CN117872936A

CN117872936A - Remote control model trolley energy consumption optimization method based on driving characteristic dimension

Info

Publication number: CN117872936A
Application number: CN202410268861.1A
Authority: CN
Inventors: 陈韬; 傅洪新; 李强
Original assignee: Shenzhen Xinshiqi Innovation Technology Co ltd
Current assignee: Shenzhen Xinshiqi Innovation Technology Co ltd
Priority date: 2024-03-11
Filing date: 2024-03-11
Publication date: 2024-04-12
Anticipated expiration: 2044-03-11
Also published as: CN117872936B

Abstract

The invention relates to the technical field of vehicles, and discloses a remote control model trolley energy consumption optimization method based on a driving characteristic dimension, which comprises the following steps: acquiring a simulation experiment environment according to a simulation experiment instruction, starting a trolley positioned at a track starting point, executing speed measurement operation on the trolley by utilizing a plurality of sensors, obtaining an initial speed measurement data set after confirming that the trolley reaches a track end point, and dividing the initial speed measurement data set to obtain an acceleration data set, a deceleration data set and an idle speed data set; calculating unit experiment total energy consumption based on a deceleration data set, an idling data set and driving total energy consumption from an acceleration data set, acquiring a plurality of unit experiment total energy consumption according to experiment times to obtain a total energy consumption data set, inputting the total energy consumption data set into a pre-constructed neural network model to obtain minimum total energy consumption, and controlling a trolley to run on a trolley track according to the minimum total energy consumption. The invention mainly aims to solve the problem of training an operation mode corresponding to the minimum total energy consumption by using an instantaneous energy consumption and neural network model.

Description

Remote control model trolley energy consumption optimization method based on driving characteristic dimension

Technical Field

The invention relates to a remote control model trolley energy consumption optimization method based on a driving characteristic dimension, and belongs to the technical field of vehicles.

Background

With the rapid development of vehicle technology, the remote control model trolley is more and more widely applied in various fields. The existing trolley comprises a fixed track and a non-fixed track, and when the remote control trolley is required to run on the fixed track, if the energy utilization efficiency is low, the cruising ability of the existing trolley is limited.

Different gradients of the fixed track can occur, and the running characteristics of the trolley generally include acceleration, deceleration and idling, and when the purely electric remote control trolley runs on the fixed track, the trolley is often driven to run on the fixed track by a fixed driving force.

Although the trolley can run on the fixed track by the method, the idle state cannot be reasonably prolonged by reasonably utilizing the shape of the track, and the trolley can be intelligently accelerated or decelerated on the fixed track, so that energy is wasted, and the cruising ability of the trolley is reduced.

Disclosure of Invention

The invention provides a remote control model trolley energy consumption optimization method and device based on a driving characteristic dimension and a computer readable storage medium, and mainly aims to solve the problem that an operation mode corresponding to minimum total energy consumption is trained by using energy consumption of a unit period and a neural network model.

In order to achieve the above purpose, the invention provides a remote control model trolley energy consumption optimization method based on a driving characteristic dimension, which comprises the following steps:

receiving a simulation experiment instruction, and acquiring a simulation experiment environment according to the simulation experiment instruction, wherein the simulation experiment environment comprises a trolley, a trolley track and a plurality of sensors, each sensor in the plurality of sensors is arranged on the trolley, and the trolley track comprises a track starting point and a track ending point;

starting a trolley at a track starting point based on a simulation experiment environment, executing speed measurement operation on the trolley by utilizing each sensor in a plurality of sensors and a pre-built speed measurement method, and obtaining an initial speed measurement data set after the trolley is confirmed to reach a track end point, wherein the sampling frequency of each sensor is a preset frequency, the speed measurement time is a trolley operation period, and the initial speed measurement data set is composed of a plurality of state matrixes;

dividing the initial speed measurement data set by using a pre-constructed working condition dividing method to obtain an acceleration data set, a deceleration data set and an idle speed data set;

sequentially extracting acceleration state matrixes from an acceleration data set, calculating unit driving force according to the extracted acceleration state matrixes, acquiring unit driving energy according to the unit driving force, calculating total driving energy based on the unit driving energy, and calculating unit experiment total energy based on a deceleration data set, an idle data set and the total driving energy, wherein the calculation formula of the unit experiment total energy is as follows:

Wherein,represents total energy consumption per unit experiment,/->Representing the driving efficiency correction factor,/->Representing the total energy consumption of the drive,representing braking efficiency correction factor,/->Indicating total braking energy consumption>Representing other total energy consumption;

acquiring a plurality of unit experiment total energy consumption according to preset experiment times and simulation experiment environments, wherein each unit experiment total energy consumption in the plurality of unit experiment total energy consumption is different, and the number of the plurality of unit experiment total energy consumption is equal to the number of the experiment times;

summarizing total energy consumption of a plurality of unit experiments to obtain a total energy consumption data set;

inputting the total energy consumption data set into a pre-constructed neural network model to obtain minimum total energy consumption;

and controlling the trolley to run on the trolley track according to the minimum total energy consumption and the pre-constructed control unit, and completing the energy consumption optimization of the trolley.

Optionally, the performing a speed measurement operation on the cart by using each of the plurality of sensors and the pre-built speed measurement method includes:

acquiring an acceleration speed measuring unit and a speed measuring unit according to a simulation experiment environment, wherein the plurality of sensors comprise a plurality of speed sensors and a plurality of acceleration sensors, at least 2 speed sensors in the acceleration speed measuring unit are arranged, and at least 2 acceleration sensors in the speed measuring unit are arranged;

Sequentially extracting acceleration sensors from the plurality of acceleration sensors by using an acceleration speed measuring unit, and performing the following operations on the extracted acceleration sensors:

acquiring track coordinates corresponding to the state matrix based on the state matrix, acquiring track gradient based on the track coordinates, acquiring an acceleration measurement value corresponding to the state matrix according to the extracted acceleration sensor, and calculating a suboptimal acceleration value by utilizing the acceleration measurement value and a pre-constructed track gradient compensation formula, wherein the track gradient compensation formula is as follows:

wherein,representing suboptimal acceleration value,/->Representing the filter coefficients, and->，/>Representing acceleration measurements corresponding to the state matrix, +.>Indicating the current moment corresponding to the state matrix as +.>Time of day (I)>Acceleration measurement value representing the moment immediately preceding the current moment, is provided>Representing a gradient coefficient, when the track gradient corresponding to the track coordinates is an ascending slope, the +.>When the track gradient corresponding to the track coordinates is downhill, the track is +.>，/>Indicating the acceleration of gravity>Representing the standard track gradient corresponding to the current track coordinates;

optimizing a suboptimal acceleration value according to a preset initial measurement error to obtain an optimized acceleration value;

acquiring an acceleration average value based on a plurality of optimized acceleration values, and sending the acceleration average value to an acceleration speed measuring unit;

And acquiring the impact rate corresponding to the state matrix according to the speed measuring unit, acquiring the speed average value of the trolley based on the impact rate and a preset impact rate threshold value, and transmitting the speed average value to the speed measuring unit.

Optionally, the obtaining, according to the speed measuring unit, the impact rate corresponding to the state matrix includes:

acquiring a plurality of speed sensors according to a speed measuring unit, sequentially extracting the speed sensors from the plurality of speed sensors, and executing the following operations on the extracted speed sensors:

performing low-pass filtering operation on the extracted speed sensor to obtain a suboptimal speed value, wherein the filtering coefficient of the low-pass filtering operation is 10;

acquiring the speed acceleration corresponding to the state matrix according to the suboptimal speed value, and calculating the impact rate based on the speed acceleration, wherein the calculation formula of the impact rate is as follows:

wherein,indicate->Impact rate corresponding to time, +.>Indicate->Speed acceleration corresponding to time, +.>Indicate->Speed acceleration corresponding to time, +.>Indicate->Time to->Time difference between moments.

Optionally, the obtaining the speed average value of the trolley based on the impact rate and the preset impact rate threshold value includes:

Comparing the impact rate with a preset impact rate threshold;

if the impact rate is smaller than the impact rate threshold, reserving a suboptimal speed value corresponding to the impact rate, otherwise, eliminating the suboptimal speed value corresponding to the impact rate;

summarizing the reserved suboptimal speed values to obtain a suboptimal speed value set;

if the suboptimal speed value set is an empty set, then based onSuboptimal speed value corresponding to moment +.>Time acceleration averaging trolley +.>Obtaining a predicted speed value according to the speed at the moment, and confirming that the predicted speed value is a speed average value;

and if the suboptimal speed value set is not the empty set, acquiring a speed average value based on the suboptimal speed value set, wherein the speed average value is the average value of all suboptimal speed values in the suboptimal speed value set.

