CN111401786B - Electric vehicle charging scheduling method considering bilateral interest balance based on road condition information - Google Patents

Electric vehicle charging scheduling method considering bilateral interest balance based on road condition information Download PDF

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CN111401786B
CN111401786B CN202010274138.6A CN202010274138A CN111401786B CN 111401786 B CN111401786 B CN 111401786B CN 202010274138 A CN202010274138 A CN 202010274138A CN 111401786 B CN111401786 B CN 111401786B
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陈光宇
王佳乐
张仰飞
郝思鹏
陆牧君
吕干云
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Nanjing Institute of Technology
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Abstract

The invention discloses an electric vehicle charging scheduling method considering bilateral interest balance based on road condition information, which comprises the following steps of: acquiring peripheral real-time traffic data, information of nearby charging stations and residual SOC of a user automobile; counting the real-time queuing conditions of all charging stations, and determining the predicted waiting time after the vehicle arrives; calculating the passing speed of the peripheral possible road section and the unit energy consumption of the electric automobile running on the road section; detecting whether a user is charged; different charging schemes are implemented in connection with the load situation of the neighboring charging stations at the moment. The invention considers the multi-dimensional factors of road network traffic, the residual electric quantity of the electric automobile, the loads of the charging station and the adjacent charging stations, the profits of the power station and the users and the like, reduces the travel time of the users or the charging cost on the premise of ensuring the charging profits of the power station, and realizes the balance of bilateral benefits.

Description

Electric vehicle charging scheduling method considering bilateral interest balance based on road condition information
Technical Field
The invention belongs to the field of electric vehicle charging scheduling, and particularly relates to an electric vehicle charging scheduling method considering bilateral interest balance based on road condition information.
Background
Along with the gradual popularization of electric vehicles, a user can upload information of real-time traffic road conditions such as vehicle positions and charging requirements to an information interaction platform by using mobile intelligent equipment such as a mobile phone when going out. Each charging station firstly acquires real-time information of an information interaction platform and uploads real-time load conditions; and then according to the real-time information received from the information interaction platform, aiming at the purposes that the total cost of the user for completing the charging process is low and the real-time income of the charging station is high, combining an analytic hierarchy process to make bilateral win-win path selection and charging navigation strategies, and feeding the strategies back to the electric vehicle user through a wireless communication network. And the user selects the charging path according to two preferred strategies recommended by the control center. Because the uploaded real-time traffic information and the uploaded charging station service information are constantly changed, the control center can timely adjust the electric vehicle routing and charging navigation strategies, and on the premise of ensuring the charging station income, the travel time of a user is reduced or the charging cost is reduced as much as possible.
The existing charging behavior scheduling method has less consideration on the aspects of road conditions, time cost and the like, and the charging guidance scheme for electric vehicle users is not comprehensive, and at least has the following two disadvantages: 1, the condition that the road conditions can influence the running speed of the electric automobile is considered, but the time cost and the energy consumption cost of a user for running to a charging station are not considered, and the obtained data is lack of accuracy; 2 does not take into account charging behaviour guidance schemes in combination with charging station revenue and user charging costs.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides an electric vehicle charging scheduling method considering bilateral interest balance based on road condition information, and the method considers the influence of road conditions on the driving speed and the power consumption of a unit mileage of an electric vehicle on the basis of considering the electric quantity of the electric vehicle and the optimal path search of a charging station position; meanwhile, based on the existing path guiding method, the time cost is quantitatively analyzed, the charging station income is combined with the user cost, and a scheme more fitting to the reality is provided.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
an electric vehicle charging scheduling method considering bilateral interest balance based on road condition information comprises the following steps:
s1, the dispatching system acquires real-time road section information and charging station information within a set radius range by taking a certain user automobile as a center, and the remaining SOC of the user automobile;
s2, the dispatching system counts the real-time queuing situation of each charging station and determines the predicted waiting time t after the user automobile arrives at each charging stationw；
S3, the dispatching system counts the accessible road section information of the user automobile and determines the predicted driving mileage S of the user automobile on each accessible road sectiondThe predicted average passing speed V, the predicted motor efficiency eta and the predicted unit energy consumption e;
s4, the dispatching system detects whether the user automobile selects charging: if charging is selected, the flow proceeds to step S8; otherwise, go to step S5;
s5, the dispatching system judges the remaining SOC of the user automobile: if the power level is lower than the power level early warning value, a low power level warning is performed and the operation goes to step S7; otherwise, go to step S6;
s6, the dispatching system counts the load of each charging station: if the load of the charging station is at the valley value, pushing preferential charging information to the user automobile; otherwise, not as;
s7, calculating the predicted total energy consumption of the user automobile to reach each charging station through various path schemes consisting of passable road sections by the dispatching system;
s8, the dispatching system selects various path schemes capable of reaching the charging station according to the remaining SOC of the user automobile: if there are more than two path schemes, go to step S9; otherwise, go to step S10;
s9, recommending two path schemes to the user automobile based on the bilateral interest weight;
and S10, pushing the path guidance scheme with the expected minimum total energy consumption to the automobile of the user.
Specifically, in step S2, the predicted waiting time t after a certain user vehicle arrives at a certain charging stationwIs determined by:
when N is more than or equal to 0 and less than m, tw＝0；
② when ym is less than or equal to N < (y +1) m,
Figure GDA0002818316850000021
wherein: m is the number of charging piles owned by the charging station, and n is the current moment t in the charging station1The total number of the user automobiles is N which is the predicted time t2The total number of the user automobiles reaching the charging station is N-x + a, and a is t1And t2User vehicle data which is driven into the charging station and is waiting for charging within a time period, a ═ λ × tdLambda is the station entering rate of the charging vehicle in unit time (Poisson distribution can be obtained through big data analysis), and x is t1And t2Within a time period ofThe charging station finishes the total number of the charged user automobiles and drives away, and the driving time td＝t2-t1，
Figure GDA0002818316850000031
And f (t) is the maximum value of the remaining charging time of the user automobile which is being charged in the charging station at the moment t, and y is a positive integer which is more than or equal to 1.
Specifically, in step S9, two types of route schemes recommended to the user' S automobile based on the bilateral profit weight are obtained as follows:
s91, based on the information obtained in the step S3, calculating a shortest total time path scheme and a shortest total energy consumption path scheme of the user automobile to each charging station by using a Dijkstra algorithm; user's automobile is from present moment t1Total time period T from start to finish chargingGeneral assemblyComprises the following steps:
Tgeneral assembly＝td+tw+tc
Wherein: t is tdFor the user from the present time t1Starting to reach the charging station time t2Time of travel in between, twWaiting time for the user's car, tcCharging time for the user's car;
s92, quantifying the time cost (namely, the time consumed by the user in the whole charging process is measured by currency), dividing a city into Z areas with the total population of the city as p, and dividing the ith area into p areasiThe total value of the national production of the people in the i-th area is GDPi(unit: yuan), then the unit time value for the city is expressed as:
Figure GDA0002818316850000032
wherein: vot is the value per unit time (different cities have different unit values, unit: Yuan/h); 250 is calculated according to the national regulation 1 year according to 250 working days, 8 is calculated according to the national regulation 1 day according to 8 working hours;
s93, calculating the total charging cost of the user automobile and the income of the charging station, comprising the following steps:
s931, calculating the total time cost H of the user automobile which is Vot multiplied by TGeneral assembly；
S932, calculating a total charge amount M ═ E for the user' S vehicleh-Sp+E)×m0(ii) a Wherein E ishFor the charge quantity expected by the user' S car at the end of charging, SpFor the charge at the beginning of charging the user's car, E is t1And t2Energy consumption in time period, m0Real-time electricity price is unit;
s933, calculating the total charging cost C of the user automobile as H + M;
s94, calculating the profit of the charging station
Figure GDA0002818316850000041
S95, performing system analysis by using an analytic hierarchy process (AHP for short) to obtain a user cost normalized value r and a charging station profit normalized value S, and dividing into three steps:
s951, constructing a scheduling model with a three-layer structure;
constructing a scheduling model with a three-layer structure: the highest layer is a target layer, namely all charging stations which can provide charging service for the user automobile, namely all charging stations within a set radius range by taking the user automobile as a center; the middle layer is a criterion layer, namely factors needing to be considered by selecting a proper charging station comprise the total charging cost of the user automobile and the income of the charging station; the bottom layer is a scheme layer, namely, an alternative scheme for providing a proper charging station for a user automobile by considering bilateral satisfaction;
s952, establishing a charging station satisfaction degree judgment matrix (paired comparison matrix) according to an analytic hierarchy process:
s9521, establishing a first comparison matrix, i.e. the relative importance of the total charging cost of the user' S vehicle and the revenue of the charging station:
Figure GDA0002818316850000042
wherein: u represents the total charging cost of the user automobile, S represents the income of the charging station, and the element in the ith row and the jth column represents the importance degree of the element representation in the ith row relative to the element representation in the jth column; for example, the benefit of the charging station is slightly more important than the total cost of the user vehicle, so the importance ratio is 3: 2; comparing the importance degrees of U and S pairwise, wherein all comparison results form the 2 multiplied by 2 comparison matrix;
s9522, establishing a second comparison matrix, namely comparing the total charging cost of the user automobiles arriving at different charging stations in pairs:
Figure GDA0002818316850000051
wherein: sA、SB、SC… … denotes charging stations A, B, C … …, C, respectivelyA、CB、CC… … respectively represent the total charging cost for a user's car to arrive at the charging station A, B, C … … for charging;
s9523, establishing a third comparison matrix, namely comparing the reciprocal of the income of the user automobile arriving at different charging stations for charging the charging stations in pairs:
Figure GDA0002818316850000052
wherein: pA、PB、PC… … respectively represent the profit of the user's car arriving at the charging station A, B, C … … for charging each charging station, the reciprocal is taken because only two variables with positive correlation coefficient to the result can be analyzed by analytic hierarchy process;
s953, obtaining the total charging cost weight w of the user automobile through the first comparison matrix by using an analytic hierarchy process1And the profit weight w of the charging station2And obtaining the total charging cost score and the income of the charging stations when the user automobile reaches each charging station for charging according to the second comparison matrix and the third comparison matrixScoring, namely a user cost normalization value r and a charging station profit normalization value s;
s96, user cost normalization value r obtained based on analytic hierarchy process and total charging cost weight w of user automobile1The charging station profit normalization value s and the profit weight w of the charging station2And calculating a bilateral satisfaction normalization value z, wherein a specific formula is as follows:
z＝(1-r)×w1+s×w2＝(1-r)×0.4+0.6s
and S97, pushing the two schemes with the maximum bilateral satisfaction normalization value z to the user automobile.
Has the advantages that: according to the electric vehicle charging scheduling method considering bilateral interest balance based on the road condition information, the influence of the road condition on the driving speed of the electric vehicle and the power consumption of unit mileage is considered, the problem of influence of the road condition on the driving of the electric vehicle is solved to a certain extent, and the method is more suitable for the actual situation of an electric vehicle user; the time cost is customized, a method for solving the path guidance under the condition of maximizing the bilateral benefits by using an analytic hierarchy process is provided, the error caused by neglecting the influence of the driving time on the total cost is solved, and the accuracy of the path guidance is improved.
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FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a schematic diagram of a scheduling model of a three-layer structure according to the present invention;
FIG. 3 is a road traffic network model diagram of an embodiment of the present invention;
fig. 4 is a charging preferred path guide diagram of an embodiment of the present invention;
fig. 5 is a schematic diagram of a terminal working flow designed based on the invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of an electric vehicle charging scheduling method considering bilateral interest balance based on road condition information, and each step is specifically described below.
And S1, the dispatching system acquires real-time road section information and charging station information within a set radius range by taking a certain user automobile as a center, and the remaining SOC of the user automobile.
S2, the dispatching system counts the real-time queuing situation of each charging station and determines the predicted waiting time t after the user automobile arrives at each charging stationw。
Predicted waiting time t after a certain user automobile arrives at a certain charging stationwIs determined by:
when N is more than or equal to 0 and less than m, tw＝0；
② when ym is less than or equal to N < (y +1) m,
Figure GDA0002818316850000061
wherein: m is the number of charging piles owned by the charging station, and n is the current moment t in the charging station1The total number of the user automobiles is N which is the predicted time t2The total number of the user automobiles reaching the charging station is N-x + a, and a is t1And t2User vehicle data which is driven into the charging station and is waiting for charging within a time period, a ═ λ × tdLambda is the station entering rate of the charging vehicle in unit time (Poisson distribution can be obtained through big data analysis), and x is t1And t2The total number of the user automobiles which are charged and driven away in the charging station in the time period td＝t2-t1，
Figure GDA0002818316850000071
And f (t) is the maximum value of the remaining charging time of the user automobile which is being charged in the charging station at the moment t, and y is a positive integer which is more than or equal to 1.
S3, the dispatching system counts the accessible road section information of the user automobile and determines the predicted driving mileage S of the user automobile on each accessible road sectiondThe predicted average passing speed V, the predicted motor efficiency eta and the predicted unit energy consumption e, and carrying out pretreatment.
Because the battery pack capacity of the electric vehicle is small and the endurance mileage is insufficient, when a user drives the electric vehicle to go out, whether the electric quantity of the electric vehicle is enough to support the user to reach a destination or not must be considered. Under different road conditions, the driving speed of the electric automobile is different, and further the motor efficiency is influenced, namely the electric automobile is different in mileage power consumption. We can classify the road conditions into four cases as table 1 for statistical analysis:
TABLE 1 electric vehicle traffic situation under different road conditions
Road conditions Average speed (km/h) Traveling mileage (km) Travel time (h) Efficiency of motor (km/kwh) Energy consumption (kwh)
Congestion working condition VC SC TC ηC EC
General city operating mode VN SN TN ηN EN
Ideal urban working condition VI SI TI ηI EI
Working conditions of highway VH SH TH ηH EH
S4, the dispatching system detects whether the user automobile selects charging: if charging is selected, the flow proceeds to step S8; otherwise, the process proceeds to step S5.
S5, the dispatching system judges the remaining SOC of the user automobile: if the power level is lower than the power level early warning value, a low power level warning is performed and the operation goes to step S7; otherwise, the process proceeds to step S6.
S6, the dispatching system counts the load of each charging station: if the load of the charging station is at the valley value, pushing preferential charging information to the user automobile; otherwise, it does not.
S7, the dispatching system calculates the predicted total energy consumption of the user' S car to reach the charging stations through various route schemes consisting of traversable road segments.
And calculating the minimum total energy consumption path scheme of the user automobile to each charging station by using a Dijkstra algorithm.
S8, the dispatching system selects various path schemes capable of reaching the charging station according to the remaining SOC of the user automobile: if there are more than two path schemes, go to step S9; otherwise, the process proceeds to step S10.
And S9, recommending two path schemes to the user automobile based on the bilateral interest weight.
S91, based on the information obtained in the step S3, calculating a shortest total time path scheme and a shortest total energy consumption path scheme of the user automobile to each charging station by using a Dijkstra algorithm; user's automobile is from present moment t1Total time period T from start to finish chargingGeneral assemblyComprises the following steps:
Tgeneral assembly＝td+tw+tc
Wherein: t is tdFor the user from the present time t1Starting to reach the charging station time t2Time of travel in between, twWaiting time for the user's car, tcCharging time for the user's car;
s92, quantifying the time cost (namely, the time consumed by the user in the whole charging process is measured by currency), dividing a city into Z areas with the total population of the city as p, and dividing the ith area into p areasiThe total value of the national production of the people in the i-th area is GDPi(unit: yuan), then the unit time value for the city is expressed as:
Figure GDA0002818316850000081
wherein: vot is the value per unit time (different cities have different unit values, unit: Yuan/h); 250 is calculated according to the national regulation 1 year according to 250 working days, 8 is calculated according to the national regulation 1 day according to 8 working hours;
s93, calculating the total charging cost of the user automobile and the income of the charging station, comprising the following steps:
s931, calculating the total time cost H of the user automobile which is Vot multiplied by TGeneral assembly；
S932, calculating a total charge amount M ═ E for the user' S vehicleh-Sp+E)×m0(ii) a Wherein E ishFor the charge quantity expected by the user' S car at the end of charging, SpFor the charge at the beginning of charging the user's car, E is t1And t2Energy consumption in time period, m0Real-time electricity price is unit;
s933, calculating the total charging cost C of the user automobile as H + M;
s94, calculating the profit of the charging station
Figure GDA0002818316850000091
S95, performing system analysis by using an analytic hierarchy process (AHP for short) to obtain a user cost normalized value r and a charging station profit normalized value S, and dividing into three steps:
s951, constructing a scheduling model with a three-layer structure;
as shown in fig. 2, a scheduling model with a three-layer structure is constructed to organize and stratify the problems: the highest layer is a target layer, namely all charging stations which can provide charging service for the user automobile, namely all charging stations within a set radius range by taking the user automobile as a center; the middle layer is a criterion layer, namely factors needing to be considered by selecting a proper charging station comprise the total charging cost of the user automobile and the income of the charging station; the bottom layer is a scheme layer, namely, an alternative scheme for providing a proper charging station for a user automobile by considering bilateral satisfaction;
s952, establishing a charging station satisfaction degree judgment matrix (paired comparison matrix) according to an analytic hierarchy process:
s9521, establishing a first comparison matrix, i.e. the relative importance of the total charging cost of the user' S vehicle and the revenue of the charging station:
Figure GDA0002818316850000092
wherein: u represents the total charging cost of the user automobile, S represents the income of the charging station, and the element in the ith row and the jth column represents the importance degree of the element representation in the ith row relative to the element representation in the jth column; for example, the benefit of the charging station is slightly more important than the total cost of the user vehicle, so the importance ratio is 3: 2; comparing the importance degrees of U and S pairwise, wherein all comparison results form the 2 multiplied by 2 comparison matrix;
s9522, establishing a second comparison matrix, namely comparing the total charging cost of the user automobiles arriving at different charging stations in pairs:
Figure GDA0002818316850000101
wherein: sA、SB、SC… … denotes charging stations A, B, C … …, C, respectivelyA、CB、CC… … respectively represent the total charging cost for a user's car to arrive at the charging station A, B, C … … for charging;
s9523, establishing a third comparison matrix, namely comparing the reciprocal of the income of the user automobile arriving at different charging stations for charging the charging stations in pairs:
Figure GDA0002818316850000102
wherein: pA、PB、PC… … respectively represent the profit of the user's car arriving at the charging station A, B, C … … for charging each charging station, the reciprocal is taken because only two variables with positive correlation coefficient to the result can be analyzed by analytic hierarchy process;
s953, obtaining the total charging cost weight w of the user automobile through the first comparison matrix by using an analytic hierarchy process1And the profit weight w of the charging station2Obtaining the total charging cost score of the user automobile reaching each charging station for charging and the income score of the charging station by the second comparison matrix and the third comparison matrix, namely a user cost normalization value r and a charging station income normalization value s;
s96, user cost normalization value r obtained based on analytic hierarchy process and total charging cost weight w of user automobile1Charging station profit normalization value s and chargingRevenue weight w for a station2And calculating a bilateral satisfaction normalization value z, wherein a specific formula is as follows:
z＝(1-r)×w1+s×w2＝(1-r)×0.4+0.6s
and S97, pushing the two schemes with the maximum bilateral satisfaction normalization value z to the user automobile.
And S10, pushing the path guidance scheme with the expected minimum total energy consumption to the automobile of the user.
One specific embodiment of the electric vehicle charging behavior guidance method applying the invention is as follows:
in this embodiment, a traffic map of a certain area is taken as an example, and a road network model is established based on road network dynamic traffic data, and the road network model is in a planar single-layer form. And (4) setting the path navigation of the electric vehicle to the optimal charging station by taking the minimum energy consumption and the minimum time consumption as an objective function. Therefore, the following settings are made: electric vehicle battery capacity E0Is 30 kWh; initial SOC of electric automobileiIs 0.5; defaulting the upper limit time of each charge of a user to be 0.5h at the peak of demand; the real-time charging electricity price of the charging station is 1.5 yuan/kwh; the average charging power of the charging pile is 30 kW; the cost value Vot of the unit time value is 20 yuan/h; considering that the importance of the user is slightly lower than that of the charging station in practice, 0.4 and 0.6 represent satisfaction weights for both matching of the electric vehicle body and the charging station body, respectively.
A schematic diagram of the road traffic network model established in the embodiment is shown in fig. 3. The area has 16 road network nodes, 24 road sections and 3 charging stations. The numerical values 1-16 marked on the nodes are road network node serial numbers, and A-C are 3 charging stations in the region.
In the embodiment, it is assumed that the road driving parameters under different working conditions are shown in table 2:
TABLE 2 road driving parameters under different working conditions
Road conditions Average speed (km/h) Efficiency of motor (km/kwh)
Congestion working condition 5.00 3.20
General city operating mode 25.00 7.50
Ideal urban working condition 60.00 9.25
Working conditions of highway 100.00 4.20
Assuming that the user clicks on the "i want to charge" option at the starting point O at the peak of demand, the remaining amount of power can reach multiple charging stations (here, it is assumed that charging station ABC can be reached). Such a situation is where it plans a bilateral optimal charging scheme.
In the present embodiment, it is assumed that the basic conditions of different charging stations are shown in table 3:
TABLE 3 different charging station base cases
Figure GDA0002818316850000111
Figure GDA0002818316850000121
The Dijkstra algorithm is used for obtaining 6 path selection schemes for the electric vehicle to reach A, B, C three charging stations for charging respectively based on the minimum energy consumption and the minimum time consumption, and the 6 path selection schemes are substituted into each formula to obtain the energy consumption/time, the user cost and the unit time profit of the charging stations corresponding to each path, as shown in table 4:
TABLE 4 energy consumption/time, user cost and per unit time profit of charging station for each path
Figure GDA0002818316850000122
The user cost normalization value r and the charging station profit normalization value s are obtained by using an analytic hierarchy process, and the bilateral satisfaction normalization value z of each path is calculated and is shown in table 5:
TABLE 5 bilateral satisfaction normalization values for each Path
Figure GDA0002818316850000123
As shown in FIG. 4, two electric vehicle preferred paths A based on bilateral satisfaction degree are pushed2、B2For selection by the user.
In summary, the invention provides an electric vehicle charging scheduling method considering bilateral interest balance based on road condition information, and on the basis of considering electric quantity of an electric vehicle and optimal path search of a charging station position, the influence of the road condition on the driving speed of the electric vehicle and the electric quantity of unit mileage is considered; meanwhile, the time cost is customized based on the existing path guiding method.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (1)

1. An electric vehicle charging scheduling method considering bilateral interest balance based on road condition information is characterized in that: the method comprises the following steps:
s1, the dispatching system acquires real-time road section information and charging station information within a set radius range by taking a certain user automobile as a center, and the remaining SOC of the user automobile;
s2, the dispatching system counts the real-time queuing situation of each charging station and determines the predicted waiting time t after the user automobile arrives at each charging stationw；twIs determined by:
when N is more than or equal to 0 and less than m, tw＝0；
② when ym is less than or equal to N < (y +1) m,
Figure FDA0002818316840000011
wherein: m is the number of charging piles owned by the charging station, and n is the current moment t in the charging station1The total number of the user automobiles is N which is the predicted time t2The total number of the user automobiles reaching the charging station is N-x + a, and a is t1And t2User vehicle data which is driven into the charging station and is waiting for charging within a time period, a ═ λ × tdλ is the arrival rate of the charging vehicle per unit time, and x is t1And t2The total number of the user automobiles which are charged and driven away in the charging station in the time period td＝t2-t1，
Figure FDA0002818316840000012
F (t) is the maximum value of the remaining charging time of the user automobile which is being charged in the charging station at the moment t, and y is a positive integer which is more than or equal to 1;
s3, the dispatching system counts the accessible road section information of the user automobile and determines the predicted driving mileage S of the user automobile on each accessible road sectiondThe predicted average passing speed V, the predicted motor efficiency eta and the predicted unit energy consumption e;
s4, the dispatching system detects whether the user automobile selects charging: if charging is selected, the flow proceeds to step S8; otherwise, go to step S5;
s5, the dispatching system judges the remaining SOC of the user automobile: if the power level is lower than the power level early warning value, a low power level warning is performed and the operation goes to step S7; otherwise, go to step S6;
s6, the dispatching system counts the load of each charging station: if the load of the charging station is at the valley value, pushing preferential charging information to the user automobile; otherwise, not as;
s7, calculating the predicted total energy consumption of the user automobile to reach each charging station through various path schemes consisting of passable road sections by the dispatching system;
s8, the dispatching system selects various path schemes capable of reaching the charging station according to the remaining SOC of the user automobile: if there are more than two path schemes, go to step S9; otherwise, go to step S10;
s9, recommending two path schemes to the user automobile based on the bilateral interest weight;
s91, based on the information obtained in the step S3, calculating a shortest total time path scheme and a shortest total energy consumption path scheme of the user automobile to each charging station by using a Dijkstra algorithm; user's automobile is from present moment t1Total time period T from start to finish chargingGeneral assemblyComprises the following steps:
Tgeneral assembly＝td+tw+tc
Wherein: t is tdFor the user from the present time t1Starting to reach the charging station time t2Time of travel in between, twWaiting time for the user's car, tcCharging time for the user's car;
s92, carrying out economic quantification on the time cost, and dividing a city into Z areas by setting the population total of the city as p, wherein the population total of the ith area is piThe total value of the national production of the people in the i-th area is GDPiThen the unit time value of the city is expressed as:
Figure FDA0002818316840000021
wherein: vot is the unit time value;
s93, calculating the total charging cost of the user automobile and the income of the charging station, comprising the following steps:
s931, calculating the total time cost H of the user automobile which is Vot multiplied by TGeneral assembly；
S932, calculating a total charge amount M ═ E for the user' S vehicleh-Sp+E)×m0(ii) a Wherein E ishFor the charge quantity expected by the user' S car at the end of charging, SpFor the charge at the beginning of charging the user's car, E is t1And t2Energy consumption in time period, m0Real-time electricity price is unit;
s933, calculating the total charging cost C of the user automobile as H + M;
s94, calculating the profit of the charging station
Figure FDA0002818316840000022
S95, performing system analysis by using an analytic hierarchy process to obtain a user cost normalization value r and a charging station profit normalization value S, and dividing into three steps:
s951, constructing a scheduling model with a three-layer structure;
constructing a scheduling model with a three-layer structure: the highest layer is a target layer, namely all charging stations which can provide charging service for the user automobile, namely all charging stations within a set radius range by taking the user automobile as a center; the middle layer is a criterion layer, namely factors needing to be considered by selecting a proper charging station comprise the total charging cost of the user automobile and the income of the charging station; the bottom layer is a scheme layer, namely, an alternative scheme for providing a proper charging station for a user automobile by considering bilateral satisfaction;
s952, establishing a charging station satisfaction degree judgment matrix according to an analytic hierarchy process:
s9521, establishing a first comparison matrix, i.e. the relative importance of the total charging cost of the user' S vehicle and the revenue of the charging station:
Figure FDA0002818316840000031
wherein: u represents the total charging cost of the user automobile, S represents the income of the charging station, and the element in the ith row and the jth column represents the importance degree of the element representation in the ith row relative to the element representation in the jth column;
s9522, establishing a second comparison matrix, namely comparing the total charging cost of the user automobiles arriving at different charging stations in pairs:
Figure FDA0002818316840000032
wherein: sA、SB、SC… … denotes charging stations A, B, C … …, C, respectivelyA、CB、CC… … respectively represent the total charging cost for a user's car to arrive at the charging station A, B, C … … for charging;
s9523, establishing a third comparison matrix, namely comparing the reciprocal of the income of the user automobile arriving at different charging stations for charging the charging stations in pairs:
Figure FDA0002818316840000041
wherein: pA、PB、PC… … respectively indicate the income of the user's automobile arriving at the charging station A, B, C … … to charge each charging station;
s953, obtaining the total charging cost weight w of the user automobile through the first comparison matrix by using an analytic hierarchy process1And the profit weight w of the charging station2Obtaining the total charging cost score of the user automobile reaching each charging station for charging and the income score of the charging station by the second comparison matrix and the third comparison matrix, namely a user cost normalization value r and a charging station income normalization value s;
s96, user cost normalization value r obtained based on analytic hierarchy process and total charging cost weight w of user automobile1The charging station profit normalization value s and the profit weight w of the charging station2Calculating bilateral satisfaction normalizationThe value z, the specific formula is as follows:
z＝(1-r)×w1+s×w2＝(1-r)×0.4+0.6s
s97, pushing the two schemes with the maximum bilateral satisfaction normalization value z to the user automobile;
and S10, pushing the path guidance scheme with the expected minimum total energy consumption to the automobile of the user.
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