CN111196163B - Intelligent network-connected electric automobile energy optimal braking speed optimization method - Google Patents
Intelligent network-connected electric automobile energy optimal braking speed optimization method Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
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- B60L7/00—Electrodynamic brake systems for vehicles in general
- B60L7/24—Electrodynamic brake systems for vehicles in general with additional mechanical or electromagnetic braking
- B60L7/26—Controlling the braking effect
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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
- B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
- B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
- B60L15/2009—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed for braking
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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
- B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
- B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
- B60L15/2045—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed for optimising the use of energy
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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
- B60L7/18—Controlling the braking effect
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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
- B60L2220/00—Electrical machine types; Structures or applications thereof
- B60L2220/40—Electrical machine applications
- B60L2220/46—Wheel motors, i.e. motor connected to only one wheel
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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
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/10—Vehicle control parameters
- B60L2240/12—Speed
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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
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/42—Drive Train control parameters related to electric machines
- B60L2240/423—Torque
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- 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
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract
The invention discloses an energy optimal braking speed optimization method for an intelligent networked electric vehicle, which comprises the steps of firstly, obtaining information such as braking distance and terminal speed by using a V2X technology, obtaining initial vehicle speed information by using a vehicle-mounted wheel speed sensor, and transmitting the information to a vehicle-mounted controller in real time; designing a front and rear axle braking force distribution method, a coaxial left and right wheel uniform distribution method and a motor with priority of motor braking force and a friction braking force coupling method based on an ideal braking force distribution curve; then, designing a vehicle speed optimization problem containing multiple constraints, establishing an energy objective function by adopting an electric vehicle energy consumption model, and calculating the optimal braking speed of energy by utilizing a dynamic programming algorithm. The method provided by the invention realizes the improvement of the energy efficiency of the electric automobile by using the intelligent networking automobile technology, is beneficial to improving the problem of short driving range of the electric automobile, and is applied and popularized to the power-assisted electric automobile.
Description
Technical Field
The invention belongs to the field of traffic energy-saving control, and particularly relates to an energy optimal braking speed optimization method for an intelligent network-connected electric automobile.
Background
With the development of the fields of automobile electronics, network communication, intelligent control and the like, automobiles and traffic are organically integrated into a whole, an intelligent traffic system is favorably constructed, the new mode and new state development of the automobiles and traffic services are promoted, and the method has important significance for improving traffic efficiency, saving resources, reducing pollution, reducing accident rate and improving traffic management.
The electric automobile has the advantages of low energy consumption, no pollution and the like, and is widely developed and researched in recent years, but due to the limitation of a battery technology, the driving range of the electric automobile is short, the daily driving requirement is difficult to meet, and the application scene of the electric automobile is greatly restricted. Because the electric automobile has the regenerative braking function, the kinetic energy of the automobile can be recovered during braking, and the kinetic energy is converted into electric energy to be stored in the battery, so that the energy efficiency of the electric automobile can be improved, and the driving range can be prolonged.
With the development of intelligent internet automobile technology in recent years, an automobile can perform information interaction and sharing (V2X) with all things, such as vehicle-to-vehicle communication V2V, vehicle-to-road communication V2I, vehicle-to-person communication V2P, vehicle-to-internet communication V2N and the like, can be combined with the electric automobile regenerative braking technology to obtain braking intention in advance and reasonably plan braking speed, can further improve regenerative braking energy, has important significance for the application of advanced control technologies such as automobile energy-saving driving, green passing, high-efficiency passing at intersections and the like, and effectively improves traffic safety, automobile energy-saving level and traffic passing efficiency.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides an energy optimal braking speed optimization method for an intelligent network-connected electric automobile, which can be integrated into advanced control technologies of automobile energy-saving driving, green traffic and the like, and can improve the energy-saving effect.
The technical scheme is as follows: the invention relates to an energy optimal braking speed optimization method for an intelligent networked electric automobile, which comprises the following steps of:
(1) Initialization: the method comprises the steps of (1) initializing a control unit, wherein the control unit comprises a braking distance, a terminal speed and an initial speed;
(2) Obtaining a braking intention: obtaining information such as braking distance, terminal speed and the like by using a V2X technology, and obtaining initial vehicle speed information by using a vehicle-mounted wheel speed sensor;
(3) Designing a regenerative braking control strategy: the static axle load proportion is used as an ideal braking force distribution curve to distribute the braking force of a front axle and a rear axle, the braking force of coaxial left and right wheels adopts an equal distribution strategy, and the braking force of a motor is preferentially distributed to the motor and the friction braking force;
(4) Designing an electric automobile energy consumption model: establishing an energy consumption model comprising a motor and a battery, and considering energy loss characteristics of related components;
(5) Optimizing the energy optimal vehicle speed: aiming at the maximum braking energy of the electric automobile, integrating travel distance constraint, upper and lower vehicle speed constraint and driving and braking force constraint for ensuring comfortableness, establishing an optimization problem, and solving the optimal vehicle speed by adopting a dynamic programming algorithm;
(6) And the electric automobile runs according to the optimal speed until the control process is finished.
Further, the step (3) is realized as follows:
the front and rear axle brake distribution strategy is as follows:
β I ＝m f /(m f +m r )
F bf ＝F b β I
F br ＝F b (1-β I )
the braking force distribution strategy of the coaxial left and right wheels is as follows:
F fl ＝F fr ＝0.5F bf
F rl ＝F rr ＝0.5F br
the distribution strategy of the motor and the friction braking force of each wheel is as follows:
F mfl ＝min{F mflmax ,F fl }
F ffl ＝F fl -F mfl
F mfr ＝min{F mfrmax ,F fr }
F ffr ＝F fr -F mfr
F mrl ＝min{F mrlmax ,F rl }
F frl ＝F rl -F mrl
F mrr ＝min{F mrrmax ,F rr }
F frr ＝F rr -F mrr
wherein the braking forces of the four wheel motors are respectively F mfl 、F mfr 、F mrl 、F mrr The maximum braking force of the four in-wheel motors is respectively F mflmax 、F mfrmax 、F mrlmax 、F mrrmax Four wheel brake forces are respectively F fl 、F fr 、F rl 、F rr A frictional braking force of F ffl 、F ffr 、F frl 、F frr Total braking force of F b Front axle braking force of F bf Rear axle braking force of F br The front and rear axle braking force distribution coefficient is beta I Front axle weight of m f Rear axle weight of m r Min () is a function for taking the minimum value; when the battery state of charge value is larger than 0.9 and the vehicle speed is larger than 120km/h, the regenerative braking system is not in use, namely, the braking force is only provided by friction braking.
Further, the step (4) is realized as follows:
the recovery power of the motor is as follows:
the recovery power of the battery is as follows:
P b ＝P m η b
wherein, P v As power of the vehicle, P m Is the motor power, P b Is the battery power, eta b For the working efficiency of the cell, η m For the motor working efficiency, n is the motor rotation speed, r w Is the wheel radius.
Further, the optimization problem in step (5) is established as follows:
the particle model is adopted to describe the automobile longitudinal dynamics model:
wherein g is the gravitational acceleration, m is the vehicle mass, f is the friction drag coefficient, theta is the road gradient, C D Is an air resistance coefficient, rho is air density, d is driving distance, delta is a conversion coefficient of the rotational inertia of the automobile, F is tractive force, and x = [ d v ]] T Represents a state quantity;
the optimization problem is then as follows:
satisfies the following conditions:
L[x(k),u(k),Δt]＝P b Δt
x(0)＝[v s ，0]
x(N)＝[v p ，D」
v(k)∈[v min ，v max ]
u(k)∈[F b (k)，F d (k)]
wherein, the recovered energy of the automobile is defined as J, the control quantity of the optimization problem is defined as u, and the initial speed is defined as v s Terminal vehicle speed v p Minimum vehicle speed v min Maximum vehicle speed v max The optimization problem length is N, and the maximum driving force is F d Maximum braking force of F b 。
Further, the implementation process of solving the optimal vehicle speed by adopting the dynamic programming algorithm in the step (5) is as follows:
defining a terminal cost function of a dynamic programming algorithm as follows:
wherein argmin () represents the control quantity and state quantity function when taking the minimum value;
the dynamic programming reverse iteration cost function is defined as:
by solving the equation, the optimal vehicle speed meeting the optimization requirement can be obtained.
Has the beneficial effects that: compared with the prior art, the invention has the following beneficial effects: 1. the intelligent networked automobile braking method based on the intelligent networked automobile has the advantages that the technical advantages of the intelligent networked automobile are considered, the regenerative braking characteristics of the electric automobile are combined, a mature information acquisition method is adopted for a typical traffic scene, and the braking speed with the optimal energy is reasonably planned based on the acquired braking intention information; 2. the invention adopts a dynamic planning algorithm, and can realize energy optimal vehicle speed optimization under the conditions of multiple constraints such as vehicle speed, acceleration/deceleration, road adhesion and the like; 3. the invention can be integrated into advanced control technologies of automobile energy-saving driving, green traffic and the like, and improves the energy-saving effect.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a diagram of an application scenario of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The intelligent networked automobile can realize communication between automobile and road, has high performance controller, radar, GPS, etc. and may be used in intelligent driving.
In traffic scenes such as approaching a destination, slow running of a vehicle ahead, arrival at an intersection and the like, energy consumption of the electric vehicle is influenced by changes of running speed, and since the electric vehicle has a regenerative braking function and can recover kinetic energy of the vehicle in a braking process, braking control in the scenes is beneficial to improvement of energy efficiency. In order to ensure that the electric motor braking force is exerted to recover energy in the braking process as much as possible, after the electric automobile obtains the braking intention information, the electric automobile runs according to the optimized energy and the optimal speed until the electric automobile reaches the destination.
In fig. 2, 0 denotes an initial stage and N denotes a terminal stage. The invention firstly utilizes V2X communication to obtain the braking distance D and the terminal speed V p Waiting intention information, and acquiring initial vehicle speed information v by using a vehicle-mounted wheel speed sensor s As shown in fig. 2 in particular; then designing a motor containing multiple constraint conditionsThe optimal braking energy of an automobile energy consumption model, a regenerative braking control method and the like; and then solving the problem by adopting a dynamic programming algorithm to obtain the energy optimal speed, and executing the speed by the electric automobile until the braking process is finished.
The implementation of the invention is explained in detail by means of the scenario of fig. 2. As shown in fig. 1, the method for optimizing the energy optimal braking speed of the intelligent networked electric vehicle specifically includes the following steps:
initialization: the initial braking distance is 0, the terminal speed is 0, the initial speed is 0/the control unit is initialized.
The first step is as follows: obtaining a braking intention; obtaining braking distance D and terminal speed V by using V2X technology p And the initial vehicle speed information is obtained by utilizing a vehicle-mounted wheel speed sensor.
The information acquisition method comprises the following steps:
the initial vehicle speed information is acquired by a vehicle-mounted wheel speed sensor, and the wheel speed sensor is mounted on a vehicle and belongs to a vehicle essential sensor; the braking distance and the terminal vehicle speed information are obtained through V2X communication, if the scene information belonging to destination parking can be obtained through vehicle-cloud communication, and the scene information belonging to vehicle following braking can be obtained through vehicle-vehicle communication.
The second step: designing a regenerative braking control strategy; the static axle load proportion is used as an ideal braking force distribution curve to distribute front and rear axle braking forces, the coaxial left and right wheel braking forces adopt an equal distribution strategy, and the motor braking force priority strategy is adopted to distribute the motor and the friction braking force.
The regenerative braking control method comprises the following steps:
the braking force of the four wheel motors is defined as F mfl 、F mfr 、F mrl 、F mrr The maximum braking force of the four hub motors is respectively F mflmax 、F mfrmax 、F mrlmax 、F mrrmax Four wheel brake forces are respectively F fl 、F fr 、F rl 、F rr Frictional braking force of F ffl 、F ffr 、F frl 、F frr Total braking force of F b Front axle braking force of F bf Rear axle braking force of F br The braking force distribution coefficient of the front and rear axles is beta I Front and rear weights of m f Rear axle weight of m r And min () is a function of taking the minimum value.
The front and rear axle brake distribution strategy is:
β I ＝m f /(m f +m r )
F bf ＝F b β I
F br ＝F b (1-β I )
the braking force distribution strategy of the coaxial left and right wheels is as follows:
F fl ＝F fr ＝0.5F bf
F rl ＝F rr ＝0.5F br
the distribution strategy of the friction braking force and the motor of each wheel is as follows:
F mfl ＝min{F mflmax ,F fl }
F ffl ＝F fl -F mfl
F mfr ＝min{F mfrmax ,F fr }
F ffr ＝F fr -F mfr
F mrl ＝min{F mrlmax ,F rl }
F frl ＝F rl -F mrl
F mrr ＝min{F mrrmax ,F rr }
F frr ＝F rr -F mrr
furthermore, when the battery state of charge value is greater than 0.9 and the vehicle speed is greater than 120km/h, the regenerative braking system is not active, i.e. the braking force is provided by friction braking only.
Thirdly, designing an energy consumption model of the electric automobile; and establishing an energy consumption model comprising a motor and a battery, and considering the energy loss characteristics of related components.
The energy model of the electric automobile is as follows:
defining vehicle power as P v The motor power is P m The battery power is P b The working efficiency of the battery is eta b The working efficiency of the motor is eta m The rotating speed of the motor is n, and the radius of the wheel is r w 。
The recovered power of the motor is as follows:
the battery recovery power is:
P b ＝P m η b
the fourth step: optimizing the energy optimal vehicle speed; the method is characterized in that the maximum braking energy of the electric automobile is taken as a target, driving distance constraint, upper and lower vehicle speed constraint and driving braking force constraint for ensuring comfortableness are integrated, an optimization problem is established, and a dynamic programming algorithm is adopted to solve the optimal vehicle speed.
The energy optimal vehicle speed optimization method comprises the following steps:
defining g as gravity acceleration, m as automobile mass, f as friction resistance coefficient, theta as road gradient, C D Is an air resistance coefficient, rho is an air density, d is a running distance, delta is a conversion coefficient of the rotational inertia of the automobile, F is a traction force (a positive value is a driving force, a negative value is a braking force), and x = [ d v] T Representing state quantity, and describing an automobile longitudinal dynamics model by using a particle model:
defining the recovered energy of the automobile as J, the control quantity of the optimization problem as u, and the initial speed as v s Terminal vehicle speed v p Minimum vehicle speed v min Maximum vehicle speed v max The optimization problem length is N, and the maximum driving force is F d (determined by road adhesion and driving comfort) and a maximum braking force of F b (determined by road adhesion and braking comfort), the optimization problem is as follows:
satisfies the following conditions:
L[x(k),u(k),Δt]＝P b Δt
x(0)＝[v s ，0]
x(N)＝[v p ，D」
v(k)∈[v min ，v max ]
u(k)∈[F b (k)，F d (k)]
defining a terminal cost function of a dynamic programming algorithm as follows:
wherein argmin () represents the control quantity and state quantity functions when taking the minimum value.
The dynamic programming inverse iteration cost function is defined as follows:
by solving the above-mentioned equations, the optimal vehicle speed meeting the optimization requirement can be obtained.
And (4) ending: and the electric automobile runs according to the optimal speed until the control process is finished.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The meaning of "and/or" as used herein is intended to include both the individual components or both.
The term "connected" as used herein may mean either a direct connection between components or an indirect connection between components via other components.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (4)
1. An energy optimal braking speed optimization method for an intelligent network-connected electric automobile is characterized by comprising the following steps:
(1) Initialization: the method comprises the steps of (1) initializing a control unit, wherein the control unit comprises a braking distance, a terminal speed and an initial speed;
(2) Obtaining a braking intention: acquiring a braking distance and a terminal speed by using a V2X technology, and acquiring initial vehicle speed information by using a vehicle-mounted wheel speed sensor;
(3) Designing a regenerative braking control strategy: the static axle load proportion is used as an ideal braking force distribution curve to distribute front and rear axle braking forces, the coaxial left and right wheel braking forces adopt an equal distribution strategy, and a strategy with priority of motor braking force is adopted to distribute a motor and friction braking force;
(4) Designing an electric automobile energy consumption model: establishing an energy consumption model comprising a motor and a battery, and considering the energy consumption characteristics of the motor and the battery;
(5) Optimizing the energy optimal vehicle speed: aiming at the maximum braking energy of the electric automobile, integrating travel distance constraint, upper and lower vehicle speed constraint and driving and braking force constraint for ensuring comfortableness, establishing an optimization problem, and solving the optimal vehicle speed by adopting a dynamic programming algorithm;
(6) The electric automobile runs according to the optimal speed until the control process is finished;
the optimization problem in step (5) is established as follows:
the particle model is adopted to describe the automobile longitudinal dynamics model:
wherein g is gravity acceleration, m is automobile mass, f is friction resistance coefficient, theta is road gradient, C D Is an air resistance coefficient, rho is air density, d is driving distance, delta is a conversion coefficient of the rotational inertia of the automobile, F is tractive force, and x = [ d v ]] T Represents a state quantity;
the optimization problem is then as follows:
satisfies the following conditions:
L[x(k),u(k),△t]＝P b △t
x(0)＝[v s ，0]
x(N)＝[v p ，D]
v(k)∈[v min ，v max ]
u(k)∈[F b (k)，F d (k)]
wherein, the recovered energy of the automobile is defined as J, the control quantity of the optimization problem is defined as u, and the initial speed is defined as v s Terminal vehicle speed v p Minimum vehicle speed v min Maximum vehicle speed v max The optimization problem length is N, and the maximum driving force is F d Maximum braking force of F b 。
2. The method for optimizing the energy-optimal braking speed of the intelligent networked electric vehicle according to claim 1, wherein the step (3) is implemented as follows:
the front and rear axle brake distribution strategy is as follows:
β I ＝m f /(m f +m r )
F bf ＝F b β I
F br ＝F b (1-β I )
the braking force distribution strategy of the coaxial left and right wheels is as follows:
F fl ＝F fr ＝0.5F bf
F rl ＝F rr ＝0.5F br
the distribution strategy of the motor and the friction braking force of each wheel is as follows:
F mfl ＝min{F mflmax ,F fl }
F ffl ＝F fl -F mfl
F mfr ＝min{F mfrmax ,F fr }
F ffr ＝F fr -F mfr
F mrl ＝min{F mrlmax ,F rl }
F frl ＝F rl -F mrl
F mrr ＝min{F mrrmax ,F rr }
F frr ＝F rr -F mrr
wherein the braking forces of the four wheel motors are respectively F mfl 、F mfr 、F mrl 、F mrr The maximum braking force of the four in-wheel motors is respectively F mflmax 、F mfrmax 、F mrlmax 、F mrrmax Four wheel braking forces are respectively F fl 、F fr 、F rl 、F rr A frictional braking force of F ffl 、F ffr 、F frl 、F frr Total braking force of F b Front axle braking force of F bf Rear axle braking force of F br The front and rear axle braking force distribution coefficient is beta I Front axle weight of m f Rear axle weight m r Min { } is a function for taking the minimum value; when the battery state of charge value is larger than 0.9 and the vehicle speed is larger than 120km/h, the regenerative braking system is not in use, namely, the braking force is only provided by friction braking.
3. The method for optimizing the energy-optimal braking speed of the intelligent networked electric vehicle according to claim 1, wherein the step (4) is implemented as follows:
the recovery power of the motor is as follows:
the recovery power of the battery is as follows:
P b ＝P m η b
wherein, P v As power of the vehicle, P m Is the motor power, P b Is the battery power, η b For the working efficiency of the cell, η m For the motor working efficiency, n is the motor rotation speed, r w Is the wheel radius.
4. The method for optimizing energy optimal braking speed of the intelligent networked electric vehicle according to claim 1, wherein the implementation process of solving the optimal vehicle speed by adopting a dynamic programming algorithm in the step (5) is as follows:
defining a terminal cost function of a dynamic programming algorithm as follows:
wherein argmin () represents a control quantity and state quantity function when taking a minimum value;
the dynamic programming reverse iteration cost function is defined as:
by solving the equation, the optimal vehicle speed meeting the optimization requirement can be obtained.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202010068538.1A CN111196163B (en) | 2020-01-21 | 2020-01-21 | Intelligent network-connected electric automobile energy optimal braking speed optimization method |
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| CN202010068538.1A CN111196163B (en) | 2020-01-21 | 2020-01-21 | Intelligent network-connected electric automobile energy optimal braking speed optimization method |
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| CN111196163B true CN111196163B (en) | 2023-01-31 |
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| CN107351840A (en) * | 2017-06-07 | 2017-11-17 | 同济大学 | A kind of vehicle energy saving path and economic speed dynamic programming method based on V2I |
| CN109823188A (en) * | 2019-01-10 | 2019-05-31 | 乾碳国际公司 | The mixed gentle speed system of dynamic commercial vehicle regenerative braking |
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| CN107351840A (en) * | 2017-06-07 | 2017-11-17 | 同济大学 | A kind of vehicle energy saving path and economic speed dynamic programming method based on V2I |
| CN109823188A (en) * | 2019-01-10 | 2019-05-31 | 乾碳国际公司 | The mixed gentle speed system of dynamic commercial vehicle regenerative braking |
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| 汽车防抱制动系统制动时的车速计算;吴诰等;《华南理工大学学报(自然科学版)》;20020228(第02期);全文 * |
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