Optionally, after the trolley is confirmed to reach the track end point, an initial speed measurement data set is obtained, including:

dividing the trolley operation time period based on preset frequency and the trolley operation time period to obtain a plurality of unit periods, wherein the unit periods are equal to the first periodTime to->Time difference between moments;

sequentially extracting unit periods from the plurality of unit periods, and performing the following operations on each extracted unit period:

the track coordinates of the trolley are acquired by utilizing a pre-constructed geodetic coordinate system, and a state matrix is acquired based on the track coordinates and a unit period, wherein the state matrix is as follows:

Wherein,indicate->State matrix of time, ">Is->Respectively representing the abscissa and ordinate of the track coordinates,/-, respectively>Representing the unit period +.>Represents the mean value of acceleration>Represents the mean value of the speed>Indicate->A unit period;

and summarizing the plurality of state matrixes to obtain an initial speed measurement data set.

Optionally, dividing the initial speed measurement data set by using a pre-constructed working condition dividing method to obtain an acceleration data set, a deceleration data set and an idle speed data set, including:

sequentially extracting state matrixes from the initial speed measurement data set, acquiring a suboptimal speed value set corresponding to the extracted state matrixes based on the extracted state matrixes, and if the suboptimal speed value set is a null set, confirming that the state matrixes are idle state matrixes, wherein the idle state matrixes comprise 0, 1 or more idle state matrixes;

removing all idle state matrixes from the initial speed measurement data set to obtain a data set to be divided;

sequentially extracting state matrixes from the data set to be divided, and acquiring acceleration average values corresponding to the state matrixes based on the extracted state matrixes;

if the acceleration average value is larger than 0, confirming that the extracted state matrix is an acceleration state matrix;

If the average value of the acceleration is smaller than 0, confirming that the extracted state matrix is a deceleration state matrix;

and respectively summarizing the idle state matrix, the plurality of acceleration state matrices and the plurality of deceleration state matrices to obtain an idle data set, an acceleration data set and a deceleration data set.

Optionally, the calculating the unit driving force according to the extracted acceleration state matrix includes:

acquiring a speed average value based on an acceleration state matrix, and calculating a unit driving force based on the speed average value and a pre-constructed driving force formula, wherein the driving force formula is as follows:

wherein,represents the driving force of unit,/->Represents +.>Acceleration state matrix->Representing the overall mass of the trolley, < >>Indicating the acceleration of gravity>Representing the rolling resistance coefficient, +.>Representing the conversion coefficient of the rotating mass>Represents air density, ++>Represents the air resistance coefficient, & lt & gt>Representing the frontal area.

Optionally, the obtaining the unit driving energy consumption according to the unit driving force includes:

the calculation formula of the total driving energy consumption is as follows:

wherein,representing the total number of acceleration state matrices in the acceleration data set,/->Indicate->The corresponding speed average in the acceleration state matrix.

Optionally, the calculating the total energy consumption per unit experiment based on the deceleration data set, the idle data set and the driving total energy consumption includes:

Sequentially extracting a deceleration state matrix from a deceleration data set, calculating unit braking force according to the extracted deceleration state matrix, acquiring unit braking energy consumption according to the unit braking force, and calculating total braking energy consumption based on the unit braking energy consumption, wherein a calculation formula of the total braking energy consumption is as follows:

wherein,representing +.>Deceleration state matrix->Representing the total number of deceleration state matrices in the deceleration data set,/->Indicate->The corresponding speed average value in the acceleration state matrix;

sequentially extracting an idle state matrix from an idle data set, acquiring unit other energy consumption based on the extracted idle state matrix, and acquiring other total energy consumption based on the unit accessory energy consumption;

the unit experimental total energy consumption is calculated based on the braking total energy consumption, the driving total energy consumption, and other total energy consumption.

