8), the ratio P is reduced until the ratio P is 0, namely the mechanical friction braking force is fully charged into the braking force of the front wheels;
B. the vehicle speed v of the whole vehicle is also divided into three subsets (L, M, H), the domain of discourse is [0,120], and the membership function adopts a trapmf (trapezoid) type, wherein L represents low vehicle speed, M represents moderate vehicle speed, and H represents high vehicle speed. When the vehicle speed is low or moderate, the braking strength is not large, and the value P can be increased as much as possible; when the vehicle speed is higher, particularly more than 100km/h, the P value is reduced as much as possible until 0, because the vehicle runs at high speed, delayed regenerative braking easily causes safety accidents, and the motor has high rotating speed, small electromagnetic torque and limited recovered energy;
C. the SOC value SOC _ n of the power battery is also divided into three subsets { L, M, H }, the domain of discourse is [0,1], and the membership function adopts gauss2mf (Gaussian 2 type), wherein L represents a low state of charge, M represents a medium state of charge, and H represents a high state of charge. When the state of charge is very high (more than 90%) or very low (less than 15%), the charging has an influence on the normal life of the power battery, so the aforementioned P value should be small, and when the state of charge is moderate, the P value should be increased as much as possible;
D. the ratio P of the regenerative braking force to the total braking force of the front wheels is divided into five subsets { VL, L, M, H, VH }, the domain of argument is [0,1], and the membership function adopts trimf (triangle). Wherein VL and L represent very low and low regenerative braking ratios, respectively, i.e. the motor generates very little regenerative braking force, the front wheels being mainly mechanical brakes; m represents a moderate regenerative braking proportion, namely the regenerative braking force generated by the motor is equivalent to the mechanical braking force; h and VH represent high and very high regenerative braking ratios, respectively, i.e. the motor generates more regenerative braking force, the front wheels being mainly regenerative braking.
Preferably, in the step S45, in the defuzzifying the output quantity P, a gravity center method is used for defuzzifying, the weight in the gravity center method is the membership of each fuzzy quantity, and the calculation formula is:
wherein i is a rule, m is a rule entry, and μ is a membership of a corresponding fuzzy quantity.
Examples
The work flow of the method refers to fig. 1, and the method comprises the following steps:
step one, calculating the distribution of the braking force of the front wheel and the rear wheel
S1: after detecting the information of the increase of the opening degree of a brake pedal, a brake pedal sensor of the forerunner electric vehicle transmits a brake signal to a vehicle controller and judges that the vehicle is in a braking stage;
s2: the brake pedal sensor detects the change size and the change rate of the opening degree of the brake pedal, the vehicle speed sensor detects a vehicle speed change signal of the whole vehicle, the signal is transmitted to the whole vehicle controller, and the braking strength z of the whole vehicle is calculated;
s3: and reasonably distributing the braking force of the front wheel and the braking force of the rear wheel according to different braking strengths z and a certain rule. The specific allocation rules are as follows:
A. when the braking strength z is less than or equal to 0.15, the braking strength of the interval is small, namely the braking force demand of the electric automobile is small, and at the moment, all the braking force is provided by the front wheels;
B. when the braking strength is more than 0.15 and less than or equal to 0.7, the braking strength in the interval is not large, and in order to enable the regenerative braking force of the front wheels to participate in the braking process as much as possible, the braking forces of the front wheels and the rear wheels are distributed according to an ECE (equal cost effectiveness) rule curve;
C. when the braking strength is more than 0.7 and less than or equal to 0.8, the braking strength in the interval is larger, and in order to improve the safety of the electric automobile during braking, the braking force of the front wheels and the braking force of the rear wheels are distributed according to a braking force distribution curve that the front wheels are firstly locked when the braking strength is 0.8;
D. when the braking strength z is larger than 0.8, the braking strength of the interval is large, and the braking force of the front wheel and the braking force of the rear wheel are distributed according to an I curve.
