CN112078371A - Energy recovery method and energy recovery device of hybrid power supply electric vehicle - Google Patents

Energy recovery method and energy recovery device of hybrid power supply electric vehicle Download PDF

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CN112078371A
CN112078371A CN202010811132.8A CN202010811132A CN112078371A CN 112078371 A CN112078371 A CN 112078371A CN 202010811132 A CN202010811132 A CN 202010811132A CN 112078371 A CN112078371 A CN 112078371A
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braking force
automobile
curve
braking
rear wheel
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姜苏杰
汪伟
罗金
张焱
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Jiangsu University of Technology
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Jiangsu University of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L7/00—Electrodynamic brake systems for vehicles in general
    • B60L7/10—Dynamic electric regenerative braking
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L50/00—Electric propulsion with power supplied within the vehicle
    • B60L50/40—Electric propulsion with power supplied within the vehicle using propulsion power supplied by capacitors
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L50/00—Electric propulsion with power supplied within the vehicle
    • B60L50/50—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells
    • B60L50/60—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells using power supplied by batteries
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L7/00—Electrodynamic brake systems for vehicles in general
    • B60L7/10—Dynamic electric regenerative braking
    • B60L7/18—Controlling the braking effect
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

The invention provides an energy recovery method and an energy recovery device of a hybrid power supply electric automobile, wherein the method comprises the following steps: according to the I curve, the f curve and the ECE braking law, a front wheel braking force distribution strategy and a rear wheel braking force distribution strategy are carried out, and the front wheel braking force and the rear wheel braking force of the automobile are obtained according to the distribution strategies; acquiring the speed, the braking strength and the SOC of a super capacitor of the automobile, inputting the speed, the braking strength and the SOC into a fuzzy controller, optimizing a membership function value of the fuzzy controller by adopting a hybrid PSO algorithm to obtain a new fuzzy controller, and substituting the new fuzzy controller with the new fuzzy controller to obtain a regenerative braking force proportion K; and acquiring the regenerative braking force of the motor according to the braking force of the front wheel and the rear wheel and the braking force K. The invention combines the ECE braking regulation with the ideal braking distribution I curve and the f curve, and optimizes the membership function value of the fuzzy controller by adopting a mixed PSO algorithm, thereby greatly improving the energy recovered by the motor, prolonging the driving mileage, and having simple control and easy implementation.

Description

Energy recovery method and energy recovery device of hybrid power supply electric vehicle
Technical Field
The invention relates to the technical field of automobiles, in particular to an energy recovery method of a hybrid power supply electric automobile, an energy recovery device of the hybrid power supply electric automobile and a non-transitory computer readable storage medium.
Background
At present, the insufficient driving mileage of the electric automobile is a main limiting factor for limiting the development of the electric automobile, wherein for the pure electric automobile, the driving mileage of the automobile can be improved by recovering energy during the braking of the automobile.
In the related art, the braking force distribution modes commonly used for electric vehicles include three types:
1. ideal brake force distribution control strategy based on I-curve: the ideal braking force distribution strategy is evolved from the basis of the I curve, and aims to enable the front wheels and the rear wheels to be locked simultaneously in the braking process of the automobile. The control strategy has the advantages of less energy recovery, more complex control system, higher requirements on hardware and control precision and difficult implementation.
2. Control strategy based on optimal braking energy recovery: the basic idea of the optimal braking energy recovery control strategy is as follows: in the braking process, the motor is enabled to participate in braking to the maximum extent. The control strategy has the best braking energy recovery effect, but the control is too complicated, and the braking stability is poor (the insufficient braking force is easily caused, and dangerous conditions are generated), so the practical value is not high.
3. The parallel regenerative braking force-based distribution control strategy comprises the following steps: the parallel regenerative braking force-based distribution control is specifically as follows: when the braking strength is low, the motor alone provides the required braking force. When the braking strength is high but emergency braking is not achieved, the regenerative braking force, the front wheel friction braking force and the rear wheel braking force are distributed according to a fixed proportion. The strategy has quick response and wide application. However, the control strategy is less in motor participation in the braking process and limited in recovered energy.
Disclosure of Invention
In order to solve the technical problems, the invention provides an energy recovery method for a hybrid power supply electric vehicle, which combines an ECE (Economic Commission of Europe, United nations European Economic Commission of automotive regulations) brake regulation with an ideal brake distribution I curve and an f curve, and optimizes a membership function value of a fuzzy controller by adopting a hybrid Particle Swarm Optimization (PSO) algorithm, so that the energy recovered by a motor can be greatly improved, the driving mileage is prolonged, and the method is simple to control and easy to implement.
The invention further provides an energy recovery device of the hybrid power supply electric automobile.
The invention also proposes a non-transitory computer-readable storage medium.
The technical scheme adopted by the invention is as follows:
the embodiment of the first aspect of the invention provides an energy recovery method for a hybrid power supply electric vehicle, wherein the hybrid power supply electric vehicle is a storage battery-super capacitor hybrid power supply electric vehicle, and the method comprises the following steps: acquiring an ideal braking distribution I curve and an ideal braking distribution f curve of the automobile according to automobile parameters; according to the I curve, the f curve and the ECE braking law, a front wheel braking force distribution strategy and a rear wheel braking force distribution strategy are carried out, and the front wheel braking force and the rear wheel braking force of the automobile are obtained according to the front wheel braking force distribution strategy and the rear wheel braking force distribution strategy; acquiring the speed, the braking strength and the SOC (State Of Charge) Of a super capacitor Of the automobile, inputting the speed, the braking strength and the SOC into a fuzzy controller, optimizing a membership function value Of the fuzzy controller by adopting a hybrid PSO algorithm to obtain a new fuzzy controller, and substituting the speed, the braking strength and the SOC Of the super capacitor Of the automobile into the new fuzzy controller to obtain a regenerative braking force proportion K; and acquiring the regenerative braking force of the motor according to the ratio K of the front wheel braking force, the rear wheel braking force and the regenerative braking force so as to recover energy according to the regenerative braking force of the motor.
