CN113222248A - Charging pile selection method for automatically driving taxi - Google Patents

Charging pile selection method for automatically driving taxi Download PDF

Info

Publication number
CN113222248A
CN113222248A CN202110519094.3A CN202110519094A CN113222248A CN 113222248 A CN113222248 A CN 113222248A CN 202110519094 A CN202110519094 A CN 202110519094A CN 113222248 A CN113222248 A CN 113222248A
Authority
CN
China
Prior art keywords
charging
charging station
taxi
time
matrix
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110519094.3A
Other languages
Chinese (zh)
Other versions
CN113222248B (en
Inventor
曾伟良
韩宇
廖立
邱高阳
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong University of Technology
Original Assignee
Guangdong University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong University of Technology filed Critical Guangdong University of Technology
Priority to CN202110519094.3A priority Critical patent/CN113222248B/en
Publication of CN113222248A publication Critical patent/CN113222248A/en
Application granted granted Critical
Publication of CN113222248B publication Critical patent/CN113222248B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations

Abstract

The invention discloses a method for selecting a charging pile of an automatic driving taxi, which comprises the following steps of: establishing a charging station traffic information environment model, which comprises a charging pile state information matrix and a queuing matrix of a charging station; establishing a charging station queuing model for predicting how many charging cars may arrive at a charging station in each time period based on the queuing matrix; calculating the total time cost and the total mileage cost of the automatically-driven taxi needing to be charged when the automatically-driven taxi goes to each charging station for charging; constructing a cost objective function of automatic driving and renting charging and a constraint condition of the objective function; and automatically driving the taxi to select a charging station with the minimum cost to charge according to the solution result of the cost objective function under the constraint condition. The method integrates and efficiently utilizes the data of the internet of vehicles in the big data era, completes the modeling of the selection strategy of the charging station of the automatic driving charging taxi, and can provide the optimal charging station strategy for selecting and estimating the taxi in real time.

