CN113650622B - Vehicle speed track planning method, device, equipment and storage medium - Google Patents
Vehicle speed track planning method, device, equipment and storage medium Download PDFInfo
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- 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
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Abstract
The invention belongs to the technical field of energy-saving driving, and discloses a vehicle speed track planning method, device, equipment and 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 distance limiting condition, the acceleration information and 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. By the method, 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 built, and the speed track of each target vehicle is planned, so that unnecessary acceleration and deceleration are reduced, and the energy consumption of the vehicle is reduced.
Description
Technical Field
The present invention relates to the field of energy-saving driving technologies, and in particular, to a vehicle speed trajectory planning method, device, equipment, and 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, in the current economic vehicle speed planning, the fixed speed limit and the arrival time are used as constraints for planning, and the real-time speed planning of the vehicle speed per se is carried out by taking the real-time acquisition of traffic information and the adjustment speed limit of surrounding vehicle information into consideration;
2. from the aspect of vehicles, the current economic vehicle speed planning is mostly aimed at a single vehicle, and the cooperative vehicle speed planning is carried out by rarely considering a plurality of vehicles, particularly vehicles with different power system characteristics.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide a vehicle speed track planning method, device, equipment and storage medium, which aim to solve the technical problems of how to reduce the total energy consumption of multiple vehicles and improve the road passing efficiency in the prior art.
In order to achieve the above object, the present invention provides a vehicle speed trajectory planning method, which includes 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 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.
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 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-distance constraint condition, the acceleration information, the speed-distance information and the 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 limit condition, an acceleration and deceleration limit condition and an inter-vehicle distance limit condition according to the speed and inter-vehicle distance limit 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 a 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 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 nonlinear programming NLP problem includes:
acquiring a running time interval of each target vehicle, and converting the running time interval into Legendre orthogonal multi-intervals;
selecting a preset number of LGR distribution points in the Legendre orthogonal multi-region;
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 the Lagrange interpolation basis function to obtain an approximation result;
converting the approximation result into a dynamic equation, and substituting the dynamic 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 speed track of each target vehicle according to the nonlinear programming NLP problem includes:
converting the nonlinear programming NLP problem into a multiplier problem by utilizing Lagrangian multipliers;
carrying out iteration for preset times on the multiplier problem to obtain a quadratic programming sub-problem;
determining a linear search equation according to a preset search direction and the quadratic programming sub-problem;
and determining the speed track of each target vehicle according to the quadratic programming sub-problem and the linear search equation.
Optionally, the step of determining the speed track of each target vehicle according to the quadratic programming sub-problem and the linear search equation includes:
determining optimal vector parameters according to the quadratic programming sub-problem 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 also 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 generation 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 and distance limiting conditions, acceleration information of the speed limiting conditions in each target vehicle and speed and distance information;
the establishing module is used for establishing a constraint objective function according to the speed distance limiting condition, the acceleration information, the speed distance information and the 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 also proposes a vehicle speed trajectory planning apparatus including: the vehicle speed trajectory planning system comprises a memory, a processor and a vehicle speed trajectory planning program stored on the memory and operable 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, in order to achieve the above object, the present invention also proposes a storage medium having stored thereon a vehicle speed trajectory planning program which, 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 distance limiting condition, the acceleration information and 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. By the method, 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 built, and the speed track of each target vehicle is planned, so that unnecessary acceleration and deceleration are reduced, and the energy consumption of the vehicle is reduced. .
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FIG. 1 is a schematic diagram of a vehicle speed trajectory planning device in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a 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 achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a vehicle speed track planning device in a hardware running 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 (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further 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 high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Those skilled in the art will appreciate that the configuration shown in fig. 1 is not limiting of the vehicle speed trajectory planning device and may include more or fewer components than shown, or may combine certain components, or may be a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a vehicle speed trajectory planning program may be included in the memory 1005 as one type of storage medium.
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 in the vehicle speed track planning device of the present invention may be provided in the vehicle speed track planning device, where the vehicle speed track planning device invokes a vehicle speed track planning program stored in the memory 1005 through the processor 1001, and executes the vehicle speed track planning method provided by the embodiment of the present invention.
The embodiment of the invention provides a vehicle speed track planning method, referring to fig. 2, fig. 2 is a flow chart of a first embodiment of the vehicle speed track planning method of the invention.
In this embodiment, the vehicle speed track 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 body of the embodiment may be a vehicle-mounted terminal on an intelligent internet-connected vehicle, which refers to an organic combination of the internet of vehicles and an intelligent vehicle, and is a new-generation vehicle that is equipped with advanced devices such as a vehicle-mounted sensor, a controller and an actuator, and integrates modern communication and network technologies, so as to realize intelligent information exchange and sharing of vehicles, people, vehicles, roads and the background, realize safe, comfortable, energy-saving and efficient running, and finally can replace people to operate.
It should be understood that the intelligent network-connected automobile and the intelligent network-connected automobiles establish network connection through the internet of vehicles or are connected through V2X, so that data intercommunication among the vehicles can be achieved, and each vehicle for data intercommunication is the target vehicle.
It should be appreciated that the speed information includes the speed and acceleration of the vehicle at different driving conditions, thereby constructing a set of speed-acceleration.
It can be appreciated that factors affecting the energy consumption of the vehicle, including the engine, speed, mass, etc. of the vehicle, so 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 accurate calculation of the energy consumption information of each target vehicle, 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 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.
The required torque of each target vehicle power source can be calculated through a longitudinal dynamics model, and the longitudinal dynamics model is as follows:
wherein m is the mass of the whole vehicle, g is the gravitational acceleration, f is the rolling resistance coefficient, alpha is the gradient angle, A is the windward area of the vehicle, and C D The air resistance coefficient, v is the vehicle speed (km/h), delta is the conversion coefficient of the rotating mass of the automobile, a is the acceleration (m/s 2), r is the rolling radius of the wheels, T t For rim torque, i g For transmission ratio, i 0 Is the main speed reduction ratio eta t Is driveline efficiency.
It should be appreciated that the vehicle power source speed n is calculated from the relationship between the power source speed and the vehicle speed as follows:
wherein n is the power source rotating speed (r/min).
In a specific implementation, the power source types of the intelligent network vehicle comprise a fuel vehicle and a new energy vehicle, wherein 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:
wherein T is e ,T m ,n e ,n m Respectively, an engine torque rotating speed and a motor torque rotating speed, m fuel (T e ,n e ) To query specific fuel consumption (g/kWh) according to universal characteristics, z d Is the gasoline heavy (N/L), Q fuel Fuel consumption (L/s), Q elec Is 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 this embodiment, the vehicle energy consumption map matrix is established with the speed information and the corresponding energy consumption information, so that the energy consumption calculation models Q to (v, a) are established.
Step S30: and acquiring speed distance limiting conditions, acceleration information of the speed limiting conditions in each target vehicle and speed distance information.
The speed limit conditions are given as speed-distance limits, and include a speed limit and a travel distance limit of each target vehicle, that is, a distance section including a speed section of the vehicle speed and a travel distance. The speed limit condition further includes a control variable (i.e., acceleration information) that is the acceleration and the rate of change of the acceleration of each target vehicle, and a state variable (i.e., speed distance information) that is the speed and the travel distance of each target vehicle. The speed limit condition is obtained by an intelligent transportation system (Intelligent Traffic System, ITS).
It can be understood that the speed-distance limitation conditions include speed, acceleration, and acceleration change rate limitation conditions of each target vehicle, limitation conditions of frequent acceleration and deceleration of each target vehicle, and distance limitation of the distance between each target vehicle.
Step S40: and establishing a constraint objective function according to the speed and distance limiting condition, the acceleration information, the speed and 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 limit condition, an acceleration and deceleration limit condition and an inter-vehicle distance limit condition according to the speed and inter-vehicle distance limit 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, according to the control variable (namely acceleration information), a control variable function of the multi-vehicle collaborative economic vehicle speed planning is established:
where a is the vehicle acceleration, i a In order to provide a rate of change of acceleration,
is a state equation.
It can be appreciated that the state variable function is built from the state variables (i.e., speed distance information) and the control variable function:
wherein s is k0 S is the initial position of the target vehicle kf S is the target position of the target vehicle k For the position of the target vehicle, t 0 Starting for driving the target vehicleStart time, t f For the end time of the target vehicle travel, k represents the vehicle number.
Speed, acceleration, and acceleration rate of change constraints:
wherein vM, aM, i a m,i a M is the upper and lower limit values of the speed, the acceleration and the acceleration change rate respectively.
Limit of frequent acceleration and deceleration:
Q a ＝∑abs(i a_k ) Equation 7;
wherein Q is a And the cost caused by punishment of frequent change of acceleration is avoided, and k represents the number of the target vehicle.
Spacing limit of vehicle distance between each target vehicle:
wherein s is k_act For ensuring the safety of the kth vehicle and the (k+1) th vehicle, sigma is the minimum safety vehicle distance, Q s The road traffic efficiency is indirectly improved by increasing the road utilization rate as much as possible on the premise of ensuring safety for the cost brought by oversized punishment of the vehicle distance.
In the present embodiment, an objective function is established based on the control variable function, the state variable function, the speed limitation condition, the acceleration/deceleration limitation condition, and the inter-vehicle distance limitation condition:
where x is the state variable, u is the control variable, ω 1 ,ω 2 ,ω 3 For weights of energy consumption cost, frequent change of acceleration cost and excessive vehicle distance cost，α elec ,α fuel The energy consumption cost coefficient (Yuan/kWh) and the oil consumption cost coefficient (Yuan/L) are respectively, 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 then a constraint objective function is established:
where β is a time penalty factor, a dichotomy can be used to adjust to determine the appropriate β to achieve the 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 speed track of each target vehicle more accurately, the model needs to be converted 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 the optimal control problem, and in the time interval, by searching the control variable, the performance index J is minimized, and one or a class of vehicle speed tracks meeting the constraint condition and the performance index are obtained. 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, a pseudo-spectrum method can be used for converting the optimal control problem model into the nonlinear programming NLP (Nonlinear Programming) problem, the pseudo-spectrum method is classified into a Gauss pseudo-spectrum method, a Legendre pseudo-spectrum method and a Radau pseudo-spectrum method, and the Radau pseudo-spectrum method has higher convergence rate when solving the numerical solution of the optimal control problem, 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 nonlinear programming NLP problem includes: acquiring a running time interval of each target vehicle, and converting the running time interval into Legendre orthogonal multi-intervals; selecting a preset number of LGR distribution points in the Legendre orthogonal multi-region; 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 the Lagrange interpolation basis function to obtain an approximation result; converting the approximation result into a dynamic equation, and substituting the dynamic 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 driving time interval is a time interval [ t ] in which each target vehicle needs to perform vehicle speed track planning 0 ,t f ]Using time domain conversion will [ t ] 0 ,t f ]Conversion to Legendre orthogonal multi-interval [ -1,1]The affine change formula is:
where τ is the normalized time.
It should be noted that the Legendre orthogonal multi-interval [ -1,1]Selecting LGR configuration points with preset number N p Discretizing state variables and control variables by N p +1 and N p A Lagrange interpolation polynomial approximates the discretized result;
the LGR fitting point is:
the discrete results were:
the approximation result is:
wherein the LGR point is an N-order Legebdre orthogonal polynomial P N The root of (tau),
respectively N p And N p -Lagrange interpolation basis function 1.
Converting the approximation result into a kinetic equation, substituting the kinetic equation into a preset state equation to obtain an algebraic equation, solving a first derivative of the state equation, and performing kinetic equation conversion:
substituting the state equation to obtain:
wherein the state equation is
The point of the distribution in the time domain is denoted as tau m 。
Converting the constraint objective function into a constraint integral weight objective function according to a preset integral weight:
wherein omega k Is the integral weight.
Through the conversion, the optimal control problem is converted into a nonlinear programming NLP problem:
where h (x, u) is the equality constraint of the energy consumption calculation model and g (x, u) is the inequality constraint in the constraint objective function.
Further, the step of determining the speed track of each target vehicle according to the nonlinear programming NLP problem includes: converting the nonlinear programming NLP problem into a multiplier problem by utilizing Lagrangian multipliers; carrying out iteration for preset times on the multiplier problem to obtain a quadratic programming sub-problem; determining a linear search equation according to a preset search direction and the quadratic programming sub-problem; and determining the speed track of each target vehicle according to the quadratic programming sub-problem and the linear search equation.
It should be noted that, the sequence quadratic programming method is used to convert the NLP problem into a series of quadratic programming sub-problems to obtain the optimal solution of the original problem, and the Lagrangian multiplier is used to convert the NLP problem into:
l (x, u, λ, μ) =j (x, u) - λh (x, u) - μg (x, u) formula 18;
assuming the current iteration is the c-th (i.e., preset number of) iteration, the values of x, u, λ, μ are x, respectively c ,u c ,λ c ,μ c ，H c Is an approximate Hessian matrix of the lagrangian multiplier function, resulting in a quadratic programming sub-problem:
where d is the solution of the quadratic programming sub-problem, which is also the solution of the first iteration (x c ,u c ) Is a search direction of (a). The linear search equation is obtained as:
(x c+1 ,u c+1 )＝(x c ,u c )+α c d c equation 20;
wherein alpha is c Is the step size of each search in the iterative process.
Further, the step of determining the speed trajectory of each target vehicle according to the quadratic programming sub-problem and the linear search equation includes: determining optimal vector parameters according to the quadratic programming 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 noted that, by calculating the quadratic programming sub-problem, the optimized vector parameters (x c+1 ,u c+1 ). The iterative process is repeated until the result converges to obtain an optimal parameter vector (x * ,u * ) And obtaining a vehicle speed track.
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 distance limiting condition, the acceleration information and 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. By the method, 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 built, and the speed track of each target vehicle is planned, so that unnecessary acceleration and deceleration are reduced, and the energy consumption of the vehicle is reduced.
In addition, the embodiment of the invention also provides a storage medium, wherein the storage medium is stored with a vehicle speed track planning program, and the vehicle speed track planning program realizes the steps of the vehicle speed track planning method when being executed by a processor.
Referring to fig. 3, fig. 3 is a block diagram illustrating 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:
an acquisition module 10 is configured to acquire speed information and energy consumption information of each target vehicle.
The generating module 20 is configured to generate an energy consumption calculation model of the target vehicle according to the energy consumption information and the speed information.
The acquiring module 10 is further configured to acquire speed and distance limiting conditions, acceleration information of the speed limiting conditions in each target vehicle, and speed and distance information.
The establishing module 30 is configured to establish a constraint objective function according to the speed and distance constraint condition, the acceleration information, the speed and distance information, and the state constraint condition.
The planning module 40 is configured to plan a vehicle speed track of each target vehicle according to the energy consumption calculation model, the speed limitation condition, the acceleration information, the speed distance information and the constraint objective function.
In an embodiment, the acquiring module 10 is further configured to acquire 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 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 one 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 limit condition, an acceleration and deceleration limit condition and an inter-vehicle distance limit condition according to the speed and inter-vehicle distance limit 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 limitation 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 multi-interval; selecting a preset number of LGR distribution points in the Legendre orthogonal multi-region; 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 the Lagrange interpolation basis function to obtain an approximation result; converting the approximation result into a dynamic equation, and substituting the dynamic 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 nonlinear programming NLP problem into a multiplier problem by using lagrangian multipliers; carrying out iteration for preset times on the multiplier problem to obtain a quadratic programming sub-problem; determining a linear search equation according to a preset search direction and the quadratic programming sub-problem; and determining the speed track of each target vehicle according to the quadratic programming sub-problem and the linear search equation.
In one embodiment, the planning module 40 is further configured to determine optimal vector parameters according to the quadratic programming 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 foregoing is illustrative only and is not limiting, and that in specific applications, those skilled in the art may set the invention as desired, and the invention is not limited thereto.
In the present embodiment, the acquisition module 10 acquires 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-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 distance limiting condition, the acceleration information and 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 limiting condition, the acceleration information, the speed distance information and the constraint objective function. By the method, 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 built, and the speed track of each target vehicle is planned, so that unnecessary acceleration and deceleration are reduced, and the energy consumption of the vehicle is reduced.
It should be noted that the above-described working procedure is merely illustrative, and does not limit the scope of the present invention, and in practical application, a person skilled in the art may select part or all of them according to actual needs to achieve the purpose of the embodiment, which is not limited herein.
In addition, technical details not described in detail in the present embodiment may refer to the vehicle speed track planning method provided in any embodiment of the present invention, which is not described herein again.
Furthermore, it should 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 one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. Read Only Memory)/RAM, magnetic disk, optical disk) and including several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (7)
1. The vehicle speed track planning method is characterized by comprising the following steps of:
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 distance limiting condition, the acceleration information, the speed distance information and the state constraint condition;
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;
the step of obtaining the speed information and the energy consumption information of each target vehicle comprises the following steps:
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;
determining 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;
the step of obtaining 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 comprises the following steps:
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;
determining the speed track of each target vehicle according to the nonlinear programming NLP problem;
the step of converting the optimal control model into a nonlinear programming NLP problem comprises the following steps:
acquiring a running time interval of each target vehicle, and converting the running time interval into Legendre orthogonal multi-intervals;
selecting a preset number of LGR distribution points in the Legendre orthogonal multi-region;
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 the Lagrange interpolation basis function to obtain an approximation result;
converting the approximation result into a dynamic equation, and substituting the dynamic 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.
2. The method of claim 1, wherein the step of establishing a constraint objective function based on the speed-distance constraint, the acceleration information, the speed-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 limit condition, an acceleration and deceleration limit condition and an inter-vehicle distance limit condition according to the speed and inter-vehicle distance limit 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.
3. The method of claim 1, wherein the step of determining a vehicle speed trajectory of each target vehicle from the nonlinear programming NLP problem comprises:
converting the nonlinear programming NLP problem into a multiplier problem by utilizing Lagrangian multipliers;
carrying out iteration for preset times on the multiplier problem to obtain a quadratic programming sub-problem;
determining a linear search equation according to a preset search direction and the quadratic programming sub-problem;
and determining the speed track of each target vehicle according to the quadratic programming sub-problem and the linear search equation.
4. The method of claim 3, wherein the step of determining a 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 sub-problem and the linear search equation;
and determining the speed track of each target vehicle according to the optimal vector parameters.
5. A vehicle speed trajectory planning device, characterized in that the vehicle speed trajectory planning device comprises:
the acquisition module is used for acquiring the speed information and the energy consumption information of each target vehicle;
the generation 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 and distance limiting conditions, acceleration information of the speed limiting conditions in each target vehicle and speed and distance information;
the establishing module is used for establishing a constraint objective function according to the speed distance limiting condition, the acceleration information, the speed distance information and the state constraint condition;
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;
the acquisition module is also used for acquiring the 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; determining 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;
the planning module is further used for 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; determining the speed track of each target vehicle according to the nonlinear programming NLP problem;
the planning module is also used for acquiring the running time interval of each target vehicle and converting the running time interval into Legendre orthogonal multi-interval; selecting a preset number of LGR distribution points in the Legendre orthogonal multi-region; 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 the Lagrange interpolation basis function to obtain an approximation result; converting the approximation result into a dynamic equation, and substituting the dynamic 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. A vehicle speed trajectory planning device, characterized in that the device comprises: a memory, a processor, and a vehicle speed trajectory planning program stored on the memory and operable on the processor, the vehicle speed trajectory planning program configured to implement the vehicle speed trajectory planning method of any one of claims 1 to 4.
7. A storage medium having stored thereon a vehicle speed trajectory planning program which when executed by a processor implements the vehicle speed trajectory planning method of any one of claims 1 to 4.
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