According to one embodiment of the invention, obtaining the ideal brake distribution I-curve of the vehicle comprises: when the front wheel and the rear wheel are simultaneously locked during the braking of the automobile, the braking force relation between the ground of the front wheel and the ground of the rear wheel of the automobile is obtained according to the following formula (1):
Figure BDA0002631000450000021
wherein, FXb1For ground-to-front wheel braking force, FXb2For ground to rear wheel braking force, G is the weight of the vehicle, hgIs the height of the mass center, L is the wheelbase, b is the distance from the center of the rear axle to the mass center of the automobile; and drawing an ideal brake distribution I curve of the automobile according to the brake force relation of the front wheel ground and the rear wheel ground of the automobile.
According to one embodiment of the invention, an f-curve of the car is obtained: when the automobile is braked, the front wheel is locked to drag to slide firstly, then the rear wheel is locked, and the braking force relation between the front wheel and the ground of the rear wheel of the automobile is obtained according to the following formula (2):
Figure BDA0002631000450000031
wherein, FXb1For ground-to-front wheel braking force, FXb2For ground-to-rear wheel braking force, FZ2In order for the ground to react in the direction of the rear wheels,
Figure BDA0002631000450000032
is the coefficient of adhesion, G is the weight of the automobile, hgIs the height of the mass center, L is the wheelbase, b is the distance from the center of the rear axle to the mass center of the automobile; and drawing an f curve of the automobile according to the braking force relation of the front wheel ground and the rear wheel ground of the automobile.
According to one embodiment of the present invention, a front and rear wheel brake force distribution strategy according to the I-curve and f-curve and ECE brake regulations includes: obtaining the braking strength of the automobile; when the braking intensity is smaller than a first preset intensity, providing a regenerative braking force by a motor, wherein the first preset braking intensity is smaller than the braking intensity corresponding to the maximum regenerative braking force of the automobile; taking the intersection point of the first preset intensity and the x axis as a starting point to cut the ECE line, and obtaining the braking intensity of the cut point B; when the braking intensity is greater than or equal to the first preset intensity and less than the braking intensity at the tangent point B, distributing the front wheel braking force and the rear wheel braking force of the automobile according to the tangent line; obtaining the braking intensity of a point C where an f curve passing through the tangent point B and the I curve intersect, and when the braking intensity is greater than or equal to the braking intensity of the tangent point and less than or equal to the braking intensity of the point C, distributing the front wheel braking force and the rear wheel braking force of the automobile according to the f curve; and when the braking intensity is greater than the braking intensity at the intersection C, distributing the front wheel braking force and the rear wheel braking force of the automobile according to the I curve.
According to an embodiment of the present invention, obtaining a motor regenerative braking force from the front wheel braking force, the rear wheel braking force, and the regenerative braking force proportion K includes: and multiplying the regenerative braking force proportion K by the front wheel braking force to obtain the motor regenerative braking force.
According to an embodiment of the present invention, the method for optimizing the membership function value of the fuzzy controller by using the hybrid PSO algorithm comprises: setting the individual number N of the hybrid particle swarm algorithm, learning factors c1 and c2, inertia weight w, hybrid probability bc, hybrid pool size ratio bs, maximum iteration number Y and space search dimension D; evaluating the fitness of each particle according to an objective function, and randomly setting the speed Vi, the position xid, the individual optimal value pBest and the global optimal extreme value gBest of each particle through a particle swarm algorithm; the current particle position and velocity are updated according to the following equations (3.1) and (3.2):
Xij(t+1)＝Xij(t)+Vij(t+1)，i＝1…n，j＝1…d (3.1)
Vij(t+1)＝w*Vij(t)+c1*r1*(Pij-xij(t))+c2*r2*(Pgj-xij(t)) (3.2)
wherein x isij(t +1) is the position of the ith particle evolution to the jth dimension of the t +1 th generation, Vij(t +1) is the speed of the ith particle evolving to the jth dimension of the t +1 th generation, c1 and c2 are the learning factors, PijFor individual optimal particle positions r1 and r2 are [0,1]]Two random numbers within a range, w being the inertial weight; comparing the fitness value of each particle with the best position of the particleIf the current values are similar to the optimal positions of the particles, the current values are used as the optimal positions of the particles, all current individual optimal values pBest and the global optimal extreme value gBest are compared, and the global optimal extreme value gBest is updated; selecting a specified number of particles according to the set hybridization probability, putting the particles into a hybridization pool, hybridizing every two particles to generate a progeny particle n with the same number, and replacing a parent particle m with the progeny particle n, wherein the position n (x) and the speed n (v) of the progeny particle n are calculated according to the following formulas (3.3) and (3.4):
n(x)＝p*ml(x)+(1-p)*m2(x) (3.3)
n(v)＝|m1(v)|[(m1(v)+m2(v))/(|m1(v)+m2(v)|)] (3.4)
wherein n (x) is the position of the daughter particle n, n (v) is the velocity of the daughter particle n, p is a random number between [0,1], ml (x) is the position of one parent particle when crossed two by two, m2(x) is the position of the other parent particle when crossed two by two, m1(v) is the velocity of one parent particle when crossed two by two, m2(v) is the velocity of the other parent particle when crossed two by two; and judging whether the stopping condition is met, if so, stopping searching, outputting a result, otherwise, skipping to the step of updating the current particle position and speed according to the following formulas (3.1) and (3.2), and searching again.
In a second aspect of the present invention, an energy recovery apparatus for a hybrid power electric vehicle is provided, where the hybrid power electric vehicle is a battery-super capacitor hybrid power electric vehicle, and the apparatus includes: the first acquisition module is used for acquiring an ideal brake distribution I curve and an ideal brake distribution f curve of the automobile according to automobile parameters; the second acquisition module is used for carrying out a front wheel braking force distribution strategy and a rear wheel braking force distribution strategy according to the I curve, the f curve and an ECE braking regulation, and acquiring the front wheel braking force and the rear wheel braking force of the automobile according to the front wheel braking force distribution strategy and the rear wheel braking force distribution strategy; the optimization module is used for acquiring the speed, the braking strength and the SOC of the super capacitor of the automobile, inputting the speed, the braking strength and the SOC into a fuzzy controller, optimizing the membership function value of the fuzzy controller by adopting a hybrid PSO algorithm to obtain a new fuzzy controller, and substituting the speed, the braking strength and the SOC of the super capacitor of the automobile into the new fuzzy controller to obtain a regenerative braking force proportion K; the energy recovery module is used for acquiring the regenerative braking force of the motor according to the front wheel braking force, the rear wheel braking force and the regenerative braking force proportion K so as to recover energy according to the regenerative braking force of the motor
In a third aspect, the present invention provides a non-transitory computer readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the energy recovery method for a hybrid power electric vehicle according to the first aspect of the present invention.
