CN113650622A - Vehicle speed trajectory planning method, device, equipment and storage medium - Google Patents

Vehicle speed trajectory planning method, device, equipment and storage medium Download PDF

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CN113650622A
CN113650622A CN202110810703.0A CN202110810703A CN113650622A CN 113650622 A CN113650622 A CN 113650622A CN 202110810703 A CN202110810703 A CN 202110810703A CN 113650622 A CN113650622 A CN 113650622A
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speed
information
vehicle
target vehicle
energy consumption
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CN113650622B (en
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黄彬
刘永刚
张志强
马洁高
覃胤合
高德坤
聂明勇
冯倍茂
朱祝宏
卢昶伯
何超兰
梁新丽
周婉清
曹秋媛
黎跃
蔡大伟
梁高松
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Dongfeng Liuzhou Motor Co Ltd
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Dongfeng Liuzhou Motor Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—Speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed

Abstract

The invention belongs to the technical field of energy-saving driving, and discloses a vehicle speed trajectory planning method, a vehicle speed trajectory planning device, vehicle speed trajectory planning equipment and a storage medium. The method comprises the following steps: acquiring speed information and energy consumption information of each target vehicle; generating an energy consumption calculation model of the target vehicle according to the energy consumption information and the speed information; acquiring speed vehicle distance limiting conditions, acceleration information of the speed limiting conditions in each target vehicle and speed distance information; establishing a constraint objective function according to the speed-vehicle distance limiting condition, the acceleration information, the speed-distance information and the state constraint condition; and planning the speed track of each target vehicle according to the energy consumption calculation model, the speed limiting condition, the acceleration information, the speed distance information and the constraint objective function. Through the mode, the energy consumption information is determined through the speed information of each target vehicle, so that an energy consumption calculation model is generated, a constraint objective function is established, and the speed track of each target vehicle is planned, so that unnecessary acceleration and deceleration are reduced, and the energy consumption of the vehicles is reduced.

Description

Vehicle speed trajectory planning method, device, equipment and storage medium
Technical Field
The invention relates to the technical field of energy-saving driving, in particular to a vehicle speed trajectory planning method, a vehicle speed trajectory planning device, vehicle speed trajectory planning equipment and a storage medium.
Background
The energy-saving driving technology can reduce unnecessary acceleration and deceleration and reduce the energy consumption of the vehicle by planning the economic speed track of the vehicle.
However, the energy-saving driving technology has the following defects:
1. from the aspect of intelligent networking, most of the current economic vehicle speed planning plans by taking fixed speed limit and arrival time as constraints, and it is rare to consider that the real-time acquisition of traffic information and the information of surrounding vehicles adjusts the speed limit and plans the vehicle speed of the vehicle in real time;
2. from the vehicle aspect, present economic speed planning is mostly directed at single vehicle, and the speed planning in coordination is carried out to a plurality of vehicles, especially the vehicle that a plurality of driving system characteristics are different rarely considered, because the vehicle driving system characteristics are different, the economic speed of vehicle self probably leads to the increase by a wide margin of back car energy consumption to cause the increase of total energy consumption, still can reduce the current efficiency of road.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a vehicle speed trajectory planning method, a vehicle speed trajectory planning device, vehicle speed trajectory planning equipment and a storage medium, and aims to solve the technical problems of reducing overall energy consumption of multiple vehicles and improving road traffic efficiency in the prior art.
In order to achieve the purpose, the invention provides a vehicle speed track planning method, which comprises the following steps:
acquiring speed information and energy consumption information of each target vehicle;
generating an energy consumption calculation model of the target vehicle according to the energy consumption information and the speed information;
acquiring speed vehicle distance limiting conditions, acceleration information of the speed limiting conditions in each target vehicle and speed distance information;
establishing a constraint objective function according to the speed-vehicle distance limiting condition, the acceleration information, the speed-distance information and a state constraint condition;
and planning the speed track of each target vehicle according to the energy consumption calculation model, the speed limiting condition, the acceleration information, the speed distance information and the constraint objective function.
Optionally, the step of acquiring speed information and energy consumption information of each target vehicle includes:
acquiring speed information of each target vehicle;
determining the required torque of each target vehicle according to the speed information;
determining the power source rotating speed of each target vehicle according to the speed information;
and determining the energy consumption information of each target vehicle according to the power source type, the required torque and the power source rotating speed of each target vehicle.
Optionally, the step of establishing a constraint objective function according to the speed-vehicle distance limit condition, the acceleration information, the speed-distance information, and a state constraint condition includes:
establishing a control variable function according to the acceleration information;
establishing a state variable function according to the speed distance information and the control variable function;
determining a speed limiting condition, an acceleration and deceleration limiting condition and a vehicle distance limiting condition according to the speed vehicle distance limiting condition;
and establishing a constraint objective function according to the control variable function, the state variable function, the speed limiting condition, the acceleration and deceleration limiting condition, the inter-vehicle distance limiting condition and the state constraint condition.
Optionally, the step of obtaining the vehicle speed trajectory of each target vehicle according to the energy consumption calculation model, the speed limit condition, the acceleration information, the speed distance information, and the constraint objective function includes:
generating an optimal control model according to the energy consumption calculation model, the speed limiting condition, the acceleration information, the speed distance information and the constraint objective function;
converting the optimal control model into a nonlinear programming (NLP) problem;
and determining the speed track of each target vehicle according to the nonlinear programming NLP problem.
Optionally, the step of converting the optimal control model into a non-linear programming NLP problem includes:
acquiring a running time interval of each target vehicle, and converting the running time interval into a Legendre orthogonal multi-interval;
selecting a preset number of LGR distribution points in the Legendre orthogonal multi-interval;
discretizing the acceleration information and the speed distance information based on the Legendre orthogonal multi-interval and the LGR distribution point to obtain a discretization result;
approximating the discretization result according to a Lagrange interpolation basis function to obtain an approximating result;
converting the approximation result into a kinetic equation, and substituting the kinetic equation into a preset state equation to obtain an algebraic equation;
converting the constraint objective function into a constraint integral weight objective function according to a preset integral weight;
and converting the optimal control model into a nonlinear programming (NLP) problem according to the algebraic equation and the constraint integral weight objective function.
Optionally, the step of determining the vehicle speed trajectory of each target vehicle according to the nonlinear programming NLP problem includes:
converting the nonlinear programming NLP problem into a multiplier problem by utilizing a Lagrange multiplier;
performing iteration on the multiplier problem for preset times to obtain a quadratic programming subproblem;
determining a linear search equation according to a preset search direction and the quadratic programming subproblem;
and determining the speed track of each target vehicle according to the secondary planning subproblem and the linear search equation.
Optionally, the step of determining the vehicle speed trajectory of each target vehicle according to the quadratic programming subproblem and the linear search equation includes:
determining optimal vector parameters according to the quadratic programming subproblem and the linear search equation;
and determining the speed track of each target vehicle according to the optimal vector parameters.
In addition, in order to achieve the above object, the present invention further provides a vehicle speed trajectory planning device, including:
the acquisition module is used for acquiring the speed information and the energy consumption information of each target vehicle;
the generating module is used for generating an energy consumption calculation model of the target vehicle according to the energy consumption information and the speed information;
the acquisition module is also used for acquiring speed vehicle distance limiting conditions, acceleration information of the speed limiting conditions in each target vehicle and speed distance information;
the establishing module is used for establishing a constraint objective function according to the speed vehicle distance limiting condition, the acceleration information, the speed distance information and a state constraint condition;
and the planning module is used for planning the speed track of each target vehicle according to the energy consumption calculation model, the speed limiting condition, the acceleration information, the speed distance information and the constraint objective function.
In addition, in order to achieve the above object, the present invention further provides a vehicle speed trajectory planning device, including: a memory, a processor and a vehicle speed trajectory planning program stored on the memory and executable on the processor, the vehicle speed trajectory planning program being configured to implement the steps of the vehicle speed trajectory planning method as described above.
In addition, to achieve the above object, the present invention further provides a storage medium, on which a vehicle speed trajectory planning program is stored, and the vehicle speed trajectory planning program, when executed by a processor, implements the steps of the vehicle speed trajectory planning method as described above.
The method comprises the steps of obtaining speed information and energy consumption information of each target vehicle; generating an energy consumption calculation model of the target vehicle according to the energy consumption information and the speed information; acquiring speed vehicle distance limiting conditions, acceleration information of the speed limiting conditions in each target vehicle and speed distance information; establishing a constraint objective function according to the speed-vehicle distance limiting condition, the acceleration information, the speed-distance information and the state constraint condition; and planning the speed track of each target vehicle according to the energy consumption calculation model, the speed limiting condition, the acceleration information, the speed distance information and the constraint objective function. Through the mode, the energy consumption information is determined through the speed information of each target vehicle, so that an energy consumption calculation model is generated, a constraint objective function is established, and the speed track of each target vehicle is planned, so that unnecessary acceleration and deceleration are reduced, and the energy consumption of the vehicles is reduced. .
Drawings
FIG. 1 is a schematic structural diagram of a vehicle speed trajectory planning device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a vehicle speed trajectory planning method according to a first embodiment of the present invention;
fig. 3 is a block diagram of a vehicle speed trajectory planning device according to a first embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a vehicle speed trajectory planning device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the vehicle speed trajectory planning apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the vehicle speed trajectory planning apparatus, and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a vehicle speed trajectory planning program.
In the vehicle speed trajectory planning device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the vehicle speed trajectory planning device of the present invention may be disposed in the vehicle speed trajectory planning device, and the vehicle speed trajectory planning device calls the vehicle speed trajectory planning program stored in the memory 1005 through the processor 1001 and executes the vehicle speed trajectory planning method provided by the embodiment of the present invention.
An embodiment of the invention provides a vehicle speed trajectory planning method, and referring to fig. 2, fig. 2 is a schematic flow diagram of a first embodiment of the vehicle speed trajectory planning method.
In this embodiment, the vehicle speed trajectory planning method includes the following steps:
step S10: and acquiring speed information and energy consumption information of each target vehicle.
It should be noted that the execution main body of this embodiment may be a vehicle-mounted terminal on an intelligent network-connected vehicle, which means that the vehicle network and the intelligent vehicle are organically combined, and the execution main body is a new generation vehicle that carries advanced vehicle-mounted sensors, controllers, actuators, and other devices, and integrates modern communication and network technologies, so as to implement intelligent information exchange and sharing between vehicles and people, vehicles, roads, backgrounds, and the like, implement safe, comfortable, energy-saving, and efficient driving, and finally may replace people to operate.
It should be understood that the intelligent networking automobile and the plurality of intelligent networking automobiles are connected through a network of the automobile network or connected through the V2X, so that data intercommunication can be performed between the vehicles, and the vehicles performing data intercommunication are target vehicles.
It should be understood that the speed information includes the speed and acceleration of the vehicle in different driving states, thereby constructing a speed-acceleration set.
It can be understood that factors affecting the energy consumption of the vehicle, including the engine, speed, mass, etc. of the vehicle, and therefore the energy consumption of different target vehicles is different and needs to be analyzed according to the current driving state of different vehicles.
Further, in order to ensure that the energy consumption information of each target vehicle is accurately calculated, step S10 includes: acquiring speed information of each target vehicle; determining the required torque of each target vehicle according to the speed information; determining the power source rotating speed of each target vehicle according to the speed information; and determining the energy consumption information of each target vehicle according to the power source type, the required torque and the power source rotating speed of each target vehicle.
It should be noted that the required torque of each target vehicle power source can be calculated by a longitudinal dynamics model, which is:
Figure RE-GDA0003311823260000061
wherein m is the total vehicle mass, g is the gravity acceleration, f is the rolling resistance coefficient, alpha is the slope angle, A is the windward area of the vehicle, CDIs an air resistance coefficient, v is a vehicle speed (km/h), delta is a conversion coefficient of rotating mass of the vehicle, a is an acceleration (m/s2), r is a rolling radius of a wheel, T is a rolling radius of the wheeltAs wheel-side torque, igTo a transmission ratio, i0Is a main reduction ratio, ηtFor driveline efficiency.
It is understood that the vehicle power source speed n is calculated from the relationship between the power source speed and the vehicle speed as follows:
Figure RE-GDA0003311823260000062
wherein n is the power source rotation speed (r/min).
In the specific implementation, the power source types of the intelligent internet vehicle comprise a fuel vehicle and a new energy vehicle, the power source of the fuel vehicle is an engine, and the power source of the new energy vehicle is a motor, so that energy consumption needs to be calculated according to different power source types; if the power source is a motor, calculating the power at the current moment according to a power calculation formula to obtain the power consumption at the current moment:
Figure RE-GDA0003311823260000071
wherein, Te,Tm,ne,nmRespectively engine torque speed and motor torque speed, mfuel(Te,ne) Specific fuel consumption (g/kWh), z, to be queried according to the universal characteristicsdIs gasoline heavy (N/L), QfuelIs fuel consumption (L/s), QelecIs the electricity consumption (kWh/s).
Step S20: and generating an energy consumption calculation model of the target vehicle according to the energy consumption information and the speed information.
In the embodiment, the speed information and the corresponding energy consumption information are used for establishing a vehicle energy consumption mapping matrix, so that energy consumption calculation models Q to (v, a) are established.
Step S30: the speed-to-vehicle distance limit condition, acceleration information of the speed limit condition in each target vehicle, and speed-to-distance information are acquired.
It should be noted that the speed limitation condition is given as a speed-distance limitation, and the speed limitation condition includes a vehicle speed limitation and a travel distance limitation of each target vehicle, that is, a speed section including a vehicle speed and a distance section including a travel distance. The speed limit condition further includes a control variable (i.e., acceleration information) indicating the acceleration and the rate of change of the acceleration of each target vehicle, and a state variable (i.e., speed-distance information) indicating the vehicle speed and the travel distance of each target vehicle. The speed limit is obtained by an Intelligent Transportation System (ITS).
It can be understood that the speed-inter-vehicle distance limitation conditions include speed, acceleration, and acceleration rate limitation conditions of the respective target vehicles, limitation conditions of frequent acceleration and deceleration of the respective target vehicles, and distance limitation of inter-vehicle distances between the respective target vehicles.
Step S40: and establishing a constraint objective function according to the speed-vehicle distance limiting condition, the acceleration information, the speed-distance information and the state constraint condition.
Further, in order to establish a constraint objective function with better planning capability, the step S40 includes: establishing a control variable function according to the acceleration information; establishing a state variable function according to the speed distance information and the control variable function; determining a speed limiting condition, an acceleration and deceleration limiting condition and a vehicle distance limiting condition according to the speed vehicle distance limiting condition; and establishing a constraint objective function according to the control variable function, the state variable function, the speed limiting condition, the acceleration and deceleration limiting condition, the inter-vehicle distance limiting condition and the state constraint condition.
It should be noted that, a control variable function of the multi-vehicle cooperative economic vehicle speed plan is established according to the control variables (i.e. acceleration information):
Figure RE-GDA0003311823260000081
wherein a is the vehicle acceleration, iaIn order to accelerate the rate of change of the degree of change,
Figure RE-GDA0003311823260000082
is an equation of state.
It can be understood that the state variable function is established from the state variable (i.e., velocity distance information) and the control variable function:
Figure RE-GDA0003311823260000083
wherein s isk0Is the initial position of the target vehicle, skfIs the target position of the target vehicle, skIs the position of the target vehicle, t0Is the starting time of the target vehicle's travel, tfK represents a vehicle number as the end time of travel of the target vehicle.
It should be noted that the speed, acceleration, and acceleration rate limiting conditions are:
Figure RE-GDA0003311823260000084
wherein vM, vM, aM, aM, iam,iaM are upper and lower limit values of the speed, acceleration and acceleration rate, respectively.
Limitation of frequent acceleration and deceleration:
Qa＝∑abs(ia_k) Equation 7;
wherein Q isaAnd the cost brought by punishment of frequent change of the acceleration is avoided, and the frequent acceleration and deceleration of the vehicle are avoided, wherein k represents the number of the target vehicle.
And (3) limiting the distance between the target vehicles:
Figure RE-GDA0003311823260000091
wherein s isk_actFor the guaranteed safe inter-vehicle distance of the kth vehicle and the (k + 1) th vehicle, sigma is the minimum safe inter-vehicle distance, QsThe cost brought by punishment of overlarge distance between vehicles is increased as much as possible on the premise of ensuring safety, and the road passing efficiency is indirectly increased.
In this embodiment, an objective function is established according to the control variable function, the state variable function, the speed limiting condition, the acceleration and deceleration limiting condition, and the inter-vehicle distance limiting condition:
Figure RE-GDA0003311823260000092
where x is the state variable, u is the control variable, ω1,ω2,ω3Are weights, alpha, of energy consumption cost, acceleration frequent change cost and inter-vehicle distance excessive cost, respectivelyelec,αfuelRespectively is a power consumption cost coefficient (yuan/kWh) and an oil consumption cost coefficient (yuan/L), and J is a performance index.
Further, the state constraint condition needs to consider a time penalty factor, and can be adjusted by adopting a dichotomy, so that a proper time penalty factor is determined to realize travel time constraint, and a constraint objective function is established:
Figure RE-GDA0003311823260000093
wherein, beta is a time penalty factor, and can be adjusted by adopting a dichotomy so as to determine proper beta to realize travel time constraint.
Step S50: and planning the speed track of each target vehicle according to the energy consumption calculation model, the speed limiting condition, the acceleration information, the speed distance information and the constraint objective function.
Further, in order to plan the vehicle speed trajectory of each target vehicle more accurately, it is necessary to convert the model into a nonlinear programming NLP problem, and step S50 includes: generating an optimal control model according to the energy consumption calculation model, the speed limiting condition, the acceleration information, the speed distance information and the constraint objective function; converting the optimal control model into a nonlinear programming (NLP) problem; and determining the speed track of each target vehicle according to the nonlinear programming NLP problem.
It should be noted that the path planning problem essentially belongs to an optimal control problem, and in a time interval, one or a plurality of vehicle speed trajectories satisfying constraint conditions and performance indexes are obtained by searching for control variables and minimizing a performance index J. And combining the energy consumption calculation model, the speed limiting condition, the acceleration information, the speed distance information and the constraint objective function to obtain an optimal control problem model.
It can be understood that the optimal control problem model is converted into a nonlinear programming NLP (nonlinear programming) problem by using a pseudo-spectrum method, which is divided into a Gauss pseudo-spectrum method, a Legendre pseudo-spectrum method and a Radau pseudo-spectrum method, and the convergence rate of the Radau pseudo-spectrum method is faster when the numerical solution of the optimal control problem is solved, so that the Radau pseudo-spectrum method is adopted to convert the optimal control problem into the nonlinear programming NLP problem.
Further, the step of converting the optimal control model into a non-linear programming NLP problem includes: acquiring a running time interval of each target vehicle, and converting the running time interval into a Legendre orthogonal multi-interval; selecting a preset number of LGR distribution points in the Legendre orthogonal multi-interval; discretizing the acceleration information and the speed distance information based on the Legendre orthogonal multi-interval and the LGR distribution point to obtain a discretization result; approximating the discretization result according to a Lagrange interpolation basis function to obtain an approximating result; converting the approximation result into a kinetic equation, and substituting the kinetic equation into a preset state equation to obtain an algebraic equation; converting the constraint objective function into a constraint integral weight objective function according to a preset integral weight; and converting the optimal control model into a nonlinear programming (NLP) problem according to the algebraic equation and the constraint integral weight objective function.
In a specific implementation, the running time interval is a time interval [ t ] for which the speed trajectory of each target vehicle needs to be planned0,tf]Using time domain conversion to [ t ]0,tf]Conversion to Legendre orthogonal multi-interval [ -1,1 [ -1]The affine transformation formula is:
Figure RE-GDA0003311823260000101
wherein τ is the normalized time.
It should be noted that Legendre orthogonal multi-interval [ -1,1 ] is required]Selecting LGR distribution points, wherein the preset number is NpDiscretizing the state variable and the control variable, respectively by Np+1 and NpApproximating the discretized result by a Lagrange interpolation polynomial;
the LGR distribution points are as follows:
Figure RE-GDA0003311823260000102
the dispersion result is:
Figure RE-GDA0003311823260000111
Figure RE-GDA0003311823260000112
the approximation result is:
Figure RE-GDA0003311823260000113
wherein, the LGR coordination point is an N-order Legebdre orthogonal polynomial PNThe root of (tau) is,
Figure RE-GDA0003311823260000114
are respectively NpAnd Np-1 Lagrange interpolation basis function.
Converting the approximation result into a kinetic equation, substituting the kinetic equation into a preset state equation to obtain an algebraic equation, and solving a first derivative of the state equation to convert the kinetic equation:
Figure RE-GDA0003311823260000115
substituting the state equation to obtain:
Figure RE-GDA0003311823260000116
wherein the state equation is
Figure RE-GDA0003311823260000117
The distribution point in the time domain is recorded as taum。
Converting the constraint objective function into a constraint integral weight objective function according to a preset integral weight:
Figure RE-GDA0003311823260000121
wherein, ω iskIs the integral weight.
Through the conversion, the optimal control problem is converted into a nonlinear programming NLP problem:
Figure RE-GDA0003311823260000122
wherein h (x, u) is an equality constraint of the energy consumption calculation model, and g (x, u) is an inequality constraint in the constraint objective function.
Further, the step of determining the vehicle speed trajectory of each target vehicle according to the nonlinear programming NLP problem includes: converting the nonlinear programming NLP problem into a multiplier problem by utilizing a Lagrange multiplier; performing iteration on the multiplier problem for preset times to obtain a quadratic programming subproblem; determining a linear search equation according to a preset search direction and the quadratic programming subproblem; and determining the speed track of each target vehicle according to the secondary planning subproblem and the linear search equation.
It should be noted that, the NLP problem is converted into a series of quadratic programming subproblems by using a sequential quadratic programming method to obtain the optimal solution of the original problem, and the NLP problem is converted by using lagrange multiplier:
l (x, u, λ, μ) ═ J (x, u) - λ h (x, u) - μ g (x, u) formula 18;
assuming that the current iteration is the c-th (i.e. the predetermined number of) iteration, the values of x, u, λ, μ are xc,uc,λc,μc，HcIs an approximate Hessian matrix of a Lagrange multiplier function to obtain a quadratic programming subproblem:
Figure RE-GDA0003311823260000123
wherein d is the solution of the quadratic programming subproblem and is also in the main iteration process (x)c,uc) The search direction of (2). The linear search equation is obtained as:
(xc+1,uc+1)＝(xc,uc)+αcdcequation 20;
wherein alpha iscIs the step size of each search in the iterative process.
Further, the step of determining the vehicle speed trajectory of each target vehicle according to the quadratic programming subproblem and the linear search equation includes: determining optimal vector parameters according to the quadratic programming subproblem and the linear search equation; and determining the speed track of each target vehicle according to the optimal vector parameters.
It should be noted that, the optimized vector parameter (x) of the next iteration process is obtained by calculating the quadratic programming subproblemc+1,uc+1). The iterative process is repeated until the result converges to obtain the optimal parameter vector (x)*,u*) And obtaining a vehicle speed track.
The embodiment obtains the speed information and the energy consumption information of each target vehicle; generating an energy consumption calculation model of the target vehicle according to the energy consumption information and the speed information; acquiring speed vehicle distance limiting conditions, acceleration information of the speed limiting conditions in each target vehicle and speed distance information; establishing a constraint objective function according to the speed-vehicle distance limiting condition, the acceleration information, the speed-distance information and the state constraint condition; and planning the speed track of each target vehicle according to the energy consumption calculation model, the speed limiting condition, the acceleration information, the speed distance information and the constraint objective function. Through the mode, the energy consumption information is determined through the speed information of each target vehicle, so that an energy consumption calculation model is generated, a constraint objective function is established, and the speed track of each target vehicle is planned, so that unnecessary acceleration and deceleration are reduced, and the energy consumption of the vehicles is reduced.
In addition, an embodiment of the present invention further provides a storage medium, where a vehicle speed trajectory planning program is stored, and when executed by a processor, the vehicle speed trajectory planning program implements the steps of the vehicle speed trajectory planning method described above.
Referring to fig. 3, fig. 3 is a block diagram of a vehicle speed trajectory planning device according to a first embodiment of the present invention.
As shown in fig. 3, a vehicle speed trajectory planning device according to an embodiment of the present invention includes:
the acquiring module 10 is used for acquiring speed information and energy consumption information of each target vehicle.
And a generating module 20, configured to generate an energy consumption calculation model of the target vehicle according to the energy consumption information and the speed information.
The obtaining module 10 is further configured to obtain speed-vehicle distance limiting conditions, acceleration information of the speed limiting conditions in each target vehicle, and speed-distance information.
And the establishing module 30 is configured to establish a constraint objective function according to the speed-vehicle distance limiting condition, the acceleration information, the speed-distance information, and the state constraint condition.
And the planning module 40 is configured to plan a vehicle speed trajectory of each target vehicle according to the energy consumption calculation model, the speed limiting condition, the acceleration information, the speed distance information, and the constraint objective function.
In an embodiment, the obtaining module 10 is further configured to obtain speed information of each target vehicle; determining the required torque of each target vehicle according to the speed information; determining the power source rotating speed of each target vehicle according to the speed information; and determining the energy consumption information of each target vehicle according to the power source type, the required torque and the power source rotating speed of each target vehicle.
In an embodiment, the establishing module 30 is further configured to establish a control variable function according to the acceleration information;
establishing a state variable function according to the speed distance information and the control variable function;
determining a speed limiting condition, an acceleration and deceleration limiting condition and a vehicle distance limiting condition according to the speed vehicle distance limiting condition;
and establishing a constraint objective function according to the control variable function, the state variable function, the speed limiting condition, the acceleration and deceleration limiting condition, the inter-vehicle distance limiting condition and the state constraint condition.
In an embodiment, the planning module 40 is further configured to generate an optimal control model according to the energy consumption calculation model, the speed limit condition, the acceleration information, the speed distance information, and the constraint objective function; converting the optimal control model into a nonlinear programming (NLP) problem; and determining the speed track of each target vehicle according to the nonlinear programming NLP problem.
In an embodiment, the planning module 40 is further configured to obtain a driving time interval of each target vehicle, and convert the driving time interval into a Legendre orthogonal multiple interval; selecting a preset number of LGR distribution points in the Legendre orthogonal multi-interval; discretizing the acceleration information and the speed distance information based on the Legendre orthogonal multi-interval and the LGR distribution point to obtain a discretization result; approximating the discretization result according to a Lagrange interpolation basis function to obtain an approximating result; converting the approximation result into a kinetic equation, and substituting the kinetic equation into a preset state equation to obtain an algebraic equation; converting the constraint objective function into a constraint integral weight objective function according to a preset integral weight; and converting the optimal control model into a nonlinear programming (NLP) problem according to the algebraic equation and the constraint integral weight objective function.
In an embodiment, the planning module 40 is further configured to convert the non-linear programming NLP problem into a multiplier problem by using lagrangian multipliers; performing iteration on the multiplier problem for preset times to obtain a quadratic programming subproblem; determining a linear search equation according to a preset search direction and the quadratic programming subproblem; and determining the speed track of each target vehicle according to the secondary planning subproblem and the linear search equation.
In an embodiment, the planning module 40 is further configured to determine an optimal vector parameter according to the quadratic planning sub-problem and the linear search equation; and determining the speed track of each target vehicle according to the optimal vector parameters.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited thereto.
In this embodiment, the obtaining module 10 obtains speed information and energy consumption information of each target vehicle; the generation module 20 generates an energy consumption calculation model of the target vehicle according to the energy consumption information and the speed information; the acquisition module 10 acquires speed vehicle distance limiting conditions, acceleration information of the speed limiting conditions in each target vehicle and speed distance information; the establishing module 30 establishes a constraint objective function according to the speed-vehicle distance limiting condition, the acceleration information, the speed-distance information and the state constraint condition; the planning module 40 plans the speed trajectory of each target vehicle according to the energy consumption calculation model, the speed limit condition, the acceleration information, the speed distance information and the constraint objective function. Through the mode, the energy consumption information is determined through the speed information of each target vehicle, so that an energy consumption calculation model is generated, a constraint objective function is established, and the speed track of each target vehicle is planned, so that unnecessary acceleration and deceleration are reduced, and the energy consumption of the vehicles is reduced.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not elaborated in this embodiment may refer to the vehicle speed trajectory planning method provided in any embodiment of the present invention, and are not described herein again.
Further, it is to be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (e.g. a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (10)

1. A vehicle speed trajectory planning method is characterized by comprising the following steps:
acquiring speed information and energy consumption information of each target vehicle;
generating an energy consumption calculation model of the target vehicle according to the energy consumption information and the speed information;
acquiring speed vehicle distance limiting conditions, acceleration information of the speed limiting conditions in each target vehicle and speed distance information;
establishing a constraint objective function according to the speed-vehicle distance limiting condition, the acceleration information, the speed-distance information and a state constraint condition;
and planning the speed track of each target vehicle according to the energy consumption calculation model, the speed limiting condition, the acceleration information, the speed distance information and the constraint objective function.
2. The method of claim 1, wherein the step of obtaining speed information and energy consumption information for each target vehicle comprises:
acquiring speed information of each target vehicle;
determining the required torque of each target vehicle according to the speed information;
determining the power source rotating speed of each target vehicle according to the speed information;
and determining the energy consumption information of each target vehicle according to the power source type, the required torque and the power source rotating speed of each target vehicle.
3. The method of claim 1, wherein said step of establishing a constraint objective function based on said speed-to-vehicle distance limit, said acceleration information, said speed-to-distance information, and a state constraint comprises:
establishing a control variable function according to the acceleration information;
establishing a state variable function according to the speed distance information and the control variable function;
determining a speed limiting condition, an acceleration and deceleration limiting condition and a vehicle distance limiting condition according to the speed vehicle distance limiting condition;
and establishing a constraint objective function according to the control variable function, the state variable function, the speed limiting condition, the acceleration and deceleration limiting condition, the inter-vehicle distance limiting condition and the state constraint condition.
4. The method of claim 1, wherein the step of deriving the vehicle speed trajectory for each target vehicle based on the energy consumption calculation model, the speed limit condition, the acceleration information, the speed distance information, and the constrained objective function comprises:
generating an optimal control model according to the energy consumption calculation model, the speed limiting condition, the acceleration information, the speed distance information and the constraint objective function;
converting the optimal control model into a nonlinear programming (NLP) problem;
and determining the speed track of each target vehicle according to the nonlinear programming NLP problem.
5. The method of claim 4, wherein the step of converting the optimal control model to a non-linear programming NLP problem comprises:
acquiring a running time interval of each target vehicle, and converting the running time interval into a Legendre orthogonal multi-interval;
selecting a preset number of LGR distribution points in the Legendre orthogonal multi-interval;
discretizing the acceleration information and the speed distance information based on the Legendre orthogonal multi-interval and the LGR distribution point to obtain a discretization result;
approximating the discretization result according to a Lagrange interpolation basis function to obtain an approximating result;
converting the approximation result into a kinetic equation, and substituting the kinetic equation into a preset state equation to obtain an algebraic equation;
converting the constraint objective function into a constraint integral weight objective function according to a preset integral weight;
and converting the optimal control model into a nonlinear programming (NLP) problem according to the algebraic equation and the constraint integral weight objective function.
6. The method of claim 4, wherein the step of determining a vehicle speed trajectory for each target vehicle from the non-linear programming NLP problem comprises:
converting the nonlinear programming NLP problem into a multiplier problem by utilizing a Lagrange multiplier;
performing iteration on the multiplier problem for preset times to obtain a quadratic programming subproblem;
determining a linear search equation according to a preset search direction and the quadratic programming subproblem;
and determining the speed track of each target vehicle according to the secondary planning subproblem and the linear search equation.
7. The method of claim 6, wherein the step of determining the vehicle speed trajectory for each target vehicle based on the quadratic programming sub-problem and the linear search equation comprises:
determining optimal vector parameters according to the quadratic programming subproblem and the linear search equation;
and determining the speed track of each target vehicle according to the optimal vector parameters.
8. A vehicle speed trajectory planning device, characterized by comprising:
the acquisition module is used for acquiring the speed information and the energy consumption information of each target vehicle;
the generating module is used for generating an energy consumption calculation model of the target vehicle according to the energy consumption information and the speed information;
the acquisition module is also used for acquiring speed vehicle distance limiting conditions, acceleration information of the speed limiting conditions in each target vehicle and speed distance information;
the establishing module is used for establishing a constraint objective function according to the speed vehicle distance limiting condition, the acceleration information, the speed distance information and a state constraint condition;
and the planning module is used for planning the speed track of each target vehicle according to the energy consumption calculation model, the speed limiting condition, the acceleration information, the speed distance information and the constraint objective function.
9. A vehicle speed trajectory planning apparatus, characterized by comprising: a memory, a processor and a vehicle speed trajectory planning program stored on the memory and executable on the processor, the vehicle speed trajectory planning program being configured to implement the vehicle speed trajectory planning method according to any one of claims 1 to 7.
10. A storage medium having stored thereon a vehicle speed trajectory planning program which, when executed by a processor, implements a vehicle speed trajectory planning method according to any one of claims 1 to 7.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114783175A (en) * 2022-03-23 2022-07-22 东南大学 Energy-saving driving control method for networked vehicles under multi-signal lamp road conditions based on pseudo-spectrum method

Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009196560A (en) * 2008-02-22 2009-09-03 Toyota Motor Corp Traveling control device for vehicle
CN105035090A (en) * 2015-06-06 2015-11-11 吉林大学 Trace prediction control method for autonomously driven vehicle based on traffic signal lamp
CN106502098A (en) * 2016-11-19 2017-03-15 合肥工业大学 A kind of optimum speed closed loop fast prediction control method and device based on car networking
CN107117170A (en) * 2017-04-28 2017-09-01 吉林大学 A kind of real-time estimate cruise control system driven based on economy
CN107139923A (en) * 2017-05-11 2017-09-08 中科院微电子研究所昆山分所 A kind of ACC decision-making techniques and system
CN108646734A (en) * 2018-04-27 2018-10-12 天津大学 Nonlinear model predictive control method based on quanta particle swarm optimization
CN110154775A (en) * 2019-05-07 2019-08-23 东风柳州汽车有限公司 The regenerating braking energy recycling and control method of electric car
CN111791887A (en) * 2020-07-03 2020-10-20 北京理工大学 Vehicle energy-saving driving method based on layered vehicle speed planning
DE102019208878A1 (en) * 2019-06-19 2020-12-24 Denso Corporation Apparatus and method for adaptive speed control
CN112677995A (en) * 2021-01-07 2021-04-20 腾讯科技（深圳）有限公司 Vehicle track planning method and device, storage medium and equipment
WO2021073079A1 (en) * 2019-10-17 2021-04-22 南京航空航天大学 Trajectory planning method for highly coupling path and speed of autonomous vehicle
CN112810598A (en) * 2021-02-22 2021-05-18 东风柳州汽车有限公司 Control method, device and equipment for clutch synchronization and storage medium
CN113050687A (en) * 2021-03-19 2021-06-29 四川大学 Multi-unmanned aerial vehicle formation recombination track planning method

Patent Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009196560A (en) * 2008-02-22 2009-09-03 Toyota Motor Corp Traveling control device for vehicle
CN105035090A (en) * 2015-06-06 2015-11-11 吉林大学 Trace prediction control method for autonomously driven vehicle based on traffic signal lamp
CN106502098A (en) * 2016-11-19 2017-03-15 合肥工业大学 A kind of optimum speed closed loop fast prediction control method and device based on car networking
CN107117170A (en) * 2017-04-28 2017-09-01 吉林大学 A kind of real-time estimate cruise control system driven based on economy
CN107139923A (en) * 2017-05-11 2017-09-08 中科院微电子研究所昆山分所 A kind of ACC decision-making techniques and system
CN108646734A (en) * 2018-04-27 2018-10-12 天津大学 Nonlinear model predictive control method based on quanta particle swarm optimization
CN110154775A (en) * 2019-05-07 2019-08-23 东风柳州汽车有限公司 The regenerating braking energy recycling and control method of electric car
DE102019208878A1 (en) * 2019-06-19 2020-12-24 Denso Corporation Apparatus and method for adaptive speed control
WO2021073079A1 (en) * 2019-10-17 2021-04-22 南京航空航天大学 Trajectory planning method for highly coupling path and speed of autonomous vehicle
CN111791887A (en) * 2020-07-03 2020-10-20 北京理工大学 Vehicle energy-saving driving method based on layered vehicle speed planning
CN112677995A (en) * 2021-01-07 2021-04-20 腾讯科技（深圳）有限公司 Vehicle track planning method and device, storage medium and equipment
CN112810598A (en) * 2021-02-22 2021-05-18 东风柳州汽车有限公司 Control method, device and equipment for clutch synchronization and storage medium
CN113050687A (en) * 2021-03-19 2021-06-29 四川大学 Multi-unmanned aerial vehicle formation recombination track planning method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
段建民;房泽平;杨晨;: "基于Radau伪谱法和MPC的智能电动车辆生态驾驶", 北京工业大学学报, no. 05 *
熊禹;周辉;张志强;廖黎;冯倍茂;丁磊;: "电动汽车的整车动力匹配仿真", 汽车电器, no. 05 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114783175A (en) * 2022-03-23 2022-07-22 东南大学 Energy-saving driving control method for networked vehicles under multi-signal lamp road conditions based on pseudo-spectrum method
CN114783175B (en) * 2022-03-23 2023-06-23 东南大学 Multi-signal lamp road condition internet-connected vehicle energy-saving driving control method based on pseudo-spectrum method

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