It should be noted that the situation that the braking strength exceeds 0.8 is rarely achieved during the driving of the civil front-wheel drive car, and if the braking with the strength is generated, the braking force is distributed according to an I curve which is an ideal braking force distribution curve of the front wheel brake and the rear wheel brake, so that the safest braking mode is realized.
Step two, calculating the distribution of the mechanical friction braking force and the regenerative braking force of the front wheel
S4: the method comprises the steps that the vehicle speed v of the whole vehicle, the SOC value SOC _ n of a power battery and the brake intensity value z of the whole vehicle, which are detected by a sensor, are used as input variables and input into a fuzzy controller of a brake system, the corresponding output variable is the ratio P of the regenerative braking force of a front wheel to the total braking force of the front wheel, and the distribution relation between the mechanical friction braking force of the front wheel and the regenerative braking force can be obtained after calculation is finished;
the specific fuzzy controller design, as mentioned above, is not discussed here.
S41, creating a fuzzy inference algorithm
The fuzzy inference algorithm of the invention is a Mandani type algorithm.
S42, variable fuzzification is carried out, and membership functions of input quantity and output quantity are determined
A. The braking strength z is divided into three subsets (L, M, H), the domain is [0,1], and the membership function adopts a trapmf (trapezoid) type, wherein L represents low braking strength, M represents medium braking strength, and H represents high braking strength. When the braking strength is low or moderate, the ratio P of the regenerative braking force to the total braking force of the front wheels is slightly larger, and when the braking strength is high, particularly in emergency braking (z is more than 0.8), the ratio P is reduced until the ratio P is 0, namely the mechanical friction braking force is fully charged into the braking force of the front wheels;
B. the vehicle speed v of the whole vehicle is also divided into three subsets (L, M, H), the domain of discourse is [0,120], and the membership function adopts a trapmf (trapezoid) type, wherein L represents low vehicle speed, M represents moderate vehicle speed, and H represents high vehicle speed. When the vehicle speed is low or moderate, the braking strength is not large, and the value P can be increased as much as possible; when the vehicle speed is higher, particularly more than 100km/h, the P value is reduced as much as possible until 0, because the vehicle runs at high speed, delayed regenerative braking easily causes safety accidents, and the motor has high rotating speed, small electromagnetic torque and limited recovered energy;
C. the SOC value SOC _ n of the power battery is also divided into three subsets { L, M, H }, the domain of discourse is [0,1], and the membership function adopts gauss2mf (Gaussian 2 type), wherein L represents a low state of charge, M represents a medium state of charge, and H represents a high state of charge. When the state of charge is very high (more than 90%) or very low (less than 15%), the charging has an influence on the normal life of the power battery, so the aforementioned P value should be small, and when the state of charge is moderate, the P value should be increased as much as possible;
D. the ratio P of the regenerative braking force to the total braking force of the front wheels is divided into five subsets { VL, L, M, H, VH }, the domain of argument is [0,1], and the membership function adopts trimf (triangle). Wherein VL and L represent very low and low regenerative braking ratios, respectively, i.e. the motor generates very little regenerative braking force, the front wheels being mainly mechanical brakes; m represents a moderate regenerative braking proportion, namely the regenerative braking force generated by the motor is equivalent to the mechanical braking force; h and VH represent high and very high regenerative braking ratios, respectively, i.e. the motor generates more regenerative braking force, the front wheels being mainly regenerative braking.
The corresponding arrangement of membership functions and image generation are not described in detail above.
S43, generating a fuzzy rule base
According to the setting of the membership function and the corresponding algorithm, a corresponding fuzzy rule control table, namely a fuzzy rule base, can be obtained, and the specific rule is as shown in table 1.
TABLE 1
It should be noted that the number of subsets and the universe of discourse value of each input quantity can be fine-tuned, and if the number of subsets changes, the number of rules in the fuzzy rule base will also increase or decrease accordingly.
S44, outputting the fuzzy quantity P
And obtaining a corresponding output value P according to the three input variables and the fuzzy rule base, wherein P is a fuzzy quantity.
