CN112070300A - Electric vehicle charging platform selection method based on multi-objective optimization - Google Patents

Electric vehicle charging platform selection method based on multi-objective optimization Download PDF

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CN112070300A
CN112070300A CN202010928113.3A CN202010928113A CN112070300A CN 112070300 A CN112070300 A CN 112070300A CN 202010928113 A CN202010928113 A CN 202010928113A CN 112070300 A CN112070300 A CN 112070300A
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陈勇
朱培坤
陈章勇
李猛
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Abstract

The invention discloses a multi-objective optimization-based electric vehicle charging platform selection method, which takes the total distance from the current position of an electric vehicle to a charging platform, the total time for completing one-time charging and the total cost as sub-objective functions; and performing multi-objective optimization on the basis of the limiting conditions of the maximum cruising distance, the road congestion condition and the total charging time accepted by the user in the current state of the electric automobile. The method comprises the following steps: 1) acquiring a charging request initiated by an electric automobile; 2) analyzing according to the charging request, the road congestion condition and the charging platform information near the electric automobile, and establishing a multi-objective optimization mathematical model; 3) and analyzing to obtain an optimal charging platform meeting the requirements of users through a deep learning algorithm. The method and the device combine a multi-objective optimization model to compare and select a plurality of charging platforms, find out an optimal charging platform through deep learning, and achieve the most intelligent and humanized user experience.

Description

Electric vehicle charging platform selection method based on multi-objective optimization
Technical Field
The invention belongs to the technical field of optimization decision, and particularly relates to a multi-objective optimization-based electric vehicle charging platform selection method.
Background
With the gradual popularization of new energy electric vehicles, the charging endurance problem of the new energy electric vehicles is worthy of further study. In an intricate urban traffic network, the road condition is very serious, and the charging stations of the new energy electric automobile are very limited. Therefore, whether the charging path is selected from the owner of the electric vehicle or the distribution of the charging platform of the electric vehicle is planned, the method is worthy of careful analysis. Aiming at the problem of the electric vehicle charging platform selection method based on multi-objective optimization, a most suitable charging platform is generated by using a big data analysis and deep learning method. The multi-objective optimization is realized by analyzing various influence factors and relating to a series of state variables, so that a reasonable scheme for relative optimization can be made. However, each sub-target of the multi-objective optimization has certain complexity, and needs to comprehensively consider relevant factors in multiple aspects and multiple levels, and various schemes are simulated through a computer by means of big data analysis and deep learning, so that relatively optimized schemes are obtained through analysis.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method overcomes the defects of the prior art, provides a multi-objective optimization-based electric vehicle charging platform selection method, performs multi-angle comprehensive consideration, fully embodies the difference and similarity of each optimized sub-target, and improves the accuracy and intelligence of selection decision.
The technical solution of the invention is as follows: the method for selecting the electric vehicle charging platform based on the multi-objective optimization comprises the following steps:
(1) acquiring charging request information initiated by the electric automobile, wherein the request content comprises the current remaining cruising distance and the current position of the electric automobile and the charging time acceptable by a user;
(2) according to the position of the electric automobile initiating the charging request, analyzing the distribution of the charging platform near the position, analyzing the cruising distance of the current automobile, carrying out single-target planning based on the traveling distance and planning the optimal charging platform under the current condition; the method comprises the steps that a single-target planning is carried out based on the time required to be consumed by combining the road congestion condition and the charging time received by a user, and a charging platform station with the optimal current condition is planned; combining the cost from the electric vehicle to the charging platform and the charging cost, performing single-target planning based on the cost, and planning an optimal charging platform station under the current condition; linearly weighting the three multiple targets, and establishing a mathematical model for multiple target optimization;
(3) and determining the weight distribution of each optimized sub-target through a deep learning algorithm to obtain an optimal charging platform which meets the user requirements through calculation.
The object of the invention is thus achieved.
The invention relates to a multi-objective optimization-based electric vehicle charging platform selection method, wherein a charging platform comprises the following steps: charging station, battery replacement station and removal storage battery car. The selection method takes the total distance from the current position of the electric automobile to a charging platform, the total time for completing one-time charging (the charging is generally called as charging through a charging station, a battery replacement station and a mobile charging vehicle), and the total cost as sub-target functions; and performing multi-objective optimization on the basis of the limiting conditions of the maximum cruising distance, the road congestion condition and the total charging time accepted by the user in the current state of the electric automobile. The method comprises the following steps: 1) acquiring a charging request initiated by an electric automobile; 2) analyzing according to the charging request, the road congestion condition and the charging platform information near the electric automobile, and establishing a multi-objective optimization mathematical model; 3) and analyzing to obtain an optimal charging platform meeting the requirements of users through a deep learning algorithm. The method and the device combine a multi-objective optimization model to compare and select a plurality of charging platforms, find out an optimal charging platform through deep learning, and achieve the most intelligent and humanized user experience.
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FIG. 1 is a flow chart of an embodiment of a multi-objective optimization-based electric vehicle charging platform selection method according to the present invention;
fig. 2 is a model diagram of an embodiment of an electric vehicle charging platform optimized based on deep learning according to the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
As shown in fig. 1, the method for selecting the electric vehicle charging platform based on multi-objective optimization comprises the following steps:
(1) acquiring charging request information initiated by the electric automobile, wherein the request content comprises the current remaining cruising distance and the current position of the electric automobile and the charging time acceptable by a user;
(2) according to the position of the electric automobile initiating the charging request, analyzing the distribution of the charging platform near the position, analyzing the cruising distance of the current automobile, carrying out single-target planning based on the traveling distance and planning the optimal charging platform under the current condition; the method comprises the steps that a single-target planning is carried out based on the time required to be consumed by combining the road congestion condition and the charging time received by a user, and a charging platform station with the optimal current condition is planned; and (4) combining the charge of the electric vehicle to the charging platform and the charging cost, carrying out single-target planning based on the cost, and planning an optimal charging platform station under the current condition. Linearly weighting the three multiple targets, and establishing a mathematical model for multiple target optimization;
(3) and determining the weight distribution of each optimized sub-target through a deep learning algorithm to obtain an optimal charging platform which meets the user requirements through calculation.
The invention discloses a multi-objective optimization-based electric vehicle charging platform selection method. Wherein the platform that charges includes: charging station, battery replacement station and removal storage battery car. The selection method takes the total distance from the current position of the electric automobile to a charging platform, the total time for completing one-time charging (the charging is generally called as charging through a charging station, a battery replacement station and a mobile charging vehicle), and the total cost as sub-target functions; and performing multi-objective optimization on the basis of the limiting conditions of the maximum cruising distance, the road congestion condition and the total charging time accepted by the user in the current state of the electric automobile. The method and the device combine a multi-objective optimization model to compare and select a plurality of charging platforms, find out an optimal charging platform through deep learning, and achieve the most intelligent and humanized user experience.
Specifically, the following steps may be adopted:
the method comprises the following steps: and determining a total target and a branch target of the research object, wherein the total target is a charging station which best meets the requirements of users, and the branch targets are a charging station which is closest to the total target, a charging station which consumes the least time and a charging station which costs the least cost.
Step two: and performing single-target planning on the charging station with the closest distance, namely the charging station with the least total route. Assuming that, during charging, a user tends to select a travel path having the smallest total distance from a starting point to an end point, a road segment between 2 nodes in a road network is called a road segment a, and a geometric length of a path k between a starting point s and an end point e is
Figure BDA0002669182080000031
The objective function T of the optimal travel path selectiondisComprises the following steps:
Figure BDA0002669182080000032
Figure BDA0002669182080000033
wherein a isiIs the geometric length of a certain road section; l ismaxThe maximum distance that the current electric quantity can be continued.
Step three: and performing single-target planning on the charging station with the least consumption time, namely the charging station with the minimized comprehensive time impedance. Integrated time impedance
Figure BDA0002669182080000041
Can be understood as the road time impedance
Figure BDA0002669182080000042
And charging time impedance
Figure BDA0002669182080000043
Two parts. Suppose that the user tends to select the scheme that consumes the least aggregate time while charging, and the maximum total time consumed that can be accepted is TmaxThen the objective function T is optimizedtimeComprises the following steps:
Figure BDA0002669182080000044
while
Figure BDA0002669182080000045
Now turning to a more detailed discussion
Figure BDA0002669182080000046
And
Figure BDA0002669182080000047
Figure BDA0002669182080000048
is the road time impedance, and its determining factors are: each road section aiCongestion rate of
Figure BDA0002669182080000049
Travel route from starting point to end point
Figure BDA00026691820800000410
Figure BDA00026691820800000411
And the vehicle is on the road section aiSpeed of upward travel
Figure BDA00026691820800000412
During the constant speed driving, the distance is speed x time. Suppose that the vehicle is on each road segment aiAt different speeds, due to different road congestion conditions
Figure BDA00026691820800000413
Keeping constant-speed motion. The road time impedance may be expressed as:
Figure BDA00026691820800000414
section aiThe average running speed of the upper vehicle is subjected to the road section congestion rate
Figure BDA00026691820800000415
The influence of (c). Under the condition that the electric quantity of the electric automobile is sufficient, if the actual running speed has linear correlation with the congestion rate, when the road is blocked (the congestion rate)
Figure BDA00026691820800000416
1), the passage cannot be realized; when the road is unobstructed (congestion rate)
Figure BDA00026691820800000417
0), the set speed can be reached
Figure BDA00026691820800000418
And (5) running.
Thus, the section aiThe upper running speed can be expressed as
Figure BDA00026691820800000419
The road time impedance is:
Figure BDA00026691820800000420
and the total travel route
Figure BDA00026691820800000421
Maximum distance L which can be continued by current electric quantitymaxThe constraint of (a), namely:
Figure BDA00026691820800000422
impedance of charging time
Figure BDA00026691820800000423
Two cases can be discussed, one is charging; and secondly, directly replacing the battery. If the battery is directly replaced, the charging time impedance can be considered
Figure BDA00026691820800000424
Is th(ii) a The charging time impedance is related to the consumed charge and the charging efficiency. If the battery of the electric automobile is full, the endurance electric quantity is EmaxThe battery consumption e has a linear relationship with the travel distance l, that is:
e(l)＝hl
wherein h is a proportionality coefficient, L is more than or equal to 0 and less than or equal to Lmax，
Figure BDA00026691820800000425
Further assuming that the charging time has a linear relationship with the amount of power e consumed by the battery to be charged and the charging efficiency η, the charging time impedance can be expressed as:
Figure BDA0002669182080000051
wherein h is a proportionality coefficient, L is more than or equal to 0 and less than or equal to Lmax，
Figure BDA0002669182080000052
Because whether to replace the battery has the subjective choice of the user, the subjective choice of the user is set as follows:
Figure BDA0002669182080000053
so that the charging time impedance
Figure BDA0002669182080000054
The parts can be represented as:
Figure BDA0002669182080000055
the objective function for minimum integrated time impedance is:
Figure BDA0002669182080000056
Figure BDA0002669182080000057
step four: and performing single-target planning on the charging station with the least expense. The cruising mode has two types: firstly, charging; and secondly, directly replacing the battery.
Suppose charging fee QcLinear relationship with the charging time, there are:
Figure BDA0002669182080000058
wherein p is a proportionality coefficient (p)>0). When the charging is selected, the charging is performed,hc0, so
Figure BDA0002669182080000059
The charge rate can therefore be expressed as:
Figure BDA00026691820800000510
wherein L is more than or equal to 0 and less than or equal to Lmax，
Figure BDA0002669182080000061
The cost for directly replacing the battery can be directly set as QhThe minimum of the endurance cost is required to be achieved, namely Q is takencAnd QhThen the objective function of the endurance cost plan is:
Qk＝min{Qc,Qh}＝min{phηl,Qh}
Figure BDA0002669182080000062
when in use
Figure BDA0002669182080000063
When is, Qk＝Qh(ii) a When in use
Figure BDA0002669182080000064
When is, QkPh η l, i.e.:
Figure BDA0002669182080000065
Figure BDA0002669182080000066
step five: as can be seen from the above discussion, the multi-objective planning model optimally selected by the electric vehicle charging platform is as follows:
Figure BDA0002669182080000067
Figure BDA0002669182080000071
in the multi-objective planning problem, because the dimensions of the sub-objectives are different, a range differentiation method can be adopted to perform dimensionless processing on variables.
The formula for dimensionless is as follows:
Figure BDA0002669182080000072
wherein, x'iIs a variable xiA dimensionless value; max (x)i) And min (x)i) Respectively represent variable xiMaxima and minima over the domain of interest. Through non-dimensionalization, each variable can be converted into [0,1 respectively]The value over the interval.
Assume that the sub-objective functions after non-dimensionalization are respectively
Figure BDA0002669182080000073
(Qk) ' then the multi-objective planning model can be expressed in the minimization criteria form, namely:
Figure BDA0002669182080000074
and weighting the multi-target planning model by using a linear weighting method, wherein the finally established model can be expressed as follows:
Figure BDA0002669182080000075
λ1λ2λ3respectively, the weight coefficients of the sub-targets.
Step six: according to the acquired big data information, the selection proportion tendency of the user to the three sub-optimization targets of shortest distance, least time consumption and least cost under the condition that the electric automobile needs to be charged is analyzed, and three specific proportion coefficients lambda are obtained1λ2λ3And preliminarily obtaining a plurality of relatively optimally selected charging platforms. And after determining the result of the multi-objective optimization, the user performs secondary judgment on the selected charging platform and selects a specific charging platform to go to.
After the user selects, recording the selection of the user, correspondingly adjusting the weight coefficients of the three according to the selection of the user, and performing a deep learning training process. After the deep learning optimization training and the adjustment of the optimization weight coefficients of the three are completed, the selection method can obtain a charging platform which meets the requirements of users and has generality and specific user specificity. And the weight coefficient after the deep learning training is uploaded, so that the selection method is convenient for the deep learning training reference when other users select.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.

Claims (4)

1. The electric vehicle charging platform selection method based on multi-objective optimization is characterized by comprising the following steps:
step 1: acquiring charging request information initiated by the electric automobile, wherein the request content comprises the current remaining cruising distance and the current position of the electric automobile and the charging time acceptable by a user;
step 2: according to the position of the electric automobile initiating the charging request, analyzing the distribution of the charging platform near the position, analyzing the cruising distance of the current automobile, carrying out single-target planning based on the traveling distance and planning the optimal charging platform under the current condition; the method comprises the steps that a single-target planning is carried out based on the time required to be consumed by combining the road congestion condition and the charging time received by a user, and a charging platform station with the optimal current condition is planned; combining the cost from the electric vehicle to the charging platform and the charging cost, performing single-target planning based on the cost, and planning an optimal charging platform station under the current condition; linearly weighting the three multiple targets, and establishing a mathematical model for multiple target optimization;
and step 3: and obtaining an optimal charging platform meeting the requirements of the user through a deep learning algorithm.
2. The multi-objective optimization-based electric vehicle charging platform selection method according to claim 1, wherein the step 2 performs multi-objective linear weighted programming, wherein the multi-objective function is:
Figure FDA0002669182070000011
wherein:
Figure FDA0002669182070000012
Figure FDA0002669182070000013
Qk＝Qh·hc+phηl·(1-hc) (4)
in the formula (1), lambda1λ2λ3Respectively representing the weight coefficients of the sub-targets, and expressing the optimization target T (k) as an optimal charging platform obtained by analysis and solution according to a multi-target optimization model;
Figure FDA0002669182070000014
representing the geometric length of the path K from the electric vehicle starting position s to the charging platform e;
Figure FDA0002669182070000015
the total time length from the starting position s of the electric automobile to the charging platform e for completing one-time charging is represented; qkRepresents the cost of completing one charge; while
Figure FDA0002669182070000016
(Qk) ' then means
Figure FDA0002669182070000017
QkA normalized expression after dimensionless; in the formula (2), aiIndicating that the position is from the starting position s of the electric vehicle to the charging platform eEach sub-segment included between the paths k; in the formula (3), the reaction mixture is,
Figure FDA0002669182070000018
represents the time consumed from the route k from the electric vehicle origin position s to the charging platform e,
Figure FDA0002669182070000019
represents the time taken for charging;
Figure FDA00026691820700000110
representing a road section aiIn the case of a congestion situation of the vehicle,
Figure FDA00026691820700000111
is shown in the section aiThe average traveling speed of the vehicle is set,hcindicating user selection of charging or direct replacement of batteries, thThe time required by charging is represented, l represents the driving distance, eta represents a charging efficiency coefficient, and h represents a undetermined parameter of the relation between the electric quantity and the distance; in the formula (4), QcIndicating the charge, QhRepresenting the cost of directly replacing the battery, and p represents a parameter to be determined of the relation between the charging cost and the charging time;
writing out the limiting conditions of the multi-target planning target:
Figure FDA0002669182070000021
the specific submodel has the following limiting conditions:
Figure FDA0002669182070000022
in the formula (6), TmaxTime to complete one charge acceptable to the user, LmaxFor the maximum distance which can be currently continued, EmaxThe current maximum electric quantity value of the electric automobile.
3. The multi-objective optimization-based electric vehicle charging platform selection method according to claim 1, wherein a range method is adopted when the multi-objective optimization model is established in the step 2 for non-dimensionalization, and a conversion formula is as follows:
Figure FDA0002669182070000023
in formula (7), x'iIn a standard form after dimensionless, xiFor general function expressions to be non-dimensionalized, max (x)i) To dimensionalize a function x to be dimensionlessiMaximum value of (c), min (x)i) To dimensionalize a function x to be dimensionlessiIs measured.
4. The multi-objective optimization-based electric vehicle charging platform selection method according to claim 1, wherein the weight coefficient λ of the sub-target in the step 31λ2λ3The weight of the user is selected and adjusted through big data analysis and multiple operation behavior preference analysis of the user, so that a proportion which is most consistent with the user tendency is achieved; the specific steps can be expressed as follows:
step 3-1: according to the acquired big data information, the selection proportion tendency of the user to the three sub-optimization targets of shortest distance, least time consumption and least cost under the condition that the electric automobile needs to be charged is analyzed, and three specific proportion coefficients lambda are obtained1λ2λ3Preliminarily obtaining a plurality of charging platforms which are relatively optimized and selected;
step 3-2: after determining the result of the multi-objective optimization in the step 3-1, the user performs secondary judgment on the selected charging platform and selects a specific charging platform to go to; after the user selects, recording the selection of the user, correspondingly adjusting the weight coefficients of the user, the user and the selection of the user, and performing a deep learning training process;
step 3-3: after the deep learning optimization training and the adjustment of the optimization weight coefficients in the step 3-2 are completed, the selection method can obtain a charging platform which meets the requirements of users and has generality and specific user specificity; and the weight coefficient after the deep learning training is uploaded, so that the selection method is convenient for the deep learning training reference when other users select.
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