An electric vehicle charging guidance method considering user satisfaction

Abstract

The present invention relates to the technical field of electric vehicle charging, and in particular to a method for guiding electric vehicle charging taking into account user satisfaction, comprising the following steps: collecting relevant information of all charging stations in a scheduling area; collecting charging requirements and location information of electric vehicles that have issued charging applications within a scheduling cycle; obtaining a charging guidance sequence for electric vehicles based on a nearby guidance strategy to ensure fairness in the charging process; constructing a user satisfaction evaluation model with the total charging time and total charging cost of electric vehicles as clustering points; guiding electric vehicles in sequence according to the charging guidance sequence, and solving a user satisfaction evaluation model with the goal of achieving the highest user satisfaction when guiding any electric vehicle, to obtain the optimal guidance result for the current electric vehicle. The present invention can not only save the time and cost costs of electric vehicle users in charging, improve user satisfaction, but also effectively take into account the utilization rate of charging stations.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging, and more particularly to an electric vehicle charging guidance method taking user satisfaction into consideration. Background Art

[0002] At present, the global energy supply shortage and environmental pollution problems are becoming increasingly serious. Therefore, the world is accelerating the development of new energy technologies, promoting the adjustment of energy structure, and encouraging new energy vehicles to replace traditional fuel vehicles. In this context, electric vehicles have been promoted on a large scale. However, the corresponding electric vehicle charging guidance measures are not perfect enough, resulting in serious queuing. This not only causes charging congestion at charging stations, reduces user satisfaction, but also wastes the unit time output rate of electric vehicle users; secondly, electric vehicle users go to different charging stations to charge, and the time and cost consumed by the road are different. It is difficult for electric vehicle users to reach a balance in the choice of time and cost. Therefore, without investing in additional hardware equipment to increase the cost of charging facility construction, some effective guidance methods are needed to alleviate the congestion caused by charging of electric vehicles, save the time and cost costs of charging for electric vehicle users, and improve the satisfaction of electric vehicle users.

[0003] At present, some analyses focus on the selection preferences of electric vehicle users, the expansion planning of electric vehicle charging stations, and the optimization of electric vehicle charging auxiliary decision-making. For example: 1) In view of the differences among electric vehicle users in selecting target charging stations, a vehicle-pile matching strategy is proposed. According to the user's charging preference, the user is divided into distance-sensitive and time-sensitive types, and a matching model with different preferences is established. Through multiple rounds of matching, the guidance results are obtained. However, when the time or cost exceeds the acceptable range of the electric vehicle user, the user's charging preference will change. Therefore, this matching model based on different user preferences obviously cannot maximize the user's satisfaction.

[0004] 2) Based on the two-layer expansion planning of electric vehicle charging stations with active charging guidance on the user side, the upper layer takes into account the investment economy and user time cost constraints, and expands the existing stations or selects new sites for regional charging stations; the lower layer obtains real-time road network information through the digital map interface, and actively guides charging with the goal of minimizing the charging time cost of electric vehicles, and returns the queuing time of vehicles to the upper layer for expansion planning. The upper and lower layers are effectively solved through multiple rounds of iterative algorithms. However, the charging infrastructure construction in many areas is still imperfect and does not have the conditions for expanding the charging station. The lower layer guides charging with the goal of minimizing the charging time cost of electric vehicles, without considering the impact of the charging cost of electric vehicles on user satisfaction. Therefore, this guidance strategy obviously cannot maximize user satisfaction.

[0005] 3) Electric vehicle charging auxiliary decision optimization method based on multi-source data. This method obtains the optimal charging plan for electric vehicles through comprehensive optimization of multi-source data, determines the shortest time charging optimization strategy and the least cost charging optimization strategy, and users select according to their subjective wishes. The charging guidance problem is a continuous nonlinear problem. This method does not construct the relationship between user satisfaction and charging time and cost. It only helps users plan the guided route and provides guidance plans for users to choose. From the perspective of overall optimization, the user's subjective choice is not conducive to the goal of overall optimization.

[0006] Through the above analysis, the problems and defects of the prior art are as follows:

[0007] (1) It is impossible to reasonably coordinate the time cost and financial cost of charging for electric vehicle users.

[0008] (2) In areas where there are no conditions for expanding charging stations, there is still a lack of effective methods to balance the time and financial costs of charging for electric vehicle users. Summary of the invention

[0009] In view of this, the present invention provides an electric vehicle charging guidance method taking into account user satisfaction, which can not only save the time and cost of charging for electric vehicle users and improve user satisfaction, but also effectively take into account the utilization rate of charging stations.

[0010] In order to achieve the above object, the present invention adopts the following technical solution:

[0011] A method for guiding electric vehicle charging taking into account user satisfaction comprises the following steps:

[0012] Collect relevant information of all charging stations in the dispatching area;

[0013] Collect the charging demand and location information of electric vehicles that apply for charging within a scheduling cycle;

[0014] Based on the nearest guidance strategy, the charging guidance sequence of electric vehicles is obtained;

[0015] Taking the total charging time and total charging cost of electric vehicles as cluster points, a user satisfaction evaluation model is constructed;

[0016] According to the charging guidance sequence, the electric vehicles are guided in turn. When guiding any electric vehicle, the highest user satisfaction is taken as the goal, and the user satisfaction evaluation model is solved to obtain the optimal guidance result of the current electric vehicle.

[0017] Furthermore, the relevant information of all charging stations in the dispatching area includes: the geographical location of each charging station, the unit price of electricity at the charging station, the number of charging piles in the charging station and the charging power of the charging piles, information about vehicles being charged in the charging station and information about vehicles queuing for charging.

[0018] Furthermore, the relevant information of all charging stations in the dispatching area is represented by a data set as follows:

[0019] S A ={1,2,3,...,N A}

[0020] S EV ={1,2,3,...,N EV}

[0021]

[0022]

[0023]

[0024] Among them, N A Represents the number of all charging stations in the dispatching area; S A Represents the set of charging stations in the dispatching area; N EV Indicates the number of electric vehicles that need to be charged in the dispatching area; S EV Indicates the collection of electric vehicles in the dispatch area; represents the initial occupancy time of the jth charging station; T A represents the initial occupancy time set of the charging station; represents the number of charging piles at the i-th charging station; represents the set of charging piles in the jth charging station; represents the unit price of electricity at the jth charging station; M A Represents the electricity price set of a charging station.

[0025] Furthermore, the charging demand is the amount of charging power that the electric vehicle user wants to achieve; and the location information is the geographical location of the electric vehicle to the charging station.

[0026] Furthermore, the charging guidance sequence of the electric vehicle is obtained based on the proximity guidance strategy, including:

[0027] For any electric vehicle that has applied for charging, calculate the time it takes for the electric vehicle to reach each charging station in the dispatching area, and arrange the time from small to large to determine the minimum time it takes for the electric vehicle to reach the corresponding charging station;

[0028] Arrange the minimum time spent by all electric vehicles from small to large, and determine the guidance order of the electric vehicles.

[0029] Furthermore, the construction process of the user satisfaction evaluation model is as follows:

[0030] Determine the time and cost factors that affect user satisfaction, as shown below:

[0031]

[0032]

[0033]

[0034]

[0035] in, is the time influencing factor of user satisfaction; is the cost influencing factor of user satisfaction; To perceive waiting time; for perceived economic loss; The waiting time for the i-th electric vehicle to arrive at the j-th charging station; The time required for the i-th electric vehicle to charge at the j-th charging station; is the time when the i-th electric vehicle arrives at the j-th charging station; The cost of charging electric vehicle i at charging station j, is the electricity cost consumed by the i-th electric vehicle on the way to the j-th charging station, and the calculation formula is:

[0036]

[0037]

[0038] Among them, P j is the charging power of charging station j; represents the unit price of electricity at the jth charging station; represents the amount of electricity consumed by electric vehicle i on the way to charging station j;

[0039] Based on the time influencing factors and the cost influencing factors, a user satisfaction model is constructed:

[0040]

[0041] Among them, γ represents the electric vehicle user satisfaction evaluation factor; It indicates the time influence factor of satisfaction; Represents the cost impact factor.

[0042] Furthermore, the process of solving the user satisfaction evaluation model includes:

[0043] Based on the decomposition and coordination strategy, the dynamic clustering method is used to classify the tasks, with the lower layer as the decomposition layer and the upper layer as the coordination layer;

[0044] According to the charging guidance order of electric vehicles, electric vehicles are guided in turn. When guiding any electric vehicle, the decomposition layer uses the total charging time and total charging cost of electric vehicles as cluster points, solves the feasible solution under the corresponding decomposition point, and calculates the total charging time and total charging cost of the electric vehicle at each station, and sends the result to the coordination layer;

[0045] The coordination layer coordinates the feasible solutions under the decomposition layer according to the user satisfaction model, obtains the solution with the highest user satisfaction, and determines the station selection result of the electric vehicle as the optimal guidance result of the electric vehicle.

[0046] Furthermore, it also includes:

[0047] In a scheduling cycle, the station selection results of all electric vehicles that have issued charging applications in the scheduling area are displayed as 0 or 1 variables.

[0048] Furthermore, when guiding the charging of all electric vehicles that have issued charging applications in the dispatching area, the following constraints are included:

[0049] Selection uniqueness constraint: After an electric vehicle user submits a charging application, he can only choose one charging station to charge during the guidance process;

[0050] Electric vehicle arrival constraint: The charging start time selected by the electric vehicle user is later than the time when the electric vehicle user arrives at the charging station, otherwise the guidance is deemed invalid;

[0051] Charging station capacity constraint: The number of vehicles that can be charged simultaneously is less than or equal to the number of charging piles at the charging station.

[0052] Furthermore, it also includes:

[0053] After a scheduling cycle is over, the charging service intensity of each charging station is calculated based on the utilization rate of the charging facilities in each charging station;

[0054] After calculating the charging service intensity of all charging stations, the variance value is calculated to obtain the charging service intensity variance. The smaller the variance, the smaller the difference in charging facility utilization between different charging stations, which means that the guidance results are more balanced and reasonable.

[0055] It can be seen from the above technical solutions that, compared with the prior art, the present invention has the following beneficial effects:

[0056] The invention obtains the order of electric vehicle guidance based on the nearest guidance strategy to meet the principle of fairness. Based on the decomposition and coordination strategy, the shortest total charging time of electric vehicles (time to go to the target charging station + queuing time + charging time) and the total charging cost of electric vehicles (electricity consumed on the road + charging electricity cost) are used as clustering points, and the results of total charging time and total charging cost are coordinated to construct a user satisfaction evaluation model; then, with the highest user satisfaction as the goal, the user satisfaction model is solved to obtain the optimization guidance result with the highest user satisfaction.

[0057] The present invention can not only save the time and expense costs of charging for electric vehicle users and improve user satisfaction, but also alleviate the charging congestion of charging stations caused by disorderly charging and effectively improve the utilization rate of charging stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0059] Figure 1 A flow chart of an electric vehicle charging guidance method taking into account user satisfaction provided by the present invention;

[0060] Figure 2 A distribution map of charging stations within a certain dispatching area provided by the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] like Figure 1 As shown, an embodiment of the present invention discloses an electric vehicle charging guidance method taking into account user satisfaction, comprising the following steps:

[0063] S1. Collect relevant information of all charging stations in the dispatching area;

[0064] S2, collecting the charging demand and location information of electric vehicles that have issued charging applications within a scheduling cycle;

[0065] S3, based on the nearest guidance strategy, obtain the charging guidance order of electric vehicles to ensure the fairness of the charging process;

[0066] S4. Using the total charging time and total charging cost of electric vehicles as cluster points, a user satisfaction evaluation model is constructed;

[0067] S5. According to the charging guidance sequence, the electric vehicles are guided in sequence. When guiding any electric vehicle, the user satisfaction is maximized as the goal, and the user satisfaction evaluation model is solved to obtain the optimal guidance result of the current electric vehicle.

[0068] The above steps are further explained below.

[0069] S1. The cloud decision-making platform collects information about all charging stations in the dispatching area, including: the geographical location of each charging station, the unit price of electricity at the charging station, the number of charging piles in the charging station and the charging power of the charging piles, the information of vehicles being charged in the charging station and the information of vehicles queuing for charging.

[0070] The relevant information of all charging stations in the dispatching area is represented by a data set as follows:

[0071] S A ={1,2,3,...,N A}

[0072] S EV ={1,2,3,...,N EV}

[0073]

[0074]

[0075]

[0076] Among them, N A Represents the number of all charging stations in the dispatching area; S A Represents the set of charging stations in the dispatching area; N EV Indicates the number of electric vehicles that need to be charged in the dispatching area; S EV Indicates the collection of electric vehicles in the dispatch area; represents the initial occupancy time of the jth charging station; T A represents the initial occupancy time set of the charging station; represents the number of charging piles at the jth charging station; represents the set of charging piles in the jth charging station; represents the unit price of electricity at the jth charging station; M A Represents the electricity price set of a charging station.

[0077] S2. After the electric vehicle sends a charging application, the cloud-based decision platform obtains the charging demand and location information of the electric vehicle; the charging demand refers to the amount of charging power that the electric vehicle user hopes to achieve; the cloud-based decision platform predicts the time required for the electric vehicle to charge based on the power supply of the charging piles in the charging station and the charging demand of the electric vehicle; the cloud-based decision platform combines with the digital map platform to determine the current geographical location of the electric vehicle to each charging station, and predicts the time required for the electric vehicle to reach the charging station based on the current geographical location of the electric vehicle and the charging station.

[0078] Suppose the time for the i-th electric car to arrive at the j-th charging station is The time required for the i-th electric car to charge at the j-th charging station is T S represents the time set of all electric vehicles arriving at the relevant charging station; T C It represents the time set of all electric vehicles charging at the corresponding charging station, which is specifically expressed as:

[0079]

[0080]

[0081] S3. Based on the nearest guidance strategy, the charging guidance sequence of the electric vehicle is obtained, which specifically includes:

[0082] For any electric vehicle that has applied for charging, calculate the time it takes for the electric vehicle to reach each charging station in the dispatching area, and arrange the time from small to large to determine the minimum time it takes for the electric vehicle to reach the corresponding charging station;

[0083] Arrange the minimum time spent by all electric vehicles from small to large, and determine the guidance order of the electric vehicles.

[0084] Table 1 shows the results of the nearest guidance.

[0085] Table 1 Results of nearby guidance

[0086]

[0087] Arrange the electric vehicles from near to far from the station, that is, vehicle 1 exists: S i,j i∈S EV ,j∈S A It means that the i-th vehicle chooses the j-th charging station which is the closest and gets the nearest guidance result.

[0088] It can be seen from Table 1 that the station on the far left is the station closest to the electric vehicle. From the arrival time at the station on the far left of the table, the electric vehicle corresponding to the smallest element arrives at the station first to charge. Therefore, according to the principle of fairness, they are arranged from small to large to obtain the electric vehicle guidance order.

[0089] like:

[0090] Then: the charging order is car No. 2, …, car No. i, …, car No. 1.

[0091] S4. Using the total charging time and total charging cost (electricity consumed on the road + charging electricity cost) of electric vehicles as clustering points, a user satisfaction evaluation model is constructed.

[0092] Among them, the total charging time and total charging cost of electric vehicles are defined as follows:

[0093]

[0094]

[0095] The total time it takes to charge the i-th electric vehicle at the j-th charging station; The total cost of charging the i-th electric vehicle at the j-th charging station; The waiting time for the i-th electric vehicle to arrive at the j-th charging station; The electricity cost for charging the i-th electric vehicle at the j-th charging station; is the electricity cost consumed by the i-th electric car on the way to the j-th charging station.

[0096] The construction process of the user satisfaction evaluation model is as follows: Investigation and research found that the first factor affecting user satisfaction is the perceived waiting time. The second factor is perceived economic loss. Based on these two factors, the time influencing factor and cost influencing factor that affect user satisfaction are determined as follows:

[0097]

[0098]

[0099]

[0100]

[0101] in, is the time influencing factor of user satisfaction; is the cost influencing factor of user satisfaction; To perceive waiting time; for perceived economic loss; The waiting time for the i-th electric vehicle to arrive at the j-th charging station; The time required for the i-th electric vehicle to charge at the j-th charging station; is the time when the i-th electric vehicle arrives at the j-th charging station; The cost of charging electric vehicle i at charging station j, is the electricity cost consumed by the i-th electric vehicle on the way to the j-th charging station, and the calculation formula is:

[0102]

[0103]

[0104] Among them, P j is the charging power of charging station j; represents the unit price of electricity at the jth charging station; represents the amount of electricity consumed by electric vehicle i on the way to charging station j;

[0105] Based on the time influencing factors and the cost influencing factors, a user satisfaction model is constructed:

[0106]

[0107] Among them, γ represents the electric vehicle user satisfaction evaluation factor; It indicates the time influence factor of satisfaction; Represents the cost impact factor.

[0108] S5. Solve the user satisfaction evaluation model, including:

[0109] Based on the decomposition and coordination strategy, the dynamic clustering method is used to classify the tasks, with the lower layer as the decomposition layer and the upper layer as the coordination layer;

[0110] According to the charging guidance order of electric vehicles, electric vehicles are guided in turn. When guiding any electric vehicle, the decomposition layer uses the total charging time and total charging cost of electric vehicles as cluster points, solves the feasible solution under the corresponding decomposition point, and calculates the total charging time and total charging cost of the electric vehicle at each station, and sends the result to the coordination layer;

[0111] The coordination layer coordinates the feasible solutions under the decomposition layer based on the user satisfaction model, takes the highest user satisfaction as the goal, and constructs the objective function as follows:

[0112]

[0113] The objective function is maximized, the solution with the highest user satisfaction is obtained, and the station selection result of the electric vehicle is determined as the optimal guidance result of the electric vehicle.

[0114] When guiding the charging of all electric vehicles that have applied for charging in the dispatching area, the following constraints are included:

[0115] 1) Selection uniqueness constraint: After an electric vehicle user issues a charging application, he can only choose one charging station to charge during the guidance process, otherwise it does not conform to the actual situation.

[0116]

[0117] 3) Electric vehicle arrival constraint: The charging start time selected by the electric vehicle user is later than the time when the electric vehicle user arrives at the charging station, otherwise the guidance is deemed invalid;

[0118]

[0119] in, It indicates the time when the electric car i starts charging, with a 2-minute margin to make it more in line with the actual situation.

[0120] 3) Charging station capacity constraint: The number of vehicles that can be charged simultaneously is less than or equal to the number of charging piles at the charging station.

[0121]

[0122] The total number of charging piles at charging station j in the above formula is expressed. The constraint purpose is achieved by selecting that the number of electric vehicles in the same charging start time window must be less than or equal to the number of charging piles.

[0123] In order to better demonstrate the proposed scheduling optimization results, within a scheduling cycle, the station selection results of all electric vehicles that have issued charging applications in the scheduling area are displayed as 0 or 1 variables. i,j :

[0124]

[0125] Among them, the variable x i,j represents the output result, which can clearly show the station selection results of electric vehicles. Ideally, the selection range of each electric vehicle charging station is all charging stations in the dispatching area. i,j If it is 1, it means that electric vehicle i chooses charging station j for charging.

[0126] In other embodiments, after a scheduling cycle ends, the charging service intensity of each charging station is calculated according to the utilization rate of the charging facilities in each charging station;

[0127] After calculating the charging service intensity of all charging stations, the variance value is calculated to obtain the charging service intensity variance. The smaller the variance, the smaller the difference in charging facility utilization between different charging stations, which means that the guidance results are more balanced and reasonable.

[0128] The method of the present invention is further described below with reference to real data from charging stations in a certain area of ​​Shanghai.

[0129] 1. Collect relevant information about charging stations and electric vehicles in the dispatching area, Figure 2 Table 2 shows the distribution of charging stations in the area, Table 3 shows the real data of charging stations in a certain area of ​​Shanghai, and Table 3 shows the real data of charging vehicles, including the estimated charging time, arrival time and other information.

[0130] Table 2 Charging station information

[0131]

[0132]

[0133] Table 3 Charging vehicle information (unit: min)

[0134]

[0135] 2. Based on the charging station information and charging vehicle information, the guidance results of each electric vehicle are generated, as shown in Table 4.

[0136] Table 4 Electric vehicle charging guidance results

[0137] serial number Stop 1 Station 2 Stop 3 Stop 4 Stop 5 Stop 6 Stop 7 Stop 8 Car 1 0 0 0 0 0 0 1 0 Car 2 0 0 0 0 0 0 1 0 Car 3 0 0 0 0 0 0 1 0 Car 4 0 0 0 0 0 0 0 1 Car 5 0 0 0 0 0 0 0 1 Car 6 0 0 0 0 0 1 0 0 Car 7 0 0 0 0 0 0 0 1 Car 8 0 0 0 0 0 1 0 0 Car 9 0 0 0 0 0 1 0 0 Car 10 0 0 0 0 0 0 0 1 …… …… …… …… …… …… …… …… …… Car 91 0 0 0 1 0 0 0 0 Car 92 0 0 0 0 0 1 0 0 Car 93 0 1 0 0 0 0 0 0 Car 94 0 1 0 0 0 0 0 0 Car 95 0 1 0 0 0 0 0 0 Car 96 0 1 0 0 0 0 0 0 Car 97 0 0 0 0 0 1 0 0 Car 98 0 0 0 0 0 1 0 0 Car 99 0 0 0 0 0 1 0 0 Car 100 0 1 0 0 0 0 0 0

[0138] 3. Guide the results analysis

[0139] After a scheduling cycle is over, the charging service intensity is used to represent the utilization rate of the charging facilities in the charging station by the guidance strategy. The calculation method is as follows:

[0140]

[0141] In the formula, represents the charging capacity in charging station j, Represents the charging capacity of all charging stations in the area. represents the ratio of the number of electric vehicles going to charging station j to the total number of electric vehicles in the area; It represents the ratio of the charging capacity of charging station j to the total charging capacity of all charging stations in the region. The closer the value of charging service intensity is to 1, the more the number of electric vehicles guided to the charging station matches the charging capacity of the charging station.

[0142] After calculating the service intensity of all charging stations, we calculate their variance values ​​and obtain the variance of charging station service intensity. The smaller the variance, the smaller the difference in charging facility utilization between different charging stations, indicating that the guidance results are more balanced and reasonable. The analysis results are shown in Table 5.

[0143] Table 5 Analysis of bootstrap results

[0144] Boot results Electric vehicle charging guidance strategy considering user satisfaction Average waiting time for electric vehicles (minutes) 8.35 Charging station service intensity variance 0.0036 Average driving distance (meters) 308.25 Average driving time (minutes) 5.04 Total charging capacity (kWh) 3909

[0145] 4. Analysis of guidance results in other scenarios. The analysis results are shown in Table 6.

[0146] Table 6 Guidance results under different vehicle penetration scenarios

[0147]

[0148] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0149] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for guiding electric vehicle charging taking into account user satisfaction, characterized in that: The following steps are involved: Collect relevant information of all charging stations in the dispatching area; Collect the charging demand and location information of electric vehicles that apply for charging within a scheduling cycle; Based on the nearest guidance strategy, the charging guidance sequence of electric vehicles is obtained; Taking the total charging time and total charging cost of electric vehicles as cluster points, a user satisfaction evaluation model is constructed; According to the charging guidance sequence, the electric vehicles are guided in turn. When guiding any electric vehicle, the highest user satisfaction is taken as the goal, and the user satisfaction evaluation model is solved to obtain the optimal guidance result of the current electric vehicle; The process of solving the user satisfaction evaluation model includes: Based on the decomposition and coordination strategy, the dynamic clustering method is used to classify the tasks, with the lower layer as the decomposition layer and the upper layer as the coordination layer; According to the charging guidance order of electric vehicles, electric vehicles are guided in turn. When guiding any electric vehicle, the decomposition layer uses the total charging time and total charging cost of electric vehicles as cluster points, solves the feasible solution under the corresponding decomposition point, and calculates the total charging time and total charging cost of the electric vehicle at each station, and sends the result to the coordination layer; The coordination layer coordinates the feasible solutions under the decomposition layer according to the user satisfaction model, obtains the solution with the highest user satisfaction, and determines the station selection result of the electric vehicle as the optimal guidance result of the electric vehicle.

2. The electric vehicle charging guidance method taking into account user satisfaction according to claim 1, characterized in that: The relevant information of all charging stations in the dispatching area includes: the geographical location of each charging station, the unit price of electricity at the charging station, the number of charging piles in the charging station and the charging power of the charging piles, information about vehicles being charged in the charging station and information about vehicles queuing for charging.

3. The electric vehicle charging guidance method taking into account user satisfaction according to claim 2, characterized in that: The relevant information of all charging stations in the dispatching area is represented by a data set as follows: S A ={1,2,3,...,N A } S EV ={1,2,3,...,N EV } Among them, N A Represents the number of all charging stations in the dispatching area; S A Represents the set of charging stations in the dispatching area; N EV Indicates the number of electric vehicles that need to be charged in the dispatching area; S EV Indicates the collection of electric vehicles in the dispatch area; represents the initial occupancy time of the jth charging station; T A represents the initial occupancy time set of the charging station; represents the number of charging piles at the jth charging station; represents the set of charging piles in the jth charging station; represents the unit price of electricity at the jth charging station; M A Represents the electricity price set of a charging station.

4. The electric vehicle charging guidance method taking into account user satisfaction according to claim 1, characterized in that: The charging demand is the amount of charging power that the electric vehicle user wants to achieve; the location information is the geographical location of the electric vehicle to the charging station.

5. The electric vehicle charging guidance method taking into account user satisfaction according to claim 1, characterized in that: The charging guidance sequence of the electric vehicle is obtained based on the proximity guidance strategy, including: For any electric vehicle that has applied for charging, calculate the time it takes for the electric vehicle to reach each charging station in the dispatching area, and arrange the time from small to large to determine the minimum time it takes for the electric vehicle to reach the corresponding charging station; Arrange the minimum time spent by all electric vehicles from small to large, and determine the guidance order of the electric vehicles.

6. The electric vehicle charging guidance method taking into account user satisfaction according to claim 3, characterized in that: The construction process of the user satisfaction evaluation model is as follows: Determine the time and cost factors that affect user satisfaction, as shown below: in, is the time influencing factor of user satisfaction; is the cost influencing factor of user satisfaction; To perceive waiting time; for perceived economic loss; The waiting time for the i-th electric vehicle to arrive at the j-th charging station; The time required for the i-th electric vehicle to charge at the j-th charging station; is the time when the j-th electric vehicle arrives at the j-th charging station; The cost of charging electric vehicle i at charging station j, is the electricity cost consumed by the i-th electric vehicle on the way to the j-th charging station, and the calculation formula is: Among them, P j is the charging power of charging station j; represents the unit price of electricity at the jth charging station; represents the amount of electricity consumed by electric vehicle i on the way to charging station j; Based on the time influencing factors and the cost influencing factors, a user satisfaction model is constructed: Among them, γ represents the electric vehicle user satisfaction evaluation factor; It indicates the time influence factor of satisfaction; Represents the cost impact factor.

7. The electric vehicle charging guidance method taking into account user satisfaction according to claim 1, characterized in that: Also includes: In a scheduling cycle, the station selection results of all electric vehicles that have issued charging applications in the scheduling area are displayed as 0 or 1 variables.

8. The electric vehicle charging guidance method taking into account user satisfaction according to claim 1, characterized in that: When guiding the charging of all electric vehicles that have applied for charging in the dispatching area, the following constraints are included: Selection uniqueness constraint: After an electric vehicle user submits a charging application, he can only choose one charging station to charge during the guidance process; Electric vehicle arrival constraint: The charging start time selected by the electric vehicle user is later than the time when the electric vehicle user arrives at the charging station, otherwise the guidance is deemed invalid; Charging station capacity constraint: The number of vehicles that can be charged simultaneously is less than or equal to the number of charging piles at the charging station.

9. The electric vehicle charging guidance method taking into account user satisfaction according to claim 1, characterized in that: Also includes: After a scheduling cycle is over, the charging service intensity of each charging station is calculated based on the utilization rate of the charging facilities in each charging station; After calculating the charging service intensity of all charging stations, the variance value is calculated to obtain the charging service intensity variance. The smaller the variance, the smaller the difference in charging facility utilization between different charging stations, which means that the guidance results are more balanced and reasonable.

Images