Description

Charging pile selection method for automatically driving taxi
Technical Field
The invention relates to the field of computers and transportation, in particular to a method for selecting a charging pile for automatically driving a taxi.
Background
Global energy scarcity and environmental problems are becoming more serious, and the high dependence on oil around the world has increased a series of problems laterally. In recent years, with the development of battery technology, the progress of the electric automobile industry is promoted, and meanwhile, the increasing strength of artificial intelligence and the vigorous development of mobile internet have brought forward large-scale data analysis application scenes such as internet of vehicles and the like.
With the maturity of internet of vehicles technology, the research of the automatic driving charging automobile has a lot of qualitative leaps. A number of taxi companies have also been added to the corner of autonomous vehicles. The paper "research on pure electric network taxi charging behavior" analyzes the driver charging behavior, but when autodrive is applied to charging taxi operations, a non-negligible problem is that: how is a suitable charging station autonomously selected by the vehicle-on-board system without human intervention? Obviously, in the traditional taxi with a driver, the driver can decide when to go to charge and which charging station to go to charge according to own experience, and in the automatic taxi, the problem needs to be solved.
Many existing researches on automatically driving and charging taxis, such as treatises "planning of station sites of electric taxi charging stations based on improved PSO algorithm", "analysis of charging demand of electric taxis based on track data", and the like, often default to the traditional "selection nearby" principle of charging taxis or how to determine the configuration orientation of the charging stations according to analysis of driver walking track data; however, in real life, the charging station is not perfectly planned from the beginning, and according to the change of the actual daily supply and demand relationship, the planned charging station only plays an auxiliary role in selecting the taxi to be charged, and the actual selection still needs to be changed according to the supply and demand relationship at the current moment.
The data collection capacity of the current big data era is very strong, a large amount of data can be obtained in real time, and according to current research, various selection modes are formulated by various scholars through analyzing data, such as ' a management method for supplying electric energy to a pure electric taxi ', an electric taxi charging recommendation strategy considering time and electricity price ' and the like, but the consideration area of the scholars is small, for example, the charging time is limited, delay caused by queuing is not considered, how long the scholars can receive the passengers again after charging is not considered, and the charging station position needs to be globally arranged again; from a commercial taxi company perspective, it is possible to maximize the benefit by completing the charge as soon as possible and starting to service the next order.
Disclosure of Invention
The invention aims to provide a method for selecting a charging pile for automatically driving a taxi, which is used for providing the best charging selection for automatically driving the taxi on the premise of fully considering the cost of time, mileage and the like.
In order to realize the task, the invention adopts the following technical scheme:
a method for selecting a charging pile of an automatically-driven taxi is used for selecting the charging pile of the automatically-driven taxi needing to be charged according to the following steps:
establishing a charging station traffic information environment model, which comprises a charging pile state information matrix and a queuing matrix of a charging station; establishing a charging station queuing model for predicting how many charging cars may arrive at a charging station in each time period based on the queuing matrix;
calculating the total time cost and the total mileage cost of the automatically-driven taxi needing to be charged for going to each charging station for charging by using the charging pile state information matrix and the charging station queuing model;
constructing a cost objective function of automatic driving, renting and charging and a constraint condition of the objective function according to the total time cost and the total mileage cost;
and automatically driving the taxi to select a charging station with the minimum cost to charge according to the solution result of the cost objective function under the constraint condition.
Further, the process of establishing the charging pile state information matrix and the queuing matrix of the charging station includes:
acquiring dynamic information of all charging stations within a range of K kilometers around the charged automatic driving taxi as a circle center, and establishing a charging pile state information matrix S and a queuing matrix Q of the charging stations;
wherein, each element S in the charging pile state information matrix Sj.iShowing the state of the ith charging pile in the jth charging station, and if the charging pile is occupied, Sj.iThe time when the charging pile is occupied; if the charging pile is not occupied, Sj.iIs set to 0;
element Q in the queuing matrix Q of a charging stationmRepresenting the queue of the mth charging station, the number of vehicles currently queued for charging.
Further, the establishment process of the charging station queuing model comprises the following steps:
constructing a customer number matrix lambda of the charging station, each element of the customer number matrix lambda
Figure BDA0003063182890000021
Expressed as:
the time of day is discretized into a time sequence according to fixed intervals, and then the average number of customers in each time period of each charging station every day is
Figure BDA0003063182890000022
j denotes the jth charging station, TiRepresents the ith time period;
establishing a charging station queuing model based on the customer number matrix lambda:
Figure BDA0003063182890000031
in the above formula Pn(T) represents a time period TiThe probability that the charging station j newly arrives at n customers in the future time period of (1), wherein t is unit time and n represents the number of customers.
Further, the calculation process of the total time cost for the automatic driving taxi needing to be charged to go to each charging station for charging comprises the following steps:
calculating the number of automatically-driven taxis reaching each charging station according to the charging station position matrix L and the current position of the automatically-driven taxisPassing time matrix pt of charging station and estimated arrival time matrix T of automatically-driven taxia(ii) a The mth element L in the position matrix L of the charging stationmIndicating the location of the mth charging station;
queuing matrix Q and estimated arrival time matrix T based on charging pile state information matrix S and charging stationaCalculating expected queuing time values of the automatically-driven taxis at all charging stations and queuing time possibly needed when the automatically-driven taxis reach all the charging stations, and constructing a queuing time matrix;
calculating the charging time of the jth charging station according to the charging power of the charging station and the w loss electric quantity of the automatically-driven taxi;
estimating the estimated time from the charging completion of the automatic taxi w to the receiving of the first order by historical data;
the total time cost of the automatic taxi w needing to be charged when the taxi w is charged at the jth charging station consists of the transit time of the w reaching the jth charging station, the expected queuing time value of the w at the jth charging station j, the charging time of the w at the jth charging station and the estimated time of the w receiving the first order after the charging is finished.
Further, the time period T of the taxi automatically driven to reach the charging station is predicted through the charging station queuing modelbWhen the charging cars of a new customer arrive at the charging station, the charging cars will join the existing queue of the charging station;
obtaining the information that other automatic driving taxies are going to the charging station and adding the information into the queuing queue, wherein the rule is as follows: at the current time TnowAn automatically-driven taxi which is determined to go to a certain charging station before arrives at the charging station before the automatically-driven taxi w; estimating when the taxi w is automatically driven at TbAnd when the taxi arrives at the charging station j, selecting the probability of each charging pile of the charging station j and the number of vehicles in queue before the queue, and calculating the expected queuing time value of the automatically-driven taxi w at the charging station j.
Further, the constraint conditions of the objective function include:
each automatic taxi can only select one charging station;
there is at least one charging station j, which is at a distance d from the taxi ww.jLess than the distance that the taxi can run with the residual electric quantity
Figure BDA0003063182890000041
w can not select the charging station which does not meet the condition, and the taxi w can only select the charging station which can be reached by the residual electric quantity;
charging pile number M of each charging stationjGreater than 1;
when the unused charging pile exists in any charging station, the queue of the charging station must be 0.
Further, the cost objective function of the automated driving rental charge is expressed as:
Figure BDA0003063182890000042
wherein x isw.jThe taxi charging station selection constraint of the taxi w is represented, when the value of the taxi w is 1, the taxi w is represented that the charging station j is selected, and when the value of the taxi w is 0, the charging station j is not selected; beta is aw.jRepresenting the total time cost t of charging an autonomous taxi w at the jth charging stationw.jValue after de-dimensional processing, thetaw.jRepresenting the dimensioned value of the total mileage cost for an autonomous taxi w to travel to a charging station j.
A driving computer for automatically driving a taxi comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the step of the method for selecting the charging pile for automatically driving the taxi is realized when the processor executes the computer program.
A computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method for automatically driving a taxi charging post selection.
Compared with the prior art, the invention has the following technical characteristics:
1. the automatic driving and charging taxi is one of the revolutionary traffic modes in the future, and can generate great social and environmental benefits. The invention integrates the data of the vehicle networking in the big data era, realizes the organic integration of people, vehicles, roads and networks, efficiently utilizes the collected data, completes the modeling of the selection strategy of the automatic driving charging taxi charging station, and can provide the optimal charging station strategy for selecting and estimating taxis in real time.
2. According to the scheme, urban grids and road nodes are combined to carry out urban grid management, the demand conditions of other charging cars except taxies to charging stations are considered by predicting the real-time taxi traffic flow of the car network and using a proper distribution probability model, and finally the best charging station selection is given to each taxi needing to be charged in real time.
3. Compared with the previous scheme, the automatic driving taxi charging station selection strategy can search for the charging station which completes charging fastest and finds the next order to a large extent, and service efficiency of the taxi is improved. In addition, the charging strategy provided by the invention has strong compatibility, can be easily expanded to other existing taxi systems, and can also be applied to a large-scale taxi system.
Drawings
FIG. 1 is a schematic diagram of a charging selection model framework;
FIG. 2 is TiA queuing schematic diagram of charging stations at the moment;
FIG. 3 is TaExample charging station queue time calculation at the moment.
Detailed Description
Referring to fig. 1, the invention provides a method for selecting a charging pile of an automatically-driven taxi, which is used for completing a charging station selection process of any automatically-driven taxi needing to be charged according to the following steps in sequence:
step 1, establishing a charging station traffic information environment model, including a charging pile state information matrix and a queuing matrix of a charging station; and establishing a charging station queuing model for predicting how many charging cars may arrive at the charging station in each time period based on the queuing matrix.
1.1 firstly acquiring dynamic information of all charging stations within a range of K kilometers around a charged automatic driving taxi as a circle center, and establishing a charging pile state information matrix S and a queuing matrix Q of the charging stations:
Figure BDA0003063182890000051
Q＝[Q1 Q2 … Qm]T (2)
the matrix S represents a charging pile state information matrix of m arranged charging stations within the K kilometers; charging stations are irregularly scattered, the number of charging piles contained in each charging station is different, and the number of charging piles contained in the jth charging station is MjRepresents; each element S in the matrix Sj.iShowing the state of the ith charging pile in the jth charging station, and if the charging pile is occupied, Sj.iThe time when the charging pile is occupied; if the charging pile is not occupied, Sj.iIs set to 0; therefore, the matrix S records the waiting time required for each charging post of the m charging stations.
The matrix Q represents how many vehicles are waiting in line per charging station, where QmRepresenting the queue of the mth charging station, i.e., the number of vehicles currently queued for charging.
1.2, acquiring the information of the m charging stations, and constructing a charging station position matrix L and a customer number matrix lambda:
L＝[L1 L2 … Lm]T (3)
Figure BDA0003063182890000052
in the above formula, LmRepresenting the position of the mth charging station, discretizing the time of day into a time series [ T ] at regular intervals1,T2,…,T∞]Then the average number of customers per charging station per day over each time period
Figure BDA0003063182890000061
j denotes the jth charging station, TiIndicating the ith time period.
1.3, a charging station queuing model is established, as shown in formula 5, and the arrival of how many charging cars are possible in each time period of the charging station is predicted:
Figure BDA0003063182890000062
in the above formula Pn(T) represents a time period TiThe probability that the charging station j newly arrives at n customers in the future time period of (1), wherein T is Ti+1-TiIs a unit time of the time of day,
Figure BDA0003063182890000063
is the jth charging station in the time period TiThe average number of customers is the inner average number of customers, n represents the number of customers, and e is the base of the natural logarithm.
The above model is a model established for other charging cars than charging autopilot cabs.
Wherein the queuing model can be built based on a queuing theory model of M/N/C/∞/∞/FCFS, for example, wherein the first symbol M represents that the distribution of the customer flow is a negative exponential distribution; n represents that the customer service time meets normal distribution; c represents that the number of charging piles of each charging station is constant; in the fourth fifth symbol, two ∞ indicates that the charging station system capacity and the customer source capacity are infinite; the sixth symbol indicates that the service rule is First Come First Served (FCFS).
And 2, calculating the total time cost and the total mileage cost of the automatic driving taxi needing to be charged for going to each charging station for charging by using the charging pile state information matrix and the charging station queuing model.
2.1 calculating a passing time matrix pt of the automatically-driven taxi reaching each charging station and a predicted arrival time matrix T of the automatically-driven taxi according to the charging station position matrix L and the current position of the automatically-driven taxi and the shortest path planned by the existing path planning algorithma：
pt＝[pt1,pt2,…,ptm] (6)
Ta＝Tnow+pt (7)
Wherein pt in the matrix ptmIndicating the transit time, T, for an automatically driven taxi to travel to the mth charging stationnowIndicating the current time of day.
2.2 queue matrix Q and estimated arrival time matrix T based on charging pile state information matrix S, charging stationaAnd calculating expected queuing time values of the automatically-driven taxis at all the charging stations and queuing time possibly needed when the automatically-driven taxis arrive at all the charging stations, and constructing a queuing time matrix.
At the current time TnowNext, an example of selection of one charging station for the autonomous taxi w is as follows:
2.2.1 As in FIG. 2, a charging station at TiThe state diagram of the time slot, 5 charging piles in the charging station, TiEvery charging pile is occupied at all times, 4 electric vehicles are queued in a queuing queue, and the 4 electric vehicles are sequentially arranged behind the corresponding charging piles according to the FCFS sequence to wait for charging. Each charging pile is not in an occupied state until all vehicles in the queue are finished; each charging pile has a corresponding value S, wherein S is the sum of the time of the current charging vehicle needing to be charged and the time of the vehicles in the corresponding queue needing to be charged.
2.2.2 predicting time period T for arrival at charging station in automatically driven taxi by charging station queuing model of formula 5bAt that time, how many new customers 'charging cars arrive at the charging station, and these charging cars will join the charging station's existing queue.
In addition, because all the charged automatic driving taxis are not in the estimation of the passenger flow, the queuing condition needs to consider that a certain automatic driving taxi needing to be charged is going to the charging station, the information is obtained by the current automatic driving taxi w through the control center and is additionally added into the queuing queue, and the default rule is at the current time TnowAn autopilot that has previously decided to travel to a certain charging station will arrive at the charging station before autopilot w. When the taxi w is automatically driven at T based on the estimationbWhen arriving at charging station, the probability behind each charging post of charging station j and how many vehicles are queuing up before the queue, as shown in fig. 3.
That is, since the customer flow follows the poisson distribution, it can be predicted by the charging station queuing prediction model how many new customers arrive at the charging station and join the queuing queue before the taxi w arrives at the charging station j. While following the previously set rules, at TnowA vehicle that decides to go to charging station j after time will by default arrive at charging station j later than taxi w. Existing queue of charging stations + new customer predicted to arrive at charging station + add other on TnowThe former other determines the number of self-driving taxis heading for the charging station, i.e., all the customers that self-driving taxi w ranks before w after arriving at the charging station. Thus, the probability of selecting each charging post of charging station j when w arrives at the charging station and how many vehicles are queuing up before the team can be estimated.
2.2.3 calculate the expected queuing time for the autopilot w at station j, as represented by equation (8):
qtw.j＝∑(Pb×wtb),b＝1,2,…,Mj (8)
wherein, PbProbability, wt, of selecting b charging pile for automatic driving taxi wbWaiting for b to charge the pile.
Based on the expected values, for an autonomous taxi w heading to all charging stations, the queuing time matrix can be estimated as:
qt＝[qt1,qt2,…,qtm] (9)
wherein, qtmQueuing time of taxi w at mth charging station for automatic driving.
2.3 according to the charging power of the charging station and the w loss electric quantity of the automatic driving taxi, calculating the charging time of the jth charging station:
Figure BDA0003063182890000081
in the above formula，EwFor automatically driving taxi W with lost electric quantity, WjCharging power for charging station j.
2.4 according to the travel regularity of the passenger, the estimated time rt from the time when the automatic taxi w finishes charging to the time when the automatic taxi w receives the first order is estimated from historical datac.j(ii) a The time may also be set to an empirical value.
2.5 total time cost of the automatically driven taxi w when charged at the jth charging station:
tw.j＝ptj+qtw.j+ctw.j+rtc.j (11)
wherein pt isjDenotes the time of passage of w to the jth charging station, qtw.jThe expected value of the queuing time of w at charging station j, ctw.jIs the charging time of w at the jth charging station, rtc.jEstimated time rt from the completion of charging for w to the receipt of the first orderc.j。
2.6 calculating the total mileage cost of the automatic driving taxi w for going to each charging station for charging, and constructing a mileage cost matrix:
d＝[dw.1,dw.2,…,dw.m] (12)
wherein d isw.mAnd the total mileage cost of w going to the charging station m is represented and calculated by the oil consumption and the mileage of the automatic driving taxi.
And 3, constructing a cost objective function of the automatic driving, renting and charging and a constraint condition of the objective function according to the total time cost and the total mileage cost.
3.1 establishing constraints for selection models
3.1.1 each autopilot taxi can only select one charging station, the charging station selected by the autopilot taxi w satisfies the constraint:
Figure BDA0003063182890000082
wherein x isw.jA taxi charging station selection constraint representing taxi w, a value of 1 representing taxi w selecting charging station j, 0A time indicates that charging station j is not selected.
3.1.2 there is at least one charging station j, which is at a distance d from the taxi ww.jLess than the distance that the taxi can run with the residual electric quantity
Figure BDA0003063182890000083
As shown in equation (14), the taxi w can only select the charging station where the remaining power can reach, as shown in equation (15), the vehicle cannot select the charging station that does not satisfy equation (14).
Figure BDA0003063182890000084
Figure BDA0003063182890000085
3.1.3 number M of charging piles per charging stationjGreater than 1:
Figure BDA0003063182890000091
3.1.4 when there is unused charging pile in arbitrary charging station, the queue of this charging station must be 0:
Figure BDA0003063182890000092
wherein Q isjRepresenting the queuing matrix of the jth charging pile, Sj.cThe status of the c-th charging pile of charging station j is shown, and k is the number of vehicles in the queue.
3.2 Dedimensioning
Because the units are not consistent, dimensionless processing needs to be performed on the total time cost and the total mileage cost of the automatic driving taxi w. Taking an automatically-driven taxi w:
Figure BDA0003063182890000093
Figure BDA0003063182890000094
Figure BDA0003063182890000095
Figure BDA0003063182890000096
wherein, betaw.jRepresenting the total time cost t of charging an autonomous taxi w at the jth charging stationw.jValue after de-dimensional processing, thetaw.jRepresenting the dimensioned value of the total mileage cost for an autonomous taxi w to travel to a charging station j.
3.3 cost objective function for autopilot rental charge, i.e. the selection model, is expressed as:
Figure BDA0003063182890000097
and 4, automatically driving the taxi w to select a charging station with the minimum cost to charge according to the solution result of the cost objective function under the constraint condition in the step 3.
The invention provides a time and mileage cost framework which needs to be considered for charging a taxi, does not need a driver decision making process, and combines historical data analysis and estimation of a queuing theory; the method has wide consideration range and is fully suitable for the charging selection scheme of a taxi company; the compatibility is strong, and the system is suitable for an automatic driving charging taxi system and can also be applied to a common charging taxi or a charging network taxi appointment management system.
The embodiment of the present application further provides a driving computer for automatically driving a taxi, where the driving computer includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor implements the steps of the method for selecting a charging pile for automatically driving a taxi when executing the computer program, for example, the steps 1 to 4 described above.
The implementation of the present application provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the above-mentioned method for selecting an automatic driving taxi charging pile, for example, the foregoing steps 1 to 4.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, in accordance with legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunications signals.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims (9)

1. The method for selecting the charging pile of the automatic driving taxi is characterized in that for the automatic driving taxi needing to be charged, the charging station is selected according to the following steps:
establishing a charging station traffic information environment model, which comprises a charging pile state information matrix and a queuing matrix of a charging station; establishing a charging station queuing model for predicting how many charging cars may arrive at a charging station in each time period based on the queuing matrix;
calculating the total time cost and the total mileage cost of the automatically-driven taxi needing to be charged for going to each charging station for charging by using the charging pile state information matrix and the charging station queuing model;
constructing a cost objective function of automatic driving, renting and charging and a constraint condition of the objective function according to the total time cost and the total mileage cost;
and automatically driving the taxi to select a charging station with the minimum cost to charge according to the solution result of the cost objective function under the constraint condition.
2. The method of claim 1, wherein the process of establishing the charging post status information matrix and the queuing matrix of the charging station comprises:
acquiring dynamic information of all charging stations within a range of K kilometers around the charged automatic driving taxi as a circle center, and establishing a charging pile state information matrix S and a queuing matrix Q of the charging stations;
wherein, each element S in the charging pile state information matrix Sj.iShowing the state of the ith charging pile in the jth charging station, and if the charging pile is occupied, Sj.iThe time when the charging pile is occupied; if the charging pile is not occupied, Sj.iIs set to 0;
queuing matrix Q of charging stationElement Q of (5)mRepresenting the queue of the mth charging station, the number of vehicles currently queued for charging.
3. The method of claim 1, wherein the building of the charging station queuing model comprises:
constructing a customer number matrix lambda of the charging station, each element of the customer number matrix lambda
Figure FDA0003063182880000011
Expressed as:
the time of day is discretized into a time sequence according to fixed intervals, and then the average number of customers in each time period of each charging station every day is
Figure FDA0003063182880000012
j denotes the jth charging station, TiRepresents the ith time period;
establishing a charging station queuing model based on the customer number matrix lambda:
Figure FDA0003063182880000013
in the above formula Pn(T) represents a time period TiThe probability that the charging station j newly arrives at n customers in the future time period of (1), wherein t is unit time and n represents the number of customers.
4. The method of claim 1, wherein the step of calculating the total time cost for traveling an autonomous taxi to each charging station to be charged comprises:
according to the charging station position matrix L and the current position of the automatic taxi, calculating a passing time matrix pt when the automatic taxi reaches each charging station and a predicted arrival time matrix T when the automatic taxi reaches each charging stationa(ii) a The position matrix of the charging stationM-th element L in LmIndicating the location of the mth charging station;
queuing matrix Q and estimated arrival time matrix T based on charging pile state information matrix S and charging stationaCalculating expected queuing time values of the automatically-driven taxis at all charging stations and queuing time possibly needed when the automatically-driven taxis reach all the charging stations, and constructing a queuing time matrix;
calculating the charging time of the jth charging station according to the charging power of the charging station and the w loss electric quantity of the automatically-driven taxi;
estimating the estimated time from the charging completion of the automatic taxi w to the receiving of the first order by historical data;
the total time cost of the automatic taxi w needing to be charged when the taxi w is charged at the jth charging station consists of the transit time of the w reaching the jth charging station, the expected queuing time value of the w at the jth charging station j, the charging time of the w at the jth charging station and the estimated time of the w receiving the first order after the charging is finished.
5. The method of claim 1, wherein the time period T when the autonomous taxi arrives at the charging station is predicted by a charging station queuing modelbWhen the charging cars of a new customer arrive at the charging station, the charging cars will join the existing queue of the charging station;
obtaining the information that other automatic driving taxies are going to the charging station and adding the information into the queuing queue, wherein the rule is as follows: at the current time TnowAn automatically-driven taxi which is determined to go to a certain charging station before arrives at the charging station before the automatically-driven taxi w; estimating when the taxi w is automatically driven at TbAnd when the taxi arrives at the charging station j, selecting the probability of each charging pile of the charging station j and the number of vehicles in queue before the queue, and calculating the expected queuing time value of the automatically-driven taxi w at the charging station i.
6. The method of claim 1, wherein the constraints of the objective function include:
each automatic taxi can only select one charging station;
there is at least one charging station j, which is at a distance d from the taxi ww.jLess than the distance that the taxi can run with the residual electric quantity
Figure FDA0003063182880000031
w can not select the charging station which does not meet the condition, and the taxi w can only select the charging station which can be reached by the residual electric quantity;
charging pile number M of each charging stationjGreater than 1;
when the unused charging pile exists in any charging station, the queue of the charging station must be 0.
7. The method of claim 1, wherein the cost objective function of the automated driving taxi charging is expressed as:
Figure FDA0003063182880000032
wherein x isw.jThe taxi charging station selection constraint of the taxi w is represented, when the value of the taxi w is 1, the taxi w is represented that the charging station j is selected, and when the value of the taxi w is 0, the charging station j is not selected; beta is aw.jRepresenting the total time cost t of charging an autonomous taxi w at the jth charging stationw.jValue after de-dimensional processing, thetaw.jRepresenting the dimensioned value of the total mileage cost for an autonomous taxi w to travel to a charging station i.
8. A cab computer for automatically driving a taxi, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for automatically driving a taxi charging post according to any one of claims 1 to 8 when the computer program is executed.
9. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for automatically driving a taxi cab charging post selection according to any one of claims 1 to 8.
CN202110519094.3A 2021-05-12 2021-05-12 Automatic taxi-driving charging pile selection method Active CN113222248B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110519094.3A CN113222248B (en) 2021-05-12 2021-05-12 Automatic taxi-driving charging pile selection method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110519094.3A CN113222248B (en) 2021-05-12 2021-05-12 Automatic taxi-driving charging pile selection method

Publications (2)

Publication Number Publication Date
CN113222248A true CN113222248A (en) 2021-08-06
CN113222248B CN113222248B (en) 2022-06-03

Family

ID=77095266

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110519094.3A Active CN113222248B (en) 2021-05-12 2021-05-12 Automatic taxi-driving charging pile selection method

Country Status (1)

Country Link
CN (1) CN113222248B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117634713A (en) * 2024-01-26 2024-03-01 南京邮电大学 Electric taxi charging cost optimization method and system based on charging pile lease

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH07290125A (en) * 1994-04-22 1995-11-07 Nippon Steel Corp Method for scheduling physical distribution in rolling mill
CN108470224A (en) * 2018-03-20 2018-08-31 李琰 Charging station selection method, medium and equipment based on electric vehicle charging
CN110570050A (en) * 2019-09-25 2019-12-13 国网浙江省电力有限公司经济技术研究院 Road-network-vehicle-related electric vehicle charging guiding method
CN110705746A (en) * 2019-08-27 2020-01-17 北京交通大学 Optimal configuration method for electric taxi quick charging station
CN111429014A (en) * 2020-03-30 2020-07-17 南京邮电大学 Electric vehicle charging station selection method based on double-queue dynamic pricing model
CN112356721A (en) * 2020-08-24 2021-02-12 黑龙江省电工仪器仪表工程技术研究中心有限公司 Electric vehicle charging guiding method and system based on cloud platform