The invention has the beneficial effects that:
the invention combines the ECE braking regulation with the ideal braking distribution I curve and the f curve, and optimizes the membership function value of the fuzzy controller by adopting a mixed PSO algorithm, thereby greatly improving the energy recovered by the motor, prolonging the driving mileage, and having simple control and easy implementation.
Drawings
FIG. 1 is a flow chart of a method of energy recovery for a hybrid power electric vehicle according to one embodiment of the present invention;
FIG. 2 is a schematic illustration of a front and rear wheel braking force distribution strategy according to one particular example of the present disclosure;
FIG. 3 is a schematic diagram of a braking force distribution framework of a fuzzy controller according to one embodiment of the present invention;
FIG. 4 is a schematic diagram of a membership function of a fuzzy controller according to one embodiment of the present invention;
FIG. 5 is a flow diagram of a hybrid PSO algorithm according to one embodiment of the invention;
fig. 6 is a block schematic diagram of an energy recovery apparatus of a hybrid power electric vehicle according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The electric automobile provided by the embodiment of the invention is a storage battery-super capacitor composite power supply pure electric automobile.
To examine the brake force distribution relationship between the front axle and the rear axle, first, the wheel state at the time of braking the vehicle is examined.
When the automobile brakes, the wheels have the following three conditions:
1. the front wheel and the rear wheel are locked at the same time (in an ideal state);
2. the front wheel is locked to drag the wheel to slide, and then the rear wheel is locked (in a stable state);
3. the rear wheels are locked to drag the wheel to slide first, and then the front wheels are locked (unstable state).
It is known from the theory of automobiles that the coefficient of adhesion of an automobile is not dependent on the road surface on which the automobile is traveling (different road surfaces)
Figure BDA0002631000450000061
Different), regardless of which of the front and rear wheels is locked first, the braking strength Z and the adhesion coefficient
Figure BDA0002631000450000065
Are all equal. At this time
Figure BDA0002631000450000062
Wherein, FxbIs the braking force of the ground,
Figure BDA0002631000450000063
is the ground adhesion, G is the gravity of the vehicle,
Figure BDA0002631000450000064
is the coefficient of adhesion.
When the wheel is in the condition 1, the front wheel and the rear wheel are locked at the same time, and at the moment, the braking force relation of the ground of the front wheel and the ground of the rear wheel of the automobile is obtained according to the following formula (1):
Figure BDA0002631000450000071
wherein, FXb1For ground-to-front wheel braking force, FXb2For ground to rear wheel braking force, G is the weight of the vehicle, hgThe height of the automobile mass center, L is the wheelbase, and b is the distance from the center of the rear axle to the automobile mass center;
the ideal braking distribution I curve of the automobile can be drawn according to the braking force relation of the ground of the front wheels and the ground of the rear wheels of the automobile, so that the distribution of the braking force is below the I curve.
When the wheels are in the condition 2, namely the front wheels are locked to drag and slide firstly, and then the rear wheels are locked, the braking force relation between the ground of the front wheels and the ground of the rear wheels of the automobile is obtained according to the following formula (2):
Figure BDA0002631000450000072
wherein, FXb1For ground-to-front wheel braking force, FXb2For ground-to-rear wheel braking force, FZ2In order for the ground to react in the direction of the rear wheels,
Figure BDA0002631000450000074
is the coefficient of adhesion, G is the weight of the automobile, hgThe automobile is the height of the mass center, L is the wheelbase, and b is the distance from the center of the rear axle to the mass center of the automobile;
and (4) drawing an f curve of the automobile according to the relation of the braking force of the front wheel and the braking force of the rear wheel of the automobile.
When the wheel is in the above condition 3, the braking force relationship between the front wheel and the rear wheel of the automobile is obtained according to the following formula (3):
Figure BDA0002631000450000073
wherein, FXb2For the ground to brake the rear wheels,
Figure BDA0002631000450000075
is the coefficient of adhesion, FZ2For ground reaction in the direction of the rear wheels, L is the wheelbase, hgThe height of the center of mass of the automobile is shown, and a is the distance from the center of the front axle to the center of mass of the automobile.
And drawing an r curve of the automobile according to the relation of the braking force of the front wheel and the braking force of the rear wheel of the automobile.
Meanwhile, the ECE makes the following regulation on the distribution of the braking force of the front wheel and the rear wheel of the two-axle car: when the braking strength Z of the automobile is in the range of [ 0.20.8 ], in order to ensure braking safety, the braking strength of the automobile needs to satisfy the following formula (4):
Figure BDA0002631000450000081
wherein Z is the braking strength of the automobile,
Figure BDA0002631000450000082
is the coefficient of adhesion.
The ECE regulation also states that the front wheel grip coefficient utilization curve must be above the rear wheel, i.e. the front wheel brake grip coefficient is greater than the rear wheel grip coefficient, and equation (5) can be derived from equation (4):
Figure BDA0002631000450000083
Figure BDA0002631000450000084
Figure BDA0002631000450000085
wherein the content of the first and second substances,
Figure BDA0002631000450000086
in order to obtain the coefficient of adhesion of the front wheels,
Figure BDA0002631000450000087
is attached to the rear wheelCoefficient of influence, Fμ1For front wheel braking force, Fμ2The braking force of the rear wheel, G is the gravity of the automobile, Z is the braking strength of the automobile, b is the distance from the center of the rear axle to the mass center of the automobile, a is the distance from the center of the front axle to the mass center of the automobile, and h is the braking force of the rear wheelgIs the height of the center of mass of the automobile, and L is the wheelbase.
The front and rear wheel braking force expressions (6), (7) can be obtained from the expression (5):
Figure BDA0002631000450000088
Fμ1＝mgZ-Fμ2 (7)
wherein, Fμ1For front wheel braking force, Fμ2The braking force of the rear wheel, G is the gravity of the automobile, Z is the braking strength of the automobile, b is the distance from the center of the rear axle to the mass center of the automobile, and hgIs the height of the mass center of the automobile, L is the wheelbase, m is the mass of the automobile, and g is the acceleration of gravity.
According to the equations (6) and (7), the front and rear wheel braking force distribution relation under the ECE R13 regulation can be obtained, as shown in the equation (8):
Figure BDA0002631000450000091
wherein, FXb1For ground-to-front wheel braking force, FXb2The braking force of the ground to the rear wheel, b is the distance from the center of the rear axle to the mass center of the automobile, L is the wheelbase, hgIs the height of the center of mass of the automobile, and G is the gravity of the automobile.
The ECE line can be drawn by substituting the above-mentioned vehicle-related parameters.
Fig. 1 is a flowchart of an energy recovery method of a hybrid power electric vehicle according to an embodiment of the present invention. As shown in fig. 1, the method comprises the steps of:
and S1, acquiring an ideal brake distribution I curve and an ideal brake distribution f curve of the automobile according to the automobile parameters. Wherein the vehicle parameters include: gravity g of the automobile and height h of the center of massgAxle distance L, distance b from center of rear axle to mass center of automobile and adhesion coefficient
Figure BDA0002631000450000092
And S2, performing a front and rear wheel braking force distribution strategy according to the I curve and the f curve and an ECE braking regulation, and acquiring the front wheel braking force and the rear wheel braking force of the automobile according to the front and rear wheel braking force distribution strategy.
Further, according to an embodiment of the present invention, a front and rear wheel brake force distribution strategy according to the I-curve and the f-curve and the ECE brake regulation includes: obtaining the braking strength Z of the automobile; when the braking intensity Z is less than a first preset intensity Z1, the motor provides regenerative braking force, wherein the first preset braking intensity Z1 is less than the braking intensity Z corresponding to the maximum regenerative braking force of the automobilemax(ii) a Taking the intersection point of the braking strength Z equal to the first preset strength Z1 and the x axis as a starting point to cut the ECE line, and acquiring the braking strength of the cut point B; when the braking strength is greater than or equal to the first preset strength and less than the braking strength at the tangent point B, distributing the front wheel braking force and the rear wheel braking force of the automobile according to the tangent line; acquiring the braking intensity of a point C of intersection of an f curve of a point B of the overcut point and the I curve, and when the braking intensity is greater than or equal to the braking intensity of the point B of the overcut point and less than or equal to the braking intensity of the point C of the intersection, distributing the braking force of the front wheel and the braking force of the rear wheel of the automobile according to the f curve; and when the braking intensity is greater than the braking intensity at the intersection C, distributing the front wheel braking force and the rear wheel braking force of the automobile according to the I curve.
And S3, acquiring the speed and the braking intensity of the automobile and the SOC of the super capacitor, inputting the speed and the braking intensity of the automobile and the SOC of the super capacitor into a fuzzy controller, optimizing the membership function value of the fuzzy controller by adopting a hybrid PSO algorithm to obtain a new fuzzy controller, and substituting the speed and the braking intensity of the automobile and the SOC of the super capacitor into the new fuzzy controller to obtain a regenerative braking force proportion K.
And S4, acquiring the motor regenerative braking force according to the front wheel braking force, the rear wheel braking force and the regenerative braking force proportion K, and recovering energy according to the motor regenerative braking force.
Specifically, as shown in fig. 2, the relevant parameters of the vehicle are substituted, for example, G1400 × 9.8, hgWhen L is 2.34 m, b is 1.3 m, and a is 1.04 m, I and F curves and ECE lines shown in fig. 2, where the vertical axis in fig. 2 represents the braking force F of the ground-facing rear wheel, may be obtainedXb2The horizontal axis represents the ground-to-front wheel braking force FXb1。
The braking intensity Z corresponding to the maximum regenerative braking force can be calculated first from the following equation (9)max。
Figure BDA0002631000450000101
Wherein Z ismaxBrake intensity corresponding to maximum regenerative braking force, TmaxTo maximum regenerative braking force, igFor a fixed speed ratio, i0The main reduction ratio is m, the mass of the automobile, g, the gravity acceleration, r, the wheel radius and eta, and the transmission efficiency.
Substituting the above-mentioned related parameters to obtain the brake intensity Z corresponding to the maximum regenerative braking forcemaxA value of 0.25, and a first preset intensity Z1 is set to be smaller than a braking intensity Z corresponding to a maximum regenerative braking force in order to ensure safety during brakingmaxIn this embodiment, the first preset intensity Z1 may be set to 0.15.
The braking strengths corresponding to the A, B, C points in fig. 2 are 0.15, 0.33, and 0.47, respectively. When the brake intensity is less than 0.15, which is braked only by the motor, the braking force follows the OA line distribution. The AB line is a tangent to the ECE line. The brake force distribution follows the AB line when the brake intensity is at [ 0.150.33 ]. And point C is the intersection point of the f line passing through point B and the curve I. The brake force distribution follows the BC line when the brake intensity is at [ 0.330.47 ]. When the brake intensity is greater than 0.47, the brake force distribution follows the CD line.
The effects affecting energy recovery are mainly related to energy storage, motor performance, vehicle speed, braking strength and the like. Therefore, the input of the fuzzy controller of the invention consists of the SOC value of the energy storage device, the vehicle speed and the braking strength. Because the composite power supply electric automobile is aimed at, the energy accumulator consists of the battery and the super capacitor, but the super capacitor bears peak power, and the SOC of the super capacitor is selected as the controllerAnd (4) inputting. In summary, as shown in FIG. 3, the input of the fuzzy controller is the SOC (SOC) of the super capacitoruc) The speed V and the braking strength Z of the automobile, and the output of the fuzzy controller is the regenerative braking force proportion K.
During braking, the total braking force is obtained and the respective braking forces of the front wheel and the rear wheel are determined. The fuzzy controller outputs the proportion K of regenerative braking force through Mamdani (Madamini fuzzy system) reasoning according to the vehicle speed V, the braking strength Z and the SOC value of the super capacitor, and the motor braking force is determined by multiplying the regenerative braking proportion K and the required braking force of the front wheels. Thus, the brake system divides the total braking force required into a front wheel friction braking force, a motor regenerative braking force, and a rear wheel friction braking force.
In the invention, the SOC (SOC) of the super capacitor is useduc) The braking intensity Z and the vehicle speed V are divided into 3 levels { L, M, H } respectively representing small, medium and large, and the regenerative braking force ratio K is divided into 11 levels, and the membership function thereof is shown in fig. 4.
Wherein the SOCuc：L:[0 0 0.2 0.26]，M:[0.15 0.24 0.68 0.78]，H:[0.68 0.78 1 1]；
V：L:[0 0 18 23]，M:[12.94 22.94 77.94 87.94]，H:[75 90 120 120]；
Z：L:[0 0 0.1 0.23]，M:[0.1 0.23 0.63 0.73]，H:[0.62 0.8 1 1]，
K：mf0:[0 0 0.1]，mf1:[0 0.1 0.2]，mf2:[0.1 0.2 0.3]，mf3:[0.2 0.3 0.4]，mf4:[0.3 0.4 0.5]，mf5:[0.4 0.5 0.6]，mf6:[0.5 0.6 0.7]，mf7:[0.6 0.7 0.8]，mf8:[0.7 0.8 0.9]，mf9:[0.8 0.9 1]，mf10[0.9 1 1]。
L, M, H are all trapezoidal functions, and mf0-mf10 are triangular functions.
The specific rules are shown in table 1 below.
TABLE 1 rules for regenerative braking fuzzy controller
Figure BDA0002631000450000111
Figure BDA0002631000450000121
Wherein NS, NE, NB, TS, M, NM, NTB in Table 1 represent the region range.
According to the invention, by means of the automobile simulation software ADVISOR, according to the brake force distribution principle introduced above, the parameters of the whole automobile are introduced, and a brake force distribution model is built and loaded into the controller, so that energy recovery effect graphs of different brake force distribution modes can be obtained.
In order to further improve the control effect, a mixed PSO algorithm can be adopted to optimize the membership function value of the controller.
Design objective this function: min [ ess _ out _ kj + ess2_ out _ kj ] (energy minimum)
Constraint conditions are as follows: the invention adopts a method for optimizing the membership value of the controller without changing the control rule, so that L, M, H and mf0-mf10 need to be subjected to upper and lower limit constraints. Setting the membership value of the SOCuc: l [ 000.20.26 ]]Set to x1-x 4; m [ 0.150.240.680.78 ]]Set to x5-x 8; h [ 0.680.7811 ]]Set to x9-x 12. The same operation is carried out on V, Z, K in sequence to finally obtain x1-x 69. Because the membership value is changed, there is a constraint of 1 by only upper and lower bounds on 69 randomly generated values: a isi≤xi≤b i 1≤i≤69
Meanwhile, if the energy consumption after optimization should be less than the energy consumption before optimization, there is a constraint of 2: fitness < ═ K; wherein K is the energy consumption before optimization, and Fitness is the energy consumption after optimization.
And finally, the generated new controller needs to meet the working condition track, and the error of the controller and the working condition track is set to be not more than 2 meters.
Then there is a constraint of 3: max (trace _ miss) < ═ 2.
The particle swarm algorithm is an optimization algorithm established based on the predation behaviors of birds. The optimal solution is found by iteration by randomly initializing a population of particles. Whenever an individual optimum (pBest) and a global optimum extremum (gBest) are found, their speed and position are updated by equations (10) (11).
Vi＝W*Vi+c1*r1(Pid-xid)+c2*r2(Pgd-xid) (10)
xid＝xid+Vi (11)
Wherein, W is an inertia weight, c1 and c2 are acceleration factors, r1 and r2 are two random numbers in the range of [0,1], pBest is a history optimal solution, and gBest is a global optimal solution; pid is the individual optimal particle position; pgd is the global optimum particle position, Vi is the particle velocity, and xid is the particle position.
However, the particle swarm optimization has a problem that the optimization of parameters is easy to fall into the problem of local optimization. In order to solve the problem, the hybrid particle swarm algorithm based on hybridization is adopted, the algorithm uses the genetic algorithm for reference, designated particles are selected to randomly hybridize pairwise in the process of each iteration, the same number of filial generation particles are generated, and the designated particles are used for replacing the father generation particles.
As shown in fig. 5, the membership function value of the fuzzy controller is optimized by using a hybrid PSO algorithm, which specifically comprises the following steps:
s301, setting the individual number N of the hybrid particle swarm algorithm, learning factors c1 and c2, inertia weight w, hybrid probability bc, hybrid pool size ratio bs, maximum iteration number Y and space search dimension D.
S302, evaluating the fitness of each particle according to the objective function, and randomly setting the speed Vi, the position xid, the individual optimal value pBest and the global optimal extreme value gBest of each particle through a particle swarm algorithm.
S303, updating the current particle position and velocity according to the following equations (3.1) and (3.2):
Xij(t+1)＝Xij(t)+Vij(t+1)，i＝1…n，j＝1…d (3.1)
Vij(t+1)＝w*Vij(t)+c1*r1*(Pij-xij(t))+c2*r2*(Pgj-xij(t)) (3.2)
wherein x isij(t +1) is the position of the ith particle evolution to the jth dimension of the t +1 th generation, Vij(t +1) is the speed of the ith particle evolving to the jth dimension of the t +1 th generation, c1 and c2 are the learning factors, PijFor individual optimal particle positions r1 and r2 are [0,1]]Two random numbers within the range, w is the inertial weight.
S304, the adaptive value of each particle is compared with the best position of the particle, if the adaptive value is similar to the best position of the particle, the current value is taken as the best position of the particle, all current individual optimal values pBest and the global optimal extreme value gBest are compared, and the global optimal extreme value gBest is updated.
S305, selecting a specified number of particles according to the set hybridization probability, putting the particles into a hybridization pool, hybridizing every two particles to generate the same number of filial generation particles n, and replacing parent particles m with the filial generation particles n. Wherein the position n (x) and velocity n (v) of the daughter particle n are calculated according to the following equations (3.3) and (3.4):
n(x)＝p*ml(x)+(1-p)*m2(x) (3.3)
n(v)＝|m1(v)|[(m1(v)+m2(v))/(|m1(v)+m2(v)|)] (3.4)
wherein n (x) is the position of the daughter particle n, n (v) is the velocity of the daughter particle n, p is a random number between [0,1], ml (x) is the position of one parent particle in pairwise hybridization, m2(x) is the position of the other parent particle in pairwise hybridization, m1(v) is the velocity of one parent particle in pairwise hybridization, and m2(v) is the velocity of the other parent particle in pairwise hybridization.
S306, judging whether the stop condition is reached. If yes, go to step S307; if not, the process returns to step S303.
S307, stopping searching and outputting the result.
In summary, according to the energy recovery method of the hybrid power supply electric vehicle of the embodiment of the invention, the ideal brake distribution I curve and f curve of the vehicle are obtained according to the vehicle parameters, the front and rear wheel brake force distribution strategies are performed according to the I curve and the f curve and the ECE brake regulation, the front wheel brake force and the rear wheel brake force of the vehicle are obtained according to the front and rear wheel brake force distribution strategies, the vehicle speed, the brake strength and the SOC of the super capacitor are obtained and input into the fuzzy controller, the membership function value of the fuzzy controller is optimized by adopting the hybrid PSO algorithm to obtain a new fuzzy controller, the vehicle speed, the brake strength and the SOC of the super capacitor of the vehicle are substituted into the new fuzzy controller to obtain the regenerative brake force proportion K, and finally, the motor regenerative brake force is obtained according to the front wheel brake force, the rear wheel brake force and the regenerative brake force proportion K, so as to recover energy according to the regenerative braking force of the motor. Therefore, the ECE braking law is combined with the ideal braking distribution I curve and the f curve, and the membership function value of the fuzzy controller is optimized by adopting the mixed PSO algorithm, so that the energy recovered by the motor can be greatly improved, the driving mileage is prolonged, and the control is simple and easy to implement.
Corresponding to the energy recovery method of the hybrid power supply electric automobile, the invention also provides an energy recovery device of the hybrid power supply electric automobile. Since the device embodiment of the present invention corresponds to the method embodiment described above, details that are not disclosed in the device embodiment may refer to the method embodiment described above, and are not described again in the present invention.
Fig. 6 is a block diagram schematically illustrating an energy recovery apparatus of a hybrid power electric vehicle according to an embodiment of the present invention. As shown in fig. 6, the hybrid power electric vehicle is a hybrid power electric vehicle of a storage battery-super capacitor, and the apparatus includes: a first acquisition module 1, a second acquisition module 2, an optimization module 3 and an energy recovery module 4.
The first acquisition module 1 is used for acquiring an ideal brake distribution I curve and an ideal brake distribution f curve of the automobile according to automobile parameters; the second acquisition module 2 is used for carrying out a front wheel braking force distribution strategy and a rear wheel braking force distribution strategy according to the I curve, the f curve and an ECE braking regulation, and acquiring the front wheel braking force and the rear wheel braking force of the automobile according to the front wheel braking force distribution strategy and the rear wheel braking force distribution strategy; the optimization module 3 is used for acquiring the speed, the braking strength and the SOC of the super capacitor of the automobile, inputting the speed, the braking strength and the SOC into the fuzzy controller, optimizing the membership function value of the fuzzy controller by adopting a hybrid PSO algorithm to obtain a new fuzzy controller, and substituting the speed, the braking strength and the SOC of the super capacitor of the automobile into the new fuzzy controller to obtain a regenerative braking force proportion K; the energy recovery module 4 is used for obtaining the motor regenerative braking force according to the front wheel braking force, the rear wheel braking force and the regenerative braking force proportion K so as to recover energy according to the motor regenerative braking force.
According to an embodiment of the present invention, the first obtaining module 1 is specifically configured to: when the front wheel and the rear wheel are simultaneously locked during the braking of the automobile, the braking force relation between the ground of the front wheel and the ground of the rear wheel of the automobile is obtained according to the following formula (1):
Figure BDA0002631000450000161
wherein, FXb1For ground-to-front wheel braking force, FXb2For ground to rear wheel braking force, G is the weight of the vehicle, hgThe height of the automobile mass center, L is the wheelbase, and b is the distance from the center of the rear axle to the automobile mass center;
and drawing an ideal brake distribution I curve of the automobile according to the brake force relation of the front wheel ground and the rear wheel ground of the automobile.
According to an embodiment of the present invention, the first obtaining module 1 is specifically configured to: when the automobile is braked, the front wheel is locked to drag the wheel to slide, then the rear wheel is locked, and the braking force relation between the front wheel and the ground of the rear wheel of the automobile is obtained according to the following formula (2):
Figure BDA0002631000450000162
wherein, FXb1For ground-to-front wheel braking force, FXb2For ground-to-rear wheel braking force, FZ2In order for the ground to react in the direction of the rear wheels,
Figure BDA0002631000450000163
is the coefficient of adhesion, G is the weight of the automobile, hgThe height of the automobile mass center, L is the wheelbase, and b is the distance from the center of the rear axle to the automobile mass center;
and drawing an f curve of the automobile according to the relation of the braking force of the front wheels and the braking force of the rear wheels of the automobile.
According to an embodiment of the present invention, the second obtaining module 2 is specifically configured to: obtaining the braking strength of the automobile; when the braking intensity is smaller than a first preset intensity, providing regenerative braking force by the motor, wherein the first preset braking intensity is smaller than the braking intensity corresponding to the maximum regenerative braking force of the automobile; taking the intersection point of the braking strength equal to the first preset strength and the x axis as a starting point to cut the ECE line, and obtaining the braking strength of the cut point B; when the braking strength is greater than or equal to the first preset strength and less than the braking strength at the tangent point B, distributing the front wheel braking force and the rear wheel braking force of the automobile according to the tangent line; acquiring the braking intensity of a point C at the intersection of an f curve of a point B at the overcut point and a curve I, and when the braking intensity is greater than or equal to the braking intensity of the point B at the overcut point and less than or equal to the braking intensity of the point C at the intersection, distributing the braking force of the front wheel and the braking force of the rear wheel of the automobile according to the f curve; and when the braking intensity is greater than the braking intensity at the intersection C, distributing the front wheel braking force and the rear wheel braking force of the automobile according to the I curve.
According to an embodiment of the invention, the energy recovery module 4 is specifically configured to: and multiplying the regenerative braking force proportion K by the front wheel braking force to obtain the motor regenerative braking force.
According to an embodiment of the present invention, the optimization module 3 is specifically configured to:
setting the individual number N of the hybrid particle swarm algorithm, learning factors c1 and c2, inertia weight w, hybrid probability bc, hybrid pool size ratio bs, maximum iteration number Y and space search dimension D;
evaluating the fitness of each particle according to an objective function, and randomly setting the speed Vi, the position xid, the individual optimal value pBest and the global optimal extreme value gBest of each particle through a particle swarm algorithm;
the current particle position and velocity are updated according to the following equations (3.1) and (3.2):
Xij(t+1)＝Xij(t)+Vij(t+1)，i＝1…n，j＝1…d (3.1)
Vij(t+1)＝w*Vij(t)+c1*r1*(Pij-xij(t))+c2*r2*(Pgj-xij(t)) (3.2)
wherein x isij(t +1) is the position of the ith particle evolution to the jth dimension of the t +1 th generation, Vij(t +1) is the speed of the ith particle evolving to the jth dimension of the t +1 th generation, c1 and c2 are the learning factors, PijFor individual optimal particle positions r1 and r2 are [0,1]]Two random numbers within the range, w being the inertial weight;
comparing the adaptive value of each particle with the best position of the particle, if the adaptive value is similar to the best position of the particle, taking the current value as the best position of the particle, comparing all current individual optimal values pBest and a global optimal extreme value gBest, and updating the global optimal extreme value gBest;
selecting a specified number of particles according to the set hybridization probability, putting the particles into a hybridization pool, hybridizing the particles two by two to generate the same number of filial generation particles n, and replacing parent particles m with the filial generation particles n. Wherein the position n (x) and velocity n (v) of the daughter particle n are calculated according to the following equations (3.3) and (3.4):
n(x)＝p*ml(x)+(1-p)*m2(x) (3.3)
n(v)＝|m1(v)|[(m1(v)+m2(v))/(|m1(v)+m2(v)|)] (3.4)
wherein n (x) is the position of the daughter particle n, n (v) is the velocity of the daughter particle n, p is a random number between [0,1], ml (x) is the position of one parent particle in pairwise hybridization, m2(x) is the position of the other parent particle in pairwise hybridization, m1(v) is the velocity of one parent particle in pairwise hybridization, m2(v) is the velocity of the other parent particle in pairwise hybridization;
and judging whether the algorithm reaches a stop condition. If yes, stopping searching and outputting a result; if not, returning to the step of updating the current particle position and speed according to the following formulas (3.1) and (3.2).
According to the energy recovery device of the hybrid power supply electric automobile provided by the embodiment of the invention, the first acquisition module acquires an ideal brake distribution I curve and an ideal brake distribution f curve of the automobile according to automobile parameters, the second acquisition module carries out front and rear wheel brake force distribution strategies according to the I curve and the f curve and an ECE brake regulation, and acquires front wheel brake force and rear wheel brake force of the automobile according to the front and rear wheel brake force distribution strategies, the optimization module acquires the speed, brake strength and SOC of a super capacitor of the automobile and inputs the speed, brake strength and SOC of the super capacitor into the fuzzy controller, the hybrid PSO algorithm is adopted to optimize a membership function value of the fuzzy controller to obtain a new fuzzy controller, the speed, brake strength and SOC of the super capacitor of the automobile are substituted into the new fuzzy controller to acquire a regenerative brake force proportion K, and the energy recovery module acquires motor regenerative brake force according to the front wheel brake force, the rear wheel brake force and the regenerative brake, so as to recover energy according to the regenerative braking force of the motor. Therefore, the ECE braking law is combined with the ideal braking distribution I curve and the f curve, and the membership function value of the fuzzy controller is optimized by adopting the mixed PSO algorithm, so that the energy recovered by the motor can be greatly improved, the driving mileage is prolonged, and the control is simple and easy to implement.
The invention also proposes a non-transitory computer-readable storage medium on which a computer program is stored which, when executed by a processor, implements the energy recovery method according to the hybrid power electric vehicle described above.
According to the non-transitory computer-readable storage medium of the embodiment of the present invention, when a program stored thereon is executed by a processor, an ideal brake distribution I curve and f curve of an automobile are obtained according to automobile parameters, a front and rear wheel brake force distribution strategy is performed according to the I curve and the f curve and an ECE brake regulation, a front wheel brake force and a rear wheel brake force of the automobile are obtained according to the front and rear wheel brake force distribution strategy, a speed, a brake intensity, and an SOC of a super capacitor of the automobile are obtained and input to a fuzzy controller, a hybrid PSO algorithm is used to optimize a membership function value of the fuzzy controller to obtain a new fuzzy controller, the speed, the brake intensity, and the SOC of the super capacitor of the automobile are substituted into the new fuzzy controller to obtain a regenerative brake force proportion K, and a motor regenerative brake force is obtained according to the front wheel brake force, the rear wheel brake force, and the regenerative brake force proportion K, the energy recovery is carried out according to the regenerative braking force of the motor, so that the energy recovered by the motor can be greatly improved, the driving mileage is prolonged, and the control is simple and easy to implement.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims (8)

1. The energy recovery method of the hybrid power supply electric vehicle is characterized in that the hybrid power supply electric vehicle is a storage battery-super capacitor hybrid power supply electric vehicle, and the method comprises the following steps:
acquiring an ideal braking distribution I curve and an ideal braking distribution f curve of the automobile according to automobile parameters;
according to the I curve, the f curve and the ECE braking law, a front wheel braking force distribution strategy and a rear wheel braking force distribution strategy are carried out, and the front wheel braking force and the rear wheel braking force of the automobile are obtained according to the front wheel braking force distribution strategy and the rear wheel braking force distribution strategy;
acquiring the speed, the braking strength and the SOC of the super capacitor of the automobile, inputting the speed, the braking strength and the SOC of the super capacitor into a fuzzy controller, optimizing a membership function value of the fuzzy controller by adopting a hybrid PSO algorithm to obtain a new fuzzy controller, and substituting the speed, the braking strength and the SOC of the super capacitor of the automobile into the new fuzzy controller to obtain a regenerative braking force proportion K;
and acquiring the regenerative braking force of the motor according to the ratio K of the front wheel braking force, the rear wheel braking force and the regenerative braking force so as to recover energy according to the regenerative braking force of the motor.
2. The energy recovery method of a hybrid power electric vehicle according to claim 1, wherein obtaining an ideal brake distribution I-curve of the vehicle comprises:
when the front wheel and the rear wheel are simultaneously locked during the braking of the automobile, the braking force relation between the ground of the front wheel and the ground of the rear wheel of the automobile is obtained according to the following formula (1):
Figure FDA0002631000440000011
wherein, FXb1For ground-to-front wheel braking force, FXb2For ground to rear wheel braking force, G is the weight of the vehicle, hgThe height of the automobile mass center, L is the wheelbase, and b is the distance from the center of the rear axle to the automobile mass center;
and drawing an ideal brake distribution I curve of the automobile according to the brake force relation of the front wheel ground and the rear wheel ground of the automobile.
3. The energy recovery method of a hybrid power electric vehicle according to claim 1, wherein obtaining an f-curve of the vehicle comprises:
when the automobile is braked, the front wheel is locked to drag to slide firstly, then the rear wheel is locked, and the braking force relation between the front wheel and the ground of the rear wheel of the automobile is obtained according to the following formula (2):
Figure FDA0002631000440000021
wherein, FXb1For ground-to-front wheel braking force, FXb2For ground-to-rear wheel braking force, FZ2In order for the ground to react in the direction of the rear wheels,
Figure FDA0002631000440000022
is the coefficient of adhesion, G is the weight of the automobile, hgThe height of the automobile mass center, L is the wheelbase, and b is the distance from the center of the rear axle to the automobile mass center;
and drawing an f curve of the automobile according to the braking force relation of the front wheel ground and the rear wheel ground of the automobile.
4. The energy recovery method of a hybrid power electric vehicle according to claim 1, wherein performing a front and rear wheel braking force distribution strategy according to the I-curve and f-curve and ECE braking regulations comprises:
obtaining the braking strength of the automobile;
when the braking intensity is smaller than a first preset intensity, providing a regenerative braking force by a motor, wherein the first preset braking intensity is smaller than the braking intensity corresponding to the maximum regenerative braking force of the automobile;
taking the intersection point of the first preset intensity and the x axis as a starting point to cut the ECE line, and obtaining the braking intensity of the cut point B;
when the braking intensity is greater than or equal to the first preset intensity and less than the braking intensity at the tangent point B, distributing the front wheel braking force and the rear wheel braking force of the automobile according to the tangent line;
obtaining the braking intensity of a point C where an f curve passing through the tangent point B and the I curve intersect, and when the braking intensity is greater than or equal to the braking intensity of the tangent point and less than or equal to the braking intensity of the point C, distributing the front wheel braking force and the rear wheel braking force of the automobile according to the f curve;
and when the braking intensity is greater than the braking intensity at the intersection C, distributing the front wheel braking force and the rear wheel braking force of the automobile according to the I curve.
5. The energy recovery method of a hybrid power supply electric vehicle according to claim 1, wherein obtaining a motor regenerative braking force from the front wheel braking force, the rear wheel braking force, and the regenerative braking force ratio K includes:
and multiplying the regenerative braking force proportion K by the front wheel braking force to obtain the motor regenerative braking force.
6. The energy recovery method of a hybrid power supply electric vehicle according to claim 1, wherein the fuzzy controller membership function value is optimized by using a hybrid PSO algorithm, and the method comprises the following steps:
setting the individual number N of the hybrid particle swarm algorithm, learning factors c1 and c2, inertia weight w, hybrid probability bc, hybrid pool size ratio bs, maximum iteration number Y and space search dimension D;
evaluating the fitness of each particle according to an objective function, and randomly setting the speed Vi, the position xid, the individual optimal value pBest and the global optimal extreme value gBest of each particle through a particle swarm algorithm;
the current particle position and velocity are updated according to the following equations (3.1) and (3.2):
Xij(t+1)＝Xij(t)+Vij(t+1)，i＝1…n，j＝1…d (3.1)
Vij(t+1)＝w*Vij(t)+c1*r1*(Pij-xij(t))+c2*r2*(Pgj-xij(t)) (3.2)
wherein x isij(t +1) is the position of the ith particle evolution to the jth dimension of the t +1 th generation, Vij(t +1) is the speed of the ith particle evolving to the jth dimension of the t +1 th generation, c1 and c2 are the learning factors, PijFor individual optimal particle positions r1 and r2 are [0,1]]Two random numbers within a range, w being the inertial weight;
comparing the adaptive value of each particle with the best position of the particle, if the adaptive value is similar to the best position of the particle, taking the current value as the best position of the particle, comparing all current individual optimal values pBest and a global optimal extreme value gBest, and updating the global optimal extreme value gBest;
selecting a specified number of particles according to the set hybridization probability, putting the particles into a hybridization pool, hybridizing every two particles to generate a same number of filial generation particles n, and replacing parent particles m with the filial generation particles n, wherein the position n (x) and the speed n (v) of the filial generation particles n are calculated according to the following formulas (5) and (6):
n(x)＝p*m1(x)+(1-p)*m2(x) (3.3)
n(v)＝|m1(v)|[(m1(v)+m2(v))/(|m1(v)+m2(v)|)] (3.4)
wherein n (x) is the position of the daughter particle n, n (v) is the velocity of the daughter particle n, p is a random number between [0,1], m1(x) is the position of one parent particle when crossed two by two, m2(x) is the position of the other parent particle when crossed two by two, m1(v) is the velocity of one parent particle when crossed two by two, and m2(v) is the velocity of the other parent particle when crossed two by two;
and judging whether a stopping condition is reached, if so, stopping searching, and outputting a result, otherwise, skipping to the step of updating the current particle position and speed according to the following formulas (3.1) and (3.2).
7. An energy recovery device of a hybrid power supply electric vehicle, which is characterized in that the hybrid power supply electric vehicle is a storage battery-super capacitor hybrid power supply electric vehicle, and the device comprises:
the first acquisition module is used for acquiring an ideal brake distribution I curve and an ideal brake distribution f curve of the automobile according to automobile parameters;
the second acquisition module is used for carrying out a front wheel braking force distribution strategy and a rear wheel braking force distribution strategy according to the I curve, the f curve and an ECE braking regulation, and acquiring the front wheel braking force and the rear wheel braking force of the automobile according to the front wheel braking force distribution strategy and the rear wheel braking force distribution strategy;
the optimization module is used for acquiring the speed, the braking strength and the SOC of the super capacitor of the automobile, inputting the speed, the braking strength and the SOC into a fuzzy controller, optimizing the membership function value of the fuzzy controller by adopting a hybrid PSO algorithm to obtain a new fuzzy controller, and substituting the speed, the braking strength and the SOC of the super capacitor of the automobile into the new fuzzy controller to obtain a regenerative braking force proportion K;
and the energy recovery module is used for acquiring the regenerative braking force of the motor according to the front wheel braking force, the rear wheel braking force and the regenerative braking force proportion K so as to recover energy according to the regenerative braking force of the motor.
8. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements the energy recovery method of a hybrid power electric vehicle according to any one of claims 1 to 6.
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