S45, defuzzification is carried out on the output quantity P
The text adopts a gravity center method for defuzzification, the weight in the gravity center method is the membership of each fuzzy quantity, and the calculation formula is as follows:
wherein i is a rule, m is a rule entry, and μ is a membership of a corresponding fuzzy quantity.
Step three: the actuator performs braking and energy recovery operations
According to the first step and the second step, the specific braking force of the front wheel and the specific braking force of the rear wheel during braking and the proportion of the regenerative braking force of the front wheel can be obtained, and the braking operation can be carried out through an actuating mechanism of a mechanical braking system and a regenerative braking system.
S5: according to the calculation of the fuzzy rule base, if the ratio P of the regenerative braking force to the total braking force of the front wheels is 0, namely because the vehicle speed, the SOC value or the braking intensity value is not suitable (for example, the SOC value exceeds 90%, or the braking intensity exceeds 0.8, or the vehicle speed exceeds 100km/h and the like), the braking energy is not recovered, and the braking force of the front wheels and the braking force of the rear wheels are completely provided by the mechanical braking force;
s6: and if the ratio P of the regenerative braking force to the total front wheel braking force is not 0, starting the motor to brake for energy recovery. At the moment, a driver steps on the brake pedal force of the brake pedal stroke, a brake master cylinder is utilized to generate corresponding front and rear axle hydraulic braking force, a rear wheel brake generates corresponding braking force according to the calculated value in the step one, the motor serves as a generator at the moment, corresponding electromagnetic braking torque is generated according to different P values, and the rest braking force is provided by a front wheel brake;
s7: the process of generating the regenerative braking force by the motor is the process of generating electricity by dragging the motor back by the wheel power, namely the process of converting the kinetic energy of the automobile into electric energy, the generated electric energy is transmitted to the power battery through a corresponding circuit of the battery management system, and the electric energy is stored in the power battery. Meanwhile, under the combined action of the braking force of the front wheel brake and the braking force of the rear wheel brake and the electromagnetic braking torque, the electric automobile completes the braking process.
It should be noted that, the maximum electromagnetic braking force that the vehicle-mounted driving motor can provide is limited, and the calculation method is as follows:
wherein, FmaxThe maximum electromagnetic braking force which can be provided by the motor is N; t ismaxIs the motor peak torque, in n.m; r is the wheel radius in m; pmaxThe unit is peak power of the motor, wherein n is motor rotating speed and nbThe unit is r/min for the rated rotating speed of the motor.
In step S7, if the motor regenerative braking force calculated from the P value is larger than FmaxThat is, when the braking force of the electric motor cannot provide a theoretical calculated value, the corresponding braking force difference is supplemented by the front wheel mechanical brake.
According to corresponding laws and theoretical knowledge, a method for distributing braking force of front and rear wheel brakes in a braking working condition is formulated for a front-wheel-drive electric automobile, a fuzzy control method is used, a proportional relation between the regenerative braking force of the front wheels and mechanical braking force is calculated, and the proportional relation is used as a theoretical basis for operation of a braking execution mechanism, so that the whole automobile realizes maximum braking energy recovery on the basis of ensuring braking safety;
the fuzzy rule is formulated according to the power characteristics of the whole vehicle brake, the service life characteristics of the battery, the brake safety and other factors, and the brake performance of the whole vehicle is improved.
Claims (5)
1. A regenerative braking control method of a forerunner electric vehicle based on fuzzy control is characterized by comprising the following steps:
step one, calculating the distribution of the braking force of front and rear wheels:
s1, judging that the whole automobile is in a braking stage after a vehicle-mounted sensor of the forerunner electric automobile detects information of the increase of the opening degree of a brake pedal;
s2, judging and calculating the braking strength z of the whole vehicle by detecting the change size and the change rate of the opening degree of the brake pedal and the vehicle speed information of the whole vehicle;
s3, distributing the braking force of the front wheel and the braking force of the rear wheel according to different braking strengths z;
step two, calculating the distribution of the mechanical friction braking force and the regenerative braking force of the front wheel:
s4, inputting the vehicle speed v of the whole vehicle, the SOC value SOC _ n of the power battery and the brake intensity value z of the whole vehicle, which are detected by the sensor, into a fuzzy controller of a brake system, wherein the corresponding output variable is the ratio P of the regenerative braking force of the front wheels to the total braking force of the front wheels, and calculating to obtain the distribution relation between the mechanical friction braking force and the regenerative braking force of the front wheels;
step three, the actuating mechanism carries out braking operation:
according to the first step and the second step, the specific braking force of the front wheel and the rear wheel during braking and the proportion of the regenerative braking force of the front wheel can be obtained, and braking operation is carried out through an actuating mechanism of a mechanical braking system and an actuating mechanism of a regenerative braking system.
2. The method for controlling the regenerative braking of the predecessor electric vehicle based on the fuzzy control as claimed in claim 1, wherein in step S3, the distribution rule for the braking force of the front and back wheels is:
A. when the braking strength z is less than or equal to 0.15, the braking strength of the interval is small, namely the braking force demand of the electric automobile is small, and at the moment, all the braking force is provided by the front wheels;
B. when the braking strength is more than 0.15 and less than or equal to 0.7, the braking strength in the interval is not large, and in order to enable the regenerative braking force of the front wheels to participate in the braking process as much as possible, the braking forces of the front wheels and the rear wheels are distributed according to an ECE (equal cost effectiveness) rule curve;
C. when the braking strength is more than 0.7 and less than or equal to 0.8, the braking strength in the interval is larger, and in order to improve the safety of the electric automobile during braking, the braking force of the front wheels and the braking force of the rear wheels are distributed according to a braking force distribution curve that the front wheels are firstly locked when the braking strength is 0.8;
D. when the braking strength z is larger than 0.8, the braking strength of the interval is large, and the braking force of the front wheel and the braking force of the rear wheel are distributed according to an I curve.
3. The method as claimed in claim 1, wherein in step S4, the fuzzy controller is designed by:
s41, creating a Mandani fuzzy inference algorithm;
s42, fuzzifying variables, and determining membership functions of input quantity and output quantity;
s43, generating a fuzzy rule control table: obtaining a corresponding fuzzy rule control table according to the membership function;
s44, outputting a fuzzy quantity P: obtaining a corresponding output value P according to the input quantity and a fuzzy rule control table, wherein the output value P is a fuzzy quantity;
s45, defuzzification is carried out on the output quantity P.
4. The method for controlling the regenerative braking of the precursor electric vehicle based on the fuzzy control as claimed in claim 3, wherein the step S42 is characterized in that the variable is fuzzified and membership functions of the input quantity and the output quantity are determined, and the method comprises the following processes:
A. the braking strength z is divided into three subsets { L, M, H }, the domain of discourse is [0,1], and the membership function adopts a trapmf type;
wherein, L represents low brake intensity, M represents medium brake intensity, and H represents high brake intensity;
when the braking strength is low or moderate, the ratio P of the regenerative braking force to the total braking force of the front wheels is slightly larger; when the braking strength is higher, the ratio P is reduced until the ratio P is 0, namely the mechanical friction braking force is fully used for filling the braking force of the front wheel;
B. the vehicle speed v of the whole vehicle is divided into three subsets { L, M, H }, the domain of discourse is [0,120], and the membership function adopts a trapmf type;
wherein L represents a lower vehicle speed, M represents a moderate vehicle speed, and H represents a higher vehicle speed;
when the vehicle speed is low or moderate, the P value is increased; when the vehicle speed exceeds 100km/h, the P value is reduced until the P value is 0, the electromagnetic torque is small, and the recovered energy is limited;
C. the SOC value SOC _ n of the power battery is divided into three subsets { L, M, H }, the domain of discourse is [0,1], and the membership function adopts a gauss2mf type;
wherein L represents a low state of charge, M represents a medium state of charge, and H represents a high state of charge;
when the state of charge exceeds 90% or the state of charge is less than 15%, the aforementioned P value is small; when the charge state is moderate, the P value is increased;
D. the ratio P of the regenerative braking force to the total braking force of the front wheels is divided into five subsets { VL, L, M, H and VH }, the domain of discourse is [0,1], and a membership function adopts a trimf type;
wherein VL and L represent very low and low regenerative braking ratios, respectively, i.e. the motor generates very little regenerative braking force, the front wheels being mainly mechanical brakes; m represents a moderate regenerative braking proportion, namely the regenerative braking force generated by the motor is equivalent to the mechanical braking force; h and VH represent high and very high regenerative braking ratios, respectively, i.e. the motor generates more regenerative braking force, the front wheels being mainly regenerative braking.
5. The method as claimed in claim 3, wherein in the step S45, the output quantity P is defuzzified by a centroid method, the weight in the centroid method is the degree of membership of each fuzzy quantity, and the calculation formula is:
wherein i is a rule, m is a rule entry, and μ is a membership of a corresponding fuzzy quantity.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011618813.9A CN112677771A (en) | 2020-12-31 | 2020-12-31 | Method for controlling regenerative braking of forerunner electric vehicle based on fuzzy control |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011618813.9A CN112677771A (en) | 2020-12-31 | 2020-12-31 | Method for controlling regenerative braking of forerunner electric vehicle based on fuzzy control |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112677771A true CN112677771A (en) | 2021-04-20 |
Family
ID=75453688
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011618813.9A Pending CN112677771A (en) | 2020-12-31 | 2020-12-31 | Method for controlling regenerative braking of forerunner electric vehicle based on fuzzy control |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112677771A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114454724A (en) * | 2022-03-11 | 2022-05-10 | 南京工业大学 | Intelligent pure electric vehicle braking energy recovery control method |
| CN114475262A (en) * | 2022-03-14 | 2022-05-13 | 万向钱潮股份有限公司 | Braking energy recovery control system and method for electric automobile |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1415839A1 (en) * | 2002-10-29 | 2004-05-06 | STMicroelectronics S.r.l. | Fuzzy logic control system for torque distribution in hybrid vehicles |
| CN104192107A (en) * | 2014-08-14 | 2014-12-10 | 济宁中科先进技术研究院有限公司 | Precursor electric automobile regenerative braking and ABS matching control method |
| CN104933264A (en) * | 2015-07-07 | 2015-09-23 | 电子科技大学 | Determining method for regenerative braking distribution coefficient of electric vehicle |
| CN109941245A (en) * | 2019-04-08 | 2019-06-28 | 哈尔滨理工大学 | A kind of electric vehicle brake force distribution method |
| CN110816282A (en) * | 2019-11-27 | 2020-02-21 | 江苏航运职业技术学院 | Regenerative braking control method for electric automobile |
| KR102142809B1 (en) * | 2019-07-03 | 2020-08-11 | (주)캠시스 | System and method for setting regenerative braking value |
| CN111572369A (en) * | 2020-05-19 | 2020-08-25 | 电子科技大学 | Fuel cell hybrid electric vehicle energy management online optimization method based on improved genetic algorithm |
| CN112078371A (en) * | 2020-08-13 | 2020-12-15 | 江苏理工学院 | Energy recovery method and energy recovery device of hybrid power supply electric vehicle |
-
2020
- 2020-12-31 CN CN202011618813.9A patent/CN112677771A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1415839A1 (en) * | 2002-10-29 | 2004-05-06 | STMicroelectronics S.r.l. | Fuzzy logic control system for torque distribution in hybrid vehicles |
| CN104192107A (en) * | 2014-08-14 | 2014-12-10 | 济宁中科先进技术研究院有限公司 | Precursor electric automobile regenerative braking and ABS matching control method |
| CN104933264A (en) * | 2015-07-07 | 2015-09-23 | 电子科技大学 | Determining method for regenerative braking distribution coefficient of electric vehicle |
| CN109941245A (en) * | 2019-04-08 | 2019-06-28 | 哈尔滨理工大学 | A kind of electric vehicle brake force distribution method |
| KR102142809B1 (en) * | 2019-07-03 | 2020-08-11 | (주)캠시스 | System and method for setting regenerative braking value |
| CN110816282A (en) * | 2019-11-27 | 2020-02-21 | 江苏航运职业技术学院 | Regenerative braking control method for electric automobile |
| CN111572369A (en) * | 2020-05-19 | 2020-08-25 | 电子科技大学 | Fuel cell hybrid electric vehicle energy management online optimization method based on improved genetic algorithm |
| CN112078371A (en) * | 2020-08-13 | 2020-12-15 | 江苏理工学院 | Energy recovery method and energy recovery device of hybrid power supply electric vehicle |
Non-Patent Citations (3)
| Title |
|---|
| FENZHU JI 等: "Energy recovery based on pedal situation for regenerative braking system of electric vehicle", 《VEHICLE SYSTEM DYNAMICS》 * |
| SHENGQIN LI 等: "Research on braking energy recovery strategy of electric vehicle based on ECE regulation and I curve", 《SCIENCE PROGRESS》 * |
| 姜良超: "多制动工况下纯电动车制动控制策略研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114454724A (en) * | 2022-03-11 | 2022-05-10 | 南京工业大学 | Intelligent pure electric vehicle braking energy recovery control method |
| CN114475262A (en) * | 2022-03-14 | 2022-05-13 | 万向钱潮股份有限公司 | Braking energy recovery control system and method for electric automobile |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CA2581643C (en) | Parallel hybrid vehicle optimal storage system | |
| Tao et al. | A regenerative braking system for electric vehicle with four in-wheel motors based on fuzzy control | |
| CN107264293B (en) | A kind of energy reclaiming method of the parallel type hybrid vehicle based on fuzzy control | |
| CN112677771A (en) | Method for controlling regenerative braking of forerunner electric vehicle based on fuzzy control | |
| CN113147412A (en) | Brake energy recovery strategy for rear-drive pure electric commercial vehicle | |
| Zhang et al. | Powertrain design and energy management of a novel coaxial series-parallel plug-in hybrid electric vehicle | |
| CN109466525B (en) | Braking force distribution method and system for trolley bus | |
| JPH08251708A (en) | Electric vehicle | |
| WO2023098465A1 (en) | Braking and gear shifting control method and system for electric vehicle | |
| CN202200825U (en) | Plug-in hybrid passenger car driving system | |
| CN215204406U (en) | Range-extending power system based on flywheel power | |
| CN106994904B (en) | Energy recovery system and method of electric automobile based on brake-by-wire | |
| CN210502292U (en) | Electric automobile regenerative braking system based on intelligent control | |
| CN116443011A (en) | Electric automobile braking energy recovery layered control system and method based on V2V communication network | |
| Jingming et al. | Optimization of parallel regenerative braking control strategy | |
| CN115027475A (en) | Hybrid electric vehicle sliding energy recovery torque control method | |
| CN113771635B (en) | Energy recovery control method based on brake-by-wire | |
| CN105922987B (en) | Downshift strategy and Mechatronic Systems in HEV/EV process of regenerative braking | |
| Ye et al. | Research on parallel regenerative braking control of the electric commercial vehicle based on fuzzy logic | |
| CN207291695U (en) | A kind of energy integrated-type hybrid vehicle based on flywheel energy storage | |
| JP2010125877A (en) | Controller for hybrid electric vehicle | |
| Zhu et al. | Design and control of regenerative braking system | |
| Zhang et al. | Adaptive fuzzy controller for hybrid traction control system based on automatic road identification | |
| Song et al. | Control strategy of energy recovery of electric vehicles based on braking strength | |
| Tallón-Ballesteros et al. | Research on the Application of Fuzzy Control in Braking of Gasoline-Electric Hybrid Vehicles |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210420 |