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH07290125A (en) * 1994-04-22 1995-11-07 Nippon Steel Corp Method for scheduling physical distribution in rolling mill
CN108470224A (en) * 2018-03-20 2018-08-31 李琰 Charging station selection method, medium and equipment based on electric vehicle charging
CN110705746A (en) * 2019-08-27 2020-01-17 北京交通大学 Optimal configuration method for electric taxi quick charging station
CN110570050A (en) * 2019-09-25 2019-12-13 国网浙江省电力有限公司经济技术研究院 Road-network-vehicle-related electric vehicle charging guiding method
CN111429014A (en) * 2020-03-30 2020-07-17 南京邮电大学 Electric vehicle charging station selection method based on double-queue dynamic pricing model
CN112356721A (en) * 2020-08-24 2021-02-12 黑龙江省电工仪器仪表工程技术研究中心有限公司 Electric vehicle charging guiding method and system based on cloud platform

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
李斌等: "基于混合整数规划的电动公交车", 《电网技术》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117634713A (en) * 2024-01-26 2024-03-01 南京邮电大学 Electric taxi charging cost optimization method and system based on charging pile lease

Also Published As

Publication number Publication date
CN113222248B (en) 2022-06-03

Similar Documents

Publication Publication Date Title
US10639995B2 (en) Methods, circuits, devices, systems and associated computer executable code for driver decision support
Amirgholy et al. Optimal design of sustainable transit systems in congested urban networks: A macroscopic approach
James Two-stage request scheduling for autonomous vehicle logistic system
DE102013202059B4 (en) CHARGER INFRASTRUCTURE FOR ELECTRIC VEHICLES (EVs) WITH OPTIMUM LOCATION SELECTION FOR CHARGING STATIONS
Guo et al. A deep reinforcement learning approach to ride-sharing vehicle dispatching in autonomous mobility-on-demand systems
Yuan et al. p^ 2charging: Proactive partial charging for electric taxi systems
US10157242B2 (en) Charger arrangement planning supporting apparatus, charger arrangement planning supporting method, and program
Tang et al. Online-to-offline mobile charging system for electric vehicles: Strategic planning and online operation
Mkahl et al. An optimal solution for charging management of electric vehicles fleets
CN109741626A (en) Parking situation prediction technique, dispatching method and system
Cai et al. A hybrid adaptive large neighborhood search and tabu search algorithm for the electric vehicle relocation problem
Sivagnanam et al. Minimizing energy use of mixed-fleet public transit for fixed-route service
CN113222248B (en) Automatic taxi-driving charging pile selection method
Miao et al. Highly Automated Electric Vehicle (HAEV)-based mobility-on-demand system modeling and optimization framework in restricted geographical areas
Uribe et al. Discrete event simulation for battery-swapping station sizing for hybrid and electric motorcycles
CN112149906B (en) Comprehensive optimization method for travel line of electric vehicle considering charging time
CN106682759B (en) Battery supply system for electric taxi and network optimization method
CN112949987A (en) Taxi dispatching and matching method, system, equipment and medium based on prediction
Ren et al. Routing optimization for shared electric vehicles with ride-sharing
CN117372055A (en) Cost of ownership optimization for fleet having electric vehicles
CN116663811A (en) Scheduling matching method and device for reciprocating dynamic carpooling of inter-city passenger transport
CN112669603B (en) Urban traffic cooperation method and device based on big data
CN109919393A (en) A kind of charging load forecasting method of electric taxi
Chen et al. Modeling and testing autonomous and shared multimodal mobility services for low-density rural areas
Chow et al. Adaptive scheduling of mixed bus services with flexible fleet size assignment under demand uncertainty

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant