CN112070300B - Multi-objective optimization-based electric vehicle charging platform selection method - Google Patents
Multi-objective optimization-based electric vehicle charging platform selection method Download PDFInfo
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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 carrying out multi-objective optimization based on the limiting conditions of the maximum endurance distance, road section congestion condition and total charging time accepted by the user of the electric automobile in the current state. The method comprises the following steps: 1) Acquiring a charging request initiated by an electric automobile; 2) Analyzing according to the charging request, road congestion conditions and charging platform information nearby the electric automobile, and establishing a multi-objective optimized mathematical model; 3) And analyzing and obtaining an optimal charging platform meeting the requirements of users through a deep learning algorithm. According to the invention, a plurality of charging platforms are compared and selected by combining with the multi-objective optimization model, and an optimal charging platform is found out through deep learning, so that the most intelligent and humanized user experience is achieved.
Description
Technical Field
The invention belongs to the technical field of optimization decision making, 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 and cruising problem of the new energy electric vehicles is worth further investigation. In a complicated urban traffic network, road conditions are serious, and charging sites of new energy electric vehicles are limited. Therefore, careful analysis is worth both selecting a charging path from the owner of the electric vehicle and planning the distribution of the charging platforms of the electric vehicle. Aiming at the problem of the multi-objective optimization-based electric vehicle charging platform selection method, the method of big data analysis and deep learning is used for generating the most suitable charging platform. The multi-objective optimization is that a reasonable scheme for relative optimization can be made by analyzing various influencing factors and involving a series of state variables. However, as each sub-objective of the multi-objective optimization has a certain complexity, related factors need to be comprehensively considered in multiple aspects and multiple layers, and various schemes are simulated by a computer by means of big data analysis and deep learning, so that the relatively optimized scheme is obtained through analysis.
Disclosure of Invention
The technical solution of the invention is as follows: the method for selecting the electric vehicle charging platform based on multi-objective optimization is provided, multi-angle comprehensive consideration is carried out, the difference and the similarity of each optimization sub-objective are fully embodied, and the accuracy and the intelligence of a selection decision are improved.
The technical scheme of the invention is as follows: 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 an electric vehicle, wherein the request content comprises the current remaining endurance and the current position of the electric vehicle, and the charging time which can be accepted by a user;
(2) According to the position of the electric vehicle initiating the charging request, analyzing the distribution of charging platforms near the position, and combining with analyzing the cruising distance of the current vehicle, carrying out single-target planning based on the driving distance to plan the optimal charging platform under the current condition; combining road congestion and charging time accepted by a user, carrying out single-target planning based on the time required to be consumed, and planning a charging platform site with optimal current conditions; the method comprises the steps of combining the charge from the electric automobile to the charging platform and the charging charge, carrying out single-target planning based on the charge, and planning an optimal charging platform station under the current condition; the three multi-objective linear weights are weighted, and a multi-objective optimized mathematical model is established;
(3) And determining weight distribution of each optimization sub-target through a deep learning algorithm to obtain an optimal charging platform meeting the user requirement through calculation.
The object of the present invention is thus achieved.
The invention discloses a multi-objective optimization-based electric vehicle charging platform selection method, wherein a charging platform comprises the following steps: charging station, battery change station and mobile charging vehicle. The selecting method takes the total distance from the current position of the electric vehicle to a charging platform and the total time for completing one-time charging (the charging is carried out by a charging station, a battery replacement station and a mobile charging vehicle for endurance, which are collectively called charging) as sub-objective functions; and carrying out multi-objective optimization based on the limiting conditions of the maximum endurance distance, road section congestion condition and total charging time accepted by the user of the electric automobile in the current state. The method comprises the following steps: 1) Acquiring a charging request initiated by an electric automobile; 2) Analyzing according to the charging request, road congestion conditions and charging platform information nearby the electric automobile, and establishing a multi-objective optimized mathematical model; 3) And analyzing and obtaining an optimal charging platform meeting the requirements of users through a deep learning algorithm. According to the invention, a plurality of charging platforms are compared and selected by combining with the multi-objective optimization model, and an optimal charging platform is found out through deep learning, so that the most intelligent and humanized user experience is achieved.
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FIG. 1 is a flowchart of a specific embodiment of a multi-objective optimization-based electric vehicle charging platform selection method of the present invention;
fig. 2 is a model diagram of an embodiment of an electric vehicle charging platform optimized based on deep learning in the present invention.
Detailed Description
The following description of the embodiments of the invention is presented in conjunction with the accompanying drawings to provide a better understanding of the invention to those skilled in the art. It is to be expressly noted that in the description below, detailed descriptions of known functions and designs are omitted here as perhaps obscuring the present invention.
As shown in fig. 1, the electric vehicle charging platform selection method based on multi-objective optimization of the invention comprises the following steps:
(1) Acquiring charging request information initiated by an electric vehicle, wherein the request content comprises the current remaining endurance and the current position of the electric vehicle, and the charging time which can be accepted by a user;
(2) According to the position of the electric vehicle initiating the charging request, analyzing the distribution of charging platforms near the position, and combining with analyzing the cruising distance of the current vehicle, carrying out single-target planning based on the driving distance to plan the optimal charging platform under the current condition; combining road congestion and charging time accepted by a user, carrying out single-target planning based on the time required to be consumed, and planning a charging platform site with optimal current conditions; and combining the charge from the electric automobile to the charging platform and the charging charge, and carrying out single-objective planning based on the charge, so as to plan an optimal charging platform station under the current condition. The three multi-objective linear weights are weighted, and a multi-objective optimized mathematical model is established;
(3) And determining weight distribution of each optimization sub-target through a deep learning algorithm to obtain an optimal charging platform meeting the user requirement 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 change station and mobile charging vehicle. The selecting method takes the total distance from the current position of the electric vehicle to a charging platform and the total time for completing one-time charging (the charging is carried out by a charging station, a battery replacement station and a mobile charging vehicle for endurance, which are collectively called charging) as sub-objective functions; and carrying out multi-objective optimization based on the limiting conditions of the maximum endurance distance, road section congestion condition and total charging time accepted by the user of the electric automobile in the current state. According to the invention, a plurality of charging platforms are compared and selected by combining with the multi-objective optimization model, and an optimal charging platform is found out through deep learning, so that the most intelligent and humanized user experience is achieved.
Specifically, the following steps may be adopted:
step one: and determining the total target and the sub-target of the research object, wherein the total target is a charging station which is the most suitable for the requirement of a user, and the sub-targets are a charging station which is the closest to the charging station, the charging station with the least time consumption and the charging station with the least cost respectively.
Step two: and carrying out single-objective planning on the charging stations closest to the charging station, namely the charging station with the minimum total distance. Assuming that the user tends to select a travel path having the smallest total distance from the start point to the end point at the time of charging, a link between 2 nodes in the road network is called a link a, and the geometric length of the path k between the start point s and the end point e is
Then the objective function T of optimal travel path selection dis The method comprises the following steps:
wherein a is i The geometric length of a road section for driving; l (L) max The maximum distance that the current power can last.
Step three: and (3) carrying out single-objective planning on the charging station with the least consumed time, namely the charging station with the least comprehensive time impedance. Integrated time impedance
It can be understood that the road time impedance +.>
And charging time impedance->
Two parts. Assuming that the user is charging, the option with the least overall time consumed is chosen, and the maximum total time consumed that can be accepted is T max Then optimize the objective function T time The method comprises the following steps:
while
Next, a careful discussion of +.>
And->
During constant speed travel, distance = speed x time. Suppose that the vehicle is on each road section a i Due to different road congestion conditions, the speed is different from each other
And keeping uniform motion. The road time impedance can be expressed as:
road section a i The average running speed of the upper vehicle is subject to the road congestion rate
Is a function of (a) and (b). Under the condition that the electric automobile has sufficient electric quantity, assuming that the actual running speed and the congestion rate have linear correlation, when the road is blocked (the congestion rate +.>
1), no passage is possible; when the road is clear (congestion rate +.>
0), the set speed can be reached>
And (5) running.
Thus, road segment a i The travel speed on can be expressed as
The path time impedance is:
and total distance travelled
Maximum distance L that should be subjected to current electric quantity for cruising max Is a constraint of (1), namely:
charging time impedance
One can discuss two cases, charging; and secondly, the battery is directly replaced. If the battery is directly replaced, the charging time impedance can be considered +.>
At t h The method comprises the steps of carrying out a first treatment on the surface of the The charge time impedance is related to the amount of consumed power and the charge efficiency. Let the continuous electric quantity of the electric automobile accumulator at full-charge be E max The battery consumption e is linear with the travel distance l, namely: />
e(l)＝hl
Wherein h is a proportionality coefficient, L is more than or equal to 0 and less than or equal to L max ，
Further assuming that the charging time is linearly related to the battery consumption e and the charging efficiency η, the charging time impedance may be expressed as:
wherein h is a proportionality coefficient, L is more than or equal to 0 and less than or equal to L max ，
Since there is a subjective choice of the user as to whether to replace the battery, the subjective choice of the user is here set as:
so that the charging time impedance
Portions may be expressed as:
the objective function with minimum integrated time impedance is:
step four: and (3) performing single-objective planning on the charging site with the least cost. There are two modes of endurance: firstly, charging; and secondly, the battery is directly replaced.
Assuming charge rate Q c In linear relation to the charging time, there are:
where p is a scaling factor (p>0). Delta upon selection of charge hc =0, so
The charge fee can be expressed as:
wherein L is more than or equal to 0 and less than or equal to L max ，
While the cost of directly replacing the battery can be directly set as Q h The minimum of the continuous voyage cost is required to be achieved, namely Q is taken c And Q is equal to h Then the objective function of the endurance charge plan is:
Q k ＝min{Q c ,Q h }＝min{phηl,Q h }
when (when)
When Q is k ＝Q h The method comprises the steps of carrying out a first treatment on the surface of the When->
When Q is k =ph ηl, i.e.:
step five: as can be seen from the above discussion, the multi-objective planning model optimally selected by the electric vehicle charging platform is:
in this multi-objective planning problem, the variables can be dimensionless processed using a polar-difference method because the dimensions of the several sub-objectives are different.
The non-dimensionalized formula is as follows:
wherein x' i As variable x i Non-dimensionalized values; max (x) i ) And min (x) i ) Respectively represent the variable x i Maximum and minimum values over the universe. The variables can be transformed into [0,1 ] respectively by dimensionless representation]Values over the interval.
Assuming that the sub-objective functions after dimensionless treatment are respectively
(Q k ) ' the multi-objective planning model may be expressed in a minimized standard form, namely:
weighting the multi-objective planning model by using a linear weighting method, the finally established model can be expressed as:
λ 1 λ 2 λ 3 respectively the weight coefficients of the sub-targets.
Step six: according to the obtained big data information, analyzing the selective specific gravity tendency of a user for three sub-optimization targets of shortest distance, least consumption time and least cost under the condition that the electric automobile needs to be charged, and obtaining three specific gravity coefficients lambda 1 λ 2 λ 3 And a plurality of charging platforms which are relatively optimized and selected are preliminarily obtained. After determining the multi-objective optimization result, the user performs secondary judgment on the selected charging platform, and selects a specific charging platform.
After the user selects, the selection of the user is recorded, and the weight coefficients of the user selection, the user selection and the user selection are correspondingly adjusted, so that the deep learning training process is performed. After the deep learning optimization training and the adjustment of the three optimization weight coefficients 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 deep learning training reference is convenient when the selection method is used for selecting other users.
While the foregoing describes illustrative embodiments of the present invention to facilitate an 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, but is to be construed as protected by the accompanying claims insofar as various changes are within the spirit and scope of the present invention as defined and defined by the appended claims.
Claims (3)
1. The electric automobile charging platform selection method based on multi-objective optimization is characterized by comprising the following steps of:
step 1: acquiring charging request information initiated by an electric vehicle, wherein the request content comprises the current remaining endurance and the current position of the electric vehicle, and the charging time which can be accepted by a user;
step 2: according to the position of the electric vehicle initiating the charging request, analyzing the distribution of charging platforms near the position, and combining with analyzing the cruising distance of the current vehicle, carrying out single-target planning based on the driving distance to plan the optimal charging platform under the current condition; combining road congestion and charging time accepted by a user, carrying out single-target planning based on the time required to be consumed, and planning a charging platform site with optimal current conditions; combining the charge of the electric automobile to the charging platform and the charging charge, and carrying out single-objective planning based on the charge to plan an optimal charging platform station under the current condition; the three single targets are weighted linearly, and a mathematical model of multi-target optimization is established;
step 3: obtaining an optimal charging platform meeting the requirements of users through a deep learning algorithm;
step 2, performing multi-objective linear weighted programming, wherein the multi-objective function is as follows:
wherein:
Q k ＝Q h ·δ hc +phηl·(1-δ hc ) (4)
in the formula (1), lambda 1 λ 2 λ 3 Respectively weighing coefficients of the sub-targets, wherein an optimization target T (k) is expressed as an optimal charging platform obtained by analysis and solution according to a multi-target optimization model;
representing the geometric length of a path K from a starting point position s of the electric automobile to a charging platform e; />
Indicating the total time length required for completing one-time charging from the starting point position s of the electric automobile to the charging platform e; q (Q) k Indicating the cost of completing a charge; but->
(Q k ) ' then means +.>
Q k Standardized expressions after dimensionless; in the formula (2), a i Representing each sub-section included between a path k from a starting point position s of the electric vehicle to the charging platform e; in the formula (3), ->
Represents the time spent from the starting position s of the electric car to the path k between the charging stations e, +.>
Indicating the charge costA compartment; />
Representing road segment a i Congestion condition of->
Indicated at road section a i Average running speed of delta hc Indicating the user's choice to charge or directly replace the battery, t h The method comprises the steps of representing the time required for charging, i representing the distance travelled, eta representing the charging efficiency coefficient, and h representing the undetermined parameters of the relation between the electric quantity and the distance; q (Q) h Representing the charge of directly replacing the battery, and p represents a pending parameter of the relationship between the charge and the charge time;
writing out the limiting conditions of the multi-target planning target:
the limiting conditions of the specific submodel are as follows:
in formula (6), T max For the time of completing one charge acceptable to the user, L max E is the maximum distance that can be continued at present max The maximum electric quantity value of the current electric automobile.
2. The method for selecting the charging platform of the electric automobile based on the multi-objective optimization according to claim 1, wherein the step 2 is characterized in that a maximum differentiation method is adopted when a multi-objective optimization model is established for dimensionless treatment, and the transformation formula is as follows:
in the formula (7), x i ' is in a standard form after dimensionless treatment, x i To be treatedNon-dimensionalized general function expression, max (x i ) To be dimensionless function x i Maximum value of (x), min (x i ) To be dimensionless function x i Is a minimum of (2).
3. The method for selecting the charging platform of the electric vehicle based on the multi-objective optimization according to claim 1, wherein the weighting coefficient lambda of the sub-objective in the step 3 is 1 λ 2 λ 3 The weight of the model (C) is selected and adjusted through big data analysis and multiple operation behavior preference analysis of a user, so that the weight which is most in line with the tendency of the user is achieved; the specific steps can be expressed as follows:
step 3-1: according to the obtained big data information, analyzing the selective specific gravity tendency of a user for three sub-optimization targets of shortest distance, least consumption time and least cost under the condition that the electric automobile needs to be charged, and obtaining three specific gravity coefficients lambda 1 λ 2 λ 3 A plurality of charging platforms which are optimized and selected relatively are obtained preliminarily;
step 3-2: after determining the multi-objective optimization result in the step 3-1, the user carries out secondary judgment on the selected charging platform, and selects a specific charging platform; after the user selects, recording the selection of the user, and correspondingly adjusting weight coefficients of the user selection and the user selection to perform a deep learning training process;
step 3-3: after the deep learning optimization training and the adjustment of the three 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 deep learning training reference is convenient when the selection method is used for selecting other users.
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