Optionally, the inputting the total energy consumption dataset into a pre-constructed neural network model to obtain the minimum total energy consumption includes:

sequentially extracting a plurality of unit experiment total energy consumption from the total energy consumption data set, and acquiring an initial speed measurement data set corresponding to the extracted unit experiment total energy consumption based on the extracted unit experiment total energy consumption;

inputting the initial velocity measurement data set into a neural network model, and constructing a simulation experiment simulation result corresponding to the extracted unit experiment total energy consumption based on the neural network model;

Summarizing a plurality of simulation experiment simulation results, inputting the simulation experiment simulation results into a neural network model, and acquiring the minimum total energy consumption based on the neural network model.

In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:

at least one processor; the method comprises the steps of,

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to implement the remote control model trolley energy consumption optimization method based on the driving feature dimension.

In order to solve the above problems, the present invention further provides a computer readable storage medium having at least one instruction stored therein, the at least one instruction being executed by a processor in an electronic device to implement the above-described remote control model car energy consumption optimization method based on the driving feature dimension.

Compared with the problems in the prior art, the method and the device for detecting the speed of the train have the advantages that the simulation experiment instruction is received, the simulation experiment environment is obtained according to the simulation experiment instruction, the train at the start point of the track is started based on the simulation experiment environment, the speed measuring operation is carried out on the train by utilizing each sensor in the plurality of sensors and the pre-built speed measuring method, and the initial speed measuring data set is obtained after the train is confirmed to reach the end point of the track. Dividing the initial speed measurement data set by using a pre-constructed working condition dividing method to obtain an acceleration data set, a deceleration data set and an idle speed data set, and describing in detail how to divide acceleration, deceleration and idle speed. The method comprises the steps of sequentially extracting acceleration state matrixes from acceleration data sets, calculating unit driving force according to the extracted acceleration state matrixes, obtaining unit driving energy according to the unit driving force, calculating driving total energy based on the unit driving energy, and calculating unit experiment total energy based on a deceleration data set, an idle speed data set and the driving total energy. According to the method, a plurality of unit experiment total energy consumption is obtained according to preset experiment times and simulation experiment environments, the unit experiment total energy consumption is summarized to obtain a total energy consumption data set, the total energy consumption data set is input into a pre-built neural network model to obtain minimum total energy consumption, a trolley is controlled to run on a trolley track according to the minimum total energy consumption and a pre-built control unit to complete trolley energy consumption optimization. Therefore, the remote control model trolley energy consumption optimization method, device, electronic equipment and computer readable storage medium based on the driving characteristic dimension mainly aims to solve the problem that the running mode corresponding to the minimum total energy consumption is trained by using the energy consumption of a unit period and the neural network model.

Drawings

Fig. 1 is a schematic flow chart of a remote control model trolley energy consumption optimization method based on a driving feature dimension according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of an electronic device for implementing the remote control model trolley energy consumption optimization method based on the driving feature dimension according to an 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.

The embodiment of the application provides a remote control model trolley energy consumption optimization method based on a driving characteristic dimension. The execution main body of the remote control model trolley energy consumption optimization method based on the driving characteristic dimension comprises at least one of electronic equipment, such as a server side, a terminal and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the remote control model car energy consumption optimization method based on the driving characteristic dimension can be executed by software or hardware installed in the terminal device or the server device. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.

Example 1:

referring to fig. 1, a flow chart of a remote control model trolley energy consumption optimization method based on a driving feature dimension according to an embodiment of the present invention is shown. In this embodiment, the remote control model trolley energy consumption optimization method based on the driving feature dimension includes:

s1, receiving a simulation experiment instruction, and acquiring a simulation experiment environment according to the simulation experiment instruction, wherein the simulation experiment environment comprises a trolley, a trolley track and a plurality of sensors, each sensor in the plurality of sensors is arranged on the trolley, and the trolley track comprises a track starting point and a track ending point.

For example, the sheet is intended to measure the energy consumption of the trolley running on a fixed track, so the sheet initiates a simulation experiment instruction, and the simulation experiment environment is the environment in which the trolley performs a simulation experiment.

In the embodiment of the invention, the trolley is controlled to run on the fixed track for multiple times, energy consumption analysis is carried out on each running track of the trolley which is controlled to run for multiple times, and the trolley is optimized based on an analysis result.

Further, the trolley track is a fixed trolley track, each experiment of the trolley in the embodiment of the invention runs along the trolley track, from the track starting point to the track ending point, in the process of running the trolley from the track starting point to the track ending point, the trolley track has track slopes such as an ascending slope, a descending slope and the like, the trolley in the embodiment of the invention is a pure electric trolley, and the trolley does not comprise an energy consumption recovery device based on economic consideration.

S2, starting a trolley positioned at a track starting point based on a simulation experiment environment, executing speed measurement operation on the trolley by utilizing each sensor in a plurality of sensors and a pre-built speed measurement method, and obtaining an initial speed measurement data set after the trolley is confirmed to reach the track end point, wherein the sampling frequency of each sensor is a preset frequency, the speed measurement time is a trolley operation period, and the initial speed measurement data set is composed of a plurality of state matrixes.

Further, the method for performing a speed measurement operation on the trolley by using each sensor of the plurality of sensors and the pre-built speed measurement method includes:

The acceleration speed measuring unit is a unit for analyzing the acceleration of the trolley by using an acceleration sensor, and the speed measuring unit is a unit for analyzing the speed and the acceleration of the trolley by using a speed sensor. The embodiment of the invention is provided with a plurality of sensors, and aims to reduce the error of speed measurement of the trolley through the plurality of sensors, thereby further reducing the error of energy consumption calculation. The state matrix is a matrix for describing the state of the trolley, and the state matrix comprises a plurality of parameters for describing the current state of the trolley. The track coordinates are coordinates based on the ground coordinate system for describing the current position of the trolley on the trolley track, and the track coordinates are not changed due to the movement of the track. The track gradient is the gradient of dolly on current dolly track, and the track gradient is 0 degrees with the horizontal plane, and anticlockwise rotation is positive, and clockwise rotation is negative, and the angle of track gradient is the acute angle. The acceleration measurement value is a value for measuring the acceleration of the trolley by using an acceleration sensor, and the track gradient compensation formula is used for correcting the influence of the track gradient on the acceleration measurement of the trolley. The suboptimal acceleration value is the value of the acceleration measured value after gradient correction, and the filter coefficient is the coefficient when the acceleration sensor is subjected to low-pass filtering. The gradient coefficient is a coefficient used to determine whether the gradient is an ascending or descending slope.

In the embodiment of the invention, in order to improve the accuracy of measurement, the suboptimal acceleration value is optimized based on the initial measurement error to obtain an optimized acceleration value.

Further, the average acceleration value is an average value of a plurality of optimized acceleration values, and the purpose of the average acceleration value is to reduce accidental errors generated when a single acceleration sensor performs acceleration measurement.

It can be understood that the impact rate is a numerical value for describing whether the trolley is in the idle state or not, in the embodiment of the invention, whether the trolley is in the idle state is judged through the impact rate, and because the motor hardly rotates when the trolley is in the idle state, the idle state is simplified to be the condition that the motor does not work, namely the neutral state, so when the impact rate is greater than the impact rate threshold value, the trolley is considered to be in the idle state, and the trolley is exemplified to be at the top of an ascending slope and is about to descend, and when the trolley is still capable of advancing in the neutral state, the energy consumption optimization can be carried out by designing a certain idle state in a fixed track.

It should be noted that the obtaining, according to the speed measuring unit, the impact rate corresponding to the state matrix includes:

It should be noted that, in the embodiment of the present invention, the acceleration sensor and the speed sensor both need to perform the filtering operation.

Further, the suboptimal speed value is a speed value obtained by performing a filtering operation on the speed measured by the extracted speed sensor, and the speed acceleration is an acceleration calculated by the speed sensor.

Further, the obtaining the speed average value of the trolley based on the impact rate and the preset impact rate threshold value includes:

Comparing the impact rate with a preset impact rate threshold;

The impact rate threshold is the maximum impact rate of the trolley in a non-idle state, and when the impact rate is larger than the impact rate threshold, the trolley is in an idle working condition, and the suboptimal speed value set is a set formed by a plurality of suboptimal speed values. The speed average is a value obtained by a plurality of sensors and most representative of the current speed.

Further, after the trolley is confirmed to reach the track end point, an initial speed measurement data set is obtained, which comprises the following steps:

It should be noted that, the initial velocity measurement data set is a set formed by a plurality of state matrixes, the preset frequency is the frequency of scanning by the sensor, the running period of the trolley is the period from the start point to the end point of the track, and the unit period in the optional embodiment of the invention is 0.5s.

And S3, dividing the initial speed measurement data set by using a pre-constructed working condition dividing method to obtain an acceleration data set, a deceleration data set and an idle speed data set.

Further, the dividing the initial speed measurement data set by using the pre-constructed working condition dividing method to obtain an acceleration data set, a deceleration data set and an idle speed data set, including:

It should be noted that, in the embodiment of the present invention, the running conditions of the trolley are divided into an acceleration condition, a deceleration condition and an idle condition, where the acceleration condition is a condition with an acceleration greater than 0, the deceleration condition is a condition with an acceleration less than 0, and the idle condition is explained in detail above and is not repeated here. The idle state matrix is a matrix for expressing that the current state of the trolley is an idle working condition, and in the embodiment of the invention, in the process of one experiment, a condition without the idle working condition may occur, so the idle state matrix comprises 0, 1 or more idle state matrices. The data set to be divided is obtained by eliminating all idle state matrixes in the initial speed measurement data set, namely the data set to be divided only comprises two working conditions of acceleration and deceleration.

Further, the acceleration state matrix is a state matrix corresponding to the current state of the trolley when accelerating, the deceleration state matrix is a state matrix corresponding to the current state of the trolley when decelerating, and it should be noted that the acceleration state matrix, the deceleration state matrix and the idle state matrix are consistent with the structure of the state matrix.

It is understood that the idle data set is a set of 0, 1 or more idle state matrices, the acceleration data set is a set of a plurality of acceleration state matrices, and the deceleration data set is a set of a plurality of deceleration state matrices.

And S4, sequentially extracting acceleration state matrixes from the acceleration data set, calculating unit driving force according to the extracted acceleration state matrixes, acquiring unit driving energy consumption according to the unit driving force, calculating driving total energy consumption based on the unit driving energy consumption, and calculating unit experimental total energy consumption based on the deceleration data set, the idle speed data set and the driving total energy consumption.

Further, the calculation formula of the total energy consumption of the unit experiment is as follows:

wherein,represents total energy consumption per unit experiment,/->Representing the driving efficiency correction factor,/->Representing the total energy consumption of the drive, Representing braking efficiency correction factor,/->Indicating total braking energy consumption>Representing other total energy consumption.

The total energy consumption of unit experiment is the total running energy consumption of the small car in one simulation experiment, the driving efficiency correction coefficient is the coefficient for correcting the mechanical efficiency and other influencing factors when the small car accelerates, the driving total energy consumption represents the total energy consumption of the motor in the acceleration running process of the small car, the braking efficiency correction coefficient is the coefficient for correcting the mechanical efficiency, friction and other influencing factors when the small car decelerates, the braking total energy consumption is the total energy consumption of the motor in the braking process of the small car, and the other total energy consumption comprises the total energy consumption corresponding to various conditions such as neutral sliding, equipment heat dissipation and idle speed. It should be noted that, in other total energy consumption, the motor does not work approximately, and in the running process, when the trolley is in an idle state in a large amount, although other total energy consumption can rise, the total energy consumption of unit experiment is, and in the actual use process, the idle state cannot appear in a large amount, and the idle state is unfavorable for prolonging the service life of the trolley, so that the idle working condition should be designed reasonably.

Further, the calculating the unit driving force according to the extracted acceleration state matrix includes:

wherein,represents the driving force of unit,/->Represents +.>Acceleration state matrix->Representing the overall mass of the trolley, < >>Indicating the acceleration of gravity>Representing the rolling resistance coefficient, +.>Representing the conversion coefficient of the rotating mass>Represents air density, ++>Indicating air resistanceForce coefficient->Representing the frontal area.

Further, the obtaining the unit driving energy consumption according to the unit driving force includes:

the calculation formula of the total driving energy consumption is as follows:

The unit driving force is traction force when the trolley is in an acceleration working condition in a unit period, and the unit driving energy consumption is energy when the trolley is in the acceleration working condition in the unit period.

Further, the calculating the unit experimental total energy consumption based on the deceleration data set, the idle speed data set and the driving total energy consumption includes:

wherein,representation subtractionFirst->Deceleration state matrix->Representing the total number of deceleration state matrices in the deceleration data set,/->Indicate->The corresponding speed average value in the acceleration state matrix;

The unit braking force is traction force of the trolley in a unit period when the trolley is in a deceleration working condition, the unit braking energy consumption is approximate instantaneous energy consumption of the trolley in the unit period when the trolley is in the deceleration working condition, and the unit other energy consumption is approximate instantaneous energy consumption of the trolley in the unit period when the trolley is in an idle working condition.

S5, acquiring a plurality of unit experiment total energy consumption according to preset experiment times and simulation experiment environments, wherein each unit experiment total energy consumption in the plurality of unit experiment total energy consumption is different, and the number of the plurality of unit experiment total energy consumption is equal to the number of the experiment times; and summarizing the total energy consumption of a plurality of unit experiments to obtain a total energy consumption data set.

It should be noted that, in the embodiment of the invention, the simulation method of the simulation test of the trolley is only described once, the preset experiment times do not affect the simulation of the trolley by the neural network model, and the total energy consumption data set is a data set formed by a state matrix corresponding to each unit period of each experiment in multiple experiments.

S6, inputting the total energy consumption data set into a pre-constructed neural network model to obtain minimum total energy consumption; and controlling the trolley to run on the trolley track according to the minimum total energy consumption and the pre-constructed control unit, and completing the energy consumption optimization of the trolley.

Further, the inputting the total energy consumption dataset into a pre-constructed neural network model to obtain the minimum total energy consumption includes:

Further, the minimum total energy consumption is the minimum unit experimental total energy consumption predicted by the neural network model, which is the prior art and will not be described herein. The simulation result of the simulation test is a result of reproducing the trolley experiment on a computer, and it is required to explain that the simulation result of the non-actual simulation experiment can be constructed through the existing simulation test simulation result and the neural network model, so that the minimum total energy consumption can be calculated through the neural network model. The control unit is a unit for remotely controlling the trolley to run on the trolley track.

It should be noted that, when the total energy consumption dataset is input to the neural network model according to the initial speed measurement dataset corresponding to the total energy consumption of the multiple unit experiments, the operation of the trolley on the trolley track can be simulated by the neural network model, and in the embodiment of the invention, the preset experiment times are enough, alternatively, 80% of the total energy consumption of the multiple unit experiments and the initial speed measurement dataset corresponding to the total energy consumption of the multiple unit experiments are used as training data, 20% of the total energy consumption of the unit experiments and the initial speed measurement dataset corresponding to the total energy consumption of the multiple unit experiments are used as correction data, and the minimum total energy consumption taking the total energy consumption of the unit experiments as an objective function can be predicted by continuously calibrating the neural network model.

The fixed track is formed by splicing a section of straight line, a section of downhill slope and a second section of straight line, and in the process that the trolley runs from the section of straight line to the section of downhill slope, the trolley can be stopped to accelerate for 1s, 2s or 3s when entering the section of downhill slope, so that the trolley enters an idle state until the trolley reaches a track end point, but the fact that the trolley cannot reach the track end point due to friction is needed to be described. The acceleration can be stopped when the vehicle descends for 1s, the vehicle starts decelerating when the vehicle enters for 1s of the second section of straight line until the vehicle reaches the track end point, and the simulation result of the simulation test is obtained based on the total energy consumption of the unit experiment, so that the test operation of executing the simulation test by the vehicle is various and is not described in detail herein. By importing the total energy consumption data set into the neural network model and predicting the minimum total energy consumption and the trolley running state corresponding to the minimum total energy consumption, the trolley can be controlled to run according to the trolley running state corresponding to the minimum total energy consumption by using the control system, so that the energy consumption optimizing effect is achieved.

Example 2:

fig. 2 is a schematic structural diagram of an electronic device for implementing a remote control model car energy consumption optimization method based on a driving feature dimension according to an embodiment of the present invention.

The electronic device 1 may comprise a processor 10, a memory 11, a bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a remote model car energy consumption optimization program based on a driving feature dimension.

The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard) or the like, provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of a remote control model car energy consumption optimization program based on the dimension of the driving characteristics, but also for temporarily storing data that has been output or is to be output.

The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (CentralProcessingunit, CPU), microprocessors, digital processing chips, graphics processors, a combination of various control chips, and the like. The processor 10 is a control unit (control unit) of the electronic device, connects the respective components of the entire electronic device using various interfaces and lines, executes or executes programs or modules stored in the memory 11 (for example, a remote control model car energy consumption optimizing program based on a driving feature dimension, etc.), and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process the data.

The bus may be an Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.

Fig. 2 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 2 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.

For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.

Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.

The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (organic light-emitting diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.

It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.

The remote control model car energy consumption optimization program stored in the memory 11 of the electronic device 1 based on the dimension of the driving characteristics is a combination of a plurality of instructions, which when running in the processor 10 can realize:

Specifically, the specific implementation method of the above instruction by the processor 10 may refer to descriptions of related steps in the corresponding embodiments of fig. 1 to 2, which are not repeated herein.

Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM).

The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:

The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.

Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. The remote control model trolley energy consumption optimization method based on the driving characteristic dimension is characterized by comprising the following steps of:

wherein,represents total energy consumption per unit experiment,/->Representing the driving efficiency correction factor,/->Representing the total energy consumption of the drive +.>Representing braking efficiency correction factor,/->Indicating total braking energy consumption>Representing other total energy consumption;

2. The method for optimizing energy consumption of a remote control model vehicle based on a driving feature dimension according to claim 1, wherein the performing a speed measurement operation on the vehicle by using each of a plurality of sensors and a pre-built speed measurement method comprises:

3. The method for optimizing energy consumption of a remote control model trolley based on driving feature dimensions according to claim 2, wherein the obtaining the impact rate corresponding to the state matrix according to the speed measuring unit comprises the following steps:

wherein,indicate->Impact rate corresponding to time, +.>Indicate->Speed acceleration corresponding to time, +.>Indicate->Speed acceleration corresponding to time, +.>Indicate->Time to- >Time difference between moments.

4. The method for optimizing energy consumption of a remote control model trolley based on driving feature dimensions according to claim 2, wherein the obtaining a speed average value of the trolley based on the impact rate and a preset impact rate threshold value comprises:

comparing the impact rate with a preset impact rate threshold;

5. The method for optimizing energy consumption of a remote control model car based on a driving characteristic dimension according to claim 4, wherein the step of obtaining an initial velocity measurement data set after confirming that the car reaches a track end point comprises the steps of:

Dividing the trolley operation time period based on preset frequency and the trolley operation time period to obtain a plurality of unit periods, wherein the unit periods are equal to the first periodTime of dayTo->Time difference between moments;

6. The method for optimizing energy consumption of a remote control model vehicle based on driving feature dimensions according to claim 1, wherein dividing the initial speed measurement data set by using a pre-constructed working condition dividing method to obtain an acceleration data set, a deceleration data set and an idle speed data set comprises:

7. The remote control model car energy consumption optimizing method based on the driving feature dimension according to claim 1, wherein the calculating the unit driving force from the extracted acceleration state matrix comprises:

wherein,represents the driving force of unit,/->Represents +.>Acceleration state matrix->Representing the overall mass of the trolley, < >>Indicating the acceleration of gravity >Representing the rolling resistance coefficient, +.>Representing the conversion coefficient of the rotating mass>Represents air density, ++>Represents the air resistance coefficient, & lt & gt>Representing the frontal area.

8. The method for optimizing energy consumption of a remote control model car based on a driving characteristic dimension according to claim 1, wherein the obtaining unit driving energy consumption according to unit driving force comprises:

the calculation formula of the total driving energy consumption is as follows:

9. The method for optimizing energy consumption of a remote control model vehicle based on a driving characteristic dimension according to claim 1, wherein the calculating the total energy consumption per unit experiment based on the deceleration data set, the idle data set and the driving total energy consumption includes:

wherein,representing +.>Deceleration state matrix- >Representing the total number of deceleration state matrices in the deceleration data set,/->Indicate->The corresponding speed average value in the acceleration state matrix;

10. The method for optimizing energy consumption of a remote control model vehicle based on a driving feature dimension according to claim 9, wherein the step of inputting the total energy consumption dataset into a pre-constructed neural network model to obtain minimum total energy consumption comprises the steps of: