CN111645685A - Economic vehicle speed planning method and device, storage medium and equipment - Google Patents
Economic vehicle speed planning method and device, storage medium and equipment 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 embodiment of the application discloses a method, a device, a storage medium and equipment for planning an economic vehicle speed. The method comprises the following steps: constructing a power consumption model of the vehicle according to the motion basic constraint condition of the vehicle; wherein the power consumption model is constructed by taking a unit time period as a construction unit; determining the optimal average acceleration of the vehicle in each unit time period by adopting a particle swarm optimization algorithm according to the optimization constraint condition; determining the optimal ending speed of each unit time period according to the optimal average acceleration and the starting speed of each unit time period; and determining a vehicle speed curve according to the optimal termination speed of each unit time period, and planning the vehicle speed according to the vehicle speed curve. By executing the scheme, the energy consumption of the vehicle can be effectively controlled through the vehicle speed curve of the vehicle in the driving process, so that the effect of reasonably planning the vehicle speed is achieved.
Description
Technical Field
The embodiment of the application relates to the technical field of vehicle control, in particular to a method, a device, a storage medium and equipment for planning an economic vehicle speed.
Background
With the rapid development of the technology level, the reduction of global fossil energy and the aggravation of environmental pollution, the new energy pure electric vehicle is rapidly developed. For a traditional fuel automobile or an electric vehicle, a front axle and rear axle torque distribution strategy is to distribute the front axle and rear axle torque according to a fixed proportion so as to realize single vehicle dynamic performance.
The single torque distribution mode is lack of flexibility, only a vehicle can deal with a plurality of fixed types of pavements, and the torque distribution proportion cannot be adjusted according to the real-time road condition of the vehicle and the requirement of a driver, so that the optimal real-time dynamic performance is realized.
Disclosure of Invention
The embodiment of the application provides a method, a device, a storage medium and equipment for planning an economic vehicle speed, which can effectively control the energy consumption of a vehicle through a vehicle speed curve of the vehicle in the driving process, thereby achieving the effect of reasonably planning the vehicle speed.
In a first aspect, an embodiment of the present application provides a method for planning an economic vehicle speed, including:
constructing a power consumption model of the vehicle according to the motion basic constraint condition of the vehicle; wherein the power consumption model is constructed by taking a unit time period as a construction unit;
determining the optimal average acceleration of the vehicle in each unit time period by adopting a particle swarm optimization algorithm according to the optimization constraint condition;
determining the optimal ending speed of each unit time period according to the optimal average acceleration and the starting speed of each unit time period;
and determining a vehicle speed curve according to the optimal termination speed of each unit time period, and planning the vehicle speed according to the vehicle speed curve.
Further, the basic constraint condition includes: an acceleration constraint condition and an electric quantity consumption constraint condition;
correspondingly, according to the vehicle motion basic constraint condition, constructing a power consumption model of the vehicle, comprising the following steps:
determining the relation between the total power consumption before the current unit time period and the average acceleration of the current unit time period according to the acceleration constraint condition and the power consumption constraint condition;
and determining the average acceleration combination of each unit time period before the current unit time period when the total electric quantity consumption takes the minimum value according to the relation between the total electric quantity consumption before the current unit time period and the average acceleration of the current unit time period.
Further, determining a relationship between the total power consumption before the current unit time period and the average acceleration of the current unit time period includes:
determining the relation between the motor torque and the acceleration according to the rolling resistance, the air resistance, the gradient resistance, the vehicle rotating mass conversion coefficient, the vehicle mass and the wheel radius;
determining a functional relation between the motor power and the acceleration according to the functional relation between the motor torque and the acceleration and the motor rotating speed;
determining a functional relation between the power of the power battery and the acceleration according to the functional relation between the power of the motor and the acceleration, the dynamic efficiency of the motor and the power of low-voltage load equipment;
and determining the relation between the total electricity consumption before the current unit time period and the average acceleration of the current unit time period according to the battery power of the current unit time period and the average acceleration of the current unit time period.
Further, the acceleration constraints include:
the ratio of the acceleration of the current unit time period to the acceleration of the previous unit time period is in accordance with a preset range; the acceleration of the current unit time period is smaller than the preset maximum acceleration;
the power consumption constraint condition comprises: the total power consumption before the current unit time period is the power consumption minimum value.
Further, the optimization constraint condition comprises a maximum speed constraint condition and a mileage constraint condition;
correspondingly, the method for determining the optimal average acceleration of the vehicle in each unit time period by adopting the particle swarm optimization algorithm according to the optimization constraint condition comprises the following steps:
and determining the optimal average acceleration of each unit time period before the current unit time period by adopting a particle swarm optimization algorithm according to the maximum speed constraint condition and the driving mileage constraint condition.
Further, the maximum speed constraint includes: the ending speed of each unit time period is less than the maximum value of the preset vehicle speed;
the mileage constraint condition includes: the total mileage before the current unit time period is greater than the predicted mileage.
Further, the method further comprises: determining the total driving range of the vehicle according to the ending speed of each unit time period; the method specifically comprises the following steps:
the total driving range is calculated by the following formula:
wherein S is the total driving range, ViIs the termination speed of the i-th unit period, and Δ t is the time length of the unit period.
In a second aspect, an embodiment of the present application provides an apparatus for planning an economic vehicle speed, including:
the power consumption model building module is used for building a power consumption model of the vehicle according to the vehicle motion basic constraint condition; wherein the power consumption model is constructed by taking a unit time period as a construction unit;
the optimal average acceleration determining module is used for determining the optimal average acceleration of the vehicle in each unit time period by adopting a particle swarm optimization algorithm according to the optimization constraint condition;
the optimal termination speed determining module is used for determining the optimal termination speed of each unit time period according to the optimal average acceleration and the initial speed of each unit time period;
and the vehicle speed planning module is used for determining a vehicle speed curve according to the optimal termination speed of each unit time period and planning the vehicle speed according to the vehicle speed curve.
In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the method for planning an economic vehicle speed according to the present application.
In a fourth aspect, embodiments of the present application provide an apparatus, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the method for planning an economic vehicle speed according to embodiments of the present application.
According to the technical scheme provided by the embodiment of the application, a power consumption model of the vehicle is constructed according to the motion basic constraint condition of the vehicle; wherein the power consumption model is constructed by taking a unit time period as a construction unit; determining the optimal average acceleration of the vehicle in each unit time period by adopting a particle swarm optimization algorithm according to the optimization constraint condition; determining the optimal ending speed of each unit time period according to the optimal average acceleration and the starting speed of each unit time period; and determining a vehicle speed curve according to the optimal termination speed of each unit time period, and planning the vehicle speed according to the vehicle speed curve. Through adopting the technical scheme that this application provided, can carry out effectual control through the speed curve of vehicle at the in-process of traveling to the energy consumption of vehicle to reach the effect of rational planning speed of a motor vehicle.
Drawings
FIG. 1 is a flow chart of a method for providing an economic vehicle speed planning in accordance with an embodiment of the present application;
FIG. 2 is a schematic illustration of a vehicle speed curve provided by an embodiment of the present application;
FIG. 3 is a schematic structural diagram of an economic vehicle speed planning device provided by an embodiment of the application;
fig. 4 is a schematic structural diagram of an apparatus provided in an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Fig. 1 is a flowchart of a method for planning an economic vehicle speed according to an embodiment of the present application, where the present embodiment is applicable to a case of planning a vehicle speed of an electric vehicle, and the method may be executed by an apparatus for planning an economic vehicle speed according to an embodiment of the present application, where the apparatus may be implemented by software and/or hardware, and may be integrated into a vehicle control system or other devices.
As shown in fig. 1, the method for planning the economical vehicle speed includes:
s110, constructing a power consumption model of the vehicle according to the motion basic constraint condition of the vehicle; wherein the power consumption model is constructed by using a unit time period as a construction unit.
The unit time periods may be divided every 1 second or every 5 seconds, and may be longer or shorter. Since the vehicle starts from a standstill in the form, the calculation of the unit time period can be performed from the moment the vehicle starts. And finishing the current unit time period at certain time intervals, and entering the next unit time period. Therefore, it can be understood that the termination speed of the previous unit period is a real speed of the current unit period.
The basic constraint condition of the vehicle motion can be some constraint conditions in the process of starting the vehicle till running to a certain speed, and can also be the constraint condition of completing the running of the whole journey.
The power consumption model of the vehicle may be a function relationship including the total power consumption Y of i unit time periods and the average acceleration of the i unit time period. The average acceleration a of each unit time period corresponding to the minimum electric quantity consumption Y can be obtained according to the relation1～ai。
In this embodiment, optionally, the basic constraint condition includes: an acceleration constraint condition and an electric quantity consumption constraint condition;
correspondingly, according to the vehicle motion basic constraint condition, constructing a power consumption model of the vehicle, comprising the following steps:
determining the relation between the total power consumption before the current unit time period and the average acceleration of the current unit time period according to the acceleration constraint condition and the power consumption constraint condition;
and determining the average acceleration combination of each unit time period before the current unit time period when the total electric quantity consumption takes the minimum value according to the relation between the total electric quantity consumption before the current unit time period and the average acceleration of the current unit time period.
Wherein, because of adopting certain constraint condition, can obtain a series of relation of total electric quantity consumption before confirming the present unit time quantum and average acceleration of present unit time quantum. Since the constraints are not able to determine a unique acceleration sequence, further processing can be performed in subsequent steps. It is understood that the total power consumption before the current unit time period is included in the current unit time period. According to the scheme, the acceleration constraint condition and the electric quantity consumption constraint condition are adopted, the average acceleration combination of each unit time period before the current unit time period is determined, and the average acceleration combination of each period is determined under the condition of lowest electric energy consumption.
In this embodiment, optionally, the acceleration constraint condition includes:
the ratio of the acceleration of the current unit time period to the acceleration of the previous unit time period is in accordance with a preset range; the acceleration of the current unit time period is smaller than the preset maximum acceleration;
the power consumption constraint condition comprises: the total power consumption before the current unit time period is the power consumption minimum value.
Specifically, the following formula can be adopted:
wherein, ai-1Is the average acceleration of the i-1 unit time period, amaxIs the maximum value of acceleration, YminIs the minimum value of the electricity consumption.
In the scheme, the energy utilization rate of the vehicle in the acceleration process can be ensured to be highest by limiting the average acceleration of the acceleration in the current unit time period and the average acceleration of the acceleration in the previous unit time period, and the energy utilization rate is always YminLimiting the power consumption and limiting the average acceleration of the current unit time period with the maximum acceleration results in a relatively stable acceleration process, such that the energy consumption of the vehicle is minimal throughout the entire driving process.
And S120, determining the optimal average acceleration of the vehicle in each unit time period by adopting a particle swarm optimization algorithm according to the optimization constraint condition.
The particle swarm optimization algorithm is also called a particle swarm optimization algorithm or a bird swarm foraging algorithm, is abbreviated as PSO, and is a new evolutionary algorithm developed by J.Kennedy, R.C.Eberhart and the like. PSO is similar to a simulated annealing algorithm, and from random, the optimal solution is searched through iteration, and the global optimum is searched by following the optimal value searched currently. The algorithm draws attention from academic circles due to the advantages of easy realization, high precision, fast convergence and the like, and shows superiority in solving practical problems. The economic vehicle speed planning of the pure electric vehicle based on the algorithm is optimal in overall energy consumption under the whole running working condition.
The PSO is initialized to a population of random particles (random solution). The optimal solution is then found by iteration. In each iteration, the particle updates itself by tracking two "extrema". The first "extremum" is the optimal solution found by the particle itself, which is called the individual pole pBest. The other "extremum" is the best solution currently found for the entire population, this extremum being the global extremum gBest. Alternatively, instead of using the entire population, only a portion of it may be used as a neighborhood for the particle, and the extremum in all the neighborhoods is the local extremum. When these two optimal values are found, the particle updates its velocity and new position according to the following formula:
v [ ] + c1 × rand () (pbest [ ] -present [ ]) + c2 × rand () (gbest [ ] -present [ ]); and
present[]＝present[]+v[]；
where v [ ] is the velocity of the particle, w is the inertial weight, present [ ] is the position of the current particle, rand () is a random number between (0, 1), c1, c2 is a learning factor, typically c1 ═ c2 ═ 2.
The pseudo code for solving the problem minimum value by the particle swarm can be:
function// function: pseudo code of particle swarm optimization algorithm
// description: this example targets the problem minimum
// parameters: n is the population size
By adopting the particle swarm optimization, an optimal average acceleration sequence can be determined in the average acceleration combination and is used as the basis of vehicle control.
In this technical solution, optionally, the optimization constraint condition includes a maximum speed constraint condition and a mileage constraint condition;
correspondingly, the method for determining the optimal average acceleration of the vehicle in each unit time period by adopting the particle swarm optimization algorithm according to the optimization constraint condition comprises the following steps:
and determining the optimal average acceleration of each unit time period before the current unit time period by adopting a particle swarm optimization algorithm according to the maximum speed constraint condition and the driving mileage constraint condition.
According to the actual situation, the pure electric vehicle cannot overspeed during running, so that the average acceleration a per unit time period obtained now is1～aiThe final requirement cannot be guaranteed, and certain requirements need to be met, that is, whether the requirements are met can be verified through the following limiting conditions:
Vi≤Vmax，Viis the final vehicle speed, V, of the i unit time periodmaxIs the maximum value of vehicle speed, VmaxCan be set according to the actual situation.
Calculating a final vehicle speed V of the i unit time period by the following formulai：
Vi＝Vi-1+Δt×ai；
Wherein, Vi-1Is the final vehicle speed of the i-1 th unit time period, aiThe final vehicle speed of each unit time period can be obtained through the formula, and whether the limiting condition is met or not is judged.
In addition, as is well known, if the pure electric vehicle does not run at all in i unit time periods, the total power consumption Y in i unit time periods must be the lowest, but the fact that the pure electric vehicle does not run obviously makes the least energy consumption for determining how to run is not meaningful, which should be the case excluded by the present invention, therefore, the following restriction conditions are also required to be verified:
S≥Sall-purposeWherein S is the total driving range of i unit time periods, SAll-purposeAnd predicting the driving mileage for the pure electric vehicle.
The total driving range S of i unit time periods can be calculated by the following formula:
wherein, ViIs the final vehicle speed of the i-th unit time period, and Δ t is the time of the unit time period.
Average acceleration a per unit time period1～aiIf the average acceleration a does not meet the requirement, returning to the particle swarm optimization algorithm, and eliminating the just obtained average acceleration a of each unit time period1～aiCorresponding minimum electric quantity consumption Y is recalculated to obtain new average acceleration a of each unit time period1～ai。
According to the scheme, the optimal average acceleration of the vehicle in each unit time period can be obtained by providing the optimal constraint condition in the actual scene and adopting the particle swarm algorithm.
And S130, determining the optimal ending speed of each unit time period according to the optimal average acceleration and the starting speed of each unit time period.
After determining the optimal average acceleration of each unit time period, the termination speed of each unit time period may be determined according to the time length of the unit time period and the optimal average acceleration of each period.
It is understood that the ending speed of the previous time period is the starting speed of the current time period.
Vi＝Vi-1+Δt×ai；
Wherein, Vi-1Is the final vehicle speed of the i-1 th unit time period, aiIs the average acceleration of the i-th unit period, and Δ t is the time of the unit period.
And S140, determining a vehicle speed curve according to the optimal termination speed of each unit time period, and planning the vehicle speed according to the vehicle speed curve.
After the optimal ending speed of each unit time period is determined, a vehicle speed curve can be obtained, and the vehicle can be controlled according to the vehicle speed curve, so that the purpose of planning the vehicle speed is achieved.
FIG. 2 is a schematic diagram of a vehicle speed curve provided by an embodiment of the present application. As shown in fig. 2, at t0To t1Acceleration of a1，t1To t2Acceleration of a2Until t is reached2000Then, the vehicle speed is not increased any more, and the constant speed driving is started.
In this embodiment, optionally, the method further includes: determining the total driving range of the vehicle according to the ending speed of each unit time period; the method specifically comprises the following steps:
the total driving range is calculated by the following formula:
wherein S is the total driving range, ViIs the termination speed of the i-th unit period, and Δ t is the time length of the unit period.
Each time segment in fig. 2 may be regarded as a trapezoid, the area of the trapezoid is sequentially obtained, and then the area is summed, so as to obtain the above formula. It is understood that the acceleration may be regarded as 0 in the uniform velocity stage. According to the scheme, through the formula, a functional relation between the distance and the ending speed of each unit time period can be established, and a data basis is provided for vehicle speed control.
According to the technical scheme provided by the embodiment of the application, a power consumption model of the vehicle is constructed according to the motion basic constraint condition of the vehicle; wherein the power consumption model is constructed by taking a unit time period as a construction unit; determining the optimal average acceleration of the vehicle in each unit time period by adopting a particle swarm optimization algorithm according to the optimization constraint condition; determining the optimal ending speed of each unit time period according to the optimal average acceleration and the starting speed of each unit time period; and determining a vehicle speed curve according to the optimal termination speed of each unit time period, and planning the vehicle speed according to the vehicle speed curve. Through adopting the technical scheme that this application provided, can carry out effectual control through the speed curve of vehicle at the in-process of traveling to the energy consumption of vehicle to reach the effect of rational planning speed of a motor vehicle.
On the basis of the foregoing technical solutions, optionally, determining a relationship between total power consumption before the current unit time period and an average acceleration of the current unit time period includes:
determining the relation between the motor torque and the acceleration according to the rolling resistance, the air resistance, the gradient resistance, the vehicle rotating mass conversion coefficient, the vehicle mass and the wheel radius;
determining a functional relation between the motor power and the acceleration according to the functional relation between the motor torque and the acceleration and the motor rotating speed;
determining a functional relation between the power of the power battery and the acceleration according to the functional relation between the power of the motor and the acceleration, the dynamic efficiency of the motor and the power of low-voltage load equipment;
and determining the relation between the total electricity consumption before the current unit time period and the average acceleration of the current unit time period according to the battery power of the current unit time period and the average acceleration of the current unit time period.
Specifically, the relationship between the motor torque and the acceleration can be determined according to rolling resistance, air resistance, gradient resistance, a vehicle rotating mass conversion coefficient, vehicle mass and wheel radius;
determining a running equation of the automobile as follows:
Ft＝Ff+Fw+Fi+Fj；
where Ft is driving force, Ff is rolling resistance, Fw is air resistance, Fi is slope resistance, and Fj is acceleration resistance.
The following formula is then determined:
Ft＝Tt/r；
wherein, TtMotor torque, r is wheel radius.
Ff＝Ga×f；
Wherein f is a rolling resistance coefficient, GaIs the wheel load.
Wherein, CdIs the air resistance coefficient, A is the windward area, ρ is the air density, and V is the relative velocity.
Fi＝G×φ；
Wherein G is the vehicle gravity and phi is the road gradient;
Ff＝§×m×a；
wherein § the vehicle rotational mass conversion factor, m the vehicle mass and a the acceleration.
By combining the above formulas, the motor torque T can be obtainedtThe functional relationship with the acceleration a is as follows:
and determining the functional relation between the motor power and the acceleration according to the functional relation between the motor torque and the acceleration and the motor rotating speed.
Determining the motor rotating speed n of the pure electric vehicle as follows:
and determining the functional relation between the power of the power battery and the acceleration according to the functional relation between the power of the motor and the acceleration, the dynamic efficiency of the motor and the power of the low-voltage load equipment.
Motor power PmComprises the following steps:
wherein, TtIs the motor torque, and n is the motor rotation speed;
so that the motor power P can be obtainedmAs a function of the acceleration a.
Knowing the motor drive efficiency η, the motor power P can be adjustedmConversion into electric drive system input power P1Obtaining the input power P of the electric drive system1As a function of the acceleration a;
the power P of the power battery is as follows:
P＝P1+P2；
wherein, P1For input of power to the electric drive system, P2Low voltage load device power (which may be assumed to be a fixed value since the load device is not changing);
thus, power P is input in the electric drive system1On the basis of the functional relation with the acceleration a, the functional relation between the power P of the power battery and the acceleration a can be obtained according to the formula. If the power P of the power battery and the acceleration a are converted into unit time, the power P of the power battery in the ith unit time can be obtainediAverage acceleration a with the i unit time periodiThe functional relationship of (a).
Then, the total power consumption Y of the i-th unit time period may be determined as:
where Δ t is the unit time period, PiIs the power of the power battery in the ith unit time period;
power P of power battery in unit time period of iiAverage acceleration a with the i unit time periodiBased on the functional relationship of (a), the total electric quantity consumption Y of i unit time periods and the average acceleration a of the i unit time period can be obtained according to the formulaiThe functional relationship of (a).
The method comprises the steps of obtaining the average acceleration of each unit time period in i unit time periods which enable the electricity consumption of the pure electric vehicle to be minimum by establishing an electricity consumption model of the pure electric vehicle based on a particle swarm optimization algorithm and utilizing the electricity consumption model of the pure electric vehicle, enabling the average acceleration of each unit time period to meet a condition, determining the final vehicle speed of each unit time period which meets the condition according to the average acceleration of each unit time period which meets the condition, determining a vehicle speed curve according to the final vehicle speed of each unit time period which meets the condition, and controlling the pure electric vehicle according to the vehicle speed curve. The method can calculate the economic vehicle speed curve with the optimal overall energy consumption in the current travel, and can accurately control the pure electric vehicle to run in the mode with the lowest energy consumption.
Fig. 3 is a schematic structural diagram of an economic vehicle speed planning device provided in an embodiment of the present application. As shown in fig. 3, the economic vehicle speed planning apparatus includes:
the power consumption model building module 310 is used for building a power consumption model of the vehicle according to the vehicle motion basic constraint condition; wherein the power consumption model is constructed by taking a unit time period as a construction unit;
the optimal average acceleration determining module 320 is configured to determine an optimal average acceleration of the vehicle in each unit time period according to the optimization constraint condition by using a particle swarm optimization algorithm;
an optimal ending speed determining module 330, configured to determine an optimal ending speed for each unit time period according to the optimal average acceleration and the starting speed of each unit time period;
and the vehicle speed planning module 340 is configured to determine a vehicle speed curve according to the optimal termination speed of each unit time period, and plan a vehicle speed according to the vehicle speed curve.
The product can execute the method provided by the embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method.
Embodiments of the present application also provide a storage medium containing computer-executable instructions that, when executed by a computer processor, perform a method of economic vehicle speed planning, the method comprising:
constructing a power consumption model of the vehicle according to the motion basic constraint condition of the vehicle; wherein the power consumption model is constructed by taking a unit time period as a construction unit;
determining the optimal average acceleration of the vehicle in each unit time period by adopting a particle swarm optimization algorithm according to the optimization constraint condition;
determining the optimal ending speed of each unit time period according to the optimal average acceleration and the starting speed of each unit time period;
and determining a vehicle speed curve according to the optimal termination speed of each unit time period, and planning the vehicle speed according to the vehicle speed curve.
Storage medium-any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Lanbas (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the computer system in which the program is executed, or may be located in a different second computer system connected to the computer system through a network (such as the internet). The second computer system may provide the program instructions to the computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations, such as in different computer systems that are connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium provided in the embodiments of the present application contains computer-executable instructions, and the computer-executable instructions are not limited to the operation of planning the economic vehicle speed as described above, and may also perform related operations in the method of planning the economic vehicle speed provided in any embodiment of the present application.
The embodiment of the application provides equipment, and the device for planning the economic vehicle speed can be integrated into the equipment. Fig. 4 is a schematic structural diagram of an apparatus provided in an embodiment of the present application. As shown in fig. 4, the present embodiment provides an apparatus 400 comprising: one or more processors 420; a storage device 410 for storing one or more programs that, when executed by the one or more processors 420, cause the one or more processors 420 to implement a method for planning an economic vehicle speed as provided by an embodiment of the present application, the method comprising:
constructing a power consumption model of the vehicle according to the motion basic constraint condition of the vehicle; wherein the power consumption model is constructed by taking a unit time period as a construction unit;
determining the optimal average acceleration of the vehicle in each unit time period by adopting a particle swarm optimization algorithm according to the optimization constraint condition;
determining the optimal ending speed of each unit time period according to the optimal average acceleration and the starting speed of each unit time period;
and determining a vehicle speed curve according to the optimal termination speed of each unit time period, and planning the vehicle speed according to the vehicle speed curve.
Of course, those skilled in the art will appreciate that the processor 420 may also implement the solution of the method for planning the economic vehicle speed provided in any of the embodiments of the present application.
The apparatus 400 shown in fig. 4 is only an example and should not bring any limitations to the functionality or scope of use of the embodiments of the present application.
As shown in fig. 4, the apparatus 400 includes a processor 420, a storage device 410, an input device 430, and an output device 440; the number of the processors 420 in the device may be one or more, and one processor 420 is taken as an example in fig. 4; the processor 420, the storage device 410, the input device 430 and the output device 440 of the apparatus may be connected by a bus or other means, for example, the bus 450 in fig. 4.
The storage device 410 is a computer-readable storage medium, and can be used to store software programs, computer-executable programs, and module units, such as program instructions corresponding to the method for planning an economic vehicle speed in the embodiment of the present application.
The storage device 410 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the storage 410 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, storage 410 may further include memory located remotely from processor 420, which may be connected via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input means 430 may be used to receive input numbers, character information or voice information, and to generate key signal inputs related to user settings and function control of the apparatus. The output device 440 may include a display screen, speakers, etc.
The equipment provided by the embodiment of the application can effectively control the energy consumption of the vehicle through the vehicle speed curve of the vehicle in the driving process, so that the effect of reasonably planning the vehicle speed is achieved.
The device, the storage medium and the equipment for planning the economic vehicle speed, which are provided by the embodiment, can execute the method for planning the economic vehicle speed provided by any embodiment of the application, and have corresponding functional modules and beneficial effects for executing the method. For details of the technology not described in detail in the above embodiments, reference may be made to a method for planning an economical vehicle speed provided in any of the embodiments of the present application.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present application and the technical principles employed. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the appended claims.
Claims (10)
1. A method for planning an economic vehicle speed, the method comprising:
constructing a power consumption model of the vehicle according to the motion basic constraint condition of the vehicle; wherein the power consumption model is constructed by taking a unit time period as a construction unit;
determining the optimal average acceleration of the vehicle in each unit time period by adopting a particle swarm optimization algorithm according to the optimization constraint condition;
determining the optimal ending speed of each unit time period according to the optimal average acceleration and the starting speed of each unit time period;
and determining a vehicle speed curve according to the optimal termination speed of each unit time period, and planning the vehicle speed according to the vehicle speed curve.
2. The method of claim 1, wherein the base constraints comprise: an acceleration constraint condition and an electric quantity consumption constraint condition;
correspondingly, according to the vehicle motion basic constraint condition, constructing a power consumption model of the vehicle, comprising the following steps:
determining the relation between the total power consumption before the current unit time period and the average acceleration of the current unit time period according to the acceleration constraint condition and the power consumption constraint condition;
and determining the average acceleration combination of each unit time period before the current unit time period when the total electric quantity consumption takes the minimum value according to the relation between the total electric quantity consumption before the current unit time period and the average acceleration of the current unit time period.
3. The method of claim 2, wherein determining the relationship of the total power consumption prior to the current time unit period to the average acceleration of the current time unit period comprises:
determining the relation between the motor torque and the acceleration according to the rolling resistance, the air resistance, the gradient resistance, the vehicle rotating mass conversion coefficient, the vehicle mass and the wheel radius;
determining a functional relation between the motor power and the acceleration according to the functional relation between the motor torque and the acceleration and the motor rotating speed;
determining a functional relation between the power of the power battery and the acceleration according to the functional relation between the power of the motor and the acceleration, the dynamic efficiency of the motor and the power of low-voltage load equipment;
and determining the relation between the total electricity consumption before the current unit time period and the average acceleration of the current unit time period according to the battery power of the current unit time period and the average acceleration of the current unit time period.
4. The method of claim 2, wherein the acceleration constraints comprise:
the ratio of the acceleration of the current unit time period to the acceleration of the previous unit time period is in accordance with a preset range; the acceleration of the current unit time period is smaller than the preset maximum acceleration;
the power consumption constraint condition comprises: the total power consumption before the current unit time period is the power consumption minimum value.
5. The method of claim 2, wherein the optimization constraints include a maximum speed constraint and a range constraint;
correspondingly, the method for determining the optimal average acceleration of the vehicle in each unit time period by adopting the particle swarm optimization algorithm according to the optimization constraint condition comprises the following steps:
and determining the optimal average acceleration of each unit time period before the current unit time period by adopting a particle swarm optimization algorithm according to the maximum speed constraint condition and the driving mileage constraint condition.
6. The method of claim 5, wherein the maximum speed constraint comprises: the ending speed of each unit time period is less than the maximum value of the preset vehicle speed;
the mileage constraint condition includes: the total mileage before the current unit time period is greater than the predicted mileage.
7. The method of claim 6, further comprising: determining the total driving range of the vehicle according to the ending speed of each unit time period; the method specifically comprises the following steps:
the total driving range is calculated by the following formula:
wherein S is the total driving range, ViIs the termination speed of the i-th unit period, and Δ t is the time length of the unit period.
8. An arrangement for planning an economical vehicle speed, characterized in that the arrangement comprises:
the power consumption model building module is used for building a power consumption model of the vehicle according to the vehicle motion basic constraint condition; wherein the power consumption model is constructed by taking a unit time period as a construction unit;
the optimal average acceleration determining module is used for determining the optimal average acceleration of the vehicle in each unit time period by adopting a particle swarm optimization algorithm according to the optimization constraint condition;
the optimal termination speed determining module is used for determining the optimal termination speed of each unit time period according to the optimal average acceleration and the initial speed of each unit time period;
and the vehicle speed planning module is used for determining a vehicle speed curve according to the optimal termination speed of each unit time period and planning the vehicle speed according to the vehicle speed curve.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out a method of planning an economical vehicle speed according to any one of claims 1-7.
10. An apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements a method of planning an economic vehicle speed as claimed in any one of claims 1 to 7.
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